Solar Plant Research Articles

Allothermally Heated Reactors for Solar-Powered Implementation of Sulphur-Based Thermochemical Cycles

Catalytic sulphur trioxide splitting is the highest-temperature (650-950oC), endothermic step of several sulphur-based thermochemical cycles targeted to production of hydrogen or solid sulphur. Concentrated solar power tower plants are an attractive renewable energy source to provide the necessary heat. Furthermore, the development of solar receivers capable of delivering solid or gaseous heat transfer fluids at these temperature ranges enable the implementation of such endothermic reactions in allothermally-heated reactors/heat exchangers placed away from the solar receiver. In this context, a 2-kW laboratory-scale shelland-tube reactor/heat exchanger to perform thermal sulphuric acid decomposition and catalytic sulphur trioxide splitting was in-house designed, built and tested with electrically heated bauxite particles, in the perspective of eventually coupling such a reactor with a centrifugal particle solar receiver. Thermal test runs demonstrated the in-principle feasibility of the concept. The temperatures reached were sufficient to ensure complete sulphuric acid evaporation. However, the ones in the SO3 splitting zone were of the order of 750°C, high enough to demonstrate SO3 splitting but not reaching the levels required for close-toequilibrium conversion of the Fe2O3 catalyst system used (~ 850oC). An improved version of the reactor is under construction incorporating design modifications based on lessons learned from the test campaigns, in the perspective of scaling up the process.

Effect of Load Cycling on High Temperature Creep of 316L Stainless Steel

For components in concentrating solar thermal plants, the creep mechanism will cause a significant portion of material damage to receivers, storage tanks, turbines and pipework. These components will undergo conditions where loads, temperature, or both load and temperature are not constatnly applied. This creep damage process will not resemble the constant load creep tests used to characterise creep of materials. Therefore, creep tests incorporating a load cycle were undertaken to obtain a better understanding of how high temperature materials respond to these cyclic conditions. These tests showed that when time under load was considered, creep undertaken under load cycling conditions were accelerated relative to constant load conditions. A modified Larson-Miller approach was used to assess this effect and determine an equivalent stress where constant load test data agrees with the cycled test data. This found that cycling the load was equivalent to if the system were under 3 and 7 MPa higher stress, for tests which were loaded for 12 hrs and 6 hrs per 24 hrs respectively. This could be a potential approach to simply and easily consider these load cycling effects for design and life analysis.

Artificial Intelligence-Based Improvement of Empirical Methods for Accurate Global Solar Radiation Forecast: Development and Comparative Analysis

Artificial intelligence (AI) technology has expanded its potential in environmental and renewable energy applications, particularly in the use of artificial neural networks (ANNs) as the most widely used technique. To address the shortage of solar measurement in various places worldwide, several solar radiation methods have been developed to forecast global solar radiation (GSR). With this consideration, this study aims to develop temperature-based GSR models using a commonly utilized approach in machine learning techniques, ANNs, to predict GSR using just temperature data. It also compares the performance of these models to the commonly used empirical technique. Additionally, it develops precise GSR models for five new sites and the entire region, which currently lacks AI-based models despite the presence of proposed solar energy plants in the area. The study also examines the impact of varying lengths of validation datasets on solar radiation models’ prediction and accuracy, which has received little attention. Furthermore, it investigates different ANN architectures for GSR estimation and introduces a comprehensive comparative study. The findings indicate that the most advanced models of both methods accurately predict GSR, with coefficient of determination, R2, values ranging from 96% to 98%. Moreover, the local and general formulas of the empirical model exhibit comparable performance at non-coastal sites. Conversely, the local and general ANN-based models perform almost identically, with a high ability to forecast GSR in any location, even during the winter months. Additionally, ANN architectures with fewer neurons in their single hidden layer generally outperform those with more. Furthermore, the efficacy and precision of the models, particularly ANN-based ones, are minimally impacted by the size of the validation data sets. This study also reveals that the performance of the empirical models was significantly influenced by weather conditions such as clouds and rain, especially at coastal sites. In contrast, the ANN-based models were less impacted by such weather variations, with a performance that was approximately 7% better than the empirical ones at coastal sites. The best-developed models, particularly the ANN-based models, are thus highly recommended. They enable the precise and rapid forecast of GSR, which is useful in the design and performance evaluation of various solar applications, with the temperature data continuously and easily recorded for various purposes.

ACWA Power’s CSP Performance Assessment

A CSP Plant and in particular the solar field has hundreds of thousands of components and the corresponding process parameters. This needs to be monitored and evaluated every day to make sure that the plant is working efficiently. A performance assessment with a production simulation is done daily where the expected and actual power production are compared. Such evaluation is usually done with the plant performance model and the necessary data is compiled in calculation programs like Excel. The work is repetitive, time consuming and human errors may easily occur. Typically, the evaluations are done from different parties with deviating results and conclusions. ACWA Power has developed an online simulation platform to automatize the data processing to increase the data reliability and reduce the time effort. The application has been implemented in the Noor Energy 1 hybrid solar plant in Dubai and shall be presented in this paper.

Performance analysis of IoT-enabled hydro-photovoltaic power systems considering electrical power mission chains

Synergistically and complementarily managing the operation of hydropower plants and other weather-dependent renewable energy generation plants (e.g., solar energy plants) provides an effective measure to improve the performance efficiency (PE) of renewable energy. However, environmental uncertainty and physical system unavailability pose challenges in assessing the PE of the power generation. This paper proposes an approach to synergistically and complementarily managing the operation of hydro-photovoltaic (HPV) power systems in IoT under uncertain environments and develops a mission chain-based PE model for quantifying the capacity of the fulfilment of a power supply mission to satisfy users’ demand for power. An importance measure-based restoration strategy is then proposed to enhance the PE of an HPV power system (PEPS). The approach is examined by taking a large-scale HPV in China in the case study. The results show that, in winters and in summers, the PEPS is higher than PE of a photovoltaic power system and a hydropower system, respectively, and the PEPS with the importance measure-based restoration strategy is higher than that under the random restoration strategy, respectively. The findings not only lay the foundation for the complementary operation of hybrid renewable energy power systems but also provide a reference for the restoration in case of power cuts.

Numerical analysis of evaporation process in the heating column of a solar water desalination plant under various geometries, ambient conditions, solar radiations, and time steps

Solar-powered desalination offers a promising and sustainable method for producing potable water. In this study, the heating column of a solar desalination powerplant is numerically investigated under different novel geometric/structure scenarios, various solar/ambient conditions and different seawater salinity through different time steps. The primary objective is to identify the optimal configuration that maximizes water evaporation within the heating cylinder which has not been carried out before. Initially, four distinct heating cylinder configurations are examined under a fixed time step. The most promising configuration is then selected and evaluated under a broader range of ambient conditions and the same time step. The influence of solar radiation throughout the day is also analyzed. Finally, the evaporation process is simulated for the selected geometrical and environmental conditions across different time steps ranging from 60 to 300 s. The findings reveal that incorporating fins alone, a glass cover alone, and a combination of both fins and a glass cover could enhance the volume fraction of vapor by approximately 8 %, 33.3 % and 10.6 %, respectively, compared to the baseline configuration without fins or a glass cover. However, the evaporation rate for the glass cover configuration increased with rising ambient air temperature. Additionally, the highest evaporation is observed at 15:00 p.m., corresponding to the peak solar radiation intensity. Under optimal conditions, the heating cylinder achieved a promising vapor volume fraction of approximately 78.2 % after 300 s. These findings offer valuable insights for optimizing solar desalination system design. By increasing the salinity of the water from 0 to 75 %, the value of steam volume fraction is reduced from 77 % to 60 % for a given time step.

A Three-Step Weather Data Approach in Solar Energy Prediction Using Machine Learning

Solar energy plays a critical part in lowering CO2 emissions and other greenhouse gases when integrated into the grid. Higher solar energy penetration is hindered by its intermittency leading to reliability issues. To forecast solar energy production, this study suggests a three-step forecasting method that selects weather variables with a moderate to strong positive correlation to solar radiation using Pearson correlation coefficient analysis. Low-level data fusion is used to combine weather inputs from a reliable local weather station and an on-site weather station, significantly improving the forecasting model’s accuracy regardless of the machine learning method used. Weather data was obtained from the Kisanhub Weather Station located in Cranfield University, UK and the meteorological station in Bedford, UK. In addition, PV power supply data was obtained from four solar plants. Using the Regression Learner app in MATLAB, the proposed architecture is tested on a utility scale solar plant (1 MW), showing a 6% and 13% prediction accuracy improvement when compared to solely using data from the on-site and local weather station respectively. It is further validated using data from three residential rooftop solar systems (8 kW, 10.5 kW and 15 kW), achieving root-mean square values of 0.0984, 0.0885, and 0.1425 respectively. The data was pre-processed using both rescaling and list-wise deletion methods. Training and testing data from the 1 MW solar plant was divided into 75% and 25% respectively, while 100% of the residential rooftop solar plants was used for validation.

Green hydrogen demand in Cameroon's energy sectors by 2040

Cameroon possesses a significant endowment of solar energy, granting it exceptional potential for the generation of hydrogen through environmentally friendly means. However, the continued expansion of the nation's petroleum industry presents an obstacle to the domestic utilization of green hydrogen due to its present costliness for energy purposes. Nonetheless, the prospect of exporting green hydrogen to developed nations remains an intriguing proposition. Indeed, a pact concerning hydrogen was established between Australia and Cameroon in the year 2021, thus opening avenues for the export of green hydrogen to facilitate the decarbonization of national energy supplies in Australia and other industrialized nations. Presently, there are no documented large-scale projects within Cameroon dedicated to the electrolytic production of hydrogen. This study projects the potential hydrogen demand in the electricity and transportation sectors up to 2040. Electricity demand is expected to be as high as 8675 GWh in 2040, while gasoline and diesel demand are expected to reach 1.75 and 3.26 million cubic meters, respectively. Therefore, the total amount of hydrogen needed to power both the electricity and transportation sectors is estimated at 0.532 megatonnes. Even a relatively modest allocation, merely 5 %, of Cameroon's land for the production of hydrogen via solar-powered electricity generation could yield a surplus. This resultant quantity of hydrogen, estimated at a substantial 16.68 megatonnes, would likely be more than enough to satisfy the projected domestic needs for both electrical and transportation uses by the year 2040.

Data analysis and review of the research landscape in performance-enhancing thermal management strategies of photovoltaic technology

New researchers in Performance-Enhancing Thermal Management Strategies (PETS) for photovoltaic (PV) technology struggle with scattered data, hindering their efforts. This research gap is addressed by extracting key information from 1,474 documents in the Web of Science and Scopus databases and conducting a thorough data analysis. The findings reveal three phases of the trend inception, gradual growth period, tech-advanced period, and rapid progress. The bibliometric results show a 24.98% growth rate, 25.01 citations per document, 2,577 researchers involved, and 275 production sources. China, India, and other Asian countries are the main collaborators in this field, emphasizing the impact of hot climate regions in Asia on the choice of PETS methods. The main researchers collaborating with PETS are Sopian. K, Jie Ji, and Pie Gang. Furthermore, a review of keywords suggests that most experimental and numerical studies have prioritized temporal and performance outcomes, often neglecting long-term practical viability considerations. Future research should prioritize the 4E (Energy, Exergy, Economic and Environmental) analysis approach to address this gap effectively. Advancements in evaporative cooling and PVT technology have evolved in offshore solar plants, PV with heat pumps, and building-integrated PV (BIPV). This analysis sets the stage for future PV advancements in PETS technology.

A novel algorithmic multi-attribute decision-making framework for solar panel selection using modified aggregations of cubic intuitionistic fuzzy hypersoft set

To address the shortcomings of the cubic intuitionistic fuzzy sets (CIFSs) for the entitlement of multi-argument approximate function, the cubic intuitionistic fuzzy hypersoft set (Ω-set) is an emerging study area. This type of setting associates the sub-parametric tuples with the collection of CIFSs. Categorizing the evaluation of parameters into their corresponding sub-parametric values based on non-overlapping sets has significance in decision making and optimization related situations. Some operations of Ω-set are proposed in this study, along with certain practical features. We provide the complement, P-order, and R-order subsets, P-union (∪P), R-union (∪R), P-intersection (∩P) and R-intersection (∩R) of Ω-sets. The internal cubic intuitionistic fuzzy hypersoft set (ΩI-set) and the external cubic intuitionistic fuzzy hypersoft set (ΩE-set) are also proposed in this paper, which will aid researchers in applying this new theory to other areas of study. We show a few examples in this context and look into some more aspects of ∪P, ∪R, ∩P and ∩R of ΩI-sets and ΩE-sets. Arguments for a few significant theorems about ΩI-sets and ΩE-sets are also presented. Lastly, an algorithm is presented that assists decision-makers in evaluating appropriate solar panels to establish solar plants. The proposed algorithm uses the idea of ∪P and ∪R for two Ω-sets constructed based on expert opinions of decision makers.

Improvement of Solar Farm Performance based on Photovoltaic Modules Selection

The emissions of greenhouse gases from conventional power plants are currently a significant cause for worry. In China, about 75% of total domestic energy is dependent on coal-fire power, which emits 50% of total SO2 and has a significant impact on the human respiratory system. Therefore, solar power plants are a viable option that can mitigate this problem. Furthermore, the efficiency of solar modules exhibits a progressive upward trend, while their price per watt experiences a corresponding decline, making it a promising source for future energy. This article examines the performance and effectiveness of several photovoltaic (PV) modules in designing solar plants on a certain land area measuring 10000 m2 (100 m * 100 m). The PV plant performance was evaluated by comparing occupation ratio (OR), PV power capacity, net energy production, performance ratio (PR) via PVsyst software, and lastly financial analysis. Consequently, the PV module (PV7), characterized by its high efficiency, low temperature coefficients, and affordable price, result in a significant OR (73.81%), increased installed PV power capacity (1568kW), enhanced net energy output (2269029 kWh/year), improved yearly PR (83.4%), and lastly, the shortest payback period (around two years). Instead of optimizing shadow length in existing research, this paper aims to improve the performance of large-scale solar farm based on PV module selection which results in less computation and structure installation efforts.

The political economy of energy transitions in Africa: Coalitions, politics and power in Tanzania

The understanding of the political economy of energy transitions in lower-income African countries is little developed. A focus on coalitions has emerged as a promising approach, but it was largely developed based on experiences from higher-income countries. This article has two interrelated purposes. First, it explores and develops the coalition approach to the study of the prioritisation between energy sources in lower-income countries by combining it with a political settlement framework that has been adapted to analysing energy transitions. Secondly, it researches the promotion and implementation of non-hydro renewable energy in mainland Tanzania as a case. The article covers the period from 2008, when the first potent coalitions around private non-hydro renewable energy emerged, up until today. Until recently, these coalitions were overtaken by stronger coalitions around state-owned gas and hydropower. Only with a new president and administration in power and a donor that was pragmatic with regard to state ownership did a large-scale solar plant materialize. Based on the Tanzanian example, the article argues first, that large-scale energy projects are of such importance politically that the analysis of coalitions at the sector level must take into account how these coalitions are embedded in a country's broader distribution of power. Secondly, that for renewable energy policies and projects to get implemented they must fit with the priorities and ideas about broader development held by a country's ruling political elite. A number of implications for the study of the political economy of energy transitions are further unfolded in the article.

Performance analysis and optimization of a zero-emission solar-driven hydrogen production system based on solar power tower plant and protonic ceramic electrolysis cells

The solar-driven high-temperature steam electrolysis is promising for efficient large-scale H2 production. In this study, a comprehensive component-to-system model and optimization framework is developed to investigate the performance of a zero-emission H2 production system based on solar power plant and protonic ceramic electrolysis cell. Compared to previous system studies, the detailed description of cell internal operating characteristics is realized by integrating multi-physics simulation and artificial neural network. After parametric analyses, it is found that the system energy/exergy efficiency and co-generation performance are complicated by each subsystem. And the optimal system performance (ηth = 50.63 %, Z = 179.63 $·h−1 and ηex = 33.03 %, Z = 178.94 $·h−1, with LCOE = 0.172 $·kWh−1 and ZH2 = 6.497 $·kg−1) is obtained considering cell operating features and system energy-exergy-economic factors through multi-objective optimizations. Besides, the tradeoff between system maximum H2 production capacity and cell internal thermal conditions is revealed. This study can facilitate the development of zero-emission green H2 production driven by renewable energy.

Long-Term Evaluation of a Ternary Mixture of Molten Salts in Solar Thermal Storage Systems: Impact on Thermophysical Properties and Corrosion.

Solar thermal plants typically undergo trough operational cycles spanning between 20 and 25 years, highlighting the critical need for accurate assessments of long-term component evolution. Among these components, the heat storage media (molten salt) is crucial in plant design, as it significantly influences both the thermophysical properties of the working fluid and the corrosion of the steel components in thermal storage systems. Our research focused on evaluating the long-term effects of operating a low-melting-point ternary mixture consisting of 30 wt% LiNO3, 57 wt% KNO3, and 13 wt% NaNO3. The ternary mixture exhibited a melting point of 129 °C and thermal stability above 550 °C. Over 15,000 h, the heat capacity decreased from 1.794 to 1.409 J/g °C. Additionally, saline components such as CaCO3 and MgCO3, as well as lithium oxides (LiO and LiO2), were detected due to the separation of the ternary mixture. A 30,000 h exposure resulted in the formation of Fe2O3 and the presence of Cl, indicating prolonged interaction with the marine environment. This investigation highlights the necessity of analyzing properties under actual operating conditions to accurately predict the lifespan and select the appropriate materials for molten salt-based thermal storage systems.

A Study on Hybrid Power Plant (Solar & Biomass)

We have designed an on-grid hybrid power plant that combines solar and biomass energy sources. The solar plant has a capacity of 6 MW, and the biomass plant has a capacity of 2 MW. The hybrid solar-biomass power plant is still a recent renewable technology. It is an ongoing investigation that not only proposes dispatchable power generation but also benefits from having a flexible input source, including solar and bioenergy. We aimed to successfully design a hybrid project using proper analysis, to reduce the cost of power generation through a grid-connected hybrid system, while also reducing greenhouse gas emissions. We used PVsyst software to simulate the solar plant and created the PV mounting structure and Single Line Diagram (SLD) using AutoCAD software. We have used Microsoft Excel software to carry out the cost analysis. We also calculated how much biomass waste and gas the biomass plant will need. We have been able to successfully design our hybrid power plant and, at the same time, reduce the power generation cost comparatively. The hybrid system design has made it possible to ensure the most convenient arrangement of electricity.

Simulation and survey-based feasibility study of concentrated solar plant in northern and central Bangladesh

A comprehensive simulation and survey-based feasibility study have been carried out for the possible implementation of a CSP (concentrated solar power) plant in the middle and northern parts of Bangladesh. Area-specific optimization of plant-related parameters like tower height, heliostat number, heliostat cost, and heliostat area has been conducted using weather data. The design and analysis of small-scale CSP plants (1, 50, and 100 MW) were performed using the System Advisor Model (SAM) platform. Also, a sensor-based study was conducted to measure the sun's direct normal irradiance, temperature, and humidity at various time intervals throughout the year. The results of the plant cost, followed by the identification of the most suitable CSP technology, and the electricity output over a certain period of time were evaluated, and a good result was observed for a 1 MW CSP installation in Bangladesh. The simulated findings suggest that the high-elevation area in northern Bangladesh, such as Rajshahi, is more suitable for generating 100 MW of power, whereas the central region of Bangladesh, such as Katiyadi and Tarakandi, demonstrated better results for generating 50 MW of power.

Thermal Analysis of Parabolic and Fresnel Linear Solar Collectors Using Compressed Gases as Heat Transfer Fluid in CSP Plants

This study introduces the use of compressed air as a heat transfer fluid in small-scale, concentrated linear solar collector technology, evaluating its possible advantages over traditional fluids. This work assumes the adoption of readily available components for both linear parabolic trough and Fresnel collectors and the coupling of the solar field with Brayton cycles for power generation. The aim is to provide a theoretical analysis of the applicability of this novel solar plant configuration for small-scale electricity generation. Firstly, a lumped thermal model was developed in a MatLab® (v. 2023a) environment to assess the thermal performance of a PT collector with an evacuated receiver tube. This model was then modified to describe the performance of a Fresnel collector. The resulting optical–thermal model was validated through literature data and appears to provide realistic estimates of temperature distribution along the entire collector length, including both the receiver tube surface and the Fresnel collector’s secondary concentrator. The analysis shows a high thermal efficiency for both Fresnel and parabolic collectors, with average values above 0.9 (in different wind conditions). Th5s study also shows that the glass covering of the Fresnel evacuated receiver, under the conditions considered (solar field outlet temperature: 550 °C), reaches significant temperatures (above 300 °C). Furthermore, due to the presence of the secondary reflector, the temperature difference between the upper and the lower part of the glass envelope can be very high, well above 100 °C in the final part of the collector string. Differently, in the case of PTs, this temperature difference is quite limited (below 30 °C).

Evaluation of renewable energy technologies in Colombia: comparative evaluation using TOPSIS and TOPSIS fuzzy metaheuristic models

The study investigates the weighting and hierarchization of renewable energy sources in specific geographical regions of Colombia using the TOPSIS and Diffuse TOPSIS metaheuristic models. 5 regions were analyzed, two of them with different scenarios: Caribbean 1 and 2, Pacific 1 and 2, Andean, Amazonian and Orinoquia. The results reveal significant differences in the evaluation of technologies between the two models. In the Caribbean 1, Diffuse TOPSIS gave a higher score to Solar Photovoltaics, while TOPSIS favored Hydropower. In the Caribbean 2, Solar Photovoltaic obtained similar scores in both models, but Wind was rated better by TOPSIS. In the Pacific Region 1, Biomass and large-scale Hydropower led according to both models. In the Pacific 2, Solar Photovoltaic was better evaluated by TOPSIS, while Wind was preferred by Diffuse TOPSIS. In the Andean Region, large-scale hydroelectric and Solar photovoltaic plants obtained high scores in both models. In the Amazon, Biomass led in both models, although with differences in scores. In Orinoquia, Solar Photovoltaic was rated higher by both models. The relevance of this research lies in its ability to address not only Colombia's immediate energy demands, but also in its ability to establish a solid and replicable methodological framework. The application of metaheuristic methods such as TOPSIS and TOPSIS with fuzzy logic is presented as a promising strategy to overcome the limitations of conventional approaches, considering the complexity and uncertainty inherent in the evaluation of renewable energy sources. By achieving a more precise weighting and hierarchization, this study will significantly contribute to strategic decision-making in the implementation of sustainable energy solutions in Colombia, serving as a valuable model for other countries with similar challenges.

Experimental study on a solar thermoelectric power generation system under non-uniform irradiation

Non-uniform irradiation caused by high light concentration significantly affects the performance of the solar thermoelectric power generation system, but the research remains at the theoretical stage. This paper establishes an experimental setup for the solar thermoelectric system and conducts a comprehensive experimental study of the system operating under non-uniform irradiation. The temperature, power and efficiency of the systems are tested for different input powers, cooling methods and structures, and irradiation uniformity. The results indicate that the output power of the solar thermoelectric system slightly increases and then decreases as the irradiation uniformity increases. The system’s output power is minimized when the irradiation uniformity approaches 100%. The optimum irradiation uniformity of the solar thermoelectric system is 47.4% and the maximum efficiency is 1.495%, which is an improvement of 7.6% compared to the efficiency of 98.4% irradiation uniformity. The performance of the solar thermoelectric system can be further improved by using a double-layer TE structure. At a concentration ratio of only 12.4, the double-layer system connected in series achieved a maximum efficiency of 2.06%, which is 0.55% higher than the single-layer system. The importance of optimizing irradiation uniformity to improve system performance is experimentally revealed. These results are important for clarifying the operating principle of the solar thermoelectric system under non-uniform irradiation and can guide the subsequent design of high-efficiency solar thermoelectric systems.

The Design Concept of Integrated Pedestrian Sidewalk with Solar Plants as a Form of Green Campus Development at the Bali Land Transportation Polytechnic

Abstract An important aspect of creating a viable campus environment is providing safe and comfortable walking paths. Proper walking paths give some advantages for students, including improved physical and mental health, transportation efficiency, and increased Student satisfaction and happiness. The pedestrian sidewalk area at Bali Land Transportation Polytechnic (Poltrada Bali) covers 3800 m2, experiencing high pedestrian activity but lacking protective roofing to enhance pedestrian comfort. Based on the pedestrian sidewalk area, a solar power plant as support for the green canopy construction combined with shading vines is possible. The generated electrical energy serves as the power source for the Public Electric Vehicle Charging Stations (PEVCS) in the campus parking lot as a support for the electric vehicle acceleration program in Bali province. In addition to PEVCS, energy extraction surplus is used as alternative energy for street lighting facilities in the campus area. In this study, the design concept of a shaded pedestrian sidewalk has been carried out, with multiple benefits as solar plants using Delphi as a variable identification and validation tool, and SketchUp software to visualize design shapes aesthetically. The results show that a pedestrian comfort level of 94% was obtained and the maximum power generation of 934,800 Wp, thus it was able to meet the 887,832 W campus operational power reserve needs, street and garden lights of 9,728 W, and the power requirements of two PEVCS units of 22,000 W with a surplus of 15,240 W electric power.