Energy storage systems are becoming increasingly important, with a growing focus on renewable energy sources that provide highly fluctuating output. Therefore, sound decisions regarding the energy storage systems to be employed need to be made, especially with a systematic and semantic approach. This paper considers the problem of evaluation and selection of energy storage technologies (ESTs). The objective of the proposed research is to decide the best technology for energy storage under the novel idea of hybrid multicriteria decision-making technique under fuzzy environment, that is, fuzzy AHP with fuzzy VIKOR. Electrochemical storage, electrical storage, magnetic storage, mechanical storage, and chemical storage are considered as five alternative energy storage technologies. Energy density, life cycles, cycle efficiency, investment level, suitability to climatic conditions, and required space are considered as six main evaluation criteria. Under each of the main criteria, a set of subcriteria are also considered. The weights of main criteria and subcriteria are determined using fuzzy AHP. With the help of the weights of each set of subcriteria, the weights of alternatives are determined using fuzzy VIKOR. Further, with the help of the main criteria weights and the weights of alternatives determined with respect to each set of subcriteria, the final normalized weights of alternatives are determined. Based on these weights, energy storage technologies are ranked. In addition, the sensitivity analysis is carried out to analyze the variation in ranking pattern of alternatives. From the research findings of this paper, the results are found to be more practical as the evaluation is carried out on an objective basis.

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This study looks into artificial intelligence methods for scaling solar power systems, such as standalone, grid-connected, and hybrid systems, in order to lessen environmental effect. When all essential information is provided, conventional sizing methods may be a feasible alternative. It is impossible to apply typical procedures in instances where data is unavailable. The new suggested artificial intelligence model employing multilayered perceptrons is employed for sizing solar systems, and this model functions on current photovoltaic modules that incorporate hybrid-sizing models; so, they should not be rejected entirely. In this work, the convergence speed of the proposed model for single diode, two diodes, and three diodes are the comparison factors to estimate the performance of the proposed model.

Freshwater scarcity is increasing across many parts of the globe; to meet this demand, seawater desalination is the best choice, and the electrical energy consumption is escalating due to urbanization and industrialization. Sustainable production of electricity and freshwater can be met by an integrating photovoltaic-thermal (PVT) module with stepped solar still (SSS). The present study focuses on the theoretical modeling of the PVT-SSS desalination system for evaluating thermal efficiency, energy efficiency, freshwater productivity, and electrical power generation. The solar still productivity will be influenced by the depth of water, insulation thickness, glass cover material, thickness and inclination, and operational factors like preheating the input water supply and water salinity. A comparative analysis has been made of summer, winter, and rainy climatic conditions of Vellore town (12.9165° N, 79.1325° E), Tamil Nadu. In the present work, a thermodynamic model based on mass and energy balance is developed for the PVT-SSS system, and it is solved by a numerical method. A Runge-Kutta technique of 4th order is employed using a Python program for solving the thermodynamic simulation model. The results from the model depict that for summer, winter, and rainy climatic seasons, the freshwater productivity of PV/T-SSS was determined to be 12.18 kg/m2day, 6.67 kg/m2day, and 2.77 kg/m2day. Also, it is found that electrical efficiency for summer, winter, and rainy seasons is 8.91%, 9.135%, and 9.53%, respectively. A maximum and minimum freshwater production of 1668 kg/m2 and 1218 kg/m2 are observed for a depth of 2 cm and 5 cm, respectively.

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