Photovoltaic Plants Research Articles

Photovoltaic-powered seasonal snow storage-assisted district cooling system: Site suitability analysis and performance assessment

District cooling (DC) plants coupled with cold thermal energy storage (TES) and photovoltaic (PV) systems are getting attention worldwide. Utilising winter snow is a successfully implemented TES technology, with little scale-up. Its site identification and cooling potential are not yet fully explored, especially for DC applications. The objective of this paper is to carry out site suitability analysis and performance assessment for PV-powered snow storage-assisted DC system. The site selection methodology is based on multi-criteria decision-making techniques and geographic information system. Performance analysis is carried out mathematically by considering ten indicators. The robustness of the study is enhanced by multiple sensitivity analysis. For the base case scenario, the dominant selection criteria are observed to be cooling load density (30 %), proximity to electric grids (17 %) and air temperature (13 %). Out of the selected land cover (vacant/crops/rangeland) in Estonia, around 34 km2 was observed to be moderately-suitable for the development of the proposed system. For a specific site, the theoretical generation potential of snow cooling facility is estimated as 1728 MWh with an annual cooling share of 80 %. With annual energy output of 1823 MWh and performance-ratio of 78 %, rooftop PV plants is capable to fully power the proposed system. A satisfactory economic and environmental performance is observed at the whole system level. This study is expected to scientifically support the effective utilisation of locally available energy sources (i.e. winter snow and solar energy) for space cooling applications in cold regions.

Design of reliable standalone utility-scale pumped hydroelectric storage powered by PV/Wind hybrid renewable system

With a target of 35 % renewable energy in the country’s energy mix by 2030, the goal of this research is to support the Libyan government’s plan to maximize the use of environmentally friendly and sustainable energy sources. This will be accomplished by making effective use of the region’s plentiful natural resources. The feasibility of wind and solar energy has been established by local research, and the presence of highlands that can store pumped hydropower (PHS) makes hybrid renewable energy systems (HRES) even more feasible. These systems might offer a sizable edge over competitors in the regional energy market. In this study, PHS was utilized to stabilize electricity production in addition to storing energy. A hydropower turbine provides electricity to the load, and the PHS system is powered by a PV/wind supply. By lowering the volatility of wind and photovoltaic power generation, the suggested system could offer a more dependable renewable energy source without requiring complicated control mechanisms. This strategy could encourage a wider adoption of renewable energy, which could be especially advantageous for developing nations. Under particular load and climate conditions, the energy production of different solar PV array and wind turbine farm configurations was estimated using the System Advisor Model (SAM) software. The ideal HRES configuration consists of a 290 MW wind farm, a 154 MW solar PV field, and a 508 MW PHS system. This configuration will provide 100 % of the annual electrical demand of 513 GWh, plus a 20 % safety margin. The levelized cost of energy (LCOE), estimated at $211.65/MWh, is reduced by this configuration. It is anticipated that the $929.02 million initial investment will be recovered in 11 years, and the system will turn a profit in the following 9 years. Also, the planned HRES will stop 532.17 tons of CO2 emissions from being released each year.

Design, modeling and cost analysis of 8.79 MW solar photovoltaic power plant at National University of Sciences and Technology (NUST), Islamabad, Pakistan

Climate change, as a critical global concern, has fueled our efforts to address it through different strategies. In response to the critical worldwide issue of climate change, we suggested a Photovoltaic (PV) system at the National University of Sciences and Technology (NUST) in Islamabad, Pakistan (latitude: 33.724530 N, longitude: 73.046869, terrain elevation: 552 m). Islamabad is located in a region blessed with enormous solar resources, boasting a daily horizontal solar irradiance of 1503.45 kWh/m2 and an average daily solar irradiance of 5.89 kWh/m2, with an exceptional solar fraction of 98.99%. The ambient air temperature, averaging 23.21 °C, reaches its maximum in June and its minimum in December. Our research thoroughly evaluates the system’s performance, accounting for various losses and utilizing modern PVsyst software. Over the course of 18 years, our PV system is expected to save 75,478.60 tons of CO2, the equivalent of planting 348,754 teak trees. Furthermore, the cost of energy generation is an affordable 0.0141 US $/kWh, much lower than traditional rates, including the Sherif cost of 0.028$/kWh. Along with the performance research, we conducted a detailed cost analysis, projecting the starting cost and cash flow, and discovered that the plant would be in surplus within 12 years of installation. Our system is positioned to generate 11,270,771 kWh/year with a respectable performance ratio (PR) of 76.2% and a Capacity Utilization Factor (CUF) of 16%. Our findings not only highlight the potential of renewable energy but also provide important insights for future sustainable energy programs.

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

Intelligent parking lot power management: Augmented epsilon‐constraint concept with correlation analysis

AbstractThis paper addresses essential aspects of decision‐making and management in energy resources. To achieve this, a tri‐objective model is proposed that seeks to find the best solution within the basic constraints framework of the optimization problem so that all three proposed objective functions can approach their ideal point. The uncertainty is used for the photovoltaic and wind power plants’ output in the proposed multi‐objective optimization problem as a scenario‐based stochastic approach. The proposed objective functions are realizing the operating cost, the amount of emission produced by generation resources, the amount of load‐shedding, and the maximum participation of responsive demands in the management program. The idea of employing plug‐in electric vehicle (PHEV) units in the form of intelligent parking lots within the network is also included in the proposed study, which can increase network flexibility and help improve the main features of the network. A modified IEEE 83‐bus test system is used to ensure the accuracy and effectiveness of the proposed model. The properties of PHEVs significantly affect the simulation results and compensate for the uncertainty associated with renewable energy sources. Randomly considering the parameters of PHEVs can also realistically bring the results of power management more realistic. In addition, the multi‐objective problem defined for each scenario is solved by the augmented epsilon‐constraint method with the correlation coefficient concept for the network under study, and the Pareto front curves are obtained separately and the best solution is extracted by a proper decision‐making method.

Performance assessment of a 30.26 kW grid-connected photovoltaic plant in Egypt

Abstract This paper presents an experimental analysis and performance evaluation of a grid-connected photovoltaic plant installed on the rooftop of the Electronics Research Institute in Cairo, Egypt. Cairo is classified as a hot-desert climate region according to the standard Koppen-Geiger climate classification system. Over a year, we monitored real-time data to assess key system performance metrics, such as energy yield, efficiencies, performance ratio, capacity factor, and losses. Based on the obtained experimental results, the highest final yield of 5.2498 hr/day was observed in the summer, whereas the lowest yield of 3.439 hr/day occurred in the winter months. The photovoltaic plant had an average annual system efficiency of 15.8%, while the photovoltaic and inverter had mean yearly efficiencies of 17.1% and 97.2%, respectively. The average annual performance ratio is 83.03%, and the capacity factor is 18.72%. The monthly total loss exhibited a linear rise alongside increasing ambient temperature and solar irradiance. The ambient temperature affected the system efficiency, photovoltaic efficiency, and performance ratio. The findings can help strengthen forecasts of future large-scale photovoltaic plants in hot desert climates. Moreover, they can guide the design, optimization, operation, and maintenance of new grid-connected photovoltaic systems.

Dynamic modelling and equilibrium manifold of multi‐converter systems: A study on grid‐forming and grid‐following converters in renewable energy power plants

AbstractThis study explores the optimal balance between grid‐forming (GFM) and grid‐following (GFL) converter capacities within power stations to ensure stable operations. The investigation introduces a novel, generic modelling approach for analysing multiple converter systems in the wind and photovoltaic power plants. The method aims to elucidate the dynamic characteristics of the converters in power plants, particularly focusing on the continuity and existence of the equilibrium manifolds and their impact on system stability. Findings reveal a pronounced difference in the recovery capabilities of GFM and GFL following synchronization losses, highlighting an asymmetry in their abilities. Specifically, GFL converters exhibit more effectiveness in reinstating synchrony after synchronization losses caused by GFM. Conversely, GFM demonstrates a lesser capacity to recover from synchronization losses induced by GFL. Furthermore, analysis indicates that when the capacity ratio of GFL to the system's short‐circuit capacity significantly exceeds that of GFM (exceeding a 1:5 ratio), the system experiences an absence of a stable equilibrium point, thereby affecting the synchronization stability of GFM. These conclusions have been validated through joint controller hardware‐in‐the‐loop testing.

Assessing Yield Disparities: Anticipated versus Optimal Rooftop Photovoltaic Systems and Implications for Prosumer Viability

Solar photovoltaics, especially rooftop systems also called distributed solar photovoltaics, are crucial in the ongoing energy transition. Modeling these systems is vital to understanding their role in a decentralized energy system. While ground‐mounted photovoltaic power plants are easier to model, generalizing yield profiles for rooftop systems is challenging. This study aims to estimate yield loss effects for rooftop solar photovoltaic systems compared to optimized ground‐mounted systems. Anticipated yield losses are 18% for residential, 7% for commercial, and 4% for industrial rooftop systems. The impact on residential prosumers’ viability is assessed by comparing prosumer system optimization results with and without yield losses. Results show a non‐uniform change in installed solar photovoltaic and battery capacities, with a tendency to compensate for reduced yields by increasing photovoltaic capacity by up to 20%, given favorable cost prospects by 2050. The annualized total cost of energy for prosumer households could therefore increase by up to 20% by 2050. Despite yield reductions, installing a solar photovoltaic prosumer system remains more favorable than relying entirely on‐grid electricity. This study highlights the importance of considering yield losses in rooftop solar photovoltaics and the significant role of prosumers despite identified yield losses.

An error-corrected deep Autoformer model via Bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction

Accurate PV power prediction is crucial for stable grid operation and rational dispatch. However, due to the instability of PV power generation, PV power prediction still has great challenges. Therefore, an Autoformer model based on secondary decomposition, Bayesian optimization and error correction for PV power prediction. In order to reduce the complexity of the data and fully extract the features, two decomposition methods are employed. First, empirical mode decomposition (EMD) is applied to decompose the PV power series at the first level. Then, the sample entropy (SE) is introduced to measure the complexity of each component, and the variational mode decomposition (VMD) is employed to implement secondary decomposition of the component with the highest complexity. Secondly, a Bayesian optimization algorithm enhanced Autoformer model is developed for predicting each component, and the predicted component results are aggregated to obtain preliminary PV power prediction results. Finally, the preliminary prediction results are error corrected using a least squares support vector machine. A four-month PV dataset from a PV power plant in Hangzhou, China is utilized to validate the effectiveness of the proposed model. The experimental results show that the model after primary decomposition is superior to the single model, and the prediction accuracy is substantially improved after secondary decomposition. The proposed model has the best prediction performance in predicting the PV power for different seasons, which shows good robustness.

Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems.

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms-grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)-were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution.

Research on a Photovoltaic Panel Dust Detection Algorithm Based on 3D Data Generation

With the rapid advancements in AI technology, UAV-based inspection has become a mainstream method for intelligent maintenance of PV power stations. To address limitations in accuracy and data acquisition, this paper presents a defect detection algorithm for PV panels based on an enhanced YOLOv8 model. The PV panel dust dataset is manually extended using 3D modeling technology, which significantly improves the model’s ability to generalize and detect fine dust particles in complex environments. SENetV2 is introduced to improve the model’s perception of dust features in cluttered backgrounds. AKConv replaces traditional convolution in the neck network, allowing for more flexible and accurate feature extraction through arbitrary kernel parameters and sampling shapes. Additionally, a DySample dynamic upsampler accelerates processing by 8.73%, improving the frame rate from 87.58 FPS to 95.23 FPS while maintaining efficiency. Experimental results show that the 3D image expansion method contributes to a 4.6% increase in detection accuracy, an 8.4% improvement in recall, a 5.7% increase in mAP@50, and a 15.1% improvement in mAP@50-95 compared to the original YOLOv8. The expanded dataset and enhanced model demonstrate the effectiveness and practicality of the proposed approach.

Locating the suitable large-scale solar farms in China's deserts with environmental considerations

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4–84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47–110 PWh which is 1.7–3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8–62.6 gigatons annually, offsetting 73–170 % of global emissions. Covering just 4.8–11.5 % of China's desert area (8 × 104–19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

A method for configuring hybrid electrolyzers based on joint wind and photovoltaic power generation modeling using copula functions

Considering the specific wind and photovoltaic power characteristics of a certain region, this study investigates the optimal ratio of Alkaline Electrolysis Cells (AEL) to Proton Exchange Membrane (PEM) electrolyzers in a hybrid electrolysis system for hydrogen production. A flexible model for configuring the hybrid electrolysis system is proposed, based on a copula function for joint wind and solar power modeling. This model generates wind and photovoltaic power generation scenarios using the copula function, incorporating a selection mechanism to ensure that the output scenarios are more representative of the actual data characteristics of wind and photovoltaic power output. Consequently, considering both the fluctuation and amplitude, the wind and photovoltaic power data are decomposed using the Ensemble empirical mode decomposition method. The decomposed components are then allocated to the two types of electrolyzers. Furthermore, the optimal configuration of the hybrid electrolysis system is determined by minimizing the costs associated with wasted power, electricity purchases, and other expenses. Finally, a case study of a 100 MW wind farm and a 50 MW photovoltaic power station in Northwest China is presented, concluding that the optimal configuration ratio of AEL to PEM electrolyzers is 2:1. In a Matlab/Simulink platform, the performance metrics of the hybrid electrolysis system were validated. It was found that the hydrogen production rate of the hybrid electrolyzer is comparable to that of the PEM electrolyzer, but with a lower required cost. Additionally, the hydrogen production rate and volume of the optimal configuration for the hybrid electrolyzer determined by the model proposed in this paper are higher than those obtained through the optimization algorithm's optimal configuration.

Comparison of Conventional and Microwave Heating

Carnot batteries (or Power-to-Heat-to-Power systems) are capable of increasing the use of electricity from photovoltaic or wind power plants to achieve decarbonisation of the electricity sector. To decouple electricity production from demand, these renewable power plants can be connected to the thermal energy storage system of a concentrated solar thermal power plant via electric heaters. As an alternative to electric heaters, microwaves are studied as a volumetric and selective heating method, avoiding the limitations of the Joule effect conventional heating when having solar salt (non-eutectic mixture of 60 wt % NaNO3 and 40 wt % KNO3), which is a storage medium with low thermal conductivity, working at a temperature very close to its affordable maximum temperature without degradation. In this work, the two heating methods are compared both experimentally and numerically. At experimental level, a microwave oven and a muffle furnace are used to record energy consumption and time for a common objective. Additionally, different crucible materials were used to test their suitability with microwaves. Only quartz glass was validated for microwave heating, while the porcelain and alumina based materials (Corundum and Alsint 99.7) failed while exposed to microwaves. Compared to conventional heating, heating solar salt with microwaves saved fifty minutes of time and ninety percent of consumption. These experiments were validated by numerical simulations, showing a different distribution in each experiment.

Augmenting a Concentrated Solar Power (CSP) Plant With a Solar PV Plant

The search for enhanced dispatchability at decreased prices has led to a surge in research on hybrid renewable energy systems. This paper explores the integration of Photovoltaic (PV) technology with Concentrating Solar Power (CSP) plants to enhance energy generation and economic feasibility, focusing on optimising the size of PV augmentation for existing CSP facilities. The study considers CSP plants with both Time-of-Day (ToD) and single tariffs, employing modelling techniques to assess the synergies between these two technologies. The cost of PV technology has significantly reduced, making it a cost-effective choice for electricity generation compared to CSP. CSP-PV augmentation promises to reduce online and offline auxiliary consumption, resulting in enhanced energy generation and a subsequent reduction in energy production costs. This study aims to explore the advantages of combining solar PV with a CSP system and determine the ideal PV size to maximise return, building upon previous studies to assess the technological and economic potential of integrating solar PV with CSP, focusing on South Africa. It utilises a rigorous analysis, combining the System Advisory Model and PVSyst modelling tool within an MS Excel framework. This economic assessment and sizing exercise aims to maximise profits while adhering to the limitations imposed by the power purchase agreement of a 100 MW CSP facility. The optimal size is 15 MWp for the ToD tariff and 16 MWp for the flat tariff. This highlights the potential for CSP-PV augmentation to improve energy dispatchability and financial viability, making it a compelling solution for existing CSP plants.

Application of multi-criteria decision making (MCDM) model for solar plant location selection

This study aims to systematically evaluate and identify the most suitable locations for establishing solar farms in the United Kingdom. It employs a comprehensive Multi-Criteria Decision-Making (MCDM) model that incorporates the Fuzzy Analytic Hierarchy Process (FAHP) method. Eight potential locations have been identified and selected for their climatic conditions and other pertinent parameters. These potential locations underwent additional assessment considering several factors including temperature, annual hours of sunshine, absolute humidity, proximity to major roads, distance from the power network, and potential demand. The data was normalized to eliminate scale effects and improve comparability. A MATLAB program has been developed with universality and user-friendliness in mind. Its purpose is to establish location selection factors graphs according to application-specific criteria and generate the matrix representation of these graphs for each potential candidate location. The permanent matrix, a composite metric synthesizing six criteria, identifies Torquay as the optimal location with a permanent value of 18.16. This quantitative approach, intersected with qualitative assessments, presents a clear hierarchy of site suitability. The model's simplicity, scalability, and proven accuracy make it a valuable tool for assessing renewable energy project viability in diverse regions.

Understanding Heat Dissipation Factors for Fixed‐Tilt and Single‐Axis Tracked Open‐Rack Photovoltaic Modules: Experimental Insights

ABSTRACTThis paper presents the results of long‐term experiments conducted on fixed‐tilt (FT) and single‐axis tracked (SAT) open‐rack photovoltaic (PV) modules in South Africa. Utilising Faiman's heat dissipation model and data filtering method, the study demonstrates favourable comparisons of FT experimental results with literature while yielding novel heat dissipation factors for SAT modules. Enhanced heat dissipation is observed in no/low wind conditions for SAT modules compared to FT modules. Analyses reveal the influence of plane‐of‐array (POA) irradiance, wind speed and direction on module temperature, with SAT modules exhibiting greater heat dissipation stability. An investigation into data filtering methods suggests minor sensitivity for both configurations, with a slightly more pronounced impact on SAT modules. Assessments comparing module temperature predictions using diverse heat dissipation factors for FT modules reveal negligible sensitivity. This suggests that exact heat dissipation factor values may not be crucial for accurate predictions of module temperature in FT open‐rack systems. Annual power output simulations using PVsyst software demonstrate a 2.9% and 3.3% enhancement for FT and SAT configurations, respectively, when employing experimentally determined heat dissipation factors. These findings highlight the importance of realistic, configuration‐specific heat dissipation factors in optimising PV system performance, particularly in the competitive context of modern PV power plant construction and techno‐economic calculations.

Design and simulation of a photovoltaic plant for maximum use of the solar resource in the Toluviejo municipality of Colombia

Currently, there are various technologies for constructing photovoltaic plants including monofacial and bifacial photovoltaic panels, centralized and string-type inverters, and fixed mounting structures with light followers, to build electrical energy generation systems. This work reviewed the advantages and disadvantages between these technologies, and the implementation of a photovoltaic plant in Toluviejo, Colombia was proposed. To justify and confirm the design, simulations were carried out with the PVsyst software, resulting in the bifacial panels, centralized inverters, and structures with light followers, the most effective solution, and with the highest production for the proposed photovoltaic power generation plant.

Junction Temperature Prediction and Lifetime Assessment in a PV Inverter Using a 10-year Mission profile

The expansion of great-scale photovoltaic (PV) power plants indicates the need for an accurate lifetime assessment of inverters to maintain energy supply availability. In this context, the study contributes in two ways. First, we use machine-learning (ML) models for junction temperature prediction. Second, we perform reliability assessments using a 10-year mission profile in three Brazilian cities. The thermal loadings are obtained through a look-up table approach. Although the ML models exhibit different performances in regression, other factors must be considered, such as easy-to-apply, interpretability, and generalization capability. The reliability assessment is typically based on an annual mission profile, assuming damage repeats until failure. However, only the historical series can confirm whether this choice was acceptable, pessimistic, or optimistic. For instance, in Campos do Jordão-SP, if the chosen mission profile is 2014, the expected failure of 10\% of inverter samples occurs three years earlier than suggested by the historical series. Regardless of the methodology used to estimate thermal loading or accumulated damage, the mission profile significantly influences photovoltaic inverter reliability, indicating that if more data is available, the chosen mission profile should align with the historical series.

Towards advanced sustainable criteria for choosing the best site for collecting solar energy in cities using multi-criteria GIS

Solar energy has become a prominent and crucial source of clean energy due to the increasing global demand for electricity. There is a prevailing global trend towards the construction of solar farms as a means of energy generation. This study aims to assess multiple criteria encompassing urban, environmental, and social aspects in order to ascertain the optimal site for constructing solar farms. The evaluation primarily focuses on characteristics such as topography, solar radiation, accessibility, land use, and proximity to roads and power stations. In order to acquire the necessary spatial fit model, the data was evaluated utilizing a Geographic Information System (GIS) program, and the criteria were subsequently consolidated into an integrated geographic information system. A comprehensive review of the existing body of literature reveals that environmental factors, specifically solar radiation and aspect, play a crucial role in determining the suitable places for the establishment of solar energy collection projects (Merrouni et al. in Energy Procedia 49:2270-2279, 2013; McKinney in J Student Res Environ Sci Appalac 4:1-14, 2014). Furthermore, other analysis factors such as proximity to built-up regions, closeness to power lines, and proximity to roadways are taken into account (Hott et al. in GIS-based Spatial Analysis For LargeScale Solar Power And Transmission Line Issues: Case Study of Wyoming, U.S. In: Proceedings of the 41st American Solar Energy Society Meeting, 2012; Effat in Int J Adv Remote Sens GIS 2:205-220, 2013). These criteria have an impact on the cost of solar farms. The objective of this study was to assess several aspects influencing the selection of an optimal location for a solar energy farm, taking into consideration the aforementioned criteria. The study was carried out in New Aswan city, utilizing data obtained from the Urban Planning Authority within the Ministry of Housing and Urban Development, as well as the New Aswan City AuthorityI generated cartographic representations and compiled quantitative data, which informed our selection of places that met the established criteria through the utilization of a Geographic Information System (GIS) software. Upon the conclusion of the study, numerous locations that satisfied the established criteria were selected. The present study focuses on regions with high capacity, and the findings are depicted in the form of spatial and objective maps. One of the primary benefits associated with the utilization of this methodology lies in its versatility, as it can be implemented across several domains. Furthermore, a straightforward adjustment of the criteria facilitates its application in the selection of optimal locations for wind farms.