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Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt

  • Received: 04 October 2022 Revised: 18 February 2023 Accepted: 20 February 2023 Published: 02 March 2023
  • The present paper examines the potential hybridization for a dispatchable hybrid renewable energy system (HRES). The plant has been examined for existence in the city of Ras Ghareb, Egypt and follows the load profile of Egypt. The proposed plant configuration contains a wind plant, a solar photovoltaic plant, vanadium redox flow batteries (VRFBs) and a hydrogen system consisting of an electrolyzer, hydrogen tanks and fuel cells (FCs), the latter of which are for both daily and seasonal storage. Professional software tools have been used to model the wind and solar resources. Simulations for both the battery and hydrogen generation and electrolyzer operation are also considered. The output of these simulations is used to configure the HRES using MATLAB. The optimization objective function of the HRES is based on the least levelized cost of energy (LCOE) with constraints for a zero loss of power supply probability (LPSP) and curtailed energy. The optimization has been achieved by using artificial neural networks and a MATLAB program. The results show that the optimal system can handle 91.2% of the load directly from the renewable energy sources (wind and solar), while the rest of the demand comes from the storage system (FCs and VRFBs). The LCOE of the optimal system configuration is (USD) 9.3 %/kWh, with both the LPSP and curtailed energy at zero values. This cost can be reduced by 14.5% if the constraint of zero curtailed energy is relaxed by 10%. Despite the load being maximum in summer, the energy storage requirement is predicted to be maximum in winter due to the low wind profile and solar radiation in winter months. Energy storage system size is dependent on both seasonal and daily variations in wind and solar profiles. In addition, energy storage size is the main factor that determines the LCOE of the system.

    Citation: Mohamed Hamdi, Hafez A. El Salmawy, Reda Ragab. Optimum configuration of a dispatchable hybrid renewable energy plant using artificial neural networks: Case study of Ras Ghareb, Egypt[J]. AIMS Energy, 2023, 11(1): 171-196. doi: 10.3934/energy.2023010

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  • The present paper examines the potential hybridization for a dispatchable hybrid renewable energy system (HRES). The plant has been examined for existence in the city of Ras Ghareb, Egypt and follows the load profile of Egypt. The proposed plant configuration contains a wind plant, a solar photovoltaic plant, vanadium redox flow batteries (VRFBs) and a hydrogen system consisting of an electrolyzer, hydrogen tanks and fuel cells (FCs), the latter of which are for both daily and seasonal storage. Professional software tools have been used to model the wind and solar resources. Simulations for both the battery and hydrogen generation and electrolyzer operation are also considered. The output of these simulations is used to configure the HRES using MATLAB. The optimization objective function of the HRES is based on the least levelized cost of energy (LCOE) with constraints for a zero loss of power supply probability (LPSP) and curtailed energy. The optimization has been achieved by using artificial neural networks and a MATLAB program. The results show that the optimal system can handle 91.2% of the load directly from the renewable energy sources (wind and solar), while the rest of the demand comes from the storage system (FCs and VRFBs). The LCOE of the optimal system configuration is (USD) 9.3 %/kWh, with both the LPSP and curtailed energy at zero values. This cost can be reduced by 14.5% if the constraint of zero curtailed energy is relaxed by 10%. Despite the load being maximum in summer, the energy storage requirement is predicted to be maximum in winter due to the low wind profile and solar radiation in winter months. Energy storage system size is dependent on both seasonal and daily variations in wind and solar profiles. In addition, energy storage size is the main factor that determines the LCOE of the system.



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