Energy Optimization at GSM Base Station Sites Located in Rural Areas

i

ANI, VINCENT ANAYOCHUKWU PG/Ph.D/08/49541

ENERGY OPTIMIZATION AT GSM BASE STATION SITES LOCATED IN RURAL AREAS

FACULTY OF ENGINEERING

DEPARTMENT OF ELECTRONIC

Ebere Omeje Digitally Signed by: Content manager’s Name

DN : CN = Webmaster’s name

O= University of Nigeria, Nsukka

OU = Innovation Centre

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ENERGY OPTIMIZATION AT GSM BASE STATION

SITES LOCATED IN RURAL AREAS

BY

ANI, VINCENT ANAYOCHUKWU

PG/Ph.D/08/49541

DEPARTMENT OF ELECTRONIC ENGINEERING

UNIVERSITY OF NIGERIA, NSUKKA

JULY, 2015

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APPROVAL PAGE

ENERGY OPTIMIZATION AT GSM BASE STATION SITES LOCATED IN

RURAL AREAS.

BY

ANI, VINCENT ANAYOCHUKWU (PG/Ph.D/08/49541)

A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE AWARD OF THE DEGREE OF DOCTOR OF PHILOSOPHY (Ph.D) IN ELECTRONIC ENGINEERING, UNIVERSITY OF NIGERIA, NSUKKA

JULY, 2015.

Vincent Anayochukwu Ani ------------------------------------ --------------------

(Student) Signature Date Engr. (Prof.) A. N. Nzeako -------------------------------------- -------------------

(Supervisor) Signature Date Engr. (Prof.) T. A. Kuku ------------------------------------- ------------------

(External Examiner) Signature Date Engr. (Prof.) C. I. Ani ------------------------------------- ------------------

(Head of Department) Signature Date Engr. (Prof.) E. S. Obe ------------------------------------- ------------------

(Chairman, Faculty Signature Date Postgraduate Committee)

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CERTIFICATION

This is to certify that ANI, VINCENT ANAYOCHUKWU, a postgraduate student

in the Department of Electronic Engineering, University of Nigeria, Nsukka, with

Registration number PG/Ph.D/08/49541 has satisfactorily completed the requirements

for the research work for the award of the degree of Doctor of Philosophy (Ph.D) in

Electronic Engineering.

---------------------------------------- ---------------------------------------

Engr. (Prof.) A. N. Nzeako Engr. (Prof.) C. I. Ani

(Supervisor) (Head of Department)

---------------------------------------------------

Engr. (Prof.) E. S. Obe

(Chairman, Faculty Postgraduate Committee)

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DECLARATION

I, Ani Vincent Anayochukwu, a postgraduate student of the Department of Electronic

Engineering, University of Nigeria, Nsukka, hereby declare that the work embodied in

this thesis is original and has not been submitted by me in part or in full for any other

academic purposes before this.

Vincent Anayochukwu Ani ------------------------------------ -------------------- PG/Ph.D/08/49541 Signature Date

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DEDICATION

This thesis is dedicated to God the Father, the Son and the Holy Spirit.

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ACKNOWLEDGEMENT

I wish to express my profound gratitude and indebtedness to the following for their

assistance in making this work a reality. First is Engr. Prof. A. N. Nzeako, my

supervisor, for his direction and inspirational guidance towards the completion of this

work. The motivation, encouragement, understanding and concessional privilege of

easy access to facilities are gratefully appreciated. I also acknowledge with thanks the

encouragement and contributions made by Engr. Prof. O. U. Oparaku during the

proposal and seminar stages that structure the work. I owe my gratitude to the Head of

Department, Prof. C. I. Ani for approving this study. I wish to appreciate the efforts of

both the academic and technical staff of the Department of Electronic Engineering,

University of Nigeria, Nsukka, for their willing disposition in the course of this

programme. My appreciations go to Engr. Prof. J. Agunwamba, a scholar and sterling

personality; Prof. (Mrs.) M. A. O. Obi and Prof. M. Madukwe – your essence

convinces one that education and strength of character remain worthy ideals. I want to

particularly thank Miss Rosemary Nwonah who went above and beyond in helping

me edit this thesis. Worthy of acknowledgement is the wonderful support given by my

parents Mr. and Mrs. Innocent Okafor Ani, both morally and financially to ensure that

the work reached a successful completion. The individual and collective sacrifices of

my brothers (Emmanuel Ani and Kingsley Ani) and Sisters (Theresa Ani, Charity

Ani, and Chidera Ani) especially that of my sister Charity Ani, is sincerely

appreciated. I owe a lot of appreciation to my family for their goodwill and

understanding while this work lasted. My sincere gratitude goes to Mrs. Dorothy

Nzeako and her family for their encouragement, support, caring and love during the

research. To my good friend Nwachukwu Arnold whose stand on my running this

programme brought out the best in me. To my brother Pastor Okechukwu Ugwu, I

never knew I could go through this without depending on anybody but you planted

independence in me and I say a million thanks for helping me to confirm the fact that

I can do all things through Christ, who strengthens me. Above all, I am most grateful

to Almighty God, for providing me with enough courage, wisdom, knowledge, good

health and travel protections in the course of this programme.

Thank You God

Ani, Vincent Anayochukwu

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ABSTRACT

The work presented in this thesis explored the potential of using a mix of renewable energy resources (hybrid power systems, HPSs) to generate electricity that meets power needs of mobile base stations at rural areas in Nigeria. The study was based on theoretical mathematical modeling and simulation using the hybrid optimization model for electric renewables (HOMER) software. A sample of eight hypothetical off-grid remote telecommunication base station (BTS) sites at various geographical locations in Nigeria was used for the study. These locations include: Abaji (Abuja, FCT), Nkanu-West (Enugu), Ikwerre (Rivers), Nembe (Bayelsa), Mopa-Muro (Kogi), Kauru (Kaduna), Guzamala (Borno), and Tureta (Sokoto), and were selected to reflect the various climatic conditions in Nigeria. Eight different combinations (HPS options) of four energy resources [small-hydro power (SHP), wind turbine generator, solar photovoltaic (SPV) and diesel generator (DG)] were studied and compared for each of the eight selected BTS sites. These are: Hybrid (Solar, Wind & Hydro) + DG; Hybrid (Solar & Hydro) + DG; Hybrid (Wind & Hydro) + DG; Hydro only + DG; Hybrid (Solar & Wind) + DG; Solar only + DG; Wind only + DG. Total Net Present Cost (NPC) and total CO2 generated are used as indices for measuring the optimization level of each energy solution, and the option with the highest optimization value is considered to be the best energy solution for that base station site. The quantitative results of the study (as reported here) show that the hybrid power system can be more cost-effective and environmentally friendly in providing energy to BTS sites than diesel generators. The results also show that there is no general least-cost option for powering GSM base station sites at different locations. It all depends on climatic conditions and available renewable energy resources. A major contribution of this work is the demonstration (by these results) that it is possible to develop an optimized energy map for appropriate locations of GSM Base Station sites in Nigeria, both as a design guide for network operators and for the formulation of energy use policies by the national telecommunications regulatory authority (the NCC). One of such policies could be the requirement that any network operator intending to site a base station in any location should first produce an optimized energy feasibility study of the location before an approval would be granted.

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TABLE OF CONTENTS

Title Page i

Approval Page ii

Certification iii

Declaration iv

Dedication v

Acknowledgements vi

Abstract vii

Table of contents viii

List of Tables xii

List of Figures xiv

CHAPTER ONE 1

INTRODUCTION 1

1.0. Introduction 1

1.1. Energy Costs in Telecommunication Industries 4

1.2. Environmental Impact and Greenhouse Gas Emissions 6

1.3 Energy Consumption at a Macro Base Transmitter Station (BTS)

Site 8

1.4 Power Solutions for BTS Sites 9

1.4.1 Mains Power 9

1.4.2 Diesel Generators 9

1.4.3. Renewable Energy Solution 10

1.4.3.1 Renewable Energy Technologies 10

1.4.3.2 Renewable Power Options at BTS Sites 11

1.4.4 Off-Grid Cell Sites and Renewable Energy Potentials in Nigeria 11

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1.5. Hybrid Power Systems (HPS) 15

1.6 Problem Statement 17

1.7. Objectives and Significance of the Study 17

CHAPTER TWO 19

LITERATURE REVIEW 19

2.0. Introduction 19

2.1. Energy Optimization 19

2.2 Simulation and Optimization Software Tools for Hybrid Systems 21

2.2.1. Hybrid Optimization Model for Electric Renewable (HOMER ) 21

2.2.2. Hybrid Power System Simulation Model (HYBRID2) 24

2.2.3. Hybrid Optimization by Genetic Algorithms (HOGA) 24

2.2.4. Transient Energy System Simulation Program (TRNSYS) 25

2.3. Component Sizing 26

2.4. Optimization 27

2.5. Optimization Techniques 33

2.5.1. Multi-Objective Design of Stand-Alone Hybrid Systems 33

2.5.2. Control Strategies 35

2.5.3. System Cost Analysis 37

2.6. Summary 40

CHAPTER THREE 41

METHODOLOGY 41


3.1. Mathematical Model 42

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3.2. System Components Used in the Modeling and Simulation 43

3.2.1 Photovoltaic (PV) Systems 44

3.2.2. Wind Generator 48

3.2.3. Micro-Hydro Power 53

3.2.4. Diesel/Gasoline Engine-Generator Power Systems 59

3.3. Energy Storage 62

3.3.1. Battery Electricity 62

3.4. Conversion Devices 65

3.5. Modeling of Hybrid Energy System Components 65

3.6. Power Generation Model 70

3.7. Mathematical Cost Model (Economic & Environmental Costs)

of Energy Systems 71

3.8. The Energy Optimization Model 79

3.9. Calibration of the Model 84

3.10. Materials and Method 88

3.11. Hybrid System Components 98

3.12. Optimal Design of Hybrid System 104

3.13. Computer Simulation 109

3.14. Supervisory Control System 112

3.15. Summary 130

CHAPTER FOUR 131

RESULTS AND DISCUSSIONS 131


4.1. The Results 132

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4.2. Analysis of the Results 137

4.2.1. Optimal Ranking of the Hybrid System Types 138

4.2.2. Energy Rating of the Hybrid Systems and Components 139

4.2.3. Economic Rating of the Hybrid System Types and Components 142

4.2.3.1. Initial Capital Costs [ICC] 142

4.2.3.2. The Total Net Present Cost [NPC] 143

4.2.4. Environmental Impact Rating of the Hybrid System Types

and Components 148

4.3. Discussions 152

4.3.1 Justification for Renewable Power Options at BTS Sites 153

4.4. Summary 156

CHAPTER FIVE 157

CONCLUSION 157

REFERENCES 160

APPENDICES 175

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LIST OF TABLES

Table Page

2.1. Hybrid Simulation and/or Optimization Software Tools 26

3.1. Load Inputs for Radio Base Station and Climate & Auxiliary

Equipment 89

3.2. Details of Solar Properties 98

3.3. Details of the Wind Parameters 99

3.4. Details of Micro-Hydro Parameters 99

3.5. The details of Diesel Generator model parameters 100

3.6. Surrette 6CS25P Battery Properties 101

3.7. Details of Converter Parameters 101

3.8. System control inputs 102

3.9. Constraints inputs 102

3.10. Economic data (Initial System Costs, Replacement Costs and Operating & Maintenance Costs) of all the components of the hybrid system used for the Simulation 103

3.11. Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day one. 120

3.12. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day two. 120 3.13. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day three. 121 3.14. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day four. 121 3.15. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day five. 122 3.16. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day six. 122 3.17. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in day seven. 123 3.18. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of January. 124


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and diesel generator) in 15th day of February 124

3.20. Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of March 125


and diesel generator) in 15th day of April 125 3.22. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of May 126 3.23. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of June 126 3.24. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of July 127 3.25. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of August 127 3.26. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of September 128 3.27. Power demand met by the hybrid energy system (PV, wind, hydro

and diesel generator) in 15th day of October 128

3.28. Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of November 129


and diesel generator) in 15th day of December 129 4.1. Economic Costs [NPC in Billions of Naira] 133

4.2. Environmental Impact [pollutant emissions in tons of CO2] 133

4.3. Percentage of Energy Generated by the Renewable Energy

Hybrid Systems Components 134

4.4. Percentage (%) of Energy Generated by Renewable Energy

Components of Each Hybrid System 137

4.5. Optimal Ranking of the Hybrid System Types

[as Generated by HOMER] 138

4.6. Environmental Impact Analysis 150

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LIST OF FIGURES

Figure Page

1.1. Power Consumption of Radio Base Station 9

3.1. Solar panel converting the sun’s energy into electricity 46

3.2. Solar power system structure and working principle 47

3.3. Wind independent power supply system 51

3.4. Micro inclined-jet water turbine generator 57

3.5. Hydro turbine generator 58

3.6. Model for choosing Power Solution for a BTS Site 83

3.7. Calibrated solar radiation in (a) Abaji, (b) Nkanu-West,

(c) Ikwerre, (d) Nembe, (e) Mopa-Muro, (f) Kauru,

(g) Guzamala and (h) Tureta locations 87

3.8. Overview of HOMER output graphic for DC Load of Radio

Base Station Equipment 89

3.9. Overview of HOMER output graphic for DC Load of

Climate & Auxiliary Equipment 90

3.10. Map of Base Station Site Locations on Study 91

3.11a. HOMER output graphic for Solar

(clearness index and daily radiation) profile for Abaji 94

3.11b. HOMER output graphic for Wind Speed profile for Abaji 94


(clearness index and daily radiation) profile for Nkanu-West 94

3.12b. HOMER output graphic for Wind Speed profile for Nkanu-West 94


(clearness index and daily radiation) profile for Ikwerre 95

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3.13b. HOMER output graphic for Wind Speed profile for Ikwerre 95


(clearness index and daily radiation) profile for Nembe 95

3.14b. HOMER output graphic for Wind Speed profile for Nembe 95


(clearness index and daily radiation) profile for Mopa-Muro 96

3.15b. HOMER output graphic for Wind Speed profile for Mopa-Muro 96


(clearness index and daily radiation) profile for Kauru 96

3.16b. HOMER output graphic for Wind Speed profile for Kauru 96


(clearness index and daily radiation) profile in Guzamala 97

3.17b. HOMER output graphic for Wind Speed profile in Guzamala 97


(clearness index and daily radiation) profile for Tureta 97

3.18b. HOMER output graphic for Wind Speed profile for Tureta 97

3.19. HOMER output graphic for measured Ngenene stream data

used for the stream flow profile for the study locations 98

3.20. Proposed Hybrid System Set-up 104

3.21. Algorithm for Hybrid PV/Wind/Hydro-Diesel System

Sizing Simulation 108

3.22. The proposed energy system for GSM Base Station Site 109

3.23. Hybrid System Controller Block Diagram 113

3.24. Overview of the Decision Strategy of Hybrid Controller and

Modes of Control for the System Operation 115

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4.1a. Economic Costs [NPC in Billions of Naira] 135

4.1b. Economic Costs [NPC in Billions of Naira] 135

4.2a. Environmental Impact [pollutant emissions in tons of CO2] 136

4.2b. Environmental Impact [pollutant emissions in tons of CO2] 136

4.3. Percentage Energy Generated by Renewable Energy

Components per Each Hybrid Type 137

4.4a. Percentage Energy Generated by PV per Renewable

Energy Hybrid Type 140

4.4b. Percentage Energy Generated by Wind per Renewable


4.4c. Percentage Energy generated by Hydro per Renewable


4.4d. Percentage Energy generated by Diesel per each Renewable


4.5. Initial Capital Costs [ICC] 143

4.6a. Economic Costs in 5 years 146

4.6b. Economic Costs in 10 years 146

4.6c. Economic Costs in 15 years 147

4.6d. Economic Costs in 20 years 147

4.6e. Economic Costs in 25 years 148

4.7. Economic Cost Differences in 5 Years Intervals 148

4.8. Operational Hour of Diesel in the Hybrid System Type 150

4.9. Fuel Consumption of Diesel per Hybrid System Type 151

4.10. Environmental Impact Analysis 151

1

CHAPTER ONE

INTRODUCTION

1.0 Introduction

Communication services have faced several challenges with the increasing spread of

wireless voice and data signals into remote areas [1]. Power supply is one of the

critical challenges the telecommunication operators confront in deploying their

networks. This challenge is readily overcome in the developed countries as a result of

well-developed power infrastructure. In the developing world, where national

electricity grid exists, it is always the energy solution of choice for powering Base

Transceiver Stations (BTSs). Unfortunately, it is not always reliable and has limited

coverage. This is complicated in developing countries like Nigeria as mobile

communication extends more and more into rural areas outside the reach of national

grid. The electrification by grid extension or secondary power station can only reach a

small minority of the population in rural areas. In view of the dispersion of localities,

the cost of production, transmission and especially distribution of electricity, would be

expensive.

In Nigeria, Airtel Nigeria (Mobile Operator) has embarked on upgrading 250 diesel-

powered stations on– sites. The company regretted that non-availability of regular

grid power supply to sites across the country is responsible for over 70% of down

time, resulting in poor QoS (Quality of Service) [2]. MTN Nigeria, one of the four

mobile telecommunications operators in Nigeria with 4,798 base stations spends a

whooping $82.8 million on generator acquisition almost every three years and $3.5

million monthly on diesel oil and generator maintenance [3]. This puts the operating

expenditure (OPEX) of generators and diesel at about $69 million annually.

2

In most of the remote and non-electrified sites, extension of utility grid lines

experiences a number of problems such as high capital investment, high lead time,

low load factor, poor voltage regulation and frequent interruptions in power supply.

The costs for installing and servicing the distribution lines are considerably high for

the remote areas [4]. Also, there will be substantial increased losses in transmission

line plus poor power supply reliability. This poor quality in power supply leads to

unsatisfactory quality of services. It also increases the capital expenditure (CAPEX)

and operating expenditure (OPEX) of telecommunication installations. Decentralized

and stand-alone systems could effectively become a viable option in these areas.

To manage this challenge, telecommunication operators in developing countries have

to generate their own electricity. At present, the problem of poor electricity supply

experienced at the telecommunication installations in Nigeria, is being tackled by

using diesel generators. These generators, however, are associated with many

problems. These include, among other things, transportation and storage of diesel

which is a major problem in rural areas, noise pollution emanating from the

generators and environmental pollution. Diesel generators exhaust harmful

hydrocarbons in the atmosphere during operations. Generators also produce

significant waste heat, which is essentially wasted energy. Diesel particulate

emissions are a short-lived climate pollutant that contains considerable black carbon

which causes harmful effects to humans such as health problems (respiratory diseases

and eye problems). They produce high proportions of health-harmful particulate

matter (PM) and CO2 emissions per kWh of power generation, contributing to air

pollution exposures as well as to climate change.

3 The operation and maintenance is relatively costly which typically accounts for 35

percent of the total cost of ownership (TCO) [5] of a BTS, but more than 50 percent in

Nigeria. Thus, it has become increasingly evident that diesel generator-powered

stations are becoming a much less viable option for network operators looking to

expand into rural areas.

Replacing diesel with sustainable alternative energy sources that are cost-effective

and clean, such as solar or wind power, allows telecommunication companies to

circumvent rising energy costs and realize an excellent return on investment (ROI).

This will make communications more accessible and again reduce the environmental

impact. For sustainable telecommunications services that benefit both the operators

and the end user, a simple, efficient, cost-effective wireless base stations that can run

on sustainable and reliable alternative energy sources is needed. Base stations

powered by alternative energy are good options that will reduce network operators’

OPEX and have a positive impact on the environment by reducing their carbon

emission. If alternative energy system can be deployed at base stations of rural

communities, telecommunication services can be extended to millions of potential

new customers.

Several studies have been carried out to evaluate the competitiveness of renewable

energy systems as an alternative to the diesel generator [6]. For sites that are already

on the grid, switching to an alternative source of energy can mean substantial cost

savings for the network operator, as well as the possibility of actually generating

revenue by reselling excess electricity that the sites produce. Renewable energy

technologies create opportunities for economic growth and also reduce greenhouse

gas emissions. Use of renewable energy, for example, helps to reduce or eliminate

4 health problems (respiratory diseases and eye problems) associated with using

conventional energy such as fossil fuel.

1.1 Energy Costs in Telecommunication Industries

Energy costs are one of the largest operating expenses for telecommunication network

operators. Energy costs account for more than half of the mobile operators’ operating

expenses and about 65% of this is for the tower site equipment [7]. As an ever-

increasing number of people around the world become connected by fixed and mobile

telecommunications networks, the challenges related to providing electricity to these

expanding networks are becoming greater as well. Telecommunication networks are

still driven largely by fossil fuel energy and the energy costs represent a significant

OPEX item. Engr. Eyo Ita advanced that global system for mobile communication

(GSM) operators had spent over N500,000 (five hundred thousand naira) on diesel

generators per year in each of their base stations with costs being transferred to

subscribers in terms of billing [8]. Mobile network operators MTN, Globacom, Airtel,

Etisalat, Starcomms, Visafone, MultiLinks, ZoomMobile and Mobitel in Nigeria were

powering their Base Transceiver Stations with over 20,000 generators, which

consume about 25 million litres of diesel monthly [167]. This means that the operators

will require a minimum of 300 million litres of diesel to power their cell sites across

the country with the potential for the figure to increase if they add new base stations.

With the price of the diesel market, the pump price of the product is between N153

and N155 per litre [167]. Providing services in all parts of the country on availability

basis, the telecommunications operators will have to spend a whopping N45.9bn to

fuel their 20,000 generators if they buy diesel at N153 a litre at an average of N3.82bn

monthly; which this huge cost of diesel was outside other logistical costs incurred in

5 procuring and transporting the product as well as the cost of servicing the generators.

The adoption of renewable energy as an alternative source of energy by Nigerian

government and network operators would reduce the cost of GSM charges drastically.

When analysing the costs and benefits of implementing renewable energy solutions to

power base stations, mobile operators look at CAPEX and OPEX and how long it will

take (payback period) to recoup CAPEX by making OPEX savings. A study on

mobile operator, Mobile Telecommunication of Nigeria (MTN) on running 10 BTS in

Uganda on solar energy indicates an average CAPEX around US$49,000 per BTS,

amounting to annual savings of the order of US$15,000 and an average payback

period of around three years, though with the latter figures varying depending on the

price of diesel [9]. Although the actual load – i.e. the total energy consumption of the

individual base station – will also affect the payback period. In Mozambique, mobile

operator, Mozambique Cellular (Mcel) has been replacing diesel generators with

100% solar powered systems on some of its sites. Up to 2010, it reports annual OPEX

savings of US$405,000, with a CAPEX payback of around 12 months per site [10].

Isabelle [11] looks at what the short and long terms options are for African mobile

operators when it comes to saving on the energy bill that they are currently running.

When oil prices are depressed, the pay-back time will be longer – a couple of years

more for most renewable energy projects. When oil prices are high, the return on

investment will take less time. The latter comparison is a big loss to the network

operators. Isabelle said that the cost of running diesel-driven base stations rose by

27% since January 2011, especially in areas with no electricity and in western Kenya

where frequent power outages mean the stations must run on diesel for up to four

hours a day and further acknowledged that the rising operating costs will need to be

addressed and a way to do so will be to increase calling rates. Charging customers

6 more is one approach; however, it has numerous pitfalls. A price increase, for

instance, can result in lower call volumes and therefore the overall revenue will not go

up.

1.2. Environmental Impact and Greenhouse Gas Emissions

The world is experiencing many environmental issues related to usage of fossil fuel.

The major environmental impact of diesel generators is constant CO2 emissions

during service life. A diesel generator used to power a BTS consumes about 18,000

litres of fuel per year [12]. Carbon Dioxide (CO2) emission from one litre of diesel

fuel is 2.68kg [12]. This emits 46.5 metric tons of CO2 annually [12].

The ability for someone to use his or her mobile phone at “any time” means that the

network must be powered up at “all times”. Over 99% of cell sites in Nigeria are

deployed with diesel generators as the primary source of electrical power [13]. The

10.7 kW per base station that we have chosen to model represents 92.715MWh per

year which would release 98.793 tonnes of CO2 if generated by natural gas [14] hence

contributing to global greenhouse gas (GHG) emissions. For these reasons, from

research and developments, recommended alternative energy sources have been

renewable energy [15 - 18].

There is no longer an excuse to continue to pollute the environment in order to meet

business goals. Doing the right thing for the environment is always a lot easier when it

also makes financial sense. Utilization of renewable energy is an effective way to

solve the energy crisis and environmental pollution problems [15]. To that end,

alternative energy sources for wireless base stations consist of eco-efficient solutions

that employ cost-effective, reliable and sustainable methods of power generation for

7 both on- and off- the- grid locations. Renewable energy solutions have positive

environmental effects. There are currently 10,233 renewable powered BTSs which

combine to reduce carbon emissions globally by 480,000 metric tons per year. In

China, the three large telecommunications providers namely: China Mobile, China

Unicom and China Telecommunication together have over 10,000 sites that operate

without grid power [13]. If these telecommunications companies were to use

renewable energy as a primary energy resource, a carbon savings of 107,000 metric

tons of CO2 per year could have been achieved.

In 2008, the GSM Association (GSMA) gathered nearly 800 worldwide mobile

operators to launch a plan for deploying renewable energy sources for 118,000 new

and existing base stations in developing countries to save 2.5billion litres of diesel and

cut CO2 emission up to 6.3 million tons per year [19]. The Mozambique mobile Mcel

initiative reports an overall annual saving over 5,000 tonnes of CO2 by turning to solar

power on several of its base stations. Mobile Telecommunications (MTC) Limited,

Namibia's largest mobile operator, swapped its diesel generator for a dual solar-wind

power system in one pilot BTS which provides an annual saving of 4.58 tonnes of

CO2 per year [20]. Also, in September 2008, the GSMA Green Power for Mobile

Programme was launched to accelerate the use of green power in the mobile industry.

It plans to install new and retrofit 118,000 off-grid BTS in developing countries by

2012 [21]. Similarly, In April 2010 the government of India initiated a programme to

promote solar power in the telecommunication sector. Under this programme,

between 30% and 50% of the cost of solar retrofits will be subsidised [13]. By this

report, 250,000 towers are expected to benefit from the scheme. Each of these

programmes is a huge step towards green energy and carbon emission reduction in the

telecommunication industry.

8 1.3. Energy Consumption at a Macro Base Transmitter Station Site

In identifying the energy consumption at GSM Base Station sites and assessing the

impact of various operational strategies, we used a macro BTS as a model. A BTS is a

tower or mast mounted with telecommunication equipment (e.g. antenna, radio

receiver and transmitters at the top of the mast) that enables the transmission and

reception of mobile signals (voice and data). At the bottom of each tower, there is a

shelter with additional transmission equipment, air conditioner, battery racks and – for

those that are off-grid or with unreliable electricity supply – in a separate room, a

diesel generator. A BTS site load profile depends on multiple parameters including

radio equipment, antenna, power conversion equipment, transmission equipment, etc.

Therefore, it is important to outline an accurate power profile in order to select the

energy components and their sizing. The energy consumption of the various

components at a typical BTS site has been categorized [22 - 25]. The categorization is

as follows:

1. Radio Equipment:

• Radio Unit [Radio Frequency (RF) Conversion and Power Amplification] =

4160W

• Base Band [Signal Processing and Control] = 2190W

2. Power Conversion Equipment:

• Power Supply & Rectifier = 1170W

3. Antenna Equipment

• RF feeder = 120W

• Remote Monitoring and Safety (aircraft warning light) = 100W

4. Transmission Equipment

• Signal Transmitting = 120W

5. Climate Equipment

• Air Conditioning = 2590W

6. Auxiliary Equipment

• Security and Lighting = 200W

9

This implies that a site consumes 10.7kW of Electricity.

Figure 1.1: Power Consumption of Radio Base Station [22]

1.4 Power Solutions for BTS Sites

A service provider has a number of possible options for power solutions when

planning for remote cell sites. The selection of an optimum solution will depend on

the local circumstances and can include:

1.4.1 Mains Power. This may already be available, or can be provided via grid

extension. However, where main grid is readily available with reliable power supply,

this will normally be the solution of choice [26]. In some cases where there are

frequent interruptions to the supply, a battery back-up unit is provided. In difficult

locations, new grid connections can be arranged though very costly.

1.4.2 Diesel Generators. Also known as Genset, (powered by gasoline, natural gas

or LPG) generators are often installed to produce electrical energy especially in areas

where power supply is not steady to ensure uninterrupted service delivery. The major

10 challenges with the use of generators are maintenance and fuel costs. Again, the

inaccessibility of many of these areas further raises OPEX costs through the

transportation of diesel [27]. Due to portability and value of both the generators and

associated fuel, they may become a target for theft [26]. A staff of the MTN

Communications identified vandalisation of generating sets powering their numerous

base stations as a major challenge presently facing her organization [28].

1.4.3. Renewable Energy Solution

Where a site is not connected to the main electrical grid or where the electricity

supply is unreliable, there are a number of cost-effective alternative renewable energy

sources available [29]. One of the widely-available renewable energy sources is the

solar energy. Renewable energy sources offer a viable alternative to the provision of

power in rural areas [30 - 34]. Utilizing alternative sources of energy such as solar,

wind, hydro and bio-fuels will make communications more accessible and will reduce

reliance on fossil fuels and further reduce the environmental impacts.

1.4.3.1. Renewable Energy Technologies

Basic concept of renewable energy relates to issues of sustainability, renewability and

pollution reduction [7]. In reality, renewable energy means anything other than

deriving energy via fossil fuel combustion. Renewable energy technologies are

designed to run on a virtually inexhaustible or replenishable supply of natural “fuels.”

The use of renewable energy sources promotes sustainable development since it runs

on infinite energy sources. Renewable energy facilities enhance the value of the

overall resource base of a country by using the country’s indigenous resources for

electricity generation to power base stations.

11

1.4.3.2. Renewable Power Options at BTS Sites

The choice of renewable power options is partly determined by the region in which

the facility is located [35]. For instance, the performance of solar and wind energy

systems (singly or in combination) is strongly dependent on the climatic conditions at

the location. Other deciding factors when choosing a renewable power system for

GSM BTS sites include: Reliability, Cost and the Environment. Possible renewable

energy options for BTS include:

• Solar Power

• Wind Power

• Pico Hydro

• Biomass

• Hybrid (Renewable) Power System

These energy options can be used as the main energy source in BTS or as a

supplement to either the grid-power or power from genset. On the other hand, power

supply at the BTS can be provided by a combination of one or more of the renewable

energy sources and a diesel generated in the hybrid system as shall be focused on in

this work.

1.4.4. Off-grid Cell Sites and Renewable Energy Potentials in Nigeria

Usually the site is off the grid because it is situated in a place which is difficult to get

to or it is not connected to the main power grid. In Nigeria, off-grid sites could be

found mostly in the rural areas. Apparently, future network expansions are focusing

on these rural areas. For instance, IT news Africa of Tuesday, 2 August, 2011 states

that MTN plans to roll out over 1,000 Universal Mobile Telecommunications Systems

12 (UMTS) base stations tagged “Rural Mobile Broadband Project” in rural areas over

the next two years. But these roll out of Rural Mobile Broadband Project in rural

areas is yet to be a reality. Providing dedicated low cost power supply for these sites

could be challenging as most of the rural areas are not connected to the electricity

grid.

The irony of this situation is that Nigeria is endowed with very abundant renewable

energy resources that remained unexplored and unexploited for alternative energy

solutions for telecommunications particularly for the largely populated rural areas in

the country. Nigeria lies along the equator, with abundant sunshine all year round. It is

endowed with an annual average daily sunshine of 6.25 hours, ranging between about

3.5 hours at the coastal areas and 9.0 hours at the far northern boundary [36].

Similarly, it has an annual average daily solar radiation of about 5.25 KWh/m2/day,

varying between 3.5kWh/m2/day at the coastal area and 7.0kWh/m2/day at the

northern boundary [37]. Nigeria receives about 4.851 x 1012

kWh of energy per day

from the sun [36]. This is equivalent to about 1.082 million tonnes of oil equivalent

(mtoe) per day, and is about 4,000 times the current daily crude oil production, and

about 13,000 times that of natural gas daily production based on energy unit [14].

This huge energy resource from the sun is available for about 26% of a whole day.

Based on the land area of 924 x 103

km2

for the country and an average of

5.535kWh/m2/day, Nigeria has an average of 1.804 x 10

15 kWh of incident solar

energy annually [37].

There are lots of canals, several minor streams and rivulets that crisscross the entire

Nigerian land mass, tributaries of main river Niger, Benue, as well as tiny waterfalls

13 having potentials for setting up mini/micro hydropower units that can power GSM

Base Station Sites [38]. These can be found mainly in coastal regions of the country.

Harnessing micro-hydro resources and setting up decentralized small-scale water

power or micro-hydro schemes are a particularly attractive option in terrain areas

without hampering the ecosystem. Paish [39] reviewed in his paper a small-scaled

hydro which is operating on the basis of run-of-river. From the review, we can see

that small-scaled hydro should be considered for power generation in Nigeria due to

cost and environmental concerns.

Two main air masses alternate with the season. During the dry season, the northeast

winds predominate while the southwest winds are dominant during the wet season,

and, furthermore, during the harmattan months (December to February) the winds are

from the Northeast. Depending on the shifts in the pressure belts in the Gulf of

Guinea, these winds are interspersed respectively by the south-eastern and the north-

western winds in different parts of the year. The wetter winds prevail for more than

70% of the period due to the strong influence of the breeze from the Atlantic Ocean.

Mean annual wind speed varies between 2 to 6 m/s. Speeds in dry season (November

- March) are lower. In the wet season (April - October), daily average speed could rise

to 15 m/s. Values of up to 25 m/s are sometimes experienced due to inducement by

convective rainfall activities and relative diffusion. Generally, the wind directions are

Southwesterly during the rainy season (February to October) and Northeasterly during

dry season (November to January). However, local variation do exists depending on

the local land breezes. For instance, in Uyo (Akwa Ibom State), surface winds from

the southwest dominate during both the wet and dry seasons. The secondary wind

direction is from the northeast, with northeasterly winds occurring more frequently

during the dry season than the wet season. The highest wind speeds occur during the

14 rainy season when storms are encountered. From meteorological centres in Nigeria,

satellite-derived meteorology and solar energy parameters from National Aeronautics

and Space Administration (NASA), the average daily wind which speeds across the

country, at 50 meter height above the earth, is within the range of 2.7m/s in the central

western parts to 5.4 m/s in the North East.

A new possibility is brought by renewable sources, mainly solar power and wind

power. In Nigeria, towards the North East of the country, temperature can rise up to

40oC during high solar activity. In Maiduguri, Nigeria, the average monthly

temperature over the year in 2009 was found to be 34.75oC [40]. In places like

Sokoto, Nigeria, the warmest months are February to April, where daytime

temperatures can exceed 45°C (113.0°F) [40]. Highest recorded temperature in

Sokoto is 47.2°C (117.0°F), which is also the highest recorded temperature, in

Nigeria. Similarly, the average sunlight hour per day within these months (i.e.

February, March and April) was 6.7, 6.4 and 6.2 respectively [40]. Also in November

and December, the average sunlight hours per day are 6.6. All these areas could utilize

PV solar cells as an alternative solution. Photovoltaic (solar) panels and wind turbine

placed on a mast can help reduce energy costs, produce a healthier living

environment, and increase the overall energy supply [38, 41]. Also, the PV solar cells

or wind power or Pico-hydro could be backed up by a fuel generator system for

redundancy which allows the cell site to work when the renewable sources are not

enough.

There is therefore a great potential of reducing OPEX by using renewable energy

sources for the telecommunications industry in Nigeria. It is a major goal of this

15 research to explore best alternative renewable energy solutions for a more sustainable

and cost-effective service delivery.

1.5. Hybrid Power Systems (HPS)

A hybrid powered system can be described as an electricity production system which

supply consists of a combination of two or more types of electricity generating

sources (e.g. solar photovoltaic panels, wind turbine generators, pico-hydro plants,

and/or fuel generators) [42 - 43]. The useful components of hybrid systems considered

in this study are the solar photovoltaic panels, wind turbine, hydro turbine generator,

and diesel generator. A diesel generator can provide energy at any time, whereas

energy from PV, wind and hydro is greatly dependent on the availability of solar

radiation, wind speed and stream flow, respectively. This makes the system

(generator) more reliable, and can be used when PV and/or wind and/or hydro fail to

satisfy the load and when the battery storage is depleted. Hybrid power systems can

be a good way of providing power to the many rural areas in the developing world

where the costs for large scale expansion of electrical grids is difficult and the

transportation costs of diesel fuel are also very high [44].

Telecommunication systems require safe, long-lasting and uninterruptible power

supply in order to provide uninterrupted service [45]. Stand-alone homogenous

renewable power systems cannot meet the power requirements of telecommunication

systems. The deployment of renewable energies for telecommunication purposes

(especially for cell sites) will require combining several sources of renewable energy

source, conventional generators (Diesel generator, LPG turbines etc.) and energy

storage systems (battery bank) which is selected based on their comparative advantage

16 while maintaining uninterrupted supply and acceptable power quality, hence the HPS

[46 - 47]. Different mode of operations for the HPS is possible. Conventional System

Source (2) could be used simultaneously with Renewable System Source (1) to

provide the required 10.7kW power or, either of the two could be used as redundancy

in case of failure. Another possible mode of operation is that, sources (1) and (2)

would each have the capacity of 10.7kW but the supply would be alternative in such a

way that, source (1) will provide power for the first 12 hrs and source (2) will provide

for the remaining 12 hrs. This configuration could prove effective and may in turn cut

the carbon emission by 50% when renewable (solar-PV or wind or hydro) and

generator systems are deployed.

A hybrid system uses advanced system control logic (also known as a dispatch

strategy) to coordinate when power should be generated by renewable energy and

when it should be generated by sources like diesel generators. The real innovation of

hybrid power generation is the realization that cost savings do not come from using

the most powerful solar panels or the most efficient diesel engine, but by closely

matching the cheapest energy production with the load. By coupling and coordinating

sources together, the system provides more reliable and higher quality electricity at

lower costs [48]. According to Faruk et al [48], HPS provide a realistic alternative for

conventional energy sources in terms of economy (fuel consumption and

maintenance) and environmentally benign although the CAPEX of such systems is

high. However, the life-cycle cost is comparatively less, considering the cost of

emissions.

17 1.6. Problem Statement

From the above overview, it is evident that lack of grid power supply in rural Nigeria

poses great challenges to all stakeholders in the telecommunications industry.

Regrettably, available solutions can best be described as begging the issues, with

much emphasis on conventional energy supplies (diesel generators). Little or no

attention has been paid to the exploitation of all other available energy (renewable)

resources in these rural areas and the latest technologies in the field. By “Energy

Optimization” here it is meant the process of assessing the energy load of any BTS at

a rural site and matching it with cost-effective and environmentally-friendly power

supply using theoretical mathematical models. This goal is pursued by selecting the

best components and their sizing, and determining the best available energy option (in

terms of economic and environmental costs) that will effectively power specific base

station sites. The selection of the best available energy option (from economic and

environmental perspectives) means the design of the most effective economic

configuration (combination of a number of power system components) from among a

variety of options (diesel generators, PV arrays, wind turbines, micro-hydro power,

etc.) available at the BTS site.

1.7. Objectives and Significance of the Study

It has been established that the main cost of telecommunication accrues from energy

consumption. Renewable energy could contribute significantly to the reduction of this

energy cost if properly integrated into the BTS energy sources. Hybrid Power Systems

(HPSs) have been described above as among the popular cost-saving renewable

energy applications in the telecommunications industry. But till date, these systems

(HPSs) have found little or no applications in Nigeria. This may be attributed to the

18 lack of information on the necessary site and system parameters required to design

suitable HPSs to meet given loads of BTS sites. Neither could these parameters be

obtained experimentally, because of cost. The work reported in this thesis has been

designed to explore the HPS potentials for powering BTS sites in rural areas in

Nigeria. The research is based on theoretical mathematical modeling and simulation

using the hybrid optimization model for electric renewables (HOMER) software. The

ultimate aim of this study is to provide a model for producing energy optimization

maps that could form a policy framework for siting BTS in Nigeria, particularly in the

rural off-grid areas.

19

CHAPTER TWO

LITERATURE REVIEW

2.0. Introduction

Energy optimization is a critical requirement in the design of a system that could

deploy the best available energy options [49] at any GSM base stations in cellular

mobile communications. Energy Optimization of a GSM base station system looks

into its sizing and the process of selecting the best components to provide cheap

efficient, reliable, environmentally friendly and cost effective power supply. The

techno-economic analysis looks at both environmental cost and the cheapest cost of

energy produced by the system components. This review focuses on the simulation

and design of hybrid systems, as well as the optimization of the hybrid systems using

software.

2.1. Energy Optimization

In energy systems, the optimization [50] of the size of the individual systems can be

made in a variety of ways, depending upon the choice of parameters of interest.

Energy Optimization models are employed as a supporting tool to develop energy

strategies as well as outline the likely future structure of the system under particular

conditions. This will help to provide insights into the technological paths, structural

evolution and policies that should be followed [51]. Several studies have been done to

evaluate the competitiveness of renewable energy systems as alternatives to the diesel

generator such as by Schmid et al [6] and feasibility of the stand-alone hybrid systems

by Elhadidy, Elhadidy et al, and Shaahid et al [52 - 54]. While it is found that the

renewable energy system is competitive and feasible for off-grid application, single

source renewable usually leads to component over-sizing, which increases the

20 operating and life cycle costs [55]. A combination of one or more resources of

renewable energy, called hybrid, will improve load factors and help in saving on

maintenance and replacement costs as the renewables can complement each other

[56]. High initial capital of the hybrid is a barrier to adopt the system thus the needs

for long-lasting, reliable and cost effective system [57]. Designing a hybrid system

would require correct components selection and sizing, with appropriate operation

strategy [58 - 59]. Initial optimization and component sizing methods are based on

worst month scenario and leads to non-optimal design with excess capacity [60].

Hybrid (photovoltaic-wind-hydro and/or Diesel) systems can offer great abilities in

the production of energy based on wind, hydro and solar energy. A Diesel generator is

often used so that energy needs are covered in case of insufficient meteorological

conditions [61]. A battery can also be used with the hybrid systems for storage of

energy when its production is more than the required loads. In regions where sunshine

and wind conditions are good, like the Greek islands, the combined use of

photovoltaics and wind turbine has great results for most of the day-night period and

also for a very large period of a year. Deepak et al [62] proposed a hybrid system that

consists of micro hydro plant, wind turbine and solar photovoltaic (PV) panels. Diesel

generator and battery bank were included as part of back-up and storage system. The

authors adopted a new approach to the PV/Wind/diesel hybrid system including a

hydro resource and compared the results with excluding it. The run-of-river plant was

used as base load plant during raining season and work as peak load plant during dry

season. The renewable energy sources in collaboration with the micro hydro and

diesel generator were evaluated to determine the feasibility of the system. Their

results show that on a cloudy, windy day when solar photovoltaic cells are producing

lower levels of energy, a wind generator is producing a lot of energy; while when

flow of water is very low the diesel generator supplies considerable amount of power

21 to the loads to meet peak load demands. Also the contribution of electrical power by

diesel generator increases in the absence of micro-hydro plant.

A lot of research has been conducted on the performance of hybrid power systems and

experimental results have been published in many articles [11, 52, 57, 63 – 67]. The

energy output of a hybrid system can be enough for the demands of a house placed in

regions where the extension of the already available electricity grid would be

financially unadvisable [63]. Such hybrid systems can also be used in various other

applications, such as telecommunications.

2.2. Simulation and Optimization Software Tools for Hybrid Systems

Simulation programs are the most common tools for evaluating performance of the

hybrid systems. By using computer simulation, the optimum configuration can be

found by comparing the performance and energy production cost of different system

configurations. Several software tools are available for designing of hybrid systems,

such as HOMER, HYBRID2, HOGA, and TRNSYS.

2.2.1. HOMER (Hybrid Optimization Model for Electric Renewables) [68],

developed by National Renewable Energy Laboratory (NREL), USA, is the most-used

optimization software for hybrid systems [69]. HOMER software can simulate a wide

variety of micro power system configurations. A micro power system is a system that

generates electricity to serve a nearby load. Such a system may employ any

combination of electrical generation and storage technologies. It is able to optimize

hybrid systems consisting of a photovoltaic generator, batteries, wind turbines,

hydraulic turbines, AC generators, fuel cells, electrolyzers, hydrogen tanks, AC–DC

bidirectional converters, and boilers. The loads can be AC, DC, and/or hydrogen

22 loads, as well as thermal loads. The simulation is carried out using 1-hour intervals,

during which all of the parameters (load, input and output power from the

components, etc.) remain constant. Two types of dispatch strategies are available in

HOMER. In the ‘load following’ strategy, the generators supply just enough power to

service the loads whenever there is insufficient renewable energy contribution. In the

‘cycle charging’ strategy, the generator (if present) runs at full power and excess

electricity is used for charging the batteries [70]. The control strategies are based on

four proposed strategies: frugal dispatch, load following, State of Charge (SOC) set-

point, and operation strategy [71]. For systems that include batteries or fuel-powered

generators like diesels, the software decides the strategy to operate generators and

charging/discharging of batteries.

The analysis and design of distribution systems can be challenging, due to the large

number of design options and the uncertainty in key parameters, such as load size and

future fuel price [72]. Renewable power sources add further complexity because their

power output may be intermittent, seasonal, and non-dispatchable, and the availability

of renewable resources may be uncertain. This software was designed to overcome

these challenges. HOMER performs three principal tasks namely simulation,

optimization, and sensitivity analysis. The simulation process determines how a

particular system configuration, a combination of system components of specific

sizes, and an operating strategy that defines how those components work together, and

would behave in a given setting over a long period of time. Its higher-level

capabilities, optimization and sensitivity analysis rely on this simulation capability.

The simulation process serves two purposes. First, it determines whether the system is

feasible. Also, it considers the system to be feasible if it can adequately serve the

electric and thermal loads and satisfy any other constraints imposed by the user.

23 Second, it estimates the life-cycle cost of the system. The quantity used to represent

the life-cycle cost of the system is the total net present cost (NPC). This single value

includes all costs and revenues that occur within the project lifetime, with future cash

flows discounted to the present. The total net present cost includes the capital cost of

the system components, the cost of any component replacements that occur within the

project lifetime, the cost of maintenance and fuel.

In the sensitivity analysis process, it was performed with multiple optimizations under

a range of input assumptions to gauge the effects of uncertainty or changes in the

model inputs. Optimization determines the optimal value of the variables over which

the system designer has control such as the mix of components that make up the

system and the size or quantity of each. It is a time-step simulator using hourly load

and environmental data inputs for renewable energy system assessment; it facilitates

the optimization of renewable energy systems based on Net Present Cost for a given

set of constraints and sensitivity variables. HOMER is most widely used for designing

and sizing hybrid systems that do not yet exist. Nayar et al [168] went into a

feasibility study to survey and design an electricity generation system for North

Thiladhunmathi Atoll Uligamu in the Republic of Maldives. The authors designed an

innovative Micro-grid Hybrid Distributed Generation system combining several small

scale wind generators, solar photovoltaic panels, battery storage, and existing diesel

generators using HOMER software. The designed system was installed and

commissioned in August 2007 and on 7th January 2008 the President of the Maldives

inaugurated the implemented Pilot Project. Preliminary performance data of this

system was accessed through a remote monitoring system and the data shows that the

result of the design is in reality. The developed and installed system provides very

good opportunities to showcase high reliability of HOMER software and the future

24 possibilities of distributed generation in remote locations. HOMER has been used

extensively in previous renewable energy system case studies [73 - 74] and in

renewable energy system validation tests [59]. Although simulations can take a long

time, depending on the number of variables used, its operation is simple and

straightforward. The advantage of the modeling approach is that it has an easy user

interface. A further advantage is that it automatically finds the system configuration

that can serve the load at lowest life cycle cost. The main disadvantage of the

simulation approach is that it is mainly economical model and the algorithms and

calculations are not visible or accessible. It can be downloaded and used free of

charge.

2.2.2. HYBRID2 (Hybrid Power System Simulation Model) was developed by the

Renewable Energy Research Laboratory (RERL) of the University of Massachusetts

[75 - 76]. It is a hybrid systems’ simulation software. The hybrid systems may include

three types of electrical loads, multiple wind turbines of different types, photovoltaic

generators, multiple diesel generators, battery storage, and four types of power

conversion devices. Other components, such as, fuel cells or electrolyzers, can be

modeled in the software. The simulation is very precise, as it can define time intervals

from 10 min to 1 h. The possibilities with regard to control strategies are very high,

but it does not optimize the system. NREL recommends optimizing the system with

HOMER and then, once the optimum system is obtained, improving the design using

HYBRID2. It can be downloaded and used free of charge.

2.2.3. HOGA (Hybrid Optimization by Genetic Algorithms) is a hybrid system

simulation and optimization program developed in C++ by José L. Bernal-Agustín

and Rodolfo Dufo-López of the Electric Engineering Department of the University of

25 Zaragoza, Spain [77]. The optimization is carried out by means of Genetic

Algorithms, and can be Mono-Objective or Multi-Objective. It allows optimizing of

hybrid systems consisting of a photovoltaic generator, batteries, wind turbines,

hydraulic turbine, AC generator, fuel cells, electrolyzer, hydrogen tank, rectifier, and

inverter. The loads can be AC, DC, and/or hydrogen loads. The simulation is carried

out using 1-hour intervals, during which all of the parameters remained constant. The

control strategies are optimized using Genetic Algorithms. It can be downloaded and

used free of charge.

2.2.4. TRNSYS (Transient Energy System Simulation Program) is an energy system

simulation software, developed in FORTRAN in 1975 by the University of Wisconsin

and the University of Colorado, USA [78]. It was initially developed to simulate

thermal systems, but, over the years, it has also become a hybrid system simulator,

including photovoltaic, thermal solar and other systems. The standard TRNSYS

library includes many of the components commonly found in thermal and electrical

renewable energy systems. The simulation is carried out with great precision,

allowing the viewing of graphics with great detail and precision. However, it does not

allow the carrying out of optimizations. It is not free of charge.

Table 2.1 summarizes the characteristics of the simulation and/or optimization

software tools.

26 Table 2.1: Hybrid Simulation and/or Optimization Software Tools

HOMER

HYBRID2

HOGA

TRANSYS

Free download and use × × × PV, Diesel, Batteries × × × × Wind × × × × Mini-Hydro × × × × Fuel cell; electrolyze and hydrogen tank × × × × Hydrogen load × × × × Thermal load × × Control strategies × × × Simulation × × × × Economical Optimization × × × Multi-Objective Optimization, Genetic Algorithms ×

2.3. Component Sizing

In order to efficiently and economically utilize the renewable energy resources, an

optimum sizing method is necessary. The optimum sizing method can help to

guarantee the lowest investment with full use of the system component, so that the

hybrid system can work at the optimum conditions in terms of investment and system

power reliability requirement. With continuous research and development efforts, it

has been established that the hybrid systems, if optimized properly, are both cost

effective and reliable compared with single power source systems [79].

Solar, hydro and wind energy systems are among the most developed renewable

energy systems (RES), with diesel generator and have been widely used in both stand-

alone and grid-connected applications. The sizing tool performs dimensioning of the

system: given an energy requirement, it determines the optimal size of each of the

different components of the system. In a hybrid system, 40% of the total energy loss

[80] is due to the non-optimal sizing of the system. Simulation tools can be used for

sizing. Simulation programs are the most common tools for evaluating performance of

the hybrid systems. This requires that the user correctly identify the key variables and

then repeatedly run the simulation, adjusting the variables manually to converge on an

acceptable sizing. Some packages automate this process. A lot of research work has

27 been carried out to optimize their size and evaluate their performance. Chedid and

Rahman [81] developed a linear programming model to optimize the size of a hybrid

system with battery storage and diesel sets. However, the solution provided did not

consider system’s expansion over a future horizon. Kellogg et al [82] presented a

simple numerical algorithm to determine the optimum size of system’s components

for three different configurations: wind alone, photovoltaic (PV) alone and hybrid

wind/PV. Karaki et al [66] presented a probabilistic model of a stand-alone wind/PV

power system. The model takes into consideration system stability, outages due to the

primary energy fluctuations and hardware failure. Gavanidou and Bakirtzis [83]

developed a multi-objective planning technique to design a hybrid system based on

minimization of both capital investment and loss of load probability (LOLP). They

applied the trade off/risk method which rejected inferior plans and gave a set of robust

scenarios to the designer. Protogeropoulos et al [67] tried to determine the optimum

size of a hybrid system and to assess its economical and technical merits against

single PV and wind stand-alone systems. Borowy and Salameh [84] reported an

algorithm based on energy concept to optimally size solar PV array in a PV/wind

hybrid system. In this study, HOMER simulation was used to find the optimum

combination and sizing of components.

2.4. Optimization

Hybrid systems with energy storage in batteries have been studied by various authors.

These systems have been installed for a number of decades, although their systems

would be substantially improved if optimization methods were applied [69].

Numerous papers have been written about the optimum economic designs of PV

and/or Wind and/or hydro and/or Diesel systems with energy storage in batteries.

Usually, the optimum design is carried out minimizing the Net Present Cost (NPC)

28 (NPC: investment costs plus the discounted present values of all future costs during

the lifetime of the system) or by minimizing the Levelized Cost of Energy (LCE)

(LCE: total cost of the entire hybrid system divided by the energy supplied by the

hybrid system).

When working with stand-alone hybrid systems for the generation of electricity,

various aspects must be taken into account [69]. Reliability and cost (economic and

environmental) are two of these aspects [85 - 86]. Yiew et al [87] studied the technical

and economical feasibility of implementing a Solar-wind Power Plant in Malaysia. A

comparison was made between the hybrid system and a conventional standalone PV

based system. From their analysis, a solar-wind hybrid power plant was highly

feasible and improves the reliability and sustainability of existing standalone solar

power plants. Muselli et al [88] proposed the optimal configuration for hybrid systems

should be determined by minimizing the kilowatt-hour (kWh) cost. Studies on genetic

algorithm (GA) are done to find the optimum sizing as well as the suitable operation

strategies to meet different load demand by, among others, Dufo-Lopez and Bernal-

Augustin, Protogeropoulos et al, Seeling-Hochmuth, and Ashok [59, 67, 89 – 90]

carried out the optimization of PV–Wind–Battery systems, modifying the size of the

batteries until a configuration that ensures sufficient autonomy is achieved. Elhadidy

and Shaahid [91] had studied the effect of the size of the batteries on the operation

hours and on the energy provided by the diesel generator in Wind–Diesel–Battery

systems. The diesel generator works only when the wind turbines do not provide

sufficient energy and, additionally, the batteries are unable to supply the demand. By

changing the size of the batteries, economic optimization of the system is carried out.

Koutroulis et al [92] presented a paper for economic optimization by means of

Genetic Algorithms on PV–Wind–Battery systems. McGowan and Manwell [93]

29 described the latest advances in PV–Wind–Diesel–Batteries hybrid systems, using

data from hybrid systems in various locations in the world. In a later paper of

McGowan et al [94], the designs for PV–Wind–Diesel–Battery systems for various

applications in South America are described. Elhadidy, Elhadidy and Shaahid [52, 95]

studied the performance of possible variances of PV–Wind–Diesel–Battery systems;

Schmidt and Patterson [96] studied the effect of energy demand management on PV–

Wind–Diesel–Battery systems.

Wiels et al [97] presented a simulation work, using Simulink, of a real hybrid PV–

Diesel–Battery system located in Alaska, comparing it with a system with only a

diesel generator and another Diesel–Battery system to supply energy for the same

load. Contaminating emissions were evaluated (CO2, NOx and particles) for the

various cases, comparing the results with those obtained by means of HOMER [68]

software. Additionally, the global efficiency of the system and its costs were

determined. The results obtained indicate that the system with only a diesel generator

had a lower installation cost, but higher operation and maintenance costs; additionally,

it was less efficient and released more contaminating emissions than the PV–Diesel–

Battery system.

Shaahid and Elhadidy [98] used the HOMER software for the economic optimization

(minimization of the NPC) of a PV–Diesel–Battery system to supply a shopping

centre located in Dhahran (Saudi Arabia). Ashok [90] presented an optimization

method for PV–Wind–Diesel–Battery systems that includes Micro-hydro. The LCE of

all of the possible component combinations was assessed. It is applied to an example

located in India. Yang et al [99 - 100] presented a method for the optimization of

hybrid PV–Wind–Battery systems which minimize the LCE. The optimization is

30 carried out by trying component combinations: changing the number of PV modules,

the orientation of PV modules, the rated power of the wind turbine, the tower height

of the wind turbine, and the capacity of the battery bank. Diaf et al [101] present an

application of hybrid PV–Wind–Battery systems in Corsica (France) which minimizes

the LCE. Dalton et al [102] carry out the optimization (minimization of NPC) by

means of HOMER in a PV–Wind–Diesel–Battery system in Australia. In addition,

simulations of the optimum system are carried out, using HOMER and HYBRIDS

[103] for this purpose, comparing the simulations obtained with each of the two

programs. Himri et al [104] optimize a Wind–Diesel system using HOMER, with no

batteries, to supply a remote village in Algeria. Shaahid and El-Amin [105] use

HOMER for the optimization of a PV–Diesel–Battery system to supply a remote

village in Saudi Arabia.

Several studies have been done demonstrating the ability to optimize configurations of

renewable energy systems in order to maximize performance while minimizing cost.

The optimization of energy systems in the context of minimizing excess energy and

cost of energy is addressed by Juhari et al [106]. The high upfront cost hybrid systems

warrant the need to optimize unit sizing for reliable and cost‐effective energy system

[57 - 58]. Kamaruzzaman et al and Lambert et al [107 - 108] used the annualized cost

of a component to derive the calculation of the total Net Present Cost (NPC) of energy

systems. Kamaruzzaman et al [107] reviewed the application of genetic algorithms in

optimization (finding optimum component sizing and operational strategy) of hybrid

system consisting of pico-hydro system, solar photovoltaic modules, diesel generator

and battery sets. It is focused on maximizing the renewal energy components while

trying minimizing the use of generator to provide for the load demand, thus

minimizing the total operation cost. The authors show that the use of pico hydro in the

31 renewable energy set-up is an important sizing determination. The main advantage is

that the turbine can operate 24 hours and can provide enough flowing water into the

gathering chamber. The price of the pico hydro turbine is much less compared with

other sources of renewable energy.

Khan and Iqbal, Kamel and Dahl [73, 109] used the HOMER software [110] to find

optimum sizing and minimizing cost for hybrid power system with specific load

demand in stand-alone applications. Ashok [90] developed a reliable system operation

model based on HOMER [68] to find an optimal hybrid system among different

renewable energy combinations while minimizing the total life cycle cost. HOMER

has been used to conduct feasibility study of hybrid systems in many locations around

the world [105, 111 – 114]. Bekele and Palm [111] determined the optimal system for

supplying electricity to a community of 200 families in Ethiopia. They found that in

the 2009 diesel price, the diesel generator/battery/converter set-up was the most cost-

effective. A 51% RE-based system was 19% more expensive but with half the GHG

emissions. With the ever increasing diesel price and continued decrease in solar PV

module prices, RE-based systems are becoming more competitive. Al-Karaghouli and

Kazmerski [115] applied HOMER to study the life cycle cost of a hybrid system for a

rural health clinic in Iraq. A system comprising PV/battery/inverter emerged as the

most economic system. Shaahid and El-Amin [105] performed a techno-economic

evaluation of PV/diesel/battery systems for rural electrification in Saudi Arabia. They

examined the effect of the increase in PV/battery on the cost of energy (COE),

operational hours of diesel generators and reduction in GHG emissions. Van-Alphen

et al [116] used HOMER to create optimal RE system designs in the Maldives.

Deepak et al [62] used HOMER to study the optimization of PV/Wind/Hydro/Diesel

Hybrid Power system in rural area in Sundargarh district of Orissa state India. They

32 configured the power system in such a way that the PV generator and battery

subsystems are connected with DC bus. Hydro, Wind energy generator and diesel

generating unit sub-systems are connected with AC bus. The electric loads connected

in the scheme are AC loads. Kansas State University [117] reported recently that

Mahdi Sadiqi, a graduate of Kansas State University and native of Afghanistan, used

the HOMER software to model robust, reliable energy systems for remote areas of

Afghanistan. From the work, it was found that the most ideal solution for the remote

areas was a hybrid system powered by renewable resources, including micro-hydro

and solar, with a battery backup.

Choice of the Software

Among the various available software, we have chosen the Hybrid Optimization

Model for Electrical Renewables (HOMER). It is a users’ friendly software that can

be easily configured, and, as for the managed information, it is complete too. This

software is a computer modelling tool that can evaluate different situations to

determine the system configuration that will provide acceptable reliability at the

lowest lifecycle cost. In addition to sizing the components of the hybrid system,

HOMER also does a comparison between two simple dispatch strategies. HOMER’s

two dispatch strategies are: Load Following and Cycle Charging. The user is able to

choose between different sources such as photovoltaic source, wind generator, hydro

generator, battery and diesel generator. From the optimization problem stated in

chapter one, the aim of the present study is to perform a hybrid system component

sizing of a GSM base station site located in rural areas with the aim of minimizing

both life cycle costs and pollutant emissions while meeting a given demand reliably.

As an optimization tool, HOMER can be used to solve this optimization problem.

33 2.5. Optimization Techniques

Various optimization techniques for hybrid system have been reported in Renewable

and Sustainable Energy Reviews [69], such as graphic construction methods,

probabilistic approach, iterative technique, artificial intelligence methods, multi-

objective design. Using feasible optimization method, optimum configurations which

meet the load requirement can be obtained [99, 118].

2.5.1. Multi-Objective Design of Stand-Alone Hybrid Systems

In any engineering field, to carry out a design, it is possible to have several objectives

simultaneously, being typical that some of them conflict with each other [119]. Multi-

Objective optimization attempts the simultaneous minimization of various objectives.

In the optimum sizing of hybrid solar–wind–hydro-diesel systems, we wish to carry

out the design considering simultaneously at least two objectives (costs and pollutant

emissions). These two objectives are in conflict, since a reduction in design costs

implies a rise in pollutant emissions and vice versa.

Therefore, the task of getting good results in problems of this kind (multi-objective) is

complicated. Given the complexity of this kind of problems, because of the large

number of variables that are usually considered and of the mathematical models

applied, classic optimization techniques may consume excessive Central Processing

Unit (CPU) time or even being incapable of taking into account all the characteristics

associated to the posed problem. In the literature [52, 59, 88] the design of these

systems is usually done by searching the configuration and/or control that yields the

lowest total cost through the useful life of the installation. However, the

environmental issues associated to this type of installations should also be taken into

account during the design process. Until now, usually, the pollutant emissions have

34 been calculated after obtaining the design that minimizes costs. In some cases, as in

the HOMER program, it is possible to consider the pollutant emissions by

economically evaluating them, and therefore becoming a part of the costs objective

function. This mapping of costs to emissions is subjective, and decisively influences

the results of the design. The method that HOMER uses for the multi-objective design

is known as the method of the weights [119, 120]. Multi-Objective Evolutionary

Algorithms (MOEAs) [121] stand out in the multi-objective design task, being applied

in numerous papers.

Coello et al [122] carried out an application of MOEA for the optimization of system

cost and CO2 emissions for a stand-alone hybrid system in which three hotels and a

town in the Tunisian Sahara were thermally and electrically supplied. The system

consisted of photovoltaic panels, diesel generators, thermo-solar panels, a hot water

accumulator, and a cooling tower (Ranking cycle). Bernal-Agustín et al [120]

presented a multi-objective optimization (NPC versus CO2 emissions) to hybrid a

Solar-wind-diesel system with battery storage based on MOEAs. In a similar

development, Dufo-López and Bernal-Agustín [123] presented a triple multi-objective

optimization to minimize simultaneously the total cost throughout the useful life of

the installation, pollutant emissions (CO2) and unmet load. For this task, a MOEAs

and a Genetic Algorithm have been used in order to find the best combination of

components and control strategies for the hybrid system. Strength Pareto Evolutionary

Algorithm was also applied to the multi-objective design of hybrid systems. The

design is posed as an optimization problem whose solution allows obtaining the

configuration of the system as well as the control strategy that simultaneously

minimizes both the total cost through the useful life of the installation and the

pollutant emissions. Performance of the hybrid system under study is assessed by

35 employing Multi-Objective models of costs and emissions for PV array, wind

turbines, hydro turbines and diesel generator.

2.5.2. Control Strategies

In hybrid systems with batteries and without diesel generators, the dispatch strategy is

very simple: the battery charges if the renewable energy is in excess after meeting the

demand, and the battery discharges if the load exceeds the renewable energy.

However, the control strategies of a hybrid system can become very complex if the

system includes a diesel generator and batteries [69]. Therefore, it is necessary to

determine how the batteries are charged and what element (batteries or diesel

generator) have priority to supply energy when the load exceeds the energy generated

from renewable energy sources.

In 1995, Barley et al [124] proposed various strategies for the operation of hybrid PV–

Diesel–Battery systems. One-hour intervals are considered, during which the system

parameters remain constant. They also consider ideal batteries, without taking into

account losses or the influence of the cycles in the lifespan of the same. The three

basic control strategies proposed are the following:

• Zero-charge strategy (Load Following Diesel): the batteries are never charged

using the diesel generator. Therefore, the Setpoint of the State of Charge

(SOC_Setpoint) is 0%.

• Full cycle-charge strategy: the batteries are charged to 100% of their capacity

every time the diesel generator is on (SOC_Setpoint = 100%).

• Predictive control strategy: the charging of the batteries depends on the

prediction of the demand and the energy expected to be generated by means of

renewable sources, so there will be a certain degree of uncertainty. With this

strategy, the energy loss from the renewable energies tends to decrease.

36 The authors proposed having an optimum point for the SOC_Setpoint between 0 and

100% in such a way that the total operation cost of the system is minimal. That is to

say, the strategy will be between Zero-charge and Full cycle-charge.

In 1996, Barley and Winn [71] improved the control strategies model of Barley et al

[124], introducing new parameters that have become of great importance in the

control strategies of the HOMER software tool. The Critical Discharge Power (CDP)

is the value as from which the net energy (that demanded by the charges minus that

supplied by the renewable sources) is more profitable when supplied by means of the

diesel generator than when supplied by means of the batteries (having previously been

charged by the diesel generator). The authors propose four control strategies:

• Frugal Dispatch strategy: if the net demand is higher than CDP, the diesel

generator is used. If it is lower, the batteries are used.

• Load Following strategy: the diesel generator never charges the batteries.

• SOC_Setpoint strategy: the diesel generator is on at full power, attempting to

charge the batteries until the SOC_Setpoint is reached.

• Operation strategy of diesel at maximum power for a minimum time (charging

the batteries).

Ashari and Nayar [125] proposed an optimization method of the control strategies

(based on Barley and Winn [71]) of a PV–Diesel–Battery system with AC load, using

Setpoints for the start-up and stop of the diesel generator and for the charging of

batteries. The Diesel generator starts up when the voltage of the batteries is lower than

a determined value or when the power of the inverter exceeds a determined

percentage. The diesel generator stops when the power to be supplied is less than a

certain percentage of its nominal power.

37 Muselli et al [85, 88] simulate a hybrid PV–Diesel–Battery system with only DC load

in such a way that all the energy from the diesel generator goes through the batteries.

The diesel generator works at nominal power, providing that the State of Charge

(SOC) of the batteries is within determined limits (SDM and SAR, in % of the battery

capacity). The decrease in costs obtained with the hybrid system, when compared

with the traditional photovoltaic system, is at least 20 or 30%. In addition, it has been

concluded that the hybrid PV–Diesel–Battery system offers greater flexibility and

efficiency than the PV–Battery system.

2.5.3. System Cost Analysis

Generally speaking, several economic criteria exist, such as the Net Present Cost,

Levelised Cost of Energy and life-cycle cost. The HOMER (Hybrid Optimization

Model for Electric Renewable) uses the total Net Present Cost to represent the life-

cycle cost of the system, assumes that all prices escalate at the same rate and takes the

‘‘annual real interest rate” rather than the ‘‘nominal interest rate”. This method allows

inflation to be factored out of the analysis [102]. The Net Present Cost also takes into

account any salvage costs, which is the value remained in a component of the system

at the end of the project lifetime. The HOMER assumes a linear depreciation of

components, meaning that the salvage value of a component is directly proportional to

its remaining life. It also assumes that the salvage value is based on the replacement

cost rather than the initial capital cost. The Levelised Cost of Energy is defined as the

ratio of the total annualized cost of the system to the annual electricity delivered by

the system [99]. It has been extensively used as an objective term to evaluate the

hybrid (solar–wind) system configurations [126]. Other economical approaches, such

as the Levelised Cost of System [118] and life-cycle cost are also widely used [127].

The mathematical model derived estimates the life-cycle cost of the system, which is

38 the total cost of installing and operating the system over its lifetime. The life-cycle

cost is a convenient metric for comparing the economics for different types of system

configurations. Life cycle cost analysis is a tool used to compare the ultimate

delivered costs of technologies with different cost structures [128]. The initial costs

are the costs incurred through purchasing equipment, and hiring labour, in order to

install an energy system. A component purchase might also generate certain

associated fixed costs for the user. For example, Seeling-Hochmuth [89] indicate that

regardless what kind of component size is purchased a certain type of transport would

always have to be paid for, or a certain sized container etc. When installing equipment

certain costs arise due to installation, labour or required accessories. These costs

depend on size and type of a component and are often given as a percentage of

individual or overall equipment purchase costs. In general, the initial purchase costs

of a component will depend on the size and type of a component, and on how many

components are bought. For example, differently sized wind turbines are available at

varying costs due to different material and labour costs in producing a turbine. In

addition, similarly sized wind turbines from different manufacturers might be priced

differently due to the use of particular designs, materials, quality standards and mark-

ups. The component size and type are subject to optimizing system design criteria

[129]. They are therefore selected as decision variables to be optimized in the

developed hybrid system design model. The component initial cost is multiplied by

the required number of components to be installed in the system in series ( ,in series)

and in parallel ( ,ix parallel). The number of components to be installed in series is

often straightforward and determined by the nominal operating voltages of the system

and the components [89]. However, the number of components to be installed in

parallel is subject to system design and its optimization and is therefore labelled with

39 an ‘x’, as is the size of a component type. Therefore, in the initial cost modelling, the

size and type of a component and the required number of parallel connected

components are taken to be decision variables to be optimized in the developed hybrid

system model. The operation costs describe costs incurred after installation in order to

run the system for a certain number of years, the so-called ‘project life’. The project

life is an important parameter for system designers as it helps to benchmark different

life cycle costs or net present value costs for different designs. The operation costs

include expenses for fuel, maintenance, components-overhaul and components

replacement. As operation costs occur in the near and distant future and are only

estimates, they are more difficult to determine than initial costs [130]. It is also

difficult to estimate component degradation as part of the replacement and

maintenance costing. In the developed model the predicted timing for maintenance,

overhaul and replacements is based on the number of hours or operational cycles a

component has been operating which is influenced by the system size and system

operation. Operation costs can be split into a number of contributing expenditures,

mainly costs for maintenance, refueling, component overhaul, component

replacement, and administration. In many projects, a maintenance person is employed

to look after a system or several systems. This person’s monthly salary or part of it

will therefore occur in the maintenance costs [89]. Often systems will need some kind

of administrative support, and then monthly administrative costs will be included in

the operation costs. The operation costs for either a component or the overall system

can contain fixed costs and costs that are counted as a percentage of initial capital

expenditure. From the above statement, we can see that the operation cost comprises

of expenses for fuel, maintenance, and component replacement. One can still separate

the expenses for the fuel and component replacement from operation cost to enable us

40 simulate the component replacement and expenses for fuel in terms of cost whereas

the maintenance remain with the operation cost.

2.6. Summary

This thesis is an optimization design study similar in form to most of the works

reviewed in this chapter. Among these works is Barley and Winn [71], which has set

guidelines for dispatch strategies. The authors proposed four control strategies, which

are: Frugal dispatch strategy, Load following strategy, State of Charge (SOC) Set-

point strategy, and Operation strategy. Among these, the SOC_Setpoint strategy is

adopted for this study. In SOC_Setpoint strategy, the diesel generator is ON at full

power, ready to charge the batteries until the SOC_Setpoint is reached. Thus, for a

Base Station Site with only DC loads, the strategy used for the control of the

(PV/Wind/Hydro/Diesel-Battery) hybrid systems in this study is designed in such a

way that the energy from the diesel generator (AC) goes through a rectifier. This is

then used to power the site DC loads, whenever there is insufficient renewable energy

contribution, and as well charge the batteries. On the other hand, all the (DC) power

by the renewable energy hybrid system is supplied directly to the site (DC) loads, and

as well used to charge the batteries. The design is shown in figures 3.15 and 3.17.

Based on this control strategy, the HOMER software is used to model several

renewable energy hybrid options, using PV, Wind, Hydro, Diesel and Battery as

primary components, and the option with the lowest Net Present Cost (NPC) and least

environmental impact (least CO2 emission) is considered as the optimal solution.

41

CHAPTER THREE

METHODOLOGY

3.0. Introduction

This chapter presents all the tools and systems used for the study. These include: the

mathematical model, the simulation & optimization software, and a supervisory

controller for the energy management. The descriptions of all the system components

(solar, wind, hydro, diesel generator, and hybrid systems), the power converters, the

energy storage (battery) and their sizing are also outlined and detailed. From the

description of the system components, a mathematical model was formulated.

Mathematical modeling of energy systems involves representing the deployment of

energy options (either single or hybrid) computationally and simulating its operation

over time or under varying conditions or scenarios. In this study, the mathematical

models developed by HOMER, based on power generation model and cost model,

were presented. A power generation model is a model of the electrical energy

generated by the system components. Cost model is the annualized cost of a

component which includes annualized capital cost, annualized replacement cost,

annual operation and maintenance (O & M) cost, emission cost and annual fuel cost

(generator). This model (economic and environmental cost model) was derived

through annualized total cost of different configurations of power system. An

algorithm was formulated to be used to solve the optimization problem which links

the power generation model and the cost model together.

HOMER software performs a simulation and optimization process to determine how a

particular system configuration would behave in a given setting over a long period of

time. Before this model (HOMER) was used to simulate the operation of the

42 PV/Wind/Hydro-Diesel Hybrid system, the calibration of the model was done to

ascertain its accuracy. The tests show that synthetic solar data produce virtually the

same simulation results as real data. So, HOMER software was used in the simulation

of all the possible system configurations. At the end of these (simulations) a list of

configurations, sorted by net present cost (NPC), was displayed. This list was used to

compare the system design options.

An algorithm for the supervisory controller (sliding mode control) was also developed

to regulate the generated power. The controller operates in 4 modes, modes 1-4

according to which of the Hybrid System Components [PV, H, W, DG] is generating

the dispatch power to the load.

3.1. Mathematical Model

Mathematical modeling has been widely utilized in science and engineering in order

to improve understanding of the behaviour of systems, explore new theoretical

concepts, predict system performance and, in an increasing number of cases, aid in the

solution of practical design problems [131]. In the latter context, mathematical models

offer the potential to reduce, or even replace, the need for physical experimentation

when exploring new material and/or process options. Given the challenges and costs

involved in conducting appropriate laboratory and pilot scale investigations, increased

ability to assess new process options through such modeling is to be welcomed [131].

Mathematical modeling of energy systems, as we mentioned earlier, involves

representing the deployment of energy options (either purely or hybrid)

computationally and simulating its operation over time or under varying conditions or

scenarios. Different types of energy systems and modeling procedures, performance

studies of energy systems, operating strategies, economic analysis and case studies

43 can be derived using Net Present Cost and Cost of Energy [132]. The Net Present

Cost is defined as the total present value of a time series of cash flows, which includes

the initial cost of all the system components, the cost of any component replacements

that occur within the project lifetime and the cost of maintenance. The system lifetime

is usually considered to be the life of the PV modules, which are the elements that

have a longer lifespan [59, 120]. Net Present Cost analysis is a tool used to compare

the ultimate delivered costs of technologies with different cost structures.

In studying energy optimization at GSM base station sites, the research focus here is

on models that would determine the best (economic and environmental cost-effective)

available energy (generator, solar, wind, hydro, and hybrid systems) option that could

effectively power GSM Base Station Sites at specific locations.

3.2. System Components Used in the Modeling and Simulation

In order to design a power system for any Base Station Site, we have to obtain some

information about a particular remote location of the Base Station, such as the load

profile that should be met by the system. Such information includes: solar radiation

for PV generation (availability of solar resources), wind speed for wind power

generation (availability of wind resources), flow rate for hydro generation (availability

of water flow), cost for each component (diesel, renewable energy generators, battery,

converter, etc.), cost of diesel fuel (price of fuel), annual interest rate, project lifetime,

etc. Outlined below are suggestions of systems that could determine the best option

for a Base station located in rural areas. The suggested systems are examples in the

literature on existing plants.

44 3.2.1. Photovoltaic Systems

Photovoltaic (PV) cells are made up of two semi-conductor layers. One layer

containing a positive charge, the other a negative charge. Sunlight consists of little

particles of solar energy called photons. As the PV cell is exposed to this

sunlight, many of the photons are reflected, and passed right through, or absorbed by

the solar cell. When enough photons are absorbed by the negative layer of the

photovoltaic cell, electrons are freed from the negative semiconductor material. Due

to the manufacturing process of the positive layer, these freed electrons naturally

migrate to the positive layer creating a voltage differential. Multiple cells can be

assembled into modules that can be wired in an array of any size. Photovoltaic

systems convert energy from the sun directly into electricity. They (PV systems) are

cost-effective in small off-grid applications, providing power, for example, to rural

homes in developing countries, off-grid cottages and motor homes in industrialized

countries, and remote telecommunications, monitoring and control systems

worldwide. PV can become cost-effective for power requirements in areas not

connected to the existing electricity grid. Studies have shown that solar energy is

becoming an increasingly viable alternative [133 - 134] where there is no access to the

electricity grid, providing the only realistic solution in some situations.

Solar energy in one form or another is the source of nearly all energy on the earth.

Humans, animals and plants, rely on the sun for warmth and food [170]. However,

people also harness the sun's energy in many other different ways. For example, fossil

fuels, plant matter from a past geological age, is used for transportation and electricity

generation and is essentially just stored solar energy from millions of years ago.

Similarly, biomass converts the sun's energy into a fuel, which can then be used for

heat, transport or electricity. Wind energy, used to provide mechanical energy or for

45 transportation, uses air currents that are created by solar heated air and the rotation of

the earth. Wind turbines convert wind power into electricity as well as its traditional

uses. Likewise, hydroelectricity is derived from the sun. Hydropower depends on the

evaporation of water by the sun, and its subsequent return to the Earth as rain to

provide water in dams. PV is a simple and elegant method of harnessing the sun's

energy. These devices (solar cells) are unique in that they directly convert the incident

solar radiation into electricity, with no noise, pollution or moving parts, making them

robust, reliable and long lasting.

How solar panels convert the sun’s energy into electricity

Photovoltaic modules, commonly called solar modules, are the key components used

to convert sunlight into electricity [169]. The sun transmits energy in the form of

electromagnetic radiation. The PV cells, which have a semiconductor feature, create

voltage and current by providing electron movement between (+) and (-) poles as a

result of the photons that strike the crystals thereby induces the “photovoltaic effect”

which generates electricity. Because of the way the cells are manufactured with layers

of material with differing atomic structures, the electrons are forced to move in one

direction, creating direct current (DC). The generated electrons then flow into an

inverter which converts the DC into alternating current (AC), to be usable in homes or

business, as shown in figures (3.1 and 3.2).

46

Figure 3.1: Solar panel converting the sun’s energy into electricity [169]

Components used to provide solar power

The four primary components for producing electricity using solar power, which

provides AC power for daily use, are: Solar panels, charge controller, batteries and

inverter. Solar panels charge the battery, and the charge regulator insures proper

charging of the battery. The battery provides DC voltage to the inverter, and the

inverter converts the DC voltage to normal AC voltage. In the figure below (figure

3.2), the solar power system structure and the working principle (the electric current

flow) are given in detail.

47

Figure 3.2: Solar power system structure and working principle [169]

PV System Design

Due to the variable nature of the energy source, one of the most expensive aspects of a

PV power system is the necessity to build in system autonomy to provide reliable

power during periods of adverse weather or increased demands. This is accomplished

by over-sizing the PV array and enlarging the battery storage, the two most costly

system components.

PV in Hybrid Systems

Improved system usage and operation may be more easily achieved with a hybrid

system than with a single-source application. Hybridising a PV system often reduces

the need for over-sizing the PV array to achieve system autonomy especially when

complementarily of different energy sources can be used effectively. Photovoltaic

systems can be combined with fossil fuel-driven generators in applications having

higher energy demands or in climates characterized by extended periods of little

sunshine (e.g. winter at high latitudes) to form hybrid systems.

48

PV Costing Variables

The initial costs of the PV array are the PV panel costs according to the size of the PV

panel type, the percentage of capital costs added for installation and BOS (Balance of

System) parts, and added fixed costs accounting for installation and BOS parts,

respectively. Operation and maintenance of PV arrays can be described with monthly

fixed costs and yearly costs as a percentage of capital costs. These payments can

cover system inspection by a maintenance person. Replacement events of PV arrays

are assumed to occur after every 25 years, so for a project life of 25 years or less,

there will be no PV replacement costs. The Annualized Total Cost of running Solar

Power only is calculated using equation (3.30).

3.2.2. Wind Generator

Wind power is the transformation of wind energy into more useable forms, typically

electricity using wind turbines [135]. Wind speeds are highly irregular; therefore wind

turbine energy production becomes highly variable. In off-grid applications it is

difficult to keep the frequency of the resulting current constant, as it depends on wind

speed which is highly variable. Therefore the current is usually rectified to give DC.

The power extracted by a wind turbine will have a mean value during a specific time

interval with variations about the mean due to fluctuations in wind speed. A power

curve is typically used to reflect the performance of a wind turbine and is the

relationship between wind speed (at the hub height) and average output power (during

the averaging time interval). Generally the output of a wind turbine is assumed to be

proportional to the cube of the wind speed. The manufacturer will usually specify a

cut-in wind speed at which the turbine starts to generate power, a rated wind speed at

which it starts to generate rated power and a cut-out wind speed at which it shuts

49 down for safety [136]. Wind power is known as ‘Green Power’, because of its

technical and commercial viability and its environment-friendly nature. The special

features of wind energy that makes it attractive are zero cost fuels, low gestation

period, quicker benefits and usefulness for sustainable economic development [137].

Wind has strong potential as a fuel-free renewable source of energy, which can

contribute to the deployment of off-shore base stations power needs. The free,

theoretically inexhaustible supply of energy holds great appeal, particularly since it is

not limited by geographic and political boundaries: no one owns the wind, and every

place receives it (to a greater or less extent, anyway). The best places for strong steady

winds are the temperate latitudes (between 40º and 50º N and S), and areas which are

close to the sea. Wind speeds increase with altitude, making hilltops favourable sites

[138]. Therefore the best sites for mounting wind turbines are on hilltops, the open

plains, through mountain passes, and near the coasts of oceans or large lakes close to

the Base Station Sites location.

Basic source of Wind Power

Wind power is actually one of the most versatile elements on Earth; a form of solar

power, because wind is caused by heat from the sun. Solar radiation heats every part

of the Earth’s surface, but not evenly or at the same speed. Different surfaces (sand,

water, stone and various types of soil) absorb, retain, reflect and release heat at

different rates, and the Earth generally gets warmer during daylight hours and cooler

at night. As a result, the air above the Earth’s surface also warms and cools at

different rates. Hot air rises, reducing the atmospheric pressure near the Earth’s

surface, which draws in cooler air to replace it. For example, during a day the air

above the water (sea or lake) is always lower than the temperature of air above the

land. According to the laws of physics, hot air always moves up, creating some

50 “vacant places” for air from colder area to move in. The speed of air movement or

wind velocity depends on the difference of air pressure: the bigger the difference is –

the higher the wind velocity is. At the coastlines it is always windy because of the

difference in air pressure above the land and water. This is the reason why many wind

power plants are located at the coastline.

Therefore, when the air masses move from one place to another, causing wind, it has

kinetic energy. With the right technology, the wind’s kinetic energy can be captured

and converted to other forms of energy such as electricity or mechanical power.

Wind turbine working principle

Wind is a form of solar energy and is a result of the uneven heating of the atmosphere

by the sun, the irregularities of the earth's surface, and the rotation of the earth [172].

Wind flow patterns and speeds vary greatly across Nigeria and are modified by bodies

of water, vegetation, and differences in terrain. Wind turbines use wind energy to

produce electricity [173]. They are machines that have a rotor with three propeller

blades. These blades are specifically arranged in a horizontal manner to propel wind

for generating electricity. The sunrays heat up the ground and this causes the

atmosphere to become warm [174]. Basically, air over the land heats up faster than

the air over the water, therefore, when warm air over the land rises, the cool air above

the water rushes to replace it creating wind energy. The wind then hits the blades of

the wind turbine making the wind turbine to turn. The wind turbine is fitted with an

internal shaft that turns inside a generator to produce electrical energy. By spinning a

magnet that is inside a coil of wires, the generator inside the wind turbine is able to

produce electrical energy.

51 The terms wind energy or wind power describes the process by which the wind is

used to generate electricity. Wind turbines convert the kinetic energy from the wind

into mechanical power; and a generator convert this mechanical power into electricity

[175]. The generated electricity is used to charge the battery through controller, and

then through the inverter, the system supply the power in AC for lightings, home

appliances, electrical tools and other loads as shown in figure 3.3.

Figure 3.3: Wind independent power supply system [176].

Wind System Design

If the generator is undersized, the turbine will reach peak power at relatively low wind

speeds and stay there until the cut out wind speed is reached. If the turbine is

oversized, then the power will increase until the cut out speed is reached [139]. The

energy output of a wind turbine can be calculated by determining the frequency

distribution of local wind speeds and then computing the expected range of power

outputs for each wind speed by using the wind turbine power curve. A direct

connection of the wind turbine to the DC bus may impose additional requirements to

the battery storage to ensure that wind energy variations do not cause voltage

fluctuations that exceed inverter input voltage limits. For reactive power management

the inverter output needs to be adjusted [136].

52 Wind Turbines in Hybrid Systems

Wind is a natural phenomenon related to the movement of air masses caused primarily

by the differential solar heating of the earth's surface. Seasonal and locational

variations in the energy received from the sun affect the strength and direction of the

wind. Wind turbine single-source systems tend to produce highly variable and

therefore unreliable power supply due to the irregular wind speeds. If the wind turbine

is combined with other sources in a hybrid system, the produced energy can become

more regular improving system performance and cost effectiveness. In some regions

wind speeds and radiation levels complement each other.

Wind Turbine Sizing

A similar listing of relations as for the PV array can be obtained for wind turbine

components. Manufacturers give the characteristic curves for wind turbines as power

output versus wind speed at the hub height. In the design tool the energy output and

current output of wind turbine components for each time instant is then computed

based on the local weather conditions and actual installation height of the turbines.

Wind turbines are usually only connected in parallel, not in series. Therefore the

number of wind turbines in series will be equal to one. Several wind turbines can be

connected in parallel to match the system current requirements. This can be done with

parallel strings of the same wind turbine type or with strings of a different wind

turbine type.

Wind Turbine Costing

The wind turbine costing variables (initial cost, operation cost, and replacement cost)

are similarly derived as for the PV array. Wind turbine operating costs comprise

maintenance and replacement costs Maintenance costs for wind turbines can vary

53 depending on the application, type of maintenance and wind turbine sizes. The wind

turbine life is often assumed to be more than 20 years; therefore in many life cycle

costings no wind turbine replacement will take place. Annualized Total Cost of

running Wind Power only is calculated using equation (3.29)

3.2.3. Micro-Hydro Power

Hydro power is the generation of electrical energy by harnessing water’s kinetic

energy created by gravity. Hydro power is centered on the efficiency of the water's

kinetic energy converting to electrical energy. In hydro power, the kinetic energy of

the water depends on two aspects, head and flow. The head refers to the vertical

distance the water travels and the flow refers to the volume of the water that passes

through the turbine in a given amount of time [140]. The head of a site is the vertical

distance from the source, the surface, to the point of the water’s outflow [141]. When

evaluating a potential site, head is usually measured in feet, meters, or units of

pressure. Head also is a function of the characteristics of the channel or pipe through

which it flows [142]. The flow of the site is a volume of fluid that passes through a

given area per unit of time [141]. The flowing water moves through the system and

pushes the turbine to make it spin. The spinning of the turbine is turned into electricity

by means of a generator. The electrical energy created is usually stored in a battery

which can then power electrical objects in house, such as appliances and lightings.

When looking at the full process of micro hydro power and the transference of energy

from one form to another, one must also take into account that there are no toxic

emissions because micro hydro is a very environmentally-friendly source of power

[140]. Small hydro systems convert the potential and kinetic energy of moving water

into electricity, by using a turbine that drives a generator. As water moves from a

higher to lower elevation, such as in rivers and waterfalls, it carries energy with it, and

54 this energy can be harnessed by small hydro systems. An appreciable, constant flow

of water is critical to the success of a commercial small hydro project. The energy

available from a hydro turbine is proportional to the quantity of water passing through

the turbine per unit of time (i.e. the flow), and the vertical difference between the

turbine and the surface of the water at the water inlet (i.e. the head - in reality, this

must be adjusted for various losses). The power supplied by falling water is the rate at

which it delivers energy, and this depends on the flow rate and water head [143].

Micro hydro power is a site-specific type of renewable energy. Each different site

requires a separate evaluation in order to determine the energy output. A micro hydro

application is generally installed in home areas or any place where a small stream can

be harnessed for power. This means that each individual site will most likely, but not

necessarily, have a low head and a low flow. The higher head a site has, the higher the

final energy output will be. Higher heads require less water to produce a given amount

of power. Used for over one hundred years, small hydro systems are a reliable and

well-understood technology that can be used to provide power to an isolated grid or

an off-grid load, and may be either run-of-river systems or include a water storage

reservoir. Small hydro technology is extremely robust (systems can last for 20 years

or more with little maintenance) and is also one of the most environmentally benign

energy technologies available [39]. Generally, small hydro projects built for

application at an isolated area are run-of-river developments, meaning that water is

not stored in a reservoir and is used only as it is available.

The main advantages of small-scale hydropower are [39]:

• it is a much more concentrated energy resource than either wind or solar

power

• the energy available is readily predictable

55

• power is usually continuously available on demand

• no fuel and only limited maintenance are required

• it is a long-lasting technology

• it has almost no environmental impact.

Against these, the main shortcomings are:

• it is a site-specific technology; sites that are both well-suited to the harnessing

of water power and close to a location where the power can be exploited are

not all that common

• there is always a maximum useful power output available from a given hydro

power site, which limits the level of expansion of activities which make use of

the power

• river flows often vary considerably with the seasons, especially where there

are monsoon-type climates, and this can limit the firm power output to quite a

small fraction of the possible peak output

• there can be conflicts with fisheries interests on low-head schemes, and with

irrigation needs on high head schemes

• lack of familiarity with the technology and how to apply it inhibits the

exploitation of hydro resources in many areas.

However, where a hydropower resource exists, experience has shown that there is no

more cost-effective, reliable and environmentally-sound means of providing power

than a hydropower system [39].

The basic working principle of hydropower systems

Water at the top of a waterfall has more gravitational potential energy than when the

water is at the bottom of the waterfall, because the water at the top is further from the

center of the Earth than at the bottom [177]. So, if the water is allowed to fall from the

56 top to the bottom, (that is, the Earth's gravitational force does work on the water

moving it), then the energy stored as potential energy at the top becomes transformed

into the kinetic energy of this water. This is the principle behind the production of

hydroelectric power. Potential energy, therefore, is the energy associated with

different positions in the force field. The water at the top of a waterfall has higher

gravitational potential energy than at the bottom because of the different positions in

the gravitational field. In hydroelectric power plants, the potential energy of water due

to its high location is converted into electrical energy. The total power generation

capacity of the hydroelectric power plants depends on the volume of water flowing

towards the water turbine and the vertical distance (known as ‘head’) the water falls

through. Hydropower systems use the energy in flowing water to produce electricity.

Although there are several ways to harness the moving water to produce energy, run-

of-the-river systems, which do not require a dam or storage facility to be constructed,

are often used for micro-hydro power systems [178]. For run-of-the-river micro-

hydropower systems, a portion of a river's water is diverted to a water conveyance -

pressurized pipeline (penstock) that delivers it to a turbine. When the water reaches

the bottom, it rotates the turbine, which spins a shaft. The motion of the shaft powers

the generator which generate electricity. The method for the installation of a micro-

hydro power system (courtesy of the Guangxi Nanning Hecong) can be found in the

diagram below (figure 3.4).

57

Figure 3.4: Micro inclined-jet water turbine generator [179]

Hydro turbine generator: How it works

A hydraulic turbine converts the energy of flowing water into mechanical energy. A

hydroelectric generator converts this mechanical energy into electricity. The operation

of a generator is based on the principles discovered by Faraday. He found that when a

magnet is moved past a conductor, it causes electricity to flow. In a large generator,

electromagnets are made by circulating direct current through loops of wire wound

around stacks of magnetic steel laminations. These are called field poles, and are

mounted on the perimeter of the rotor. The rotor is attached to the turbine shaft, and

rotates at a fixed speed. When the rotor turns, it causes the field poles (the

electromagnets) to move past the conductors mounted in the stator. This, in turn,

cause

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59 Hydro Turbine Costing

The hydro turbine costing variables (initial cost, operation cost, and replacement cost)

are similarly derived as for the PV array and wind turbine. Annualized Total Cost of

running Hydro Power only is calculated using equation (3.31).

3.2.4. Diesel/Gasoline Engine-Generator Power Systems

Diesel generators are the most common generators in a large number of small and

remote power systems throughout the world. By and large they provide a dependable

AC output, but diesel fuel at these locations can often be very expensive due to the

additional transport costs involved [139]. A typical configuration is an

engine/generator set, where the shaft output of a diesel engine drives an electrical

generator, usually via a clutch or similar mechanism. A diesel generator has a higher

efficiency (35-45 percent), and can use a range of fuels including light oil, residual oil

and, even, palm or coconut oil. Diesel engines also have a wide capacity range, from 2

kW to 20 MW. Diesel generators are available in sizes ranging from under 1kW to

over a megawatt.

Diesel Generator Design

A diesel generator should be designed such that it meets the load reliably but also runs

on average at very high load levels. If a battery for short-term storage is installed in

the diesel generator system it can help to overcome peak loads and thereby reduce the

design capacity of the diesel generator, and system costs, if the inverter is sized

accordingly. The diesel generator is charging the battery via a battery charger that

converts the AC energy into DC energy. The battery allows the diesel engine to

operate close to its rated power and it can help reduce the start/stop cycles of the

generator resulting in a decrease in fuel consumption and maintenance costs.

60 Diesel Generator Sizing

The nominal voltage of the diesel generator in most cases matches the AC bus or DC

bus nominal voltage. Several diesel generators can run in parallel, so as to be able to

output different load levels at good capacity factors. It is assumed here that the

number of diesels in parallel does not exceed 2 which is a reasonable assumption.

Diesel Generator in a Hybrid System

The integration of renewable energy sources into a diesel system is far from

straightforward, especially where the renewable energy sources are expected to have a

large contribution. Depending on the specific control strategy, problems can include

[136].

• Unacceptably high numbers of generator stop-start cycles if it is operated as a

back-up generator. This is due to the variability of the renewable energy

sources and the consumer load.

• Prolonged low running of the diesel generator, which will lead to, increased

wear and maintenance together with a reduction in working lifetime. This is

the case if the diesel generator is over-sized and/or the renewable/battery

sources are designed as back-up sources and are not sufficient to cover the

load over some time periods.

• An increase in the specific fuel consumption of the diesel at low load

conditions which results from the last two points.

Criteria should thus be set such that the diesel is run favourably and these can include

next to the ones recommended generally for diesel generator operation [136].

• Starting criteria – Diesels are started for one of two reasons: firstly if the

renewable energies and the battery cannot meet the load and secondly if the

battery state of charge has fallen below a specified value.

61

• Shut off criteria – Generally diesels may be shut off if there is sufficient

power available from the renewable energies and the battery to supply the load

or if the battery has attained a respectable state of charge.

Many of these points can be achieved in a well-designed hybrid system as opposed to

a single-source system. In a hybrid system the diesel generator is often not used all the

time but only in certain instances (such as battery charging, covering peak loads)

where it can run at a high loading levels thus reducing wear and maintenance on the

generator. It can be easier to have the diesel shut-off during required times (e.g. at

night), as other energy sources and storage means are available. Setting minimum run-

times, where the diesel is forced to stay on for some defined period of time, is more

appropriate in a hybrid system as there is often a destination for excess energy

(battery charging, dump loads). A diesel generator in a renewable hybrid system often

eliminates the need to build in system autonomy and adds to the system reliability. In

addition, the design capacity of hybrid system component can often be reduced as

compared with their required sizing in single source systems.

Diesel Generator Costing

The initial costs of the diesel gensets are the diesel generator cost according to the size

of the diesel generator type, the percentage of capital costs added for installation and

BOS parts for diesel generator type and the corresponding added fixed costs. The

diesel generator operating costs comprise fuel costs, and maintenance, overhaul and

replacement costs. The fuel costs occur whenever the fuel storage tanks are refilled.

Overhaul is assumed half way through the diesel lifetime. Replacement occurs after a

certain number of diesel generators run time hours. The effective lifetime of a diesel

generator is defined by the time until a mechanical overhaul becomes uneconomic

(i.e. when overhaul costs exceed 60% of the replacement costs). Factors that affect

62 lifetime include the quality and regularity of maintenance, the average capacity factor

and the number of start-ups. An air-cooled machine is likely to have a shorter life than

a water-cooled unit that can keep the operating temperature down. Annualized Total

Cost of running Diesel Generator only is calculated using equation (3.28). The

installation costs, balance of system costs, fuel tank and shelter costs are included in

the fixed costs or as a percentage of initial costs. The diesel fuel consumption varies

according to generator size and loading factor and is non-linear.

3.3. Energy Storage

A battery is a device that stores Direct Current (DC) electrical energy in

electrochemical form for later use. The amount of energy that will be stored or

delivered from the battery is managed by the inverter. The inverter inverts the DC

electrical energy to Alternative Current (AC) electrical energy, which is the energy

that most residential homes use.

3.3.1. Battery Electricity

A battery is used as a backup system and it also maintains constant voltage across the

load. They are used to store excess energy for later use. Electrical energy is stored in a

battery in electrochemical form and is the most widely used device for energy storage

in a variety of applications. The conversion efficiency of batteries is not perfect.

Energy is lost as heat and in the chemical reaction, during charging or recharging.

Battery lifetime is measured both in terms of energy taken out of the battery and by

float life. A battery is weak or inactive when all available energy has been taken out

of it or when the average battery capacity has been reduced to 20% of its original

value.

63 Battery Design

When selecting a battery type, usually lead acid type batteries are chosen. In general

lead acid batteries are more-cost effective than NiCad batteries, but the latter may be

the better choice if greater battery ruggedness is an important consideration [139].

This is because the NiCad is the only battery type that performs well under rigorous

working conditions. In fact, a periodic full discharge is so important that, if omitted,

large crystals will form on the cell plates (also referred to as memory) and the NiCad

will gradually lose its performance. Selection of battery voltage depends on inverter

and generation controller equipment generally available. They come in specific

voltages from 12, 24, 48 up to 120 and 240V DC and thus batteries must be selected

and combined in series to meet this voltage requirement. The number of battery

strings that can be connected in parallel is limited to about five without rigorous

monitoring and higher maintenance costs. This means that once the general battery

bank capacity has been selected the size of the individual battery type must be chosen

accordingly. Batteries should be installed in a vented, enclosed area. Batteries may be

connected in series to increase the battery bank voltage and in parallel to increase the

capacity. The batteries in a bank should all be of the same brand, model and age

[139]. This is because different brands of batteries can have different charging and

discharging characteristics, with some accepting a charge or delivering current faster

than others. That can be true even if the batteries are the same size. Different types of

batteries (flooded or Absorbed Glass Mat (AGM)) also can have different

charge/discharge characteristics. When you connect two or more batteries that don't

charge and discharge at the same rate, one battery will probably end up overcharged

and/or one battery will end up undercharged. The same is also true of batteries that are

identical in every way, except that one battery is older than the other. As batteries age

(or get used), their charge/discharge profile changes.

64

Batteries in a Hybrid System

Battery operation in a hybrid system, as opposed to a single-source application, may

result in certain advantages with respect to battery lifetime optimisation. This can be

attributed to the fact that there is often more sophisticated control installed in a hybrid

system due to the interaction of many components. This requires better regulation of

components and will result in better treatment of the battery. Moreover, there are

more energy sources available resulting in the battery not being utilised to as high a

degree as in single-source systems. Reduced cycling leads to increased lifetime and

more time (and resources) available for recharging and boost charging [136]. Batteries

are costly and can often be sized smaller in a hybrid system than in a single-source

system.

Battery Sizing

Battery sizing consists in calculating the number of batteries needed for a renewable

energy system. This mainly depends on the days of autonomy desired. Days of

autonomy are the number of days a battery system will supply a given load without

being recharged by a PV array, wind turbine or another source. If the load being

supplied is not critical then 2 to 3 autonomy days are commonly used. For critical

loads 5 days of autonomy are recommended. A critical load is a load that must be

used all the time. The battery’s capacity will decrease at lower temperatures and

increase at higher temperature. The battery’s life increases at lower temperature and

decreases at higher. It is recommended to keep the battery’s storage system at 25 ºC.

At 25 ºC the derate factor is one.

65 Battery Costing

The initial battery costs are the installation costs and balance of system costs which

will be accounted for in the fixed costs or as a percentage of initial battery capital

costs. The battery operation costs depend on the battery cycling during system

operation and also include fixed costs and costs at regular intervals such as

maintenance costs. Maintenance includes checking the battery electrolyte levels [144].

Battery maintenance costs are often included in maintenance costs of the overall

system or individual electricity generators. Replacement costs occur whenever a

battery needs to be exchanged with a new or newer one. Annualized Total Cost of

running Battery only is calculated using equation (3.32).

3.4. Conversion Devices

Power converters are used to convert DC power, e.g. from PV panels, hydro turbines,

batteries and wind turbines, to AC power, which is required by most electrical

appliances, and vice versa. Engine generators typically produce AC power which can

be converted to DC power with the help of a rectifying battery charger in order to be

used to charge batteries. The most common power conversion devices are electronic

and include inverters (DC to AC), rectifiers (AC to DC) and bi-directional converters

(both directions) [136, 139].

3.5. Modeling of Hybrid Energy System Components

Before going to the computer simulation, mathematical modeling of hybrid energy

system components is described below. The proposed system contains micro hydro

generating subsystem, wind energy sub-system, PV sub-system, diesel generators unit

and battery storage sub-system. The theoretical aspects are given below (A, B, C, D,

E, and F) and are based on the works of Gupta et al, and Ashok [34, 90].

66

A. Mathematical Model of Micro-Hydro Generator

In the proposed scheme the micro hydro generator will supply power to the base load.

The capacity factor of the resources is taken as unity. The sub-system is considered as

run of river with small pondage. The available power will be dependent of seasonal

variation of the resources.

The electrical power generated by small hydropower generator is given by [62, 107]:

( )1000

81.9 hQtP Water

HydroMHP×××

=ρ

η ----------------------------------------------------- (3.1)

and the total energy in kWh is given by

( ) ( ) ttPtE MHPMHP ×= ---------------------------------------------------------------------- (3.2)

where ( )tPMHP is the electrical power generated by micro hydropower

generator

Hydroη is the hydro efficiency

Waterρ is the density of water

h is the falling height, head (m)

( )tEMHP is the electrical energy generated by micro hydropower

generator

t is time in Hour

B. Mathematical Model of Solar Photovoltaic Generator

Using the solar radiation available, the hourly energy output of the PV generator

( )PVGE can be calculated according to the following equation [62, 107]:

( ) PVGPVG PAtGE η×××= ---------------------------------------------------------------- (3.3)

An assumption was made that the temperature effects (on PV cells) will be ignored.

67

where ( )tG is the hourly irradiance in 2/ mkWh

A is the surface area in m2

P is the PV penetration level factor

PVGη is the efficiency of PV generator

C. Mathematical Model of Wind Energy Generator

Hourly energy generated by wind generator ( )WEGE with rated power output ( )WEGP is

defined by the following expression [62,107, 145]:

( ) gtPWindWEG CAvP ηηβλρ ××= ,21 3 ----------------------------------------------------- (3.4)

( ) tPtE WEGWEG ×= -------------------------------------------------------------------------- (3.5)

where Windρ is the density of air in 1.22kg/m3

A is the swept area (m2)

v is the wind speed (m/s)

PC is the performance coefficient of the turbine

λ is the tip speed ratio of the rotor blade tip speed to wind speed

β is the blade pitch angle (deg) as 0o

tη is the wind turbine efficiency

gη is the generator efficiency

D. Mathematical Model of Diesel Generator

Hourly energy generated by diesel generator ( )DEGE with rated power output ( )DEGP

is defined by the following expression [62]:

( ) ( ) DEGDEGDEG tPtE η×= ------------------------------------------------------------------- (3.6)

68

where DEGη is the diesel generator efficiency

For better performance and higher efficiency the diesel generator will always operate

between 80 and 100% of their kW rating.

E. Mathematical Model of Converter

In the proposed scheme, a converter contains both rectifier and inverter. PV, hydro,

wind energy generator and battery sub-systems are connected with DC bus while

diesel generating unit sub-system is connected with AC bus. The electric loads

connected in this scheme are DC loads.

The rectifier is used to transform the surplus AC power from the diesel electric

generator to DC power of constant voltage. The diesel electric generator will be

powering the load and at the same time charging the battery. The rectifier model is

given below:

( ) ( ) RECINRECOUTREC tEtE η×= −− ----------------------------------------------------------- (3.7)

( ) ( )tEtE ACSURINREC −− = --------------------------------------------------------------------- (3.8)

At any time t,

( ) ( ) ( )tEtEtE LoadDEGACSUR −=− ---------------------------------------------------------- (3.9)

where ( )tE OUTREC− is the hourly energy output from rectifier, kWh

( )tE INREC− is the hourly energy input to rectifier, kWh

RECη is the efficiency of rectifier

( )tE ACSUR− is the amount of surplus energy from AC sources, kWh

( )tEDEG is the hourly energy generated by diesel generator

69 F. Mathematical Model of Charge Controller

To prevent overcharging of a battery, a charge controller is used to sense when the

batteries are fully charged and to stop or reduce the amount of energy flowing from

the energy source to the batteries. The model of the charge controller is presented

below:

( ) ( ) CCINCCOUTCC tEtE η×= −− ------------------------------------------------------------- (3.10)

( ) ( ) ( )tEtEtE DCSUROUTRECINCC −−− += --------------------------------------------------- (3.11)

where ( )tE OUTCC− is the hourly energy output from charge controller, kWh

( )tE INCC− is the hourly energy input to charge controller, kWh

CCη is the efficiency of charge controller

( )tE OUTREC− is the hourly energy output from rectifier, kWh

( )tE DCSUR− is the amount of surplus energy from DC sources, kWh

G. Mathematical Model of Battery Bank

The battery state of charge (SOC) is the cumulative sum of the daily charge/discharge

transfers. The battery serves as an energy source entity when discharging and a load

when charging. At any time, t, the state of battery is related to the previous state of

charge and to the energy production and consumption situation of the system during

the time from t -1 to t.

During the charging process, when the total output of all generators exceeds the load

demand, the available battery bank capacity at time, t, can be described by [107, 145]:

( ) ( ) ( ) CHGOUTCCBATBAT tEtEtE η×−−= −1 ----------------------------------------------- (3.12)

where ( )tEBAT is the energy stored in battery at hour t, kWh

( )1−tEBAT is the energy stored in battery at hour t-1, kWh

70

CHGη is the battery charging efficiency

On the other hand, when the load demand is greater than the available energy

generated, the battery bank is in discharging state. Therefore, the available battery

bank capacity at time, t, can be expressed as [107]:

( ) ( ) ( )tEtEtE NeededBATBAT −−= 1 -------------------------------------------------------- (3.13)

where ( )tENeeded is the hourly load demand or energy needed at a particular

period of time.

Let d be the ratio of minimum allowable SOC voltage limit to the maximum SOC

voltage across the battery terminals when it is fully charged. So, the Depth of

Discharge (DOD)

DOD = (1− d) ×100----------------------------------------------------------------------- (3.14)

DOD is a measure of how much energy has been withdrawn from a storage device,

expressed as a percentage of full capacity. The maximum value of SOC is 1, and the

minimum SOC is determined by maximum depth of discharge (DOD),

1001 DODSOC Min −= ---------------------------------------------------------------------- (3.15)

3.6. Power Generation Model

Total hybrid power generated at any time t, is given by [62, 107]:

( ) ∑ ∑ ∑ ∑= = = =

+++=H W P DN

MHP

N

WEG

N

PVG

N

DEGDEGPVGWEGMHP PPPPtP

1 1 1 1-------------------------------- (3.16)

where MN is the number of micro-hydro generators unit

WN is the number of wind generators unit

PN is the number of PV cells unit

DN is the number of diesel generators unit

71 This generated power will feed to the loads. The diesel generator has the constraint to

always operate between 80 and 100% of their kW rating. When this generated power

exceeds the load demand then the surplus of energy will be stored in the battery bank.

This energy will be used when deficiency of power occurs in order to meet the load

demand. The charged quantity of the battery bank has the constraint

( ) MAXMIN SOCtSOCSOC ≤≤ . The dump load will draw excess energy produced by

the renewable generators or diesel generators but unused when the load does not need

all the energy and the battery has reached its maximum capacity and cannot store

more energy. The approach involves the minimization of a cost function subject to a

set of equality and inequality constraints.

3.7. Mathematical Cost Model (Economic & Environmental Costs) of Energy Systems This work developed a mathematical model of a system that could deploy the best

renewable energy options available at any base station located in any part of a country

in the world; where the term Best represents the integral (total sum) of the minimum

economic and environmental (health and safety) costs of the considered options. For

this purpose we adopt the Hybrid Optimization Model for Electric Renewable

(HOMER).

The Annualized Cost of a Component

The annualized cost of a component includes annualized capital cost, annualized

replacement cost, annual O&M cost, emissions cost and annual fuel cost (generator).

Operation cost is calculated hourly on daily basis. [107 - 108]:

72 Annualized Capital Cost

The annualized capital cost of a system component is equal to the total initial capital

cost multiplied by the capital recovery factor. Annualized capital cost is calculated

using [107 - 108]:

( )projcapacap RiCRFCC ,⋅= --------------------------------------------------------------- (3.17)

where ( )projRiCRF , =capital recovery factor

capC =initial capital cost of the component

Annualized Replacement Cost

The annualized replacement cost of a system component is the annualized value of all

the replacement costs occurring throughout the lifetime of the project, minus the

salvage value at the end of the project lifetime [107 - 108].

Annualized replacement cost is calculated using [107 - 108]:

( ) ( )projcomprepreparep RiSFFSRiSFFfCC ,, ⋅−⋅⋅= ------------------------------------ (3.18)

where repC = replacement cost of the component

( )SFF = sinking fund factor

i = interest rate

compR = lifetime of the component

projR = project lifetime

N = number of years

repf , a factor arising because the component lifetime can be different from the project

lifetime, is given by:

73

( ) ( )⎪⎩

⎪⎨⎧

=

>=

0 , 0

0 ,,,

rep

reprepprojrep R

RRiCRFRiCRFf --------------------------------------- (3.19)

repR , the replacement cost duration, is given by:

⎟⎟⎠

⎞⎜⎜⎝

⎛⋅=

comp

projcomprep R

RINTRR --------------------------------------------------------------- (3.20)

where ( )INT is the integer function, returning the integer portion of a real

value.

( )SFF , the sinking fund factor is a ratio used to calculate the future value of a series

of equal annual cash flows, is given by:

( )( ) 11

,−+

= NiiNiSFF ------------------------------------------------------------------ (3.21)

where N = number of years

The salvaged value of the component at the end of the project lifetime is proportional

to its remaining life. Therefore the salvage value S is given by:

comp

remrep R

RCS ⋅= --------------------------------------------------------------------------- (3.22)

remR , the remaining life of the component at the end of the project lifetime is given

by:

( )repprojcomprem RRRR −−= ------------------------------------------------------------- (3.23)

Annualized Operating Cost

The operating cost is the annualized value of all costs and revenues other than initial

capital costs and is calculated using: [107 - 108]:

( )[ ]∑ ∑= = ⎭

⎬⎫

⎩⎨⎧

=365

1

24

1t tocaop tCC ------------------------------------------------------------------- (3.24)

where ( )tCoc is the Cost of operating component.

74

Cost of Emissions

The following equation is used to calculate the cost of emissions [107 - 108]:

( )25.31000

2222 −−−−−+++++

= XX NONOSOSOPMPMUHCUHCCOCOCOCOemissions

McMcMcMcMcMcC

where 2COc = cost for emissions of 2CO [$/t]

COc = cost for emissions of CO [$/t]

UHCc =cost for emissions of unburned hydrocarbons (UHC) [$/t]

PMc =cost for emissions of particulate matter (PM) [$/t]

2SOc =cost for emissions of 2SO [$/t]

xNOc =cost for emissions of xNO [$/t]

2COM =annual emissions of 2CO [kg/yr]

COM =annual emissions of CO [kg/yr]

UHCM =annual emissions of unburned hydrocarbons (UHC) [kg/yr]

PMM =annual emissions of particulate matter (PM) [kg/yr]

2SOM =annual emissions of 2SO [kg/yr]

xNOM =annual emissions of xNO [kg/yr]

Total cost of a component = Economic cost + Environmental cost

where Economic cost = Capital cost + Replacement cost + Operation &

Maintenance cost + Fuel cost (Generator)

Environmental cost = Emissions cost

75 Annualized cost of a component is calculated using [107 - 108]:

emissionsaoparepacapann CCCCC +++= --------------------------------------------------- (3.26)

Annualized total cost of a component is calculated using [107 - 108]:

( )∑=

+++=cN

cemissionscaopcarepcacapctotann CCCCC

1,,,,, ------------------------------------- (3.27)

where cacapC , = Annualized capital cost of a component

carepC , = Annualized replacement cost of a component

caopC , = Annualized operating cost of a component

From equation (3.27), the Economic and Environmental cost model through

Annualized Total Cost of different Configurations of Power System were derived as

follows:

Economic and Environmental cost model of running Diesel Generator only is

calculated using:

( )∑=

++++=gN

gafgemissionsgaopgarepgacapgtotann CCCCCC

1,,,,, ---------------------------- (3.28)

where gacapC , = Annualized Capital Cost of Diesel Generator

garepC , = Annualized Replacement Cost of Diesel Generator

gaopC , = Annualized Operating Cost of Diesel Generator

gafC , = Annualized Fuel Cost for Diesel Generator

Economic and Environmental cost model of running Wind Power only is calculated

as:

( )∑=

+++=wN

wemissionswaopwarepwacapwtotann CCCCC

1,,,,, ------------------------------------ (3.29)

where wacapC , = Annualized Capital Cost of Wind Power

76

warepC , = Annualized Replacement Cost of Wind Power

waopC , = Annualized Operating Cost of Wind Power

Economic and Environmental cost model of running Solar Power only is calculated

as:

( )∑=

+++=sN

semissionssaopsarepsacapstotann CCCCC

1,,,,, ------------------------------------- (3.30)

where sacapC , = Annualized Capital Cost of Solar Power

sarepC , = Annualized Replacement Cost of Solar Power

saopC , = Annualized Operating Cost of Solar Power

Economic and Environmental cost model of running Hydro Power only is calculated

as:

( )∑=

+++=hN

hemissionshaopharephacaphtotann CCCCC

1,,,,, ------------------------------------- (3.31)

where hacapC , = Annualized Capital Cost of Hydro Power

harepC , = Annualized Replacement Cost of Hydro Power

haopC , = Annualized Operating Cost of Hydro Power

Economic and Environmental cost model of running Batteries is calculated as:

( )∑=

+++=bN

bemissionsbaopbarepbacapbtotann CCCCC

1,,,,, ------------------------------------- (3.32)

where bacapC , = Annualized Capital Cost of Batteries Power

barepC , = Annualized Replacement Cost of Batteries Power

baopC , = Annualized Operating Cost of Batteries Power

77 Economic cost model of Converter is calculated as:

( )∑=

++=cN

ccaopcarepcacapctotann CCCC

1,,,,,

where cacapC , = Annualized Capital Cost of Converter Power

carepC , = Annualized Replacement Cost of Converter Power

caopC , = Annualized Operating Cost of Converter Power

Hybridizing the renewable energy generators (PV, Wind, and Hydro) with existing

energy (Diesel) results in seven (7) different topologies as:

Economic and Environmental cost model of running Solar only + Diesel Generator

+ Batteries + Converter is calculated as:

( ) ( )

( ) ( ) )33.3( 1

,,,1

,,,

1,,,

1,,,,,

−−−−−−−++++++

+++++++++=

∑∑

∑∑

==

==+++

cb

gs

N

ccaopcarepcacap

N

bemissionsbaopbarepbacap

N

gafgemissionsgaopgarepgacap

N

semissionssaopsarepsacapcbgstotann

CCCCCCC

CCCCCCCCCC

Economic and Environmental cost model of running Wind only + Diesel Generator


( ) ( )

( ) ( ) )34.3( 1

,,,1

,,,

1,,,

1,,,,,

−−−−−−−−++++++

+++++++++=

∑∑

∑∑

==

==+++

cb

gw

N

ccaopcarepcacap

N


N


N

wemissionswaopwarepwacapcbgwtotann

CCCCCCC

CCCCCCCCCC

Economic and Environmental cost model of running Hydro only + Diesel Generator


( ) ( )

( ) ( ) )35.3( 1

,,,1

,,,

1,,,

1,,,,,

−−−−−−−−++++++

+++++++++=

∑∑

∑∑

==

==+++

cb

gh

N

ccaopcarepcacap

N


N


N

hemissionshaopharephacapcbghtotann

CCCCCCC

CCCCCCCCCC

78 Economic and Environmental cost model of running Hybrid (Wind & Solar) +

Diesel Generator + Batteries + Converter is calculated as:

( ) ( )

( ) ( )

( ) )36.3(1

,,,

1,,,

1,,,

1 1,,,,,,,,

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−++

+++++++++

++++++++=

∑

∑∑

∑ ∑

=

==

= =++++

c

bg

w s

N

ccaopcarepcacap

N


N


N

w

N

semissionssaopsarepsacapemissionswaopwarepwacapcbgswtotann

CCC

CCCCCCCCC

CCCCCCCCC

Economic and Environmental cost model of running Hybrid (Hydro & Solar) +


( ) ( )

( )

( ) ( ) )37.3(1 1

,,,,,,

1,,,

1 1,,,,,,,,

−−−−−−−−−−−−−−−++++++

+++++

++++++++=

∑ ∑

∑

∑ ∑

= =

=

= =++++

b c

g

h s

N

b

N

ccaopcarepcacapemissionsbaopbarepbacap

N


N

h

N

semissionssaopsarepsacapemissionshaopharephacapcbgshtotann

CCCCCCC

CCCCC

CCCCCCCCC

Economic and Environmental cost model of running Hybrid (Hydro & Wind) +


( ) ( )

( )

( ) ( ) )38.3(1 1

,,,,,,

1,,,

1 1,,,,,,,,

−−−−−−−−−−−−−−++++++

+++++

++++++++=

∑ ∑

∑

∑ ∑

= =

=

= =++++

b c

g

h w

N

b

N

ccaopcarepcacapemissionsbaopbarepbacap

N


N

h

N

wemissionswaopwarepwacapemissionshaopharephacapcbgwhtotann

CCCCCCC

CCCCC

CCCCCCCCC

79 Economic and Environmental cost model of running Hybrid (Hydro/ Solar/wind) +


( ) ( )

( )

( ) ( )

( ) )39.3(1

,,,

1 1,,,,,,,

1,,,

1 1,,,,,,,,

−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−++

+++++++++

++++

++++++++=

∑

∑ ∑

∑

∑ ∑

=

= =

=

= =+++++

c

g b

w

h s

N

ccaopcarepcacap

N

g

N

bemmisionsbaopbarepbacapgafemissionsgaopgarepgacap

N

wemissionswaopwarepwacap

N

h

N

semissionssaopsarepsacapemissionshaopharephacapcbgwshtotann

CCC

CCCCCCCCC

CCCC

CCCCCCCCC

The mathematical model derived estimates the life-cycle cost of the system, which is

the total cost of installing and operating the system over its lifetime. The output when

run with HOMER software/tool will give us the optimal configuration of the energy

system that takes into account technical and economic performance of supply options

(rated power characteristics for solar Photovoltaic (PV), power curve characteristics

for wind turbine (WT), fuel consumption characteristics for diesel generators (DG)

and minimum and maximum state of charge (SOC) of a battery bank), the 25-year life

cycle cost (LCC) of equipment, locally available energy resources (hourly solar

insolation data (W/m2), hourly wind speed (m/s), hourly stream flow (L/s), as well as

cost of fossil fuels), environmental costs, and system reliability.

3.8. The Energy Optimization Model

The process of optimization had been of concern for man for many ages [146].

Previously there were no defined and scientific rules for optimum conditions. But

with the passage of time and advancement in science and technology, everything was

considered to be based on certain reasons or logics. Mathematical calculations

involving process of optimization have become more famous in recent years. True

meaning of optimization is to find the best answer for a particular problem. For

80 example, problems dealing with the cost will require the best cost to be as less as

possible. On the other hand, problems dealing with profit will see the maximum value

as the best answer. So ‘Optimum’ is the word which is used to demonstrate the

meaning of best, and the process of finding the best solution to a particular problem is

known as the process of optimization [146 - 147]. To solve an optimization problem,

we require an optimization algorithm. An optimization algorithm is the algorithm

which is used to define an optimized solution for a particular function. Thus, for the

stand-alone PV/Wind/Hydro-Diesel hybrid system with constraints under study, we

have the following optimization algorithm.

Minimize

( )40.3−−−−−+++++= ∑∑∑∑∑∑n

CCBm

Bl

DGDGk

HHj

WWi

PVPV nnmmllkkjjiiNCNCNCNCNCNCCost

Subject to the constraints

( )41.3−−−−−−−−−−−−−−−−−−−+++≤ ∑∑ ∑ ∑l

DGDGi j k

HHWWPVPV llkkjjiiNENENENELoad

( )42.3agePower Watt Maximum −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−≤ ∑n

CC nnNP

( ) ( )43.3maxmin −−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−−≤≤ SOCtSOCSOC

where iPVC = Cost of a photovoltaic module

jWC = Cost of wind turbine

kHC = Cost of hydro turbine

lDGC = Cost of a diesel generator

mBC = Cost of a battery

nCC = Cost of a converter

iPVN = Number of photovoltaic modules

81

jWN = Number of wind turbines

kHN = Number of hydro turbines

lDGN = Number of diesel generators

mBN = Number of battery bank to be use.

nCN = Number of converters

iPVE = kWh generated by photovoltaic module i

jWE = kWh generated by wind turbine j

kHE = kWh generated by hydro turbine k

lDGE = kWh generated by a diesel generator l

nCP = Maximum output power of converter n

minSOC = State of battery charge at minimum

maxSOC = State of battery charge at maximum

In the optimization procedure, the sizes of system components are decision variables,

and their costs and the pollutant emissions are the objective function.

Objective Function

The objective functions to be minimized are the total costs (N) and the pollutant

emissions (kg of CO2).

Costs

The costs objective function is the Total Net Present Cost of the system (NPC), which

includes the cost of the initial investment plus the discounted present values of all

future costs throughout the total life of the installation. The life of the system is

82 usually considered to be the life of the PV panels - which are the elements that have a

longer lifespan. In the following paragraph, the costs taken into account are indicated.

• Cost for purchasing the PV panels, the wind turbine, the hydro turbine, the

batteries, the inverter, the charge regulator, and the Diesel generator.

• Costs of maintenance of the components.

• Costs of replacing the components throughout the life of the system.

• Costs of operation and maintenance of components throughout the life of the

system.

• Cost of the fuel consumed throughout the life of the system.

A more detailed description of its calculation can be found in [59, 120, 148-149].

Pollutant Emissions

In order to measure the pollutant emissions, the weight of CO2, in kg, is considered; it

represents the largest percentage of all emissions when Fuel is burnt [150], and it is

the main cause of the greenhouse effect. It is considered that the total amount of kg of

CO2 produced by the hybrid system throughout one year is the correct measure of the

pollutant emissions and, therefore, it can be used as the objective to be minimized.

The developed algorithm has as input data the number of kg of CO2 produced per litre

of fuel consumed by the Diesel generator. This value depends on the characteristics of

the Diesel generator and of the characteristics of the fuel, and it usually falls in the

2.4-2.8 kg/l range [150]. Energy Optimization in base stations is being carried out

globally by industrial giants such as Ericsson, Chevron and Energy Cybernetics [151 -

153] etc. It involves the use of alternatives such as bio fuel, solar energy, wind and

hydro energy. In this thesis, the parameters useful for making the decision on the type

of energy solution suitable for a site and its location are grouped into the following:

• Total Cost of Energy Generation and;

83

• Total Environmental impact of each energy solution

These two summarize all the factors we proposed for evaluating the suitability of

energy solution for any BTS and location. The model is produced here in the figure

(3.6) below.

Figure 3.6: Model for choosing Power Solution for a BTS Site.

Net Present Cost (NPC) for each component

The total net present cost (NPC) of a system is the present value of all the costs that it

incurs over its lifetime minus the present value of all the revenue that it earns over its

lifetime. Revenues include salvage value and grid sales revenue. The Net Present Cost

(NPC) for each component is derived using [108 - 109]:

( )proj

totannNPC RiCRF

CC

,,= --------------------------------------------------------------------- (3.44)

Site load

Determine the size and configuration of the power solutions

Determine the cost (economic &

environmental) of the power solutions

Determine the best Energy solution to

meet cost and performance targets

Location and Meteorological

Conditions

84 The capital recovery factor (CRF) is [108 - 109]:

( )( ) 11

1−+

+⋅= N

N

iiiCRF ------------------------------------------------------------------------ (3.45)

The economic optimization identifies the most financially attractive solution for eight

scenarios - various combinations of Diesel Generator, Hydro, wind, and solar power.

The work uses HOMER but this study has tried to go beyond by including a detailed

load analysis and suggesting ways of analysing the results. Data for the eight

optimized scenarios are reported and graphed to present the viability and financial

impact of each scenario.

3.9. Calibration of the Model

Without validating data coupled with optimization and modelling there is little reason

to believe that the conclusions stated in any paper has applicability beyond the

immediate circumstances stated in each specific paper. Before using the measured

data gotten from NASA datasets in simulating the individual components of a

PV/Wind/Hydro-Diesel Hybrid system, HOMER accuracy must be established. If the

simulated data predicted by the software programs do not fall within the bounds of the

measured data, then there is either a problem with how the models are formulated or a

problem with the models that the programs use. If HOMER is proven sufficiently

accurate, the software program will be used for the simulation and optimization of the

hybrid PV/Wind/Hydro-Diesel system.

85 The algorithm that HOMER uses to synthesize solar data is based on the work of

Graham [154]. The realistic nature of synthetic data created by this algorithm is

demonstrated in the figures below.

0

1

2

3

4

5

6

7

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Abaji

Measured

Simulated

(a)

0

1

2

3

4

5

6

7

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Nkanu‐West

Measured

Simulated

(b)

86

0

1

2

3

4

5

6

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Ikwerre

Measured

Simulated

(c)

0

1

2

3

4

5

6

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Nembe

Measured

Simulated

(d)

0

1

2

3

4

5

6

7

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Mopa‐Muro

Measured

Simulated

(e)

87

Figure 3.7: Caliberated solar radiation in (a) Abaji, (b) Nkanu-West, (c) Ikwerre, (d) Nembe, (e) Mopa-Muro, (f) Kauru, (g) Guzamala and (h) Tureta locations.

0

1

2

3

4

5

6

7

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Kauru

Measured

Simulated

(f)

012345678

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Guzamala

Measured

Simulated

(g)

012345678

Daily Rad

iation

(kWh/m

2 /d)

Month of the Year

Tureta

Measured

Simulated

(h)

88

The blue colour shows the measured solar data from NASA for the study locations,

while the red colour shows the synthetic data created by specifying the study

location's latitude and monthly average radiation values as shown in figure 3.7. From

these graphs, it was shown that the simulated data from HOMER fall within the

bounds of the measured data (solar radiation). Our tests show that synthetic solar data

produce virtually the same simulation results as real data.

3.10. Materials and Method

HOMER assists researchers in designing an optimal hybrid power system based on

comparative economic analysis. The HOMER software determines optimal hybrid

systems using combinations of photovoltaics, wind turbines, micro-hydro, diesel

generation, battery storage, and inverter capacity. HOMER also takes into account

both seasonal and hourly load variations as well as variations in resource availability

such as wind and sunlight [155]. In addition, HOMER outputs multiple options

ranked in order of least net present cost, which is based on a 25-yr lifecycle cost

including interest.

LOAD

Hourly load demand (Macro Base Station Site perspective) has been given as an input

in HOMER and then it generates daily and monthly load profile for a year (figure 3.8

and 3.9). It has been found that this site consumes energy around 254kWh/day with a

peak demand of nearly 10.67kW as shown in figure 3.22. The table below (table 3.1)

shows the hourly load demand for radio base station and climate & auxiliary

equipment.

89 Table 3.1: Load Inputs for Radio Base Station and Climate & Auxiliary Equipment.

Hour Radio Base Station Baseline Data Load (kW)

Climate & Auxiliary Equipment Baseline Data Load (kW)

00:00 - 01:00 7.860 2.790 01:00 - 02:00 7.860 2.790 02:00 - 03:00 7.860 2.790 03:00 - 04:00 7.860 2.790 04:00 - 05:00 7.860 2.790 05:00 - 06:00 7.860 2.790 06:00 - 07:00 7.860 2.790 07:00 - 08:00 7.860 2.590 08:00 - 09:00 7.860 2.590 09:00 - 10:00 7.860 2.590 10:00 - 11:00 7.860 2.590 11:00 - 12:00 7.860 2.590 12:00 - 13:00 7.860 2.590 13:00 - 14:00 7.860 2.590 14:00 - 15:00 7.860 2.590 15:00 - 16:00 7.860 2.590 16:00 - 17:00 7.860 2.590 17:00 - 18:00 7.860 2.790 18:00 - 19:00 7.860 2.790 19:00 - 20:00 7.860 2.790 20:00 - 21:00 7.860 2.790 21:00 - 22:00 7.860 2.790 22:00 - 23:00 7.860 2.790 23:00 - 00:00 7.860 2.790

Average (kWh/d) 189 65.0

Figure 3.8: Overview of HOMER output graphic for DC Load of Radio Base Station Equipment.

90

Figure 3.9: Overview of HOMER output graphic for DC Load of Climate & Auxiliary Equipment

Resources Assessment

In the system designed by HOMER, resource is anything that can be used to generate

electricity and comes from outside the system. RES available at a location can differ

considerably from site to site and this is a vital aspect in developing the hybrid

system. As RES like wind, solar and hydro are naturally available and intermittent,

they are the best option to be combined into a hybridized diesel system. All of these

resources depend on different factors – apart from seasonal or even hourly changes:

Whereas the amount of solar energy available is dependent on climate and latitude,

the hydro resource depends from the location’s topography and its rainfall patterns;

the wind resource is influenced by atmospheric circulation patterns and geographic

aspects. The resources’ dependence of various factors in turn influences when and

how much power can be generated and thus the behaviour and economics of the

hybrid system. As a consequence, successful system modelling significantly depends

on the accurate modelling of the RES.

91 Choice of BTS Site Locations

The locations of the hypothetical BTS sites are chosen to reflect the various

geographical and climatic conditions in Nigeria. It is known that the performances of

certain renewable energy components like wind and solar are influenced by the

geographical location and climatic conditions [35]. These locations are:

1). Nkanu-West (Enugu State), Nembe (Bayelsa State), and Ikwerre (Rivers State) on

the equatorial climatic region.

2). Mopa-Muro (Kogi State), Guzamala (Borno State), Kauru (Kaduna State), and

Tureta (Sokoto State) on the arid climatic region, and

3). Abaji (Abuja, FCT) in the tropical climatic region.

and these are indicated by red circles in the geographical map of Nigeria below.

Figure 3.10: Map of Base Station Site Locations on Study

92 Since weather data are important factor for pre- feasibility study [38] of renewable

hybrid energy system for any particular site, the data for monthly average solar

radiation and wind speed for a given month, averaged for that month (and for year)

over a 10-year period (July 1983 - June 1993) were obtained from the meteorological

department, Oshodi and from satellite – derived meteorological and solar energy

parameter tables (of 2012) from NASA [156], while the data for the hydro resource

were gotten from the flow measurement of Ngenene stream (see the location and

description in the appendix A), which was used in establishing a general micro

hydropower for the studied 8 hypothetical BTS site locations in the rural areas across

the country. Obviously many rural communities with rivers/streams in Nigeria have

almost the same parameter with that of Ngenene stream which can be used to develop

a small hydro power scheme for telecommunication. The specific geographical

locations of the sites based on solar and wind resources are as follows:

• Abaji (Abuja, FCT) at a location of 9° 00' N latitude and 7° 00' E longitude

with annual average solar daily radiation of 5.45 kWh/m²/d whereas its annual

average wind is 2.4m/s. Figures 3.11a and 3.11b show the solar and wind

resource profile of the location.

• Nkanu-West (Enugu State) at a location of 6° 00' N latitude and 7° 00' E

longitude with annual average solar (clearness index and daily radiation) of

4.95kWh/m²/d whereas its annual average wind is 2.1m/s. Figures 3.12a and

3.12b show the solar resource profile and wind resource profile in Nkanu-

West.

• Ikwerre (River State) at a location of 4° 00' N latitude and 7° 00' E longitude

with annual average solar (clearness index and daily radiation) of


3.13b show the solar and wind resource profile of this location.

93

• Nembe (Bayelsa State) at a location of 4° 17' N latitude and 6° 25' E longitude



3.14b show the solar and wind resource profile of this area.

• Mopa-Muro (Kogi State) at a location of 7° 00' N latitude and 6° 00' E



3.15b show the solar and wind resource profile of this location.

• Kauru (Kaduna State) at a location of 10° 00' N latitude and 7° 00' E longitude



3.16b show the solar and wind resource profile of this area.

• Guzamala (Borno State) at a location of 11° 05' N latitude and 13° 00' E


5.89 kWh/m²/d whereas its annual average wind is 2.1m/s. Figures 3.17a and

3.17b show the solar and wind resource profile of the location.

• Tureta (Sokoto State) at a location of 13° 00' N latitude and 5° 00' E longitude



3.18b show the solar and wind resource profile of the location.

94

Figure 3.11a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Abaji

Figure 3.11b: HOMER output graphic for Wind Speed profile for Abaji

Figure 3.12a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Nkanu-West

Figure 3.12b: HOMER output graphic for Wind Speed profile for Nkanu-West

0.0

0.2

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0

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ly R

adia

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²/d)

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arne

ss In

dex

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Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec0.0

0.5

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95

Figure 3.13a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Ikwerre

Figure 3.13b: HOMER output graphic for Wind Speed profile for Ikwerre

Figure 3.14a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Nembe

Figure 3.14b: HOMER output graphic for Wind Speed profile for Nembe

0.0

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96

Figure 3.15a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Mopa-Muro

Figure 3.15b: HOMER output graphic for Wind Speed profile for Mopa-Muro

Figure 3.16a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Kauru

Figure 3.16b: HOMER output graphic for Wind Speed profile for Kauru

0.0

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97

Figure 3.17a: HOMER output graphic for Solar (clearness index and daily radiation) profile in Guzamala.

Figure 3.17b: HOMER output graphic for Wind Speed profile in Guzamala.

Figure 3.18a: HOMER output graphic for Solar (clearness index and daily radiation) profile for Tureta

Figure 3.18b: HOMER output graphic for Wind Speed profile for Tureta

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98

Figure 3.19: HOMER output graphic for measured Ngenene stream data used for the stream flow profile for the study locations.

3.11. Hybrid System Components

Photovoltaic Module

The PV modules used were 140W Maximum Power connected in a series and parallel

configuration. The PV module has a derating factor of 90% and a ground reflectance

of 20%. The Photovoltaic system was considered to have no tracking system for the

purpose of the study in order to determine the worst case resource from each of the

sites. The details of solar properties are shown in table 3.2:

Table 3.2: Details of Solar Properties

Solar Module type: SolarWorld SW 140 poly R6A Module Size (kW) 0.140 Array Size (kW) 10.7 Lifetime 25 yr PV Control Derating factor 90% Tracking system No Tracking Slope 4 - 13 deg Azimuth 0 deg Ground reflectance 20%

Wind Turbine Model

The number of Generic 10kW wind turbines considered for simulation is one. The

details of wind parameters are presented in table 3.3.


5

10

15

20

Str

eam

Flo

w (L

/s)

Hydro Resource

99 Table 3.3: Details of the Wind Parameters

Wind Turbine type: Generic 10kW Nominal Power (kW) 10 Quantities to Consider 0, 1 Lifetime 20 yr Wind Turbine Control Weibull k 2.00 Autocorrelation factor 0.850 Anemometer height 10m Altitude 0m Wind shear profile Logarithmic Surface roughness length 0.01 m Hub height 25m

Micro-Hydro Turbine Model

The micro hydro model in HOMER software is not designed for a particular water

resource. Certain assumptions are taken about available head, design flow rate,

maximum and minimum flow ratio and efficiency to the turbines. The life time of the

micro hydro model in simulation is taken as 20 years. The details of micro hydro

parameters are shown in table 3.4.

Table 3.4: Details of Micro-Hydro Parameters

Hydro Turbine Nominal Power (kW) 10.3 Quantities to Consider 0, 1 Lifetime 20 yr Hydro Turbine Control Available head 56 m Design flow rate 25 L/s Minimum flow ratio 25 % Maximum flow ratio 100 % Turbine efficiency 75 % Pipe head loss 10.5%

Diesel Generator Model

Diesel generator technology is widespread and the development of the power plant is

relatively easy. The price of diesel fuel is N165 ($1.0/L) based on federal government

100 approved pump price in Nigeria as at July, 2012. This price varies considerably based

on region, transportation costs, and current market price. The details of diesel

generator model parameters are presented in table 3.5. The diesel back-up system is

operated at times when the output from wind, hydro and solar systems fails to satisfy

the load and when the battery storage is depleted.

Table 3.5: The details of Diesel Generator model parameters.

AC Generator type: 20kVA Diesel Generator Size Considered (kW) 16 Quantity Considered 1 Lifetime 20,000 hrs Diesel Generator Control Minimum load ratio 30 % Heat recovery ratio 0 % Fuel used Diesel Fuel curve intercept 0.08 L/hr/kW Fuel curve slope 0.25 L/hr/kW Fuel: Diesel Price N165 ($1.0/L) Lower heating value 43.2 MJ/kg Density 820 kg/m3 Carbon content 88.0% Sulphur content 0.330%

Storage Battery

The variations of solar and wind energy generation do not match the time distribution

of the demand. The storage battery chosen was Surrette 6CS25P. These batteries were

configured such that each string consisted of two batteries, with a total of forty-eight

strings. This means the total batteries used were 96 units. From the datasheet given by

HOMER software, the minimum state of charge of the battery is 40%. Its round trip

efficiency is 80%. Batteries are considered as a major cost factor in small-scale stand-

alone power systems. The details of storage battery model parameters are given in

table 3.6.

101

Table 3.6: Surrette 6CS25P Battery Properties

Battery type: Surrette 6CS25P Quantities to Consider 96, 192 Lifetime throughput 9,645 kWh Battery: Surrette 6CS25P Control Nominal capacity 1,156 Ah Voltage 6 V

Converter

Here converter is used which can work both as an inverter and rectifier depending on

the direction of flow of power. In the present case, the size of the converter ranges

from 0 to 50 kW for simulation purposes. The details of converter parameters are

given in table 3.7.

Table 3.7: Details of Converter Parameters

Converter Sizes to Consider (kW) 25, 50 Lifetime 20 yr Converter Control Inverter efficiency 85% Inverter can parallel with AC generator Yes Rectifier relative capacity 100% Rectifier efficiency 85%

The storage batteries is a key factor in a hybrid system of renewable energy, it allows

to minimize the number of starting/stopping cycle of the diesel generator (problem

stated in section 3.2.4), which reduces the problem of its premature wear, and to

satisfy the request of the load in spite of renewable sources fluctuations. The system

control inputs used are shown in table 3.8.

102 Table 3.8: System control inputs

Simulation Simulation time step (minutes) 60 Dispatch Strategy Allow systems with multiple generators: Yes Allow multiple generators to operate simultaneously: Yes Allow systems with generator capacity less than peak load: Yes Generator control Check load following: No Check cycle charging: Yes Setpoint state of charge: 80%

Economics and Constraints

The project lifetime is estimated at 25 years. The annual interest rate is fixed at 6%.

There is no capacity shortage for the system and operating reserve is 10% of hourly

load. The operating reserve as a percentage of hourly load was 10%. Meanwhile, the

operating reserve as a percentage of solar power output and wind power output was

25% and 50% respectively. Operating reserve is the safety margin that helps ensure

reliability of the supply despite variability in electric load, solar power supply and the

wind power supply. The constraints inputs required by software are given in table 3.9.

Table 3.9: Constraints inputs

Maximum annual capacity shortage: 0% Minimum renewable fraction: 0% Operating reserve as percentage of hourly load: 10% Operating reserve as percentage of annual peak load: 0% Operating reserve as percentage of solar power output: 25% Operating reserve as percentage of wind power output: 50%

System Economics

The capital costs for all system components including PV module, wind turbine,

hydro turbine, diesel generator, inverter, battery and balance of system prices are

based on quotes from PV system suppliers in Nigeria [157]. These costs are estimates

based on a limited number of internet enquiries and prices. They are likely to vary for

103 the actual system quotes due to many market factors. The figures used in the analysis

are therefore only indicative.

The replacement costs of equipment are estimated to be 20% – 30% lower than the

initial costs, but because decommissioning and installation costs need to be added, it

was assumed that they are the same as the initial costs.

The PV array, wind turbine, hydro turbine, diesel generator, Inverter and battery

maintenance costs are estimates based on approximate time required and estimated

wages for this sort of work in a remote area of Nigeria. All initial costs including

installation and commissioning, replacement costs and operating & Maintenance costs

at site are summarized in Table (3.10). All costs presented are in Nigerian currencies

Naira (N).

Table 3.10: Economic data (Initial System Costs, Replacement Costs and Operating &

Maintenance Costs) of all the components of the hybrid system used for the

Simulation Item Initial System Costs Replacement Costs Operating &

Maintenance Costs PV modules N 324/W ($2) N 291.6/W ($1.8) N 16,200/kW/yr ($100) Generic 10kW Wind turbine N 4,374,000 ($27,000) N 3,402,000 ($21,000) N 48,600 ($300) Hydro turbine N 8,100,000 ($50,000) N 6,480,000 ($40,000) N 162,000 ($1,000) 20kVA Diesel Generator N 2,106,000 ($13,000) N 2,106,000 ($13,000) N 405/hr ($2.5) Surrete 6CS25P battery N 185,490 ($1,145) N 162,000 ($1,000) N 16,200 ($100) Converter N 324/W ($2) N 324/W ($2) N 16,200/kW/yr ($100) Labour N 6,480,000 ($40,000) N/A N/A

NA: Not Applicable Inputs to the HOMER software in simulation have been described (the technical and

economic data of all the components of the hybrid system).

The input parameters and system constraints, as described above, were used to

simulate hybrid systems and perform optimization analysis. HOMER determines the

104 optimal system by choosing suitable system components (system configuration)

depending on parameters like solar radiation, water flowrate, wind speed, diesel price

and maximum annual capacity shortage. The feasibility of a configuration is based on

the NPC and hourly performance. The results are presented in graphical and tabular

forms in chapter four.

3.12. Optimal Design of Hybrid System

The hybrid system model to be described is the core of the simulation. Apart from

correct costing and optimization, the quality and accuracy of the model and its

implementation in the algorithm, greatly determines the usefulness of the simulation

results. Figure 3.20 shows the Proposed Hybrid System Set-up.

Figure 3.20: Proposed Hybrid System Set-up

Battery

Hybrid Controller

Battery Charger

Hydro

Diesel

Solar PV

Wind DC Load IRE

IDG

IBat

Pcharger(t)Ph(t)

Pw(t)

Pp(t)

PL(t)

105

Given the values of irradiation on tilted planes, speed of the wind, stream flow and the

consumption patterns previously described, the system behavior can be simulated

using an hourly time step. Based on a system energy balance and on the storage

continuity equation, the simulation method used here is similar to that used by others

[158 - 159]. Considering the battery charger output power Pcharger(t), the PV output

power Pp(t), the Wind output power Pw(t), the Hydro output power Ph(t) and the load

power PL(t) on the simulation step ∆t, the battery energy benefit during a charge time

∆t1 is given by (∆t1< ∆t):

( ) ( ) ( ) ( ) ( ) ( )[ ]∫Δ

−+++=1

arg1t

lerchhwpch dttPtPtPtPtPtC ρ ------------------------------- (3.46)

The battery energy loss during a discharge time ∆t2 is given by (∆t2<∆t):

( ) ( ) ( ) ( ) ( ) ( )[ ]∫Δ

−+++⎟⎟⎠

⎞⎜⎜⎝

⎛=

2

arg11

tlerchhwp

dch

dttPtPtPtPtPtCρ

-------------------------- (3.47)

The state of charge of the battery is defined during the simulation time-step ∆t by:

( ) ( ) ( ) ( )tCtCttCtC 21 ++Δ−= ---------------------------------------------------------- (3.48)

If C(t) reaches Stopping threshold (SAR) by an energy benefit C1(t) during the charge

period with the engine-generator working, the generator has to be stopped and the

charge time ∆t1 during ∆t is calculated assuming a linear relation:

( )( )tC

ttCSARtt

1

1 Δ−−=

ΔΔ

------------------------------------------------------------------- (3.49)

106 Moreover, if during the discharge period when the engine generator is stopped, C(t)

reaches Starting threshold (SDM), the motor is started and the discharge time ∆t2

during ∆t is calculated by a linear relation as:

( )( )tC

SDMttCtt

2

2 −Δ−=

ΔΔ

----------------------------------------------------------------- (3.50)

As an input of a simulation time-step ∆t (taken as 1h), several variables must be

determined: PV output power, Wind output power, Hydro output power, load power,

battery state of charge, and back-up generator state (ON or OFF). A battery energy

balance indicates the operating strategy of the PV/Wind/Hydro-Diesel hybrid system:

charge (energy balance positive) or discharge (energy balance negative). If SOC(t)

falls below SDM, the motor is started; and if SOC(t) exceeds SAR, it is stopped. So,

the charge and discharge times (Eqs. 3.49 and 3.50) must be calculated on the

simulation time-step in order to compute the different energy flows in the system

(Eqs. 3.46 and 3.47).

An Algorithm for Hybrid PV/Wind/Hydro-Diesel System Sizing Simulation

The algorithm for the hybrid system of renewable and conventional energy used in the

simulation is presented in figure 3.21. Inputs of the algorithm are the technical and

economic data of all the components of the system. These data cover the climatic

variables, the load and the constraints on the operation of the system.

For each combination n, the total power, RE(t) (Renewable Energies) and CE(t)

(Conventional Energy), generated by the PV generator, Wind turbine and Hydro

turbine, and Diesel at hour t is calculated using equation 3.16.

107 During system operation of the hybrid PV/Wind/Hydro-Diesel system, different

situations may appear:

The total energy generated by the PV, Wind and Hydro generators can be greater than

the load demand (EL(t)). In this situation, the energy surplus is stored in the batteries

(Echar) after calculation, as a preliminary, the maximum amount of energy that can be

charged (Echar_max(t)) in the battery bank. The excess of energy (E.E(t)), if there exist,

is calculated for each hour.

The demand of the load can be greater than the total energy generated by the PV,

Wind and Hydro generators. In this case, the load must be covered by the energy

stored in batteries (Edisch) after calculating, as a preliminary, the maximum amount of

energy that can be discharged (Edisch_max(t)) from the battery bank. The deficit of

energy (D.E(t)) is zero.

The load demand can be equal to the total energy generated by the PV, Wind and

Hydro generator, the batteries capacity remains unchanged.

The demand of the load can be greater than both the total energy generated by

renewable energy (PV, Wind and Hydro) and the energy stored in the batteries (Edisch).

In this case, the load must be covered by the conventional source (Diesel) and excess

electricity is used for charging the batteries.

Finally, an economic and environmental study is done allowing a classification of the

feasible combinations according to the total net present cost of the system.

108

Figure 3.21: Algorithm for Hybrid PV/Wind/Hydro-Diesel System Sizing Simulation [160]

RE(t)=EL(t)

Hourly data (irradiation, wind speed, stream flow, load), technical and economic data of the components

n=1

t=1

Calculate RE(t)

RE(t)>EL(t)

SOC<or≥than SOC%

Calculate CE(t)

Calculate Edisch_max(t) (Battery discharging)

Edisch=EL(t)‐RE(t)

Calculate Echar_max(t) (Battery charging)

E(t)‐EL(t)<Echar_max(t)

Echar=E(t)‐EL(t) E.E(t)=0

D.E(t)=0

t=8760 Save for Economic and Environmental Study

Echar=Echar_max(t) Calculate E.E(t) D.E=0; E.E=0

n=n+1

t=t+1

NoYes

YesNo

Yes No

SOC≥SOC% SOC<SOC%

YesNo

109 3.13. Computer Simulation

HOMER is an optimization program based on energy cost (Economic and

Environmental) calculations. The basic idea is to design an approximately optimal

system and have a general idea of the optimal system amongst the feasible systems

considered by HOMER [161]. The total net present cost of the system, which includes

the investment costs and all future costs during the lifetime of the system, is the

parameter to minimize in the optimisation process. It is therefore necessary to

simulate the system throughout its lifetime. To produce the cheapest energy, the costs

of providing the required energy using each technology must be evaluated.

Based on the energy consumption of mobile base station and the availability of

renewable energy sources, it was decided to implement an innovative stand alone

Hybrid Energy System [162 - 163] combining solar photo-voltaic panels, small wind

turbine-generator, pico-hydro turbine-generator, diesel generator, battery storage, and

bi-directional converter. The system architecture employed in the hybrid system is DC

coupled where the renewable energy sources [164] feed into the DC side of the

network while the conventional diesel generators [165] feed into the AC side of the

network as depicted in Figure 3.22.

Figure 3.22: The proposed energy system for GSM Base Station Site.

110

To obtain this optimal system, a few assumptions and restrictions have been made.

Firstly, the Generic 10kW wind turbine has not been modified, and the number of

wind turbines in the system has been fixed at 1. Moreover, willing to keep all the

different components within the system, the minimum power of each component is

limited to 10kW. Finally, in order to favour the use of renewables over the use of the

Diesel Generator, the Generator size has been limited to 16kW to match its energy

production. This optimal sizing has been obtained step by step by modifying gradually

the size of the different elements with the objectives to minimize their size for cost

interests and to reduce as far as possible the use of the diesel generators for

environmental interests.

It is also important to note that this optimal system has been obtained with particular

capital, replacement, operation and maintenance costs for each component. HOMER

basing its optimization process on costs calculations, it is obvious that changes in

these costs would generate different results and therefore a different optimal system.

However, these costs seem quite logical and in accordance with the prices of the

market.

The total Net Present Cost (NPC) for economic and environmental evaluation of

Hybrid (Solar, Wind & Hydro) + DG, Hybrid (Solar & Hydro) + DG, Hybrid (Hydro

& Wind) + DG, Hydro only + DG, Hybrid (Solar & Wind) + DG, Solar only + DG,

Wind only + DG, DG system have been developed and simulated using the model

which results in eight different topologies:

• Diesel only system

• Wind-diesel system

111

• Solar-diesel system

• Hydro-diesel system

• PV/hydro-diesel system

• PV/wind-diesel system

• Hydro/wind-diesel system and

• PV/wind/hydro-diesel system.

From the outlined design above, we were able to compare the cost-effectiveness of

adding renewable energy components to the existing energy (Diesel):

1. The standard diesel generator configuration with renewable hybrids (wind &

solar), (wind & hydro), (solar & hydro) and (wind/solar/hydro).

2. The standard diesel generator configuration with pure wind, pure hydro and pure

solar models.

In the optimization process, the proposed system was simulated with many different

system configurations in search of the one that satisfies the technical constraints at the

lowest life-cycle cost. In this work the categorized list displays eight different

configurations, in descending order by the most effective NPC as follows:

1. Hybrid (Solar, Wind & Hydro) + DG

2. Hybrid (Solar & Hydro) + DG

3. Hybrid (Wind & Hydro) + DG

4. Hydro only + DG

5. Hybrid (Solar & Wind) + DG

6. Solar only + DG

7. Wind only + DG

8. DG only

In order to achieve a realistic result, different locations were considered.

112

3.14. Supervisory Control System

An operational control strategy consists of certain predetermined control settings that

are set when installing the system. Such settings concern the setpoint of when to

switch on the diesel or not, based on certain values representing the system state, such

as the battery state of charge and the demand placed on the system. The time-

independent controller setting in the developed design hybrid system is shown in

figure 3.23, and its flowchart is shown in figure 3.24.

The Hybrid System Control consists of 4 modes. PV is chosen as the primary energy

generation mechanism and therefore mode 1 is used when solely the PV power

generated is sufficient to power the system. This power is regulated using sliding

mode control as shown in figure 3.24. In mode 2, the PV power is generated at its

maximum and wind energy generation then tracks the load power using sliding mode

control. In mode 3, the PV power and the wind power are generated at their maximum

and hydro energy generation then tracks the load power using sliding mode control. In

mode 1, 2 and 3, the battery is recharged and therefore adds to the load on the system.

In mode 4, the load is greater than what the PV, wind and hydro can supply and the

Hybrid System Control connects to the program which determines what element

(batteries or diesel generator) have priority to supply energy to enable the necessary

load to be met. This is done by monitoring the power needed by the load and the

power of the power generating mechanism.

113

Figure 3.23: Hybrid System Controller Block Diagram [166]

Control Simulation Algorithm for Hybrid PV/Wind/Hydro-Diesel System

Control strategies have been recognized as an efficient way to improve process

profitability. In fact, the major benefit of integrating control modeling and simulation

into the energy development process is a significant reduction in total cost of

ownership. A sliding control has been used for this, using PV energy generation as the

primary source of energy, wind and hydro energy generation as the secondary source,

the battery as the supplement and the generator as the back-up source of energy. The

system moves between different modes dependant on the power needed by the load

and the power able to be supplied by each of the sources. Figure 3.24 outlines the

flow between the different modes.

Hybrid Controller

+

_

Hybrid Energy System

Monitoring

• Load Power Level • Renewable Power Level • State of Charge of a battery bank

Controller Outputs

• Diesel generator start/stop

• Battery charging start/stop

• Battery discharging start/stop

114 Initially, the power supplied by the PV panels, the wind turbine and the hydro turbine

is calculated for each hour over the year and stored in matrices, so that power

availability in each hour can be accessed easily. The control process then begins at

hour 1.

The first decision loop looks at the power that can be supplied by the PV panel in this

hour and the power required by the load. If the power generated by the PV panel is

sufficient to match the load, the system enters Mode 1.

If the PV panel cannot provide sufficient energy for the load, the control looks at the

total amount of energy that can be provided by the PV panel and the wind turbine

together. If these together are sufficient to provide power for the load, the system

enters Mode 2.

If the combined energy supplied by the PV panels and the wind turbine is not

sufficient to supply the load, the control looks at the total amount of energy that can

be provided by the PV panel, the wind turbine and hydro turbine together. If these

together are sufficient to provide power for the load, the system enters Mode 3.

If the combined energy supplied by the PV panels, the wind turbine and the hydro

turbine is not sufficient to supply the load, then the system goes to the decision mode

where the program determines what element (batteries or diesel generator) have

priority to supply energy using decision rules based on constraints.

115

Figure 3.24: Overview of the Decision Strategy of Hybrid Controller and Modes of Control for the System Operation.

Calculate Power Supplied by each energy sources at each hour

PPV,PWT,PHT

PPV(t)≥EL(t)

PPV(t)+PWT(t)≥EL(t)

PPV(t)+PWT(t)+PHT(t)≥EL(t)

Mode 1 PPV(t) Supply=EL(t)

Mode 3PPV(t)+PWT(t)+PHT(t) Supply=EL(t)

Mode 2 PPV(t)+PWT(t) Supply=EL(t)

Decision ModeSOC<or≥than SOC%

Calculate Edisch_max(t) (Battery discharging)

Start the generator

SOC≥SOC% SOC<SOC%

Is SOC<SOC%?

Is SOC≥SOC%?

Yes Yes

Continue discharging the battery

Continue using the generator

No No

Stop the generator

Stop discharging the battery

No

NoYes

Yes

YesNo

116 Mode 1

Mode 1 uses solely the energy generated by the PV panels to supply the load. When

the system is in mode 1, sometimes the energy available from the wind turbine and

hydro turbine might be in excess of what is needed by the load and therefore the

amount of energy supplied to the load must be matched to the load demand. This is

called sliding control. As the wind turbine and hydro turbine are connected to the

system, but not used to supply the load in this mode, the energy generated by the wind

turbine and hydro turbine as well as any excess energy from the PV panels can be

used to charge the battery.

Mode 2

Mode 2 uses the power of the PV panels plus the power of the wind turbine to supply

the load. In mode 2, if the energy available from the PV panels and the wind turbine

combined is in excess of what is needed by the load, then the full power available

from the PV panel is used to supply the load and the power from the wind turbine is

supplied using sliding control to match the power required by the load. As the hydro

turbine are connected to the system, but not used to supply the load in this mode, the

energy generated by the hydro turbine as well as any excess energy from the PV

panels and wind turbine can be used to charge the battery as in mode 1.

Mode 3

The system enters mode 3 when the power generated by the PV panel and wind

turbine is not sufficient to supply the load. In this mode, if the energy available from

the PV panels, the wind turbine and hydro turbine combined is in excess of what is

needed by the load, then the full power available from the PV panel and wind turbine

is used to supply the load and the power from the hydro turbine is supplied using

117 sliding control to match the power required by the load. The excess energy from the

PV panels, the wind turbine and hydro turbine can be used to charge the battery, as in

mode 1 and 2.

Decision mode

There is however a possibility, a time that the amount of power required by the load is

not able to be supplied by mode 3, then the program determines what element

(batteries or diesel generator) have priority to supply energy.

The program determines what element has priority to supply energy based on:

• If the SOC of the battery is greater than the minimum amount and therefore

the battery is able to supply power to the load. The battery will be used.

• If the load cannot be supplied by the energy sources i.e. the combined power

of the PV panels, wind turbine and the hydro turbine is not sufficient to supply

the load and the battery is at its minimum SOC and therefore cannot be used to

supply the deficit of power required. Then the diesel generator will be used.

From this control simulation we are able to see the performance of the system over the

course of the year to see which modes the system spends most time in, the power

supplied by each of the energy sources over the year, and the power required by the

load over the year.

Developed Software

HOMER software was used to build the hybrid (PV/Wind/Hydro-Diesel) system

model. A TURBO PASCAL computer program was developed to determine the

performance of the system over the course of the year to see which modes the system

spends most time in, and the power supplied by each of the energy system over the

year; given the load conditions and taking into account the technical factors.

118

The designed software results were carried out followed with HOMER data to

validate the analysis. The comparison shows a close agreement between results

obtained from designed software module and results obtained from HOMER setup.

The designed software was used to monitor the hybrid (PV/Wind/Hydro-Diesel)

system. This is a very useful manner to check how the system is being supplied and

which source of energy is the most proficient in supplying the load. The results of the

demand met by the hybrid energy system (PV/Wind/Hydro-Diesel) for the first seven

days of February and that met for the 15th day of each month of the year are shown in

Tables (3.11, 3.12, 3.13, 3.14, 3.15, 3.16, and 3.17) and Tables (3.18, 3.19, 3.20, 3.21,

3.22, 3.23, 3.24, 3.25, 3.26, 3.27, 3.28, and 3.29), respectively.

Demand met by the Hybrid Energy System (PV/Wind/Hydro-Diesel) for the first

seven days of February

Tables (3.11, 3.12, 3.13, 3.14, 3.15, 3.16, and 3.17) show how the demand is met by

the hybrid energy system (PV, wind, hydro and diesel generator) for the first seven

days of February. It shows how the sources were allocated according to the load

demand and availability. It was observed that the variation is not only in the demand

but also the availability of sources. The battery or the diesel generator compensates

the shortage depending on the decision mode. The entire operations of the hybrid

controller can be seen from Figure 3.24.

From the simulation results, the wind power is poor and variable. The hydro system

has a steady power supply. The PV power supply is between 8:00 h to 19:00 h while

the radiation peak is between 12:00 h to 14:00 h as can be seen in Tables (3.11, 3.12,

119 3.13, 3.14, 3.15, 3.16, and 3.17). Between 12:00 h and 14:00 h there is no deficit in

the system and the renewable energy supplies the load and charges the battery. There

is likely to be deficit in other remaining hours due to poor radiation, and the deficit is

being completed by either the battery or the diesel generator.

Hybrid controller switches the batteries into charging mode whenever excess power is

available from the renewable sources, and switch to discharging mode whenever there

was a shortage of power from sources. Battery power indicates the operating strategy

of the hybrid system: charging (power positive) or discharging (power negative). It

shows that the hybrid controller utilizes the battery bank effectively.

It was mentioned in the Figure 3.23, that the hybrid controller turns off the diesel

generator when the load demand can be met together by the PV, wind, hydro and

battery bank. For example on the typical day (day six), at 10:00 hours when the

battery state of charge is 80.57%, the hybrid controller turns off the diesel generator

and allocates PV, wind, and hydro to supply the load demand as well as charging the

battery.

From the fourth day (4:00 h) till the sixth day (10:00 h), the hybrid controller

allocated the diesel generator as shown in Tables (3.14, 3.15, and 3.16). The demand

of the other remaining hours and days was met by the renewable energy sources (PV

+ wind + hydro) along with the battery bank. It reduces the operational hours of the

diesel generator thereby reducing the running cost of the hybrid energy system as well

as the pollutant emissions.

120 Table 3.11: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day one.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)

Battery state of charge

(%) 0:00 0.000 0.000 2.505 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 47.021 1:00 0.000 0.000 2.440 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.774 2:00 0.000 0.000 1.332 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.527 3:00 0.000 0.000 2.540 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.281 4:00 0.000 0.000 2.430 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.034 5:00 0.000 0.000 2.190 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.788 6:00 0.000 0.000 2.385 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.541 7:00 0.001 0.000 1.072 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.295 8:00 0.126 0.223 1.431 19.981 10.467 2.146 0.000 7.368 0.000 -0.953 45.224 9:00 0.329 0.411 0.876 19.981 10.467 3.957 0.000 7.368 0.000 0.858 45.275 10:00 0.570 0.660 1.907 19.981 10.467 6.356 0.000 7.368 0.000 3.257 45.470 11:00 0.834 0.925 1.894 19.981 10.467 8.906 0.000 7.368 0.000 5.807 45.817 12:00 1.022 1.102 2.096 19.981 10.467 10.615 0.000 7.368 0.000 7.516 46.266 13:00 1.064 1.134 2.123 19.981 10.467 10.925 0.000 7.368 0.000 7.826 46.734 14:00 1.082 1.159 2.133 19.981 10.467 11.157 0.000 7.368 0.000 8.058 47.216 15:00 0.888 0.967 3.038 19.981 10.467 9.315 0.084 7.368 0.000 6.300 47.592 16:00 0.652 0.730 2.521 19.981 10.467 7.028 0.000 7.368 0.000 3.929 47.827 17:00 0.493 0.604 2.227 19.981 10.467 5.812 0.000 7.368 0.000 2.713 47.989 18:00 0.258 0.384 1.819 19.981 10.667 3.700 0.000 7.368 0.000 0.401 48.013 19:00 0.041 0.161 2.825 19.981 10.667 1.546 0.038 7.368 0.000 -1.715 47.885 20:00 0.000 0.000 3.571 19.981 10.667 0.000 0.198 7.368 0.000 -3.101 47.653 21:00 0.000 0.000 2.070 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 47.407 22:00 0.000 0.000 2.537 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 47.160 23:00 0.000 0.000 1.523 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.914

Table 3.12: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day two.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.871 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.667 1:00 0.000 0.000 0.381 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.421 2:00 0.000 0.000 0.947 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 46.174 3:00 0.000 0.000 1.425 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.928 4:00 0.000 0.000 1.575 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.681 5:00 0.000 0.000 1.463 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.435 6:00 0.000 0.000 0.932 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 45.188 7:00 0.000 0.000 1.560 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 44.942 8:00 0.054 0.052 1.337 19.981 10.467 0.498 0.000 7.368 0.000 -2.601 44.747 9:00 0.178 0.171 1.761 19.981 10.467 1.648 0.000 7.368 0.000 -1.451 44.639 10:00 0.184 0.169 2.611 19.981 10.467 1.628 0.000 7.368 0.000 -1.471 44.529 11:00 0.212 0.194 3.542 19.981 10.467 1.872 0.192 7.368 0.000 -1.035 44.452 12:00 0.364 0.337 3.946 19.981 10.467 3.243 0.274 7.368 0.000 0.418 44.477 13:00 0.742 0.759 4.698 19.981 10.467 7.313 0.550 7.368 0.000 4.764 44.761 14:00 0.460 0.433 4.898 19.981 10.467 4.171 0.677 7.368 0.000 1.749 44.866 15:00 0.253 0.232 4.089 19.981 10.467 2.235 0.304 7.368 0.000 -0.561 44.824 16:00 0.349 0.332 5.444 19.981 10.467 3.196 1.083 7.368 0.000 1.180 44.895 17:00 0.315 0.326 5.207 19.981 10.467 3.139 0.872 7.368 0.000 0.912 44.949 18:00 0.192 0.235 4.404 19.981 10.667 2.260 0.368 7.368 0.000 -0.671 44.899 19:00 0.039 0.141 4.547 19.981 10.667 1.354 0.454 7.368 0.000 -1.490 44.788 20:00 0.000 0.000 4.711 19.981 10.667 0.000 0.558 7.368 0.000 -2.741 44.583 21:00 0.000 0.000 3.881 19.981 10.667 0.000 0.261 7.368 0.000 -3.038 44.356 22:00 0.000 0.000 4.610 19.981 10.667 0.000 0.494 7.368 0.000 -2.805 44.146 23:00 0.000 0.000 2.370 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 43.900

121 Table 3.13: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day three.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 2.125 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 43.653 1:00 0.000 0.000 1.086 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 43.407 2:00 0.000 0.000 0.666 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 43.160 3:00 0.000 0.000 1.265 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 42.914 4:00 0.000 0.000 1.483 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 42.667 5:00 0.000 0.000 1.008 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 42.421 6:00 0.000 0.000 0.724 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 42.174 7:00 0.001 0.000 0.486 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 41.928 8:00 0.145 0.282 1.100 19.981 10.467 2.719 0.000 7.368 0.000 -0.380 41.899 9:00 0.244 0.260 1.374 19.981 10.467 2.499 0.000 7.368 0.000 -0.600 41.854 10:00 0.306 0.294 1.616 19.981 10.467 2.827 0.000 7.368 0.000 -0.272 41.834 11:00 0.512 0.513 2.558 19.981 10.467 4.936 0.000 7.368 0.000 1.837 41.944 12:00 0.611 0.610 1.931 19.981 10.467 5.871 0.000 7.368 0.000 2.772 42.110 13:00 0.614 0.603 1.603 19.981 10.467 5.805 0.000 7.368 0.000 2.705 42.271 14:00 0.568 0.553 1.544 19.981 10.467 5.321 0.000 7.368 0.000 2.222 42.404 15:00 0.428 0.405 1.351 19.981 10.467 3.899 0.000 7.368 0.000 0.800 42.452 16:00 0.460 0.466 2.025 19.981 10.467 4.485 0.000 7.368 0.000 1.386 42.535 17:00 0.266 0.260 2.642 19.981 10.467 2.504 0.000 7.368 0.000 -0.595 42.490 18:00 0.127 0.125 1.907 19.981 10.667 1.202 0.000 7.368 0.000 -2.097 42.334 19:00 0.029 0.056 2.072 19.981 10.667 0.536 0.000 7.368 0.000 -2.764 42.127 20:00 0.000 0.000 2.537 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 41.881 21:00 0.000 0.000 2.548 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 41.634 22:00 0.000 0.000 2.066 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 41.388 23:00 0.000 0.000 1.953 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 41.141

Table 3.14: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day four.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 2.068 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 40.895 1:00 0.000 0.000 1.935 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 40.648 2:00 0.000 0.000 2.606 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 40.402 3:00 0.000 0.000 2.211 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 40.155 4:00 0.000 0.000 2.790 19.981 10.667 0.000 0.030 7.368 16.000 10.331 40.773 5:00 0.000 0.000 3.049 19.981 10.667 0.000 0.086 7.368 16.000 10.387 41.393 6:00 0.000 0.000 1.710 19.981 10.667 0.000 0.000 7.368 16.000 10.301 42.009 7:00 0.001 0.000 1.590 19.981 10.667 0.000 0.000 7.368 16.000 10.301 42.625 8:00 0.100 0.136 2.188 19.981 10.467 1.307 0.000 7.368 16.000 11.808 43.331 9:00 0.299 0.350 2.159 19.981 10.467 3.369 0.000 7.368 16.000 13.870 44.160 10:00 0.443 0.470 3.337 19.981 10.467 4.522 0.148 7.368 16.000 15.171 45.067 11:00 0.492 0.486 2.767 19.981 10.467 4.685 0.025 7.368 16.000 15.211 45.976 12:00 0.900 0.957 2.651 19.981 10.467 9.215 0.001 7.368 16.000 19.716 47.154 13:00 0.963 1.014 3.038 19.981 10.467 9.769 0.084 7.368 16.000 20.353 48.371 14:00 0.505 0.481 2.639 19.981 10.467 4.630 0.000 7.368 16.000 15.131 49.275 15:00 0.575 0.575 3.239 19.981 10.467 5.533 0.127 7.368 16.000 16.161 50.241 16:00 0.606 0.659 3.243 19.981 10.467 6.345 0.128 7.368 16.000 16.974 51.256 17:00 0.453 0.533 2.206 19.981 10.467 5.137 0.000 7.368 16.000 15.638 52.190 18:00 0.246 0.346 1.767 19.981 10.667 3.328 0.000 7.368 16.000 13.629 53.005 19:00 0.026 0.041 1.599 19.981 10.667 0.398 0.000 7.368 16.000 10.698 53.645 20:00 0.000 0.000 2.197 19.981 10.667 0.000 0.000 7.368 16.000 10.301 54.260 21:00 0.000 0.000 2.756 19.981 10.667 0.000 0.023 7.368 16.000 10.324 54.877 22:00 0.000 0.000 3.184 19.981 10.667 0.000 0.115 7.368 16.000 10.416 55.500 23:00 0.000 0.000 2.945 19.981 10.667 0.000 0.064 7.368 16.000 10.365 56.119

122 Table 3.15: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day five.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 1.844 19.981 10.667 0.000 0.000 7.368 16.000 10.301 56.735 1:00 0.000 0.000 3.040 19.981 10.667 0.000 0.084 7.368 16.000 10.385 57.356 2:00 0.000 0.000 3.459 19.981 10.667 0.000 0.174 7.368 16.000 10.475 57.982 3:00 0.000 0.000 2.988 19.981 10.667 0.000 0.073 7.368 16.000 10.374 58.602 4:00 0.000 0.000 2.342 19.981 10.667 0.000 0.000 7.368 16.000 10.301 59.218 5:00 0.000 0.000 1.146 19.981 10.667 0.000 0.000 7.368 16.000 10.301 59.833 6:00 0.000 0.000 0.840 19.981 10.667 0.000 0.000 7.368 16.000 10.301 60.449 7:00 0.001 0.000 1.118 19.981 10.667 0.000 0.000 7.368 16.000 10.301 61.065 8:00 0.059 0.057 1.719 19.981 10.467 0.554 0.000 7.368 16.000 11.055 61.726 9:00 0.256 0.277 2.918 19.981 10.467 2.664 0.058 7.368 16.000 13.223 62.516 10:00 0.276 0.260 3.242 19.981 10.467 2.503 0.128 7.368 16.000 13.132 63.301 11:00 0.399 0.379 2.492 19.981 10.467 3.648 0.000 7.368 16.000 14.149 64.147 12:00 0.779 0.811 3.585 19.981 10.467 7.813 0.201 7.368 16.000 18.514 65.253 13:00 0.905 0.946 3.327 19.981 10.467 9.109 0.146 7.368 16.000 19.756 66.434 14:00 0.709 0.717 4.743 19.981 10.467 6.903 0.578 7.368 16.000 17.982 67.509 15:00 0.327 0.302 4.263 19.981 10.467 2.906 0.339 7.368 16.000 13.746 68.331 16:00 0.267 0.246 4.253 19.981 10.467 2.372 0.337 7.368 16.000 13.210 69.120 17:00 0.201 0.187 3.865 19.981 10.467 1.803 0.258 7.368 16.000 12.562 69.871 18:00 0.038 0.034 3.766 19.981 10.667 0.332 0.238 7.368 16.000 10.871 70.521 19:00 0.022 0.026 3.267 19.981 10.667 0.252 0.133 7.368 16.000 10.686 71.160 20:00 0.000 0.000 3.418 19.981 10.667 0.000 0.166 7.368 16.000 10.466 71.785 21:00 0.000 0.000 2.576 19.981 10.667 0.000 0.000 7.368 16.000 10.301 72.401 22:00 0.000 0.000 1.894 19.981 10.667 0.000 0.000 7.368 16.000 10.301 73.017 23:00 0.000 0.000 0.732 19.981 10.667 0.000 0.000 7.368 16.000 10.301 73.632

Table 3.16: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day six.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.821 19.981 10.667 0.000 0.000 7.368 16.000 10.301 74.248 1:00 0.000 0.000 1.665 19.981 10.667 0.000 0.000 7.368 16.000 10.301 74.864 2:00 0.000 0.000 0.998 19.981 10.667 0.000 0.000 7.368 16.000 10.301 75.479 3:00 0.000 0.000 0.956 19.981 10.667 0.000 0.000 7.368 16.000 10.301 76.095 4:00 0.000 0.000 2.549 19.981 10.667 0.000 0.000 7.368 16.000 10.301 76.711 5:00 0.000 0.000 2.558 19.981 10.667 0.000 0.000 7.368 16.000 10.301 77.326 6:00 0.000 0.000 2.775 19.981 10.667 0.000 0.027 7.368 16.000 10.328 77.944 7:00 0.002 0.000 3.754 19.981 10.667 0.000 0.235 7.368 16.000 10.536 78.574 8:00 0.141 0.260 2.948 19.981 10.467 2.506 0.064 7.368 16.000 13.071 79.355 9:00 0.417 0.543 2.828 19.981 10.467 5.227 0.038 7.368 16.000 15.766 80.297 10:00 0.687 0.791 2.870 19.981 10.467 7.616 0.048 7.368 0.000 4.564 80.570 11:00 0.940 1.025 2.522 19.981 10.467 9.866 0.000 7.368 0.000 6.767 80.975 12:00 1.062 1.126 1.766 19.981 10.467 10.843 0.000 7.368 0.000 7.744 81.437 13:00 1.061 1.113 2.576 19.981 10.467 10.714 0.000 7.368 0.000 7.615 81.893 14:00 0.978 1.028 2.017 19.981 10.467 9.895 0.000 7.368 0.000 6.796 82.299 15:00 0.846 0.901 2.282 19.981 10.467 8.679 0.000 7.368 0.000 5.580 82.632 16:00 0.679 0.748 3.116 19.981 10.467 7.204 0.101 7.368 0.000 4.206 82.884 17:00 0.464 0.544 2.626 19.981 10.467 5.234 0.000 7.368 0.000 2.135 83.011 18:00 0.208 0.257 3.427 19.981 10.667 2.479 0.168 7.368 0.000 -0.653 82.963 19:00 0.043 0.165 2.972 19.981 10.667 1.591 0.070 7.368 0.000 -1.638 82.840 20:00 0.000 0.000 2.543 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 82.594 21:00 0.000 0.000 2.336 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 82.347 22:00 0.000 0.000 1.863 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 82.101 23:00 0.000 0.000 1.231 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.854

123 Table 3.17: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in day seven.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.662 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.608 1:00 0.000 0.000 1.001 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.361 2:00 0.000 0.000 1.087 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.115 3:00 0.000 0.000 1.245 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 80.868 4:00 0.000 0.000 1.376 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 80.622 5:00 0.000 0.000 1.610 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 80.375 6:00 0.000 0.000 1.410 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 80.129 7:00 0.001 0.000 1.556 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 79.882 8:00 0.110 0.162 3.552 19.981 10.467 1.557 0.194 7.368 0.000 -1.348 79.782 9:00 0.291 0.332 3.950 19.981 10.467 3.198 0.275 7.368 0.000 0.374 79.804 10:00 0.694 0.795 3.981 19.981 10.467 7.659 0.282 7.368 0.000 4.842 80.093 11:00 0.882 0.956 4.516 19.981 10.467 9.207 0.434 7.368 0.000 6.542 80.484 12:00 1.013 1.070 5.015 19.981 10.467 10.303 0.751 7.368 0.000 7.955 80.960 13:00 1.086 1.135 4.786 19.981 10.467 10.931 0.606 7.368 0.000 8.438 81.464 14:00 0.963 1.008 3.934 19.981 10.467 9.704 0.272 7.368 0.000 6.877 81.875 15:00 0.709 0.735 2.874 19.981 10.467 7.080 0.048 7.368 0.000 4.030 82.116 16:00 0.654 0.713 3.349 19.981 10.467 6.868 0.151 7.368 0.000 3.919 82.350 17:00 0.440 0.504 3.467 19.981 10.467 4.855 0.176 7.368 0.000 1.932 82.466 18:00 0.261 0.367 3.692 19.981 10.667 3.531 0.223 7.368 0.000 0.454 82.493 19:00 0.027 0.041 3.068 19.981 10.667 0.397 0.090 7.368 0.000 -2.812 82.283 20:00 0.000 0.000 2.623 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 82.036 21:00 0.000 0.000 2.285 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.790 22:00 0.000 0.000 1.692 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.543 23:00 0.000 0.000 1.261 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 81.297

Tables (3.18, 3.19, 3.20, 3.21, 3.22, 3.23, 3.24, 3.25, 3.26, 3.27, 3.28, and 3.29) show

how the demand is met by the hybrid energy system (PV/Wind/Hydro – Diesel) on

the 15th day of each of the months of January, February, March, April, May, June,

July, August, September, October, November, and December, respectively.

In tables (3.18, 3.19, 3.20, 3.21, 3.22, 3.25, 3.26, 3.27, 3.28, and 3.29), the hybrid

controller allocates PV, Wind, and Hydro along with the battery bank to supply the

load. Meanwhile, in table (3.23) as from 08:00 h, the renewable energy sources with

the battery cannot met the load demand and the controller allocated the diesel

generator. The same thing happened in the month of July as shown in table 3.24. It is

observed that the hybrid controller allocates the sources optimally according to the

demand and availability.

124 Demand met by the hybrid energy system (PV/Wind/Hydro-Diesel) for the 15th day of each month of the year Table 3.18: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of January.

Time (h)

Global solar

(kW/m2)

Incident solar

(kW/m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%)0:00 0.000 0.000 1.751 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 72.080 1:00 0.000 0.000 1.544 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 71.819 2:00 0.000 0.000 2.156 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 71.559 3:00 0.000 0.000 2.067 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 71.299 4:00 0.000 0.000 2.089 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 71.039 5:00 0.000 0.000 3.574 19.481 10.667 0.000 0.199 7.184 0.000 -3.285 70.793 6:00 0.000 0.000 3.549 19.481 10.667 0.000 0.194 7.184 0.000 -3.290 70.547 7:00 0.003 0.000 3.882 19.481 10.667 0.000 0.261 7.184 0.000 -3.222 70.307 8:00 0.180 0.405 3.829 19.481 10.467 3.899 0.251 7.184 0.000 0.866 70.358 9:00 0.477 0.687 3.402 19.481 10.467 6.618 0.162 7.184 0.000 3.497 70.567 10:00 0.676 0.841 2.284 19.481 10.467 8.095 0.000 7.184 0.000 4.811 70.855 11:00 0.792 0.918 2.071 19.481 10.467 8.840 0.000 7.184 0.000 5.557 71.187 12:00 0.968 1.090 1.784 19.481 10.467 10.500 0.000 7.184 0.000 7.216 71.619 13:00 0.866 0.953 1.725 19.481 10.467 9.181 0.000 7.184 0.000 5.898 71.971 14:00 0.949 1.062 2.161 19.481 10.467 10.229 0.000 7.184 0.000 6.946 72.386 15:00 0.839 0.960 2.736 19.481 10.467 9.243 0.019 7.184 0.000 5.978 72.744 16:00 0.536 0.607 2.240 19.481 10.467 5.849 0.000 7.184 0.000 2.565 72.897 17:00 0.455 0.602 2.443 19.481 10.467 5.795 0.000 7.184 0.000 2.512 73.047 18:00 0.215 0.352 2.506 19.481 10.667 3.387 0.000 7.184 0.000 -0.096 73.040 19:00 0.030 0.133 1.867 19.481 10.667 1.282 0.000 7.184 0.000 -2.201 72.875 20:00 0.000 0.000 1.403 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 72.615 21:00 0.000 0.000 2.151 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 72.355 22:00 0.000 0.000 3.231 19.481 10.667 0.000 0.125 7.184 0.000 -3.358 72.104 23:00 0.000 0.000 2.141 19.481 10.667 0.000 0.000 7.184 0.000 -3.483 71.844

Table 3.19: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of February.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 2.201 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 63.953 1:00 0.000 0.000 3.520 19.981 10.667 0.000 0.187 7.368 0.000 -3.112 63.720 2:00 0.000 0.000 2.154 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 63.474 3:00 0.000 0.000 2.537 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 63.227 4:00 0.000 0.000 2.523 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 62.981 5:00 0.000 0.000 3.306 19.981 10.667 0.000 0.141 7.368 0.000 -3.158 62.745 6:00 0.000 0.000 3.472 19.981 10.667 0.000 0.177 7.368 0.000 -3.122 62.512 7:00 0.002 0.000 3.842 19.981 10.667 0.000 0.253 7.368 0.000 -3.046 62.284 8:00 0.171 0.301 4.370 19.981 10.467 2.902 0.361 7.368 0.000 0.164 62.294 9:00 0.387 0.466 5.922 19.981 10.467 4.488 1.581 7.368 0.000 2.969 62.471 10:00 0.652 0.715 7.463 19.981 10.467 6.884 4.173 7.368 0.000 7.957 62.947 11:00 0.796 0.831 5.635 19.981 10.467 8.005 1.282 7.368 0.000 6.188 63.317 12:00 0.805 0.814 5.600 19.981 10.467 7.838 1.245 7.368 0.000 5.985 63.675 13:00 1.019 1.033 5.063 19.981 10.467 9.952 0.782 7.368 0.000 7.634 64.131 14:00 1.059 1.077 4.665 19.981 10.467 10.371 0.529 7.368 0.000 7.801 64.597 15:00 0.907 0.936 3.771 19.981 10.467 9.011 0.239 7.368 0.000 6.151 64.965 16:00 0.752 0.800 2.441 19.981 10.467 7.702 0.000 7.368 0.000 4.603 65.240 17:00 0.477 0.532 3.319 19.981 10.467 5.120 0.144 7.368 0.000 2.166 65.370 18:00 0.264 0.342 3.066 19.981 10.667 3.298 0.090 7.368 0.000 0.089 65.375 19:00 0.035 0.067 3.599 19.981 10.667 0.646 0.204 7.368 0.000 -2.449 65.192 20:00 0.000 0.000 2.423 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 64.945 21:00 0.000 0.000 2.167 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 64.699 22:00 0.000 0.000 1.916 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 64.452 23:00 0.000 0.000 2.270 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 64.206

125

Table 3.20: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of March.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.273 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 59.926 1:00 0.000 0.000 0.869 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 59.680 2:00 0.000 0.000 0.778 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 59.433 3:00 0.000 0.000 1.151 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 59.187 4:00 0.000 0.000 1.883 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 58.940 5:00 0.000 0.000 2.481 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 58.694 6:00 0.000 0.000 3.011 19.981 10.667 0.000 0.078 7.368 0.000 -3.221 58.453 7:00 0.007 0.000 3.169 19.981 10.667 0.000 0.112 7.368 0.000 -3.187 58.215 8:00 0.125 0.120 3.820 19.981 10.467 1.160 0.249 7.368 0.000 -1.691 58.089 9:00 0.394 0.370 2.606 19.981 10.467 3.561 0.000 7.368 0.000 0.462 58.116 10:00 0.669 0.612 2.294 19.981 10.467 5.892 0.000 7.368 0.000 2.793 58.283 11:00 0.761 0.690 1.510 19.981 10.467 6.645 0.000 7.368 0.000 3.546 58.495 12:00 1.050 0.943 1.695 19.981 10.467 9.077 0.000 7.368 0.000 5.978 58.853 13:00 1.042 0.935 1.967 19.981 10.467 9.000 0.000 7.368 0.000 5.901 59.205 14:00 1.067 0.957 2.144 19.981 10.467 9.215 0.000 7.368 0.000 6.116 59.571 15:00 0.824 0.745 1.912 19.981 10.467 7.174 0.000 7.368 0.000 4.075 59.815 16:00 0.489 0.449 1.492 19.981 10.467 4.326 0.000 7.368 0.000 1.227 59.888 17:00 0.536 0.496 2.207 19.981 10.467 4.778 0.000 7.368 0.000 1.679 59.988 18:00 0.211 0.201 2.909 19.981 10.667 1.933 0.056 7.368 0.000 -1.310 59.890 19:00 0.035 0.039 2.091 19.981 10.667 0.376 0.000 7.368 0.000 -2.923 59.672 20:00 0.000 0.000 1.888 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 59.425 21:00 0.000 0.000 2.691 19.981 10.667 0.000 0.009 7.368 0.000 -3.290 59.180 22:00 0.000 0.000 2.063 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 58.933 23:00 0.000 0.000 1.814 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 58.687

Table 3.21: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of April.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.998 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 73.106 1:00 0.000 0.000 0.316 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 72.860 2:00 0.000 0.000 0.287 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 72.613 3:00 0.000 0.000 0.447 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 72.367 4:00 0.000 0.000 0.502 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 72.120 5:00 0.000 0.000 0.574 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 71.874 6:00 0.000 0.000 0.415 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 71.627 7:00 0.035 0.003 0.765 19.981 10.667 0.032 0.000 7.368 0.000 -3.268 71.383 8:00 0.279 0.138 1.077 19.981 10.467 1.327 0.000 7.368 0.000 -1.772 71.251 9:00 0.459 0.318 1.262 19.981 10.467 3.063 0.000 7.368 0.000 -0.036 71.248 10:00 0.733 0.533 1.223 19.981 10.467 5.131 0.000 7.368 0.000 2.031 71.370 11:00 0.679 0.546 1.300 19.981 10.467 5.257 0.000 7.368 0.000 2.158 71.499 12:00 0.844 0.665 1.418 19.981 10.467 6.400 0.000 7.368 0.000 3.301 71.696 13:00 0.997 0.768 1.335 19.981 10.467 7.392 0.000 7.368 0.000 4.293 71.952 14:00 0.948 0.729 1.868 19.981 10.467 7.020 0.000 7.368 0.000 3.921 72.187 15:00 0.816 0.625 1.679 19.981 10.467 6.015 0.000 7.368 0.000 2.916 72.361 16:00 0.633 0.479 1.618 19.981 10.467 4.617 0.000 7.368 0.000 1.517 72.452 17:00 0.487 0.338 1.403 19.981 10.467 3.252 0.000 7.368 0.000 0.153 72.461 18:00 0.235 0.143 1.203 19.981 10.667 1.372 0.000 7.368 0.000 -1.927 72.317 19:00 0.035 0.015 2.262 19.981 10.667 0.140 0.000 7.368 0.000 -3.159 72.081 20:00 0.000 0.000 3.036 19.981 10.667 0.000 0.083 7.368 0.000 -3.216 71.841 21:00 0.000 0.000 2.982 19.981 10.667 0.000 0.072 7.368 0.000 -3.227 71.600 22:00 0.000 0.000 2.374 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 71.353 23:00 0.000 0.000 1.558 19.981 10.667 0.000 0.000 7.368 0.000 -3.299 71.107

126 Table 3.22: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of May.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 3.083 18.982 10.667 0.000 0.094 7.000 0.000 -3.574 69.338 1:00 0.000 0.000 1.903 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 69.063 2:00 0.000 0.000 1.719 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 68.789 3:00 0.000 0.000 1.210 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 68.515 4:00 0.000 0.000 1.035 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 68.241 5:00 0.000 0.000 0.938 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 67.967 6:00 0.000 0.000 1.361 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 67.693 7:00 0.034 0.020 1.627 18.982 10.667 0.195 0.000 7.000 0.000 -3.473 67.434 8:00 0.203 0.101 1.710 18.982 10.467 0.976 0.000 7.000 0.000 -2.491 67.248 9:00 0.394 0.244 2.513 18.982 10.467 2.352 0.000 7.000 0.000 -1.115 67.164 10:00 0.528 0.373 3.951 18.982 10.467 3.595 0.275 7.000 0.000 0.403 67.188 11:00 0.478 0.404 3.579 18.982 10.467 3.887 0.200 7.000 0.000 0.619 67.225 12:00 0.690 0.527 3.828 18.982 10.467 5.070 0.250 7.000 0.000 1.853 67.336 13:00 1.018 0.675 4.284 18.982 10.467 6.502 0.343 7.000 0.000 3.378 67.538 14:00 0.913 0.607 2.482 18.982 10.467 5.846 0.000 7.000 0.000 2.379 67.680 15:00 0.859 0.545 2.968 18.982 10.467 5.252 0.069 7.000 0.000 1.853 67.791 16:00 0.715 0.417 2.997 18.982 10.467 4.020 0.075 7.000 0.000 0.628 67.829 17:00 0.464 0.233 4.274 18.982 10.467 2.240 0.341 7.000 0.000 -0.886 67.762 18:00 0.172 0.103 4.156 18.982 10.667 0.988 0.317 7.000 0.000 -2.362 67.586 19:00 0.033 0.013 3.687 18.982 10.667 0.124 0.222 7.000 0.000 -3.322 67.338 20:00 0.000 0.000 3.310 18.982 10.667 0.000 0.142 7.000 0.000 -3.525 67.074 21:00 0.000 0.000 2.473 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 66.800 22:00 0.000 0.000 1.717 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 66.526 23:00 0.000 0.000 1.880 18.982 10.667 0.000 0.000 7.000 0.000 -3.668 66.252

Table 3.23: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of June.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.453 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 42.404 1:00 0.000 0.000 0.733 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 42.103 2:00 0.000 0.000 0.311 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 41.801 3:00 0.000 0.000 0.579 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 41.500 4:00 0.000 0.000 0.828 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 41.198 5:00 0.000 0.000 0.600 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 40.896 6:00 0.000 0.000 0.546 17.983 10.667 0.000 0.000 6.631 0.000 -4.036 40.595 7:00 0.050 0.004 0.859 17.983 10.667 0.039 0.000 6.631 0.000 -3.997 40.296 8:00 0.260 0.027 1.097 17.983 10.467 0.259 0.000 6.631 16.000 10.023 40.895 9:00 0.402 0.196 1.127 17.983 10.467 1.885 0.000 6.631 16.000 11.649 41.592 10:00 0.695 0.355 1.584 17.983 10.467 3.419 0.000 6.631 16.000 13.183 42.380 11:00 0.822 0.474 1.607 17.983 10.467 4.564 0.000 6.631 16.000 14.328 43.236 12:00 0.749 0.503 2.285 17.983 10.467 4.843 0.000 6.631 16.000 14.607 44.109 13:00 0.998 0.608 1.788 17.983 10.467 5.857 0.000 6.631 16.000 15.621 45.043 14:00 0.912 0.554 1.841 17.983 10.467 5.335 0.000 6.631 16.000 15.099 45.945 15:00 0.867 0.498 2.389 17.983 10.467 4.791 0.000 6.631 16.000 14.555 46.815 16:00 0.563 0.339 2.858 17.983 10.467 3.269 0.045 6.631 16.000 13.078 47.597 17:00 0.392 0.210 3.130 17.983 10.467 2.025 0.104 6.631 16.000 11.892 48.308 18:00 0.214 0.076 3.281 17.983 10.667 0.734 0.136 6.631 16.000 10.434 48.932 19:00 0.039 0.021 3.153 17.983 10.667 0.198 0.108 6.631 16.000 9.870 49.522 20:00 0.000 0.000 2.264 17.983 10.667 0.000 0.000 6.631 16.000 9.564 50.093 21:00 0.000 0.000 2.536 17.983 10.667 0.000 0.000 6.631 16.000 9.564 50.665 22:00 0.000 0.000 2.004 17.983 10.667 0.000 0.000 6.631 16.000 9.564 51.237 23:00 0.000 0.000 1.415 17.983 10.667 0.000 0.000 6.631 16.000 9.564 51.808

127 Table 3.24: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of July.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.262 15.985 10.667 0.000 0.000 5.895 16.000 8.827 40.808 1:00 0.000 0.000 0.278 15.985 10.667 0.000 0.000 5.895 16.000 8.827 41.336 2:00 0.000 0.000 0.361 15.985 10.667 0.000 0.000 5.895 16.000 8.827 41.864 3:00 0.000 0.000 0.392 15.985 10.667 0.000 0.000 5.895 16.000 8.827 42.391 4:00 0.000 0.000 0.426 15.985 10.667 0.000 0.000 5.895 16.000 8.827 42.919 5:00 0.000 0.000 0.299 15.985 10.667 0.000 0.000 5.895 16.000 8.827 43.447 6:00 0.000 0.000 0.350 15.985 10.667 0.000 0.000 5.895 16.000 8.827 43.974 7:00 0.023 0.011 1.130 15.985 10.667 0.108 0.000 5.895 16.000 8.935 44.508 8:00 0.075 0.067 0.751 15.985 10.467 0.641 0.000 5.895 16.000 9.669 45.086 9:00 0.280 0.208 0.811 15.985 10.467 1.999 0.000 5.895 16.000 11.026 45.745 10:00 0.525 0.332 1.845 15.985 10.467 3.201 0.000 5.895 16.000 12.228 46.476 11:00 0.784 0.471 1.452 15.985 10.467 4.539 0.000 5.895 16.000 13.567 47.287 12:00 0.776 0.520 1.508 15.985 10.467 5.008 0.000 5.895 16.000 14.035 48.126 13:00 0.438 0.386 2.144 15.985 10.467 3.714 0.000 5.895 16.000 12.741 48.888 14:00 0.568 0.462 2.941 15.985 10.467 4.453 0.063 5.895 16.000 13.543 49.697 15:00 0.730 0.472 3.598 15.985 10.467 4.546 0.203 5.895 16.000 13.777 50.521 16:00 0.497 0.356 4.075 15.985 10.467 3.424 0.301 5.895 16.000 12.752 51.283 17:00 0.400 0.236 4.009 15.985 10.467 2.273 0.287 5.895 16.000 11.588 51.975 18:00 0.194 0.115 2.869 15.985 10.667 1.105 0.047 5.895 16.000 9.980 52.572 19:00 0.056 0.021 1.741 15.985 10.667 0.200 0.000 5.895 16.000 9.027 53.111 20:00 0.000 0.000 1.376 15.985 10.667 0.000 0.000 5.895 16.000 8.827 53.639 21:00 0.000 0.000 1.475 15.985 10.667 0.000 0.000 5.895 16.000 8.827 54.167 22:00 0.000 0.000 1.315 15.985 10.667 0.000 0.000 5.895 16.000 8.827 54.694 23:00 0.000 0.000 0.898 15.985 10.667 0.000 0.000 5.895 16.000 8.827 55.222

Table 3.25: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of August.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 4.346 12.988 10.667 0.000 0.356 4.789 0.000 -5.522 66.679 1:00 0.000 0.000 3.943 12.988 10.667 0.000 0.274 4.789 0.000 -5.604 66.260 2:00 0.000 0.000 3.120 12.988 10.667 0.000 0.102 4.789 0.000 -5.777 65.829 3:00 0.000 0.000 2.953 12.988 10.667 0.000 0.065 4.789 0.000 -5.813 65.394 4:00 0.000 0.000 3.004 12.988 10.667 0.000 0.077 4.789 0.000 -5.802 64.961 5:00 0.000 0.000 2.951 12.988 10.667 0.000 0.065 4.789 0.000 -5.813 64.526 6:00 0.000 0.000 1.683 12.988 10.667 0.000 0.000 4.789 0.000 -5.878 64.087 7:00 0.024 0.000 3.012 12.988 10.667 0.000 0.078 4.789 0.000 -5.800 63.654 8:00 0.199 0.083 3.573 12.988 10.467 0.796 0.198 4.789 0.000 -4.684 63.304 9:00 0.453 0.260 4.168 12.988 10.467 2.504 0.320 4.789 0.000 -2.854 63.091 10:00 0.645 0.426 4.283 12.988 10.467 4.105 0.343 4.789 0.000 -1.229 62.999 11:00 0.765 0.542 5.018 12.988 10.467 5.219 0.753 4.789 0.000 0.294 63.016 12:00 0.727 0.564 4.174 12.988 10.467 5.430 0.321 4.789 0.000 0.073 63.021 13:00 0.844 0.630 3.700 12.988 10.467 6.072 0.224 4.789 0.000 0.618 63.058 14:00 0.798 0.600 4.322 12.988 10.467 5.782 0.351 4.789 0.000 0.455 63.085 15:00 0.936 0.647 3.640 12.988 10.467 6.230 0.212 4.789 0.000 0.765 63.131 16:00 0.496 0.391 3.334 12.988 10.467 3.770 0.147 4.789 0.000 -1.761 62.999 17:00 0.327 0.261 2.607 12.988 10.467 2.511 0.000 4.789 0.000 -3.167 62.762 18:00 0.241 0.124 2.217 12.988 10.667 1.199 0.000 4.789 0.000 -4.679 62.413 19:00 0.046 0.021 1.707 12.988 10.667 0.201 0.000 4.789 0.000 -5.677 61.989 20:00 0.000 0.000 2.036 12.988 10.667 0.000 0.000 4.789 0.000 -5.878 61.550 21:00 0.000 0.000 2.658 12.988 10.667 0.000 0.002 4.789 0.000 -5.876 61.111 22:00 0.000 0.000 3.779 12.988 10.667 0.000 0.240 4.789 0.000 -5.638 60.689 23:00 0.000 0.000 2.370 12.988 10.667 0.000 0.000 4.789 0.000 -5.878 60.250

128 Table 3.26: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of September.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 0.794 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 77.053 1:00 0.000 0.000 1.418 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 76.628 2:00 0.000 0.000 1.036 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 76.202 3:00 0.000 0.000 0.912 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 75.777 4:00 0.000 0.000 0.797 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 75.351 5:00 0.000 0.000 0.903 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 74.926 6:00 0.000 0.000 1.610 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 74.501 7:00 0.005 0.004 1.660 13.487 10.667 0.040 0.000 4.974 0.000 -5.653 74.078 8:00 0.056 0.051 1.872 13.487 10.467 0.491 0.000 4.974 0.000 -5.003 73.704 9:00 0.049 0.045 2.735 13.487 10.467 0.430 0.019 4.974 0.000 -5.045 73.327 10:00 0.168 0.153 3.506 13.487 10.467 1.473 0.184 4.974 0.000 -3.837 73.041 11:00 0.079 0.072 3.518 13.487 10.467 0.694 0.187 4.974 0.000 -4.612 72.696 12:00 0.037 0.034 3.566 13.487 10.467 0.326 0.197 4.974 0.000 -4.970 72.325 13:00 0.054 0.049 2.908 13.487 10.467 0.474 0.056 4.974 0.000 -4.964 71.954 14:00 0.059 0.053 2.516 13.487 10.467 0.513 0.000 4.974 0.000 -4.981 71.582 15:00 0.060 0.054 0.892 13.487 10.467 0.522 0.000 4.974 0.000 -4.972 71.210 16:00 0.104 0.095 0.955 13.487 10.467 0.910 0.000 4.974 0.000 -4.583 70.868 17:00 0.032 0.029 1.206 13.487 10.467 0.279 0.000 4.974 0.000 -5.215 70.478 18:00 0.017 0.015 0.960 13.487 10.667 0.148 0.000 4.974 0.000 -5.546 70.064 19:00 0.021 0.000 1.063 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 69.638 20:00 0.000 0.000 0.416 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 69.213 21:00 0.000 0.000 1.271 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 68.787 22:00 0.000 0.000 1.280 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 68.362 23:00 0.000 0.000 0.659 13.487 10.667 0.000 0.000 4.974 0.000 -5.694 67.937

Table 3.27: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of October.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 1.691 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 49.077 1:00 0.000 0.000 1.909 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 48.679 2:00 0.000 0.000 1.946 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 48.282 3:00 0.000 0.000 1.063 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 47.884 4:00 0.000 0.000 0.583 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 47.486 5:00 0.000 0.000 0.443 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 47.088 6:00 0.000 0.000 0.565 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 46.690 7:00 0.018 0.018 0.912 14.486 10.667 0.175 0.000 5.342 0.000 -5.150 46.305 8:00 0.088 0.081 1.094 14.486 10.467 0.777 0.000 5.342 0.000 -4.348 45.980 9:00 0.153 0.140 0.811 14.486 10.467 1.346 0.000 5.342 0.000 -3.779 45.698 10:00 0.270 0.248 1.289 14.486 10.467 2.391 0.000 5.342 0.000 -2.735 45.494 11:00 0.297 0.272 2.212 14.486 10.467 2.618 0.000 5.342 0.000 -2.507 45.306 12:00 0.391 0.359 3.084 14.486 10.467 3.458 0.094 5.342 0.000 -1.574 45.189 13:00 0.267 0.244 3.250 14.486 10.467 2.351 0.129 5.342 0.000 -2.645 44.991 14:00 0.385 0.354 2.124 14.486 10.467 3.408 0.000 5.342 0.000 -1.718 44.863 15:00 0.338 0.311 1.930 14.486 10.467 2.996 0.000 5.342 0.000 -2.129 44.704 16:00 0.461 0.450 1.828 14.486 10.467 4.334 0.000 5.342 0.000 -0.791 44.644 17:00 0.365 0.387 1.402 14.486 10.467 3.727 0.000 5.342 0.000 -1.398 44.540 18:00 0.072 0.067 1.850 14.486 10.667 0.649 0.000 5.342 0.000 -4.677 44.191 19:00 0.003 0.000 2.715 14.486 10.667 0.000 0.014 5.342 0.000 -5.311 43.794 20:00 0.000 0.000 2.596 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 43.396 21:00 0.000 0.000 2.069 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 42.998 22:00 0.000 0.000 2.412 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 42.600 23:00 0.000 0.000 1.682 14.486 10.667 0.000 0.000 5.342 0.000 -5.325 42.202

129 Table 3.28: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of November.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 1.292 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 47.465 1:00 0.000 0.000 0.782 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 47.108 2:00 0.000 0.000 0.556 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 46.752 3:00 0.000 0.000 0.573 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 46.395 4:00 0.000 0.000 0.969 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 46.039 5:00 0.000 0.000 1.352 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 45.682 6:00 0.000 0.000 1.688 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 45.325 7:00 0.037 0.156 2.501 15.985 10.667 1.504 0.000 5.895 0.000 -3.268 45.081 8:00 0.278 0.448 3.220 15.985 10.467 4.310 0.123 5.895 0.000 -0.140 45.071 9:00 0.563 0.716 3.286 15.985 10.467 6.899 0.137 5.895 0.000 2.464 45.218 10:00 0.689 0.794 3.333 15.985 10.467 7.649 0.147 5.895 0.000 3.224 45.411 11:00 0.846 0.935 3.491 15.985 10.467 9.003 0.181 5.895 0.000 4.611 45.686 12:00 0.909 0.982 3.227 15.985 10.467 9.455 0.124 5.895 0.000 5.007 45.986 13:00 0.952 1.027 2.207 15.985 10.467 9.887 0.000 5.895 0.000 5.314 46.303 14:00 0.801 0.862 2.396 15.985 10.467 8.299 0.000 5.895 0.000 3.726 46.526 15:00 0.770 0.861 2.378 15.985 10.467 8.296 0.000 5.895 0.000 3.723 46.748 16:00 0.590 0.699 2.831 15.985 10.467 6.730 0.039 5.895 0.000 2.197 46.880 17:00 0.411 0.561 2.094 15.985 10.467 5.406 0.000 5.895 0.000 0.834 46.930 18:00 0.162 0.341 2.560 15.985 10.667 3.282 0.000 5.895 0.000 -1.491 46.818 19:00 0.002 0.000 1.287 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 46.462 20:00 0.000 0.000 0.611 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 46.105 21:00 0.000 0.000 0.479 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 45.748 22:00 0.000 0.000 0.324 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 45.392 23:00 0.000 0.000 0.135 15.985 10.667 0.000 0.000 5.895 0.000 -4.773 45.035

Table 3.29: Power demand met by the hybrid energy system (PV, wind, hydro and diesel generator) in 15th day of December.

Time (h)

Global solar

(kW/ m2)

Incident solar

(kW/ m2)

Wind Speed (m/s)

Stream Flow (L/s)

Dc Load (kW)

PV Output (kW)

Wind Output (kW)

Hydro Output (kW)

Diesel Output (kW)

Battery Input (kWh)


(%) 0:00 0.000 0.000 1.550 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 43.347 1:00 0.000 0.000 1.476 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 43.060 2:00 0.000 0.000 0.471 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 42.772 3:00 0.000 0.000 1.004 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 42.484 4:00 0.000 0.000 0.551 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 42.196 5:00 0.000 0.000 1.087 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 41.908 6:00 0.000 0.000 1.102 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 41.621 7:00 0.018 0.000 0.413 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 41.333 8:00 0.202 0.393 0.883 18.482 10.467 3.783 0.000 6.816 0.000 0.132 41.341 9:00 0.481 0.687 0.698 18.482 10.467 6.616 0.000 6.816 0.000 2.964 41.518 10:00 0.666 0.834 0.973 18.482 10.467 8.032 0.000 6.816 0.000 4.381 41.780 11:00 0.916 1.083 0.470 18.482 10.467 10.432 0.000 6.816 0.000 6.781 42.185 12:00 0.923 1.059 1.141 18.482 10.467 10.199 0.000 6.816 0.000 6.547 42.576 13:00 0.937 1.065 2.067 18.482 10.467 10.253 0.000 6.816 0.000 6.602 42.971 14:00 0.966 1.111 2.283 18.482 10.467 10.702 0.000 6.816 0.000 7.050 43.392 15:00 0.806 0.955 2.793 18.482 10.467 9.200 0.031 6.816 0.000 5.580 43.726 16:00 0.629 0.794 2.373 18.482 10.467 7.645 0.000 6.816 0.000 3.993 43.965 17:00 0.396 0.565 2.892 18.482 10.467 5.440 0.052 6.816 0.000 1.841 44.075 18:00 0.163 0.324 1.987 18.482 10.667 3.120 0.000 6.816 0.000 -0.732 44.020 19:00 0.006 0.000 2.588 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 43.732 20:00 0.000 0.000 2.483 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 43.444 21:00 0.000 0.000 2.275 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 43.157 22:00 0.000 0.000 1.281 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 42.869 23:00 0.000 0.000 2.353 18.482 10.667 0.000 0.000 6.816 0.000 -3.852 42.581

130

3.15. Summary

The chapter has discussed the simulation of optimal alternative power options (of

hybrid PV/Wind/Hydro/Diesel configurations) for eight hypothetical BTS sites in

selected rural areas in Nigeria, using the HOMER software. The selected locations

were chosen to reflect the various geographical and climatic conditions in Nigeria. Of

particular interest are: system life cycle costs, available resources, environmental

costs and reliability requirements to meet the required loads of the site. The

mathematical model for each power generating option was also described, along with

decision variables (such as components sizing) and associated constraints. The

HOMER software was calibrated using standard data from NASA, and an

optimization algorithm was also developed. Other developments here include a

supervisory controller (sliding mode control) to monitor and regulate the power

generation by the hybrid systems components [PV, H, W, DG] according to the load

requirements of each BTS site.

131

CHAPTER FOUR

RESULTS AND DISCUSSIONS

4.0. Introduction

This chapter presents the results of the HOMER simulations of each of the 8 hybrid

energy system types at each of the 8 selected BTS location sites. The HOMER

performs three principal tasks namely simulation, optimization, and sensitivity

analysis. The simulation process determines how a particular system configuration

would behave in a given setting over a long period of time, and serves two purposes.

First, it determines whether the system is feasible. A system is said to be feasible if it

can adequately serve the electric and thermal loads as well as satisfy any other

constraints imposed by the user. Secondly, it estimates the life-cycle cost of the

system. The quantity used to represent the life-cycle cost of the system is the total net

present cost (NPC). The total net present cost of a system is the present value of all

the costs that it incurs over its lifetime, minus the present value of all the revenue that

it earns over its lifetime. Costs include capital costs, replacement costs, operation and

maintenance costs, fuel costs, emissions penalties, and the costs of buying power from

the grid. Revenues include salvage value and grid sales revenue. The optimization and

sensitivity analysis rely on this simulation capability for a given set of constraints and

sensitivity variables, which include, among other things, the electric energy (kWh)

generated and the pollutant emissions (in tons of CO2) produced by the hybrid energy

system type under study. A hybrid energy system is considered as an optimal solution

for any particular BTS site if it meets the required loads of the site at minimum total

economic costs (NPC) and minimum adverse environmental impact. Thus, the

simulation results are collated and classified according to these three major factors,

namely: the total economic costs (NPC in N), the environmental impact (pollutant

132 emissions in tons of CO2), and the electric energy (kWh) generated by each hybrid

system type. The results are presented below in tables 4.1-4.3, respectively. More

detailed analysis of these results highlights the following major findings of this study:

1) the superiority of the PV/Wind/Hydro/Diesel hybrid system over any of the other

seven configurations in meeting the objectives of the study; 2) the more renewable

energy components in a hybrid system, the higher the initial capital cost, but the lower

the total net present cost (NPC) in the long run; 3) similarly, the more renewable

energy components in a hybrid system, the lower the environmental impact by the

hybrid system type; 4) both the net present cost (NPC) and the environmental impact

of the hybrid energy system types under study do not vary (significantly) with the

locations of the BTS sites; 5) the same applies to the percentage of energy generated

by the hydro component of the hybrid systems, it is independent of the BTS location

sites, and is consistently higher than that of each of the other two components (solar,

wind) and that of their combination (PV + W); 6) on the other hand, the percentage of

energy generated by both the PV (solar) and the wind renewable energy components

of each of the hybrid system types tends to vary with the locations of the BTS sites,

The significance and implications of these results are extensively discussed below in

the chapter.

4.1. The Results

As stated above, tables 4.1 – 4.3 show the classification of the simulation results

according to: i) the total net present economic costs (NPC in naira N) [table 4.1], ii)

the environmental impact (pollutant emissions in kg of CO2) [table 4.2], and iii) the

electric energy (kWh) generated by each hybrid system type [table 4.3].

133 Table 4.1: Economic Costs [NPC in Billions of Naira (N))]

BTS SITE LOCATION HYBRID SYSTEM TYPE [10-9] S/N 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

1 Abaji 0.90 0.93 1.61 1.64 3.05 3.07 3.66 3.69 2 Guzamala 0.62 0.88 1.37 1.63 2.78 3.03 3.46 3.69 3 Ikwerre 1.03 1.10 1.56 1.63 3.17 3.23 3.62 3.69 4 Kauru 0.86 0.91 1.60 1.65 3.01 3.05 3.66 3.695 Mopa-Muro 0.97 0.99 1.61 1.63 3.11 3.13 3.66 3.69 6 Nembe 1.01 1.11 1.54 1.63 3.15 3.24 3.61 3.69 7 Nkanu-West 0.99 1.02 1.62 1.63 3.14 3.15 3.68 3.69 8 Tureta 0.80 0.84 1.60 1.65 2.95 2.99 3.66 3.69

The figures in the table are classified from the least cost (in red) to the highest cost (in

blue) per hybrid type as well as per locations. For instance, PV/W/H+DG hybrid

system type has the least NPC [0.62 at Guzamala]. This is followed by PV/H +DG

[0.84 at Tureta] and H/W+DG [1.37 at Guzamala], While Diesel only has the highest

NPC [3.69 in all the BTS site location studied] as shown in the table.

Table 4.2: Environmental Impact [(pollutant emissions in tons of CO2)]

BTS SITE LOCATION HYBRID SYSTEM TYPE [10-3] S/N 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

1 Abaji 22.62 23.46 41.92 43.26 80.91 81.84 97.64 101.342 Guzamala 14.63 21.99 35.33 42.64 73.49 80.49 92.24 101.343 Ikwerre 25.85 27.95 40.48 42.64 84.05 85.91 96.59 101.344 Kauru 21.24 22.93 41.63 43.26 79.69 81.00 97.71 101.345 Mopa-Muro 24.17 24.85 41.80 42.64 82.31 83.20 97.69 101.346 Nembe 25.24 28.06 39.96 42.64 83.57 86.05 96.28 101.347 Nkanu-West 24.85 25.83 42.24 42.64 83.19 83.62 98.38 101.348 Tureta 19.71 20.94 41.84 43.26 78.09 79.44 97.67 101.34

Similarly, the figures in this table (4.2) are classified from the least environmental

pollution [in red] to the highest environmental pollution [in blue] per hybrid type as

well as per location. For instance, PV/W/H+DG hybrid system type has the least

environmental pollution [14.63 at Guzamala]. This is followed by PV/H+DG [20.94

at Tureta] and H/W+DG [35.33 at Guzamala], while Diesel only has the highest

environmental pollution [101.34 in all the BTS site location studied] as shown in the

table.

134 Table 4.3: Percentage of Energy Generated by the Renewable Energy Hybrid Systems

Components. BTS SITE LOCATION HYBRID SYSTEM TYPE [%]

[contributions made by the hybrid system components are demarcated by forward slashes(/)] S/N 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

1 Abaji 18/1/55/26 18/55/27 53/1/46 53/47 16/1/83 16/84 1/99 100 2 Guzamala 20/7/56/17 20/55/25 54/7/39 53/47 18/6/76 17/83 6/94 100 3 Ikwerre 13/3/55/29 13/55/32 53/2/45 53/47 12/2/86 12/88 2/98 100 4 Kauru 19/1/56/24 19/55/26 53/1/46 53/47 17/1/82 17/83 1/99 100 5 Mopa-Muro 16/1/55/28 16/55/29 53/1/46 53/47 15/1/84 15/85 1/99 100 6 Nembe 13/2/55/30 13/55/32 53/3/44 53/47 12/2/86 12/88 2/98 100 7 Nkanu-West 16/1/55/28 16/55/29 53/1/46 53/47 14/1/85 14/86 0/100 100 8 Tureta 21/1/56/23 21/55/24 53/1/46 53/47 19/1/80 18/82 1/99 100

The same applies to the figures in this table (4.3). They are arranged from the highest

energy generated by the renewable energy [in red] to the least energy generated by

the renewable energy [in blue]. For instance, PV/W/H+DG hybrid system type has

the highest energy generated by the renewable energy [20/7/56/17 at Guzamala]. This

is followed by PV/H+DG [21/55/24 at Tureta] and H/W+DG [54/7/39 at Guzamala],

while W+DG has the least energy generated by the renewable energy [0/100 at

Nkanu-West], as shown in the table.

These results are further illustrated with bar charts and line graphs in figures 4.1

(NPC), 4.2 (environmental impact) and 4.3 (energy generated).

135

Figure 4.1 (a): Economic Costs [NPC in Billions of Naira]

Figure 4.1 (b): Economic Costs [NPC in Billions of Naira]

00.51

1.52

2.53

3.54

Cost in (N

109 )

Economic Costs

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

00.51

1.52

2.53

3.54

Cost in (N

109 )

BTS Site Location

Economic Costs

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

136

Figure 4.2 (a): Environmental Impact [(pollutant emissions in tons of CO2)]

Figure 4.2 (b): Environmental Impact [(pollutant emissions in tons of CO2)]

0

20

40

60

80

100

120

Pollu

tant Emission

in to

ns

BTS Site Location

Environmental Impact

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

0

20

40

60

80

100

120

Pollu

tant Emission

in to

ns

BTS Site Location

Environmental Impact

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

137 Table 4.4: Percentage (%) of Energy Generated by Renewable Energy Components of

Each Hybrid System BTS SITE LOCATION HYBRID SYSTEM TYPE [%] S/N 1 2 3 4 5 6 7

PV/W/H PV/H H/W H only PV/W PV only W only 1 Abaji 74 73 54 53 17 16 1 2 Guzamala 83 75 61 53 24 17 6 3 Ikwerre 71 68 55 53 14 12 2 4 Kauru 76 74 54 53 18 17 15 Mopa-Muro 72 71 54 53 16 15 1 6 Nembe 70 68 56 53 14 12 2 7 Nkanu-West 72 71 54 53 15 14 0 8 Tureta 78 76 54 53 20 18 1

Figure 4.3: Percentage Energy Generated by Renewable Energy Components per

Each Hybrid Type.

4.2. Analysis of the Results

Both the tables (4.1-4.2) and the figures (4.1-4.3) described above reflect the 8X8 (8

hybrid energy types and 8 base station location sites) design of this study. This design

was intended to enable us investigate the potential of local applications of these

various (8) renewable energy hybrid configurations in the telecommunications

industry, using 8 hypothetical BTS site locations in the rural areas across the country,

as case studies. The detailed analysis of the results of this investigation is presented

below:

0102030405060708090

Percen

tage Ene

rgy Gen

erated

BTS Site Location

Percentage Energy Generated by the Renewable Energy Components per Each Hybrid Type

PV/W/H

PV/H

H/W

H only

PV/W

PV only

W only

138 4.2.1. Optimal Ranking of the Hybrid System Types

As stated above, a hybrid energy system may be considered as an optimal solution for

any particular BTS site if it meets the required loads of the site at minimum total net

present economic costs (NPC) and minimum adverse environmental impact. Thus, in

one of the simulations, HOMER generated table 4.5 (below) illustrating this

statement; where the hybrid system types are ranked both: 1) in a descending order of:

the highest percentage of energy generated (see Table 4.4 and Figure 4.3 above), and

2) in an ascending order of: a) the least economic cost (NPC) [see Figs. 4.1(a) and

4.1(b) above], and b) the least environmental impact [see Figs. 4.2(a) and 4.2(b)

above], by the hybrid system types.

Table 4.5: Optimal Ranking of the Hybrid System Types [as Generated by HOMER]

S/N HOMER Optimization Results 1 Hybrid (Solar, Wind & Hydro) + DG 2 Hybrid (Solar & Hydro) + DG 3 Hybrid (Wind & Hydro) + DG4 Hydro only + DG5 Hybrid (Solar & Wind) + DG 6 Solar only + DG 7 Wind only + DG 8 DG

In other words, table 4.5 tends to illustrate the superiority of the

PV/Wind/Hydro/Diesel hybrid system over any of the other seven configurations in

meeting the objectives of this study. That is, the Solar/Wind/Hydro/DG hybrid energy

system type could be considered as the most appropriate (optimal) hybrid renewable

energy solution option for powering any base station at each of the BTS location sites

studied. This option is followed by i) Solar/Hydro/DG hybrid energy system, ii)

Wind/Hydro/DG hybrid energy system, iii) Hydro/DG hybrid energy system, iv)

Solar/Wind/DG hybrid energy system, v) Solar/DG hybrid energy system, vi)

Wind/DG hybrid energy system, and vii) DG alone, in that order. The significance of

the above results (Table 4.5) is presented in the subsequent sections below.

139

4.2.2. Energy Rating of the Hybrid Systems and Components

As stated earlier in the introduction, a hybrid renewable energy system is said to be

feasible if it can adequately serve the electric and thermal loads as well as satisfy any

other constraints imposed by the user. This makes energy the first criterion in the

above optimal ranking of the hybrid system types, and thereby imposes certain

constraints not only on the choice and sizing of the individual hybrid components

(PV, wind and hydro, DG and battery), but also on their configurations (number and

combinations) for a given load demand at a BTS site. As illustrated in Table 4.4 and

Figure 4.3, the percentage energy generated by the renewable energy components

(solar, wind, hydro) of each hybrid system type depends on the number and

combination of these components; the more the number the higher the percentage.

The latter (generated energy) also depends on the combination, with PV/W/H and

PV/H combinations generating higher percentages than the other two combinations

(PV/W, H/W), and this is by far higher than that of the individual components alone

(except diesel). These results agree with the results of many other works in the

literature [62, 82, 90]. For example, in the work of Deepar Kumar Lal et al [62], the 3

type renewable energy combination (PV/W/H) generate higher percentage of energy

than 2 type renewable energy combinations (PV/H, H/W, and PV/W) and far higher

than that of the individual renewable components alone (PV, W, and H). Furthermore,

while the percentage energy generated by certain combinations of the renewables

(PV/H/W or PV/W) tends to vary with the locations (geographical areas) of the BTS

sites, those generated by others (PV/H, H/W or H) do not. The former is due to the

predominance of both PV (solar) and wind, the energy features and potentials (solar

radiation and wind speed) of which depend on climatic conditions (of site locations),

while the latter is because of the presence of H (hydro system), the energy potential of

140 which has no significant variation with climatic conditions. The above results are

further illustrated in Figs.4.4 (a)–(d). For instance, Figs. 4.4 (a) and 4.4 (b) illustrate

significant variation of the energy (%) generated by PV and W (wind), respectively,

per Renewable Energy Hybrid Type. More specifically, in Figure 4.4 (a) PV has

higher energy picks at Guzamara, Kauru and Tureta, and lower picks at Ikwere and

Nembe BTS sites, respectively, while in Figure 4.4 (b) W (wind) also has a very high

energy pick at Guzamara and almost zero at Nkanu-West. On the other hand, it can be

seen in Figure 4.4 (c) that the energy (%) generated by H (hydro) is almost the same

at all the BTS sites. The design implications of these results include components

sizing and portability; the choice and sizing of the hydro system could be portable

(i.e., the same size and type of a hydro system could be deployed at more than one

BTS site), while both PV (solar) and wind could be said to be more site specific [35,

62].

Figure 4.4 (a): Percentage Energy Generated by PV per Renewable Energy Hybrid

Type.

0

5

10

15

20

25

Energy Gen

erated

(%)

BTS Site Location

Percentage Energy Generated by PVper each Renewable Energy Hybrid Type

PV/W/H+DG

PV/H+DG

PV/W+DG

PV+DG

141

Figure 4.4 (b): Percentage Energy Generated by Wind per Renewable Energy Hybrid

Type.

Figure 4.4 (c): Percentage Energy generated by Hydro per Renewable Energy Hybrid

Type.

012345678

Energy Gen

erated

(%)

BTS Site Location

Percentage Energy Generated by Wind per Renewable Energy Hybrid Type

PV/W/H+DG

H/W+ DG

PV/W+DG

W+DG

51525354555657

Energy Gen

erated

(%)

BTS Site Location

Percentage Energy Generated by Hydro per Renewable Energy Hybrid Type

PV/W/H+DG

PV/H+ DG

H/W+DG

H+DG

142

Figure 4.4 (d): Percentage Energy generated by Diesel per each Renewable Energy

Hybrid Type.

4.2.3. Economic Rating of the Hybrid System Types and Components

The second most important criterion for assessing an optimal solution (renewable

energy hybrid system) for any particular BTS site is the economic cost of the system.

The cost ratings here are discussed in terms of two major cost components: 1) the

initial capital cost (ICC), and 2) the total net present cost (NPC), the former being

completely exclusive (i.e., ICC excludes other costs), while the latter is inclusive (i.e.

includes the present value of all the costs that it incurs over its lifetime).

4.2.3.1. Initial Capital Costs [ICC]

The initial capital cost of a component is the total installed cost of that component at

the beginning of the project. The results illustrated in figure 4.5 shows that the more

the renewable energy components in a hybrid system, the higher the initial capital cost

[ICC], and this cost has no significant variation with the BTS sites. On the other hand,

diesel only system has lower initial capital cost [ICC] as shown in figure 4.5, but

higher [NPC] as illustrated in figures 4.1 (a & b). These results tend to agree with

020406080

100120

Energy Gen

erated

(%)

BTS Site Location

Percentage Energy Generated by Diesel per each Renewable Energy Hybrid Type

DG in PV/W/H

DG in PV/H

DG in H/W

DG in H

DG in PV/W

DG in PV

DG in W

DG only

143 many other works in the literature in this area [55, 57, 79]. For example, Bagul et al,

in their paper [55] stated that single source renewable energy usually leads to

component over-sizing, which increases the operating and life cycle cost. Also, Laidi

et al [79] stated that hybrid systems if properly optimized are both cost effective and

reliable compared to single power systems.

Figure 4.5: Initial Capital Costs [ICC]

4.2.3.2. The Total Net Present Cost [NPC]

The total net present cost (NPC) of a system has been described as the present value

of all the costs that it incurs over its lifetime, minus the present value of all the

revenue that it earns over its lifetime. Costs include capital costs, replacement costs,

operation and maintenance costs, fuel costs, emissions penalties, and the costs of

buying power from the grid. Revenues include salvage value and grid sales revenue.

However, the analysis presented here considers neither the costs of buying power

from the grid nor grid sales revenue, since the focus of this study is on BTS sites in

rural areas without grid connections. To appreciate the significance of the life cycle

cost (NPC) in the choice of optimal combination of renewable energy components

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type

Initial Capital Costs

Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

144 and the ranking of optimized renewable energy hybrid systems (as illustrated in table

4.5) for a typical rural BTS site, further simulation runs were conducted at 5 years

intervals (for a period of 25 years) for each of the 8 X 8 design configurations [8

renewable energy component combinations (hybrid types) and 8 BTS sites]. The

results of these simulations are illustrated in Figs. 4.6 [(a) – (e)] and 4.7 as well as in

Tables (in appendix B). The following general observations were made from these

results:

1) The total net present cost (NPC) increases with the decrease of renewable

energy components in the hybrid systems [from 3 to 1] (see figs. 4.6 (a) &

(b)). This observation agrees with the work of Bagul et al [55]. According to

their paper, single source renewable energy usually leads to component over-

sizing, which increases the operating and life cycle cost. However, there are

two exceptions to the above observation, and these are: (a) type 4 (hydro only

+ DG) and (b) type 5 (PV + W + DG) hybrid systems, respectively, where the

NPC of the former (a) is significantly lower than that of the latter (b), and this

difference is consistent in all the five interval periods investigated [see figs.

4.6 (a) - 4.6 (e)]. There are two possible explanations to these two exceptions:

i) the highest percentage of energy generated by type 5 (PV/W + DG) hybrid

system is by far lower (24%) than that (53%) by type 4 (H + DG) hybrid

system [see tables in appendix B]. This means more operational hours (5,286

hrs. vs. 3,067 hrs.) and more fuel consumption (27,906 vs. 16,194) by the

diesel generator (DG) [as shown in figures 4.8 and 4.9], and consequently

higher NPC, in type 5 (PV/W + DG) than in type 4 (H + DG) hybrid systems,

respectively. This result is supported by Laidi et al [79] with the comparison

of operational hour and fuel consumption of diesel with renewable energy and

without renewable energy. Their results are as follows: PV/Wind/Diesel

145

(4,220; 3,068), Wind/Diesel (3,819; 2.798), PV/Diesel (8,116; 5,465), and

Diesel only (8,618; 5,939). ii) components replacement costs for type 5 (PV/W

+ DG) hybrid system could apparently be higher than that of type 4 (H only +

DG) system. These observations are supported by Deepak et al [62] in which

the replacement of a PV/W+DG hybrid system cost about $17,946, whereas H

only +DG cost $9,977.

2) as shown in figure 4.7, the differences in costs (NPC) between the intervals,

and also the NPC decreases as the lifetime of the hybrid systems increases,

and these differences vary significantly from one hybrid system type to

another (but not with the BTS sites); with those of (PV/H/W + DG) and (H

only + DG) hybrid types being lower than those of (DG alone) and (W only +

DG) hybrid types. This observation highlights the differences in the total net

present costs (NPC) among the various hybrid system types, as observed

above. It also makes a lot of difference not only in the choice of the renewable

energy components, but also in their combinations and sizing, for particular

BTS sites.

3) Judging from the two cost components, the initial capital cost (ICC) and the

total net present costs (NPC), as well as the amount (%) of energy generated,

the hydro system appears to be the most cost effective renewable energy

component that could be deployed for BTS sites in the rural areas of Nigeria.

This observation agrees with many other works in the literature [39, 107]. For

example, Paish and Kamaruzzaman et al [39, 107] pointed out in their papers

that the price of the hydro turbine is much less compared to other renewables

(such as wind turbine and PV panels).

146

Figure 4.6 (a): Economic Costs in 5 years

Figure 4.6 (b): Economic Costs in 10 years

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type

Economic Costs in 5 years

Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

0

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type


Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

147

Figure 4.6 (c): Economic Costs in 15 years

Figure 4.6 (d): Economic Costs in 20 years

0

0.5

1

1.5

2

2.5

3

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type


Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

0

0.5

1

1.5

2

2.5

3

3.5

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type


Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

148

Figure 4.6 (e): Economic Costs in 25 years

Figure 4.7: Economic Cost Differences in 5 Years Intervals

4.2.4. Environmental Impact Rating of the Hybrid System Types and

Components

One of the major attractions of renewable energy systems is their environmental

friendliness, and this informs their preferences in hybrid systems for various

applications. In this study, for instance, one of the objectives is to investigate

alternative energy solutions that are not only feasible, but also optimal for powering

0

0.5

1

1.5

2

2.5

3

3.5

4

1 2 3 4 5 6 7 8

Cost in (N

109 )

Hybrid System Type


Abaji

Guzamala

Ikwerre

Kauru

Mopa‐Muro

Nembe

Nkanu‐West

Tureta

0

0.2

0.4

0.6

0.8

1

1.2

1.4

0‐5yr 5‐10yr 10‐15yr 15‐20yr 20‐25yr

Cost in

(N10

9 )

Years

Economic Cost Differences in 5 Years Intervals

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

149 BTS sites in non-grid serviced rural areas in Nigeria. As stated above, a hybrid energy

system is considered as an optimal solution for any particular BTS site if it meets the

required loads of the site not only at minimum economic costs (NPC), but also at

minimum adverse environmental impact. More specifically, if there is minimum CO2

emission to the environment by the diesel generator component of the hybrid system.

As illustrated in table 4.6 and figure 4.10, this condition obtains only when any

combination of the renewable energy components (PV, H, W) in the hybrid system

generates as much (maximum) energy as would reduce to a minimum: i) the

operational hour, ii) the fuel consumption, and iii) the percentage energy generated,

by the diesel generator. It could be seen in both table 4.6 and figure 4.10 that this is

achieved only by the PV/H/W + DG hybrid configuration. It is also shown that, as the

energy (%) generated by the other combinations [(PV/H + DG), (H/W + DG), (H only

+ DG), (PV/W + DG), (PV only + DG) and (W only + DG)] decreases, the CO2

generated by the generator increases proportionately, so also is each of the other two

related parameters, as mentioned above. In summary, it could therefore be stated that

the more the number of the renewable energy components (in combination with a

diesel generator (DG)) in any renewable energy hybrid system, the lower the CO2

generated by the generator and hence the less (minimum) is the adverse

environmental impact of the hybrid system, and consequently the optimum is the

renewable energy solution at any given BTS site. However, the point (at H only +

DG) where the energy generated by the combinations of the three renewable energy

components (PV, H, W) and that generated by DG only intersect indicates a

significant exception to the above phenomenon. This again points to the over-riding

superiority of the hydro system over the other components (PV and W) in any

renewable energy hybrid configurations. Evidently, H generates more than twice the

energy generated by either PV or W only, or a combination of the two, in all the

150 hybrid types and BTS sites studied. These were supported by Kamaruzzaman et al

[107] in which they show in their paper that the use of hydro turbine in the renewable

energy set-up is an important sizing determination. Their reason being that the turbine

can operate 24 hours provided enough flowing water into the gathering chamber.

Table 4.6: Environmental Impact Analysis

HYBRID SYSTEM TYPE

Percentage of Energy Generated by Renewable Energy components

Percentage Energy Generated by Diesel

Operational Hour of Diesel in the Hybrid System Type [10-3]

Fuel Consumption of Diesel [10-3]

CO2 Emission [10-3]

PV/W/H + DG 83 17 1.06 5.56 14.63 PV/H + DG 75 25 1.58 8.35 21.99 H/W + DG 61 39 2.54 13.42 35.33 H only + DG 53 47 3.07 16.19 42.64 PV/W + DG 24 76 5.29 27.91 73.49 PV only + DG 17 83 5.79 30.57 80.49 W only + DG 6 94 6.64 35.03 92.24 DG only 100 8.76 38.49 101.34

Figure 4.8: Operational Hour of Diesel in the Hybrid System Type

01,0002,0003,0004,0005,0006,0007,0008,0009,00010,000

Ope

ration

al Hou

r (hr/yr)

BTS Site Location

Operational Hour of Diesel in the Hybrid Systems

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

151

Figure 4.9: Fuel Consumption of Diesel per Hybrid System Type

Figure 4.10: Environmental Impact Analysis

05,00010,00015,00020,00025,00030,00035,00040,00045,000

Fuel Con

sumption (L/yr)

BTS Site Location

Fuel Consumption of Diesel per Hybrid System Type

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

0

20

40

60

80

100

120 % of Energy Generated by Renewable Energy components

% Energy Generated by Diesel

Operational Hour of Diesel in the Hybrid System Type [10‐3]

Fuel Consumption of Diesel [10‐3]

CO2 Emission [10‐3]

152 4.3. Discussions

Solar, hydro and wind energy systems are among the most developed renewable

energy systems (RES), and these have found wide and popular applications. Among

these (applications) are the hybrid renewable energy systems, which have been

described as any combinations of one (or two or all) of the three systems (solar, hydro

and wind) with a diesel generator and a battery. The popularity of both the individual

renewable energy systems (oftentimes simply termed “the renewables”) and their

hybrid configurations stems from many inherent natural features and technological

developments. These include:

1. the renewables are:

i) inexhaustible and readily available natural resources, predominantly

present in most parts of Africa;

ii) relatively environmentally friendly;

iii) very scalable, from micro- to mega- hydro/wind turbines, and from

single-panel to multiple-panel PV (solar) systems; and

iv) they have long lifetime (20-50 years) and very low maintenance

cost;

2. the hybrid systems:

i) provide more reliable and higher quality energy at lower economic

and environmental costs; and

ii) are more environmentally friendly than any of the conventional

energy generators

Unfortunately, despite these attractions and the abundance of these renewable energy

resources, neither the individual renewables nor their hybrid configuration has found

considerable applications in Nigeria. The telecommunications sector of the economy

is a case in point. This study has demonstrated that the telecommunications industry is

153 one of the areas where renewable energy systems could be deployed to maximum

national economic and environmental benefits, as obtains in many developed and

developing countries, including a number of countries in Africa [13, 19, 20, 21], as

discussed in chapter 1, section 1.1. More specifically, this study has shown that GSM

Base Stations in different rural areas of the country could adequately be powered by

renewable hybrid energy systems at less (minimum) economic and environmental

costs than the use of conventional diesel generators only, as it is the case now.

Although this statement has been substantiated in many instances presented in the

analysis above (in sections 4.2.1- 4.2.4), the highlights are summarized below.

4.3.1. Justification for Renewable Power Options at BTS Sites

When one considers the geographical location of Nigeria, the favourable climatic

conditions across the country, her endowment with abundant renewable energy

sources and current developments in renewable energy technology, whereby the cost

of renewable energy components continues to fall every day, the continued powering

of BTS sites in any part of Nigeria solely with diesel generators is no longer

justifiable. This is the case this study is making, more so when the same

telecommunication operators in Nigeria are deploying the same renewable technology

in many parts of the world, including a number of countries in Asia and Africa [10,

13, 19, 20, 21] less endowed with these renewable energy resources than Nigeria. If

hitherto there was an excuse of no availability of reliable information to justify an

investment in this area, such information could now be found in this and related works

reviewed in this study. One such vital information for judicious investment is

components sizing. As stated earlier in chapter two (literature review), in order to

efficiently and economically utilize the renewable energy resources, an optimum

sizing of the renewable energy components is necessary. The optimum sizing can help

154 guarantee the lowest investment with full use of the system components, so that the

hybrid system can work at the optimum conditions in terms of investment and system

power reliability requirement. Thus, in the study is presented a comprehensive review

of current optimum sizing methods, such as simulation tools [80], linear programming

models [81], numerical algorithm [82], probabilistic model [66], multi-objective

planning technique [89], etc. Not only that, using one of the simulation tools

(HOMER) to find the optimum combination and sizing of components, the study was

able to evaluate the performances of 8 different hybrid system configurations at each

of 8 different BTS sites located in (8) different geographical/climatic areas/conditions

across the country. More specifically, the simulation results analyzed above

demonstrated the ability to optimize configurations of hybrid energy systems in order

to maximize performance while minimizing both economic and environmental costs.

For instance, among the 8 configurations studied, the PV-H-W-DG-Battery system

was found to be the best (optimum) combination to power each of the 8 BTS sites

investigated.

The study also confirmed the significance of facilities’ locations in the choice of

renewable power options. The literature [35] has it that the performance of solar and

wind energy systems (singly or in combination) are strongly dependent on the

climatic conditions at the location. This has been discussed extensively in section

4.2.2 above, and illustrated in figures 4.4 (a) and 4.4 (b) in the same section. Much

ground was also covered in the literature review (section 2.5.3) on economic costs not

only as important determining factors for the choice of renewable power options, but

also as very relevant information for making critical decisions on systems design and

economic investment. Many assertions (or observations) in the review were either

confirmed numerically (as shown in the tables) or illustrated graphically by the

simulation results presented here in the study. These include: i) that the more the

155 renewable energy components in a hybrid system, the higher the initial capital cost

[ICC], and this cost has no significant variation with the BTS sites (see figure 4.5). On

the other hand, diesel only system has lower initial capital cost [ICC], but higher total

net present cost [NPC]; ii) that the total net present cost (NPC) increases with the

decrease of renewable energy components in the hybrid systems (see figs. 4.6 (a) &

(b)), but decreases as the lifetime of the hybrid systems increases, and these

differences vary significantly from one hybrid system type to another, but not with the

BTS sites, as illustrated in figure 4.7. and iii) that the hydro system appears to be the

most cost effective renewable energy component that could be deployed for BTS sites

in any rural area in Nigeria.

This study has also addressed the issue of pollution reduction as a major justification

for alternative energy applications, as adequately reviewed in the literature (in

sections 2.4 & 2.5.1) and quantitatively demonstrated by the simulation results (in

tables 4.3 & 4.6 and figure 4.10). Given that the major source of pollution in any

alternative energy application, such as the hybrid system, is CO2 emitted by the diesel

generator component of the application, this study confirmed the observations by

many works in the literature [97, 120, 122, 123] that the more the number of the

renewable energy components (in combination with a diesel generator (DG)) in any

renewable energy hybrid system, the lower the CO2 generated by the generator and

hence the minimum is the pollution of the environment by the hybrid system. In other

words, by increasing the renewable energy penetration in any alternative energy

system, the power drawn from the diesel component of the system is minimized,

thereby reducing the environmental pollution (the amount of CO2 emitted) by the

system. Laidi et al [79] in their paper confirmed this observation with the comparison

of pollutant emissions of CO2 with renewable energy and without renewable energy

156 as follows: PV/Wind/Diesel (7.460), Wind/Diesel (8.419), PV/Diesel (12.872), and

Diesel only (13.799).

4.4. Summary

The significance of this study has been reflected by the results presented in this

chapter. This can be summarized as follows. The study has not only highlighted some

of the basic concepts and attractions of renewable energy systems and their

applications, it has also demonstrated that these systems and applications could also

be deployed very effectively in Nigeria. Specifically, they can contribute significantly

to the reduction of energy costs, believed to be the most costly items in the delivery of

telecommunications services particularly to the rural off-grid communities of this

country. Pollution reduction is another attraction of renewable energy applications in

the telecommunications industry that has been highlighted and demonstrated in this

study. Finally, the study has also provided useful information (such as component

sizing tools, solar radiation, wind speed, water head and flow rate, climate conditions,

geographical locations, etc.) on the choice of sites and systems as well as necessary

parameters required to design suitable hybrid power systems (HPSs) to meet given

loads of BTS sites in any geographical region of Nigeria. This information could not

only lead to the development of (renewable) energy optimization maps of Nigeria, but

also serve as useful tools for policy formulation on BTS sites by the Nigerian

telecommunications regulation authority (the NCC). One such policy could be a

requirement for Green Energy Pre-feasibility Studies of any intended location for BTS

sites, particularly in the rural off-grid areas. Such Green Policies are already in

existence in many countries of the developed world, particularly in US where it is a

prerequisite for the approval of any proposal for a physical project.

157

CHAPTER FIVE

CONCLUSION

The aim of this thesis as stated in chapter one was to demonstrate the potential of

hybridized diesel system with battery as a source of power for mobile base station

sites in rural locations, in the perspective of technical and economic analysis. Eight

hybrid configurations were considered and the potential of each of them was

simulated for eight hypothetical BTS sites at eight different locations selected as case

study areas in Nigeria. Climatic data in the form of solar irradiance and wind speed

were collected for each of these eight areas, which were used to study the effects of

different climatic data on the optimization of the sizing of the hybrid energy system.

The optimization was run on each of the case study sets of data and results were

generated. These results (the load power level, PV power level, wind power level and

hydro power level, and state of charge of a battery bank), were used as inputs to the

controller, which was developed to control the amount of energy supplied to the load,

and to ensure that the load was met as much as possible. This controller has four

sliding control modes: mode 1 to supply from the PV panels, mode 2 to supply from

the PV panels and wind turbine, mode 3 to supply from the PV panels, wind turbine

and hydro turbine, and decision mode (4) where the program determines what

element (batteries or diesel generator) has priority to supply energy. The controller

also makes sure that the state of charge of the batteries stays between 40% capacity to

100% capacity, the minimum and maximum bounds of the batteries, respectively.

Running the controller, the system enters all four modes at different points in the year.

The controller shows the breakdown of energy generated and supplied for each hour

over the year as well as the mode of control used in each hour.

158 The simulation results show the economic analysis of adopting each hybrid energy

resource over a period of 25 years and compare it with using only a diesel generator.

It has been shown that using any of the two or three-component hybrid systems

reduces the energy cost in all the test BTS sites. By using PV/Wind/Hydro-Diesel

system for example, the network operators can save as high as 2.5 billion Naira over a

25-year period and can supply at least 72% of the energy demand. In terms of

reducing the carbon dioxide emission, the PV/Wind/Hydro-Diesel system has the least

CO2 emission (between 400 and 600 tons) and saves 1400 - 1600 tons of CO2 from

entering into the atmosphere when compared with using only diesel generator.

In summary, the hybrid power system can be more cost-effective and environmentally

friendly in providing energy to BTS sites than diesel generators. The proposed scheme

is highly preferable for rural and remote areas where there are no grid connections.

However, it is important to note that there is no general least-cost option for powering

GSM base station sites at different locations. It all depends on climatic conditions and

available renewable energy resources. It is demonstrated that it is possible to develop

an optimized energy map for appropriate locations of GSM Base Station sites in

Nigeria, both as a design guide for network operators and for the formulation of

energy use policies by the national telecommunications regulatory authority (the

NCC). One of such policies could be the requirement that any network operator

intending to site a base station in any location should first produce an optimized

energy feasibility study of the location before an approval would be granted.

159 Recommendation

Due to cost, we couldn’t build the prototype of the hybrid system. Therefore, we

recommend that the hybrid system (data) proposed here in the thesis should be tested

in a real life hybrid system as a continuation of this research work.

For further study, we recommend the formulation of energy use policies (such as the

development of an optimized energy map for appropriate locations) for siting BTS in

Nigeria, particularly in the rural off-grid areas.

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175

Appendix A

The river used for the study is Onungenene stream located in Ogologo-Eji Ndiagu

Akpugo in Nkanu-West Local Government Area of Enugu State. In carrying out the

flow measurement of the stream, the length, width, and depth of the stream were

measured. For the surface speed a floating object was used, the physics mechanism,

which is another technique for the determination of surface speed. The time taken for

the float to move from one point to another was taken. These readings were taken

over a period of 2yrs (2010-2012). The readings for the year 2012 and the result

analysis obtained from the Ngenene stream are shown in the Table below.

Table A1: The readings and the result analysis obtained from the Ngenene stream.

Months Time taken for the float to move from point A to B (sec)

Surface speed (m/s)

Average speed (m/s)

Stream Discharge (Q) M3/s L/s

January 1200.0 0.0063 0.00100 0.0195 19.5 February 1170.0 0.0064 0.00102 0.0200 20.0 March 1170.0 0.0064 0.00102 0.0200 20.0 April 1170.0 0.0064 0.00102 0.0200 20.0 May 1231.5 0.0061 0.00097 0.0190 19.0 June 1300.0 0.0058 0.00092 0.0180 18.0 July 1462.5 0.0051 0.00082 0.0160 16.0 August 1799.9 0.0042 0.00067 0.0130 13.0 September 1733.3 0.0043 0.00069 0.0135 13.5 October 1613.8 0.0046 0.00074 0.0145 14.5 November 1462.5 0.0051 0.00082 0.0160 16.0 December 1264.9 0.0059 0.00095 0.0185 18.5

1 meter³/second [m³/s] = 1000 liter/second [L/s]

176 Width of the stream – 7.5m

Depth of the stream – 2.6m

Length of the stream – 7.5m

Constant factor of the stream – 0.16

Sectional Area (A) = Depth X Width

Surface speed (Ss) = Length/Time

Average speed (Sa) = Surface speed X Constant factor

Stream Discharge (Q) = Average speed X Sectional Area

177

Appendix B

Table B1: Economic cost differences in 5 years intervals

Economic Cost Differences in years

HYBRID SYSTEM TYPE [10-9] 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

0-5yr 0.20 0.24 0.41 0.49 0.86 0.95 1.10 1.18 5-10yr 0.15 0.21 0.33 0.40 0.68 0.75 0.85 0.90 10-15yr 0.11 0.16 0.24 0.29 0.51 0.55 0.63 0.68 15-20yr 0.08 0.11 0.26 0.23 0.38 0.41 0.48 0.50 20-25yr 0.06 0.09 0.07 0.16 0.28 0.31 0.35 0.38

Table B2: Percentage (%) of Energy Generated by Renewable Energy Components of

Each Hybrid System

BTS SITE LOCATION HYBRID SYSTEM TYPE [%] S/N 1 2 3 4 5 6 7

PV/W/H PV/H H/W H only PV/W PV only W only 1 Abaji 74 73 54 53 17 16 1 2 Guzamala 83 75 61 53 24 17 6 3 Ikwerre 71 68 55 53 14 12 2 4 Kauru 76 74 54 53 18 17 1 5 Mopa-Muro 72 71 54 53 16 15 1 6 Nembe 70 68 56 53 14 12 2 7 Nkanu-West 72 71 54 53 15 14 08 Tureta 78 76 54 53 20 18 1

Table B3: Percentage Energy Generated by Diesel per each Renewable Energy Hybrid Type

BTS SITE LOCATION HYBRID SYSTEM TYPE [%] S/N 1 2 3 4 5 6 7 8

DG in PV/W/H

DG in PV/H

DG in H/W

DG in H DG in PV/W

DG in PV DG in W DG only

1 Abaji 26 27 46 47 83 84 99 100 2 Guzamala 17 25 39 47 76 83 94 100 3 Ikwerre 29 32 45 47 86 88 98 100 4 Kauru 24 26 46 47 82 83 99 100 5 Mopa-Muro 28 29 46 47 84 85 99 100 6 Nembe 30 32 44 47 86 88 98 100 7 Nkanu-West 28 29 46 47 85 86 100 100 8 Tureta 23 24 46 47 80 82 99 100

178 Table B4: Operational Hour of Diesel in the Hybrid System Type

BTS SITE LOCATION OPERATIONAL HOUR OF DIESEL IN THE HYBRID SYSTEM TYPE [hr/yr] S/N 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

1 Abaji 1,627 1,687 3,015 3,111 5,820 5,887 7,023 8,760 2 Guzamala 1,059 1,582 2,541 3,067 5,286 5,790 6,635 8,760 3 Ikwerre 1,859 2,010 2,911 3,067 6,046 6,180 6,947 8,760 4 Kauru 1,528 1,649 2,994 3,111 5,732 5,826 7,016 8,7605 Mopa-Muro 1,738 1,787 3,006 3,067 5,921 5,985 7,027 8,760 6 Nembe 1,815 2,018 2,874 3,067 6,011 6,190 6,925 8,760 7 Nkanu-West 1,787 1,858 3,038 3,067 5,984 6,015 7,076 8,760 8 Tureta 1,422 1,508 3,009 3,111 5,617 5,714 7,025 8,760

Table B5: Fuel Consumption of Diesel per Hybrid System Type

BTS SITE LOCATION FUEL CONSUMPTION OF DIESEL IN HYBRID SYSTEM TYPE [L/yr] S/N 1 2 3 4 5 6 7 8

PV/W/H + DG

PV/H + DG

H/W + DG

H only + DG

PV/W + DG

PV only + DG

W only + DG

DG only

1 Abaji 8,590 8,907 15,919 16,426 30,725 31,079 37,078 38,485 2 Guzamala 5,555 8,350 13,417 16,194 27,906 30,567 35,029 38,485 3 Ikwerre 9,815 10,613 15,370 16,194 31,919 32,626 36,678 38,485 4 Kauru 8,065 8,707 15,809 16,426 30,261 30,758 37,042 38,485 5 Mopa-Muro 9,177 9,435 15,872 16,194 31,258 31,596 37,099 38,485 6 Nembe 9,583 10,655 15,175 16,194 31,734 32,679 36,561 38,485 7 Nkanu-West 9,435 9,810 16,041 16,194 31,591 31,755 37,358 38,485 8 Tureta 7,483 7,951 15,888 16,426 29,654 30,166 37,089 38,485

Table B6: Percentage Energy Generated by PV per Renewable Energy Hybrid Type

BTS SITE LOCATION PV IN HYBRID SYSTEM [%] S/N 1 2 3 4

PV/W/H+DG PV/H+DG PV/W+DG PV+DG 1 Abaji 18 18 16 16 2 Guzamala 20 20 18 17 3 Ikwerre 13 13 12 12 4 Kauru 19 19 17 17 5 Mopa-Muro 16 16 15 15 6 Nembe 13 13 12 12 7 Nkanu-West 16 16 14 14 8 Tureta 21 21 19 18

179 Table B7: Percentage Energy Generated by Wind per Renewable Energy Hybrid Type

BTS SITE LOCATION WIND IN HYBRID SYSTEM [%] S/N 1 2 3 4

PV/W/H+DG H/W+ DG PV/W+DG W+DG 1 Abaji 1 1 1 1 2 Guzamala 7 7 6 6 3 Ikwerre 3 2 2 2 4 Kauru 1 1 1 1 5 Mopa-Muro 1 1 1 1 6 Nembe 2 3 2 2 7 Nkanu-West 1 1 1 0 8 Tureta 1 1 1 1

Table B8: Percentage Energy Generated by Hydro per Renewable Energy Hybrid Type

BTS SITE LOCATION HYDRO IN HYBRID SYSTEM [%] S/N 1 2 3 4

PV/W/H+DG PV/H+ DG H/W+DG H+DG 1 Abaji 55 55 53 53 2 Guzamala 56 55 54 53 3 Ikwerre 55 55 53 53 4 Kauru 56 55 53 53 5 Mopa-Muro 55 55 53 53 6 Nembe 55 55 53 53 7 Nkanu-West 55 55 53 53 8 Tureta 56 55 53 53

Table B9: Environmental Impact Analysis

HYBRID SYSTEM TYPE

Percentage of Energy Generated by Renewable Energy components

Percentage Energy Generated by Diesel

Operational Hour of Diesel in the Hybrid System Type [10-3]

Fuel Consumption of Diesel [10-3]

CO2 Emission [10-3]

PV/W/H + DG 83 17 1.06 5.56 14.63 PV/H + DG 75 25 1.58 8.35 21.99 H/W + DG 61 39 2.54 13.42 35.33 H only + DG 53 47 3.07 16.19 42.64 PV/W + DG 24 76 5.29 27.91 73.49 PV only + DG 17 83 5.79 30.57 80.49 W only + DG 6 94 6.64 35.03 92.24 DG only 100 8.76 38.49 101.34

Energy Optimization at GSM Base Station Sites Located in Rural Areas

Engineering

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