Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities

Abstract

This study proposes a decentralized hybrid energy system consisting of solar photovoltaics (PV) and wind turbines (WT) connected with the local power grid for a small Najran, Saudi Arabia community. The goal is to provide the selected community with sustainable energy to cover a partial load of the residential buildings and the power requirements for irrigation. For this, a dynamic model was constructed to estimate the hourly energy demand for residential buildings consisting of 20 apartments with a total floor area of 4640 and the energy requirements for irrigation to supply a farm of 10,000 with water. Subsequently, HOMER software was used to optimize the proposed hybrid energy system. Even considering the hourly fluctuations of renewable energies, the artificial neural network (ANN) successfully estimated PV and wind energy. Based on the mathematical calculations, the final R-square values were 0.928 and 0.993 for PV and wind energy, respectively. According to the findings, the cost of energy (COE) for the optimized hybrid energy system is $0.1053/kWh with a renewable energy penetration of 65%. In addition, the proposed system will save 233 tons of greenhouse gases annually.

Shaping Sustainable Futures: Public Policies and Renewable Energy Insights Based on Global Bibliometric Analysis

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Application of Fuzzy Control and Neural Network Control in the Commercial Development of Sustainable Energy System

“…Therefore, the SES is not only a technical challenge but also a strategic undertaking for the sustainable development of global society and the economy [5]. Thanks to continual advancements in science and technology, sustainable energy technologies have evolved rapidly, and solar energy, wind energy, and bioenergy have gradually become the main energy sources [6,7].…”

Model building of urban energy system and effect analysis of green low‐carbon transition under the background of carbon peaking and carbon neutrality

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