Bibliographic Details
| Title: |
An optimization scheduling strategy for electric-heat-hydrogen integrated energy systems based on memory-enhanced deep reinforcement learning. |
| Alternate Title: |
基于记忆增强深度强化学习的电-热-氢集成能源系统优化调度策略. (Chinese) |
| Authors: |
Bai, Zhongli, Gao, Qiang, Zhang, Hongzhi, Liu, Junjie, Ji, Yuehui, Song, Yu, Cheng, Xu |
| Source: |
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering); Aug2026, Vol. 27 Issue 8, p809-824, 16p |
| Subject Terms: |
Scheduling, Electric power, Heat, Hydrogen as fuel, Mixed integer linear programming, Reinforcement learning, Entropy, Short-term memory |
| Abstract (English): |
High wind-solar penetration drives integrated energy systems (IESs) 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-h 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 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. [ABSTRACT FROM AUTHOR] |
| Abstract (Chinese): |
摘要: 目的: 本研究专注于高可再生能源渗透的能源园区,利用双向激励需求响应(B-IDR)机制实现需求侧响应,通过电-电量状态(SOC)软惩罚降低储能使用频率,延长储能设备使用寿命。 创新点: 1. 提出了一种电-SOC软惩罚机制,以保持储能设备的灵活性和延长使用寿命。2. 提出了基于B-IDR机制,通过反馈价格波动捕捉不对称需求响应。3. 结合长短期记忆网络(LSTM)的时序感知能力,提高了强化学习调度的稳定性和效率。 方法: 1. 提出一种基于LSTM-SAC调度器的优化框架,用于解决高渗透可再生能源下的电-热-氢综合能源系统(IES)调度问题。该方法结合多能储存与双向激励需求响应(B-IDR)机制,通过LSTM增强的柔性动作-评价(SAC)调度器捕捉风电、光伏输出及负荷变化等短期动态特征,优化能源管理策略。2. 在低碳经济调度模型中,综合考虑能源采购、碳排放、设备运行损耗等多个成本因素,并通过多场景模拟验证该方法在降低系统总成本、碳排放和可再生能源削减成本方面的有效性。 结论: 1. 通过结合B-IDR和储能系统的调度优化,本研究提出的方法显著降低了储能系统的循环负荷,提高了系统的经济性和碳减排效果。2. 随着多能耦合技术的应用,尤其是在氢气储能路径的引入下,系统的低碳调度效果得到了显著改善。本研究的成果为高渗透可再生能源的低碳优化调度提供了新思路,并为未来能源市场的优化调度策略提供了实践依据。 [ABSTRACT FROM AUTHOR] |
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| Database: |
Complementary Index |