Towards a Dynamic Multi-criteria Approach based on GIS and

MCDM for Wind Farm Site-selection in Morocco

Asmae Azzioui

, Rafika Hajji

, Mohammed Ettarid

and Moulay Hafid Bouhamidi

Exploration Department, Masen (The Moroccan Agency for Sustainable Energy), Rabat, Morocco

Department of Geodesy and Topography, Hassan 2

Institute of Agronomy and Veterinary Medicine, Rabat, Morocco

Department of Photogrammetry and Cartography, Hassan 2

Institute of Agronomy and Veterinary Medicine, Rabat,

Morocco

Keywords: Wind

Energy, Site-selection, GIS, MCDM, Criteria.

Abstract: Wind energy is one of the most available energies in Morocco that could contribute appreciably to the

improvement of national energy mix. Thus, identifying optimal locations for wind farm energy is a key issue

in the wind energy development process. However, site selection is a complex study that involves not only

technical considerations, but also economic, social and environmental requirements. Our research aims to

develop a dynamic, comprehensive, multiscale and multi-criteria approach for the assessment of quality wind

power sites resulting from an in-depth bibliographic study and extensive consultation of a set of professionals

in the field. Our approach is based on Geographic Information System (GIS) and Multi-Criteria Decision

Making analysis (MCDM) to assess suitable locations for wind farm energy in Morocco. The approach is

aimed to be dynamic by consideration of relevant criteria for site selection and by using data of high quality

and resolution. This paper aims to present the framework of our research. We start by exposing and analysing

basic concepts and methods of wind farm site selection from the literature, then we present and discuss the

first methodological guidelines of the research.

1 INTRODUCTION

During the current century, energy has become one of

the most critical issues in human life, due to global

warming, air pollution and other issues caused by

fossil fuels. Energy is one of the important inputs for

economic development and power generation (El

Khchine et al., 2019).

Wind is one of the renewable sources of energy

which have an important role in the mitigation of

climate change. In the world, the global wind

installed capacity was around 651 GW at the end of

2019. It accounted for around 5.3% of global

electricity production in 2019

(www.connaissancedesenergies.org). Wind energy is

a clean energy (a wind turbine does not consume

water and is not pollutant) which is characterized by

a very low surface footprint and a negligible impact

on biodiversity (IEA, 2013). In particular, offshore

wind energy is becoming more attractive due to the

https://orcid.org/0000-0003-3367-336X

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restrictions of land availability for onshore

installations. The cumulative annual capacity of

offshore wind energy has tripled over the past five

years, reaching around 28.3 GW (Sönnichsen, 2020).

Offshore resources are by far the most interesting in

terms of potential thanks to the regularity of their

wind (no turbulence created by landforms or

buildings and locally characterized by low roughness)

and their limited impact on the terrestrial landscape.

Morocco benefits from an exceptional wind

potential due to its good climatic and geographic

conditions. Its potential is estimated at 25 GW (1215

MW installed until the end of 2018) in identified on-

shore regions and at 250 GW along 3,500 km of off-

shore regions (the equivalent of 10 times the national

wind potential in on-shore (MEM, 2015)). Several

actions have been undertaken to increase the access

to electricity produced from renewable energies as

part of the objectives of reducing Green House Gas

(GHG) emissions and ensuring 52% of the country’s

energy mix from renewable sources by 2030. Masen

(Moroccan Agency for Sustainable Energy), is the

national agency responsible for managing renewable

energy in Morocco by developing integrated projects

to reach an objective of an additional 3,000 MW of

clean electricity capacity by 2020 and a further 6,000

MW in 2030 (www.masen.ma).

Wind project development begins with a

prospecting phase which consists of identifying

potential wind power sites with maximum energy

production and minimum of CAPEX (CAPital

EXpenditure) (EWEA, 2009). Site selection occurs at

a stage in the development process before significant

resources have been allocated to a particular site

(Shaheen et al., 2016). Furthermore, building a wind

power plant is a costly process. Although the average

service life of a typical wind turbine is about 20 years,

site selection has to consider the return of investment

on them.

Site selection of wind farms is a complex study.

This is due to the multiplicity of constraints and

parameters to be considered (environmental,

topographical and geographical, public opposition,

regulatory barriers, etc). In other words, planners are

facing a double challenge as they have to design

projects that will contribute to economic growth

while minimizing environmental risks and reducing

opposition from local stakeholders. Consequently, it

is required to identify (assess) suitable locations for

the development of wind farms.

"Multi-Criteria Decision Making analysis

(MCDM) aims to provide a decision-maker with the

tools to progress in solving the decision problem

where several points of view, often contradictory,

must be taken into account"(Chakhar, 2006). MCDM

is one of the best-known branches of decision

analysis in research which contribute to solve

problems involving variety of factors. The MCDM

deals with the decision-making process in the

presence of multiple objectives. The goal is to choose

among several alternatives using a number of

decision criteria (Ben Mena, 2000). In the literature,

the process of choosing wind farm sites is generally

treated under a MCDM approach combined to GIS to

analyze the potential locations of a wind farm. The

particular characteristics of GIS and MCDM

complement each other. GIS has great capabilities for

manipulating, storing, managing, analyzing and

visualizing geospatial data, while MCDM provides a

collection of procedures, techniques and algorithms

to solve complexities in decision making, for

structuring, designing, evaluating and prioritizing

alternative decisions (Gigović et al., 2017).

This paper aims to provide a bibliographical

review of the methods of wind farm site selection and

to highlight and discuss the first methodological

guidelines for our ongoing research. The first part

(section 2) is devoted to a state of the art of the used

approaches for wind site-selection both in the global

and local context of Morocco. The second section

analyses and discusses the existing methodologies

and then defines the objective and research

methodology. While the third section presents and

discusses the proposed approach. Finally, the paper

ends with a conclusion.

2 BACKGROUND

2.1 Wind Farms Site-selection Criteria

There are two types of variables in an MCDM

approach for site selection (Eastman et al. 1993):

 Constraints or exclusion criteria: based on

Boolean criteria (true/false), are in the form of

a limit threshold, a buffer zone, a setback

distance, allowing the exclusion of some zones

upstream of the site selection procedure

(Sánchez-Lozano et al., 2016).

 Selection or ranking criteria (also called

factors) allows assessing the degree of

opportunity of a site. They are associated with

preference parameters (e.g. weight,

discrimination thresholds, etc.) according to

their importance (Chakhar, 2006). They define

areas or alternatives based on a continuous

measure of suitability, reinforcing or

diminishing the importance of an alternative

resulting from the exclusion of areas defined by

restrictions (Gigović et al., 2017).

Selecting a site for a wind farm requires taking

into account several criteria and adopting assessment

methods to determine the best possible location and

to minimize or eliminate obstacles to the development

of wind power. Fig. 1 shows an example of the

hierarchical structure of the decision process. It

contains four levels: goal, constraints, objectives or

criteria and factors.

Figure 1: Decision Process hierarchy (Adapted from

Cathcart, 2011).

The success of a wind project depends on the

correct choice of a site which takes into consideration

a variety of criteria: technical aspects (regular wind

potential without excessive turbulence, topography,

proximity to the electricity and road network, size of

the project / land, etc.), technical easement constraints

(aviation easements, housing and forest), socio-

economic constraints and aspects of land use

planning, local acceptance, environmental constraints

(visual and sound impact, land use, protected areas

and issues related to birds and bats) and the landscape

(emblematic sites, remarkable landscapes, registered

or classified sites, etc.). Table 1 represents the results

of a bibliographical review about site selection

criteria, covering a large number of studies carried out

in various countries around the world.

Site selection is also the cornerstone to the success

of offshore wind farm projects, both economically

and technically. An offshore site must be selected in

terms of wind speed, depth of the sea, territorial

waters, military zones, civil aviation, maritime traffic

(shipping roads), pipelines and submarine cables,

aquaculture, sand and gravel extraction areas, marine

archaeology sites, seascapes as public heritage,

offshore renewable energy projects already installed

in the region of interest and their corresponding

characteristics (water depth, distance to shore,

distance from the operation and maintenance base,

seabed geology, social and regulatory issues, safety).

In addition, relatively minor environmental and social

concerns, such as noise and visual impact from wind

farms, movement of birds and mammals, may place

restrictions on offshore wind farm sites. Many other

limitations, such as the location of oil and gas

platforms and mining areas, are typically taken into

account in other countries when identifying the site of

a wind farm. Bathymetry and properties of the seabed

should be carefully considered as soil structure

influences the cost of turbine installations (Argin et

al., 2018).

2.2 Wind Site-selection Approaches

Many studies have associated GIS with topics related

to wind energy (Gigović et al., 2017) (Sánchez-

Lozano et al., 2016) (Hansen, 2005). GIS capabilities

facilitate the work of decision-makers to identify

potential sites for wind turbines, and can so save time

and reduce the financial costs of a project (Mari et al.,

2011). The input database is made up of dozens of

parameters to be evaluated (map of wind speeds,

distance to roads, etc.), and the resulting output is a

suitability map of the optimal sites for the installation

of wind turbines. GIS are efficient tools for spatial

and multi-criteria analysis. However, they lack a set

of mechanisms allowing the integration and the

evaluation of conflicting objectives and criteria

(Laaribi, 2000). While MCDM methods are

considered as an efficient approach to such a complex

decision problem.

(Malczewski, 2006) lists 319 works which

integrated GIS and MCDM during the period 1990-

2004, in the field of science, urban planning,

environment, transport, agriculture, ecology, remote

sensing, biology and engineering. In particular, the

number of studies using GIS-based MCDM for

planning of wind farms is low and some of them are

to be reviewed here. The GIS-based MCDM

approach gained significant interest in early 2000’s

and has been utilized in several countries like Turkey,

Greece, Denmark, USA, UK, Germany, Poland,

Vietnam and Sweden. Several researchers have

grouped site selection problems under various topics

such as network layout, mixed integer programming,

capacity-limited, hierarchical, single/multiple

product, fixed/flexible demand, static/dynamic

period, deterministic/ stochastic, single/multiple

objective models, etc (Malczewski, 2006).

The main categories of methodologies include: a)

Outranking methods; b) Value / Utility based

methods; c) Interactive methods – programming and

d) Other methods, which we expose in the next

paragraphs.

a) Outranking methods, such as the ELECTRE

(Elimination and Choice Translating Reality)

families, the PROMETHEE (Preference Ranking

Organization Method for Enrichment Evaluation)

and TOPSIS method (Technique for Order of

Preference by Similarity to Ideal Solution): These

methods use pairwise comparisons between potential

alternatives and establish an outranking relationship

between them.

b) Value / Utility based methods (American

School), such as Multi-Attribute Utility Theory

(MAUT), Simple Multi-Attribute Rated Technique

(SMART), Analytic Hierarchy Process (AHP), Simple

Additive Weighting (SAW): their purpose is to create

a utility function of values that groups together the

decision-maker preferences about evaluation criteria.

This formula provides a quantitative mode which

guides the decision maker.

c) Interactive methods – programming: like

Artificial Neural Network (ANN). They are based on

an iterative process. At the start, the analyst

establishes an initial solution. The decision-maker

responds by providing additional data about the

preferences. This additional information is then

introduced into the model during the next calculation

step. The procedure is repeated until an acceptable

solution is reached (Aydogan et al., 2017).

d) Other methods such as NAIADE (Novel

Approach to Imprecise Assessment and Decision

Environment), Flag model and SMAA (Stochastic

Multi-Criteria Acceptability analysis), among others:

there are just different types of techniques that are

difficult to put into any of the categories mentioned

above.

Regarding the application of MCDM methods,

restricted or exclusion zones where it is strictly

forbidden to install wind turbines are excluded from

the beginning of the studies (Gigović et al., 2017)

(Atici et al., 2015) (Noorollahi et al., 2016), either by

the Fuzzy method (Aydin et al., 2009), or by the

Boolean method (Latinopoulos et al., 2015).

Subsequently, the most important factor in the

MCDM is how to affect "weights" to a set of criteria

according to their importance. According to (de

Lourdes Vazquez et al., 2011), the weight of factors

is calculated from interviews with different actors and

professionals. They estimate their scores based on

political, environmental and economic standards.

(Hansen, 2005) used direct assignments of criteria

weights based on common sense or subjective

opinion of authors. (Latinopoulos et al., 2015);

(Sánchez-Lozano et al., 2016) ; (Bennui et al., 2007)

evaluated the weights of the criteria by the AHP

method, which is the most widely used method to

quantify the weight according to the expert opinion.

It consists in ranking the criteria through a

comparison matrix. To overcome the drawback of

inconsistently when assigning weight, a Fuzzy

Analytic Hierarchy Process (FAHP) which combines

fuzzy theory with AHP can be used on each factor to

determine the fuzziness weight of its attributes. It is

an improvement addressing “the vagueness,

imprecision and uncertainty associated with the

process” of traditional hierarchy process (Asakereh et

al., 2017). (Baban et al., 2000) tested two methods.

First, the authors standardized the factors into the

same number of classes. Then, the first method

consists in superimposing the factors with equal

weights (Aydin et al., 2009), and the second, in

combining them with weights derived from AHP.

Their results favor the second method. (Sánchez-

Lozano et al., 2016) combined the AHP method for

analyzing and weighting the factors and the TOPSIS

method (Villacreses et al., 2017) for assessment of

alternatives. TOPSIS is a method based on the

concept that the chosen alternative should have the

shortest distance from the positive ideal solution and

the farthest from the negative one (Sánchez-Lozano

et al., 2013). The final ranking is obtained using a

closeness index. In terms of the combination of the

criteria, the three most frequent methods are

Weighted Linear Combination (WLC) (Latinopoulos

et al., 2015) (Hansen, 2005), Weighted Index Overlay

(WIO) (Noorollahi et al., 2016), Ordered Weighted

Averaging (OWA) (Aydin et al., 2009); (Villacreses

et al., 2017). Thematic maps must first be

standardized into the same number of classes for the

three methods. The difference between the methods

is that the OWA studies two variables, the order of

importance of the factor and its weight. This method

is used in conjunction with the Fuzzy approach.

While the WLC and WIO methods build the decision

map using just the weight of the factors.

In the category c) of methods, MCDM hybrid

model combining fuzzy multi-criteria analysis with

analytical capabilities that SOLAP systems (Spatial

OnLine Analytical Processing) can provide is used to

evaluate, rank and select the strategic industrial

location for implanting new business corporation in

the region of Casablanca (Hanine et al., 2013). In this

kind of models, data is well organized multi-

dimensionally so that the decision makers could

analyze them interactively and iteratively at a detailed

and/or aggregated level. The main difference between

these techniques, and others consist at the ability to

control the temporal evolution (time dimension’s

role) of a given problem.

ANN method was applied to identify suitable

areas for the installation of Photo-Voltaic (PV)

systems. The final index is determined by combining

the quantitative criteria using an ANN, trained with

values corresponding to the sites of existing PV plants

in the given region (Mondino et al., 2015). (Ari et al.,

2020) proposes within the scope of linear

programming perspective, two models using mixed

integer linear programming based on power

maximization. In (Ari et al., 2020), three approaches

were examined based on MCDM methods: SMAA (a

simulation-based approach with different kinds of

uncertain information), AHP (a conventional

deterministic approach), and AHP-SMAA (a hybrid

approach) were applied separately in Turkey.

(Shaheen et al., 2016) proposed an efficient method

for utilization of data mining techniques in wind site

selection.

Dynamic multi-criteria decision making

(DMCDM) is an emerging subject in the decision-

making field until the challenge to consider time as an

interesting variable has become important.

(Campanella et al., 2011) proposed a flexible

framework as a general DMCDM model that

combines feedback information (historical data) with

current information, for each alternative, in a spatial-

temporal decision process. Further, the dynamic

decision model was adapted for a business-to-

business general supplier selection process. (Jassbi et

al., 2014) investigates an MCDM model for group

decision making, by taking into consideration its

dynamic perspective. A case-study about hotel

ranking, involving multi-groups in the decision-

making process is sketched to illustrate the approach.

(Jassbi et al., 2014) introduces a DMCDM with future

knowledge for supplier selection. In this work, the

authors extend a dynamic spatial-temporal

framework, designed to deal with historical data

(feedback), to address the problem of considering

future information/knowledge (feed-forward).

Recently, (Thong et al., 2020) proposed an extension

of dynamic internal-valued neutrosophic sets. Based

on this extension, the authors develop some operators

and a TOPSIS method to deal with the change of both

criteria, alternatives, and decision-makers by time.

(Dissanayake et al., 2020) explicitly incorporated

linkages between inter-temporal price changes and

location of selected and future reserve sites in a

dynamic optimization framework. This study

presents a two-period linear integer programming

model for conservation reserve design that

incorporates amenity driven price feedback effects

inherent in the reserve development problem.

(González-Prida et al., 2014) presented the proposed

methodology called DAHP (Dynamic AHP). In short,

the DAHP applies the same AHP methodology but

considers the influence of the decisions in the

boundary conditions. In other words, while the AHP

provides a fixed picture of a system in a specific

moment with its best local decision, the DAHP

provides a motion picture of the system where the

best decision can be different to the ones calculated

in determined moments.

2.3 Analysis and Discussion

Several studies relative to the development of wind

energy aimed at assessing the suitability for sites

selection based on various MCDM methods. The

adopted techniques have both advantages and

disadvantages, which are summarized in (Choudhary

et al., 2012). The most widely used MCDM method

is the AHP method proposed by (Saaty, 1980) and

WLC for energy planning. AHP is universally

recognized for its robustness, flexibility, ease of

application and its suitability for complex decision-

making processes. Furthermore, it allows the

integration of qualitative and quantitative criteria and

permits testing the consistency of the weight

allocation process by reducing the bias in the

decision-making progression. However, some

authors who use AHP do not provide a consistency

ratio and do not include pairwise comparison

matrices. The authors also pointed out that because of

the specificity of the decision-making process in

energy planning, hybrid methods of MCDM are

increasingly used. Some of the drawbacks of the AHP

method are the large number of pairwise comparisons

needed as the number of alternatives increases

(Choudhary et al., 2012) and the critic regarding the

measurement scale of the pairwise comparisons. In

addition, in many cases, AHP cannot give a good

representation of reality, as general preferences from

a point of view are very difficult to model by a single

function.

Moreover, in the literature, no consensus on the

ranking order nor the relative importance of the

criteria could be found. In some cases, authors assign

weights based on their previous experiences or by

using questionnaires and interviews (involving

experts, planners and students). According to (Uyan,

2017), collecting expert opinion is the best option for

assigning relative weights. However, it is important

that the experts should be familiar with the study area.

According to (Uyan, 2017), using the same criteria

and restrictions for different areas is a mistake. Some

relevant criteria may be applied in the same way

around the world (e.g. wind speed), but others vary

widely due to market differences in national

regulations and laws (e.g. distance from urban areas).

While the constraints are similar in the works

mentioned (Table 1), some differences exist in the

nuances of the thresholds between countries, which

are linked to land and landscape development and

planning.

They are more or less rigorous from one

Table 1: Bibliographical review of site selection criteria.

region to another depending on local issues. For

example, in Turkey and Iran which are located in

areas with high seismic risk, the proximity factor to

faults is studied for security reasons but with low

weight compared to other factors (Atici et al., 2015)

(Noorollahi et al., 2016). In addition, the karstic

geomorphological structure was excluded from the

study on the area between Washington State and

Oregon, since the risks associated with the

development of this formation are varied. (Bennui et

al., 2007) is the most demanding on the distance

between a wind farm and rural (> 2,500 m) and urban

(> 1,000 m) area. Their choice may be related to the

vast rural and populated areas of Thailand. Another

example is about Turkish legislation which imposes

thresholds concerning noise pollution, safety, nature

reserves and the size of surfaces occupied by wind

turbines (Atici et al., 2015). On the other hand, it

considers forests as potential sites (Aydin et al.,

2009). The distance to the electrical grid is one of the

10 most important criteria defined by the American

Wind Energy Association for the construction of

wind farms (AWEA, 2007). However, the importance

of this criterion in the literature is ambiguous. It

seems to depend strongly on the location of the study

area. The majority of studies use wind speed as the

first rank criterion. However, wind speed is a

parameter which varies significantly both spatially

and temporally. We therefore propose to opt for the

Wind Power Density (WPD) (Liu et al., 2020) which

is calculated on the basis of the frequency distribution

and allows to clearly understand the turbulence of the

resource.

2.4 The Moroccan Context

In Morocco, MCDM combined with GIS has been

used in recent years in the process of site selection for

the development of renewable energies. Particularly,

in solar site prospecting, the most quoted regional

studies are (Tahri et al., 2015); (Tazi et al., 2018);

(Azmi et al., 2017); (Sedrati et al., 2019) and (Kamli

et al., 2016) which are largely based on Boolean and

AHP methods. In the case of wind energy, we can

quote only three studies. The first one is conducted by

the CDER (Centre de Developpement des Energies

Renouvelables) and has resulted in a wind potential

map. However, the adopted method does not allow a

refined assessment of the suitability of a site. It is

mainly based on wind potential criterion for choosing

sites without considering exclusion criteria.

Moreover, the wind used data isn’t based on the

Moroccan Wind Atlas set up by Masen in the form of

a GIS database with high spatial resolution (2km)

covering the whole country with a buffer zone of 30

km offshore along the Moroccan coast.

The second study of (Elmahmoudi et al., 2020)

investigated the selection of the location of wind

farms in the Tarfaya region of Morocco. In order to

calculate the weight to be assigned to each criterion,

AHP, Fuzzy-AHP algorithm from Buckley and the

geometric mean Fuzzy-AHP method, were combined

to GIS. The third study of (Achbab et al., 2020)

presented a model based on GIS coupled with a

MCDM using the Fuzzy AHP method to locate a

hybrid solar-wind energy system with high potential

in the Dakhla region located in the south of Morocco.

Looking at the two studies, it turns out that they only

concern a small, particular region of Morocco, with a

limited number of criteria and no exclusion criteria

has been considered for the first one. In addition,

impacts on avifauna are not considered. Furthermore,

the used data have a limited spatial resolution (wind

data and electrical network).

In general, we can state that most current wind

farm site selection procedures lack systematic

methods and models, and are mainly based on ad hoc

decisions and individual experiences of the decision

makers or planners in charge. Therefore, this

motivates us to conduct this research in order to

propose an innovative approach based on a spatial

decision support system and advanced modelling

techniques which allow precise and dynamic

simulations of wind farm sites in Morocco.

3 OUR APPROACH

Our research aims to develop a dynamic approach for

wind sites selection, based on precise data and a

detailed analysis of relevant criteria for wind site

selection by including those related to environment

and social impact. Our approach will allow

simulation and assessment of various resulting

scenarios through a dynamic platform. Therefore, we

can arise the following questions: 1) What criteria are

relevant for site selection of wind turbines? 2) How

these criteria can be weighted? 3) Which approach to

be adopted for modelling the process and assessing

the potential location of wind turbines?

Some researches already exist in DMCDM area

but when compared with static decision-making

models, DMCDM needs more work to be applicable

in real industrial problems. The purpose of our study

is to deal with the change of criteria, alternatives, and

decision-makers during time. In a recent systematic

literature review (Shao et al., 2020) of MCDM

applications for renewable energy site selection

performed, covering a total of 85 papers published

from 2001 to 2018 in high-level journals, no article

has dealt with a dynamic simulation for wind site

selection.

Our research will also lead to two main outputs:

1) a proposal of a national standard for site selection

of wind turbines and 2) a national map for wind

potential which can serve as a support for the

establishment of the electricity grid in some

unconnected areas.

3.1 Methodological Workflow

Firstly, the aim of the process is to mask and eliminate

all the constrained areas. Then, the DAHP method

will be used to determine the weight of the factors.

The final map will be drawn by a weighted overlay of

thematic layers (WLC). Thus, an overall relevance or

suitability index (SI) will be calculated for each cell.

This method can be modified to meet the

requirements of experts and in the field. Sensitivity

analysis (SA) is a beneficial measure to include in

MCDM approaches because it allows a better

understanding of the sensitivity of outputs (i.e. areas

suitable for development) to errors, erroneous

assumptions, or disturbances in input values (ie,

criterion values and / or criterion weights). SA helps

to assess the accuracy and limitations of the model

(Chen et al., 2010). AS can therefore help to identify

areas of greatest uncertainty, and criteria that need to

be assessed. more carefully (Chang et al., 2008).

Wind farm sites will be identified after a

comprehensive approach is carried out upstream, over

a large area to locate potential areas for hosting wind

turbines. The identified areas are delimited and

prioritized and analysed in a more detailed way in

order to reach a suitable portfolio of sites.

Accordingly, the model will be applied first to the

national territory then by downscaling to each area of

interest. Finer resolution data will be used for refining

the site choice.

Figure 2 shows the basic steps of the

methodological workflow.

Figure 2: Methodological Workflow.

3.2 Material

Table 2 below summarizes the data to be used in the

process of wind site selection.

Since wind speed and WPD are the main criterion

for the wind sites assessment, the use of high quality

wind atlas is crucial. In the literature, most of the

authors use low quality and resolution (or

interpolated) wind maps (Liu et al., 2020).

Table 2: Data characteristics.

Data Format Source

Wind speed /

energy

Raster layer of wind

speeds, Wind power

density at 2 km

resolution at 60m,

80m and 120m above

ground

Masen (based on

mesoscale

simulation of

reanalysis data)

Cities and

towns

Vector layer

Digitized on

Google Maps

background

Road

Network

Map

Vector layer

http://www.diva-

gis.org/

Electric grid

map

Vector layer ONEE

Map of the

hydrographic

network

Vector layer of

watercourses and

bodies of water (dams

and lakes)

http://www.diva-

gis.org/

Airports

Vector layer

ICAO database

(International Civil

Aviation

Organization)

Data Format Source

Elevation

Digital elevation

model SRTM at 30m

resolution

SRTM (Shuttle

Radar Topography

Mission)

http://srtm.csi.cgiar

.org

Slope

Map of slopes in (%)

at 30m resolution

Calculated on the

basis of the DEM

Landuse

Raster layer of 24 soil

classes at 1km

resolution

USGS

Coasts

Vector layer (nearly

3500 km)

Digitized on border

of Morocco

Bodies of

water

Vector layer

http://www.diva-

gis.org/

Military

installation

& Dense

forest with

significant

heigh

Vector layer

Extracted from the

land use map

National

parks /

reserve /

protected

areas /

Ramsar /

SIBE

Vector layer from the

World Database on

Protected Areas

WDPA (Version

3.1)

Bird and bats

flight

corridor

Vector layer of IBA

(Important Bird

Area)

International

Database (Birdlife

International)

Furthermore, the database is to be completed by

physical or environmental data like the layer of

existing wind projects, summary map of technical

easements, map of regulatory protection of heritage

and landscapes, flood areas, landslide and karst risk

areas, map of landscape entities and other non-spatial

data (planning standards, environmental standard,

safety requirements, and location of sensitive

buildings).

3.3 Expected Results

This research aims to draw up a dynamic map of areas

suitable for wind power development and to elaborate

a procedural guide / standards for the choice of sites

for wind power projects. In the absence of legislation

and regulations related to wind energy in Morocco,

we used the bibliography and knowledge of the field

to choose the threshold of constraints and local

factors for wind prospecting. The most recurrent and

relevant factors and constraints in the literature and

adapted to Morocco were selected. Pairwise

comparison values were assigned based on the

literature analysis and our knowledge of the study

area to establish a criteria weighting matrix specific

to Masen.

4 CONCLUSIONS

Morocco has many natural resources and many assets

in terms of space which can allow reconciling the

development of sustainable energies, land use

planning and the preservation of the environment.

Our research focuses on wind energy which is one of

the most important sources of clean energy with high

potential in Morocco. We aim at developing a new

method for potential site selection. As the process is

multidimensional, the adopted approach should deal

with all the variables and the aspects of the decisional

process.

This research aims to set up a dynamic, innovative

and multiscale site choice approach using as input

very high quality data based on the combination of

GIS and MCDM in order to simulate location of

potential sites for large -scale wind power projects.

The input data and the weight of the criteria are

fundamental in defining the final result. Therefore,

the factors and criteria must correspond as much as

possible to the characteristics of the studied territory.

We are aware that the final decision is also the result

of other processes, such as political strategies.

However, the "scientific" identification of the best

solution is undoubtedly an important decision-

making support.

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