Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities
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...
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| Veröffentlicht in: | Sustainability 2022-11, Vol.14 (21), p.13792 |
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| creator | Mustafa, Jawed Almehmadi, Fahad Awjah Alqaed, Saeed Sharifpur, Mohsen |
| description | 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 m2, and the energy requirements for irrigation to supply a farm of 10,000 m2 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. |
| doi_str_mv | 10.3390/su142113792 |
| format | Article |
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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 m2, and the energy requirements for irrigation to supply a farm of 10,000 m2 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%. 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Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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| subjects | Algorithms Alternative energy sources Buildings Design and construction Dynamic models Electric power systems Emissions Energy demand Energy industry Energy prices Energy requirements Energy storage Energy use Environmental aspects Farms Green buildings Greenhouse effect Greenhouse gases Irrigation Neural networks Optimization techniques Photovoltaics Renewable resources Residential areas Residential buildings Residential energy Rural areas Rural communities Saudi Arabia Solar energy Sparsely populated areas Sustainability Turbines Wind power |
| title | Building a Sustainable Energy Community: Design and Integrate Variable Renewable Energy Systems for Rural Communities |
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