Optimization of the Energy Lack and Surplus in a Stand-Alone Photovoltaic System

Andam, Meriem; Alami, Jamila El; Louartassi, Younes

doi:10.1109/irsec48032.2019.9078210

Cited by 2 publications

(2 citation statements)

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“…In another study, the optimization was used to obtain the minimum size of an independent system from the Malaysia power grid; which again minimizes the possibility of energy shortage and the cost of the life cycle of the system 16 . Another investigation was carried out on the optimization of the shortage of excess power to find the optimum size of the system in a rural area in Morocco 17 . Another research was focused on a new design and optimization technique for a solar power supply system independent from the power grid to supply the power demand of a residential building in Cameron 18 .…”

Section: Introductionmentioning

confidence: 99%

“…16 Another investigation was carried out on the optimization of the shortage of excess power to find the optimum size of the system in a rural area in Morocco. 17 Another research was focused on a new design and optimization technique for a solar power supply system independent from the power grid to supply the power demand of a residential building in Cameron. 18 In another investigation, MATLAB Software and the Gray Wolf algorithm were used for the cost optimization of a solar system during its life cycle in Brunei.…”

Section: Introductionmentioning

confidence: 99%

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Using deep learning artificial intelligence and multiobjective optimization in obtaining the optimum ratio of a fuel cell to electrolyzer power in a hydrogen storage system

Mansir

Musharavati

Abubakar

2022

Intl J of Energy Research

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Summary Due to the depletion of fossil fuel sources and their pollution, renewable energy sources have attracted researchers' attention for energy supply; power generation using solar energy is one of the most important examples of these sources. This energy needs to be saved because of climate changes, to be used with high confidence. In this investigation, the heating and cooling of 100 Conex in Riyadh, Saudi Arabia, was studied, which used hydrogen storage as the central storage system. The main purpose of this project is to determine the ratio of fuel cell power to the electrolyzer power in this storage system, to reach the minimum dependency of the system on the urban power grid and at the same time, the minimum cost of electrolyzer and fuel cell. Because the shelters were simulated by Energy Plus and Open Studio Software and the energy supply system by TRNSYS Software, the optimization was not possible. Therefore, this system was changed into a network in MATLAB Software after simulation with deep learning artificial intelligence; then, it was optimized by a genetic algorithm. In order to accurately form a network, different parameters of artificial intelligence were studied precisely. The best result was obtained from a neural network with two hidden layers, each one containing 10 neurons, sigmoid activation function, and without dropout, with a correlation coefficient of 0.9998, which shows an excellent accuracy. The results of optimization also indicated that at all points, the Pareto front for fuel cell power is less than or equal to the power of the electrolyzer, and this ratio is 0.47 at the optimum point in terms of cost and electric consumption.

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Section: Introductionmentioning

confidence: 99%