1268631/FULLTEXT01.pdf · -II- Master of Science Thesis TRITA-ITM-EX 2018:612 On the Market...
Transcript of 1268631/FULLTEXT01.pdf · -II- Master of Science Thesis TRITA-ITM-EX 2018:612 On the Market...
Master of Science Thesis
KTH School of Industrial Engineering and Management
Energy Technology EGI- TRITA-ITM-EX 2018:612
Division of Heat and Power Technology
SE-100 44 STOCKHOLM
On the Market Potential of
Modular Stirling CSP Systems
With Storage in the MENA
Youssef Benmakhlouf Andaloussi
-II-
Master of Science Thesis TRITA-ITM-EX
2018:612
On the Market Potential of Modular Stirling
CSP Systems With Storage in the MENA
Youssef Benmakhlouf Andaloussi
Approved
Examiner
Björn Laumert
Supervisor
Rafael Guédez
Commissioner
Contact person
Abstract
Given the intermittent nature of renewable energy sources, integrated storage solutions are necessary to
accomplish the energy shift necessary for sustainable development. In the case of solar, PV-BESS tend to
be highly capital intensive, especially for long storage hours most needed to guarantee stable electricity
production day and night. This study presents a methodology to quantify the market potential for a novel
distributed CSP technology with cost competitive thermal energy technology, where the cost target is 30%
cheaper than PV-BESS. The system in question is similar to the one developed by Cleanergy AB, where a
13 kW Stirling engine is powered by heat collected from a heliostat field and stored in an integrated latent
heat storage unit. Morocco, Tunisia, Egypt, Jordan and Saudi Arabia are chosen as representative countries
of the MENA for the study. The study is done by detailed investigation of the macro-environment of each
country, developing a methodology to rank identified business opportunities. Said opportunities are
restricted to companies within the industrial sector, based on the assumption that such customers would be
interested in a solution guarantying stable electricity production. First, a techno-economical optimisation is
done to find optimal plant configurations to service a particular energy need for each business opportunity.
Second, the multi-criteria analysis scores and ranks the latter with respected to different criteria that can be
conflicting. Finally, the top business opportunity identified by the MCA in each country are compared
through a scenario analysis, assuming different rates at which the electricity generated by the system can be
sold. With a global market potential above 40 GW in the whole MENA, industrial sectors such as mining
and cement hold the best prospects in terms of market share. The achievable costs of generation vary
depending on the DNI of the sites considered but prove to be lower compared with conventional distributed
generation (diesel gensets or PV-BESS). However, several countries in the MENA, although having high
DNI resource, still offer low electricity utility prices to industrial customers for distributed CSP to become
competitive with on-grid electricity procurement. Hence, Jordan is ranked first with the MCA, both because
of the high DN in the country, and its high electricity rates, despite having the smallest market share in
terms of capacity to install. The amount of subsidies necessary for the technology to be profitable and cons
competitive were found respectively. Except in Jordan where the system is competitive with utility rates, all
other countries needs to implement feed-in-tariffs schemes for distributed CSP with storage to become
viable. The observed trend of increasing electricity prices in the MENA however, coupled with decreasing
LCOE values due to high volumes of production indicate that economic viability in the countries with low
present rates can be achieved in the future.
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Sammanfattning
Eftersom förnybara energikällor har en oförutsägbar energiproduktion krävs välutvecklade
energilagringssystem för att samhället ska gå över till förnybara energi. Solenergi kräver PV-BESS, vilket
tenderar att vara kapital intensivt, speciellt vid energilagring över lång tid som krävs för stabil
energiproduktion under nattetid. Denna studie tar fram en metodologi för att kvantifiera
marknadspotentialen för nya distribuerade CSP teknologier med termisk lagring. Kostnadsmålet för sådan
termisk lagring är 30% lägre än för PV-BESS. Som exempel för CSP systemet används tekniken utvecklat
av Cleanergy AB, vilket består av en 13 kW Stirlingmotor som är driven av hettan från ett heliostatfält och
lagrat i en integrerad latent värmelagringsenhet. Marocko, Tunisien, Egypten, Jordanien och Saudi Arabien
används för att representera länder från MENA i denna studie. Analysen består av en djupgående forskning
av makroekonomiska faktorer som används för att identifiera och ranka affärsmöjligheter. Dessa
affärsmöjligheter är begränsade till den industriella sektorn som kräver stabil energiproduktion. Först görs
en teknologisk och ekonomisk optimering för att hitta den bästa konfiguringen av energianläggningen för
kunden. För det andra poängterar multikriterieanalysen (MCA) och rankar kunderna med respekt för olika
kriterier som kan vara motstridiga. Slutligen jämförs de bästa affärsmöjligheterna som identifierats av MCA
i varje land genom en scenarioanalys, förutsatt att det är olika priser för elektricitet. Med en global
marknadspotential på över 40 GW i hela MENA, har industrisektorer som gruv och cement de bästa
utsikterna när det gäller marknadsandelar. De uppnådda LCOE varierar beroende på de undersökta
platsernas DNI men är ändå lägre jämfört med alternativa distribuerad generation (dieselgeneratorer eller
PV-BESS). Men flera länder i MENA , trots att de har en hög DNI-resurs, fortfarande erbjuda låga
elverktygspriser till industrikunder för distribuerad CSP för att bli konkurrenskraftiga med elförsörjning på
nätet. Därför rankas Jordan först med MCA, både på grund av den höga DN i landet och höga elpriser,
trots att den minsta marknadsandelen. ängden subventioner som är nödvändiga för att tekniken ska vara
lönsam och konkurrensbegränsad hittades. Förutom i Jordanien där systemet är konkurrenskraftigt med
nyttjandepriser måste alla andra länder genomföra inmatningstullsystem för distribuerad CSP med lagring
för att bli lönsam. Den observerade trenden med att öka elpriserna i MENA, i kombination med minskande
LCOE-värden på grund av stora volymer av produktion tyder på att ekonomisk lönsamhet i länder med
låga priser kan uppnås i framtiden.
-IV-
Acknowledgements
My first line of acknowledgments is bound to be addressed to Rafael Guédez, my supervisor, who played
a central part in giving me the opportunities and experience I have today. Such small recognition cannot
get right how grateful I am to him. To that effect, an additional appendix is needed in this report, so I can
enumerate all the things he taught me. Naturally, Jonas Wallmander comes next in this thank you note, who
directed me during the internship, and played a big role in the professional opportunities which came with
it. Very much thanks also to Monika Topel, who always opened (literally) the door of her office to me. The
laughter and good discussions we had there contributed a lot to this modest work. Special mention also to
Osama Zaalouk in whom I found a precious ally against the Venezuelan mafia of the Energy department.
Various reasons almost pushed me not to pursue this double degree master in KTH, but at the
end, I am glad I went with it. Some of my closest friends now are people I met during these two
years, and I am grateful to all one of them. Although Sweden is known for its cold and dark winters, my
overall experience was one of warm memories. Finally, thanks to my family and mother most notably, whose
unconditional love and words of wisdom will always resonate with me.
شكر كلمة
البحت هذا في ما فهم بإمكانهم يكن لم فإن. أمي و أبي لشكر صغيرة، كانت لو و حتى فقرة، أخصص أن علي الواجب من
بقدر مؤلفوه فهم لذلك. مثله فهم حتى أو نشره، بإمكاني كان لما تربيتي، في وتضحياتهم دعمهم ولوال فلوالهم، اللغة، بحكم
المستقبل في مكانة أوأي اليوم، مكانتي في لهم يرجع الفضل كل. مالكه أنا ما .
مرة، كل في. المشوار هذا لحظات أصعب في بجانبي كانت واللتي فرنسا، في تمدرسي دعم في السباقة كانت التي أمي، إلى
لمؤانستي المسافات أطول قطع حبها .
في لرغبتي إليه، يرجع العلمي بالمجال اهتمامي. دراستي خطوات كل وثبت الحياة في التدبر كيفية علمني الذي أبي، إلى
إلدهاشه الرياضيات مادة في الدرجات أعلى على الحصول أحاول كنت حين صغري، مند وذلك بي، فخورا جعله
.
-V-
Abbreviations
AFEX Arab Future Energy Index
BESS Battery Electricity Storage Systems
CAPEX Capital Expenditure(s)
CSP Concentrated Solar Power
CT Central Tower
DNI Direct Normal Irradiance
DS Dish Stirling
GDP Gross Domestic Product
GIS Geographic Information System
HTF Heat Transfer Fluid
IPP Independent Power Producer
IRENA International Renewable Energy Agency
IRR Internal Rate of Return
LCOE Levelized Cost of Electricity
LF Linear Fresnel
MCA Multi Criteria Analysis
MENA Middle East North Africa
MGT Micro Gas Turbine
NPV Net Present Value
O&M Operation and Maintenance
OPEX Operational Expenditure(s)
PCM Phase Changing Material
PT Parabolic Through
PV Photovoltaic
RE Renewable Energy
RES Renewable Energy Systems
SAM Serviceable Achievable Market
SOM Serviceable Obtainable Market
STEALS Investor-Owned Utility
TAM Total Addressable Market
TES Thermal Energy Storage
WACC Weighted Average Cost of Capital
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List of figures
Figure 1 : Cleanergy Stirling dish demonstration plants, Dubai (right), Mongolia (left) [3] .............................. 2
Figure 2 DESERTEC project map. The red squares represent the area need for solar power plant to power
the whole world, Europe and Germany [7] .............................................................................................................. 4
Figure 3 PV installed capacity growth [10] ................................................................................................................ 5
Figure 4 CSP installed capacity growth [10] .............................................................................................................. 5
Figure 5 Flow diagram of a typical CSP plant [11] .................................................................................................. 6
Figure 6 CSP technologies [14] ................................................................................................................................... 6
Figure 7 CSP market trends [15] ................................................................................................................................. 8
Figure 8 247Solar Plant [18] ......................................................................................................................................... 9
Figure 9 Vast Solar CSP system [19] ........................................................................................................................10
Figure 10 Design configuration of STEALS [22]...................................................................................................11
Figure 11 Cleanergy's Alpha type Stirling engine [27] (adapted)..........................................................................12
Figure 12 Cleanergy's initial target market (2021-2025) for the TES system design ........................................13
Figure 13 Model of one modular Cleanergy CSP unit with the three main components: concentrator,
receiver with storage (10 hours) and Heat Engine (Stirling) ................................................................................14
Figure 14 PV-BESS LCOE in 2021 .........................................................................................................................15
Figure 15 Market size estimation [34] ......................................................................................................................16
Figure 16 Selection methodology for business opportunities [36] ......................................................................17
Figure 17 AFEX Renewable Energy 2016 [37] ......................................................................................................18
Figure 18 Techno-economical analysis process ......................................................................................................22
Figure 19 LCOE vs Reflective area ..........................................................................................................................23
Figure 20 NES 2030 targets [59] ...............................................................................................................................28
Figure 21 Electricity market Morocco [62] .............................................................................................................29
Figure 22 RE National Program 2017-2020, Tunisia [86] ....................................................................................32
Figure 23 Electricity market, Tunisia [85]................................................................................................................32
Figure 24 Government power generation expansion plans [91] ..........................................................................35
Figure 25 Egypt power market structure .................................................................................................................36
Figure 26 RE projects in Jordan 2016 [108] ............................................................................................................39
Figure 27 Jordan's electricity market [110] ..............................................................................................................40
Figure 28 Long-term renewable energy targets, Saudi Arabia [117]....................................................................42
Figure 29 Power market structure, Saudi Arabia [25] ............................................................................................43
Figure 30 RE private Investment Increase (2013-2016) [37] ...............................................................................48
Figure 31 LCOE vs TES size (Cleanergy’s cost functions) ..................................................................................49
Figure 32 LCOE vs TES size (STEALS cost data) ...............................................................................................50
Figure 33 Site positioning map, Morocco ...............................................................................................................51
Figure 34 company positioning map MENA (Industry).......................................................................................52
Figure 35 Scaling up the SAM to the MENA .........................................................................................................54
Figure 36 SAM in the MENA region by country ...................................................................................................55
Figure 37 Cleanergy's CAPEX breakdown .............................................................................................................55
Figure 38 LCOE sensitivity analysis .........................................................................................................................56
Figure 39 NPV(k€) sensitivity analysis .....................................................................................................................57
Figure 40 MCA country score (1-10) .......................................................................................................................59
Figure 41 Multi Criteria Analysis – Ranking of business opportunities (1-10) .................................................60
Figure 42 Expected utility electricity price for industry 2021 (€/MWh) ............................................................66
Figure 43 IRR vs Power price ...................................................................................................................................67
Figure 44 NPV vs Power price .................................................................................................................................68
Figure 45 NPV vs Power price (zoom) ...................................................................................................................68
Figure 46 Normalized LCOE evolution ..................................................................................................................69
Figure 47 Case 1 ..........................................................................................................................................................99
-VII-
Figure 48 Case 2 ..........................................................................................................................................................99
Figure 49 Case 3 ..........................................................................................................................................................99
Figure 50 Case 4 ....................................................................................................................................................... 100
Figure 51 Case 5 ....................................................................................................................................................... 100
Figure 52 Case 6 ....................................................................................................................................................... 100
Figure 53 Case 7 ....................................................................................................................................................... 101
Figure 54 Case 8 ....................................................................................................................................................... 101
List of tables
Table 1 CSP technology comparison [15] ................................................................................................................. 7
Table 2 Entry modes categories [31] ........................................................................................................................19
Table 3 Financial model inputs .................................................................................................................................21
Table 4 Industry electricity rates ...............................................................................................................................25
Table 5 Attractiveness scoring table .........................................................................................................................25
Table 6 Morocco generation units 2015 [58] ..........................................................................................................27
Table 7 High voltage industry general rate, Morocco [51] ...................................................................................30
Table 8 Identified industry companies, Morocco ..................................................................................................30
Table 9 Identified industry companies, Tunisia......................................................................................................33
Table 10 Identified industry companies, Egypt ......................................................................................................37
Table 11 Identified industry companies, Jordan.....................................................................................................41
Table 12 Electricity rate, Saudi Arabia [121] [120] .................................................................................................44
Table 13 Identified industry companies, Saudi Arabia ..........................................................................................45
Table 14 Countries Performance under International Indices [124] [125] [126] ..............................................46
Table 15 Business model country comparison .......................................................................................................46
Table 16 Country score ranking ................................................................................................................................48
Table 17 Market potential for the MENA (industry), with optimum configuration ........................................52
Table 18 SAM in the MENA, industry (grid connected, VHV-HV-MV) ..........................................................53
Table 19 Market potential for the MENA (industry), with optimum configuration using STEALS cost data
........................................................................................................................................................................................56
Table 20 Most competitive business cases under the MCA (per country).........................................................58
Table 21 Country score, by criterion (1-10) ............................................................................................................61
Table 22 Country score, additional criteria .............................................................................................................62
Table 23 Weighting factors case definition .............................................................................................................63
Table 24 MCA sensitivity (top 5 business opportunities) .....................................................................................63
Table 25 Scenario analysis results, Morocco (MM31), WACC = 4,5% ..............................................................64
Table 26 Scenario analysis results, Tunisia (TC11), WACC = 4,8% ...................................................................64
Table 27 Scenario analysis results, Egypt (EM21), WACC = 4,9% ....................................................................64
Table 28 Scenario analysis results, Jordan (JCh31), WACC = 4,8% ...................................................................65
Table 29 Scenario analysis results, Saudi Arabia (SC11), WACC=5% ...............................................................65
-VIII-
1 Contents
Abstract .......................................................................................................................................................................... II
Sammanfattning .......................................................................................................................................................... III
Acknowledgements .................................................................................................................................................... IV
Abbreviations ........................................................................................................................................................... V
List of figures ......................................................................................................................................................... VI
List of tables ......................................................................................................................................................... VII
1 Introduction .......................................................................................................................................................... 1
1.1 Cleanergy AB ............................................................................................................................................... 1
1.2 Objectives ..................................................................................................................................................... 2
1.3 Thesis structure ........................................................................................................................................... 3
2 Theoretical Framework ....................................................................................................................................... 4
2.1 Solar energy overview ................................................................................................................................. 4
2.2 Solar CSP technologies .............................................................................................................................. 6
2.3 Stirling-based CSP systems ........................................................................................................................ 9
2.3.1 Small scale CSP .................................................................................................................................. 9
2.3.2 Cleanergy CSP systems ...................................................................................................................11
2.3.3 New design with TES .....................................................................................................................12
3 Methodology .......................................................................................................................................................16
3.1 Geographical limitation ............................................................................................................................17
3.2 Market analysis/entry ...............................................................................................................................18
3.3 Financial model .........................................................................................................................................19
3.4 Techno-economical analysis ....................................................................................................................21
3.5 Multi-criteria analysis ................................................................................................................................24
3.6 Scenarios definition...................................................................................................................................26
4 National environment, industry and firms’ specifics ....................................................................................27
4.1 Morocco .....................................................................................................................................................27
4.1.1 Energy context .................................................................................................................................27
4.1.2 Electricity market .............................................................................................................................28
4.1.3 Industrial companies .......................................................................................................................30
4.2 Tunisia .........................................................................................................................................................31
4.2.1 Energy context .................................................................................................................................31
4.2.2 Electricity market .............................................................................................................................32
4.2.3 Industrial companies .......................................................................................................................33
4.3 Egypt ...........................................................................................................................................................34
4.3.1 Energy context .................................................................................................................................34
4.3.2 Electricity market .............................................................................................................................35
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4.3.3 Industrial companies .......................................................................................................................37
4.4 Jordan ..........................................................................................................................................................38
4.4.1 Energy context .................................................................................................................................38
4.4.2 Electricity market .............................................................................................................................39
4.4.3 Industrial companies .......................................................................................................................41
4.5 Saudi Arabia ...............................................................................................................................................42
4.5.1 Energy context .................................................................................................................................42
4.5.2 Electricity market .............................................................................................................................43
4.5.3 Industrial companies .......................................................................................................................44
4.6 Country comparison .................................................................................................................................45
5 Comparison/Analysis ........................................................................................................................................49
5.1 Optimum configurations .........................................................................................................................49
5.2 Potential/Serviceable achievable market ...............................................................................................52
5.2.1 Results ................................................................................................................................................52
5.2.2 Sensitivity analysis ............................................................................................................................55
5.3 Multi Criteria Analysis ..............................................................................................................................57
5.3.1 Results ................................................................................................................................................57
5.3.2 Sensitivity analysis ............................................................................................................................61
5.4 Scenario analysis ........................................................................................................................................63
5.4.1 Results ................................................................................................................................................63
5.4.2 Sensitivity analysis ............................................................................................................................66
6 Conclusions .........................................................................................................................................................70
7 Appendixes ..........................................................................................................................................................72
Bibliography .............................................................................................................................................................. 102
-1-
1 Introduction
Being one of our era’s most critical problems, securing electricity access in a cost-effective way and without
harming the environment is a big challenge that countries worldwide are trying to tackle. Consequently,
electricity generation based on renewable sources is on the rise and has seen rapid growth in the last decade
globally. Investments and technological innovations are the drivers of the shift in the current energy system,
going away from centralized generation stations to decentralized and distributed units that can
accommodate different configurations. Among the multiple technologies and renewable sources that can
be used, solar energy ranks in the top due to its huge potential in providing electricity access on a global
scale. The DESERTEC initiative suggested that in only six hours, the African desert receives as much energy
from the sun as humankind consumes in a whole year [1].
However, and by definition, renewable and sustainable energy sources are intermittent, which hinders their
full adoption and penetration into the grid. Solar energy is no exception to that, being available only when
the sun shines, and unavailable after, period where the demand profiles are the highest. Specifically,
intermittent solar production cannot fit around the clock the clock load profiles. The solution is then to
couple the solar technology with a storage system that would generate electricity on demand, later in the day
when there is no sun. Storage systems can refer either to chemical batteries in the case of PV plants, or
Battery Energy Storage Systems (BESS), or thermal energy storage (TES) in the case of CSP systems. BESS
are highly capital intensive and are only viable when considering large scale projects, but are not suitable,
nor competitive to smaller distributed systems with large storage requirements [2] . Likewise, CSP systems,
such through or tower with TES are proven to cost competitive in large scale sizes, and do not fit for
distributed generation as their efficiencies drop when dealing with smaller systems.
Cleanergy AB, a privately owned Swedish company, is in the crossroad of all these considerations, offering
a new modularized designed on-demand electricity production technology with distributed energy storage
that can provide a high efficiency solar power plant and be built cost efficient in any size from the range of
10kW to hundreds of MW. This Master thesis, done in the form of an internship at Cleanergy AB, aims at
elaborating business strategies for the company that wishes to develop its new technological solar innovation
in the MENA and Sub-Saharan African regions where the solar resource is abundant and perfect for its
product (DNI greater than 2000 kWh/m²/year)
1.1 Cleanergy AB
Cleanergy is a privately held Swedish company that was founded in 2008 and that focuses on Stirling engine-
based renewable energy solutions. The company is a small and medium sized enterprise that has two sites
of production, Uddevalla and Åmål, alongside offices in Stockholm and Gothenburg [3]. Its core expertise
is the production and manufacture of Stirling engines which convert heat into electricity. The first target
segment market was gas-fuelled power production that started in 2008 with the GasBox. This product is
fully commercialized today in different countries (United Kingdom, Norway, Sweden…) [3]. In parallel,
Cleanergy started developing solutions for CSP systems with the modified version of the Stirling engine
called SunBox. The company proposes a modular CSP dish Stirling unit, comprising of a parabolic dish
capable of tracking the sun that uses SunBox for electricity generation. While the generation is caped at
13kW per unit, the Cleanergy SunBox unit is well suited for large utility scales ranging from kW to MW
scales thanks to its modular and autonomous design. No water is consumed in the power production cycle,
which is a key competitive advantage over other CSP technologies such as linear trough and tower systems,
especially in areas of high ambient temperature where high levels of Direct Normal Irradiation are usually
to be found and water resources are usually scarce. It also holds the highest conversion efficiency from sun-
to-electricity among all solar technologies, reaching 30% [3]. Currently, Cleanergy has commissioned 3
demonstration plants: 110 kW installed capacity in both Mongolia and Dubai seen in Fel! Ogiltig s
jälvreferens i bokmärke., and a 13kW unit in Ouarzazate, Morocco. The company is now focused on
-2-
developing a thermal energy storage system of 10 hours to be coupled with its CSP unit, allowing for on-
demand electricity production, increasing the grid stability, its flexibility and thus its attractiveness.
1.2 Objectives
The CSP system the company is developing is best suited for locations with a DNI greater than 2000
kWh/m²/year, which is why Cleanergy wishes to study the potential opportunities laying in the MENA.
Indeed, most of the countries in the MENA are considered to be part of the Sunbelt, where solar radiation
is well above the threshold mentioned [4]. More specifically, the countries studied in this thesis are: Morocco,
Tunisia, Egypt, Jordan and Saudi Arabia. The choice of these 5 countries is strategic, as they all have enough
resemblance to draw conclusions and strategies to be applied to MENA in general, but present in the same
time enough differences for them to stand out and challenge the company in its way of doing business.
The main goal of this thesis is to identify how a small scale distributed CSP system with TES, such as
Cleanergy’s, can enter a specific marketplace and propose strategies the company should develop for its
modular technology to successfully penetrate the MENA. Detailed prospective customer profiles are
identified in each country, and for each customer-type a techno-economic analysis has been carried out to
determine best configuration (in terms of key component size and operation) that would minimize the
generation costs of the system in order to ultimately assess the competitiveness of a business case based on
such a technology when compared to the current way of procuring electricity. At last, specific
recommendations for technology developers and potential off-takers are provided.
As follows, this work needs to:
• Present a comprehensive and detailed market profile for each of the countries selected. This
includes description of the electricity sector, generation capacities, regulatory framework in place
for the power sector, future trends in terms of energy policy and generation.
• Identify potential customers in each of the countries selected, by validating the company’s
assumptions on the market application, sizing the total addressable market, in terms of MW to
install, in each one and identifying which customers are in need of dispatchable renewable
electricity.
• Carry out a techno-economic analysis for each customer to determine best configuration (in terms
of key component size and operation) that would minimize the generation costs of the system.
• Set up business cases for selected companies/customers based on cash flow calculations to outline
the attractiveness of Cleanergy’s technology for them and its competitiveness when compared to
current way of procuring electricity, but also provide proof of business profitability for Cleanergy.
Figure 1 : Cleanergy Stirling dish demonstration plants, Dubai (right), Mongolia (left) [3]
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• Suggest business development strategies to achieve successful market entry in the MENA region.
The go-to market strategies to be developed will be based on the analysis of the countries selected
and comparison between the customer identified. It will also include suggestions on how to build
communication channels and strategic partnerships with the most promising customers.
1.3 Thesis structure
The report is constructed around the above objectives and follows the structure detailed below:
• Section 1, Theoretical framework: An overview of all the theory behind the concepts to be
developed and used throughout the report, describing in detail Cleanergy’s value proposition and
outlining the different solar technologies that compete with it, reviewing the market research theory
and detailing the financial metrics that will be used as key performance indicators to rank and qualify
the market.
• Section 2, Methodology: A step by step explanation of the methods followed to perform this
research work, ranging from the data mining approach used, description of the economic model
built to the actual market analysis and comparative approach applied.
• Section 3, National environment, industry and firm’s specifics: Acts as market analysis of the
different countries reviewed, by setting up country profiles detailing market status and regulations,
understanding how macro-environmental factors will help or hinder Cleanergy’s business,
quantifying the total addressable market by identifying potential industrial customers, and carrying
out techno-economical assessment for each.
• Section 4, Comparison/Analysis: Cross-country, cross-customer comparison and analysis to
identify the most promising and profitable market for Cleanergy. The analysis will be based on
individual business cases built for selected industrials and firms to understand the market behaviour
and need, by considering different market shares scenarios for the future and different localization
estimates for Cleanergy’s product.
• Section 5, Go-to market strategies: This section draws strategies and suggestions for Cleanergy
to develop its business in each of the countries selected based on the market analysis done and
delimits risks inherent in each one of them.
• Section 6, Conclusions: A summary of all the findings of the research work, and suggestion on
how to proceed in the future.
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2 Theoretical Framework
2.1 Solar energy overview
With regards to the shift in energy systems currently deployed around the world, solar energy technologies
are well prioritized thanks to their numerous advantages. The old-fashioned way of producing electricity
relying on fossil fuel brings many problems, from environmental concerns such as damage to the earth,
pollution of the atmosphere and water, but also socio-political issues when considering a country’s need to
secure energy access and the shrinking availability of fossil fuels and volatility of their prices. Relying on the
sun for electricity production solves above issues, as the yearly received energy from the sun is 1500 times
largen than the world energy use [5]. In fact, the DESERTEC project suggested that solar power plants
located in the African desert covering 0,3% of its area, could power up all nations around the world given
the right transmission infrastructure built [6]. Figure 2 shows the area requirement of such a project
Figure 2 DESERTEC project map. The red squares represent the area need for solar power plant to power the whole
world, Europe and Germany [7]
While solar energy is usually associated with PV power generation, the sun’s irradiance also delivers its
energy in the form of heat that can be used for power generation in CSP systems. In 2016, 76,6 GW of solar
were installed, making the total solar capacity reach 306,5 GW, representing a 33% increase compared to
2015. Asia leads the solar market, dominating with 48% of the total install capacity making it the largest
solar powered region. The global forecasted capacity to be installed in 2017 is 387 GW, while this figure will
surpass 700 GW after 2030 [8]. The evolution of the global installed solar capacity throughout the years
from 2006 till 2016, for both PV and CSP can be seen in Figure 3 and Figure 4. Solar CSP power plants are
capital intensive, needing huge investments for their erection compared to PV plants, which explains the
difference between the installed capacities of both [9].
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Figure 3 PV installed capacity growth [10]
Figure 4 CSP installed capacity growth [10]
-6-
2.2 Solar CSP technologies
Unlike photovoltaic cells or flat plate solar thermal collectors, CSP power plants do not use the global solar
irradiation, namely disregarding the diffuse part which results from scattering of the direct sunlight by
clouds, particles and molecules in the air because they cannot be concentrated. The CSP technology is based
essentially on the direct solar radiation, which is collected through a concentrator to a receiver. This makes
CSP power plants best suited for locations with high percentage of clear sky days, which do not have smog
or dust. The concentrated heat is then used to run a power conversion cycle to produce electricity. A
schematic of a conventional CSP plant is given in Figure 5.
Figure 5 Flow diagram of a typical CSP plant [11]
The most common CSP systems are shown in Figure 6 .A brief overview of each is given below, while Table
1 compares the technological specifications of each [12] [13].
Figure 6 CSP technologies [14]
• Parabolic trough (PT): It is the most deployed CSP technology. Trough-shaped mirrors concentrate
sunlight into a linear focus on a receiver tube that follows the parabola’s focal line. The mirror and
receiver tube structure are mounted on a frame that follows the daily sun movement on one axis,
while the seasonal movement of the sun are tracked with lateral movements of the line focus. The
heat is collected from the receiver tubes via a heat transfer fluid and is used to feed a power block
for electricity generation.
-7-
• Linear Fresnel reflectors (LF): Variation of the parabolic trough collectors. Their main difference
from parabolic trough collectors is that, instead of using parabolic bent mirrors to concentrate
sunlight, they use several parallel flat mirrors to concentrate it onto one receiver, which is located
several meters above the primary mirror field. The secondary mirror structure is necessary to
account for the astigmatism distortion caused by the optical principles of Fresnel collectors.
• Centrale receiver tower (CT): This design contains an array of heliostats (large mirror structures
with double axis tracking) that concentrates the solar radiation into a central receiver mounted on
the top part of a tower. This configuration gives high efficiency energy conversion into the large
receiver point, yielding higher concentration ratios compared to linear focusing systems. It permits
the power cycle to work at higher temperatures with reduced losses.
• Parabolic dishes (DS): Similarly to the trough design, dish systems rely on the geometric properties
of a three-dimensional paraboloid to concentrate direct solar radiation to a point focus receiver,
reaching in optimum condition temperatures over 1,000ºC, similar to tower systems. The latter
gives them the advantage of having the highest solar conversion efficiency, since they always have
the aperture facing the sun and avoid the cosine loss effect. These systems have a power conversion
unit, namely Stirling engine, that transforms the concentrated heat into electricity. This will be
further explained in the following sections, as it is Cleanergy’s key product.
Table 1 CSP technology comparison [15]
Technology PT LF DS CT
Typical size (MW) 10 – 280 1 – 125 1 10 – 135
Concentration
Factor 70 – 80 25 – 100 600 – 4000 600 – 1200
Capacity Factor
(%) 30 – 50 20 – 30 20 – 30 40 – 70
Operation
Temperature (ºC) 293 – 393 140 – 275 250 – 700 290 – 565
Sun-to-Electricity
efficiency (%) 16 – 18 9 – 11 12 – 25 16 – 20
Installed worldwide
(MW) 4336 319 3 689
Use of land
(MWh/(ha·year)) 600 – 1000 600 – 1000 400 – 800 400 – 800
Maturity Commercial Commercial Demo Commercial
Reflector Parabolic
mirror
Flat/curved
mirror
Paraboloid
mirror Curved mirror
Receiver
Absorber tube
w/vacuum
cover
Absorber tube
w/concentrator
Stirling
engine/Gas
turbine
External /
Cavity
HTF
Thermal oil
Saturated steam
Air
Molten salt /
Water-steam
TES
Molten salts,
indirect
Steam
accumulator
N/A
Molten salts,
direct / steam
accumulator
TES capacity 4 – 12 hours < 1 hour N/A 6 – 14 / < 1
hours
Hybridization Yes, existing Yes Unlikely Yes
-8-
In terms of market deployment, parabolic through systems dominate the CSP plants in operation globally
as of 2016, followed by central tower systems. This is due to the huge solar investments Spain made in the
past regarding solar CSP, considering that at that time trough design was the most developed and proven
technologically. However, and as it is seen in Figure 7 , the majority of planned projects, as well as the ones
under development are central tower systems, proving that there is a shift in the market justified by better
efficiency due to the higher temperatures achieved in the receiver, compared to through systems. Central
tower systems capitalize on that as well with regards to heat storage in molten salts, as the high temperatures
reached in the receiver allow for a reduced cost of energy storage unlike trough systems [16]. Figure 7 also
shows which players are investing the most in solar energy. While Spain lead the market in the early 2000s,
near zero projects are planned or developed there in 2016. This is mainly due to the fact that the country
suffers from a huge electricity tariff deficit that pushed officials to halt capital intensive projects. MENA
countries, on the other hand, are investing heavily in solar and renewable energy in general, acknowledging
their need for sustainable energy access and security, and capitalizing on the perfect renewable resources
they have. It is suggested that PV alone has a potential of 7GW by 2020 in the MENA, and 27 GW by 2030
[4]. Nevertheless, the increasing number of large-scale solar projects planned and developed in that region
make it a promising market for companies like Cleanergy, as its countries show strong commitment to
energy transition.
Figure 7 CSP market trends [15]
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2.3 Stirling-based CSP systems
2.3.1 Small scale CSP
Whereas conventional CSP technologies are competitive in large-scale parks, they have yet to become
attractive for small-scale distributed and dispatchable generation (< 5MW). On the other hand, PV-BESS
plant can be seen as best suited for that type of use, as they are more competitiveness to diesel generators
[2], which they mostly compete with. Both alternatives can offer on-demand electricity production and
represent a stable input to the existing grid generation. But PV-BESS joys from several advantages, in the
sense that they are not subject to volatile oil prices, or CO2 abatement policies and restrictions, that often
put extra financial hurdle on fossil fuel generation. In spite of that, the current cost of chemical batteries for
PV systems is too high and render it not competitive, nor its 2030 future projections [2].
As a result, a number of companies are currently developing modular and distributed cost-effective CSP
systems with TES. For instance, 247Solar develops a dispatchable 300 kWe system consisting of a heliostat
field-tower system, a micro gas turbine (MGT), and a brick-based dry TES. Specifically, a small heliostat
field concentrate solar power into a receiver mounted in a 35 m tower. The receiver heats air passing through
it to about 980 °C that in turn warms up turbine's compressor air. The microturbine is then powered by the
super-heated compressor air, thus spinning a generator to produce electricity. The system uses no
water/steam, salts, oils, hydrogen or helium. Not all the hot air from the receiver is used from power
generation, and serve as heating source to the TES, in the form of firebricks or small pieces of ceramic.).
When the sun isn't shining, air is blown through the hot TES to heat the turbine's compressor air. Natural
gas or biofuels (e.g., from landfills) provides backup power when there is not enough solar power, or during
nighttime [17]. An illustration of the 247Solar system is given in Figure 8.
Figure 8 247Solar Plant [18]
The same idea is reprised by Australian company Vast Solar, which already constructed three CSP research
and demonstration facilities, where a small heliostat field concentrates sunlight into a 30 m tower and
receiver, for electricity to be generated through a small steam turbine. Storage is achieved thanks to molten
salts. A pilot demonstration plant (6MWth, 1,1 MWe with 3 hours of storage) was commissioned in 2016,
and later connected to the Australian grid, making it the first CSP plant with storage connected in Australia.
The plant consists of five solar array modules. Each module consists of one tower of approximately 30m, a
thermal energy receiver and about 700 heliostats. Modules connect to a central energy storage tank with
molten salts, and from there the stored thermal energy is passed through a steam generator to make steam
for a small (1.1MWe) turbine and electricity generator [19]. The schematic of the plant is given in Figure 9.
-10-
Figure 9 Vast Solar CSP system [19]
A similar concept was proposed by AORA-Solar, a developer in solar-biogas hybrid power technology that
specializes in small-scale off-grid solutions. The design relies on the same heliostat field/tower
configuration, but the electricity is produced by a Stirling engine instead of a MGT. AORA-Solar however
do not propose TES, relying on natural gas, biofuels or diesel to offer around the clock electricity generation.
The lack of updated information and literature about the company and its projects means most likely that it
went bankrupt, as of today, its website is shut-down [20] [21].
Subsequently, promising alternatives are based on solar-powered Stirling engines integrated with TES,
leveraging from both the modularity (10-40 kW) and the high efficiency of the engine (e.g. when compared
to MGTs, and even to conventional cycles in large CSP plants). Indeed, a recent study performed by several
U.S. research institutions and funded by the U.S. Department of Energy [22] studies the performance
modelling and economical viability of a CSP tower small scale system with latent storage, referred to as
STEALS. The research study reprises the heliostat field design, that reflect sunlight on top of a tower, where
the entire thermal system is located. The cavity receiver heats up the bottom of the TES tank (and its PCM),
while sodium heat pipes extend vertically from the bottom of the tank to the top, distributing heat in the
storage material. A thermosyphon-based thermal valve acts as an interface between the TES tank and the
Stirling engine used for electricity generation. The heat flow from the storage to the power block is
controlled by a valve, as the Stirling engines considered in the study range from 0.1 to 1 MW. This modular
design results in minimal balance of system costs and enables high deployment rates with a rapid realization
of economies of scale, where generation costs reach values well below 100 €/MWh [22]. A schematic of the
whole system is given in Figure 10.
-11-
Figure 10 Design configuration of STEALS [22]
Against this background, previous research have investigated the potential small distributed CSP systems
can have, be it with storage or without [23] [24]. These studies provide summary information about market
application for such a design, and are narrow in scope as they don’t go beyond a general classification of
sector application: industries, villages…, or give a quantification of the potential, in terms of MW to install.
Neither have these proposed a business model that would allow small scale CSP systems with TES to secure
its market niche, based on the risks hindering its market entry. On the other hand, when it comes to analysis
work that measures the potential of a new technology, and specifically CSP in different countries, one can
name the HYSOL project work package 2 [25], which studies the economic feasibility and market
penetration of an innovative configuration for a fully renewable hybrid CSP plant. The study is carried out
for four selected countries: Kingdom of Saudi Arabia, Chile, Mexico and South Africa. It first assesses
regulatory and policy framework regarding renewable energies in each to understand the power market,
renewables energy targets…, to then carry out a corporate economic assessment for the decision-making
process based on metrics such as LCOE, NPV and IRR for the different countries. Based on the mentioned
analysis, it finally draws conclusions on the potential of the new technology in each prospective. Another
project tutored by Apricum, the strategy consultancy firm specialized on renewable energies deals with the
assessment of business opportunities present in the solar industry for Saudi Arabian companies. It
represents a prefeasibility analysis for a Saudi Company to enter the solar industry, by analysing the solar
market’s value chain and performing a multi criteria analysis of different business opportunities. Two
representative business cases are presented afterwards to showcase the value of the best identified
opportunities [26].
2.3.2 Cleanergy CSP systems
2.3.2.1 Legacy product
The company’s CSP solution “SunBox” is based on one core competence and component, which is the
Stirling engine. Cleanergy modified this two-hundred-year-old technology to better suit solar applications,
converting concentrated solar radiation to electricity with the engine [27]. The Stirling cycle is a closed cycle
that contains a unique and fixed volume working fluid (gas) that is heated, expanded, cooled, and
compressed, thus driving a piston for power generation [28]. Cleanergy’s system relies on an air cooled,
Alpha type Stirling engine. This configuration, shown in Figure 11, is characterized by two different pistons
in two separated cylinders. They are connected in series through a heater, a regenerator and a cooler. By
doing so, one piston acts as the “hot” part of the engine, and the other as the “cold” part. The heat coming
from the parabolic dish and receiver heats up the working fluid (hydrogen), causing it to expand, pushing
-12-
the hot piston and driving the crankshaft to create momentum. As a result, the cold piston is compressed,
moving the working gas into the cold heat exchanger and regenerator. The generator connected to the
moving crankshaft generates electricity, while warm air is rejected through the air cooling system [27] [3]
[28].
Figure 11 Cleanergy's Alpha type Stirling engine [27] (adapted)
As mentioned before, Cleanergy is currently developing a Stirling based CSP solution with TES. However,
the company had a previous design based on the Dish Stirling configuration. The latter will be referred to
as “legacy product” in this report, since the company no longer focus on it. Dish Stirling systems use
parabolic shaped mirrors to concentrate solar radiation into a receiver. The heat in the receiver feeds the
Stirling engine to produce electricity as explained above. “SunBox” refers to the unit Cleanergy produces,
which contains its modified version of the Stirling engine alongside the receiver. With a nominal capacity of
13kW, the SunBox unit is mounted onto the parabolic dish structure that can track sun movement
throughout the day. The main advantage of this system is its modularity, that allows it to operate individually
in remote locations such as small villages or off-grid locations. It also offers the possibility for medium to
large scale application by associating multiple Dish Stirling systems. With regards to the technical
specifications and comparison made in Table 1, the demonstration unit installed in Dubai reached a
conversion efficiency of 30%, record value for any solar technology. The system is best suited in hot arid
climate zones as mentioned before, where there is high level of direct normal solar irradiance. The water
scarcity characterizing these regions does not hinder Cleanergy’s system operation, as no water is required
for power generation. Moreover, the majority of the components of the system have near zero degradation
over a period of twenty-five years, rendering Cleanergy’s product sustainable, efficient, robust and the
perfect alternative for power generation during the day [3] [29] [27]. The addition of a thermal storage unit
would allow for dispatchable electricity, which is being developed at Cleanergy, and will be further detailed
below.
2.3.3 New design with TES
As seen in both Figure 3 and Figure 4, the installed capacity of PV power plants exceeds by far CSP. The
reason for this is the cheap price of PV cells and modules compared to solar thermal. As a point of reference,
PV power plants’ bidding price reached 30$/kWh in 2016, while CSP tower bidding price was 94.5$/kWh
[30]. However, while PV is attractive for its cheap and simple design, it becomes less appealing when the
sun falls, period when electricity demand is on the rise and PV output fading. The solution is to couple the
photovoltaic panels with storage batteries to produce reliable electricity like CSP systems do with TES. Li-
ion battery is most often the best choice, but offsets PV’s greatest advantage of being cheap. As of 2017,
the cost of Li-on battery pack was around 230$/kWh, but degradation considerations make it even more
costly: it may be needed to replace batteries 4 to 5 times during a PV power plant lifetime [30]. TES does
-13-
not present these limitations, and Cleanergy plans to capitalize on that to compete with PV and other CSP
technologies.
Cleanergy’s target market and strategy can be seen in Figure 12. In a nutshell, while PV technology is
definitely cheaper than any other renewable alternative from small scale installations(residential) to utility
scale plants, dispatchability and storage features make it way more expensive than the other solar systems,
especially in medium and large-scale projects needing storage capabilities greater than 4 hours. On the other
hand, CPS tower and through systems with TES systems are commercially viable for large installed capacities
of over 50 MW as was seen previously. Cleanergy then positions itself in markets of installed capacity from
100kW to 50 MW with long hours of storage above four hours. This market segment is a niche, meaning
that it is a blank spot where no product with competitive added value has been proposed yet.
Figure 12 Cleanergy's initial target market (2021-2025) for the TES system design
To do so, the company started the R&D on a new CSP Stirling based system that incorporates TES of 10
hours or more. Figure 13 shows the proposed design for the new system. In this configuration, a solar field
of small heliostats concentrate sunlight into a receiver mounted onto the top of a 10 m tower. The receiver
is linked to the TES system, which uses a phase changing material (PCM). The PCM transfers the heat
collected with a heat transfer fluid (HTF) that powers up the Stirling engine through a heat exchanger.
-14-
This new design is currently being researched and developed with the following technical specifications:
• Each modular system (solar field, tower and Stirling engine) will have a rated power of
13kW. This way the technology can still be used for distributed configurations but allows
for large scale deployment as well.
• The TES should have a capacity of 10 hours or more, storing enough heat for electricity
production when the solar resource is not available, but to also contribute to grid stability
by providing a firm electrical output. A storage utilization fraction of 90% is targeted.
• The Stirling engine’s performance depends to a great extent on the input temperature, the
change operated in the system (compared to the old dish configuration) should not affect
the thermal to electricity efficiency of 30%.
Based on these technical objectives, the new product must be competitive with other technologies on the
market, namely PV-BESS, but also conventional power generation system (Diesel based generators).
Cleanergy’s value proposition, especially in the niche mentioned above, must have the lowest cost of
electricity generation. More specifically, the company aims for a 25-30% lower cost of electricity production
compared to PV-BESS and Diesel Gensets based on forecasted generation costs for said technologies in
2021. Figure 14 shows the LCOE evolution of PV-BESS with storage hours placed in Ouarzazate, Morocco
with a DNI of 2630 kWh/m²/year. The costs used are 2021 projections based on several forecast sources
(Lazard [32] , IRENA [2] , NREL [33]). The target LCOE of the company should be lower than a similar
PV-BESS or diesel generator system with the same amount of storage hours, i.e.. 10 hours or more for a
similar location. The long-term goal is to reach a LCOE of around 35€/MWh in the year 2030 with the
setting (location, storage size, rated power). The reduction in cost is expected to be driven by higher volumes
Figure 13 Model of one modular Cleanergy CSP unit with the three main components: concentrator, receiver with storage (10 hours) and Heat Engine (Stirling)
-15-
of production, as well as reduced costs of installation, engineering and O&M. The reported learning rate for
CSP technologies with storage is 30% for the period 2010-2022 [31].
Figure 14 PV-BESS LCOE in 2021
0
20
40
60
80
100
120
140
2 3 4 5 6 7 8 9 10 11 12 13 14
LC
OE
(E
UR
/M
Wh
)
Storage (h)
IRENA PV-BESS Lazard PV-BESS NREL PV-BESS
-16-
3 Methodology
The present work aims at estimating the market potential of a dispatchable small scale CSP system with
TES, such as the one Cleanergy is currently developing, limited in the spatial boundary of the MENA. In
other words, the size of such a market must be quantified in relevant figures, such as MW to install, or units
(solar field, tower and Stirling engine) to deploy. Market size determination relies generally on three key
concepts as visually depicted in Figure 15 [34] :
• Total Addressable Market (TAM): represents the size of the market if the product analysed were to
meet all the demand, disregarding any kind of competition. In Cleanergy’s case, the TAM would be
the number of plants to install in the MENA region to meet all of its electricity demand.
• Serviceable Achievable Market (SAM): represents the portion of the TAM that the product assessed
is actually targeted to and geographically reachable, excluding any competition. In the scope of this
work, business opportunities to potential industrial companies (or any other local company) that
would be interested in the product. Industry accounts for 42% of the electrical consumption
globally [35], needing most often continuous electricity supply for its processes and activities. It is
then a perfect sector Cleanergy targets to seek business opportunities.
• Serviceable Obtainable Market (SOM): is the selected business opportunities within the SAM that
a company targets first to grow and develop its product.
Figure 15 Market size estimation [34]
At this stage, Cleanergy is most concerned with understanding its SAM, sizing its magnitude and numbering
all business opportunities within that area. From that, the company can clearly sees which of them are the
most promising, and that will represent its SOM. Thus, this work needs to present a way to accurately
estimate the SAM for Cleanergy, and propose a way to sort the best cases forming its SOM. To do so, the
approach depicted in Figure 16 is followed. The business opportunities are investigated in representative
countries of the MENA: Morocco, Tunisia, Egypt, Jordan and Saudi Arabia. Business opportunities refer
here to potential industrial companies (or any other local company) that would be interested in the product.
Industry accounts for 42% of the electrical consumption globally [35], needing most often continuous
electricity supply for its processes and activities. The methodology revolves around 2 steps:
• Filtering: In each country, a market analysis is done, where the main electricity intensive industrial
companies are identified, and regrouped by sector: mining, cement, chemical, metallurgy,
agriculture. Based on publicly available data, the electricity consumption of each company is
estimated and broken down to each of their consumption sites, for which exact location coordinates
and respective weather data are gathered. For each of these sites, a techno-economical analysis is
carried out based on the company’s simulation model. Different plant configurations, in terms of
installed capacity, storage size, and mirror area were evaluated, from which the optimal
configuration able to reduce the LCOE is selected. The knock-out criteria to filter the business
opportunities in this step is LCOE, and the lowest value it can reach for each site. Through this
-17-
step, a quantification of the SAM is made in each country, alongside identification of companies
that could populate the SOM of Cleanergy. The choice of which will Cleanergy should effectively
engage with will be the result of the second step.
• Scoring: The above-mentioned optimal configurations are regrouped by country in order to
estimate the country-specific potential in terms of installed capacity. With such information, a multi-
criteria analysis (MCA) is performed in order to be able to compare amongst the different markets
(by country), considering not only the potential for installed installations in MW, but also the lowest
cost at which parks could be built, macro-environmental factors in the country, and existing
infrastructure, among others. The MCA is used to score each business opportunity, to finally select
the top ones. The latter represent the lower bound of the SOM, while its higher bound is the SAM
which is also sized using global industry electricity consumption.
• Scenario analysis: Once top business opportunities are identified with the MCA, three scenarios are
defined to further the comparison between the opportunities and countries, in terms of profitability
for Cleanergy.
Figure 16 Selection methodology for business opportunities [36]
Cleanergy aims at positioning itself in the solar energy market as technology provider. In other words, its
business model will repose solely on its ability to find customers interested in owning and operating their
own power generation facilities. However, for such a novel technology like Cleanergy’s, it will be hard to
garner the desired market interest, due to it being unknown and not yet proven. Consequently, taking an
active part in the first projects to be deployed, e.g. being a co-developer can be more strategic. Doing so
will help introduce the product to the market, by showcasing that Cleanergy is equity shareholder in the
power plants based on its technology, thus build general trust in its product. In that sense, the analysis to
be performed will assume a different business model for Cleanergy, which will act as Independent Power
Producer (IPP) and operate the first projects under a Build-Own-Operate scheme. For such models,
producers compete in a liberalized power market, where off-takers decide freely the source of their electricity
procurement, which can be the national electricity utilities, other IPPs (wind, solar PV…) or even invest in
their own generation capacity. Hence, all calculations and discussions are done assuming the IPP model for
Cleanergy, rather being just a supplier, to assess the economic viability of the projects (primarily on a LCOE
basis). Indeed, although Cleanergy’s interest as suppliers is to sell as much systems as possible, the project
perspective matters in reality to the company as only a viable/competitive project will make the technology
be chosen. Conversely, while the IPP models encompasses the opportunities laying in the grid-connected
market, it lacks including off-grid users, as by definition that business model is not valid for such setting.
For that purpose, and not to disregard the potential business opportunities of users not connected to the
grid, the highest scores/weight are given to such projects in the MCA.
3.1 Geographical limitation
As mentioned previously, the study revolves around the MENA, with a special focus on Morocco, Tunisia,
Egypt, Jordan and Saudi Arabia. The choice of these 5 countries is motivated by is considered to be strategic,
as they all have enough resemblance to draw conclusions and strategies to be applied to MENA in general,
but present in the same time enough differences for them to stand out and challenge the company in its
-18-
way of doing business. Beyond that, those specific markets were included in the AFEX (Arab Future Energy
Index) Renewable Energy survey, ranking among the top 10 countries with most potential in RE
investments as seen in Figure 17. The AFEX is a policy assessment and benchmark tool that provides a
detailed comparison of renewable energy development in 17 countries of the Arab region on more than 30
different indicators. Such indicators include market structure, policy framework, private investment
regulations… [37].
Figure 17 AFEX Renewable Energy 2016 [37]
Hence, Morocco, Jordan and Egypt are representative of countries with the most potential when it comes
to renewables. Saudi Arabia, though ranked 10th, is also investigated in this work considering its market size,
and the strategic role it plays in the region, and in the world as a dominant oil producer. Tunisia is included
as well, as a representative under-developed country transitioning to renewables.
Once the SAM in these 5 markets is estimated, a scaling up process is necessary in order to have the same
figure for the whole MENA region. This can be achieved by considering the contribution of the industrial
sector as added value in each country’s GDP and interpolating the corresponding SAM as a function of the
latter. This is made possible because the SAM sizing deals exclusively with the industrial sector electricity
consumption. The data obtained for the 5 countries mentioned above serve as the basis for the interpolation.
The choice to include Saudi Arabia as country to research gets then even more meaning as it is the number
one ranking industrial economy in the MENA [38] [39], thus facilitating the scaling up process and make it
somewhat more precise.
The MENA is a region encompassing approximately 22 countries in the Middle East and North Africa.
While there is no standardized list of which countries are included in the MENA, the following are typically
included in MENA: Algeria, Bahrain, Egypt, Iran, Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco,
Oman, Qatar, Palestine, Saudi Arabia, Syria, Tunisia, United Arab Emirates, and Yemen [40]. Subsequently,
the SAM in these locations needs to be estimated to give a general figure about the MENA.
3.2 Market analysis/entry
To complete the task defined above, a comprehensive study of the five countries is carried out to assess
their macro-environment, but also to identify potential industrial customers. The latter are individually
researched by analysing their electricity need in terms of power capacity Cleanergy can install to cover their
demand. This data mining step is crucial, as it is the basis of the analysis to be carried out later on and that
will contribute in building the go-to market strategies. The information for each company is gathered
through research of publicly available studies, governmental report, utility annual activity reports, journal
articles… However, the data looked for, namely electricity consumption figures, load profiles, is most often
not made public, or hard to find, and was estimated based on several assumptions. This may lead to multiple
inaccuracies in the analysis done. As a result, finding ways to validate it is also primordial and is investigated.
-19-
Appendix 2 represents one way of doing so: a questionnaire made for the Moroccan companies, and that
was designed to help assess and validate the information collected.
Beyond that, once an optimal prospective list of customers defined, there is a need to understand the
environment around those customers, and how the company can do business in the desired markets.
Renewable policy targets, regulatory framework, business risk and fraud are all factors that need to be
considered and understood. Thereafter, an entry mode needs to be chosen to actively penetrate new
markets. The choice of entry mode into a foreign market is a very important decision for companies whose
activities are directed toward the international and wishes to expand there. Entry modes are based on the
firm’s involvement level, or the degree of influence and confidence it has over its operations but are also
based of equity of ownership and control [41]. Different motives explain a company’s choice of an entry
mode over another. For example, a firm will find that entry mode yielding the higher percentage of return
on investments the best suitable, while another would prefer an entry mode guaranteeing close to zero risk
[42]. Entry modes fall into three different categories as detailed in Table 2. A company can choose between
them when entering a new foreign market. Those categories depend on the firm’s level of control, and are
differentiated by their types of arrangements [43]
Table 2 Entry modes categories [31]
Categories Entry modes Arrangements
High Control Modes Wholly Owned Subsidiaries
The owner of the parent
company has full control over
the business in the new market.
Intermediate Modes
Strategic Alliances Partners agree to share
technology, jobs, and resources
& provide support to each other
during the agreed time. Joint Ventures
Low Control Modes
Indirect Export
The parent company use
independent organization located
in the home country/ third
country
Direct Export
The parent company sells directly
to a distributor, agent or importer
based in the market
3.3 Financial model
The power price that is set during RES tenders is the key point for a project to be awarded. These prices
are based on the cost of energy and quantifies the profits the projects stakeholders will make over the
lifetime of the power plant. They are determined by doing financial analysis that include all factors and risk
predictions to yield the best profits. The key metrics used to rank electricity production technologies are the
Levelized Cost of Electricity (LCOE), Net Present Value (NPV) and Internal Rate of Return (IRR). It is
important to be rigorous on their definition as various methodologies (inflation, nominal rates, taxes
issues…) are used in the industry and academia, and it can mislead decision makers when comparing projects
whose indicators do not follow the same method of calculation. Appendix 1 is a report done prior to this
thesis work, which compiles and explains in detail the methodologies and calculations of different metrics
used to financially valuate solar power plants. Below is a brief description of the above-mentioned metrics,
but the extensive explanations, definitions, terminology and symbols used in Appendix 1 will be referenced
in this research work.
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Levelized Cost of Electricity
The levelized cost of electricity is the most frequently used economic performance metric for power
generation plants. LCOE is used to assess/compare the performance and profitability of any form of
generation technology, and not only concerns solar or renewable sources [44]. It is defined as the constant
per unit cost of energy which over the system’s lifetime will bring all the project cash flows to zero. In other
words, it is the ‘break even’ constant sale price of energy [45]. Another way to view the LCOE is it being
the price at which the electricity must be sold to recover all the costs incurred during the lifetime of the
project. Equation 1 gives the general formula for calculating the LCOE. All used symbols and terms are
explained in Appendix 1
𝐿𝐶𝑂𝐸 =
∑𝐶𝐴𝑃𝐸𝑋∗𝐸𝑞%
𝑁𝑐𝑜𝑛𝑠×(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛−1𝑡=0 −∑
𝐷𝐸𝑃×𝑇
(1+𝑅𝑂𝐸)𝑡
𝑁𝑐𝑜𝑛+𝑁𝑑𝑒𝑝−1
𝑡=𝑁𝑐𝑜𝑛+∑
𝐼𝑁𝑇𝑡×(1−𝑇)
(1+𝑅𝑂𝐸)𝑡 +∑𝑃𝑅𝐼𝑁𝑡
(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛
𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛
∑𝐸𝑡
(1+𝑅𝑂𝐸)𝑡𝑁𝑡=0
+
∑𝑂𝑃𝐸𝑋×(1−𝑇)
(1+𝑅𝑂𝐸)𝑡𝑁𝑐𝑜𝑛+𝑁𝑜𝑝−1
𝑡= 𝑁𝑐𝑜𝑛+∑
𝐷𝑒𝑐𝑜
𝑁𝑑𝑒𝑐×(1+𝑅𝑂𝐸)𝑡𝑁−1𝑡= 𝑁𝑐𝑜𝑛+𝑁𝑜𝑝
∑𝐸𝑡
(1+𝑅𝑂𝐸)𝑡𝑁𝑡=0
(1)
Net Present Value and Internal Rate of Return
The Net Present Value (NPV) of a proposed project is most often used as the primary absolute metric to
compare/assess investments and serves as a base for decision making [46]. The NPV is the sum of the
discounted cash-flows over the lifetime of the project using an appropriate discount rate as discussed above.
The cash-flows represent the yearly difference between the revenues and costs incurred each year. It is then
linked primarily to the CAPEX, OPEX, decommission costs, the yearly energy yield or output and finally
the price at which the electricity is sold. Equation gives the general formula for calculating the NPV. All
used symbols and terms are explained in Appendix 1.
𝑁𝑃𝑉 = ∑(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡
(1 + 𝑟)𝑡
𝑛
𝑡=0
= 0 (2)
Another fundamental economical metric that is used to rank projects and get a hold of their profitability is
the internal rate of return or IRR [44]. The internal rate of return is the discount rate that would be used in
an NPV calculation and would make it equal to zero, as seen in Equation 3.
𝑁𝑃𝑉 = ∑(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡
(1 + 𝐼𝑅𝑅)𝑡
𝑛
𝑡=0
= 0 (3)
The IRR is then the interest rate that would break even the project accounting for the costs incurred and
revenues generated during the lifetime of the plant. It is a measure of the profitability of a project and is
used mainly by developers and financial institutions to base their investments decisions. Each company has
its own predictions on how much profit can be made of a project and has usually a target return on
investments. If the IRR is higher than that required target, the project is financially acceptable. To compare
different projects and financing opportunities, the higher the IRR the better [44].
The input data for the financial model are described in Table 3 . The specific component costs (solar field,
receiver, TES, Stirling engine) are confidential to Cleanergy, but cost values from a similar technology
concept, known as STEALS and described in a recent study [47]. The latter are given, and used as a reference
to compare the results, in terms of LCOE values and so on. It should be noted though that cost projections
for the STEALS project are optimistic, and represent idealized future system cost, rather than the cost of a
system that could be built today. Not all manufacturing costs were considered for example and consider
large scale production rate. Moreover, heliostats are the equipment that weigh the most in the project’s
CAPEX, and the STEALS study considers a low value compared to existing plans. As underlined by the
-21-
project report, it is assumed that improvements will be made by the greater CSP community to reach such
low values, especially considering that heliostats are used in many systems [47].
Table 3 Financial model inputs
Parameter Value
Stirling engine cost 808 €/kWe
Heliostat cost 61 €/m²
Tower/receiver cost 90 €/kWe
TES cost 25 €/kWh
OPEX cost 25 €/kWe
Project lifetime 30 years
Inflation 0%
Power price escalation factor 0%
Equity financing 25%
Equity IRR 8%
Debt financing 75%
Cost of debt 5%
Debt amortization 15 years
Depreciation 25 years
Corporate tax rate
Morocco: 31%,
Tunisia: 25%
Egypt: 22,5%
Jordan: 25%
Saudi Arabia: 20%
Naturally, LCOE figures inherent to Cleanergy’s system cannot be shown due to confidentiality reasons.
However, since the analysis done revolves to great extent on that metric, normalized values of the calculated
LCOE will be shown later in the report. In each data set to be calculated, the normalization will be done
taking as a reference the minimum value of said data set.
3.4 Techno-economical analysis
As explained previously, the analysis to be carried out consists of identifying potential off-takers in the form
of industrial companies and trying to assess the electricity consumption on each of their sites, exact location
coordinates and respective weather data, which serve as input data for the simulation model the company
developed. The latter calculates the system size (MW) of Cleanergy’s product needed to service that
electricity consumption. This in turn serves as input data for the financial model described above, that
calculates the LCOE. Different plant configurations, in terms of installed capacity, storage size, and solar
field area are evaluated, from which the optimal configuration able to reduce the LCOE is selected. A
schematic of the process is shown Figure 18.
-22-
Figure 18 Techno-economical analysis process
DNI resource estimation
The online tool developed by IRENA called Global Atlas for Renewable Energy was used [48]. This GIS
tools comprises solar radiation maps, among other resources, of different countries around the world.
Practically, once a site was determined and its location coordinates were found, the tool was used to obtain
the DNI prevalent in that area. The obtained figure was further confirmed with the solar maps accessible at
SolarGIS for each of the countries analysed [49].
TES size
The underlying premise of the storage design is that the system is cost-effective at long storage hours, ie. 10
hours and more. Thus, the storage size is varied for each location from 10 to 14 hours to validate that with
actual LCOE figures. Also, load profiles and exact demand requirements from specific off-takers are still
unknown, so the analysis is made on a range of TES sizes to consider all possibilities, and consequently,
decide on which is optimal. Moreover, the optimisation of the storage size can have an effect on the total
solar field area: with varying solar resource, charging a TES of 14 hours for example can require more or
less mirror area, hence affect the total system costs, and the capacity installed.
Mirror area
For each 13kW Stirling engine, a solar field concentrates sunlight into its correspondent receiver/tower.
The area of the solar field, or reflective area of the mirrors is varied from 150 m² to 220 m² with a 10 m²
increment, in order to see how performance evolves with that change, but more importantly, to understand
the impact on the LCOE, and later help in the design criteria to be chosen. The natural course of thought
would be to consider that a higher reflective area per field would lead to higher CAPEX and LCOE figures.
However, increasing the solar field size will yield in better energy output, capacity factors, which in turn
contributes in decreasing the LCOE. The effect of the mirror area needs then to be investigated in detail.
Figure 19 shows how the LCOE varies with increasing solar field area, different DNI values and over the
range of storage hours considered.
-23-
Figure 19 LCOE vs Reflective area
In this figure, the scattered points refer to LCOE values obtained for all possible storage sizes considered
when varying the field mirror area, with respect to several DNI conditions, highlighted by the colours in the
graph. Correspondent coloured doted lines represent the general trend said scattered points follow, giving
a more precise depiction of the behaviour. As it can be seen, minimal LCOE numbers are always obtained
for the maximal reflective area allowed (220 m²), whatever for all storage sizes. Hence, the latter value is
used in the analysts and results sections of the report.
Electricity consumption estimation
The electricity consumption figures are estimations based on annual reports, goods production rate per year
and referenced electricity intensities. For example, if a cement company produces annually 0,85 MT in a
particular location, given the referenced energy intensity of cement production [50] (110 kWh/Tons), the
electricity consumption of that site is found. As a result of this approach, the numerical data used are not
completely accurate with regards to the real consumption rates, since the energy intensities used may not be
the same for all companies, due to different production process, better/worse efficiency measures, etc.…
However, the figures are valid enough to provide a first quantification of the specific electricity consumption
in each identified site, and that may need to be adjusted in the future.
In practical terms, Cleanergy provided yield simulations for 1 unit (13kW), for 6 DNI values
(kWh/m²/year): 1913, 1987, 2196, 2467, 2640 and 2728. Hence, this data needs to be interpolated to be
able to compute the yield for a range of DNIs in between 2000 kWh/m²/year and 3000 kWh/m²/year.
Moreover, thanks to the modularity of the system design, once the yield of 1 unit is known, and given the
electricity consumption in a particular site, it is easy to determine the number of units needed to produce
that exact energy yield, thus find how much capacity is required to be installed to service a particular site.
Finally, and in order to ease the comparison later on, especially when dealing with all 5 countries of the
analysis, each location or site is given a name code depending on the country, the company and the type of
industry. The nomenclature of the code is as follow: XX##
• X: First letter of the country dealt with: Morocco, Tunisia, Egypt, Jordan, Saudi Arabia
80
90
100
110
120
130
140
150
140 150 160 170 180 190 200 210 220 230
LCO
E (€
/MW
h)
Field reflective area (m2)
1915
2015
2115
2215
2315
2415
2515
DNI (kWhh/m²/year)
......Trend lines
-24-
• X: First letter of the industry type: Mining, Cement, Agriculture, Metallurgy, Chemical
• #: Rank of the company in each sector
• #: Rank of the site for each company
3.5 Multi-criteria analysis
Once a preliminary list of potential business opportunities is clearly defined, each option is evaluated, scored
and then ranked based on criteria jointly selected with Cleanergy. The selected criteria are both numerical
and cognitive. This work attempts to give a quantitative approach to a qualitative assessment, and this is
done by attributing weights to each of the criteria, and then scoring the opportunities on a scale of 1-10
based on the criteria. The qualitative and quantitative criteria used to perform the assessment of all business
opportunities are listed below, alongside their respective weights and scoring method. Table 5 represents a
summary of the following:
• Criteria 1: Potential (MW)
Cleanergy’s interest, which represent how much units of its product it can install. In other words,
how much electrical power capacity the industrial company uses to run all its factories. This
approach is conservative, since it is hard to define clearly how much an industrial would want
replaced by a CSP system, but it gives an idea about the potential of the market. The input figures
here are taken directly from the results of the electricity consumption estimation defined earlier.
The scoring method used is linear, with a maximum capacity of 50 MW given the highest score 10.
Anything below gets a linearly decreasing score.
• Criteria 2: DNI resource (kWh/m²/year)
As explained previously, Cleanergy’s CSP system relies on good solar conditions, the higher the
better the electricity is, and the better the business case will be. When screening the potential
customers, all locations with a DNI less than 2000 kWh/m²/year were disregarded. The input data
here are taken from the market analysis performed for each country.
The scoring method is linear here as well, with the score increasing between 0 and 10 linearly from
the values 2000 and 3000.
• Criteria 3: LCOE (€/MWh)
LCOE: This metric is very much location and project dependent, but it will be the primary figure
industrials will evaluate to make a decision on whether or not adopt Cleanergy’s system. If the
technology has a generation price higher than solar PV for example or conventional fuel-based
generation, adopting it would have no financial sense. It is also a relevant decision metric for
Cleanergy, as the company should target customers with attractive business cases, eg. low LCOE
and cost competitive with other alternatives, such as PV-BESS or diesel gensets. The input data are
taken here directly from the results of the techno-economic analysis.
The LCOE is scored in comparison with two price signals: P1, which is the local average utility
price (the values used are reported in Table 4) and P2, which is Cleanergy’s LCOE target in 2021.
Hence, there are two cases:
• If P2>P1, then
o If P1≤LCOE≤P2 then the score attributed is in the range [10:5]
o If P2 ≤LCOE≤P2+10 then the score attributed is in the range [5:0]
• If P1>P2, then
o If P2≤LCOE≤P1 then the score attributed is in the range [10:5]
o If LCOE>P1, then the score is 0
-25-
Table 4 Industry electricity rates
Average utility price for the industry (€/MWh) Source
Morocco 91 [51]
Tunisia 59 [52]
Egypt 53 [53]
Jordan 160 [54]
Saudi Arabia 41 [55]
• Criteria 4: Grid access
An off-grid site represents more opportunity in terms of business case to Cleanergy, as its modular
CSP system is best suited for remote locations, where grid access is inexistent. Industry owners are
often reluctant to pay for grid-connection due to its high capital costs, and the relatively low return
on investment it brings. Using diesel generators is the alternative they go by in such cases, but
Cleanergy’s product can bring great added value, and profit in the long term.
A score of 10 is given to off-grid sites, and 5 for grid-connected locations
• Criteria 5: Macro-environmental factors
Represents a country’s attractiveness over another, due to political, societal, legal factors… The
country analysis to be performed will include insights about regulatory framework, future prospects
in terms of RE policies, targets and major stakeholders in place. This description will serve then as
basis to score the attractiveness of each country, in terms of opportunities and appeal it presents
for a company like Cleanergy to engage with in business. The country score (1-10) is given
considering the following sub-criteria, that in turn are given a score with associated weights:
• Business models in place for RE projects (50% weight). An emphasis is made on the ability for
IPPs to engage in sells contracts with large consumers, and the permissibility of self-
consumption. The presence of a scheme rewarding excess electricity sells to the grid is a plus
• Business environment (25%). based on three international indices: World Bank Ease of Doing
Business, BTI status score and Global Competitiveness Index
• Participation share of the private sector in RE energy projects. (25%)
Table 5 Attractiveness scoring table
Criteria Relative importance (1-10) Scoring method
Potential (MW) 7 Linear, the higher the better
DNI (kWh/m²/year) 9 If <2000, 0
If ≥ 3000, 10
linear in between
LCOE (EUR/MWh) 10 Related to P1 and P2
P1: local average utility price
P2: Cleanergy’s target
Grid access 10 If off-grid, 10
If not, 5
Macro-environmental factors 5 See above
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3.6 Scenarios definition
The MCA analysis ranks business opportunities according to the terms explained above, and indicates which
site Cleanergy should engage in with first. The criteria used in the MCA, while encompassing a number of
aspects primordial to Cleanergy’s business approach (costs, macro-environmental factors…), do not include
profitability, or the notion of NPV and IRR. As a matter of fact, and referring to the terminology of
Appendix 1, the LCOE used in the MCA is the ‘’societal perspective’’ LCOE, which does not account for
financing costs related to debt, interest payment nor tax issues. The choice to use the simplified version of
the LCOE in the MCA lies behind the fact that each country differs in corporate taxation as seen in Table
3, or utility electricity prices reported in Table 4, which would have made the overall comparison not
accurate. If the ‘’developer perspective’’ LCOE would have been used, tax related costs/benefits would
change from country to country, impacting the LCOE, and the comparison would have not been fair. An
intrinsic comparison based solely on technology costs, ie. simplified LCOE was then preferred for the MCA.
However, and not to disregard these aspects, the best business opportunity in each country are further
investigated with regards to profitability, namely NPV and IRR, to assess their real economic feasibility. In
doing so, a better insight of the investment profitability in each country is quantified and gives a better
country comparison. To calculate the NPV, a power price must be set, at which the electricity generated
will be sold. Electricity prices are uncertain as they are often linked to oil prices, but even more so for
renewables, as the presence or not of incentives can have a huge impact on the viability of a project. Hence,
the best business opportunities identified with the MCA in each country are analysed in the light of three
scenarios:
• Zero subsidy: the hypothesis here is that there will not be any kind of support mechanism for
renewables (in the form of feed-in tariff). The power price at which the electricity from Cleanergy’s
plant will be sold is assumed to be equal to the price national utilities sell their power, as described
in Table 4.
• Break-even subsidy: this scenario evaluates the power price at which the project would guarantee
a NPV equal to zero over its lifetime. That value can be regarded as the minimum amount of
subsidy the project needs to break even and recuperate all the costs, and reach the profits expected.
• Utility competitive: in this scenario, the analysis is performed to find the right input parameters
that would guarantee Cleanergy technology to be competitive, e.g. that the resulting LCOE would
be 20-30% lower than the power price set. The difference between the power price identified in
this case and the utility price represents the surplus that must be paid on top of the average national
power price to make investments in CSP profitable. This can be relevant when assessing the
feasibility of Cleanergy’s technology if regulatory/support frameworks are announced in the
countries studied.
-27-
4 National environment, industry and firms’ specifics
4.1 Morocco
4.1.1 Energy context
Unlike any other neighbouring North African country, the Kingdom of Morocco has close to zero natural
oil resources, making it highly dependent on fossil fuel imports for its energy needs. Petroleum products
account for 41% of the primary energy supply, while crude oil coal and peat account for 31%, 17% and 4%
respectively [56]. Morocco has also an electricity interconnection with Spain of 1.4 GW [56] and another
exists with Algeria, which is only used for grid balancing purposes [56]. Due to a rapid economic growth of
5% per year [56], the energy demand, and therefore the primary energy supply increased in Morocco,
reaching 17,7283 kTOE in 2015, increasing by 0.56 TOE against 0.36 TOE in 2002. This trend was logically
the same in the electricity sector, where the average growth rate for electricity consumption is 7% since
2002, with residential and industrial sectors growing the fastest at 8% and 7.4% respectively. Together, these
two classes account for more than 75% of the national electricity consumption, which amounted to 34,413
GWh at the end of 2015 [57]. The high rural electrification rate, which jumped from 18 % in 1996 to 99%
nowadays contributes also to the electricity demand growth. The electricity demand is met by a variety of
sources as detailed in Table 6. The total generation capacity stood at 8160 MW in 2015 [57], with hydro and
solar-wind accounting for 22% and 12% respectively. During the same year, renewables generated around
14% of the total electricity produced, but Morocco has an ambitious sustainable development program that
aims at increasing renewables share in the generation mix in the future
Table 6 Morocco generation units 2015 [58]
Generation units in 2015 MW %
Classical Hydro 1306 17%
STEP 464 6%
Total Hydro 1770 22%
Private wind farms (13-09 & Auto) 241 3%
IPP Wind farms CED + Tarfaya 352 4%
CCGT Ain Beni Mathar (Solar) 20% 0.2%
Ouarzazate Solar Power Plant (Noor 1) 160 2%
Total wind & solar 979 12%
Total thermal 5411 66%
The country joys from perfect solar irradiation conditions, with annual average DNI values reaching 2700
kWh/m² in some locations [56], as well as favourable wind resources in its northern and southern parts.
The country capitalized on that when revealing its renewable energy targets for 2020 and 2030, aiming for
42% and 52% respectively of the total installed capacity [59]. With this National Energy Strategy (NES),
Morocco led the way in 2009 in terms of sustainable development in order for the country to secure its
energy access from renewables and be independent for its supply. The strategy relies on two programs [59]:
the solar program Noor aims to reach 2000 MW installed solar power capacity by 2020, and around 4800
by 2030. The Moroccan integrated Wind programs aims at achieving 2000 MW of installed wind power
capacity by 2020, and up to 5000 MW by 2030 as shown in Figure 20. The Moroccan Agency for Sustainable
Energy (MASEN) oversees the development of such projects.
-28-
Figure 20 NES 2030 targets [59]
4.1.2 Electricity market
ONEE is the national electricity utility in Morocco and is the main player in its power sector. It is under
administrative and technical control of the Ministry of Energy, Mines, Water and Environment. The
ministry has also under its umbrella the following institutions that deal with renewable energy [56]:
• MASEN: Previously known as the Moroccan Agency for Solar Energy, MASEN became in 2016
the Moroccan Agency for Sustainable Energy, after it was decided that it will oversee the
development of all kind of renewable projects. Under the PPA tariff scheme, the agency has in the
pipeline 3000 MW renewable projects by 2020 and 6000 MW by 2030 as mentioned above. This
consolidation of all types of renewables under one umbrella helps MASEN optimize the generation
cost of electricity, especially when dealing with CSP. By auctioning the projects it seeks to
implement, MASEN has reached competitive electricity costs for its parks: NOOR 1 (CSP) 150
EUR/MWh, NOOR 4 (PV) 40 EUR/MWh [57].
• AMEE: Previously known as the National Agency for the Development of Renewable Energy and
Energy Efficiency and established in 2010, it became in 2016 the Moroccan agency for Energy
Efficiency, with the goal of focusing only on energy efficiency [57]. This move of focus is strategic
as it allows the agency to allocate all its resources to energy efficiency programs that were not
sufficient compared with the untapped potential efficiency measures can have on reducing the
overall consumption in the country [56]
• EIS: The Energy Investment Corporation was created in June 2009 to boost the development of
renewable projects and has a national interest capital of MAD 1 billion [60]. It is mainly involved
with small and medium scale projects, such as the use of PV for street lightning.
• IRESEN: The Institute for Solar Energy and New Energy was established in February 2011.
IRESEN aims to consolidate the needs of different stakeholders and to ensure the implementation
and enhancement of various research projects [57].
ONEE is the single buyer of power produced across the country as a governmental entity, and acts
throughout the whole value chain of the electricity market (generation, transmission and distribution; for
the latter, local utilities operate in some parts of the country as well), but the country is slowly liberalizing it
[56]. The following legislative developments were taken to achieve that liberalization, which gives the market
structure depicted in Figure 21:
• Act No. 16-08: Law on self-production that authorizes for the firs time any natural or legal person
to produce electricity for its own consumption. It is however subject to some conditions and
authorizations, such as a capacity limitation set to 50 MW, an obligation to use the produced
-29-
electricity for the exclusivity of the producer. As of today, only large industrial groups in the cement
or the mining sector like OCP or Lafarge use this ability [61].
• Law No. 13–09: Dubbed the renewable energy law, it served as the legal base for the NES of
Morocco. Law 13-9 allows any natural or legal person to produce energy from renewable sources.
This concerns both self-production for own needs’ service and production intended to be injected
into the high/medium voltage network and sold to buyers, provided with the right grid connections.
The law sets out also a power generation scheme based on the capacity of the renewable power
plants. However, the law also determines that the supply of electricity has to be undertaken through
the national grid with the exception of electricity generated for export or due to formal agreements
with ONEE [59]
• Law No. 58-15: Amends Law 13-09. The main changes reside in the fact that it makes it possible
to sell the surplus of electricity production from renewables, up to a certain level (no more than
20% of the annual production, and only to the high/very-high voltage grid) [62]. The law also lays
the ground for the liberalization of the medium/low voltages market as the decree of its application
has yet to be completed. This prevents large-scale decentralized injection by private individuals or
small businesses [61]. The decree aims at gradually opening the medium voltage network for
renewables, smoothening the effects and establishing a transparent framework for the investors
[62].
• Law No. 48-15: Related to the regulation of the electricity sector and the creation of the ANRE,
the National Agency for Electricity Regulation. With the goal of fully liberalizing the power market
and opening the access to the low and medium voltage grids, the law establishes a regulatory
authority to manage conflicts between operators, producers and networks users Moreover, and in
order to prevent discrimination against new independent producers , the ONEE high-voltage
network will be administered independently of energy [61].
Figure 21 Electricity market Morocco [62]
While there are no directs subsidies for renewable electricity production such as feed-in tariff for privates,
some institutions in Morocco are present to help finance and invest in renewable projects. Such institution
is MorSEFF, the Morocco Sustainable Energy Financing Facility, which is a 110 million euros credit facility
dedicated to financing energy efficiency and small-scale renewable energy investments of private companies
in Morocco [63]. MorSEFF offers bank financing, loans, free technical assistance and investment incentives
to improve quality equipment, reduce operating costs and improve competitiveness. To be eligible, projects
must abide by some criteria, such a carbon reduction targets, energy saving targets or addition of small-scale
renewable energy systems, but they have to also use equipment from pre-approved suppliers by the
-30-
institution. The MorSEFF also offers loans to suppliers of energy efficiency and renewable energy
equipment, with the goal of distribution and production capacity expansion [63].
4.1.3 Industrial companies
Industry stands for more than 40% of the national electricity consumption in Morocco [57]. More
specifically, ONEE sold around 4000 GWh to industrial connected to the high/very-high voltage grid. The
mining industry presented the highest consumption, with a total share of 29.5%, followed by the metallurgy
sector with 20.6% and the cement sector with 19.1% [64]. It was then only natural to further the
investigation in these sectors and make them a priority when identifying potential industrial companies that
would be interested in investing in a technology such as Cleanergy’s for self-production. Electricity rates for
the industrials sector in Morocco vary depending on the tariff scheme subscribed. For example, a super
peak tariff can be offered to push industrials to reduce consumption during peak periods. Table 7 details
the general high voltage tariff scheme. However, based on discussions with local industry players, the
average price paid for electricity procurement is around 0.8 MAD/ kWh. In comparison, the average
generation cost of diesel gensets in remote areas with difficult grid access is around 2.7 MAD/ kWh.
Table 7 High voltage industry general rate, Morocco [51]
Fixed rate (MAD/kVA/year) 494.09
Peak period, 17h-22h (MAD/kWh) 1.3645
Regular period, 7h-17h(MAD/kWh) 0.9736
Off-peak period, 22h-7h(MAD/kWh) 0.7131
Table 8 summarizes all the data gathered for the identified companies in Morocco that are heavy consumers
of electricity, and that represents potential customers for Cleanergy. The data was estimated on par with the
methodology described in 3.4. The list is not exhaustive, as the companies were sorted based on data
availability.
Table 8 Identified industry companies, Morocco
Company Site code Location DNI Energy consumed
(MWh) Source
OCP MM11 32.880157, -6.918677 2115 501441
[65] MM12
32.196702, -8.262906
2115 167147
MANAGEM
MM21 29.393149, -8.247911
2215 184581
[66] [67]
MM22 30.517327, -6.907685
2315 6243
MM23 30.366155, -6.463931
2415 149890
MM24 30.641392, -5.105671
2315 102285
MM25 31.413536, -8.400378
2115 315166
Maya MM31 30.777097, -7.790089 2515 14366 [68] [69]
Kasbah Ressources MM41 33.521702, -5.814462 2215 63208 [70]
LafargeHolcim MC11
27.149969, -13.202520
2215 22000 [50] [71]
[72] MC12 29.925619, -9.228238 2215 93500
-31-
MC13 30.482480, -8.879743
2115 93500
Ciments du Maroc
MC21 30.068723, -9.152878
2215 242000
[73] [50] [71]
MC22 32.300810, -9.226861
1915 11000
MC23 31.630052, -7.981257
2015 154000
MC24 27.149969, -13.202520
2215 55000
CIMAT MC31
34.775521, -4.529098
2015 17600 [74] [50]
[71] MC32
32.362069, -6.383365
2015 17600
CIMSUD MC41 27.149969, -13.202520
2215 55000 [75] [50]
Univers Acier MMe11 32.917874, -7.270166
2015 173448
[76] [77] [78]
Cosumar MA11 32.003820, -6.578683
2015 21600 [79]
Lesieur Cristal MA21 32.229224, -7,923771
2115 1095 [80]
4.2 Tunisia
4.2.1 Energy context
Tunisia is an energy-dependent country with modest oil and gas reserves. The primary energy consumption
more than doubled in Tunisia between 1990 and 2015, rising from 4,5 kTOE to 9,4 kTOE, while fossil fuel
production stagnated at 7 kTOE during the whole period. This rising imbalance between production and
consumption created lots of pressure on the Tunisian energy system [81]. The natural gas production in the
country covers only about 53% of the primary energy consumption, while Algerian gas imports ensures the
rest, where 73% of the total natural gas consumption is dedicated to electricity generation. Moreover, the
national oil production covers about 40% of the primary energy consumption while the rest is imported.
This situation leaves Tunisia very dependent on imports, not securing its energy access nor independency
[81]. By the end of 2016, the total installed capacity amounted to 5224 MW. Natural gas power plants
accounts for almost 95% of the installed capacity, while the remaining 5% are shared between 68 MW of
hydropower, 254 MW of wind and around 15 MW of residential solar PV systems [82]. The installed capacity
is expected to reach 7500 MW by 2021 as a response to the imbalances the electricity system suffers,
especially in the residential and industrial sectors which stand for more than half of the national electricity
consumption [83] . Additionally, the country’s electrification rate improved over the years, reaching 99,9%
in 2012 compared to 95% in 2000. This can partially explain the increase in electricity consumption in
Tunisia, coupled with the general economical and demographical growth the country has witnessed [82].
Higher standards of living, especially in urban areas, mean that people tend to use more electrical appliances
in households and cities, and that is meant to be tackled by the energy efficiency and conservation plans of
the country [82]. Nevertheless, aware of the necessity to shift from fossil fuels to secure its energy
procurement, Tunisia is actively transitioning from conventional power generation to renewables. DNI
values can reach 2600 kWh/m²/year in the best locations of the country, and the wind potential of the
country is estimated to 8 GW [84]. More specifically, Tunisia launched in November 2016 the Renewable
Energy Action Plan 2030, which aims integrating wind and solar PV in its generation mix, with the goals of
-32-
12% by 2020 and 30% by 2030 (of total TWh produced). The plan sets also targets in terms of renewable
capacity to be installed, including 1000 MW for 2017-2020, and the addition of 1250 MW during the period
2021-2030 [85]. The gradual suppression of energy subsidies in the country, that began in 2013, can push
private investors take the leap and contribute to the overall country energy transition [84].
Figure 22 RE National Program 2017-2020, Tunisia [86]
4.2.2 Electricity market
The Tunisian power market has a simple and coherent market structure as depicted in Figure 23. Three
main players are involved [82] :
• The Ministry of Industry, Energy and Mines: Legislative authority when it comes to the energy
sector. The Ministry elaborates governmental policies to promote research and exploitation of its
natural resources, promotes the usage of a clean source of energy by setting the legislative basis for
the energy transition
• Société Tunisienne d’Electricité et de Gaz (STEG) : The national electricity utility in Tunisia. It is
responsible of the management of the production, transportation and the distribution of electricity
and gas in Tunisia. It owns up to 80% of electricity generation facilities in the country, managing
power facilities from diverse sources such as thermic, hydraulic and wind. STEG handles also the
transport of electricity and the development of high-tension grid lines, as well as the distribution
and the administration of medium to low voltage lines.
• STEG-ER: Acts as the renewable subsidiary of STEG. Its main activities consist of realizing the
goals and targets set by the Ministry of Industry, Energy and Mines, when it comes to the energy
transition, and the effective realization of the RE National Plan. As such, it operates in all the value
chain of renewable energy projects: development with feasibility studies, realization by setting the
guidelines for ownership, supervision…, and finally exploitation and maintenance
• Agence National pour la Maitrise de l’Energie (ANME): Under the administration of the Ministry
of Energy, the ANME’s role consists of applying Tunisia’s energy management policies.
Figure 23 Electricity market, Tunisia [85]
-33-
As seen in Figure 23, independent power production is permissible, and is regulated by law 62-08 and law
96-27, which allow the generation of electricity for self-consumption and to sell the surplus to STEG, but
authorizes also IPP concessions of power generation for exclusive sale to STEG by a PPA [82].
Subsequently, and on par with its RE National Plan, Tunisia has issued several law texts and decrees to
regulate the production of electricity from renewables and have a comprehensive framework detailing the
guidelines and obligations for the establishment of such projects. Law 2015-12 defines the legal framework
for the realization of installations of electricity production from renewable energies. It was later detailed in
Decree 2016-1123, that laid down the terms and conditions for the realization of projects and sales of
electricity production from renewable energy sources [85]. The projects to be developed must fall under
four different “regimes” that are shown in Figure 22 :
• Large-scale projects, subject to concession (tender process)
• Small-scale projects, subject to authorization
• Self-production projects, also subject to authorization
• Export projects, subject to concession
The distinction between large and small-scale projects is a capacity threshold, that depends on the type
of generation: 10 MW for solar PV and solar CSP, and 30 MW for wind energy. For the first phase of
the RE National Plan, all projects will be built under the Build, Own, Operate scheme (BOO), with all
the electricity sold exclusively to STEG according to 20 years PPAs agreements. The first phase
projects will follow the authorization regime [85].
4.2.3 Industrial companies
Industry stands for more than 35 % of the total electricity consumption in Tunisia. Construction
industries such as cement lead the way with 20 %, flowed by chemical and metallurgy industries.
Electricity sales prices are not high enough to cover the costs of generation and distribution. As a result,
STEG is heavily subsidized from the government. However, the government plans to gradually remove
the latter, until they disappear in 3 to 6 years. In parallel, the government has planned a yearly increase
in electricity rates. For example, the tariff for the cement industry grew by 35% in 2014.
Following the same method described in 3.4, to identify potential industrial companies that could become
customers for Cleanergy, Table 9 was built
Table 9 Identified industry companies, Tunisia
Company Site code Location DNI Energy consumed
(MWh) Source
SCG TC11 33.874221,9.993171 2015 137500 [87] [50]
Sotacib TC21 34.937597,8.529963 2015 66000 [87] [50]
CPG TM11 34.213960, 8.606903 2015 94900 [87] [65]
GCT
TCh11 34.294721, 10.070836 2015 13200 [87] [88] [89]
TCh12 34.294721, 10.070836 2015 15000 [87] [88] [89]
TCh13 34.389871, 8.746074 2015 18600 [87] [88] [89]
TCh14 33.927670, 10.083885 2015 56400 [87] [88] [89]
TCh15 33.927670, 10.083885 2015 19500 [87] [88] [89]
CPG TCh21
33.916571,10.097558
2015 56400 [87] [88] [89]
TCh22 34.702332,10.724291 2015 15600 [87] [88] [89]
-34-
Tunisian
Indian
Fertilizers
TCh31 34.349608,10.149897
2015
43200
[87] [88] [89]
4.3 Egypt
4.3.1 Energy context
Egypt is the largest non-OPEC oil producer and the second largest natural gas producer in Africa and plays
a major role in the energy market trades of the MENA, through the operation of the Suez Canal and the
Suez-Mediterranean (SUMED) Pipeline. The country is also the largest oil and natural gas consumer in
Africa. In 2013, the country accounted for about 20% of petroleum and other liquids consumption, and
40% of dry gas consumption in Africa. As such, Egypt has been dependent on oil and natural gas for 91%
of its energy needs, the remaining 8% coming from the Aswan High Dam (2100 MW installed capacity),
and solar and wind for the last 1%. Nevertheless, the country switched from net oil producer to net oil
importer after 2013 when the local oil production became insufficient to meet the demand. The country
especially experiences multiples power shortages in summer periods. At its peak, the energy demand in 2014
was 30 GW, while generating facilities shad a capacity of 26 GW [90]. The industrial and residential sectors
lead the way in terms of electricity consumption, representing more than 70% of the national consumption,
which grows at a 6% rate. The latter is seen by the government as challenge to overcome in its reforms [91]
[92]. The stress and pressure the energy sector in Egypt suffers is the consequence of multiples factors,
among them historical ‘mal-planning’, and the political turmoil following 2011 revolution [93]. Heavily
subsidized energy prices have for instance contributed to a constant growth of the energy demand, alongside
rising state deficit [94]. As such, the government announced that all energy subsidies would be halted
gradually by 2029, and that further electricity laws would allow the liberalization of the market, opening
competition, brining investment in the electricity sector and crucially making renewables more competitive
[95]. In that sense, Egypt is building its renewable energy plans on the country’s well perfect renewable
natural resources. Average wind speeds approach 11m/s along the Suez Gulf, and DNI values are between
2000 and 3000 kWh/m²/year, as Egypt is considered a “sun belt” country. The Integrated and Sustainable
Energy Strategy till 2035 was issued to find out the necessary approach to restructure supply mix of
electricity and envisions the addition of 42 GW of large scale and distributed on-grid renewable capacity by
2030, to reach 52 GW added by 2035 [96]. On the medium term, Egypt plans to supply 22% of the total
electricity generation from renewables by 2022, with wind accounting for 12%, hydro 5,8% and solar 2,2%.
In terms of installed capacity, its evolution till 2022 can be seen in Figure 24. Egypt aims to install 2,8 GW
of PV and 700 MW of CSP by 2027 as part of its solar energy plan [97]. Plans after 2022 include also coal
(12 GW) and nuclear (1,2 GW) power plants [98].
-35-
Figure 24 Government power generation expansion plans [91]
4.3.2 Electricity market
The Ministry of Electricity and Renewable Energy (MOERE) is in charge of developing and implementing
the country’s energy strategy by setting the targets, framework…, and governs all players of the Egyptian
power market Its main. The main stakeholders are [95] :
• Egyptian Electricity Holding Company (EEHC): Owns and operates almost all generation facilities,
alongside transmission and distribution networks through its multiple subsidiaries. It is state owned.
• Egyptian Electricity Production Company (EEPC): Affiliate company of EEHC. Owns 6
regionally-based companies.
• Egyptian Electric Transmission Company (EETC): Affiliated to EEHC, it manages and operates
and maintains the transmission network across the country. It is the major off-taker and PPA party
of wind and solar power projects under the FIT scheme. EEHC also issues renewable power plans
tenders
• Egyptian Distribution Company (EDC): Owns 9 distribution companies that serve the residential
customers with electricity.
• Egyptian Electric Utility and Consumer Protection Regulatory Agency (EgyptERA): Oversees
regulation and supervision of the generation, transmission and distribution systems. EgyptERA
license to private entities, set electricity tariffs and sets the requirement for renewable energy FIT
programs
• New and Renewable Energy Authority (NREA): Public entity in charge of the operational
implementation of national renewable energy policies, through tenders and own capital
investments. NREA is also closely involved with EgyptERA in the application of the FIT, and with
EETC when it comes to the implementation of competitive bidding, and the development of solar
and wind projects with EPC tender schemes.
• General Authority for Investment and Free Zones (GAFI): Governmental authority to regulate and
facilitate investment. It is mainly involved with the FIT scheme.
As seen in Figure 25 the Egyptian power market follows a single buyer model, embodied in the EETC.
EEHC owns 90% of generation capabilities, while the private sector participates with 3 long term BOOT
contracts with PPA. The NREA procures small IPPs and wind farms. The single off-taker, EETC, is
licensed for VHV and HV electricity transmission, and sells the electrical energy to the distribution
companies. It also handles direct contract with about 100 consumers directly connected to the VHV and
-36-
HV networks [95]. However, Egypt’s plan of liberalizing the market are slowly changing its structure. In
that sense, the government plans to create a two-tiered electricity market. First tier will be competition
based, and concerns only HV customers, who will independently choose electricity generation suppliers
based on bilateral contracts and negotiated electricity prices. Second tier will be more regulated, and will
concern MV and LV customers, who will pay a regulated tariff for electricity, procuring from the distribution
companies supplied by a single Wholesale Public Trader [91].
Figure 25 Egypt power market structure
The above-mentioned transition and transformation of the Egyptian electricity market was initiated in 2015
with the passing of the “Electricity Law”, and the establishment of its regulations in 2016 [97]. The other
most noticeable regulatory and law text concerning renewable energy is the “Renewable Energy Law”
enacted end of 2014. This law establishes NREA as responsible for launching renewable projects EPC
tenders and operating them thereby, but also introduces 3 private : development schemes [99] [100] :
• Competitive bidding: EETC launches tenders to establish and operate renewable power generation
plant, with the investor agreeing on terms with EETC to sell the electricity produced. Project size
here is above 100 MW. As of 2018, 4 BOO tenders have already begun: in Kom Ombo with
200MW PV, West of Nile area with 200 MW PV, 250 MW wind and 100 MW CSP [101]
• Merchant or IPP scheme: Based on terms agreed with EETC, IPPs can use the distribution and
transmission network to enter into direct bilateral contract with private off-takers to sell sale
electricity generated from renewable sources. This is particularly useful for energy intensive
industries such a cement.
• Feed-in-Tariff (FIT): Here, pre-qualified investors may establish, own and operate renewable power
plants, with the produced electricity sold to ETTC, based on 25 years PPAs and in consideration
for a predeterminant tariff fixed for the term of the agreement. Large scale 20 MW to 50 MW
renewable energy projects are found under the FIT program. Roof top and small scale solar power
generation is also included in the FIT, for installed capacities not exceeding 500 kW. A net-metering
process was introduced by EgyptEra in 2013, encouraging distributed renewable power generation.
Circular No.1/2013 allows small scale projects to feed in electricity to the grid by discounting the
surplus from the balance through the net metering process.
Off-grid renewable projects, especially solar are not widespread in Egypt, but are highly encouraged by
EETC. When they do exist, they often rely on PV generation, thus lacking reliability and certainty because
of its intermittent nature. BESS are not common in Egypt, but storage considerations in general are expected
to become prevalent in the private sector with the gradual removal of fuel/electricity subsidies that will
make conventional generation less appealing and render renewable electricity generation cost competitive.
In that sense, the previously mentioned CSP tender includes a TES [100]. Concerning the FIT program, the
price signals relevant to foreign investors are as follow : Large scale projects mentioned above were entitled
to US$14.34/kWh, reduced to US$8.40/kWh, while wind projects of the same capacity were paid a tariff
between US$4.60/kWh and US$11.48/kWh, reduced to a tariff between US$4/kWh and US$7.96/kWh
depending on the maximum operating hours of the wind plant [100]. Even though prices are announced in
-37-
dollar USD, the renumeration is paid in EGP. The investor will have to bear a part of the foreign currency
risk according to the formula used. However, the FIT program will not see a third tariff phase and was
announced to an end in July 2017. Reasons for this include disputes with the government over financing
and arbitration locations [102] In addition, and following its desire to boost private participation in the
renewable shift of the country, Egypt’ Investment law of 2017 grants investment incentives to renewable
projects. Such financial relief consist of a 30% deduction of the net taxable profit for the first 7 years of
renewable electricity generation projects. Similarly, Egypt encourages foreign investor to establish renewable
projects there. To do so, they are required to set up a project company in Egypt without any shareholding
nationality requirements. Moreover, the government backs up the import of renewable energy equipment
and machinery required to the erection of plants, with a unified customs rate of 2% (5% is the typical rate)
[100].
Similarly to MorSEFF in Morocco, the Egypt Sustainable Energy Financing Facility (EgyptSEFF) is a credit
line dedicated to energy efficiency and renewable energy investments in Egypt. The credit line is developed
by the European Bank for Reconstruction and Development (EBRD) and is available to clients in Egypt
through the National Bank of Egypt (NBE). It offers loans and credit facility to the nation’s energy
conscious business community to develop their sustainable energy projects. The maximum loan amount is
USD 5 million with a repayment period of up to five years [103].
4.3.3 Industrial companies
Industry is the second largest electricity consumer in Egypt, after the residential sector. Metallurgy, cement
and mining are among the top electricity intensive sub-sectors. As mentioned before, electricity prices were
heavily subsidized in the past, but the country is effectively and gradually lifting the subsidies, which makes
electricity procurement more and more expensive, especially for industrial companies. Fossil fuels are also
concerned with the subsidy lift, which will represent an increased pressure on privates and entities relying
on diesel generators for their electricity supply. Similarly to 3.4 Table 10 was built.
Table 10 Identified industry companies, Egypt
Company Site
code Location DNI
Energy
consumed
(MWh)
Source
Lafarge Cement EC11 29.804432,32.089027
2315 979000
[50]
[104]
CEMEX (Assiut cement) EC21 27.170898,31.016183
2215 627000
Suez Cement
EC31 29.918392,31.533223
2015 165000
EC32 29.922371,31.288751
2015 374000
EC33 29.822361,31.308686
2015 330000
EC34 28.301581,30.746935
2215 33000
Sinai Cement company EC41 30.723626,33.774297
2515 363000
Arabian Cement Company EC51 29.796459,32.147373
2415 550000
-38-
Egyptian Iron and Steel Co EMe11
29.775674,31.315963 2015 272000
[104]
[77]
[78]
Misr National Steel EMe21
29.913912,32.448197 2115 51000
[104]
[77]
[78]
Kandil Steel EMe31
30.284382,31.793107 2115 34000
[104]
[77]
[78]
Ezz steel
EMe41 29.692945,32.319054
2215 170000 [104]
[77]
[78] EMe42 30.247967,31.740243
2115 85000
EgyptAluminum EM11 25.989233,32.331704
2215 4500000
[104]
[105]
Centamin EM²1 24.959837,34.712845 2415 282201 [106]
4.4 Jordan
4.4.1 Energy context
Jordan is considered to be a low-middle income country, with its population reaching 9,5 million capita at
the end of 2015. The country suffers a scarcity in natural resources, including water, fossil fuels and
commercial minerals. Historically, the nation has been almost entirely relying on oil imports from Iraq at
discounted rates, but the 2003 war on Iraq shook the Jordanian energy system, and the country had to
procure its energy elsewhere. Subsequently, Jordan put in place the National Energy Strategy which aimed
at securing energy access of the kingdom, by studying its domestic sources, both renewable and non-
renewable Indeed, the Kingdom has huge shale oil reserves among other resources that can be exploited,
such as uranium. In parallel of that development, Jordan signed an agreement with Egypt, which became
the natural gas supplier to the Kingdom, supplying the country with all the quantity it needs to produce
electricity and distribute it domestically at discounted prices. As a result, the period from 2003-2010 saw
Jordan relying again almost completely on an external source for its energy procurement, and not taking any
concrete step of the national energy strategy. However, as stated in 4.3.1, Egypt experienced a drastic
reduction in its natural gas production that started in 2010, which put again Jordan’s energy security at risk.
Jordan stopped importing from Egypt in 2014, but initiated again a short-term energy plan, with the
objectives of importing liquefied natural gas (LNG) for the period 2015-2025, but in the same time heavily
investing in renewables, targeting 7% in the primary energy mix in 2015, and 10% by 2020 [107] [108] [109].
As of 2016, the annual growth of primary energy demand was 7%, while electricity demand growth rate was
2,5%. The total electricity consumption amounted to 16843 GWh in 2016, with households representing
43% and the industry 24% [108] [107]. The total installed capacity was 4644 MW the same year, of which
544 MW are split between solar and wind. The peak load was 3250 MW to which renewable electricity
generation contributes to approximatively 5,44%. Figure 26 shows the breakdown of the existing and
upcoming renewable energy projects occurring in Jordan for the year 2016. The later are either wind or
solar, as the nation enjoys class quality solar and wind energy resources: the country lays in the sun-belt
zone, with 5-7 kWh/m²/day and 300 sunny days per year; while wind speeds reach up to 9m/s in the best
locations. Consequently, the National Energy Strategy aims at securing a 10% renewable capacity by 2020,
but the farms and parks outlined in Figure 26 will represent 22% of the total generation capacity, therefore
surpassing the initial target. This shows the strong commitment the country has to diversifying its energy
capabilities, but also showcases the interest and confidence foreign investors put into the country’s potential
-39-
and attractiveness [108]. In Figure 26, all solar projects are based on the PV technology, as it is the most
mature in Jordan for the time being. The government acknowledges that CSP and CPV need further
development and conducted together with the World Bank a feasibility study on CSP. The results indicated
that CSP can be viable in the Kingdom starting 2023, especially capitalizing on dispatchability advantages
[108].
Figure 26 RE projects in Jordan 2016 [108]
4.4.2 Electricity market
The Jordanian power sector is structured in a single buyer model (NEPCO), as seen in Figure 27. The main
stakeholders are [107]:
• Ministry of Energy and Mineral Resources (MEMR): The MEMR was established in 1984, it is
entrusted with administering and organizing the energy sector, so it achieves the national objectives.
In light of the restructuring process of the energy sector, the responsibilities of the Ministry were
amended to include the comprehensive planning process of the sector. They also set the general
plans and ensure implementation in a way that achieves the general objectives of the energy sector.
• National Electrical Power Company (NEPCO): has an independent financial and administrative
existence. It regulates the electricity sector in Jordan, with respect to power generation and
transmission.
• Electricity Regulatory Commission (ERC): ERC was established based on the Council of Ministers
decision issued on January 15, 2001. The ERC’s objective is to ensure the rights of consumers and
to resolve any complaints that may occur between the consumer and Electricity companies
• National Research Energy Center (NERC): was established in Amman - Jordan for the purposes
of research, development and training in the fields of new and renewable energy. This research
center is considered a specialized science and technological center working under the umbrella of
the Higher Council for Science and Technology.
• Generation companies:
o Central Electricity Generating Company (Cegco), 40% state-owned, nominal capacity
~1,669MW
o o Samra Electric Power Generation Company (Sepgco), state-owned company, nominal
capacity ~880MW
o o Jordan has currently 4 IPPs
• Distribution companies:
o Jordan Electric Power Company -Middle areas- (Jepco) private company under concession
agreement
o o Electricity Distribution Company -Southern and Eastern areas- (Edco), state-owned
company
o o Irbid District Electricity Company (Ideco) -Northern areas- state-owned company
-40-
Figure 27 Jordan's electricity market [110]
Concerning the regulatory framework surround the energy shift in the country, Jordan has produced several
text laws applicable to renewable energy projects. The main ones are:
• Law No. 13 of 2012, Renewable Energy & Energy Efficiency Law: Sets up a Renewable Energy
and Energy Efficiency Fund, and regulated project development. The law establishes two different
business models for large-scale RE generation facilities (above 5MW):
o Direct proposals: On a BOO basis, investors can freely choose the site and propose the
project to MEMR, following ERC’s Direct Proposal guidelines. The proposals must not
exceed the price ceiling set by the ERC (Wind: 85 Fils/kWh; CSP: 135 Fils/kWh, PV: 120
Fils/kWh, with a 15% increase for plants with Jordanian origin). MEMER approves or not
the project upon consideration
o Government tenders(EPC): Nepco and MEMR tender projects together in pre-selected
sites that are part of a Land-use-list. The awarded projects have a PPA agreement with
Nepco.
• By-Law No. 10 of 2013, tax exemption for RE and EE, whereby RE equipment is exempted from
custom duties and sales tax (incl. products needed for manufacture, spare and wear parts, and
measurement)
• Regulations 3579 and 3583 on transmission for RE: Sets the framework for renewable electricity
transmission. The costs of connection to the grid are bared by the TSO Nepco, but are refunded
later by project developers. Although there is no direct priority dispatch in the grid code, Nepco
has the obligation to purchase all the power produced.
As stated previously, NEPCO is the single buyer of all the electricity produced, under any regime. Therefore,
power projects are not allowed to sell the energy produced directly to large consumers. Nevertheless, net-
metering and wheeling schemes are present for small and medium scale projects. The latter should not
exceed a total installed capacity of 5MW. In practice, these options were introduced by the Renewable
Energy & Efficiency law that allows electricity consumers who operate renewable systems (mostly PV in
Jordan) to self-consume, but also receive energy credits for any excess electricity their system generates
within a billing period. The difference between the self-consumption and self-production fed to the grid is
credited to a later time, where there is not enough production from the renewable system [111].
The large deployment of renewable the Kingdom of Jordan envisions will put a lot of pressure of the existing
transmission grid for two reasons. First because the renewable potential of the country is located in the
south, while loads are in the northern regions. Thus, the country plans for grid extension corridors (400KV).
Second, because of the intermittent nature of solar and wind. To this matter, MEMER is actively
-41-
investigating storage solutions to ensure grid stability, and store re excess production for later use. For
example, MEMER has announced a 30 MW with two hours storage system, followed by a 70 MW projects
with up to 4 hours. These plants will not be coupled with wind or PV as the goal is to test and see how they
can stabilize the network. Ultimately, they will serve a ramp up control solutions to both solar PV and wind,
enable energy shift of curtailed renewable energy. Battery storage, together with pump hydro and CSP with
TES are all being considered, assessing their potential to decide in the future on which is the most beneficial
and cost competitive [108]. In order for Jordan to boost international private investment and participation
in the renewable projects of the country, the Renewable Energy & Energy Efficiency law stipulates that all
RE equipment and systems will be exempted from customs and sales taxes. Even more, sales of energy
from RE systems are not taxable for the first 10 years of the project [112].
4.4.3 Industrial companies
Industries in Jordan rely heavily on the national grid for their electricity procurement, as self-generation is
rare, and is mostly used as backup, instead of primary electricity source. Industry represents about 25% of
the total electricity consumption in Jordan, with a average growth rate of 4,14%. Regarding firms that have
self-generation capacity, most of them use either diesel reciprocating generators, or steam generators that
use fuel oil or coal. The later are more present as they can be utilized for co-generation as well. Hence,
renewable energy penetration in the industrial sector is still slow, with only few projects coming up by
selected companies: the Arab Pothash company plans for a 33 MW PV plant. Jordanian industries can
largely benefit from renewables, as they will secure their electricity generation cost, and not be subject to
fluctuating utility rates linked to the volatile oil price [113] .
Up until 2012, the average electricity selling price was below generation and transmission cost, with NEPCO
having to cover the difference in price. This situation led to a non-negligible deficit of JD 2,3 billion, which
pushed the MEMER to propose a plan that foresees the adjustment of electricity tariffs, aiming that by
2017, NEPCO would be able to cover all its cost [111]. However, prices stay closely linked to the average
market oil price [114]. Similarly, to 3.4, Table 11Table 10 was built.
Table 11 Identified industry companies, Jordan
Company Site code
Location DNI Energy
consumed
Sourc
e
Jordan Cement Company JC11 31.998069,35.781927 2215 8700
[115]
[116]
JC12 30.678506,35.631543 2515 98600
Al-Hadeetha Cement Company
JC21 - 2315 100000
Al-Rajihi Cement Company JC31 32.214893,36.202212 2415 144100
Quatrana Cement Company JC41 31.333889,36.129281 2415 74700
JC42 31.184202,36.073883 2515 74700
El-Hasa Phosphate JM11 31.184202,36.073883 2515 39400
Sheidiyah Phosphate JM21 29.912817,36.183768 2715 33300
Potash Co. JM31 31.042041, 35.488194 2015 165550
JM32 29.537524, 35.006558 2515 165550
Fertilizer Company JCh1
1 29.529386, 35.007802 2315 47600
Indo-Jordan Chemicals Company
JCh21
29.912817,36.183768 2715 26100
Indo-Jordan Fertilizer Company
JCh31
29.912817,36.183768 2715 61300
Jordan Petroleum Refinery Company
JCh41
31.720732, 35.993288 2415 105300
-42-
4.5 Saudi Arabia
4.5.1 Energy context
Saudi Arabia has a tremendous amount of natural energy sources, both renewable and non-renewable. The
country is one of the world’s largest producers of oil, and with an estimated 267 BBO proved reserves it’s
also the country owning the largest oil resource all over the globe. Despite that, Saudi Arabia is facing an
energy crisis, as the increasingly growing pressure of energy demand risks to put the economy of the country
at risk, by halting its oil exports in the future. In fact, following present growth trends, domestic energy
consumption could reach 9,3 MBOE/day by 2028, which will force oil exports to stop, thus weakening the
country’s main revenue source. Two reasons fuel the rapid growth of energy demand in Saudi Arabia: a
rising population, with an expected 30 % rise between 2010 to 2030, greater than China’s expected increase
of 7%, or even India’s with 23%. Second reason can be seen as the effect of the very low and cheap energy
prices prevalent in Saudi Arabia, which attracts large investment in energy intensive industries, together with
wasteful consumption due to extremely low electricity rates. The later do not exceed 0,07 USD7kWh all
sectors included, with industry caped at 0,032 USD/kWh, regardless of the electricity consumption rate
[25]. The extreme aridity of the region contributes to the country’s high energy intensity, as it has an
enormous desalination program that processes more than 3,5 million cubic meters of seawater per day.
Parallel t being one of the world’s largest oil producers, Saudi Arabia is also a large oil consumer, ranking as
5th in 2016, with a consumption rate of 3,2 million BBL/day, of which a large proportion is used in power
plants. The electricity system of the KSA is the largest by capacity in the Arab world, with total installed
capabilities of nearly 70 GW, and a peak load of 54 GW in 2013. Electricity is supplied mainly by gas turbines
(40%), followed by steam turbines (32%) and coined cycles. All power plants are fuelled either by the
country’s own natural gas production, or petroleum products [25]. The sectorial distribution of electricity
demand is 51%, 13%, 13%, 19% and 4% for residential, governmental, commercial, industrial and other
sectors respectively. The industrial sector is experiencing an aggressive development, which results in a
growing electricity demand [117]. Due to its geographical location and climate, Saudi Arabia joys from
perfect renewable energy sources. Solar insolation can reach 28000 kWh/m²/year, with 3000 hours of
sunshine per year with clear skies. Moreover, there are two vast wind regions in the kingdom, along the
Arabian Gulf and the Red Sea coastlines, where average annual speeds can exceed 6 m/s. Acknowledging
this untapped potential, the government established King Abdullah City of Atomic and Renewable Energy
(KACARE), that seeks to utilize the country’s indigenous energy resources. Its strategic plan concerning
renewables is to have 72 GW installed capacity of renewable energy by 2032, with solar PV (16 GW), solar
CSP (25 GW), wind (9GW), nuclear (17,6 GW), waste-to-energy (3GW) and geothermal (1GW). The
expected evolution in the installed capacity can be seen in Figure 28. It was announced in 2015 that these
targets will be pushed back to 2040 [117].
Figure 28 Long-term renewable energy targets, Saudi Arabia [117]
-43-
4.5.2 Electricity market
The Ministry of Energy, Industry and Mineral Resources (MOEIMR) is the governmental entity handling
all policy planning in the power sector. The latter is organized around the Saudi Electricity Company (SEC),
as seen in Figure 29. SEC is a government owned entity that owns most of the generation facilities of the
country, with a generation capacity of 68 GW in 2015 (4 generation subsidiaries). It is also responsible for
the transmission (NGSA) and distribution (DISTCO). Besides, the other main players in the Saudi Arabia
power market are [25] :
• Saudi Armaco: Government entity in charge of Saudi Arabia’s oil and gas production. Alongside
SEC, it manages also power generation.
• Saline Water Conversion Corporation (SWCC): is a government corporation that
operates desalinization plants and power stations in Saudi Arabia. It is the second largest electrical
provider in the country.
• Electricity and Co-Generation Regulatory Authority (ECRA): Independent regulatory entity for
Saudi’s Arabia energy sector.
• King Abdullah City of Atomic and Renewable Energy (KACARE): A 2010 royal decree established
KACARE, with the tasks of focusing on nuclear and renewable energy, as well as technology
localization for renewables.
• Power and Water Utility Company (MARAFIQ): Government owned company that services most
of the electricity to the two industrial cities in the KSA: Jubail and Yanbu.
• Sustainable Energy Procurement Company (SEPC): a separate standalone government-guaranteed
entity, responsible for administering the procurement and executing and managing the power
purchase agreements (PPA).
Figure 29 Power market structure, Saudi Arabia [25]
The ECRA launched in 2011 a plan to unbundle the vertically integrated electricity market, to promote a
competitive environment for private investments in the future. Called the “Development of the Electricity
Industry Restructuring Plan”, it will help create an independent transmission company that will guarantee
unselective access to the grid for all producers and large consumers. It will also establish competition in
distribution, by creating several local distribution companies. A “Principle Buyer” entity will also be created
in order to manage the electricity industry income, independently of generation and transmission
management [25].
When it comes to renewables, and the regulatory framework setting the business models for projects, Saudi
Arabia has the following [118]:
-44-
• KACARE program (IPP tenders): main procurement way to reach the national RE penetration
targets. KACARE issued in 2013 a white paper detailing the approach of bids and tenders to
projects developers, structured around 3 rounds, the first one (overall capacity of 500-800 MW)
will be located in pre-packaged sites, while the subsequent ones will not be bound to a specific
location, and will cover a capacity up to 7000 MW. The negotiated PPAs will be payed in Saudi
Riyals, but currency adjustments to cover exchange rate (US-KSA) is considered, but not regulated
in detail. Although not a prerequisite in the first round, local content and localization plays a major
role in the national RE program. In the first round, bidders with high content localization will be
more advantageous. Comparatively, developers who use less Saudi national equipment may be
subject to a fine and may be not eligible for future rounds. The minim set by KACARE is 25%
local content. Developers bear the cost of connection to the grid, while the TSO eventually
upgrades the grid beyond existing connection point
• IPPs selling to large consumers: Saudi Arabia’s law concerning bilateral contract between SEC,
IPPs and large electricity consumers does not distinguish between renewable and conventional
generation. It is practically difficult to establish projects of that nature because they are outside
KACARE’s scope of operation
• Self-production: The Saudi Arabian Electricity Law allows self-production. Moreover, a net-
metering scheme set to come in force starting July 2018 will foster private investment in renewable
energy. Targeted at small scale renewable energy systems (< 2MW), it will allow prosumers to
operate their own generation systems and export the unused excess to the national grid with a cash
payment [119].
The high localization content the Kingdom is aiming for can be a challenge for foreign investors wishing to
develop renewable projects in Saudi Arabia. The country has strong incentives and detailed programs to
localize the electricity industries, such a preferential price, priority over foreign companies… In that sense,
foreign investments are regulated, with the Saudi Arabia Investment Authority (SAGIA) facilitating the
investment process for non-Saudi investors. Beyond that, the extreme low electricity prices prevalent in
Saudi Arabia can represent a hurdle to enter the market. As mentioned previously, electricity prices are
heavily subsidized. The wealth redistribution resulting from the country’s oil natural resources make
electricity generation extremely cheap, among the lowest in the world. Still, aware of the burden this situation
will have on the energy demand in the future, the KSA announced late 2017 that electricity tariffs will
increase in the beginning of 2018. This move comes together with the country’s ambition to rationalize
renewable energy investment and make them cost-competitive with attractive payback period for financers
[120].
4.5.3 Industrial companies
A large chunk of the country’s electricity consumption is dedicated to the residential sector, and AC systems
in building as a result of the hot temperatures. Historically, electricity prices were really cheap, all sectors
included with 0,08 USD7kWh while industry was capped at 0,048 USD/kWh. However, as stated above,
the KSA is changing is tariff strategy to lessen stress on future energy demand and promote competitiveness
of renewable technologies. The announced rise in price can be seen in Table 12. Similarly to 3.4, Table 13
was built.
Table 12 Electricity rate, Saudi Arabia [121] [120]
Prior to 2018 (Halala/kWh) 2018 (Halala/kWh)
Commercial
1-4000 kWh/month 10 16
40001-8000 kWh/month 18 24
>8000 kWh/month 24 30
Industrial All brackets ¨12 18
-45-
Table 13 Identified industry companies, Saudi Arabia
Company Site
code Location DNI
Energy
consumed
(MWh)
Source
MAADEN
SM11 31.502471,39.922940 2315 153300 [65]
[122]
SM12 31.502471,39.922941 2315 164250
SM13 23.781456,45.073226 2315 44203
[123]
[122]
SM14 24.983122,41.598602 2315 44203
SM15 23.501225,40.866544 2415 44203
SM16 19.981040,42.012451 2415 44203
Industrial Minerals - Al
Ghazalah Mine SM21 27.252438,40.500585 2015 240000
[122]
[67] Al Masane Al Kobra Mining
Company SM31 18.135165,43.859965 2315 40500
Tabuk Cement Company
(TCC) SC11 27.531459,35.538953 2715 143000
[122]
[50]
Al Jouf Cement Company
(JCC) SC21 31.414052,38.683258 2515 187000
Yanbu Cement Company
(YCC) SC31 24.270600,37.561681 2315 154000
Al Safwa Cement Company SC41 22.553124,39.435419 2115 220000
Qassim Cement Company
(QCC) SC51 26.491166,43.962721 2115 220000
Southern Province Cement
Company (SPCC) SC61 19.519343,42.538318 2115 198000
4.6 Country comparison
RE business models
-46-
Table 15 presents a comparison of the business models applicable to renewable electricity generation in the
selected markets. Small scale generation is generally not much developed, with all 5 countries heavily
focusing on large scale project development schemes, by setting up detailed and concise project guidelines
for establishing tenders and PPAs. Moreover, and although all the markets considered rely on a single buyer
model for the electricity, most often embodied by the TSO, unbundling the power market and liberalizing
electricity sales are underway. Morocco, Egypt and Saudi Arabia make it possible to offset the TSO to sell
electricity directly to large consumers. Those PPAs agreements are not completely unrelated to the TSO
though, as it is through its grid that electricity is transmitted. Jordan and Tunisia still lack this ability. This is
an important consideration for a smalls scale CSP system with TES like Cleanergy’s, as the company should
promote its technology to local developers and showcase that there is a market need (large industrials with
high energy intensity), and that the framework in place allows said developers to sell electricity directly to
those customers. There is a strategic added value Cleanergy can offer to developers with its technology,
namely cost-competitive on demand electricity generation. Similarly, all 5 countries allow privates to self-
produce from renewables for their own energy procurement. Net metering options are offered in all of
them, with variation depending on the project size. This last treat is also desirable as it will facilitate market
entry for Cleanergy, in the sense that the company can go directly contact selected industrial firms and
propose them cost-competitive offers to secure their energy access with no grid or state dependence, while
in the case of surplus production, inject it to the grid and gain a premium.
Business environment
The business environment of a market can play a pivotal role in the failure or success of a new product
since external factors (to the company, i.e., Cleanergy) such as political stability, security of investment,
competitive landscape, currency rate fluctuations… can hinder its activities To that purpose, three
international indexes help analyse these particular factors. First, the World Bank Ease of Doing Business
Index, the BTI status score and the Global competitiveness index, as seen in Table 14.
Table 14 Countries Performance under International Indices [124] [125] [126]
Morocco Tunisia Egypt Jordan Saudi Arabia
World Bank Ease of Doing Business 69 88 128 103 92
BTI Status score 4,61 6,27 4,28 5,22 4,27
Global competitiveness Index 71 95 100 65 30
Table 15 Business model country comparison
Morocco Tunisia Egypt Jordan
Saudi
Arabia
IPP selling
to single
buyer
IPPs can sell to
ONE with
PPAs.
For projects
under the wind
and solar plan,
the PPA is
granted as a
result of a tender
process referring
Permissible for
large scale
projects under
the concession
scheme (tender
process),
through BOO
and 20 years
PPAs with
STEG
Competitive
bidding
organized by
EETC,
responsible for
the PPAs
Government
tenders (EPC):
in pre-selected
sites. PPA
signed with
Nepco
Direct
proposals:
BOO basis, on
freely selected
locations
KACARE
white papers
details
several
tendering
rounds for
IPPs. The
PPA is
signed with
SEPC
-47-
to pre-selected
sites
IPP selling
to large
consumers
Law 13-09
allows RE IPPs
to sell to large
consumers or a
group thereof.
Regulatory
development for
access to
medium voltage
is currently in
progress and will
make this option
easier to
implement.
No, STEG is
the single buyer
Merchant IPP
scheme with
bilateral
contracts
between IPP,
large consumer
and EETC (for
grid use)
No, Nepco is
the single
buyer
According
to the Saudi
Arabian
Electricity
Law, IPPs
can sell to
large
consumers
Self-
production
Current
regulation allows
self-production,
20 % of annual
production can
be sold to the
grid
Permissible and
subject to
authorization.
The surplus can
be sold to
STEG (up to
30% for large
and medium
scale)
Egypt’s
electricity law
allows private
production of
electricity for
self-
consumption
and third party
sales
Existing
regulation
allows energy
consumers to
produce their
own electricity
and to use the
transmission
grid.
Self-
production
is allowed,
with no
distinction
for
renewables
Support
mechanism
medium-
small scale
RE
Medium and low
voltage RE
integration
underway for
IPPs
Net metering
scheme, with no
monetary
transfer, only
energy flows
between billing
periods
FIT
mechanism
that was
stopped
middle 2017.
Only net
metering still
in place
Net metering
and wheeling
projects (<5
MW)
Net
metering
scheme to
begin in
2018 for
capacities
under 2 MW
First, the Ease of Doing Business index outlines the ability of establishing and operating a commercial
enterprise (taxes, bureaucracy, construction permits…). This index ranks economies from 1 to 190 [124].
Among the countries vetted, Morocco ranks first, followed by Tunisia, Saudi Arabia, Jordan and finally
Egypt. Although Egypt is last, the country experiences every year small upgrade (ranked 131 in 2016).
Second, the Bertelsmann Stiftung Transformation (BTI) Status Index (1-10) which assesses political
participation, social integration, stability of institutions, organization of the market and competition [125].
Political and economical transformations are both investigated by this index. Within the countries analysed,
-48-
it puts Tunisia on the forefront with its stabilizing political landscape and healthier economic state. Jordan
ranks second, followed by Morocco, Egypt and finally Saudi Arabia.
Third is the Global Competitiveness Index. Developed by the World Economic Forum, it looks into the
factors determining a country’s level of productivity. The latter include macroeconomic environment,
institutions infrastructure, labour market…. This index ranks 137 economies, and puts Saudi Arabia first
among the countries investigated, followed by Jordan, Morocco, Tunisia and finally Egypt.
Private sector participation
Private participation in renewable projects investments is a mandatory criterion to quantify in order to
decide on which market to target first. For a foreign company like Cleanergy, countries that are able to
promote and attract private investments and contribution to the renewable energy sector should be the
priority. Previous sections somewhat detailed how each of the countries deal with foreign and private
investment, in the light of policies in place, financial facilities, etc. The efficiency of such governmental
decisions are shown in Figure 30, where the increase of private investment in RE projects between 2013
and 2016 is shown. Morocco was the only country with private actors in 2013 and is the one with the highest
increase in private financing, achieving nearly 18% growth. The strong regulatory framework backing up
IPPs, and the ongoing liberalization of the electricity market play a significant role in catalysing private
investments. Jordan follows next, as it experienced a 10% increase. Although the country still relies on a
single buyer model for large scale electricity generation plants, the small-medium scale schemes (net
metering, wheeling) push forward the participation of the private sector. The last three countries, Egypt,
Tunisia and Saudi Arabia respectively didn’t achieve more than 2% increase. For Egypt, this can be explained
by the halt the FIT tariff program saw as a result of payments delays at the end of its first round, and
payments disputes with the government. As a result, a general unwillingness to invest rose in the country.
Finally, Saudi Arabia ranks last with its difficult private financing environment, favouriting local Saudi
companies over international ones.
Figure 30 RE private Investment Increase (2013-2016) [37]
Country rank
The final score of each country is given in Table 16, and will serve later for the MCA.
Table 16 Country score ranking
Morocco Tunisia Egypt Jordan Saudi Arabia
RE Business Models 8 6 8 6 6,5
Business environment 7 8 5 7 6
Private participation 9 4 5 8 3
Final score 8 6 6,5 6,75 5,5
0,00%
5,00%
10,00%
15,00%
20,00%
Morocco Jordan Egypt Tunisia Saudi arabia
2013 2016
-49-
5 Comparison/Analysis
5.1 Optimum configurations
The results of the techno-economic optimization can be seen in Appendix 3Appendix 3. As a reminder the
optimization searches for the lowest LCOE achievable by varying several parameters: installed capacity,
mirror area and storage hours. Appendix 3 presents for each identified site, the optimal technical parameters
of a fictive power plant that should service the energy need of said site, at the lowest LCOE possible. LCOE
numbers are given in normalized values. Although the storage size was varied during the optimization from
10 to 14 hours, only a handful of configurations have 14 hours as optimum, while the initial guess would be
the larger the storage, the higher the energy production, thus the lower LCOE. As a matter of fact, most
locations have 10 to 11 hours TES. When plotting all possible values of the LCOE with regards to the TES
size for all sites as shown in Figure 31, the optimal conditions for lowering the LCOE can be deducted. As
it can be seen, the lowest LCOE point is achieved for 14h of storage in the case of high DNI, eg. 2715
kWh/m²/year. The more the DNI decreases, the farther on the left that point gradually shifts, from the
maximal storage capacity to the lowest (13h for 2515 and 2415 kWh/m²/year, 12h for 2315kWh/m²/year
and so on). Below 2215 kWh/m²/year, the lowest LCOE is always obtained for a 10h TES. Hence, the
benefits of having a larger storage system, in terms of yearly extra power output, are not capable to offset
the capital investment of such a large TES, solely due to inadequate DNI conditions, and it is more
economically viable to opt for smaller storage hours. For such cases, hybridization with PV can represent
an alternative, and shortcut the high electricity cost by having the PV system produce during the day, and
the CSP-TES system acting as a storage for the PV and generating power during the night. Conversely,
high DNI locations allow to recuperate high storage capital, which contributes in lowering the LCOE.
Figure 31 LCOE vs TES size (Cleanergy’s cost functions)
Furthering that idea, the same plot as in Figure 31 is shown Figure 32, using the cost data from the STEALS
report instead of Cleanergy’s, as given in 3.3. The corresponding optimal configurations for that case are
found in Appendix 4. As it can be seen in this case, only a TES of 14 hours is sorted out as optimum, since
the higher the storage, the lower the LCOE. Obviously, having a lower capital investment will give lower
generation costs, but also makes the use of a large TES rational and profitable, regardless of the DNI
1
1,05
1,1
1,15
1,2
1,25
1,3
1,35
1,4
1,45
8 9 10 11 12 13 14 15 16
No
rmal
ized
LC
OE
TES size (hours)
1915
2015
2115
2215
2315
2415
2515
2715
......
Trend lines
-50-
condition, and leverages on the benefits of large storage hours to produce more electricity and lower its
generation cost.
Figure 32 LCOE vs TES size (STEALS cost data)
The data from Appendix 3 can be rearranged to form a map that gives valuable insight on where Cleanergy
should position itself in a chosen country, or the MENA as a whole. Figure 33 is a chart with such purpose,
that positions each site in Morocco (bubble) with regards to its target LCOE (y axis), energy consumed (x
axis) and capacity installed (size of the bubble), while the colour indicates the industry type. It is then a way
to identify which sites or industrial companies to target first when entering the market, but also which type
of industry holds the highest potential in terms of size. Obviously, the sites positioned on the far right of
the chart represent the most profitable cases to Cleanergy, since they have the largest energy consumption,
hence the largest capacities to install. For example, the site indexed MM11 (owned by OCP) consumes
annually 501,4 GWh. By installing a 85 MW park with 10 hours storage nearby its location, OCP will be
able to procure its electricity at a normalized LCOE of 0.66. Comparatively, a smaller consumption site such
as MC22 with 11GWh can be serviced with a smaller park size of 2 MW and 10 hours TES, but not
necessarily lower LCOE, as in that case, the normalized cost of electricity generation is 0.98. This is mainly
due to the weather conditions of each site, and their respective DNIs : the higher, the better business case,
independently of the system size. However, it may be more strategic to target first companies and sites with
low LCOE values, such as MM31 (Maya Gold&Silver), since it will be easier for Cleanergy to prove its
storage solution’s added value. This is an important consideration to make, as for any new product that is
unknown to a market, there might be relatively high resistance and lack of confidence in its attributes, which
will hinder its adoption by players in said market. The way to-go should be then to target the companies
whit the most strategically profitable business case (lowest LCOE) to quickly attract them. Those first
customers might be small in terms of market share, but once few players adopt the new technology, it will
be easier for Cleanergy to engage with more prominent companies, that might be more traditional in their
way of doing business. In fact, Cleanergy should capitalize on the competitiveness of its target LCOEs and
stress out the benefits of on-demand electricity production, especially with the small awareness there is
about CSP in Morocco, or in the MENA in general when it comes to small/medium scales.
1
1,1
1,2
1,3
1,4
1,5
1,6
8 9 10 11 12 13 14 15 16
No
rmal
ized
LC
OE
TES size (hours)
1915
2015
2115
2215
2315
2415
2515
2715
......
Trend lines
-51-
Figure 33 Site positioning map, Morocco
The positioning map regrouping all five countries representative of the MENA is given in Appendix 5. For better clarity, the same map is shown Figure 34, but positions this time the companies, instead of their individual sites. The chart axes remain the same but refer hereafter to the average values of all sites within a company, while the bubble colour refer to the country. Along the companies and countries identified, it appears that Egypt has the highest potential in terms of market size, especially within its mining companies. This last observation holds for most of the countries, as all bubbles to the right, all countries included, belong either to mining or cement companies. These two segments are the most energy intensive, with round the clock load profiles that can greatly benefit from a dispatchable CSP system. Mines are often isolated or have weak grid access which makes Cleanergy’s offer the more appealing, as it can represent a better alternative than diesel generators that these can of companies tend to use on their sites. As stated for Morocco before, locations with high DNI tend to have low LCOE values, which is the case for Jordan, where the cheapest electricity generation cost is achieved for the mining company Sheidiyah Phosphate. A 4 MW plant with 14 hours of storage can be built in one of its sites, with close to 2700 kWh/m²/year DNI and produce enough electricity to cover all its energy needs with a normalized LCOE of 0.01. Likewise, the Saudi Tabuk Cement Company can be proposed a 18 MW park with 14 hours of storage at the targeted price of 0.01 in one of its facilities located in similar weather conditions (around 2800 kWh/m²/year).
MM11MM12
MM21
MM22
MM23
MM24
MM25
MM31
MM41
MC11 MC12
MC13
MC21
MC22
MC23
MC24
MC31
MC32
MMe11MA11
MA21
1,05
1,1
1,15
1,2
1,25
1,3
1,35
1,4
0 100000 200000 300000 400000 500000 600000
No
rmal
ized
LC
OE
Energy consummed (MWh)
Mining
Cement
Mettalurgy
Agriculture
-52-
Figure 34 company positioning map MENA (Industry)
5.2 Potential/Serviceable achievable market
5.2.1 Results
A total of 58 industrial companies was investigated in this research work (11 for Morocco, 11 for Tunisia,
15 for Egypt, 12 for Jordan and 9 for Saudi Arabia), each broken down to their respective sites, which lead
to 76 different sites. These companies belong to one of the following industrial segments : mining, cement,
metallurgy, chemical and agriculture, which are the biggest industrial sub-sector in each country Table 17
shows the market size for a technology such as Cleanergy’s with regards to the 58 customers identified,
together with the respective optimum configuration found for the best business case among all sites considered
in each country, including its target LCOE and industry type. A total market potential of 2672 MW is
estimated, with Egypt and Saudi Arabia in the forefront. It would be possible to reach LCOE values below
Cleanergy’s target for each of the most promising sites in each country (expect Tunisia), thus making such
a distributed CSP technology highly competitive.
Table 17 Market potential for the MENA (industry), with optimum configuration
Market
size
(MW)
Most competitive business case (optimum configuration and industry type)
Index Normalized
LCOE
DNI
(kWh/m²/year)
Park
Size
(MW)
TES
(hours)
Mirror
Area
(m²)
CF
(%)
Industry
type
Morocco 403 MM31 1,07 2425 2 13 220 84% Mining
Tunisia 94 TChe11 1,27 2000 2,3 10 220 65% Chemical
Egypt 1400 EC41 1,07 2415 50 13 220 85% Cement
-53-
Jordan 165 JM21 1 2680 40 14 220 92% Mining
Saudi
Arabia 610 SC11 1,01 2700 17 14 220 92% Cement
While this approach gives an initial idea about the market size in the MENA for the identified countries, it
can be argued that it is not complete since it is limited to the 58 companies vetted. The real potential is
bigger as there are several other companies (both in the industrial segments considered and other smaller
ones) that are part of the industry and the economy of those markets, hence unaccounted for by this method.
This inaccuracy can be explained by a lack of data, meaning that the analysis considered only companies
that had enough data (namely energy consumption estimates, site location) to run the tecno-economical
model, and disregarded all others for which those information were unavailable. Companies that publish
annual reports and reference their production values and site locations are most often established industrials,
with big enough market share to be representative of their representative segments. Nonetheless, and as a
result of this method, the market size obtained in each country represents a fraction of the real potential,
which renders the country comparison somewhat useless: under this approach, Egypt may seem having the
lion share in terms of market share, followed by Saudi Arabia, but in reality, it can be the opposite when
accounting for all industrial companies in both. As a matter of fact, Saudi Arabia is likely to have the lion’s
share of the real SAM, because of the cheap electricity prices in the Kingdom that push for bad consumption
behaviours, energy waste, inefficient industrial processes… This is particularly relevant, as Saudi Arabia’s
electric consumption per capita is 9 times greater than Morocco’s, showcasing a fairly different energy use
culture in those two countries [127]. Hence, the companies that were investigated represent potential
customers that can populate the SOM area of Cleanergy in the future, while the market potential estimated
based on them (2672 MW) represents a lower bound of that space.
A possible method to quantify the SAM in those 5 countries would be to study the total electricity
consumption of the industrial sector as a whole in each and find the optimum technical configuration to
service that electricity need, assuming an average DNI for the whole country. This method is not as precise
as previously, especially since it relies on input parameters not completely accurate (the DNI in Egypt varies
between 2000 kWh/m²/year and 3000 kWh/m²/year, and assuming a 2500 kWh/m²/year for all possible
locations favours some over the other) but is sufficient in providing a first-hand quantification of the market
opportunity present in each country’s industrial sector. The results of such approach are given in Table 18,
where the market size shown correspond to the needed capacity of Cleanergy’s technology to produce the
indicated electricity consumption, with the average DNIs shown for each country. A total SAM of 16,2 GW
is estimated in this approach, with Saudi Arabia being the largest market (8 GW), followed by Egypt (6 GW)
as suspected before. Morocco is third with 1,2 GW, followed closely by Tunisia (0,86 GW). Jordan on the
other hand is the smallest market, with a 205 MW market size. These SAM values consider all kind of
industry types, including ones that do not necessarily need a continuous electricity supply, or that would be
interested in a storage solution. The previous method however accounted for industry segments and actors
that could have a benefit from adoption a small scale CSP system with TES. As such, both methods are
complementary, since the second one compares the countries in terms of total electricity need in the
industrial sector, while the first one targets individual companies and sorts out the most promising ones in
each market.
Table 18 SAM in the MENA, industry (grid connected, VHV-HV-MV)
Industry
electricity
consumption
(GWh)
Average DNI
(kWh/m²/year) SAM (MW) Source
Morocco 6972 2115 1200 [64]
-54-
Tunisia 4915 2015 860 [128]
Egypt 38310 2315 6000 [129]
Jordan 1383 2515 205 [115]
Saudi Arabia 56240 2515 8000 [117]
The market size embodied in the SAM figures represents how much Cleanergy would have to install of its
technology in order to supply all the electricity needed in the industrial sector of each of the 5 countries. As
a result, to scale up the SAM in the MENA region, these figures are plotted against the industry added value
in the GDP of each country [39]. The resulting graph Figure 35 shows how that the higher the contribution
of the industrial sector as a whole to a country’s economy, the more likely it will be energy intensive, hence
results in a higher SAM. A polynomial trendline was used to interpolate the 5 set of points to get the
respective SAM sizes of each MENA country.
Figure 35 Scaling up the SAM to the MENA
The results of such approach can be seen in Figure 36. As a whole, the MENA region has a market potential
in the industrial sector of approximatively 47,2 GW. Saudi Arabia leads the way naturally, followed by Iran,
Egypt, the Emirates and Iraq. Interestingly, 4 countries of the latter are OPEC members, having their own
fossil fuel resources. Egypt, up until 2013 was an important gas exporter in the region, which explains its
presence in the top 5 countries. As stated previously in the case of Saudi Arabia, the abundance of fossil
fuel assets pushes for an inefficient energy use, which drives the electricity consumption up. Even more,
electricity prices in these countries tend to be very low, which represents a barrier for Cleanergy, as it will
be in direct competition with grid connected solutions since most often, industrial are grid connected and
care only about the price paid. In brief, even if large energy intensive countries represent have the biggest
market size for Cleanergy, there is a trade-off to make considering energy prices in said markets.
The small difference in numbers between Table 18 and Figure 36 for Saudi Arabia, Egypt, Morocco, Jordan
and Tunisia is a result of the interpolation method used.
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
0 100 200 300 400 500 600 700 800 900
SAM
(M
W)
Industry added value in GDP (billion USD)
-55-
Figure 36 SAM in the MENA region by country
5.2.2 Sensitivity analysis
The general breakdown of the CAPEX of Cleanergy’s system is shown Figure 37. As it can be seen, the
solar field account for nearly 40% of the capital investment, followed by the thermal storage and the Stirling
engine unit. The repartition sown is for a 8 MW system with 10 hours of storage and 220m² mirror reflective
area, configuration determined to be the optimal for this plant located around 2000 kWh/m²/year DNI. As
discussed briefly earlier, few optimal configurations have 14 h of TES, showcasing that the added value in
terms of additional kWh produced cannot offset the high investment cost linked to the storage size. Also,
the cost data from the STEALS report (which are lower than Cleanergy’s) validate that, as all of the optimal
designs rely on a 14h TES: the extra energy produced thanks to higher storage capacity (compared to 10-13
hours) make the use of 14 TES economically viable, but also have an effect on another parameter of the
configurations, namely the system capacity. Indeed, having the maximum TES size makes the system smaller
in terms of MW to install, compared with a site with the exact same DNI resource, electricity consumption,
but smaller TES (10h to 13 hours). As a reminder, the optimal configurations are determined by having as
an input the DNI and electricity consumption of a particular site, and by varying TES size and the mirror
area.
Figure 37 Cleanergy's CAPEX breakdown
Furthermore, this can be seen when exanimating the SAM with the STEALS cost data in Table 19, where
although the previously identified optimal configurations in Table 17 stayed the same, but the market size
in each country, is lower, thus impacting the total potential, that decreases from 2672 MW to 2430 MW.
045
178322346
508610
143714551487
29533045
38054341
53515925
74117984
PalestineSyria
YemenLebanon
JordanBahrainTunisia
LibyaIsrael
OmanMorocco
QuatarKoweitAlgeria
IrakEmirates
EgyptIran
Saudi Arabia
SAM (MW)
-56-
The same reasoning can be made when estimating the SAM for each country as explained before, that would
result in a lower value than 16,2 GW by calculating it with the STEALS cost data.
Table 19 Market potential for the MENA (industry), with optimum configuration using STEALS cost data
Market
size
(MW)
Most competitive business case (optimum configuration and industry type)
Index LCOE
(EUR/MWh)
DNI
(kWh/m²/year)
Park
Size
(MW)
TES
(hours)
Mirror
Area
(m²)
CF
(%)
Industry
type
Morocco 380 MM31 48,2 2425 2 14 220 86% Mining
Tunisia 88 TChe11 59,7 2000 3 14 220 69% Chemical
Egypt 1220 EC41 48,1 2415 15 14 220 85% Cement
Jordan 160 JM21 44,7 2680 4 14 220 92% Mining
Saudi
Arabia 582 SC11 44,7 2700 17 14
220 92% Cement
This leads to the conclusion that a change in the system costs does not affect the optimum sites identified
previously, but rather has a slight effect on the parameters of the configurations, thus making the general
ranking not price sensitive, nor the MCA. However, the sensitivity analysis should be conducted to analyse
inputs’ effect on individual configurations. Here, the most competitive business case (based on the
LCOE) identified earlier will be used to perform the analysis: JM²1 (see Table 17). The parameters chosen
for the sensitivity analysis are: the discount rate, and selected CAPEX components (storage cost, solar
field cost and Stirling engine cost). Each parameter is varied within the range -20% to 20% to understand
the changes it has on the LCOE (Figure 38) and NPV (Figure 39) of the JM21 site. To compute the NPV,
the zero-subsidy scenario is followed, assuming that the electricity is sold at the utility price of Jordan.
Figure 38 LCOE sensitivity analysis
-0,3 -0,2 -0,1 0 0,1 0,2 0,3
Stirling engine
Solar field
Storage
Discount rate
+20%
-20%
-57-
Figure 39 NPV(k€) sensitivity analysis
As it can be expected, the change in LCOE values follows closely the changes in CAPEX components such
as the storage and solar field, but not so much of the Stirling engine: an increase of 20% in the latter pushes
the normalized LCOE up by almost 5%, while the same increase for the storage and solar field costs nearly
pushes the normalized LCOE by 20% up. The same trend is observed with the NPV changes, however in
a smaller order of magnitude. The effect these two parameters have both on the NPV and LCOE can be
traced back to their importance in the CAPEX breakdown from Figure 37, accounting for more than 60%
together. Conversely, the changes in the discount rates have more effects on the NPV than the LCOE,
compared with specific components costs: achieving 20% decrease in the cost of capital yields a greater
NPV (more than 20% surplus) than with combining the same cost reduction of all three components
previously described. This leads to the conclusion that acquiring cheap financing may be more beneficial
and profitable than investing in R&D to decrease costs. The latter however contributes more in lowering
the LCOE, rendering the project more competitive.
5.3 Multi Criteria Analysis
5.3.1 Results
Each business opportunity (indexed by its site code) is given a score under the criteria mentioned in 3.4, as
seen in Appendix 6. The final score (1-10) obtained is shown in Figure 41. Under this model, the most
promising site to approach first for Cleanergy is MM31 with a score of 7.15, followed by EM21 with a score
of 6.92. These sites both correspond to mining companies who have their mines in off-grid locations, which
contributed in pushing them on top of the list of all prospective sites. Business opportunities with a score
over 5 all belong to either the mining or cement sector, confirming the appeal these segments should have
for Cleanergy when entering any specific market. Alternatively, while the previous results suggested that
Egypt, Saudi Arabia and Morocco are the most interesting markets because of their size, the MCA suggests
that Jordan is attractive as well, namely because of the low generation cost Cleanergy’s technology can
achieve there. Indeed, the high DNI in Jordan allows for cheap prices (average of 74 EUR/MWh) below
the 77 EUR/MWh target, and the high industry electricity price proposed by the national utility (160
EUR/MWh) puts Jordanian companies among the top business opportunities to consider. This is furthered
by the fact that even though Saudi Arabia has similar DNI conditions, several of its indexed sites have a
score below 5 due to the low industry utility rate in place (41 EUR/MWh) which makes Cleanergy’s offer
for them not very attractive. A similar case can be made for Egypt, although having two sites scored above
5 (EM21, score 6,92 and EC51, score 6,02), has most of its sites in the lower bound of the ranking because
of the cheap electricity price (53 EUR/MWh) compared to the average achievable LCOE of 81 EUR/MWh.
40000 45000 50000 55000 60000
Stirling engine
Solar field
Storage
Discount rate
+20%
-20%
-58-
Morocco, on the other hand ranks quite well in all indexed sites, thanks to comparatively cheaper prices
than the utility (82 EUR/MWh average against 91 EUR/MWh), and a strong ecosystem in the country that
favourites renewable adoption. Finally, Tunisia’s sites rank the lowest with high LCOE figures (86
EUR/MWh average), which is not offset by its small market size or low DNI values.
Table 20 presents the most competitive business cases with regards to the MCA findings, in each country.
Interestingly, the sites that the MCA concludes are the most competitive for Egypt and Jordan (EM21 and
JCh31) are not the same when looking only at the LCOE as decision criteria. Table 17 previously showed
that EC41 and JM21 were the top sites for these two countries. Moreover, both locations have a lower
LCOE than the ones indicated by the MCA. The Multi Criteria Analysis is built in a fashion that compares
locations to several parameters, and not only pure cost effects, and tries to determine a trade-off between
the importance of each parameter : EC41 might generate a lower LCOE than EM21 but the fact that the
latter is off-grid is more important as it makes more sense to propose a modular CSP technology with
storage to a mine disconnected from the electrical grid than to a cement company that sources its electricity
needs from the local utility. In parallel, JM21 and JCh31 present an infinitesimal difference in LCOE figures,
while JCh31 is twice JM21 in terms of park size (7,6 MW against 4 MW). Clearly, having a small difference
margin in LCOE numbers is less important than capacity to install, thus making JCh31 better to target than
JM21.
Table 20 Most competitive business cases under the MCA (per country)
Index MCA score Normalized
LCOE
DNI
(kWh/m²/year)
Park
Size
(MW)
TES
(hours)
Mirror
Area
(m²)
CF
(%)
Industry
type
Morocco MM31 7,15 1,07 2425 2 13 220 84% Mining
Tunisia TC11 2,67 1,28 2000 24 10 220 65% Cement
Egypt EM21 6,92 1,12 2415 40 13 220 85% Mining
Jordan JCh31 6,31 1 2715 8 14 220 92% Chemical
Saudi
Arabia SC11 5,67 1,01 2700 17 14 220 92% Cement
The results of the MCA are regrouped by country (average score of all sites in each) and represented in
Figure 40. Jordan is the market with the highest score, followed by Morocco, while Egypt and Saudi Arabia
share more or less the same rank, and Tunisia is the last. Following this, Cleanergy should first target markets
where its product can be the most competitive, not only with other renewable technologies (PV-BESS,
diesel gensets), but also to the electricity utility price as most often, industrial are already connected to the
grid, and do not necessarily see as added value an independent power generation system, that can produce
electricity on demand. This means that it may not be strategic to engage the biggest companies in terms of
size, but the focus should be put on choosing locations that showcase to the maximum the benefits of the
TES Cleanergy’s product has, which enables it to generate electricity at attractive prices. These optimum
business opportunities are mostly defined by: their grid access, the DNI available but also the conventional
power cost paid in the country.
-59-
Figure 40 MCA country score (1-10)
2,79
4,33
4,46
5,26
6,26
Tunisia
Saudi Arabia
Egypt
Morocco
Jordan
-60-
Figure 41 Multi Criteria Analysis – Ranking of business opportunities (1-10)
2,232,232,242,252,252,292,332,382,382,41
2,522,67
2,872,97
3,173,23
3,513,55
3,703,823,823,863,953,953,994,014,01
4,134,134,16
4,264,404,404,49
4,584,584,64
4,955,045,055,055,065,095,10
5,275,275,315,335,375,415,475,475,48
5,635,675,69
5,795,855,905,905,965,976,046,046,06
6,196,216,216,226,286,306,316,386,39
6,927,15
TF11TF12TF22TF13TF15
MC22TF31TF14TF21TC21
TM11TC11EI31EI21EI42
EC31EC34
SM21SC61SC41SC51EI11
EC32EC33
SM31SM13SM14MC31MC32MA11
EI41SM15SM16MA21SM11SC31
SM12MC23MC13EC21
EM11MMe11
JC11MC11MC24MC41
MM41JM31SC21
MM22MM12MC12JCh11EC11SC11JC21JC41
JM11JCh41
MM24MM21
JC42JC31EC51JC12
JCh21MM25MM11
JM21MC21JM32JCh31EC41
MM23EM21
MM31
-61-
5.3.2 Sensitivity analysis
5.3.2.1 Scoring method
In Table 21, the individual scores of each country are given, unweighted for the 5 considered criteria. The
LCOE criteria appears to have the highest contribution in setting the ranking result, given the disparities
between the country score for given criterion, but also because of the small difference the scores in the
other criteria have for each country. How the LCOE criteria is scored with regards to each site (and hence
country) seems to influence to the results to the highest degree. As a result, another scoring approach is
proposed below, to quantify how sensitive the results to the method followed.
Table 21 Country score, by criterion (1-10)
Size DNI LCOE Macro-
environmental factors
Grid access
Morocco 3,3 2,4 7,8 6,0 5,2
Tunisia 1,7 1,0 0,0 8,0 5,0
Egypt 7,0 2,7 2,7 6,5 5,2
Jordan 2,4 5,0 9,9 6,8 5,0
Saudi Arabia
4,9 3,7 3,9 5,5 5,0
As described in 3.5, the LCOE of each studied site is compared to two figures: Cleanergy’s targeted market
price and the average utility price for industry in each country. These two price signals are unrelated to each
other, the first being a goal Cleanergy is set to reach, while the other is directly linked to the specific electricity
market conditions of each country. A more relevant way of comparison would be to quantify the difference
the LCOE has with each, and scoring them independently in the MCA. Thus, the new scoring method will
decouple the latter, by replacing the LCOE criterion with two:
• Price target, which will act as an indicator of how realistic Cleanergy’s price target is compared to
the generation costs the company is able to reach in each specific country. Practically, the value of
this criteria for each site is calculated with equation 5, and the scores are attributed according to:
𝐷𝑖𝑓𝑓%1 = 𝑇𝑎𝑟𝑔𝑒𝑡 − 𝐿𝐶𝑂𝐸
𝑇𝑎𝑟𝑔𝑒𝑡∗ 100 (4)
o If −𝐷1 ≤ 𝐷𝑖𝑓𝑓%1 ≤ 𝐷1, then the score is: 5 +𝐷𝑖𝑓𝑓%1
10. 𝐷1 is the margin for which the
price difference with regards to Cleanergy’s target is still considered acceptable. Several
values of 𝐷1 are considered in the sensitivity to underatnd its impact on the MCA.
o Else if 𝐷𝑖𝑓𝑓%1 > 𝐷1 the score is 10, otherwise the attributed score is 0
• Price hedge, which will measure how much Cleanergy’s technology will protect the potential
customer from the variations in electricity prices. Often, national utilities set their electricity price
following crude oil and natural gas price variations, or as seen previously, changes the prices
following subsidies and environmental policies. These puts several risks on their revenues and
operations. Companies will often invest in solutions to shield them from such fluctuations, in the
form of future contracts, options and so on. An investment in a self-generating renewable power
technology can then be considered a hedge alternative. Hence, the value of this criteria will be
determined by equation 6, and the scores are attributed according to :
𝐷𝑖𝑓𝑓%2 = 𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑎𝑟𝑘𝑒𝑡 𝑝𝑟𝑖𝑐𝑒 − 𝐿𝐶𝑂𝐸
𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑚𝑎𝑟𝑘𝑒𝑡 𝑝𝑟𝑖𝑐𝑒∗ 100 (5)
-62-
o If −𝐷2 ≤ 𝐷𝑖𝑓𝑓%2 ≤ 𝐷2, then the score is: 5 +𝐷𝑖𝑓𝑓%1
10. 𝐷2 is the margin for wich the
hedge investment can be considered profitable. In this case, 𝐷2 will be equal to the
forecasted growth rates of electricity prics in each country.
o Else if 𝐷𝑖𝑓𝑓%2 > 𝐷2 the score is 10, otherwise the attributed score is 0
Table 22 shows the average score by country, for the new criteria defined. Jordan and Morocco are the only
countries where Cleanergy can propose generation costs that will prove to be profitable, even when
considering that electricity prices will grow over time. Specifically, Jordan’s utility rates are so high compared
to the average price Cleanergy can propose on the Jordanian sites that all of them score 10, showcasing the
competitiveness of CSP with storage in that particular market. On the other hand, the price target criteria
ranking shows different results, depending on the margin 𝐷1 choosen. For a price tolerance from 5% to
10%, Jordan, Saudi Arabia and Egypt are the top 3 countries where Cleanergy can approach its desired cost
target. This in turn reveals how much that target is relevant to the specific markets, ie. that it is not suitable
in Tunisia for example, where the score is 0. However, for a 15% margin, all countries have the same score,
meaning that a there is a maximal 15% difference (in absolute terms) between what Cleanergy’s target is and
its actual value proposition, all sites considered. Factoring in these two new criteria in the MCA, the new
weighted country rankings are show in Appendix 7. As it can be seen, even by changing the scoring
methodology, Jordan and Morocco still lead in terms of countries hiding the most potential for business.
This is mainly due to the fact that in the other countries, electricity price are still low for Cleanergy to
compete with, even when considering the project rise in prices.
Table 22 Country score, additional criteria
Price target, 𝑫𝟏= 5% (case 1)
Price target, 𝑫𝟏= 10% (case 2)
Price target, 𝑫𝟏= 15% (case 3)
Price hedge
Morocco 2,73 3,63 4,99 8,64
Tunisia 0 0,00 4,99 0
Egypt 3 3,66 4,99 0
Jordan 7,14 5,72 5 10
Saudi Arabia 4,37 5,00 5 0
5.3.2.2 Weighting factors
The weights chosen for the MCA favour off grid locations with high DNI and low LCOE. As explained
before, off-grid configurations are where Cleanergy’s offer is the most competitive, as usually the alternatives
require either costly grid connection or diesel generators. Much emphasis is put on the LCOE and the DNI
is a consequence of the infant stage of the technology. Being an innovation that has still to enter the market
and gather attention, Cleanergy’s best interest is to consider customer’s where generation costs are the
lowest. As a result, a lower weight is attributed to the system size. Yet, another set of weighting factors can
be chosen in the case the goal of the MCA was to sort the best opportunities in size. To that effect, a
sensitivity analysis of these factors is carried out to see if they affect the general ranking obtained, and more
specifically, which are the most critical weighting factors. The previous section showed the extent to which
the LCOE affects the final results. It is then interesting to offset that criteria in the MCA (from here
onwards, said criterion is given 0 as a weight) and vary the other factors in the sensitivity, as depicted in
Table 23.
-63-
Table 23 Weighting factors case definition
Potential DNI LCOE Macro-environmental factors Grid access
Case 4 5 5 0 5 5
Case 5 10 5 0 5 5
Case 6 5 10 0 5 5
Case 7 5 5 0 10 5
Case 8 5 5 0 5 10
The results of the weights variation by case can be seen in Appendix 7. In case 5, although Jordan performs
better in the DNI score from Table 21, Egypt is on the lead. Similarly, Egypt ranks first in case 6, although
it is far behind in the macro-environmental factor score from Table 21, where Morocco is leader. The criteria
where Egypt is first compared to Morocco and Jordan is the Potential criteria, which means it is the second
most critical assumption when attributing the weight for the MCA, after the LCOE. As a result, it is safe to
assume that the other criteria (DNI, macro-environmental factors, grid access) participate in determining
the ranks in a smaller order of magnitude. Naturally, the business opportunities individual ranks also change
depending on the case studied. Table 24 shows the 5 first business opportunities identified by the MCA in
each of the cases considered. While the ranks follow the logic described in the previous paragraphs, where
first 3 cases are most influenced by the LCOE criteria of each site, and the remaining focus on a different
one depending on the case, it is worth noticing which business opportunities hold the most occurrence all
cases considered. These locations represent the first companies to approach for Cleanergy, since they rank
among the top for different focus parameters (criteria of the MCA). Business opportunity MM31 stands out
first, as it is present in 5 of the cases studied, both where the emphasis is put on the price attractiveness
(case 1 to case 3), and the other criteria (other cases). Comes then business opportunity EM21, also present
in 5 of the 8 cases, but only when the price criteria was offset. Both EM21 and MM31 are off-grid, proving
the necessity of such criteria for Cleanergy’s best interest. None of the business opportunities identified in
Saudi Arabia belong to the top 5, even though the country joys of high solar irradiation. The extremely low
utility electricity price, coupled with the harsh business environment of the kingdom explains that absence.
Table 24 MCA sensitivity (top 5 business opportunities)
Case 1 Case 2 Case 3 Case 4 Case 5 Case 6 Case 7 Case 8
Top 5 business
opportunities
MM31
JM32
JCh31
JM21
JCh21
JCh31
JM21
JCh21
MM31
MM11
MM31
MM11
MM25
MC21
JM32
EM21
EC41
EC51
EC11
MM25
EM21
EC41
EC51
EC11
MM25
EM21
EC41
EC51
MM31
EC11
EM21
EC41
MM25
MM11
EC51
EM21
MM31
EC41
EC51
EC11
5.4 Scenario analysis
5.4.1 Results
Following the scenarios defined in 3.6, the 5 top business opportunities in each country, as described in
Table 20, are analysed by looking at their pure financial viability. As a reminder, the assessment of the
economic feasibility of a project relies often on the NPV and IRR, for which a power price (the price
Cleanergy’s electricity will be bought) needs to be set to be able to calculate the yearly cash flows. Against
that, the LCOE (developer perspective) changes since tax costs/benefits vary with varying power prices,
and affect also the discount rate used (see Appendix 1). Table 25 to Table 29 present the results of such
analysis for the optimal business cases per country, for each scenario:
-64-
• Zero subsidy: takes as input the current industry electricity utility price to calculate the cashflows
for the projects, considering the equity IRR (8%) and cost of capital of the project.
• Break-even subsidy: determines the necessary power price Cleanergy will sell its production in
order to reach 0 as a NPV value after the end of the project’s lifetime, hence making the Equity
Rate of Return (ERR) equal to the equity IRR (8%, desired value).
• Utility competitive: determines the power price at which Cleanergy’s technology can be considered
competitive, ie 20% to 30% cheaper than the utility rates.
It has to be noted that in the tables below, ERR and IRR need to be separated. The former is linked to the
equity party profitability (when accounting for tax costs, loan repayments…), while the latter is the intrinsic
project IRR, that stems from cashflow calculations that concern only pure technology costs. In the first
case, the discount rate used is the equity IRR (thus finding its value when the NPV = 0), while in the second,
the WACC is used.
Table 25 Scenario analysis results, Morocco (MM31), WACC = 4,5%
Scenario Normalized
power price NPV (k€) ERR (%) IRR (%)
Normalized
LCOE
Zero subsidy 1 -525 7,08 7,09 1,03
Break-even
subsidy 1,05 0 8 7,6 1,05
Utility
competitive 1,53 4938 17,5 12,6 1,2
Table 26 Scenario analysis results, Tunisia (TC11), WACC = 4,8%
Scenario Normalized
power price NPV (k€) ERR (%) IRR (%)
Normalized
LCOE
Zero subsidy 1 -71117 -1,9 1 1,77
Break-even
subsidy 1,9 0 8 7,4 1,9
Utility
competitive 2,7 55372 17 11,9 2,1
Table 27 Scenario analysis results, Egypt (EM21), WACC = 4,9%
Scenario Normalized
power price NPV (k€) ERR (%) IRR (%)
Normalized
LCOE
Zero subsidy 1 -122104 -1,4 1,3 1,7
Break-even
subsidy 1,82 0 8 7,3 1,82
Utility
competitive 2,45 81362 15,3 10,9 1,96
-65-
Table 28 Scenario analysis results, Jordan (JCh31), WACC = 4,8%
Scenario Normalized
power price NPV (k€) ERR (%) IRR (%)
Normalized
LCOE
Zero subsidy 1,83 37595 27 16 1,2
Break-even
subsidy 1 0 8 7,4 1
Utility
competitive 1,37 16911 16 8,4 1,1
Table 29 Scenario analysis results, Saudi Arabia (SC11), WACC=5%
Scenario Normalized
power price NPV (k€) ERR (%) IRR (%)
Normalized
LCOE
Zero subsidy 1 -65105 -3 0,1 1,99
Break-even
subsidy 2,1 0 8 7,1 2,1
Utility
competitive 2,7 31205 14 10,2 2,2
An initial takeaway is that given the current level of power price, Cleanergy’s technology would not be
profitable in any of the countries, besides Jordan. In all 4 other countries, both NPV and IRR are negative,
or below the 8% equity rate of return expected, rendering investing under that scenario not viable. Power
prices are still too small to recuperate all the investment costs and profits. Jordan represents an exception,
as the very high-power prices of the country not only break even the project (JCh31 site) but make highly
profitable with 27% IRR. Under this scenario, Jordan would be the only country where Cleanergy’s
investment would make financial sense.
The break-even subsidy scenario does not concern Jordan since the current power prices already make it
profitable. In fact, for the JCh31 business case to reach a null NPV, electricity price musts drop by nearly
50%, which is unlikely to happen in the future, deeming Jordan to always be a profitable location for
investments. In the other countries, this scenario implies that power prices must increase in various ranges
to break-even the projects: by 5% in Morocco, more than 80% in Tunisia, Egypt and more than double in
Saudi Arabia The LCOE results of this scenario suggest that the generation cost of Cleanergy and power
price are equal, but in reality, the LCOE is always higher. This can be explained by the inflation rate set to
zero in the financial model (Table 3) in all countries. As detailed in Appendix 1, the power purchase price
and LCOE are linked, especially in the presence of inflation, and power price escalation factors, which are
often equal. In reality, each country experiences inflation in a different fashion with varying rates. Egypt for
example, with the deregulation of its currency, has seen inflation more than double, situation not present in
any of the other countries considered. For this reason, the analysis did not consider inflationary effects. As
such, under the break-even subsidy scenario, the LCOE is always higher than the power price calculated to
make the projects profitable, thus not competitive.
Similarly to the second scenario, Jordan is not concerned with the third scenario results: it was already
competitive with no support scheme as seen in the first case. Hence, for the JCh31 business case to be only
20-30% competitive with the utility price, the latter should decrease by 25%. For Morocco, Tunisia, Egypt
and Saudi Arabia, the power price must increase (compared to the utility prices) by more than 50%, 140%,
150% and 160% respectively in order for the business opportunities in each country to be one third cheaper
than what utilities offer. These premiums must be paid on top of the power price of the concerned countries
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to make CSP with storage competitive at a modular level. Interestingly so, electricity prices are on the rise
in the MENA region. Based on current announced regulations in the electricity sector of each country, and
the expected rise in crude oil prices, the forecasted industry electricity price in each country are found and
presented in Figure 42. As it can be seen, Jordan will still witness high utility rates, while the subsidies to the
electricity sector will gradually phase out to increase prices in the other 4 countries. The expected rise in
power price will bring most of the projects closer to the conditions of the second scenario, but further
subsidies from the government to renewables, and CSP in particular in the form of FIT must be introduced
to make them profitable (break-even subsidy scenario) and cheaper than the national offer (Utility
competitive scenario).
Figure 42 Expected utility electricity price for industry 2021 (€/MWh)
5.4.2 Sensitivity analysis
The power price being the unique input in the scenario-based study described above, its variation effects on
the projects IRR and NPV are found. In Figure 43, the IRR of the 5 best optimum projects per country
(according to the MCA) is plotted against varying power prices. The higher the requested rate of return, the
higher the power price must be, following a polynomial fashion. For the 5 projects to reach the same IRR,
different power prices must be offered by Cleanergy, but whichever business opportunity reaches the IRR
with the lowest power price would then be the best for investment. As it can be seen, Saudi Arabia’s SC11
fulfils that requirement, making it the safest project to consider. Moreover, whatever power price
considered, that location seems to be always profitable, as the IRR is each time higher than the 4 other
projects. The IRR representing how fast an investment recuperates its incurred costs and generates revenue,
deciding to approach the Tabuk Cement Company (SC11) could be of a profit to Cleanergy. This is
interesting to note as the SC11 and JCh31 projects are smaller in size than EM21 (17 MW and 8MW against
40 MW), but yet yield a better rate of return for all electricity prices.
180
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59
Jordan Morocco Egypt Saudi Arabia Tunisia
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Figure 43 IRR vs Power price
Naturally, the NPV of the projects increases also with increasing power prices but following this time a
linear trend as shown in Figure 44. It may seem strange that the NPV of 5 different projects is compared,
but the MCA suggested that among all considered business opportunities, those 5 were the optimal. Hence,
the pure financial viability of the latter should also be taken in consideration to contribute in the decision-
making process of prioritizing a project over another. At first glance, mining project EM21 brings the
highest profits to Cleanergy, mainly due to its size of 40 MW, the highest among the 5 considered sites.
While this appears to contradict the IRR results stated above, a closer look at the NPV graph presented in
Figure 45, (power prices range here between 50 €/MWh and 150€/MWh) conveys the same message as
before. More specifically, although EM21 is bigger in capacity to install (and thus revenues of energy sold),
for power prices in between 85€/MWh and 110€/MWh, the Saudi cement project SC11 garners higher
positive cashflows over its lifetime than the Egyptian mining project. Even more so, this still holds after the
break-even point of EM21 located at a power price of around 96€/MWh. Additionality, just as for the IRR,
JCh31presents higher NPV values than EM21 for prices between 85€/MWh and 100€/MWh, while there
is a 5-time difference in system size. The same can be said about JCh31 and TC11: even if the Tunisian
cement plant presents a high potential for Cleanergy in terms of capacity to install, it lags in cash-flow
generation compared to the Jordanian chemical project JCh31 for power sold in the range of 85€/MWh to
130€/MWh. However, after a certain point (110€/MWh for EM21 and 130€/MWh for TC11), the large
size/revenue is able to offset the high rate of return of the competing projects, resulting in better economic
performance with increasing power prices.
Considering the above, the main take way from the scenario analysis is that in general, the technology has
yet to be competitive, mirrored with current (and even forecasted) grid prices. However, a decrease in
investment costs over time is expected due to several reasons. Factors including economies of scale, high
volumes of production and risk depreciation (linked to the ability of building strong partnerships when
developing first projects) will all contribute into making the technology described viable in said markets. In
Figure 46, such evolution is shown, where the LCOE decreases gradually as the technology gets momentum
and is increasingly adopted. As such, and to experience this high product adoption over time, a larger
opportunity for market introduction may be present in countries that face grid unreliability issues, or where
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0%
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40%
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100%
0 100 200 300 400
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Power Price (€/MWh)
EM21
JCh31
MM31
TC11
SC11
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the share of off-grid users is consequent. Indeed, modular CSP with long hours of storage close to the end
user hedges against unreliability of the grid and can retrofit the use of diesel generators.
Figure 44 NPV vs Power price
Figure 45 NPV vs Power price (zoom)
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Figure 46 Normalized LCOE evolution
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6 Conclusions
This research work revolved about studying the market potential a modular CSP technology with storage
can have in selected countries of the MENA region: Morocco, Tunisia, Egypt, Jordan and Saudi Arabia,
from which conclusions about the whole region can stem later on. Cleanergy, a Swedish company specialized
in solar power production, was taken to illustrate the market entry case of an innovative CSP technology,
comprised of a Stirling engine, a field of heliostats, and a latent-heat TES. Specifically, the work aimed at
sizing the SAM of the company, and pinpointing at the most promising customers to potentially populate
its SOM. To do so, a methodology was developed to compare markets, countries and business
opportunities, with the goal of identifying the optimal ones that holds the highest potential for Cleanergy.
The technology the company is developing relies on a TES of high magnitude (above 10 hours), thus the
potential customer screening was limited to the industrial sector, which is often very energy intensive with
round the clock electricity demand. An extensive market review of the 5 countries has been carried out,
aiming at understanding their respective power market eco-systems, and identifying industrial companies
prone to be interested in Cleanergy’s value proposition. The electricity consumption of each company was
estimated based on referenced electricity intensities needed to produce the product of each industrial. It was
also broken down to each of their consumption sites, for which exact location coordinates and respective
weather data were gathered. Through techno-economical analysis, optimal plant configurations for each
were determined to reduce the LCOE, in terms of installed capacity, storage size, and mirror area. With
such information, a multi-criteria analysis, where each criterion is scored differently, was performed in order
to be able to compare amongst the different markets (by country), considering not only the potential for
installed installations in MW, but also the lowest cost at which parks could be built, macro-environmental
factors in the country, and existing infrastructure. First ranking business opportunities in each country, with
respect to the MCA, were further investigated to assess their economic feasibility, computing NPV and IRR
figures for each in the three scenarios considered.
The market size for such a technology exceeds 40GW in the MENA, albeit it is limited to the industry
sector. Within the latter, mining and cement are the two sub-sectors that hold the most opportunities in
terms of size, all countries considered. Most notably, mining companies can prove to be very attractive for
Cleanergy for their frequent off-grid design, making Cleanergy’s technology a way to off-set the often
expensive and volatile fuel prices, in the case of diesel gensets: the two best business opportunities from the
MCA are both mines, in Morocco and Egypt respectively. All three different analysis carried out (optimal
configurations screening, MCA, scenario analysis) make Jordan the country Cleanergy must prioritize, even
though it is small in size of capacity to install. Two reasons explain this result: the country joys from
extremely good solar resource, which de facto brings generation costs down and rationalizes the use of large
storage capacity, but also because of the high electricity price Jordanian industrial currently pay. Investing
in a dispatchable CSP system with long storage hours is the perfect hedging option in the Jordanian market
conditions. The latter is protecting risk averse industrial from the continual rise in electricity rates,
independently secure their energy procurement while making profits, considering the avoided cost of
conventional procurement. Following this rational, Morocco ranks second as market to pursue, followed by
Egypt and Saudi Arabia. Interestingly, even though the former two have better DNI conditions than
Morocco, thus reach low LCOE values, and are bigger in market share, Cleanergy has yet to be competitive
in those markets due to low electricity prices. Unlike Saudi Arabia and Egypt, Morocco has no fossil fuel
resource on its own, which drives rates up. In general, countries in the MENA are well on the way for the
renewable energy transition, with detailed political framework and goals, including gradual removal of fossil
fuel subsidies. Yet, they lack for the most part targeted support mechanism for private small-to-medium
scale generation in the form of feed in tariffs or carbon tax that would allow Cleanergy’s technology to be
competitive, as all seem to favour large bid projects. This represents an additional hurdle for Cleanergy,
especially considering its technology as an innovation that has yet to diffuse in and gain momentum. In
markets with no targeted subsidies and low utility electricity price, the gap in price is too high to make
industrials consider an unknown technology such as Cleanergy’s economically viable, thus preventing them
from crossing the innovation chasm. On the matter of subsidies, the scenario analysis quantified the support
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tariffs necessary for the system to be profitable and utility competitive in each country. Being profitable
doesn’t necessarily mean being competitive with the utilities, as the LCOE of the best cases in the break-
even scenario was higher than the purchase power price. In Morocco, Tunisia, Egypt and Saudi Araba, the
generated electricity with Cleanergy system has to be sold at prices double the utility rates to make the
generation cost 30% cheaper. The opposite case is made for Jordan as a result of the high electricity price
which make projects there always profitable and competitive. The latter highlights that it may be optimal to
target unreliable grid countries for market introduction, before addressing regions with stable grid as in the
MENA, where it will become viable as lower costs are attained. In these regards, Cleanergy should seek
different partners, partnerships and joint ventures when setting up projects to achieve competitive cases.
The goal being to elaborate innovative business models that would make the technology economically
viable. Wheeling schemes, ie. using the grid connection in exchange of a fee, can represent such approach.
The power plant to be built can be located in the highest DNI location possible of the country, while the
energy produced is wheeled to the off-taker. Doing so would allow leveraging on the location, hence land
cost, but also on the capacity to be installed, since there would be the possibility to bundle different off-
takers power needs into one site, but have separate power purchase agreement with each, while benefitting
from economies of scale. Even more, this business model can be more relevant considering that the analysis
done assumed a very low cost of capital, only accessible through international funding instrument, that are
often reluctant approving small isolated projects.
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7 Appendix 1
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KTH ROYAL INSTITUTE OF TECHNOLOGY
Master of Science in Sustainable Energy Engineering
SUMMER INTERNSHIP REPORT
Metrics and methods for financial valuation of solar power plants
PhD Professor MSc Student
Rafael Guédez Youssef Benmakhlouf
August 2017
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1 Table of content
Introduction ................................................................................................................................................................. 75
Deliverables ............................................................................................................................................................. 75
Review of solar tender process ................................................................................................................................. 75
Financial terminology ................................................................................................................................................. 76
Discount rate/Inflation ......................................................................................................................................... 76
Taxes ......................................................................................................................................................................... 76
Depreciation ............................................................................................................................................................ 77
Financing- Debt/Equity ........................................................................................................................................ 77
Risk management indicators ................................................................................................................................. 78
Economical metrics ..................................................................................................................................................... 78
Capital expenditures (CAPEX) ............................................................................................................................ 79
Operational Expenditures (OPEX) ..................................................................................................................... 79
Levelized Cost of Electricity (LCOE) ................................................................................................................. 80
Net Present Value (NPV) and Internal Rate of Return (IRR) ........................................................................ 81
LCOE: different approaches ..................................................................................................................................... 82
Societal perspective ................................................................................................................................................ 83
Business/developer perspective ........................................................................................................................... 85
LCOE for PPA projects ........................................................................................................................................ 86
Bibliography ................................................................................................................................................................. 87
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1.1 Introduction
The first year of the sustainable energy master at KTH was an eye-opener for me, as after each course taken,
I realized how much decision making and planning had great effects on the energy landscape of a country.
Furthermore, I had the chance to take part in the course ‘’Large Scale Solar Power’’ given by Rafael Guédez,
PHd professor at KTH. The main take away from that course was that even if a 100% efficient renewable
technology existed, the capitalistic nature of our society dictates that if no profits can be made from said
technology, no one would invest in it. Profit is what drives investments, and as such, I realized that it is
important to have a full grasp on the financial aspect of renewable energy projects, to be able to optimize
gains later on. Unfortunately, I did not have any economical financial knowledge prior, considering my
engineering background but wanted to learn and improve my knowledge in that field. The energy
department at KTH has developed several tools for techno-economical optimization of solar power plants,
and my supervisor Rafael Guédez, suggested this internship, as a way for me to deepen my knowledge in
this field, and a mean for the department to have a report summarizing and comparing how financial
calculations are done. Indeed, there are several ways of calculating financial metrics for renewable energy
projects (LCOE for example), and the literature and studies done do not often precise the methodology
followed or the assumptions taken.
1.2 Deliverables
As deliverables, the following report, summarizing and comparing the different ways present in the literature
for doing financial calculations. The discounted cash flow method is thoroughly followed and explained. As
mentioned, I have no economical background whatsoever. This report is written then in a way that explains
basic notions from scratch and can serve as an introduction to whomever interested in financial of renewable
projects, but lacks the proper knowledge. For the most introverted reads however, they can skip the first
sections and jump in directly into the most advanced parts. Another deliverable is an Excel spreadsheet that
takes as input financial structure of a project and returns cash flow analysis of revenues and costs, as well as
the necessary indicators needed for project valuation. This report focuses on solar power plants, but the
presented information can be applied to all kinds of renewable energy projects.
2 Review of solar tender process
Renewable energy tenders are somewhat a new way for governments to acquire renewable electricity
production [1]. When a government or entity tenders, it invites bids for a specified project, and based on
predefined criteria, the most competitive bid is chosen to carry put the project [2]. In the case of renewable
energy, tendering schemes are a competitive method for allocating and securing financial support to RES
projects. The tenders are foremost based on the cost of electricity production, to ensure the best cost-
efficient way of energy production [3].To enter a RES tenders, bidders need to comply a certain number of
criteria that make them qualified for the participation in the project. These requirements include references,
financial solidity, etc.., but also technical and commercial liability related to RES projects.
The procedure of bidding is done in the form of a reverse multi-unit auction: The sole buyer (usually the
government) ranks the bids based on their unit price starting from the lowest. Tenders can concern either
RES capacity in MW or units of produced electricity in MWh. The submitted bidding prices can be known
by all actors, or they can stay undisclosed, so that each bidder does not know competitors’ offers and react
to them. This latest form of bidding, sealed bid slows down competition as it is a static auction [3].Once the
bids are submitted and the choice made by the buyer, the price determination can either be pay-as bid (the
utility is paid the indicated price in its bid) or common price for all bidders, meaning that the awarded price
is the one of the most expensive successful bid (marginal price). Bid bond guarantees are often required by
successful bidders to protect themselves from eventual delays caused by the RES investor, and that hurdles
the accomplishment of the project [3].
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Tenders are more and more used as an allocating mechanism for new solar power plants [4]. Example of
recent tenders in Saudi Arabia of a 300MW solar PV power plant [5] or the 2000MW solar thermal plant
with storage tender in Dubai [6] show the global preference for tenders to governments wiling to promote
renewable energy. This is mainly due to the fact that the tendering mechanism stimulates competition
between the different operators and bidders, thing that lead to achieving record low energy prices, as seen
lately in the Dubai tender : The Dubai Electricity and Water Authority (Dewa) announced the prices from
the auction, that hit the low record of $US94.50/MWh, which represents the lowest price for solar thermal
and storage [6].Tendering contributes also in revealing the true cost of RES and sets off overcompensation,
this way, the governmental financial support goes exclusively to the best performing plants [4].
The bidding price is the essential criteria on which tenders are centered, and serve as a tool for the RES
investor to award the project to a specified utility or contractor. It is then obligatory to have a full grasp on
how this price is defined and set. The following sections present all the factors necessary to determine power
purchase agreement price, and all the metrics related to it.
3 Financial terminology
As said above, the power price that is set during RES tenders is the key point for a project to be awarded.
These prices are based on the cost of energy and quantifies the profits the projects stakeholders will make
over the lifetime of the power plant. They are determined by doing financial analysis that include all factors
and risk predictions to yield the best profits. It is therefore important to have an overview of the basic
concepts and elements of economic and financial analyses that will be needed to understand how the cost
of energy is defined.
3.1 Discount rate/Inflation
All power plants and energy projects are long time investments that require multiple years of construction,
operation and decommissioning. When doing a financial analysis however, all predicted cash flows of the
project must be expressed in the present so that the investor can make an accurate decision based on these
predictions. Indeed, whenever an investment is made, a certain return is expected, and the time that return
occurs is important: A dollar received today is worth more than a dollar received tomorrow since the dollar
today can be invested to earn interest immediately. The discount rate acts then as a measure of this time
value, and expresses the profit that is expected to occur considering the time value of money. Discount rates
are also indicators of how much risk is tolerated by an investor, and how much premium is expected as a
result of that risk [7].
On the other hand, inflation represents the rate at which the level of prices for goods and services is rising
over time [8]. When forecasting cash flows and benefits, it must be specified if inflation is taken into account
or not, as money can be expressed in “nominal” dollars or “real” dollars. “Nominal” dollars cash flows
represent the actual number of dollars required in the year the payment or cost occurs. “Real” dollars refer
to the number of dollars that would have been required if the payment or cost was paid in the base year of
the financial study.
Since the discount rate is related to the time value of money, it is also linked to the inflation rate. As a result,
two discount rates should be differentiated: real discount rate that includes inflation, and nominal discount
rate that excludes inflationary effects [7].
3.2 Taxes
From an investor’s point of view, accounting for taxes in the financial study is primordial as taxable income
is a non-negligible cost the company endures. Since in the case of energy production, the income is related
to the units of energy sold, taxes related cost take a considerable portion of the balance sheet, depending
on the taxes scheme in place. As such, it is important to express all cash flows as after-tax flows, and all
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analysis should be based on them. Each country sets the corporate tax according to its legislations and
voting decisions. For example, the United States uses a progressive taxe system, where the tax rate increases
with the taxable income [9].On the other hand; the taxable income can be reduced by necessary expenses a
business occurs. In the case of RES power plants, the yearly operation costs are fully tax deductible. Taxable
income can also be reduced when financing is done through debt and interest payments. Moreover, and as
an encouragement for renewable energy investments, taxable income can be further reduced with
Renewable Energy Tax Credits. The latter refer to an immediate reduction in income taxes equal to a
percentage of the installed cost of a new investment. Of course, the taxing schemes and way of renewable
support vary
from country to country, and there is no general rule to follow. Dependent on the project’s location, a
different tax plan and tax support will be put in place.
3.3 Depreciation
Depreciation is the process by which a company allocates an asset's cost over the duration of its useful life.
Each time a company prepares its financial statements, it records a depreciation expense to allocate a portion
of the cost to the current fiscal year. The purpose of recording depreciation as an expense is to spread the
initial price of the asset over its useful life [10]. Depreciation is a mean of reducing the taxable income, as it
is an expense included in the income statement of the company. Since the tax rate in related to the income,
the higher the depreciation expenses, the lower the amount of taxes owed to the government.
There are different ways of setting depreciation expenses related to the capital expenditure. The easiest one
being dividing the CAPEX cost by the number of years the power plants is running. This simple method is
called linear or straight-line depreciation, but others exists such as double declining method and non-linear
methods [11]. Here again, each government sets the rules for how depreciation should be used as a tax relief.
For instance, accelerated depreciation in India is a major incentive for solar as it offers a tax break in the
first year of operation, and enables great deductions in the early life of the asset. The higher the deductions,
the lower the overall tax burden [12].
3.4 Financing- Debt/Equity
Investing in solar energy projects requires large sums of money: indeed, the initial investment or capital
expenditure, CAPEX, represents the majority of all the costs related to the project, and securing funding
for it is the key for securing the construction and development.
A company can finance its assets either by equity, debt, or a combination of both. Equity financing means
that the company has enough capital to invest and cover the whole initial cost of the project. Stakeholders
of the company are then investing in the project and are owners, each one with its share, and each one
assumes the risk of the project failure: If the power plant construction does not occur, there are no back
payments to recover the failure. On the other hand, profits, if any, are shared and distributed among all
stakeholders [13].
Debt financing on the other hand means that the company developing the project seeks loans and debts in
order to cover the initial cost. This method of financing comes with annual loan payments linked to the
interest rate that the financial institution has set at the beginning. The advantage of debt financing compared
to equity financing is that the company does not share ownership of the project with multiple foreign
investors, and is fully in control. Another benefit is the tax shield debt financing represents, as the interest
payed on the loan is tax deductible, and thus lowers the company’s tax liability each year [14].
Nevertheless, when it comes to energy related investments and power plants construction, most of the
companies use a combination of debt and equity to finance their businesses. This means that the initial
capital expenditure needed at the beginning of the project is secured by a share of equity funding from the
company, and the complementary share by external loans and debts. Each of the investors, equity and debt
expect a return on the investment based on their desired profit and risk assessment of the project. This is
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translated by the cost of equity and the cost of debt, e.g. the discount rate mirroring the profits each type
of investor seeks to make from a particular investment. As such, the overall cost of capital of the project
capital is derived from a weighted average of all capital sources, widely known as the weighted average cost
of capital (WACC). Since the cost of capital represents a hurdle rate that a company must overcome before
it can generate value, it is extensively used in the capital budgeting process to determine whether the
company should proceed with a project [15].
The amount of equity invested in a project is dependent on the company’s financial assets and its ability to
attract external investors. An optimal debt to equity ratio is one that covers the project and investors
exposure to risk while simultaneously minimizing the cost of capital [16]. But a low equity share may not be
a good choice as it shows potential investors that the company is not willing to put its own money on the
table. Why should they do it then? A trade off must be chosen then. Many solar tenders and projects report
a 80-20 or 70-30 debt to equity ratios, as stated by professionals working in the field [17] [16].
3.5 Risk management indicators
When dealing with large financial investments that occur over a long period of time, risk is a crucial element
to consider when planning financing and operations. It is crucial in the sense that it is what encourages or
prohibits lenders and debt investors to take part in the project. A successful project must handle the risk
incurred and must guarantee that the operations (electricity production in the case of power plants) will
generate enough revenues to cover for the expected return, premiums and credit risk. Renewable sources
projects are often the subject of high risks that lenders and investors must take into account [18]: country
risk, political risk, foreign exchange risk, inflation risk, interest rate risk, appraisals, availability of permits
and licenses, operating performance risk, fuel prices, force majeure risk, and legal risk.
Looking at all these potential sources of risks for the completion of the project, the lender and investor will
judge the project ability to withstand them by investigating different ratios, called debt service coverage
ratios, which are function of the specific project risk. DSCRs analyze the financial expenses of the project
with retrospect to its ability to cover them. Usually, when debt investing is involved, the annual loan
payments should be as close as possible to the projects ability to generate cash, since the lenders want to be
assured that over the lifetime of the project, the revenues can service the debt. This is done with the annual
debt service coverage ratio (ADSCR) which is the ratio of yearly after-tax cash flow to the amount of debt
(principal and interest) payment incurred each year. Lenders require that the ADSCR should not be lower
than a minimal value usually comprised between 1.2 and 1.5, but the latter can vary according to the project
specifications. The loan life coverage ratio LLCR is also an indicator assessing the project’s ability to
withstand the risk. The LLCR is the ratio of the present value of cash available over the projects lifetime to
the outstanding debt. As such, the LLCR looks at the financial vitality of the project under the period it is
required to pay off the loan [18]. The following equations present the general form of these indicators, but
their components are detailed later in the report.
𝐴𝐷𝑆𝐶𝑅(𝑡) =
𝐴𝑓𝑡𝑒𝑟 𝑡𝑎𝑥 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑡
𝐿𝑜𝑎𝑛 𝑝𝑎𝑦𝑚𝑒𝑛𝑡𝑡
(6)
𝐿𝐿𝐶𝑅 =
𝑁𝑒𝑡 𝑝𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑐𝑎𝑠ℎ 𝑓𝑙𝑜𝑤𝑠
𝑃𝑟𝑒𝑠𝑒𝑛𝑡 𝑣𝑎𝑙𝑢𝑒 𝑜𝑓 𝑓𝑖𝑛𝑎𝑐𝑖𝑎𝑙 𝑑𝑒𝑏𝑡
(7)
4 Economical metrics
In order to assess the profitability of a solar power plant, and determine the costs and revenue generated by
its exploitation, economical performance indicators must be defined. This section outlines the most
economical metrics used by the industry and the academic field. [17].
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4.1 Capital expenditures (CAPEX)
A capital expenditure is incurred when a business spends money either to buy fixed assets or to add to the
value of an existing fixed asset with a useful life extending beyond the taxable year [19]. Accounting for the
largest part of the solar investment, capital expenditures or CAPEX refers to all the investment occurred
during the development and construction of the project, including all direct and indirect costs. Purchase
and installation of equipment is direct cost while all other expenses spent during the years of construction,
such as taxation and project development, are called indirect costs. A traditional breakdown of the CAPEX
of a CSP power plant can be seen in figure 1. As stated above, equipment accounts for the biggest part of
the initial investment, the solar field here accounting for 27% of the total CAPEX.
Figure 1 : CAPEX Breakdown
As seen in the pie chart, the CAPEX regroups multiple costs made in the beginning of the project for plant
erection. Particularly, Sales Tax expenses are worth mentioning as they are often subject to confusion, or
are disregarded. They simply refer to the additional cost paid when a certain component is imported from
a country different than the one where the plant is built. Indeed, considering the complexity of the CSP
technology, all the equipment needed is often manufactured all around the world and is bought from
different parties. For example, the true price of a power block manufactured by Siemens would be its first
hand price plus a fraction of that price as a sale tax paid when it is imported.
There are different ways of estimating the CAPEX at the early stages of the project. The cost model reported
in [17] uses cost functions for component cost scaling based on cost values from reference plants and
respective material and labor cost multipliers, to ensure that results are sensitive to the specific location
considered. The CAPEX is then the sum of all the components cost. The scaling factors are a sensitive
choice, as they are linked to the equipment, technology but also the location. For this reason, it is mandatory
for the decision maker to be fully aware of the model used, as it influences to a big extent the resulting
CAPEX.
4.2 Operational Expenditures (OPEX)
An operating expense results from the ongoing costs a company pays to run its basic business [20]. OPEX
is then a recurrent cost, that the utility pays every year to run its day to day operations related to all
operational and maintenance (O&M) during a typical year. Typical refers to a year with no unexpected power
plant shortages or failures, and a normal operation of the power plant [17]. The breakdown of the OPEX
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cost of a CSP power plant can be seen in figure 2. OPEX covers all costs related to utility costs, service
costs, labor costs, insurance costs and other miscellaneous.
Figure 2 OPEX Breakdown
Similarly, to the CAPEX estimation, scaling methods are used to get a first assessment of the operation and
maintenance costs [17]. Here again, reference prices are taken, and then the actual values are obtained by
applying the corresponding scaling factors for each one of the parts of the OPEX, which is at the end is the
sum of all the latter.
4.3 Levelized Cost of Electricity (LCOE)
The levelized cost of electricity is the most frequently known economic performance metric for power
generation plant. LCOE is used to assess/compare the performance and profitability of any form of
generation technology, and not only concerns solar or renewable sources [17]. It is defined as the constant
per unit cost of energy which over the system’s lifetime will bring all the project cash flows to zero. In other
words it is the ‘break even’ constant sale price of energy [150].Another way to view the LCOE is it being
the price at which the electricity must be sold to recover all the costs incurred during the lifetime of the
project.
𝐿𝐶𝑂𝐸 =
𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑐𝑜𝑠𝑡
𝑇𝑜𝑡𝑎𝑙 𝑙𝑖𝑓𝑒𝑡𝑖𝑚𝑒 𝑒𝑛𝑒𝑟𝑔𝑦 𝑝𝑟𝑜𝑑𝑢𝑐𝑡𝑖𝑜𝑛
(8)
As explained above, the major cost of a solar power plant is the initial investment, plus the annual OPEX
cost to cover for the plant’s operations, and decommissioning costs at the end of operational lifetime of the
plant. Unlike fossil fuel, renewable sources projects do not account as a big cost fuel, as by definition, the
fuel is free, be it a solar or wind farm. The rest of this report will detail the various components and ways
of calculation the LCOE.
Previous sections outlined the size taxation can have in a companies’ balance sheet, whether as a cost in the
form of tax on income, or as a tax shield in the case of depreciation. However, from a pure societal
perspective, it can be argued that tax issues can be left out of the LCOE. But, from a plant
owner/business/commercial entity owning a system perspective, the prevailing assumption is that, in order
to break even, it must be assumed that energy produced is taxed at the standard corporate tax rate. Against
this, interest, depreciation and operating costs are tax deductible [150]. The reason for this is that at the
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societal level, some cash flows such as subsidies and income taxes are only a representation of a
redistribution and reallocation of resources to the government, and hence should be excluded. From the
business point of view, these issues affect balance sheets and profitability and are fundamentally included
[22].
The LCOE is the price that would break the project even in the present, but is based on costs and payments
that occur in the future, and as explained previously, inflation effects change the value of the money over
the time. The OPEX is usually the metric affected by inflation, as it happens each year of the plant’s life,
the CAPEX being paid in the very beginning of the project. Hence, LCOEs can be in real (inflation
independent) or nominal terms. A nominal LCOE represents a hypothetical income that declines in real
value year by year, whereas a real LCOE has a constant ‘value’. the nominal LCOE will be the higher of the
two. Real LCOEs are typically used for future long-term technology projections, whereas nominal ones are
often used for short-term actual projects [150].
4.4 Net Present Value (NPV) and Internal Rate of Return (IRR)
The Net Present Value (NPV) of a proposed project is most often used as the primary absolute metric to
compare/assesses investments, and serves as a base for decision making [23]. The NPV is the sum of the
discounted cash-flows over the lifetime of the project using an appropriate discount rate as discussed above.
The cash-flows represent the yearly difference between the revenues and costs incurred each year. It is then
linked primarily to the CAPEX, OPEX, decommission costs, the yearly energy yield or output and finally
the price at which the electricity is sold.
𝑁𝑃𝑉 = ∑
(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡
(1 + 𝑟)𝑡
𝑛
𝑡=0
(9)
Equation 4 is a simplified version of the NPV, where r is the discount rate, and n the lifetime of the project.
The costs part will be detailed further down, but the revenues are simply the amount of money generated
by the sale of electricity at the decided price, as seen in equation 5 where Et is the net annual electrical output
and PPA is the power purchase agreement price.
𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 = 𝐸𝑡 × 𝑃𝑃𝐴 (10)
The discount rate that is often used if NPV and LCOE calculations is the WACC, or weighted average cost
of capital [17]. As explained in the definitions section, financing RES projects requires different types of
investors, and involves frequently debt/equity financing. The WACC is in that case the appropriate discount
rate that reflects the expected return of the investment from all the parties involved in financing the project.
It can be calculated by means of equation 6, where Eq%/Debt% is the equity debt ratio, or how much of
the project is financed by equity/debt. IRR% and idebt are the discount rates of expected return by both
the equity financers and debt financers, and Tcorp is the corporate taxes. As mentioned before, debt
financing is attractive for large utility projects as it is tax deductible, and that is reflected in the equation by
having the complementary (1 − Tcorp) as a factor in the debt weight.
𝑊𝐴𝐶𝐶 = 𝐸𝑞% × 𝐼𝑅𝑅% + 𝐷𝑒𝑏𝑡% × 𝑖𝑑𝑒𝑏𝑡 × (1 − 𝑇𝑐𝑜𝑟𝑝) (11)
A financially viable project would yield at the lowest a null NPV or a positive one. A null NPV means that
the present value of revenues and cost are equal with regards to the expected profit and risk embodied in
the WACC.
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Another fundamental economical metric that is used to rank projects and get a hold of their profitability is
the internal rate of return or IRR [17]. The internal rate of return is the discount rate that would be used in
an NPV calculation and would make it equal to zero, as seen in equation 7.
𝑁𝑃𝑉 = ∑
(𝑅𝑒𝑣𝑒𝑛𝑢𝑒𝑠 − 𝐶𝑜𝑠𝑡𝑠)𝑡
(1 + 𝐼𝑅𝑅)𝑡
𝑛
𝑡=0
= 0 (12)
The IRR is then the interest rate that would break even the project accounting for the costs incurred and
revenues generated during the lifetime of the plant. It is a measure of the profitability of a project and is
used mainly by developers and financial institutions to base their investments decisions. Each company has
its own predictions on how much profit can be made of a project and has usually a target return on
investments. If the IRR is higher than that required target, the project is financially acceptable. To compare
different projects and financing opportunities, the higher the IRR the better [17] .
It has to be noted that the IRR used in equation 7 is not the same as the equity IRR mentioned in equation
6. The latter is the internal rate of return of the equity financers, or the discount rate reflecting the expected
profits and risk premium of the equity investor. The IRR of the project is the discount rate that set the NPV
of the project to zero.
Limitations of the NPV can be see as it requires a known discount rate, and assumes that this rate will be
stable over the life of the project, which is not true in real life. It also assumes that cash revenues will be
reinvested at the same discount rate. Here again, this may not hold in reality when interest rates in the market
are fluctuating. As for the IRR, while not holding the same limitations since it is purely a function of the in
and out cashflows of a particular investment, it does not give a quantified picture of the financial impact the
investment will have on the firm. It is only a target to meet [24].
The paper [23] acknowledges these limitations and others related to financing during the construction time,
and proposes another metric, 𝑁𝑃𝑉% or annualized and normalized NPV and defined as seen in equation 8.
𝑁𝑃𝑉% =
𝑁𝑃𝑉𝑝𝑟𝑜𝑗
𝐶𝐴𝑃𝐸𝑋 × 𝐿𝑖𝑓𝑒𝑡𝑖𝑚𝑒
(13)
𝑁𝑃𝑉%, the resulting quantity, is a normalized measure of pro- fitability expressed as a rate (%/year). It can
be compared directly with market rates of interest or anticipated returns from projects with similar levels of
risk. 𝑁𝑃𝑉𝑝𝑟𝑜𝑗 can be found by means of equation 9. To understand𝑁𝑃𝑉𝑝𝑟𝑜𝑗, it is necessary to remember
that in constructing power plants and operating them, a life span of 25-30 years is necessary. Considering
present as year 0, decisions are made in the years preceding the construction time. NPV evaluated at the
time of project initiation, i.e., 𝑁𝑃𝑉𝑝𝑟𝑜𝑗represents the potential increase in net worth of the company if a
prospective project is undertaken. It is obtained by projecting 𝑁𝑃𝑉0 back to the time a decision is made to
start the project [23].
𝑁𝑃𝑉𝑝𝑟𝑜𝑗 = 𝑁𝑃𝑉0 × (1 + 𝑑𝑖𝑠𝑜𝑢𝑛𝑡 𝑟𝑎𝑡𝑒)𝑁𝑐𝑜𝑛𝑠𝑡𝑟𝑢𝑐𝑡𝑖𝑜𝑛 (14)
5 LCOE: different approaches
As stated in the definition of the LCOE, depending on the perspective taken, societal or business owner,
costs can vary and as a result, the LCOE. Hence, there are various types of ways to calculate the LCOE,
added to the real/nominal LCOE. This section will explain in detail how to calculate the LCOE, and the
different methodologies followed.
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5.1 Societal perspective
Tax related costs or benefits are disregarded in this approach, and therefore, the only costs that need to be
taken into account in the LCOE calculations are CAPEX, OPEX and decommissioning costs. Following a
discounted cash flow method, and considering that the LCOE is the price at which electricity needs to be
sold to make costs and revenues equal over the lifetime of the project, the formula of the LCOE is given
by equation 11, as a rearrangement of equation 10 [17] [18]. The costs included here are derived from the
NPV formula reported in [17].
∑ (
𝐿𝐶𝑂𝐸
(1 + 𝑊𝐴𝐶𝐶)𝑡× 𝐸𝑡) = ∑
𝐶𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁
𝑡=0
𝑁
𝑡=0
(15)
𝐿𝐶𝑂𝐸 = ∑
𝐶𝐴𝑃𝐸𝑋𝑁𝑐𝑜𝑛𝑠 × (1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁𝑐𝑜𝑛𝑠−1𝑡=0 + ∑
𝑂𝑃𝐸𝑋(1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁𝑐𝑜𝑛𝑠+𝑁𝑜𝑝−1
𝑡=𝑁𝑐𝑜𝑛𝑠+ ∑
𝐷𝑒𝑐𝑜𝑁𝑑𝑒𝑐 × (1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁−1𝑁𝑐𝑜𝑛𝑠+𝑁𝑜𝑝
∑𝐸𝑡 × (1 − 𝑆𝐷𝑅)𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑡=0
(16)
In equation 11, 𝑁𝑐𝑜𝑛𝑠 stands for years of construction, 𝑁𝑜𝑝 year of operation, 𝑁𝑑𝑒𝑐 decommission years
and N is the lifetime of the plant. 𝐷𝑒𝑐𝑜 is the decomissiong cost, 𝐸𝑡the annual energy yield multiplied by
the system degradation rate SDR, which is often negligible for CSP [17]. Equation 11 does not account for
inflation, and therefore the LCOE expressed is a nominal LCOE. When the inflation rate is known, the
nominal LCOE can be calculated by means of the same equation, with replacing the WACC by the nominal
WACC, and applying inflation to the OPEX. To calculate the nominal WACC, equation is 6 is used, where
the interest rates 𝐼𝑅𝑅% and 𝑖𝑑𝑒𝑏𝑡 are nominal, instead of real. To switch from a real discount rate to a
nominal one when inflation 𝑖 is known, equation 12 is used [7].
𝑑𝑟𝑒𝑎𝑙 =
(1 + 𝑑𝑛𝑜𝑚𝑖𝑛𝑎𝑙)
(1 + 𝑖)− 1
(17)
Equation 11 appears to be a bit complex, but it is a straightforward method using basic discounted cash
flows for the determination of the LCOE. In [17] [154] however, a simplified version of this formula is
reported, and that does not require utilizing annual cash flows, but instead relies on annualized costs and
energy production. Equation 13 gives then another method for calculating the LCOE.
𝐿𝐶𝑂𝐸 =
𝛼 × 𝐶𝐴𝑃𝐸𝑋 + 𝑂𝑃𝐸𝑋 + 𝛽 × 𝐷𝑒𝑐𝑜
𝐸𝑛𝑒𝑡
(18)
The annual electricity output 𝐸𝑛𝑒𝑡 used in the above formula is the net annual electricity output estimated
for the first year of operation, often calculated from performing dynamic power plant simulations using
typical meteorological year (TMY) data [17]. Since this method uses annual values of the costs and energy
yield, the long term costs like CAPEX and decommissioning need to be transformed into annual payment.
This is done through the α and β factors. The capital recovery factor α determined by equation 14, can be
seen as the amount of equal (or uniform) payments to be received for n years such that the total present
value of all these equal payments is equivalent to a payment of one dollar at present [26]. It is a function of
the discount factor d, 𝑁𝑜𝑝 years of operation and the annual plant insurance rate.
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𝛼 = 𝑓𝑐𝑜𝑛 ×
𝑑 × (1 + 𝑑)𝑁𝑜𝑝
(1 + 𝑑)𝑁𝑜𝑝 − 1+ 𝑘𝑖𝑛𝑠
(19)
As seen in equation 11, the CAPEX is discounted over the course of the plant construction, as it is not an
overnight payment that happens instantly. To reflect this, the 𝑓𝑐𝑜𝑛 factor is used in equation 14. It translates
the fact that during erection of the power plant, interest begins to accumulate on the money that has been
borrowed to finance the construction. The longer it takes to build the plant, the more interest that
accumulates and the greater the total revenue that needs to be generated [154]. Equation 15 gives the
formula for 𝑓𝑐𝑜𝑛.
𝑓𝑐𝑜𝑛 =
(1 + 𝑑)𝑁𝑐𝑜𝑛 − 1
𝑁𝑐𝑜𝑛 × 𝑑
(20)
The other factor 𝛽 serves the same purpose, but applied to the decommissioning costs. By means of
equations 16 and 17, 𝛽 is determined. The additional factor𝑓𝑑𝑒𝑐 takes into account the number of years𝑁𝑑𝑒𝑐
that it takes to decommission the power plant. Longer decommissioning times allow part of the costs to be
pushed further into the future, reducing the impact of the decommissioning costs on the overall levelized
cost of electricity [154].
𝛽 = 𝑓𝑑𝑒𝑐 ×𝑑
(1 + 𝑑)𝑁𝑜𝑝 − 1
(21)
𝑓𝑑𝑒𝑐 =
(1 + 𝑑)𝑁𝑑𝑒𝑐 − 1
𝑑 × 𝑁𝑑𝑒𝑐 × (1 + 𝑑)𝑁𝑑𝑒𝑐−1
(22)
Beside the fact that the two mentioned methods of calculating the LCOE differ by their definitions, the
second method incorporate an insurance rate materialized by the means of 𝑘𝑖𝑛𝑠 in the capital recovery
factor. This adds a cost that is disregarded in the first methodology. However, it can easily be corrected in
equation 11, by changing the first summation in the numerator to ∑(1+𝑘𝑖𝑛𝑠)×𝐶𝐴𝑃𝐸𝑋
(1+𝑊𝐴𝐶𝐶)𝑡 .
The social perspective does not account for tax related costs as seen in equation 11. Nevertheless, the use
of the WACC calculated by means of equation 6 in this LCOE calculation implies accounting for tax benefits
related to the debt financing. As explicated above, the complementary tax factor in equation 6 translates the
benefit of tax deduction financing with loans has on the project. It can be argued therefore that in this
method, a simplified WACC should be used instead, where there is no factor related to corporate tax.
However, keeping it the calculation that way approaches the real cost of electricity and gives an idea of the
impact of debt financing on the performance of the plant. The simplified version of the LCOE presented
above, which is generally used to have a quick grasp on the performance of power plants, can keep the tax
in the WACC, since it gives only an idea about the value of the LCOE, and not the actual one that should
be determined with the discounted cash flow method.
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5.2 Business/developer perspective
To fully evaluate the LCOE of a solar power plant, cash flow determination needs to be precise and has to
embody all key issues that can be encountered. Typically, these are [150] :
• As debt financing is usually involved, the loans may be paid off over a different timescale than equity investments.
• Tax benefits linked to debt vary according to jurisdictions.
• Depreciation and its tax shield may have a shorter time than the plant’s operations.
• As construction of the plant takes several years, the interest rate for finance during those years is higher
• System output may take some time to stabilize as commissioning processes proceed after first start-up
• Major plant upgrade expenditures may be predicted at certain times in addition to overall continuous O&M
• Various inputs may be subject to different escalation rates
Naturally, these considerations are very project and location dependent, but also on the developers status
and choice of technology. Nevertheless, equation 18 can be constructed for the LCOE in this perspective.
It is implied in this formula that the LCOE is nominal, with the WACC being nominal, and the OPEX
increasing yearly with inflation. As [150] states, it is a somewhat simplified approach that allows sufficient
complexity to allow issues of tax, cost of equity and cost of debt to be examined
𝐿𝐶𝑂𝐸 =
∑𝑇𝐶𝐼
𝑁𝑐𝑜𝑛𝑠 × (1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑐𝑜𝑛−1𝑡=0 − ∑
𝐷𝐸𝑃 × 𝑇(1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁𝑐𝑜𝑛+𝑁𝑑𝑒𝑝−1
𝑡=𝑁𝑐𝑜𝑛+ ∑
𝐼𝑁𝑇𝑡 × (1 − 𝑇)(1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁𝑐𝑜𝑛+𝑁𝐿−1𝑡= 𝑁𝑐𝑜𝑛
∑𝐸𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑡=0
+
∑𝑂𝑃𝐸𝑋 × (1 − 𝑇)
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑐𝑜𝑛+𝑁𝑜𝑝−1
𝑡= 𝑁𝑐𝑜𝑛+ ∑
𝐷𝑒𝑐𝑜𝑁𝑑𝑒𝑐 × (1 + 𝑊𝐴𝐶𝐶)𝑡
𝑁−1𝑡= 𝑁𝑐𝑜𝑛+𝑁𝑜𝑝
∑𝐸𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑁𝑡=0
Equation 18 [150] [27]build on the previous formula shown for LCOE by adding up tax related elements:
- TCI as explained in [23] is the true “up-front” capital requirements, including interest that would
be charged since construction takes multiples years.
- The second summation represents the tax deduction due to the use of asset depreciation, with Ndep
being the number of years of depreciation, and DEP the amount of yearly depreciation.
- Third term refers to the tax shield gained by having a portion of the capital investment financed by
debt. NL stands for the duration of loan repayment and INT is the annual interest payment. The
tax benefit concerns only the interest of the loan, and not the principal. Repaying off the principal
is not included in the costs for the LCOE as from the project’s point of view, the cost of securing
debt is the interests paid. The principal is the company’s money to run the project.
- Interest payment during construction period results in higher loans because of no generated
revenue during that period. Therefore the CAPEX rises by, for example two or three years of
interest payment. CAPEX including the loan payments during the construction period
(construction loan with a construction interest rate 𝐶𝑅) are the total capital investment (TCI) [23],
and can be calculated with equation 19 in the example of a construction time of 3 years. How
depreciation is done varies from jurisdiction to jurisdiction, and therefore a simplified and linear
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model is shown in equation 20. It simply depreciates the CAPEX over the years of operation of
the plant. Other models include accelerated depreciation with various percentages each year [28].
Finally, the interest of the loan payed yearly 𝐼𝑁𝑇𝑡 is determined by calculation of the yearly total
debt payment (principal+interest) 𝐷𝑡𝑜𝑡 by means of the loan constant 𝑐𝑙𝑜𝑎𝑛, then subtracting the
principal payment each year. This is done with equations 21 to 24. The NPV calculation in this
perspective is the same, with the only difference that now, tax deduction occurs in the revenues
each year, so equation 5 needs to be multiplied by the complementary factor of the corporate tax
rate [29] [16].
𝑇𝐶𝐼 = 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)2 + 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)1 + 𝐶𝐴𝑃𝐸𝑋 × (1 + 𝐶𝑅)0 (23)
𝐷𝐸𝑃 = 𝐶𝐴𝑃𝐸𝑋/𝑁𝑜𝑝 (24)
𝑐𝑙𝑜𝑎𝑛 =
𝑖𝑑𝑒𝑏𝑡
1 − (1 + 𝑖𝑑𝑒𝑏𝑡)−𝑁𝐿
(25)
𝐷𝑡𝑜𝑡 = 𝑐𝑙𝑜𝑎𝑛 × 𝐶𝐴𝑃𝐸𝑋 × 𝐷𝑒𝑏𝑡% = 𝐼𝑁𝑇𝑡 + 𝑃𝑅𝐼𝑁𝑡
(26)
𝐼𝑁𝑇𝑡 = 𝑖𝑑𝑒𝑏𝑡 × 𝐷𝑡 (27)
𝐷𝑡 = 𝐷𝑡−1 − 𝑃𝑅𝐼𝑁𝑡−1 𝑎𝑛𝑑 𝐷0 = 𝐷𝑒𝑏𝑡% × 𝐶𝐴𝑃𝐸𝑋 (28)
5.3 LCOE for PPA projects
While the LCOE is the price at which electricity needs to be sold to recover all the costs of the project, the
PPA price is the power purchase bid price for projects involved in a RES tender, where project developers
sell electricity at a price negotiated through a power purchase agreement (PPA). As mentioned in the
beginning, those tenders base mainly their decision to award the project to a developer on that price.
The PPA price is determined in a way to meet the specified internal rate of return of the developer. This is
done by solving equation 7, iteratively finding the price of electricity that would bring the NPV to a null or
positive value. This way, a minimal value of the PPA price is found that would produce an NPV greater or
equal to zero [27].
Typically, the PPA for a power plant project will escalate with a certain percentage each year, primarily to
account for inflation or to comply with an agreed rate with the generation off-taker. This annual value of
the PPA tariff (PPAt) can be expressed by using the first-year PPA tariff (PPA1) and an annual escalation
rate (resc) [27], so that
𝑃𝑃𝐴𝑡 = 𝑃𝑃𝐴1 × (1 + 𝑟𝑒𝑠𝑐)𝑡−1 (29)
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The relationship between the PPA and the LCOE can be seen in equation 26, where the LCOE stands for
the amount the project must receive for each unit of energy to cover the projects costs and the additional
revenue required to meet the target internal rate of return [30]. This offers a way to determine the PPA once
the LCOE is known, and vice versa.
𝐿𝐶𝑂𝐸 = ∑
𝑃𝑃𝐴𝑡 × 𝐸𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑡=𝑁𝑡=0
∑𝐸𝑡
(1 + 𝑊𝐴𝐶𝐶)𝑡𝑡=𝑁𝑡=0
(30)
Figure X points this relationship, by comparing the LCOE and PPA price with regards to inflation and
escalation rates. The calculated values are for a 64 MW sample wind farm that generates 176 GWh of
electricity in its first year with a total installed cost of $2,000/kW and a 2.2 cent/kWh production tax credit.
The shades of color in the table show the relative magnitude of the values (higher values are darker than
lower values)
More specifically, the following can be mentioned [31] :
When the inflation rate and PPA price escalation rate are both zero, the PPA price, nominal LCOE and real
LCOE are equal.
When the inflation rate is zero, the real and nominal LCOE are equal.
When the PPA price escalation rate is zero, the PPA price and nominal LCOE are equal.
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7 Appendix 2
Cleanergy is a Swedish cleantech company specialized in renewable solutions based on the Stirling engine technology. The Sunbox is a 13 kW concentrated solar power (CSP) system in which a Stirling engine is powered with energy from the Sun, collected by an array of mirrors. Cleanergy and the Moroccan Agency for Sustainable Development (MASEN) have recently entered in a Cooperation Framework Agreement to jointly develop a Thermal Energy Storage system that shall be coupled to Cleanergy’s CSP-Stirling based solar electricity technology. The agreement also seeks to jointly identify business opportunities for such a novel technology in the Kingdom of Morocco. The following questionnaire consists of a set of 10 questions which are aimed at providing a better understanding of the Moroccan market. Please note that no third parties will be given access to individual company data. Data will be analyzed anonymously and used for research purposes only.
1. Please rank, from cheapest to most expensive, the electricity providers powering your sites.
ONEE Independent Power Producer (IPP ) Self production
2. What is the average electricity price paid by the company?
☐ Following ONEE tariff scheme ☐ Other
3. In the case of self-production. when there is excess electricity, do you sell it back to the grid?
☐ Yes. Specify the rate ☐ No
Compared to the other solar technologies, the Sunbox has the highest sun-to-electricity efficiency (30%), it is modular and can be located near consumption sites where the energy is needed, thus saving grid infrastructure cost. The thermal energy storage system will enable electricity production on- demand in a more cost-effective way than through the use of electrical batteries, especially for large storage requirements. The integrated solution with storage will be demonstrated in 2019.
4. In the future, how is the company planning to answer its growing electricity needs?
☐ Expand ONEE contracts ☐Expand/Use IPP contracts ☐Invest in self-production
5. Is your company interested in renewable energy through IPPS/self-production?
☐ Yes. Rank by order of likelihood: Solar Wind Biomass
☐ No
6. In the future, how likely will the company be open to invest in solar CSP?
☐ Likely to invest ☐Technology neutral ☐ Not likely to invest
7. Considering its advantages, would you contemplate purchasing electricity from the SunBox?
☐ Yes ☐ No
8. If yes, what is the maximum price you would be willing to pay for as an off-taker?
☐ Less than 0,6 DHS/kWh ☐ 0,6-0,9DHS/kWh ☐More than 0,9 DHS/kWh
9. In the case one of the demo-plants of Cleanergy AB is located near one of your sites, would you be interested in becoming off-taker after verification and validation is completed?
☐ Yes ☐ No
10. Would you like to receive updates about the technology development and its demonstration?
☐ Yes ☐ No
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8 Appendix 3
Site Size (MW) Normalized LCOE Mirror area (m²) TES size (hours) CF (%)
MM11 84,3 1,239 220 10 68%
MM12 28,1 1,239 220 10 68%
MM21 29,2 1,194 220 11 72%
MM22 1,0 1,158 220 11 74%
MM23 21,2 1,121 220 13 81%
MM24 15,3 1,158 220 12 76%
MM25 53,0 1,239 220 10 68%
MM31 1,9 1,071 220 13 84%
MM41 10,0 1,195 220 11 72%
MC11 3,5 1,193 220 11 72%
MC12 14,8 1,194 220 11 72%
MC13 15,7 1,239 220 10 68%
MC21 38,3 1,194 220 11 72%
MC22 2,0 1,361 220 10 62%
MC23 26,9 1,286 220 10 65%
MC24 8,7 1,194 220 11 72%
MC31 3,1 1,287 220 10 65%
MC32 3,1 1,287 220 10 65%
MC41 8,7 1,194 220 11 72%
MMe11 30,3 1,287 220 10 65%
MA11 3,8 1,286 220 10 65%
MA21 0,2 1,243 220 12 71%
TC11 24,0 1,287 220 10 65%
TC21 11,5 1,286 220 10 65%
TM11 16,6 1,287 220 10 65%
TF11 2,3 1,286 220 10 65%
TF12 2,6 1,286 220 10 65%
TF13 3,2 1,287 220 10 65%
TF14 9,8 1,287 220 10 65%
TF15 3,4 1,289 220 10 65%
TF21 9,8 1,287 220 10 65%
TF22 2,7 1,288 220 10 65%
TF31 7,5 1,287 220 10 65%
EC11 146,6 1,158 220 12 76%
EC21 99,2 1,194 220 11 72%
EC31 28,8 1,287 220 10 65%
EC32 65,3 1,287 220 10 65%
EC33 57,6 1,287 220 10 65%
EC34 5,2 1,195 220 11 72%
EC41 49,2 1,072 220 13 84%
EC51 78,0 1,121 220 13 81%
EI11 47,5 1,287 220 10 65%
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EI21 8,6 1,239 220 10 68%
EI31 5,6 1,239 220 11 69%
EI41 26,9 1,194 220 11 72%
EI42 14,3 1,239 220 10 68%
EM11 177,8 1,194 220 11 72%
EM21 40,0 1,121 220 13 81%
JC11 1,4 1,197 220 10 70%
JC12 13,4 1,073 220 13 84%
JC21 15,0 1,158 220 12 76%
JC31 20,4 1,121 220 13 81%
JC41 10,6 1,121 220 13 81%
JC42 10,1 1,073 220 13 84%
JM11 5,3 1,073 220 13 84%
JM21 4,1 1,000 220 14 92%
JM31 28,9 1,287 220 10 65%
JM32 22,4 1,073 220 13 84%
JF11 7,1 1,157 220 12 76%
JF21 3,2 1,000 220 14 92%
JF31 7,6 1,001 220 14 92%
JF41 14,9 1,121 220 13 81%
SM11 23,0 1,157 220 12 76%
SM12 24,6 1,157 220 12 76%
SM13 6,6 1,158 220 12 76%
SM13 6,6 1,158 220 12 76%
SM15 6,3 1,121 220 13 81%
SM15 6,3 1,121 220 13 81%
SM21 41,9 1,287 220 10 65%
SM31 6,1 1,159 220 12 76%
SC11 17,7 1,000 220 14 92%
SC21 25,4 1,073 220 13 84%
SC31 23,1 1,158 220 12 76%
SC41 37,0 1,239 220 10 68%
SC41 37,0 1,239 220 10 68%
SC61 33,3 1,239 220 10 68%
9 Appendix 4
Site Size (MW) Normalized LCOE Mirror area (m²) TES size (hours) CF (%)
MM11 78,68 1,269 220 14 73%
MM12 26,23 1,269 220 14 73%
MM21 27,65 1,212 220 14 76%
MM22 0,90 1,173 220 14 79%
MM23 20,87 1,127 220 14 82%
MM24 14,76 1,167 220 14 79%
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MM25 49,46 1,269 220 14 73%
MM31 1,91 1,077 220 14 86%
MM41 9,47 1,212 220 14 76%
MC11 3,30 1,211 220 14 76%
MC12 14,00 1,211 220 14 76%
MC13 14,67 1,270 220 14 73%
MC21 36,25 1,211 220 14 76%
MC22 1,92 1,415 220 14 65%
MC23 25,43 1,335 220 14 69%
MC24 8,24 1,213 220 14 76%
MC31 2,91 1,339 220 14 69%
MC32 2,91 1,339 220 14 69%
MC41 8,24 1,213 220 14 76%
MMe11 28,64 1,335 220 14 69%
MA11 3,57 1,335 220 14 69%
MA21 0,18 1,319 220 12 71%
TC11 22,70 1,335 220 14 69%
TC21 10,90 1,335 220 14 69%
TM11 15,67 1,336 220 14 69%
TF11 2,18 1,339 220 14 69%
TF12 2,48 1,336 220 14 69%
TF13 3,07 1,338 220 14 69%
TF14 9,31 1,335 220 14 69%
TF15 3,22 1,335 220 14 69%
TF21 9,31 1,335 220 14 69%
TF22 2,58 1,335 220 14 69%
TF31 7,13 1,336 220 14 69%
EC11 141,29 1,168 220 14 79%
EC21 93,91 1,211 220 14 76%
EC31 27,24 1,336 220 14 69%
EC32 61,75 1,335 220 14 69%
EC33 54,48 1,335 220 14 69%
EC34 4,94 1,212 220 14 76%
EC41 48,28 1,076 220 14 86%
EC51 76,59 1,127 220 14 82%
EI11 44,91 1,335 220 14 69%
EI21 8,00 1,270 220 14 73%
EI31 5,34 1,269 220 14 73%
EI41 25,46 1,212 220 14 76%
EI42 13,34 1,269 220 14 73%
EM11 134,80 1,211 220 14 76%
EM21 39,30 1,127 220 14 82%
JC11 1,30 1,215 220 14 76%
JC12 13,11 1,077 220 14 86%
JC21 14,43 1,168 220 14 79%
-95-
JC31 20,07 1,127 220 14 82%
JC41 10,40 1,127 220 14 82%
JC42 9,94 1,076 220 14 86%
JM11 5,24 1,078 220 14 86%
JM21 4,12 1,000 220 14 92%
JM31 27,33 1,335 220 14 69%
JM32 22,02 1,076 220 14 86%
JF11 6,87 1,169 220 14 79%
JF21 3,23 1,000 220 14 92%
JF31 7,58 1,001 220 14 92%
JF41 14,66 1,127 220 14 82%
SM11 22,13 1,167 220 14 79%
SM12 23,71 1,168 220 14 79%
SM13 6,38 1,167 220 14 79%
SM13 6,38 1,167 220 14 79%
SM15 6,16 1,127 220 14 82%
SM15 6,16 1,127 220 14 82%
SM21 39,63 1,335 220 14 69%
SM31 5,85 1,168 220 14 79%
SC11 17,68 1,000 220 14 92%
SC21 24,87 1,076 220 14 86%
SC31 22,23 1,168 220 14 79%
SC41 34,52 1,269 220 14 73%
SC41 34,52 1,269 220 14 73%
SC61 31,07 1,269 220 14 73%
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10 Appendix 5
MM11
MM12
MM21
MM22
MM23
MM24
MM25
MM31
MM41
MC11
MC12
MC13
MC21
MC22
MC23
MC24
MC31MC32
MC41
MMe11MA11
MA21
TC11
TC21
TM11
TF11
TF12
TF13 TF14TF15
TF21
TF22
TF31
EC11
EC21
EC31
EC32EC33
EC34
EC41
EC51
EI11
EI21EI31
EI41
EI42
EM11
EM21
JC11
JC12
JC21
JC31
JC41
JC42JM11
JM21
JM31
JM32
JF11
JF21 JF31
JF41
SM11SM12
SM13
SM13
SM15SM15
SM21
SM31
SC11
SC21
SC31
SC41SC41
SC61
1,000
1,050
1,100
1,150
1,200
1,250
1,300
1,350
0 200000 400000 600000 800000 1000000 1200000
No
rmal
ized
LC
OE
(€/M
Wh
)
Energy consumed (MWh)
Morocco
Tunisia
Egypt
Jordan
Saudi Arabia
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11 Appendix 6
Potential (MW)
DNI (kWh/m²/year)
Normalized LCOE
Macro-environmental
factors (%)
Grid access
Final score
Weighting factors
7,00 9,00 10,00 5,00 10,00 41,00
Value Score Value Score Value Score Score Score
MM11 84,33 10,00 2115 1,95 1,239 7,71 8,00 5,00 6,21
MM12 28,11 5,62 2115 1,95 1,239 7,72 8,00 5,00 5,47
MM21 29,20 5,84 2215 2,86 1,194 8,79 8,00 5,00 5,96
MM22 0,96 0,19 2315 3,77 1,158 9,67 8,00 5,00 5,41
MM23 21,25 4,25 2415 4,68 1,121 10,00 8,00 5,00 6,39
MM24 15,32 3,06 2315 3,77 1,158 9,66 8,00 5,00 5,90
MM25 53,00 10,00 2115 1,95 1,239 7,71 8,00 5,00 6,21
MM31 1,95 0,39 2515 5,59 1,071 10,00 8,00 10,00 7,15
MM41 10,00 2,00 2215 2,86 1,195 8,78 8,00 5,00 5,31
MC11 3,48 0,70 2215 2,86 1,193 8,83 8,00 5,00 5,10
MC12 14,79 2,96 2215 2,86 1,194 8,79 8,00 5,00 5,47
MC13 15,72 3,14 2115 1,95 1,239 7,71 8,00 5,00 5,04
MC21 38,29 7,66 2215 2,86 1,194 8,79 8,00 5,00 6,28
MC22 2,03 0,41 1915 0,14 1,361 0,00 8,00 5,00 2,29
MC23 26,89 5,38 2015 1,05 1,286 6,57 8,00 5,00 4,95
MC24 8,70 1,74 2215 2,86 1,194 8,79 8,00 5,00 5,27
MC31 3,07 0,61 2015 1,05 1,287 6,55 8,00 5,00 4,13
MC32 3,07 0,61 2015 1,05 1,287 6,55 8,00 5,00 4,13
MC41 8,70 1,74 2215 2,86 1,194 8,79 8,00 5,00 5,27
MMe11 30,29 6,06 2015 1,05 1,287 6,57 8,00 5,00 5,06
MA11 3,77 0,75 2015 1,05 1,286 6,59 8,00 5,00 4,16
MA21 0,18 0,04 2115 1,95 1,243 7,62 8,00 5,00 4,49
TC11 24,01 4,80 2015 1,05 1,287 0,00 6,00 5,00 3,00
TC21 11,53 2,31 2015 1,05 1,286 0,00 6,00 5,00 2,57
TM11 16,57 3,31 2015 1,05 1,287 0,00 6,00 5,00 2,75
TF11 2,31 0,46 2015 1,05 1,286 0,00 6,00 5,00 2,26
TF12 2,62 0,52 2015 1,05 1,286 0,00 6,00 5,00 2,27
TF13 3,25 0,65 2015 1,05 1,287 0,00 6,00 5,00 2,29
TF14 9,85 1,97 2015 1,05 1,287 0,00 6,00 5,00 2,52
TF15 3,41 0,68 2015 1,05 1,289 0,00 6,00 5,00 2,30
TF21 9,85 1,97 2015 1,05 1,287 0,00 6,00 5,00 2,52
TF22 2,72 0,54 2015 1,05 1,288 0,00 6,00 5,00 2,27
TF31 7,54 1,51 2015 1,05 1,287 0,00 6,00 5,00 2,44
EC11 146,62 10,00 2315 3,77 1,158 4,54 6,50 5,00 5,66
EC21 99,20 10,00 2215 2,86 1,194 3,31 6,50 5,00 5,15
EC31 28,81 5,76 2015 1,05 1,287 0,20 6,50 5,00 3,27
EC32 65,31 10,00 2015 1,05 1,287 0,20 6,50 5,00 4,00
EC33 57,63 10,00 2015 1,05 1,287 0,19 6,50 5,00 4,00
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EC34 5,22 1,04 2215 2,86 1,195 3,28 6,50 5,00 3,62
EC41 49,22 9,84 2515 5,59 1,072 6,00 6,50 5,00 6,38
EC51 77,96 10,00 2415 4,68 1,121 5,32 6,50 5,00 6,04
EI11 47,50 9,50 2015 1,05 1,287 0,20 6,50 5,00 3,91
EI21 8,58 1,72 2115 1,95 1,239 1,79 6,50 5,00 3,17
EI31 5,59 1,12 2115 1,95 1,239 1,78 6,50 5,00 3,07
EI41 26,90 5,38 2215 2,86 1,194 3,30 6,50 5,00 4,36
EI42 14,30 2,86 2115 1,95 1,239 1,80 6,50 5,00 3,37
EM11 711,93 10,00 2215 2,86 1,194 3,31 6,50 5,00 5,15
EM21 40,00 8,00 2415 4,68 1,121 5,32 6,50 10,00 6,92
JC11 1,41 0,49 2215 2,86 1,197 9,78 6,75 5,00 5,14
JC12 13,37 2,67 2515 5,59 1,073 10,00 6,75 5,00 6,17
JC21 14,98 3,00 2315 3,77 1,158 9,94 6,75 5,00 5,81
JC31 20,43 4,09 2415 4,68 1,121 10,00 6,75 5,00 6,21
JC41 10,59 2,12 2415 4,68 1,121 10,00 6,75 5,00 5,87
JC42 10,13 2,03 2515 5,59 1,073 10,00 6,75 5,00 6,05
JM11 5,34 1,07 2515 5,59 1,073 10,00 6,75 5,00 5,89
JM21 4,12 0,82 2715 7,41 1,000 10,00 6,75 5,00 6,25
JM31 28,91 5,78 2015 1,05 1,287 9,42 6,75 5,00 5,56
JM32 22,45 4,49 2515 5,59 1,073 10,00 6,75 5,00 6,48
JF11 7,13 1,43 2315 3,77 1,157 9,95 6,75 5,00 5,54
JF21 3,23 0,65 2715 7,41 1,000 10,00 6,75 5,00 6,22
JF31 7,58 1,52 2715 7,41 1,001 10,00 6,75 5,00 6,37
JF41 14,93 2,99 2415 4,68 1,121 10,00 6,75 5,00 6,02
SM11 22,96 4,59 2315 3,77 1,157 4,54 5,50 5,00 4,61
SM12 24,60 4,92 2315 3,77 1,157 4,54 5,50 5,00 4,67
SM13 6,62 1,32 2315 3,77 1,158 4,51 5,50 5,00 4,04
SM13 6,62 1,32 2315 3,77 1,158 4,51 5,50 5,00 4,04
SM15 6,27 1,25 2415 4,68 1,121 5,21 5,50 5,00 4,40
SM15 6,27 1,25 2415 4,68 1,121 5,21 5,50 5,00 4,40
SM21 41,91 8,38 2015 1,05 1,287 0,20 5,50 5,00 3,60
SM31 6,07 1,21 2315 3,77 1,159 4,50 5,50 5,00 4,02
SC11 17,68 3,54 2715 7,41 1,000 6,34 5,50 5,00 5,67
SC21 25,36 5,07 2515 5,59 1,073 5,67 5,50 5,00 5,37
SC31 23,06 4,61 2315 3,77 1,158 4,54 5,50 5,00 4,61
SC41 37,00 7,40 2115 1,95 1,239 1,79 5,50 5,00 4,02
SC41 37,00 7,40 2115 1,95 1,239 1,79 5,50 5,00 4,02
SC61 33,30 6,66 2115 1,95 1,239 1,80 5,50 5,00 3,89
-99-
12 Appendix 7
Figure 47 Case 1
Figure 48 Case 2
Figure 49 Case 3
3,31
4,29
6,45
2,33
5,41
Tunisia
Egypt
Saudi Arabia
Morocco
Jordan
3,51
4,35
6,24
2,50
5,47
Tunisia
Egypt
Saudi Arabia
Morocco
Jordan
3,87
6,07
4,50
3,13
5,49
Tunisia
Saudi Arabia
Egypt
Morocco
Jordan
-100-
Figure 50 Case 4
Figure 51 Case 5
Figure 52 Case 6
3,44
4,74
4,77
4,78
5,38
Tunisia
Morocco
Saudi Arabia
Jordan
Egypt
3,09
4,30
4,46
4,81
5,71
Tunisia
Jordan
Morocco
Saudi Arabia
Egypt
2,96
4,27
4,55
4,83
4,84
Tunisia
Morocco
Saudi Arabia
Jordan
Egypt
-101-
Figure 53 Case 7
Figure 54 Case 8
3,95
4,92
5,17
5,39
5,61
Tunisia
Saudi Arabia
Jordan
Morocco
Egypt
3,75
4,819
4,824
4,84
5,37
Tunisia
Saudi Arabia
Jordan
Morocco
Egypt
-102-
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