Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm
The electric–hydrogen coupled integrated energy system (EHCS) is a critical pathway for the low-carbon transition of energy systems. However, the inherent uncertainties of renewable energy sources present significant challenges to optimal energy management in the EHCS. To address these challenges, t...
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MDPI AG
2025-07-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/15/3925 |
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| author | Jingbo Zhao Zhengping Gao Zhe Chen |
| author_facet | Jingbo Zhao Zhengping Gao Zhe Chen |
| author_sort | Jingbo Zhao |
| collection | DOAJ |
| description | The electric–hydrogen coupled integrated energy system (EHCS) is a critical pathway for the low-carbon transition of energy systems. However, the inherent uncertainties of renewable energy sources present significant challenges to optimal energy management in the EHCS. To address these challenges, this paper proposes an energy management method for the EHCS based on an improved proximal policy optimization (IPPO) algorithm. This method aims to overcome the limitations of traditional heuristic algorithms, such as low solution accuracy, and the inefficiencies of mathematical programming methods. First, a mathematical model for the EHCS is established. Then, by introducing the Markov decision process (MDP), this mathematical model is transformed into a deep reinforcement learning framework. On this basis, the state space and action space of the system are defined, and a reward function is designed to guide the agent to learn to the optimal strategy, which takes into account the constraints of the system. Finally, the efficacy and economic viability of the proposed method are validated through numerical simulation. |
| format | Article |
| id | doaj-art-76d57dfe9d9d435888fbf1507e4176ef |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-76d57dfe9d9d435888fbf1507e4176ef2025-08-20T03:36:35ZengMDPI AGEnergies1996-10732025-07-011815392510.3390/en18153925Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization AlgorithmJingbo Zhao0Zhengping Gao1Zhe Chen2State Grid Jiangsu Electric Power Co., Ltd., Research Institute, Nanjing 210023, ChinaState Grid Jiangsu Electric Power Co., Ltd., Nanjing 210023, ChinaState Grid Jiangsu Electric Power Co., Ltd., Research Institute, Nanjing 210023, ChinaThe electric–hydrogen coupled integrated energy system (EHCS) is a critical pathway for the low-carbon transition of energy systems. However, the inherent uncertainties of renewable energy sources present significant challenges to optimal energy management in the EHCS. To address these challenges, this paper proposes an energy management method for the EHCS based on an improved proximal policy optimization (IPPO) algorithm. This method aims to overcome the limitations of traditional heuristic algorithms, such as low solution accuracy, and the inefficiencies of mathematical programming methods. First, a mathematical model for the EHCS is established. Then, by introducing the Markov decision process (MDP), this mathematical model is transformed into a deep reinforcement learning framework. On this basis, the state space and action space of the system are defined, and a reward function is designed to guide the agent to learn to the optimal strategy, which takes into account the constraints of the system. Finally, the efficacy and economic viability of the proposed method are validated through numerical simulation.https://www.mdpi.com/1996-1073/18/15/3925proximal policy optimization algorithmelectric–hydrogen coupled integrated energy systemenergy managementrenewable energy |
| spellingShingle | Jingbo Zhao Zhengping Gao Zhe Chen Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm Energies proximal policy optimization algorithm electric–hydrogen coupled integrated energy system energy management renewable energy |
| title | Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm |
| title_full | Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm |
| title_fullStr | Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm |
| title_full_unstemmed | Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm |
| title_short | Energy Management of Electric–Hydrogen Coupled Integrated Energy System Based on Improved Proximal Policy Optimization Algorithm |
| title_sort | energy management of electric hydrogen coupled integrated energy system based on improved proximal policy optimization algorithm |
| topic | proximal policy optimization algorithm electric–hydrogen coupled integrated energy system energy management renewable energy |
| url | https://www.mdpi.com/1996-1073/18/15/3925 |
| work_keys_str_mv | AT jingbozhao energymanagementofelectrichydrogencoupledintegratedenergysystembasedonimprovedproximalpolicyoptimizationalgorithm AT zhengpinggao energymanagementofelectrichydrogencoupledintegratedenergysystembasedonimprovedproximalpolicyoptimizationalgorithm AT zhechen energymanagementofelectrichydrogencoupledintegratedenergysystembasedonimprovedproximalpolicyoptimizationalgorithm |