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Udawatta, Lanka, Madawala, Udaya, Muthumuni, Dharshana, & Vilathga-muwa, Don(2012)Control of solar powered micro-grids using electric vehicles.In Adhikary, B & Vilathgamuwa, D M (Eds.) Proceedings of the 2012 IEEEThird International Conference on Sustainable Energy Technologies (IC-SET).Institute of Electrical and Electronics Engineers Inc., United States ofAmerica, pp. 270-275.

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https://doi.org/10.1109/ICSET.2012.6357410

Control of Solar Powered Micro-grids Using Electric Vehicles

Lanka Udawatta*, Udaya K. Madawala*, Dharshana Muthumuni+ and Mahinda Vilathgamuwa++

Department of Electrical and Computer Engineering, University of Auckland*, New Zealand Manitoba HVDC Centre+, Canada and Nanyang Technological University++, Singapore

lanka@ieee.org, u.madawala@auckland.ac.nz, dharshana@hvdc.ca, emahinda@ntu.edu.sg

Abstract- Large scale solar plants are gaining recognition as potential energy sources for future. In this paper, the feasibility of using electric vehicles (EVs) to control a solar powered micro-grid is investigated in detail. The paper presents a PSCAD/EMTDC based model for the solar powered micro-grid with EVs. EVs are expected to have both the vehicle-to-grid (V2G) and grid-to-vehicle (G2V) capability, through which energy can either be injected into or extracted from the solar powered micro-grid to control its energy imbalance. Using the model, the behaviour of the micro-grid is investigated under a given load profile, and the results indicate that a minimum number of EVs are required to meet the energy imbalance and it is time dependent and influenced by various factors such as depth of charge, commuting profiles, reliability etc..

I. INTRODUCTION

Scarcity of energy resources with ever increasing demand challenges researchers to explore new energy systems. Distributed generation (DG) is gradually replacing centralised or grid based generation in order to utilise scattered energy resources. Micro-grid is one of the approaches, yet needs further analysis and investigations, especially when delivering power under autonomous operation [1], [2]. Micro-grids are becoming more attractive, in particular, as a sustainable solution for the energy crisis. Micro-grids or distributed generation systems are small-scale power generation solutions, consisting of local energy resources and facilities which are having self-sustaining capabilities. In a micro-grid, generation can be from mini or micro hydro, diesel, fuel cells, wind, solar, and other conventional resources or micro sources, making small-scale renewable power generation more viable. Furthermore, Smart Grid concepts will have information technology (IT) enabled infrastructure with smart devices for managing the generation, transmission and utilization of power in a micro-grid. Thus, Micro-grids is expected be smarter and more intelligent in the near future. Nevertheless, there are still a number of unsolved technical and operational problems, yet to be addressed in micro-grids [3].

Recent studies on micro-grid have addressed a large number of technical and practical problems [3]-[5]. Potentially, micro-grids experience several problems at islanding or when operating as an autonomous system [3]. When the micro-grid detaches from the main grid, the voltage phase angles at each source in the micro-grid will change, resulting in an apparent reduction in local frequency. To control frequency, system needs fast and accurate control of

the islanded system whenever it separates from the main grid. In fact, this would be a challenging task if the system is heavily reliant on renewable energy resources, especially under the influence of wind and solar generation [6], [7]. This issue has been investigated extensively in some research studies. In [6], resource scheduling under uncertainty in a smart grid with renewables and plug-in electric vehicles was analised. Further, in this study, an optimization algorithm is used to minimize the expected cost components and emissions. In future, there will be different requirements for micro-grids and related infrastructure for accessibility [8]-[11]. One of the major provisions of micro-grid infrastructure would be rapid charging and energy dispatching facilities for electric vehicles. One of the advantageous of EVs or hybrid electric vehicles (HEVs) is that they bring the opportunity of shifting or diversifying the transportation energy demands from petroleum to electricity. Furthermore, these vehicles can be employed to transfer energy from one physical location to another effectively. It has been estimated that in the US alone there will be more than one million electric vehicles by 2015 [12]. To cater for this demand, it needs rapid growth in the energy supply sector and connected infrastructure. Nevertheless, it is important to investigate on resource assessment of renewable energy bases. Focusing on wind and solar, renewable resource assessment for New Zealand was conducted in [13]. Results of this study are useful in designing future micro-grids which are powered by renewable resources. In developing EV charging stations and peripherals, there are few research studies, in particularly, contactless charging interface for electric vehicles in V2G systems [8], charging control for plug-in vehicles [10], and power management and power flow control with back-to-back converters [14]. Furthermore, an analysis has been carried out on cost of utilising EV batteries as energy storage devices in power grids under vehicle to grid concept [15]. In [16], “Living and Mobility”, an integrated approach is explained under V2G.

According to the literature, there are a large number of studies on micro-grids, addressing various technical and practical issues. This paper presents a solar power based micro-grid and proposes a control strategy, based on the transfer of day time solar generation to night time consumption using the V2G and G2V concept, to regulate the energy imbalance in the system. This control concept can also be applied to micro-grids with other renewable sources.

Main GridPCC

Wind Conventional

Solar

Loads StoragesEVs

~ ~

Fig. 1. Schematic of micro-grid connected to the main grid via point of common coupling (CCP), comprising different generators and loads.

II. MICRO-GRID MODEL

In general, micro-grid can be functioned either in grid connected mode or in autonomous mode. Fig. 1 shows a schematic representation of a typical micro-grid consisting of loads, generators and energy storage units. It is assumed that the micro-grid is connected to the main grid via point of common coupling (PCC), without compromising grid reliability or protection schemes. The load curve shown in Fig. 2 represents an actual load curve of Northland, New Zealand. For developing the model, scaled-down load curve is used. Moreover, micro-grid network is designed to operate largely on solar energy while a single diesel generator is connected to control the small variations and meeting the spinning reserve. Fig. 3 shows a structural schema of 1500 kW solar powered micro-grid which is designed on a solar radiation map [13].

Fig. 2. Actual load curve of Northland, New Zealand on 29 April, 2012.

1500kW generation on a clear day will approximately meet the load. After supplying the daytime energy demand, there will be a surplus which should be stored or exchanged. With the help of electrical vehicles, surplus energy produced in the daytime can be transferred to the nighttime demand. In Fig. 2, it can be observed that night peak is occurred between 1800 to 2000 hours. Each EV carries a 25kWh energy envelope, in other words, effectively, 50kW power source for a period of 30 minutes. In this network, it is assumed that there are no other generators, except one synchronous generator for maintaining the spinning reserve.

Fig. 3. Structural schema of 1500 kW Solar Micro-grid for Northland.

Fig. 5. Schematic of the micro-grid connected to 250kW photovoltaic module under PSCAD/EMTDC simulation model.

III. SYSTEM MODELLING

The micro-grid is mainly powered by 6 X 250kW solar units. Any power shortage is handled by the diesel generator rated at 60kW. Fig. 5 shows the PSCAD/EMTDC model which simulates the micro-grid. This model consists of PV array, DC-DC converter, three-phase inverter and step up transformer unit. Solar irradiance data are fed into the model to generate solar power. Micro-grid bus is rated at 11kV. Fig. 6 shows typical solar irradiance data under two different weather conditions. However, it is noteworthy to mention that detailed simulation is carried out on irradiance data of a clear day. Electric vehicles used in this analysis have the parameters as given in TAB. 1.

TABLE 1

ELECTRIC VEHICLE DATA FOR THE ANALYSIS Energy storage capacity per vehicle 25kWh

Three-phase rapid charging time 30 min

Converter efficiency 98.5%

Number of minimum deep discharges per day 1

Allowable maximum deep discharge 80%

It is assumed that smart connections could inform EV users

to charge and discharge as requested by the grid operator. This has been analysed in different research topics. However, in this analysis, an optimum criterion of dispatching of electric vehicles is not taken into account. In fact, it focuses on transferring excess daytime solar energy to night-time. Energy balance is kept by controlling the number of plug-in EVs which virtually maintains the active power-frequency balance. In a sudden islanding, it applies in stand-alone mode of easy operation when the utility grid fails or disconnects from the micro-grid. Thus, for a given instant, active power supplied from distributed generations should satisfy the load demand and the system losses, which leads to,

2 2

1 1 1 1

( ) ( ) ( ) ( ) ( ) ( )p qn m

SG S V G G Vi j k L D l

i j k l

P t P t P t P t P t P t

(1) Here, ( )SG

iP t is the power supplied by the ith synchronous generator, ( )S

jP t is the power supplied by the jth solar unit, 2 ( )V G

kP t is the V2G power by the kth EV, and 2 ( )G VlP t is the

lth EV charging unit at time t. PL(t) and PD(t) represent the power demand and power loss of the network at time t respectively. Note here that, similarly, reactive power can also be balanced.

IV. CONCEPT OF MICRO-GRID CONTROL AND ENERGY TRANSFER

The available EVs which are plugged into the micro-grid over daytime can charge the EV batteries and store energy from solar generation. During the night or night peak hours, they can sell it back to the grid at a higher price. This concept is applied in the analysis in order to absorb the excess energy generated from 1500kW solar panels, especially from 800 to 1600 hours as shown in Fig. 7. Moreover, simulation results from PSCAD/EMTDC model on individual modules, i.e., 250kW units, are given in Section V. It can be assumed that in each charging and discharging operation, enough energy is left over to drive a short trip. In terms of energy, this can be computed and accommodated by keeping a safety margin. TAB. 2 shows micro-grid data for the simulations and required number of EVs.

Fig. 6. Solar irradiance used in the PV based power generation.

TABLE 2

MICRO-GRID DATA FOR SIMULATION

Excess energy from the solar (Refer Fig. 7) 4634kWh EV carrier energy envelop (inject 50% of full capacity) 12.5kWh No. of vehicles required to transfer 371 Increased reliability of the fleet by adding 15% 56 No. of minimum EVs required in the pool 427 Solar capacity 250kW X 6

Synchronous generator for spinning reserve 60kW

Peak power in the MG load curve 619.5 KW

Minimum power in the MG load curve 248.2 kW

In calculating the number of minimum electric vehicles

required, excess energy is taken into account in keeping a margin for career reliability. To increase the reliability further, operating leverage of 15% is added.

Fig. 7. Micro-grid load curve and PV generation.

It is assumed that each EV must have three basic features in order to realise this concept, specifically, a connection to the micro-grid for electrical energy flow, control or smart connection for communication with the grid operator, and controls and metering facilities built in or on-board. It is also assumed that synchronous generator is providing the spinning reserve in a case of islanding. To improve the reliability, in this case, there shall be two small generators. Fig. 8 shows the excess amount of solar energy that transferred from daytime to night-time via electric vehicles under G2V.

Fig. 7. Micro-grid load curve and PV generation.

V. RESULTS

The real and reactive power exchange between the micro-grid and the PV system was investigated using PSCAD/EMTDC simulation. Fig. 8 shows the number of electric vehicles which are operating as sources or loads in a 24 hour period. In controlling the excess energy, during the daytime, EVs are operating as loads, i.e., G2V transfer. Note that individual scheduling is not taken into account.

Fig. 8. Number of cars operating as generators (V2G) and load (G2V).

Fig. 9 shows the amount of energy transferred to the micro-grid under the concept of V2G. This reflects the energy balance, i.e., controlling the excess energy while managing the total load curve alone the solar generation. It is assumed that the synchronous generator will be responsible for the overall control task, especially absorbing small power fluctuations in the micro-grid providing the spinning reserve.

Fig. 10 shows solar irradiance data used for

PSCAD/EMTDC Simulation. Note that on certain days, there might be severe variations in the irradiance level.

Fig. 10. Solar irradiance for PSCAD/EMTD Simulation

Fig. 11 shows the output power of a single 250kW solar panel unit subjected to solar irradiation level in Fig. 10. Moreover, Fig. 12 shows the voltage and current transients. This refers to Fig. 5 and data are taken at the measuring point shown in the PSCAD/EMTDC simulation model.

Fig. 11. Power generation of 250kW under PSCAD/EMTD Simulation

Nevertheless, it is import to investigate the worst case

scenario in a cloudy day. It is a fact that 1500kW solar system alone cannot be sustained. However, the system can be designed to operate on the above load curve, only with solar power, if the total solar system is rated and designed to 5050kW. However, in this study, cost-benefit analysis on solar based investment is not carried out. If the power requirement of the load in micro-grid is more than the power generated by the above system, the balance power is supplied by the main grid as shown in Fig. 1.

Fig. 12. Transient response of the voltage and current profile

Furthermore, this simulation is extended to investigate the

extreme conditions, i.e., under bad weather conditions (refer Fig. 6). It is a fact that, to have the same solar generation to meet the demand of load curve, the installed system capacity should be increased. To control and meet the energy demand, only through solar generations, it needs a system of 5050kW. In Fig. 13, it can be noted that even during the daytime, solar power is not enough to cater the load. Nevertheless, same demand can be met by the solar energy alone. Moreover, investment on this system might be infeasible at this economic condition. Further, computational intelligence algorithms can be effectively applied to optimise under different constraints [17], [18]. With the incorporation of intelligent controlling, scheduling and coordinating between electric vehicles and micro-grid, situation can be improved, even under extreme conditions.

Fig. 9. Power transfer to the micro-grid directly and through V2G

It is worthy to note that scheduling, controlling and

coordinating of individual electric vehicles are not taken into account. Moreover, this would be a multi-dimensional complex optimization problem, comprising of a large number of constraints under different scenarios. Noteworthy mentioning that the solar powered micro-grid is under the strain of heavy charging and discharging. In fact high charging power is a source of harmonic, which can worsen the power quality of a stand-alone micro-grid. The main problem with Vehicle-2-Grid technology would be the difficulty in getting the infrastructure up and running, especially the locations where renewable resources are greatly available.

VI. CONCLUSIONS

A solar powered micro-grid, controlled using V2G and G2V concepts, has been presented. To control the energy imbalance of the micro-grid, bi-directional energy transfer using electric vehicles is demonstrated. A PSCAD/EMTDC based simulation model has been presented to demonstrate the behaviour of the solar powered micro-grid. Under a given solar irradiation profile, generated power of 1500kW micro-grid is simulated on the PSCAD/EMTDC model. Scaled-down real load curve is used to demonstrate the proposed V2G and G2V concepts. In this study, it could be simulated that electric vehicles can be effectively dispatched in order to absorb or release energy, as required by the micro-grid. Optimum number of electric vehicles required to meet the energy imbalance is computed. The proposed control philosophy can also be applied to micro-grids powered by renewable energy sources of stochastic nature.

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Fig. 13. Controlling the micro-grid under extremely low irradiation