A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices
Optimal energy hub scheduling (EHS) has emerged as a promising strategy for improving the efficiency and flexibility of power systems. Energy hubs (EHs) offer several advantages over conventional power grids, including enhanced flexibility, reduced emissions, and improved efficiency. However, EHS po...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1537703/full |
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| author | Hossein Faramarzi Navid Ghaffarzadeh Farhad Shahnia |
| author_facet | Hossein Faramarzi Navid Ghaffarzadeh Farhad Shahnia |
| author_sort | Hossein Faramarzi |
| collection | DOAJ |
| description | Optimal energy hub scheduling (EHS) has emerged as a promising strategy for improving the efficiency and flexibility of power systems. Energy hubs (EHs) offer several advantages over conventional power grids, including enhanced flexibility, reduced emissions, and improved efficiency. However, EHS poses several challenges, including uncertainty, complexity, and computational burden. To tackle these challenges, this paper proposes an innovative optimal scheme for the operation of an integrated PV/wind energy system. The scheme incorporates a comprehensive set of components, including combined heat and power (CHP), power-to-gas (P2G), energy storage systems (ESSs), heat storage systems (HSSs), gas storage (GS), and electric boilers (EBs) and gas boilers (GBs). A demand response (DR) program is implemented for both electric and thermal loads to address the inherent uncertainty of renewable energy sources (RESs) and electrical load fluctuations. The proposed optimal management model is a multi-objective optimization problem aiming to minimize total losses, cost, and emissions while meeting energy demands. This novel approach offers significant advantages for utilities in terms of reducing losses, cost, and air pollution, contributing to a more sustainable energy system. The optimal management scheme is designed based on the optimized objective functions and implemented through steady-state energy analysis. Non-dominated sorting genetic algorithm III (NSGA-III) is employed to efficiently search for the optimal solutions. Scenario analysis is adopted to address the stochastic nature of RESs and load demand, and the Sim&Corrloss clustering strategy is used to reduce the computational burden. To demonstrate the effectiveness of the proposed approach, the results obtained from applying the proposed algorithm are compared with the results from analyzing the problem using GAMS software and the multi-objective seagull optimization algorithm (MOSOA). The proposed method enhances flexibility and ultimately increases system stability while maintaining diversity in energy sources. Additionally, the utilization of equipment such as various storage devices and P2G enhances system resilience, reducing load fluctuations and improving resource utilization. The results demonstrate that the proposed method significantly improves system performance and can effectively contribute to energy management in multi-energy systems. The superior performance of the proposed algorithm is demonstrated under various operating scenarios. |
| format | Article |
| id | doaj-art-c14306e9ff254c2983e90423c5997d02 |
| institution | Kabale University |
| issn | 2296-598X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-c14306e9ff254c2983e90423c5997d022025-08-20T03:58:36ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2025-07-011310.3389/fenrg.2025.15377031537703A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devicesHossein Faramarzi0Navid Ghaffarzadeh1Farhad Shahnia2Faculty of Technical and Engineering, Imam Khomeini International University, Qazvin, IranFaculty of Technical and Engineering, Imam Khomeini International University, Qazvin, IranSchool of Engineering and Energy, Murdoch University, Perth, WA, AustraliaOptimal energy hub scheduling (EHS) has emerged as a promising strategy for improving the efficiency and flexibility of power systems. Energy hubs (EHs) offer several advantages over conventional power grids, including enhanced flexibility, reduced emissions, and improved efficiency. However, EHS poses several challenges, including uncertainty, complexity, and computational burden. To tackle these challenges, this paper proposes an innovative optimal scheme for the operation of an integrated PV/wind energy system. The scheme incorporates a comprehensive set of components, including combined heat and power (CHP), power-to-gas (P2G), energy storage systems (ESSs), heat storage systems (HSSs), gas storage (GS), and electric boilers (EBs) and gas boilers (GBs). A demand response (DR) program is implemented for both electric and thermal loads to address the inherent uncertainty of renewable energy sources (RESs) and electrical load fluctuations. The proposed optimal management model is a multi-objective optimization problem aiming to minimize total losses, cost, and emissions while meeting energy demands. This novel approach offers significant advantages for utilities in terms of reducing losses, cost, and air pollution, contributing to a more sustainable energy system. The optimal management scheme is designed based on the optimized objective functions and implemented through steady-state energy analysis. Non-dominated sorting genetic algorithm III (NSGA-III) is employed to efficiently search for the optimal solutions. Scenario analysis is adopted to address the stochastic nature of RESs and load demand, and the Sim&Corrloss clustering strategy is used to reduce the computational burden. To demonstrate the effectiveness of the proposed approach, the results obtained from applying the proposed algorithm are compared with the results from analyzing the problem using GAMS software and the multi-objective seagull optimization algorithm (MOSOA). The proposed method enhances flexibility and ultimately increases system stability while maintaining diversity in energy sources. Additionally, the utilization of equipment such as various storage devices and P2G enhances system resilience, reducing load fluctuations and improving resource utilization. The results demonstrate that the proposed method significantly improves system performance and can effectively contribute to energy management in multi-energy systems. The superior performance of the proposed algorithm is demonstrated under various operating scenarios.https://www.frontiersin.org/articles/10.3389/fenrg.2025.1537703/fullhub managementclustering algorithmuncertaintymany-objective functionnon-dominated sorting genetic algorithm IIIintegrated energy systems |
| spellingShingle | Hossein Faramarzi Navid Ghaffarzadeh Farhad Shahnia A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices Frontiers in Energy Research hub management clustering algorithm uncertainty many-objective function non-dominated sorting genetic algorithm III integrated energy systems |
| title | A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices |
| title_full | A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices |
| title_fullStr | A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices |
| title_full_unstemmed | A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices |
| title_short | A new stochastic multi-objective model for the optimal management of a PV/wind integrated energy system with demand response, P2G, and energy storage devices |
| title_sort | new stochastic multi objective model for the optimal management of a pv wind integrated energy system with demand response p2g and energy storage devices |
| topic | hub management clustering algorithm uncertainty many-objective function non-dominated sorting genetic algorithm III integrated energy systems |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2025.1537703/full |
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