An AHP-GIS based site suitability analysis for integrating large-scale hybrid CSP+PV plants in Morocco: An approach to address the intermittency of solar energy

Jbaihi, Ouafae; Ouchani, Fatima-zahra; Merrouni, Ahmed Alami; Cherkaoui, Mohamed; Ghennioui, Abdellatif; Maâroufi, Mohamed

doi:10.1016/j.jclepro.2022.133250

Cited by 55 publications

(8 citation statements)

References 57 publications

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“…For instance, Díaz-Cuevas et al ( 2021) and Gouareh et al (2021), using the same approach, found similar key criteria for the successful adaptation of PV-wind hybrid and CSP technologies in Algeria. Furthermore, in comparison with similar works conducted in Egypt, Morocco, and Kenya (Effat and El-Zeiny, 2022; Elkadeem Jbaihi et al, 2022), our outcome was found to be significant and provide very competitive opportunities for Tataouine and the entire country as a whole. This validates our findings and emphasizes that hybrid power systems, including CSP-PV, PV-wind, and CSP-wind, could be a solution for regions sharing similar attributes.…”

Section: Discussionsupporting

confidence: 58%

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

Rekik,

El Alimi

2023

Energy Exploration & Exploitation

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Renewable energy systems have emerged as a viable option to mitigate the environmental impacts of traditional fossil fuels. However, the intermittent nature of these renewables, such as solar and wind, makes it challenging to ensure a stable energy supply using only one type. Therefore, combining more than a single technology offers significant advantages in addressing the limitations associated with each individual system. Nevertheless, developing these systems requires substantial financial investments, making it crucial to identify the most suitable locations prior to installing them. In this article, the prime objective was to propose a preliminary evaluation of land suitability for constructing solar and wind hybrid facilities (PV–wind, PV–CSP, and CS–wind) in Tataouine, southern Tunisia. To this end, a GIS-based MCDA methodology was developed based on an extensive literature review and experts’ feedback while considering climate, topography, accessibility, and environmental factors. The results obtained revealed that the optimal area for a CSP–PV hybrid system is about 793 km2, indicating that this combination has the highest potential in terms of available resources and compatibility. On the other hand, well-suited locations for hosting CSP–wind and PV–wind systems covered areas of 412 and 333 km2, respectively. Such specific locations are capable of generating an annual technical potential of 316.169, 91.252, and 62.970 TWh for CSP–PV, CSP–wind, and PV–wind, respectively. Interestingly, comprising almost all of the most appropriate sites, Remada and Dhiba stand as the ideal locations for accommodating such hybrid systems. Considering this outcome, Tataouine can position itself as a model for renewable energy adoption in Tunisia. Therefore, it is imperative for policymakers, investors, and local communities to collaborate and embrace these hybrid systems to capitalize on this immense potential and pave the way for a greener and more prosperous future.

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Section: Discussionsupporting

confidence: 58%

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

Rekik,

El Alimi

2023

Energy Exploration & Exploitation

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“…This area was equal to 44,863 km 2 of 59,514 km 2 [33]. In recent years, other MCDM methods such as the Electre, Topsis, Vikor, Oreste and Saw have been used as prioritizing options and weighting criteria [34][35][36][37][38][39][40][41]. For example, in 2014, Jun et al conducted a site selection for wind/solar power plants using the Electre II method, in which the sites were ranked from the best to the worst [39].…”

Section: Literature Review 21 Overview Of Common Methods In Site Sele...mentioning

confidence: 99%

A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP

et al. 2023

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Currently, worldwide attention to clean energy and sustainable energy has been expedited because of its many environmental benefits. In fact, wind and solar energies play a prime role in decarbonizing the energy market. However, finding the most suitable locations for wind/solar power plants is difficult because of the non-homogeneous distribution of these sources. This paper presents a novel method for selecting the optimal locations for wind and solar farms by mapping the space of the decision criteria to the site score. In addition, the multiple linear regression model was used, with the help of the combination of GIS and AHP methods, to model the siting of wind and solar power plants. The site scoring method used in this study is reliable and globally evaluated; therefore, the scores are accurate and effective. To reveal the ability of the proposed method, two study areas were investigated and researched. The results achieved based on the introduced method showed that, in case study 1, areas with an area of about 9, 4 and 7 km2 are suitable for the construction of wind, solar and wind/solar power plants, respectively. This paper also used fourteen existing wind/solar, wind and solar farms from five continents around the world. The results showed that the suggested model acts the same as the real data. In addition to the interest these results hold for the development of renewable energy in the study area, this novel approach may be applied elsewhere to select optimum sites for wind, solar, and combined wind and solar farms.

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“…Furthermore, sunny hours and average temperature have been identified as significant parameters affecting EG in solar farms [8,9]. Therefore, precise utilization of climatic param-eters becomes crucial in any research related to solar energy estimation, enabling the identification of suitable areas for the implementation of solar power plants [10,11]. Consequently, it is essential to explore feature selection algorithms and conduct energy forecasting to effectively capture the relationship between EG and climatic conditions.…”

Section: Introductionmentioning

confidence: 99%

Geographic information system‐based prediction of solar power plant production using deep neural networks

Mokarram

Aghaei

Mokarram

et al. 2023

IET Renewable Power Gen

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The study aims to predict solar energy generation to ensure the successful operation of solar power plants. This objective is crucial in light of the increasing energy demand, global warming concerns, and greenhouse gas emissions. To achieve this, the study employs multiple linear regression and feature selection techniques to calculate energy generation. Additionally, long short‐term memory (LSTM) is used to predict energy generation levels based on climate conditions. Furthermore, the spatial distribution of energy generation is analyzed using inverse distance weighting. The results of the study reveal that temperature, solar radiation, relative humidity, wind speed, wind direction, and vapor pressure deficit are the most significant parameters for predicting energy generation. The LSTM method proves to be highly accurate in predicting fluctuating energy generation patterns. Notably, the southern regions of the study area exhibit a greater potential for energy generation compared to the northern regions. Approximately 30% of the region generates over 1400 kWh, with the southern areas, characterized by hot and dry climates, producing around 1500 kWh, while the northern regions, with cold and humid climates, generate approximately 1100 kWh.

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An AHP-GIS based site suitability analysis for integrating large-scale hybrid CSP+PV plants in Morocco: An approach to address the intermittency of solar energy

Cited by 55 publications

References 57 publications

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

A spatial perspective on renewable energy optimization: Case study of southern Tunisia Using GIS and multicriteria decision making

A New Decision Framework for Hybrid Solar and Wind Power Plant Site Selection Using Linear Regression Modeling Based on GIS-AHP

Geographic information system‐based prediction of solar power plant production using deep neural networks

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