INSE 6421: Systems Integration and Testing
Survey Paper Title:
Solar Energy Site Selection using GIS-based with MultiCriteria Decision Making
Prepared by: Hassan Al Garni
ID# 9737634
To Professor:
Dr. Rachida Dssouli
April 11th, 2014
Contents
Abstract ........................................................................................................................................... 2
1.
Introduction ............................................................................................................................. 3
2.
Overview of Multi-Criteria Decision making methods ............................................................ 5
2.1
Multi-Attribute Utility Theory (MAUT) ......................................................................... 7
2.2
The Elimination and Choice Translating Reality (ELECTRE) ............................................. 7
2.3
Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) ... 8
2.4
Analytical Hierarchy Process (AHP) ................................................................................. 9
2.5
Technique for Order of Preference by Similarity to Ideal Solution ............................... 12
3.
Application of MCDM in Renewable Energy ......................................................................... 13
4.
Geographical Information Systems (GIS)............................................................................... 15
4.1
GIS in Renewable Energy Applications .......................................................................... 16
4.2
Integrating GIS and MCDM............................................................................................ 18
5.
Solar Energy site selection using GIS-based with MCDM:..................................................... 19
6.
Conclusion ............................................................................................................................. 21
7.
References ............................................................................................................................. 23
Abstract
Renewable energy resources present a sustainable, environment friendly and cost effective
energy in long term. In last decade, solar energy considered the fastest growing renewable
energy resource. One of the barriers to the solar energy development is its variability which
can be different geographically from one place to another. The Integration of GIS and
MCDM offers an effective decision support system for solar farms site selection by
providing results that can be meaningfully displayed using GIS. This review paper
particularly examines the recent published papers that applied GIS-MCDM to achieve the
optimal
placement
of
solar
energy
systems
with
comparison
analysis.
1. Introduction
Energy is the essential key element for sustainable development and prosperity of a
society in this era [1]. According to U.S. Energy Information Administration (EIA), Energy
sources are divided into two main groups: Nonrenewable resources that we are using up
and cannot recreate and Renewable that can be easily replenished .Renewable energy
sources include: Solar energy, which can be turned into electricity and heat, Wind energy,
geothermal energy from the Earth heat, Biomass from plants, and Hydropower from hydroturbines at a dam [2]. Those resources are free sources, sustainable, environment friendly
and more economical in long term.
The International Energy Agency (IEA) in its World Energy Outlook 2013 indicates that
global energy demand increases by one-third from 2011 to 2035. Demand grows for all
forms of energy while the contribution of fossil fuels in the world’s energy mix drops from
82% to 76% in 2035. Renewable and nuclear energy resources provide around 40% of the
growth in primary energy demand. Renewable energy resources almost supply half of the
net increase in electricity generation [3].
Solar energy could provide up to one-third of the world’s final energy demand after 2060
according to IEA analysis as shown in figure 1.1. Solar energy has two main kinds: Solar
photovoltaic (PV) that converts solar energy into electrical power by a Photovoltaic cell
made of a semiconductor material and Concentrating solar power (CSP) that has devices
to collect the sun’s rays to heat a receiver to high temperatures and then transformed first
into mechanical energy (by turbines or other engines) and then into electricity. Due to its
availability, environmental advantages, government incentives and advanced technology,
the Solar PV was the fastest growing renewable power technology worldwide over the
period 2000-2011. [4]
Figure 1.1 Total final energy by sources, 2060 [4]
One of the barriers to the solar energy development is its limitations and variability which
can be different geographically from one place to another. Using Multiple Criteria decision
making (MCDM) can help to facilitate the decision making related to site selection for
photovoltaic solar energy systems. Since the Solar energy is a natural resource with
inconsistent or limited availability, the strategic location selection can play a role to
maximize the energy collected and the output power generated [5]. MCDM offers useful
assistant to decision maker in mapping out the problem by providing a flexible tools to
handle and bring together a wide range of variables evaluated in different ways [6].
The Geographical Information system (GIS) is a powerful tool for consulting, analyzing
and editing data, map and spatial information. In recent years, GIS-based MCDM has
become increasingly popular as a tool for different site selection studies specially for the
energy planning. The integration of GIS and MCDM results in a useful tool to solve the
site selection problems for solar energy systems [7]. The main objectives of this survey is
to support decision makers in the field of solar energy farms developments and planning
where there are multiple tools of GIS-based integrated with MCDM can solve the
problems. Addressing such problems could correspond to select the “best” alternative from
potential alternatives or classifying alternatives into different categories sets.
2. Overview of Multi-Criteria Decision making methods
Multi-criteria decision making is a well-known area of decision making which considered
as a one branch of operation research models. It deals with solving decision problems under
numbers of decision criteria. A decision maker is able to choose among quantifiable or
non-quantifiable and multiple criteria. The problem solution is extremely dependent on the
choice of the decision maker and must be a compromise [10].
There are two major classes: Multi-attribute decision making (MADM) which is the
evaluation of a set of alternatives against a set of criteria and ultimate choices are made
among possible alternatives. On the other hand, Multi-objectives decision making
(MODM) described by DM’s multiple objectives which could be a statement about the
desired state of the system. MODM issues work with the objectives that require developing
specific relationships between attributes of the alternatives [8, 31].
In each of the above classes there are several methods. Priority based, outranking, distance
based and mixed methods that can be deployed to solve problems. Also, it can be classified
into deterministic, stochastic and fuzzy methods. Many recent researches have integrated
more than one method to reach optimal solution. [9].
MADM is one of the most popular MCDM methods Applied to solve problems from
different prospective [11]. With respect to each attribute, usually the best alternative is
selected by making comparisons between alternatives as shown in multi-criteria decision
process in figure 2.1[10]:
Formulation
of Options
Criteria
Selection
Selection of Decision Process
Performance Evaluation
Decide Decision Parameters
Application of the method
Evaluation of Result
Decison
Figure 2.1. Multi-criteria decision process [10].
MCDM has been applied in many areas such as integrated manufacturing system,
evaluations of technology investment, water and agriculture management and energy
planning. [10]. The most commonly MADM methods used in energy planning are:
Analytical Hierarchy Process (AHP), Preference ranking organization method for
enrichment evaluation (PROMETHEE), The elimination and choice translation reality
(ELECTRE), Multi-attribute utility theory (MAUT) [10,11].
2.1 Multi-Attribute Utility Theory (MAUT)
Multi-attribute utility theory (MAUT) is one of the most common used MCDM methods
to solve problem associated with different important issues. The simplest model is the
additive utility function as the follows:
K
U(Ai ) = ∑ wk uk (xik )
k=1
Where U(Ai ) represents the utility of the alternative i, wk represents the weight of the
attribute/criteria k, and uk (xik ) is the utility of attribute/criterion k of alternative i given
that the value of attribute/criterion j of alternative i is xik . The utility of each
attribute/criteria is not necessary to be linear. There are three basic models represented the
decision maker (DM) risk attitudes, linear (risk-neutral), concave (risk-averse) and convex
shape (risk-seeker). After the utility evaluation of each criterion, the integrated utility of
each alternative is assessed by weighted sum of the all attributes values of alternatives. The
highest integrated utility value considered the best alternative which should be selected by
Decision maker [11].
2.2 The Elimination and Choice Translating Reality (ELECTRE)
The elimination and choice translating reality (ELECTRE) tool is able to handle discrete
criteria of both quantitative and qualitative by providing order of all the alternatives. The
concern with this method is that to be so formulated that is chooses alternatives that are
preferred over most of the criteria and not less than acceptable level of any other criteria.
The concordance, discordance, indices and threshold values are applied in this technique
and a graph of relationship is developed which can be used to obtain the ranking of the
alternatives. The index which in the range of 0 and 1, denotes the degree of credibility of
each outranking relation and test the performance of each alternative [10].
The equation below represents the index of global concordance 𝐶𝑖𝑘 which denotes the
amount of evidence to support the concordance among all criteria.
𝑚
𝑚
𝐶𝑖𝑘 = ∑ 𝑊𝑗 𝑐𝑗 (𝐴𝑖 𝐴𝑘)/ ∑ 𝑊𝑗
𝑗=1
𝑗=1
Assuming that 𝐴𝑖 outranks Ak and 𝑊𝑗 is the weight of jth criteria. The limitation of this
method is that sometimes unable to obtain the preferred alternative. ELECTRE is more
suitable with large number of alternatives and few criteria due to its capability to
eliminate less preferable ones [10, 12].
2.3 Preference Ranking Organization Method for Enrichment
Evaluation (PROMETHEE)
PROMETHEE and ELECTRE are the main families of methods in the French school which
depends on the outranking of one alternative over another. By saying a outranks an
alternative b , we indicate that a should at least as good as b considering all criteria. [12].
Using this method in order to make up a preference function for each criterion a pair-wise
comparison is performed for all alternatives. A preference index for a over b is developed
based on the preference function [12]. PROMETHEE developed by Brans et al. in 1986
[13] with six generalized criteria functions for preference namely, usual criterion, quasi
criterion, criterion with linear preference, level criterion, criterion with linear preference
and indifference area and Gaussian criterion [10, 13].
𝑃𝑗 (𝑎, 𝑏) is a preference function used in this method which denotes the difference 𝑑𝑗
between alternatives a and b for any criteria j which also can be expressed as dj= f(a, j) −
f (b, j) where f(a, j) and f (b, j) are the values of alternatives a and b respectively for
criteria j. Depends on the type of the criteria function, the indifference and preference
thresholds q, and p, could be useful. The weighted average of the preference function
Pj (a, b) for all criteria is called Multi-criteria preference index π = (a, b) and defined as
π = (a, b) = ∑Jj=1 wj Pj(a, b)/ ∑Jj=1 wj
φ+ (a) = ∑ π = (a, b)
A
φ− (a) = ∑ π = (b, a)
A
φ(a) = φ+ (a) − φ− (a)
Considering wj is the weight allocated to the criterion j, 𝜑 + is the outranking index of a in
the alternative set A, 𝜑 − is the outranked index of a in the alternative set A. [10,13].The
maximum net ranking 𝜑 considered more preferred i.e.
𝑎 outranks 𝑏 𝑖𝑓𝑓 𝜑(𝑎) > 𝜑(𝑏)
2.4 Analytical Hierarchy Process (AHP)
The Analytical Hierarchy Process (AHP) introduced by Thomas L. Saaty in 1980 [14]. It
is an effective tool to solve complex decisions by providing DM with obvious ranking for
the best alternative selection. AHP shows it’s powerful for solving complicated problems
that may have correlation and interactions among multiple objectives. It has three main
levels including top level which is the goal, middle one which has the criteria and subcriteria, and the bottom level in the hierarchy which defined the alternatives. In each
hierarchy level a pair-wise comparison between the elements considered as input from
experts.
The AHP is an eigenvalues technique that can provides a numerical fundamental scale
ranges from 1 to 9 to calibrate the qualitative and quantitative performances of priorities.
Table 2.1 illustrates the verbal terms of the Saaty's fundamental scale [14, 15].
Scale of aij
Interpretation
1
i and j are equally important
3
i is slightly more important than j
5
i is more important than j
7
i is strongly more important than j
9
i is absolutely more important than j
Table2.1 Table of Saaty's fundamental scale.
The process of applying AHP method for decision making approach is demonstrated in
figure 2.2. Initially the DM should state the goal of the process and develop the main three
levels of hierarchy. After assessing all elements using the verbal scale, AHP computes and
aggregates the eigenvectors to obtain the composite final vector of weight coefficients for
all alternatives. The entries of final weight coefficients vector indicate the importance and
preference of each alternative towards the goal at the top of the hierarchy [10, 14, 15].
Define the Problem
Develop the Hierarchy
for Goal,criteria and
Alternatives
Expert input to weigh
against each criteria
using scale 1-9
Calculate Eigenvalues
Consistency index(CI)
Consistency ratio (CR)
for criteria/alternative
Yes
Matrix has CR>0.1
No
Select Alternative with
highest weight
Figure 2.2 AHP Process
Regarding the weight vector, the pair-wise comparison of matrix A is developed by
expert’s inputs at a given level.
𝑎11
𝑎
𝐴 = [ 21
𝑎𝑛1
𝑎12
𝑎21
𝑎𝑛2
.
.
.
𝑎1𝑛
𝑎2𝑛 ]
𝑎𝑛𝑛
A multiplication with weight coefficient of the element at the higher level should be made
after getting the weight vector. This process is repeatable upward for each level until reach
the top level of the hierarchy. The alternative with the highest weight coefficient considered
as the best alternative.
AHP has many advantages that make it an effective tool for DM such as ability to check
the inconsistency. Consistency index (CI) can assist DM and assure that judgment was
consistent and selection of the best alternative will be reliable. The value of CI should be
less than 0.1 otherwise re-evaluation of pair-wise comparison is required. [10, 14, 15]. AHP
has powerful performance in dealing with interdependent criteria involving both
quantitative and qualitative [16]. More details on how to calculate the weight vector and
consistency index are in references [14, 15].
AHP has widely criticized for some drawbacks such as pair-wise should be completed by
the experts only and its tedious process especially if huge numbers of criteria or alternatives
are involved. Experts may not make their judgments conscientiously due to feel tired and
lose patience during this process and therefore. To overcome such drawback, the DM can
consider only reasonable and manageable amounts of criteria in the process. [16]. While
AHP mathematical process is too long and complex, normally is performed by specially
designed computer programs. [12]
In some studies, a combination of different MCDM applied to use the strengths of both
methods. The AHP technique has been mostly popular for combination with other methods
[12].
2.5 Technique for Order of Preference by Similarity to Ideal Solution
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a method
of MCDM which based on the concept that the chosen alternative should have the shortest
geometric distance from the positive ideal solution (PIS) and the longest geometric distance
from the negative ideal solution (NIS). The final ranking is obtained by means of the
closeness index. The figure 2.3 illustrates the procedure of TOPSIS [22].
Step 1
Step 2
Step 3
Step 4
Step 5
Step 6
Step 7
•Establish a performance matrix
•Normalize the decision matrix
•Calculate the weighted normalized decision matrix
•Determine the +ve Ideal and -ve ideal solution
•Calculate the separation measures
•Calculate the relative closeness to the ideal solution
•Rank the Preferanace order
Figure 2.3 TOPSIS Steps [22]
3. Application of MCDM in Renewable Energy
Multi-criteria decision making analysis has been utilized to solve many real world
problems including energy sector issues. Energy system is a complex analysis that can be
determined as a multi-dimensional space with different boundaries and dynamic. Applying
MCDM provides a reliable insight to evaluate renewable energy resources and
technologies in face of different and conflict alternatives and criteria. [17]. In this section
examples of literature that covered application of MCDM in renewable energy.
A literature presented by Pohekar et al in review of MCDM in sustainable energy planning
[10]. More than 90 papers discussed and analyzed in terms of applicability and
methodologies used up to 1990 and beyond 1990. Study showed that MCDM applications
are highly popular for renewable energy planning (34%) as shown in figure 2.4.
Number of Refernces in [10]
7%
Renewable Energy Planning
12%
34%
Electric utility planning
Energy Resource Allocation
13%
Building Energy Management
Project Planning
15%
Others
19%
Figure 2.4 Categories of references [10]
Pohekar has also classified MCDM methods that have been applied in different areas of
energy planning and conclude that AHP was the most popular technique as a multiple
attribute decision making followed by outranking techniques PROMETHEE and
ELECTRE as shown in figure 2.5 [10].
Number of Refrences in [10]
25
20
22
15
14
10
13
10
5
7
4
0
Multi-Objective
MAUT
AHP
PROMETHEE
ELECTRE
Others
Number of Refrences in [10]
Figure 2.5 Tools of references [10]
Insights regarding the suitability of multi-criteria techniques in the context of renewable
energy planning provided by Polatidis et al. They demonstrate clearly that there is not one
method that can work superiorly to all identified attributes. Their main conclusion was
important criteria must be identified first, and then suitable methods should be selected.
Overall, ELECTRE III and PROMETHEE II seem to perform better in the context of
renewable energy problems at hand [18]. Loken claimed that energy planning is a field that
is quite suitable for the use of MCDA. He demonstrated many MCDM Methods with its
application in energy planning.
There was no preference given to any method. He determined that choice of method
generally depends on the preference of the DM with the importance to consider the
suitability, validity, and user-friendliness of the methods [12].
Wang et al analyzed different stages of multi-criteria decision-making for sustainable
energy, including criteria selection, criteria weighting, evaluation, and final aggregation.
He observed MCDA methods have been widely employed to sustainable energy decisionmaking considering multi-criteria and that AHP is the most popular comprehensive tool in
the rank-order weighting method. [19]
4. Geographical Information Systems (GIS)
According to international supplier of Geographic Information System, A geographic
information system (GIS) can be defined as a hardware, software, and data for consulting,
manipulating, analyzing, and displaying geographically information. GIS provide ability
to understand, interpret, and visualize data in many ways that disclose relationships,
patterns, and trends in the different forms. There are many benefits of GIS to organizations
of all sizes and in almost every industry including cost savings, managing geographically,
better decision making, enhanced communication, improved recordkeeping and increased
efficiency. [20, 21].
GIS handles the geographical data so the user can select the necessary data for a particular
project for study and analysis. The user allowed adding layers of information to a basemap
of real world locations. Various types of data sets are tied together geographically to
provide spatial context, such as power lines, road networks, urban mapping, land cover,
and demographic data can contain a multitude of information about a specific feature
(figure ) [22].
Figure 2.6 GIS layers model. [23]
4.1 GIS in Renewable Energy Applications
GIS and Renewable energy has been common combination to study renewable energy
problems and analyze data. Based on Scopus (www.scopus.com) data figures 2.7 and 2.8
demonstrate the trend of documents published integrating the two areas. More details are
given in [32].
Figure 2.7 GIS and Renewable energy publications 1990-2014 (March).
Scopus analyze results values - Country
80
70
60
50
40
30
20
10
0
Figure 2.8 GIS and Renewable energy publications 1990-2014 (March) based on Country.
The GIS can be used to eliminate unsuitable or restricted areas (undeveloped land,
community sites, infrastructure, etc.) which can reduce the study areas. Constraints or
restrictive criteria will make it possible to reduce the area of study by discarding those areas
that prevent the implementation of renewable energy plants. The restrictions are entered
into GIS using layers defined from the current legislation of the area. [22, 36].
4.2
Integrating GIS and MCDM
GIS has demonstrated its major potential for utilizing geographical information to develop
a decision support system. The integration of GIS with MCDM develops a better insight
for the decision makers to improve their selection. GIS-based MCDM tool is applied in
spatial analysis to obtain the most favorable sites for different approaches such as landfill
site selection [26, 27], renewable energy sites [34] and urban planning and development
[25, 35].
Jankowski clarify the role of GIS and multicriteria decision-making methods in supporting
spatial decision-making, and present a framework for integrating GIS with MCDM [24].
Greene et al provided an overview of the methods of MCDA and its spatial extension using
GIS. He suggested improving integration of MCDA with GIS software for increasing
accessibility. [28]. Imtiaz et al. highlight extensively the use of GIS-based analytic
hierarchy process (AHP) as a multicriteria decision analysis instrument. They found the
integration of the GIS with the AHP is a useful method in considering the land suitability
analysis for development and feature to facilitate efficiency from economic point of view.
[25]. Rumbayan and Nagaska employed AHP and GIS to rank the prioritization of
renewable energy (Solar, wind and Geothermal|) potential sites in Indonesia. [34]. Tools
and applications in GIS-based MCDA continue to expand in research output to offers an
effective decision support system for DM. [28].
5. Solar Energy site selection using GIS-based with
MCDM:
The integration of GIS with MCDM offers a reliable decision support system for DM. In
Egypt, Effat used GIS and remote sensing tools and applied AHP to calculate the criteria
weight to Spatial Multicriteria Evaluation (SMCE) model. A weighted overlay was used to
produce a suitability index map for solar energy power. The methodology proves to be
useful for DM to develop solar energy farms [33]. Uyan presented an integration of GIS
and AHP in his study for determining suitable site selection for solar farm in Konya,
Turkey. He used land suitability index to group the suitable areas into four categories (low
suitable, moderate, suitable, and best suitable) [7]. Obviously AHP method is the most
popular tool used with GIS however it is often criticized for its inability to adequately
handle the inherent uncertainty and imprecision associated with the mapping of the DM’s
perception to exact numbers.
The Fuzzy AHP method is derived from the AHP with advanced analytical process. Fuzzy
AHP presents a powerful decisions technique which has ability for adequate modeling of
the uncertainty in human behavior. Kengpol et al has proposed guideline to identify
potential solar power plant site selection. They implemented Fuzzy Analytical Hierarchy
Process (Fuzzy AHP) technique to consolidate the environment and social aspects in
electrical demand. GIS used first to exclude unsuitable sites such as mountains and screen
the possible sites under favorable conditions. This is an advantages since DM able to
optimize functional criteria and able to provide the significant weight priority as required.
In order to deal with uncertainty of decision making problem in AHP, fuzzy AHP is
preferred in such site selection. [32].
Charabi and Gastli conducted assessment study of the land suitability for large PV farms
implementation in Oman. They proposed to use the AHP-OWA using Fuzzy quantifiers in
GIS. Fuzzy Logic Ordered Weight Averaging (FLOWA) module is an integrated tool
within ESRI ARrcMap. They claim such models will incorporates uncertainty of expert
opinions on the criteria and their weights and delivers a mechanism for aiding the decisionmaking through the multi-criteria combination technique [29]. Aydin et al found
determined feasible locations in terms of environmental and economic feasibility through
a fuzzy decision-making procedure that uses ordered weighted averaging algorithm for
aggregating multiple objectives. Then, preferable sites are recognized separately for wind
and solar energy systems by using GIS and at the end, the related maps are overlaid to
obtain the most feasible locations for hybrid wind solar-PV systems. They claim that such
approach can overcome the intermittent of the renewable energy resources. They used
mathematical tools of Fuzzy Set Theory and a MCDM approach to evaluate environmental
factors together with economic feasibility objectives of wind and solar energies. [30]
To attain more information about how much alternative is suitable TOPSIS can be applied.
In Spain, Sanchez et al. combine GIS and MCDM (AHP and TOPSIS) to attain the
evaluation of the optimal sites of PV-solar power plant in the area of Cartagena, Spain.
TOPSIS has additional value through alternatives assessment according to their degree of
suitability. Also, TOPSIS is not required input from experts for each alternative and it can
be employed directly within GIS database. [22]
Instead of finding the best suitable site to implement solar farms, the potential locations
can be classified into different categories. Recently Sanchez et al. classified the potential
sites according to multiple evaluation aspects by developing a multicriteria model and
applying the ELECTRE-TRI method using decision support system software [36]. The
table below shows the selected contributions to GIS-based for solar site selections with
MCDM techniques. It is clear that using AHP is very popular in GIS-based for solar site
selection.
Reference
Effat [33]
Charabi [29]
Aydin [30]
Kengpol [32]
Uyan [7]
Sanchez [22]
Sanchez [36]
Fuzzy



AHP


ELECTRE
TOPSIS





Table 5.1 Recent Publication on GIS combined with MCDM on solar site selection
6. Conclusion
Solar energy system is one of the current renewable energy resources that can play a large
role in the power industries. Overcoming the limitation and barriers of such technology
will enhance the efficiency of the renewable energy sector, will help to reduce the green
gas emissions as well as the economical benefits on long term. From energy point of view,
selecting the optimal sites for solar farms can increase the power output and avoid many
acceptance issues and unnecessary costs.
Undoubtedly, GIS shows its usefulness as a powerful tool to eliminate unsuitable sites from
the study and use the weight of criteria to provide the best suitable site or the most
preferable category for such projects. Decision maker plays a major rule in selecting the
criteria to be evaluated and MCDM technique to be utilized considering different
objectives and constraints.
Choosing the multicriteria decision tool such as AHP, Fuzzy AHP, ELECTRE and
TOPSIS, depends on the DM objectives. AHP which is the most popular tool due to its
simplicity and consistency control has no consideration of human uncertainty while fuzzy
AHP has overcome this issue. ELECTRE-TRI has the ability to classify the feasible
locations into a set of categories without pointing the best alternative while TOPSIS has
strength of selecting alternative that close as possible to the ideal solution and furthermost
from the negative-ideal solution with extremely limited input from DM. Both ELECTRE
and TOPSIS are preferable for huge number of alternatives and criteria although they have
no control over the consistency.
This review presents the integration of GIS and MCDM which offers an effective decision
support system for solar farms site selection by providing results that can be meaningfully
displayed using GIS. The most common MCDM tools in energy planning (AHP, FAHP,
TOPSIS, and ELECTRE) have been evaluated for solar site selection. The discussion of
selection of criteria parameters such as environmental, economic, social are not covered in
this review.
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Energy
Agency
“World
Solar
(PV
and
CSP)”
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