Location Of Solar Power Plants Research Articles

Decision-Making Approach to Design a Sustainable Photovoltaic Closed-Loop Supply Chain Considering Market Share for Electric Vehicle Energy

This study aims to develop a model for the closed-loop supply chain of photovoltaic (PV) systems. The primary objective addresses strategic and tactical decision-making using a two-stage approach. To pinpoint suitable locations for solar power plants, the PROMETHEE II method is utilized, which is a component of multi-attribute decision making (MADM) approaches. Next, a multi-objective modeling of the closed-loop PV supply chain is conducted. This model aims to minimize total supply chain costs, reduce environmental impacts, mitigate adverse social effects, maximize the on-time delivery (OTD) of manufactured products, and maximize market share. Additionally, a robust fuzzy mathematical model is introduced to examine the model’s sustainability under various uncertainties. An evaluation of the effectiveness and utility of this model is conducted in Tehran city. Furthermore, a comprehensive analysis of various supply chain costs indicates that production centers have the highest costs, while separation centers have the lowest costs.

Solar Power Plant Location Selection Problem by Using Electre-III Method in Pythagorean Neutrosophic Programming Approach (A Case Study on Green Energy in India)

Solar Power Plant Location Selection Problem by Using Electre-III Method in Pythagorean Neutrosophic Programming Approach (A Case Study on Green Energy in India)

Unveiling the SALSTM-M5T model and its python implementation for precise solar radiation prediction

The world is increasingly embracing cleaner and more sustainable energy sources, with solar energy playing a crucial role in mitigating greenhouse gas emissions and addressing climate change. Accurate solar radiation predictions are vital for optimizing solar energy resource utilization and identifying suitable locations for solar power plants. Therefore, our study introduces a new model to advance solar and renewable energy systems. In this paper, we suggest a novel hybrid model, Self-attention (SA) mechanism-long short-term memory neural network (LSTM)-M5Tree (SALSTM5T) model, for predicting solar radiation. The SALSTM-M5T model combines the advantages of the Self-attention- LSTM (SALSTM) and M5Tree models. The LSTM component captures long-term solar radiation dependencies. SA improves the accuracy of LSTM and M5T models by focusing on relevant input features at different time steps. The study utilizes K-fold cross-validation to overcome the limitations of traditional methods for determining the size of training and testing data. By combining the advantages of the SALSTM and M5Tree models, our research presents a new framework for accurate solar radiation prediction. Existing techniques are developed by using SA and K-fold cross-validation. Furthermore, our paper emphasizes the practical applications of solar radiation prediction, such as identifying suitable areas for solar power plants and optimizing energy production. Our study concluded that the self-attention mechanism and LSTM model improved the efficiency of M5T models for analyzing solar time series data as these models can attend to important features. The centralized root mean square error (CRMSE) of the SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models was 0.04, 0.17, 0.25, 0.49, and 0.70, respectively. The SALSTM-M5T, LSTM-M5T, LSTM, ANN, and M5T models' correlation coefficients were 0.99, 0.98, 0.96, 0.89, and 0.82, respectively. Our SALSTM-M5T model contributes to advancing renewable energy planning and decision-making. The main innovation of the current article is the development of the M5T model using the capabilities of the SA and LSTM models. The SALSTM-M5T hybrid model, which combines self-attention, LSTM and M5Tree techniques, is proposed as an effective approach for solar radiation prediction. The results show its superiority over other models in terms of accuracy and suitability, which can be useful for practical applications in the field of renewable energy, such as site selection for solar power plants and optimization of energy production. Furthermore, this paper aligns with the advancement of digital sensors to enhance the accuracy of solar radiation prediction.

Site Selection of Solar Power Plants Using Hybrid MCDM Models: A Case Study in Indonesia

Among developing countries in Asia, Indonesia has realized the importance of transitioning from fossil fuels to renewable energy sources such as solar power. Careful consideration must be given to the strategic placement of solar power installations to fully leverage the benefits of solar energy. This study proposes a methodology to optimize the site selection of solar power plants in Indonesia by integrating Data Envelopment Analysis (DEA), Fuzzy Analytic Hierarchy Process (F-AHP), and Fuzzy Measurement of Alternatives and Ranking according to Compromise Solution (F-MARCOS) models. The proposed methodology considers quantitative and qualitative criteria to evaluate potential locations for solar power plants. In the first stage, DEA is used to identify the most efficient locations based on quantitative measures such as solar radiation, land availability, and grid connectivity. In the second stage, qualitative factors such as technological, economic, environmental, and socio-political aspects are evaluated using F-AHP to prioritize the most important criteria for site selection. Finally, F-MARCOS ranks potential locations based on the selected criteria. The methodology was tested using data from Indonesia as a case study. The results show that the proposed hybrid model optimizes Indonesia’s solar power plant site selection. The optimal locations can contribute to a cost-effective long-term renewable energy supply nationwide. The findings from this study are relevant to policymakers, industry stakeholders, and researchers interested in renewable energy development and site selection. However, to promote sustainable solar energy development, governments and local authorities must also enact supportive policies and mechanisms that encourage the adoption and growth of renewable energy technologies in Indonesia.

Land-legal, environmental and cadastral issues of location of solar power plants: a scientific and practical view

Land-legal, environmental and cadastral issues of location of solar power plants: a scientific and practical view

Development of a Best-Worst Method based MCDM approach for solar power plant location selection: An Application to Tunceli, Turkey

Development of a Best-Worst Method based MCDM approach for solar power plant location selection: An Application to Tunceli, Turkey

Development of a best-worst method based MCDM approach for solar power plant location selection: an application to Tunceli, Turkey

Development of a best-worst method based MCDM approach for solar power plant location selection: an application to Tunceli, Turkey

An Integrated GIS-Based ANP Analysis for Selecting Solar Farm Installation Locations: Case Study in Cumra Region, Turkey

With the depletion of traditional energy sources, which are defined as fossil fuels, the importance of renewable energy sources has increased even more. Renewable energy is a sustainable energy source that can be reproduced with the use of existing resources and has almost no harm to the environment. Solar energy is one of the leading types of energy among sustainable energy sources and is an inexhaustible source of energy for humanity that can be used for generations. Selecting an appropriate site for the installation of solar power plants is an important decision that increases the efficiency of a solar farm. In order to minimize the negative environmental, economic and social effects in the solar farm location studies, all criteria affecting the study should be taken into consideration. This study presents the application of combining analytical network process, which is one of the multi-criteria evaluation methods, with GIS, in order to determine the most suitable locations for solar power plants in Cumra Region, Konya, Turkey. Six main criteria have been defined for the most suitable site selection in this area. Criteria maps were prepared using the GIS software and weight values determined from analytical network process and combined for the site suitability map. Suitability map was divided into three categories as low, moderate and high suitable by using an equidistant classification method.

A Multicriteria Decision Framework for Solar Power Plant Location Selection Problem with Pythagorean Fuzzy Data: A Case Study on Green Energy in Turkey

Turkey is one of the most suitable countries for solar power plants, owing to its ideal location in terms of receiving solar radiation; accordingly, plans are in place to expand its solar power plant system to fulfill the increasing energy demand. In this study, a combination of multicriteria decision-making and fuzzy logic was used to evaluate potential locations (cities in southern Turkey) to install new solar power plants subject to different criteria of an uncertain nature. The proposed methodology has several attractive features, which are described throughout this study. The criteria were selected based on the literature and the opinions of experts. In addition, a new criterion (capacity of existing solar power plants) was added to achieve more precise results. Ten criteria and eighteen cities were selected to form the decision matrix for the problem. First, the weight of each criterion was computed by stepwise weight assessment ratio analysis (SWARA). Then, the TOPSIS approach was extended to the Pythagorean fuzzy form in ranking the locations of the decision matrix as a new solution procedure. The results show that the best candidate city to install a new solar power plant is Antalya, followed by Karaman and Malatya as the second and third best candidates, respectively. Finally, to measure the impact of the changes in the weight of the criteria, a sensitivity analysis was conducted. Multiple scenarios were considered, and the results indicated that Antalya was the best alternative in most of the scenarios.

Solar power plant location selection using integrated fuzzy DEMATEL and fuzzy MOORA method

Nowadays, global warming is one of the biggest problems of humanity and the main reason is energy production. Therefore, renewable energies such as wind, rain, tides, waves and sunlight are an appropriate way for producing clean energy. In this study, suitable locations for solar power plant are analysed. To do this, data and information acquired from three decision-making groups are implemented to improve an analytical tool that integrates modified fuzzy DEMATEL and fuzzy MOORA. The weights of criteria and sub-criteria are calculated by the fuzzy DEMATEL method. Decision-making groups select five eligible regions for a solar power plant in Turkey, and the fuzzy MOORA method is used to rank regions. Also, a sensitivity analysis was performed using the fuzzy AHP method and as a result, it was observed whether there was a change in the order of the alternatives.

Solar Energy Deployment for the Sustainable Future of Vietnam: Hybrid SWOC-FAHP-WASPAS Analysis

In recent years, solar power has developed significantly in Vietnam, making an important contribution to ensuring energy conservation and decreasing greenhouse gas exposure. Recently, Vietnam has experienced impressive growth in the solar and wind energy sectors, showing the high potential of using renewable electricity in addressing energy needs. The target of this study was to construct a fuzzy multicriteria decision-making, model including strengths-weaknesses-opportunities-challenges (SWOC) analysis, the fuzzy analytic hierarchy process (F-AHP) model, and the weighted aggregates sum product assessment (WASPAS) model, to select the location of a solar power plant in south Vietnam. The proposed fuzzy multicriteria decision-making model (MCDM) model is the first solar power plant location selection in southern Vietnam that utilizes literature reviews and expert interviews. Moreover, this is the first study to provide a case study on evaluating locations during solar power plant location selection that utilizes a combination of the SWOC, FAHP, and WASPAS models. The findings of this study provide valuable knowledge for the assessment and selection of suitable locations for renewable energy projects, including both solar power energy projects and other renewable energy projects.

Fuzzy Multi-Criteria Decision Making for Solar Power Plant Location Selection

Vietnam is one of Southeast Asian countries with a rapid GDP growth rate, ranging from 6.5% to 7% annually, leading to an average increase in energy demand of 11% per year. This demand creates many new opportunities in the energy industry, especially renewable energy, to ensure sustainable development in the future for the country with applications of solar energy growing at the present, and other opportunities to expand in the future. In Vietnam, thanks to favorable weather, climate, terrain characteristics and many preferential support policies, there are many great opportunities in the field of solar energy exploitation and application. Location selection is an important problem in all renewable energy projects. Therefore, the author proposed a fuzzy Multi-criteria Decision-Making Model (MCDM) model for solar power plant location selection in this study, and as a result, location 5 is the optimal solution. The contribution of this study is to propose a MCDM for solar power plant location selection in Vietnam under fuzzy environmental conditions.

Assessing new intra-daily temperature-based machine learning models to outperform solar radiation predictions in different conditions

The measure of solar radiation is costly, as well as its maintenance and calibration needs; therefore, reliable datasets are scarce. In this work, several machine learning models to predict solar radiation have been developed and assessed at nine locations (Southern Spain and North Carolina in the USA), representing different geo-climatic conditions (aridity, sea distance, and elevation). As a novelty, due to the ease of providing air temperature measurements, different new input variables from intra-daily temperature datasets were used. According to the results, all the models highly outperformed self-calibrated empirical methods such as Hargreaves-Samani and Bristow-Campbell, with improvements in RMSE ranging from 7.56% in arid climate to 45.65% in humid. Moreover, regarding mean NSE and R2 values, several inland locations obtained values above 0.9. In summer, the highest statistics for all sites (more than a 60% improvement in NSE and R2) were obtained, whereas the worst were given in winter (more than an 18% improvement in NSE and R2). Besides, when assessing the models in different non-used locations with similar climatic characteristics, the reduction in RMSE was from 0.305 W m−2 to 0.252 W m−2 in a semiarid coastal climate and from 0.344 W m−2 to 0.233 W m−2 in dry sub-humid climate, compared to Hargreaves-Samani method. Overall, the MLP obtained the highest performance using the new proposed variables in all locations with medium aridity values, whereas in the aridest and most humid sites, SVM and RF models were preferred. Therefore, the temperature-based models developed in this work can predict solar radiation more accurately than the current ones. This is crucial in locations with no available datasets or missing/low quality and can be used to optimize the determination of the potential locations for solar power plants' construction.

Improvement Plan Through NONAGREEMENT CONSULTATION Analysis of Small Scale Environmental Impact Assessment

Purpose: Small-scale environmental impact assessment means an assessment conducted by survey-ing, forecasting, and assessing the appropriateness of a site location and the environmental impact to provide ecological conservation measures when it is intended to implement a development project in an area requiring planned development. However, there are many cases of raising problems because of environmental information s lack and uncertainty about ecological issues. Therefore, this study aimed to improve the small-scale environmental impact assessment consultation for the development project by analyzing the reasons for the nonagreement of consultation work, focusing on regulations related to the environment and law. Method: This study analyzed 357 cases notified the result of ‘nonagreement’ consultation of small-scale environmental impact assessments for 9 years from 2012 to 2020. We conducted a one-way ANO-VA analysis to determine the differences in the reasons for nonagreement consultation by year, by con-sulting agency, by project zone, and by project type. Post-hoc analysis was performed by the Scheff test. To conduct statistical analysis, we used Spss Statistics 21 statistical program. Results: The sharp increase in the number of yearly nonagreements in 2018 can be attributed to en-vironmental policy variations following government change in 2017. This increase is because the basis for determining the contents of small-scale environmental impact assessment consultations has become more stringent. We believe that the high rate of nonagreement consultation regarding guidelines and regulations in 2018 and 2019 is also an amendment in environmental policy following the change of government in 2017. The ratio of energy projects was high as the reason for nonagreement consulta-tion by project type. It is believed that this result was inevitable to lead to ‘nonagreement’ because the Ministry of Environment established the ‘Environmental effects evaluation consultation guideline for inland solar power generation’ in 2018 to limit the location of solar power plants. Conclusion: To efficiently operate the small-scale environmental impact assessment and achieve the environmental assessment objectives, the Ministry of Environment should apply the relaxed criteria as a detailed guideline for each area in small-scale environmental impact assessments with small areas. Also, the differences in the analysis results by year and by consulting agency seem to be because most of the consultation workers are public administrative officers, and their working departments were changed according to the interchange of personnel every year. Public officials in charge of consultation are bound to rely on consultation review institutions such as the Korea Environment Institute, the Korea Environment Corporation, the National Institute of Ecology, and the National Institute of Environmental Research. They have no choice but to rely on them. Therefore, we suggest that the officials in charge of consultation should be technical officials rather than administrative officials to improve their professio-nalism. For efficient operation, frequent personnel transfers of public officials in charge of consultation should also be improved.

Optimal site selection for sitting a solar park using a novel GIS- SWA’TEL model: A case study in Libya

ABSTRACT The present paper proposes a spatial model for the purpose of helping Decision Makers (DM) to select a location for Solar Power Plants (SPPs) in the Misrata District, Western Libya. In this regard, the use of the Geographic Information System (GIS), SWARA (Step-wise Weight Assessment Ratio Analysis), and DEMATEL (Decision-Making Trial and Evaluation Laboratory) multi-criteria methods is suggested. The goal of this paper is to develop a new multi-criteria spatial model to identify the best locations for Solar Power Plants (SPP). Such model considers six constraints and eight assessment criteria that are gathered into financial, social, and specialized measures. The hybrid SWARA-DEMATEL (SWA’TEL) model is utilized to assess the weight coefficients of the suggested criteria. The final suitability map is presented utilizing raster cells (alternatives) that are given values ranging from 1 (the least suitable) to 5 (the most appropriate). As the outcomes indicate, an area of 1 667 km2 in Misrata is entirely fit for the SPPs establishment. A sensitivity analysis performed by changing the criteria weights shows that the model is useful in selecting appropriate locations for developing SPPs projects. The model could also be used effectively to help locate appropriate sites for solar farms being installed in other areas with similar geographical conditions.

Optimal site selection for the installation of solar PV plants: a case study in Nakhchivan Ar, Azerbaijan

Since the electrical power produced by converting total solar radiation on a horizontal surface, composed of direct and diffuse components of PV cells, has low output power, it is necessary to identify areas with high power factor for more efficient power generation. However, due to the low efficiency of PV panels (14-18%) and the low intensity of total solar radiation on a horizontal surface, large installation space is required to achieve a certain power level. Due to the high cost of installing solar power plants, a comprehensive systematic assessment of the geographic factors of the region is required to select the most suitable location. The reason we chose Nakhchivan as the study area is that the radiation level is high compared to other regions of Azerbaijan (1220-1699 kWh/m2-year), and the number of hours of sunshine per year exceeds 2500. Since the creation of solar power plants in regions with high values of the total radiation on a horizontal surface depends on technical, economic and environmental criteria, descriptive criteria are used to determine the optimal areas. The analytical hierarchy process model, based on multi-criteria decision-making methods, was used to identify suitable locations for solar power plants. In the first phase of the study, seven criteria were analysed to determine suitable locations: Total solar radiation on a horizontal surface, slope, and land use, buffer distance from areas with high annual solar energy potential to residential areas, proximity to substations, highways, and power lines. At the second stage, the level of accessibility and suitability of areas within the framework of certain criteria was determined using the weighted overlay tool in geographic information systems. At the second stage, using the weighted overlay tool in GIS, the level of suitability of territories was determined according to certain criteria. As a result, the study, it was concluded that 9.5% (510 km2) of the land of Nakhchivan have high suitability, 12% (645 km2) - average suitability and 24% (1290 km2) - low suitability for placing solar power plants. The remaining 54.5% (2930 km2) of the region belongs to the territories that are not suitable for use due to low radiation, high slope, the presence of protected areas, settlements, agricultural areas and poorly developed infrastructure. Optimal locations cover mainly the southern and eastern parts of the region, as shown in the polygon shape on the suitability map.   The electricity generated by converting full solar radiation to the horizontal surface consists of direct and diffuse components of PV cells, which, due to their low output power, can be used in areas with high energy efficiency for efficient power generation. &nbsp

Alternatives to Solar Power Plant Location Through GIS and AHP: Case of Karaman, Turkey

In meeting today's increasing energy needs, the use of renewable energy sources becomes widespread comparing with the thermal and nuclear power plants, which cause great harm to nature. While hydroelectric power plants are most common among renewable energy plants in Turkey, national policies towards increasing wind power plants and solar power plants are gaining momentum. Due to its geographical location, Turkey is more advantageous position compared to many other countries in terms of solar energy potential. The region receiving the most solar energy in Turkey is Southeastern Anatolia, followed by the Mediterranean and Eastern Anatolia. It is seen that the solar energy potentials of Antalya, Karaman, Mersin and Van provinces are higher than other provinces of Turkey. With the help of a well-known Turkey map on the solar potential of cities, it is possible to determine the advantageous cities which solar power plants (SPP) can be placed. However, there is a need for a multi-criteria decision-making method regarding where position solar power plants in these cities. With this work; according to the solar radiation values of Turkey, it is aimed to determine the alternatives for the most suitable SPP locations in Karaman Province, which has an important potential for the establishment of a solar power plant. Appropriate locations were determined by a multi-criteria and geographic information systems (GIS) supported method. Eleven criteria with data for the city of Karaman have been identified among the criteria mentioned in the related literature. The scores obtained from these criteria (in grids of 100x100 meters) are classified into five categories. The weighted scores were then standardized to a range of 1-5 with tools to reclassify in GIS environment. Reclassified weighted criteria were overlapped with Weighted Overlay Analysis to determine the most suitable regions for SPP investment.

Decision Support Technique Based on Neutrosophic Yager Aggregation Operators: Application in Solar Power Plant Locations—Case Study of Bahawalpur, Pakistan

The problem of energy crisis and environmental pollution has been mitigated by the generation and use of solar power; however, the choice of locations for solar power plants is a difficult task because the decision-making process includes political, socio-economic, and environmental aspects. Thus, several adverse consequences have been created by the choice of suboptimal locations. The objective of this paper is to address the integrated qualitative and quantitative multicriteria decision-making framework for the selection of solar power plant locations. Neutrosophic sets (NSs) are the latest extension of the ordinary fuzzy sets. The main characteristic of the neutrosophic sets is satisfying the condition that the sum of the truth, indeterminacy, and falsity grades must be at least zero and at most three. In this research, we establish novel operational laws based on the Yager t-norm and t-conorm under neutrosophic environments (NE). Furthermore, based on these Yager operational laws, we develop a list of novel aggregation operators under NE. In addition, we design an algorithm to tackle the uncertainty to investigating the best solar power plant selection in five potential locations in Pakistan. A numerical example of solar power plant location problem is considered to show the supremacy and effectiveness of the proposed study. Also, a detailed comparison is constructed to evaluate the performance and validity of the established technique.

GIS-AHP Multi Criteria Decision Analysis for the optimal location of solar energy plants at Indonesia

A reliable tool for site-suitability assessment of solar power plants capable to account for the protection of cultural, natural, and ecological conservation areas is proposed. The tool integrates an Analytic Hierarchy Process (AHP) based Multi Criteria Decision Analysis (MCDA) algorithm into a Geographical Information System (GIS) package, which consists of layers of satellite-derived data for energy resources and locally obtained data such as land usage, topography, community settlement, road lines, and electrical network, considered as the criteria layers for the assessment of site suitability. The study is focused on the West Kalimantan Province (WKP) which imposes significant challenges due to the wide diversification of protected areas that need to be considered, particularly in landmarks that demand for high resolution imaging of Global Horizontal Irradiation (GHI) within ±4° of the equator. To overcome these challenges, a GIS spatial weighted overlay analysis of criteria layers has been performed within three approximation schemes distinguished by the proximity to existing infrastructures. It has been found that although WKP has relatively high values of GHI over its entire area of 146,807km2, when the protected areas are accounted, only 34% of the area is available for solar power plant deployment. Further analysis using the AHP-MCDA approach, with consideration of the best-suitable conditions, significantly reduces the search of optimal location of solar power plants into just 0.07% to 0.03% of the WKP area. This corresponds to an area of 46.60−108.58km2 with an estimated generation capacity of 2,034−4,785MW, what indicates that the abundant resources of WKP could be sufficient to meet the national renewable energy target. The results of this research should provide a model of decision support system for development of large-scale solar power plants in tropical countries, where the protection of forest and biodiversity is a global concern.

Determining Optimal Solar Power Plant Locations Based on Remote Sensing and GIS Methods: A Case Study from Croatia

In the last few years, the world has been turning to the exploitation of renewable energy sources due to increased awareness of environmental protection and increased consumption of fossil fuels. In this research, by applying geographic information systems and integrating them with multi-criteria decision making methods, an area suitable for the construction and exploitation of renewable energy sources is determined. The research uses not only climate, spatial, environmental, and geomorphological parameters but also socioeconomic parameters, population, unemployment, and number of tourist nights as well as electricity consumption. By applying spatial analysis, rasters of all parameters were created using GRASS GIS software. Using the analytic hierarchy process, the calculated rasters are assigned with weight coefficients, and the sum of all those rasters gives the final raster of optimal locations for the construction of solar power plants in Croatia. To test the accuracy of the obtained results, sensitivity analysis was performed using different weight coefficients of the parameters. From the sensitivity analysis results, as well as a histogram and statistical indicators of the three rasters, it is apparent that raster F1 gives the best results. The most decisive parameters in determining the optimal solar plant locations that result from this research are GHI, land cover, and distance to the electricity network.