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An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning
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Zhongli BAI1,2, Qiang GAO1,2,3, Hongzhi ZHANG1,2, Junjie LIU1,2, Yuehui JI1,2, Yu SONG1,2, Xu CHENG1,4. An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning[J]. Journal of Zhejiang University Science A, 1998, -1(-1): .
@article{title="An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning",
author="Zhongli BAI1,2, Qiang GAO1,2,3, Hongzhi ZHANG1,2, Junjie LIU1,2, Yuehui JI1,2, Yu SONG1,2, Xu CHENG1,4",
journal="Journal of Zhejiang University Science A",
volume="-1",
number="-1",
pages="",
year="1998",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.A2500598"
}
%0 Journal Article
%T An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning
%A Zhongli BAI1
%A 2
%A Qiang GAO1
%A 2
%A 3
%A Hongzhi ZHANG1
%A 2
%A Junjie LIU1
%A 2
%A Yuehui JI1
%A 2
%A Yu SONG1
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%A Xu CHENG1
%A 4
%J Journal of Zhejiang University SCIENCE A
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%N -1
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%@ 1673-565X
%D 1998
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A2500598
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T1 - An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning
A1 - Zhongli BAI1
A1 - 2
A1 - Qiang GAO1
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A1 - 3
A1 - Hongzhi ZHANG1
A1 - 2
A1 - Junjie LIU1
A1 - 2
A1 - Yuehui JI1
A1 - 2
A1 - Yu SONG1
A1 - 2
A1 - Xu CHENG1
A1 - 4
J0 - Journal of Zhejiang University Science A
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%@ 1673-565X
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A2500598
Abstract: High wind-solar penetration drives integrated energy systems (IES) to act as cross-vector buffers that absorb surplus electricity via hybrid storage, or convert it into heat and hydrogen for cross-medium energy peak shaving. However, conventional mixed-integer linear programming (MILP) solvers and static incentive schemes often struggle with the high-dimensional, strongly coupled, non-convex, and time-varying nature of electric-heat-hydrogen scheduling. Thus, we propose an end-to-end optimization framework for a renewable electric-heat-hydrogen IES, and evaluate it through offline dispatch computations and cost settlement on a 24-hour simulated case study. The proposed framework incorporates a coupled power-state of charge (SOC) dual penalty mechanism to ensure consistent storage operation over time. Additionally, it includes a bidirectional incentive-based demand response (B-IDR) mapping that is differentiable and can capture asymmetric feedback in response to price fluctuations. Furthermore, a long-term short-term memory (LSTM)-augmented maximum-entropy Soft Actor-Critic (SAC) scheduler is utilized for stable and efficient control in this continuous and high-dimensional setting. Comparisons with other methods under identical settings show that the proposed method achieves lower operating costs and carbon emissions, as well as improved training stability.
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