| 引用本文: | 姜智霖,郝峰杰,袁志昌,等.考虑SOC优化设定的电-氢混合储能系统的运行优化[J].电力系统保护与控制,2024,52(8):65-76.[点击复制] |
| JIANG Zhilin,HAO Fengjie,YUAN Zhichang,et al.Optimal operation of an electro-hydrogen hybrid energy storage system considering SOC optimization setting[J].Power System Protection and Control,2024,52(8):65-76[点击复制] |
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| 摘要: |
| 针对含电-氢混合储能的源网荷储系统，为提高新能源的消纳水平并降低系统运行成本，提出了考虑SOC优化设定的电氢混合储能系统的运行优化方法，实现系统的日前实时优化调度。首先提出了大容量储能系统SOC优化设定的方法，以确定储能系统日前的始末SOC优化设定值。随后，基于双延迟深度确定性策略梯度算法，提出了一种日前实时优化调度模型训练方法。结合储能SOC的优化设定值和日前运行数据，建立了源网荷储系统的实时优化调度模型，实现日前和实时综合优化调度。最后，通过算例分析验证了所提运行优化方法的有效性。结果表明，大容量储能系统的SOC优化设定方法可以有效提高系统收益，日前-实时优化调度模型则在日前优化调度的基础上减少了预测误差带来的影响。 |
| 关键词: 混合储能 氢储能系统 SOC优化设定 深度强化学习 日前-实时调度 |
| DOI：10.19783/j.cnki.pspc.231371 |
| 投稿时间：2023-10-24修订日期：2024-03-01 |
| 基金项目:国家自然科学基金专项项目资助(52241701)；中国长江三峡集团有限公司科研项目资助(202103417) |
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| Optimal operation of an electro-hydrogen hybrid energy storage system considering SOC optimization setting |
| JIANG Zhilin1,HAO Fengjie2,YUAN Zhichang1,ZHU Xiaoyi2,GUO Peiqian1,PAN Haining2,XIANG Miaoyi1,HE Ningyi1 |
| (1. Department of Electrical Engineering, Tsinghua University, Beijing 100084, China;
2. China Three Gorges Corporation, Beijing 100038, China) |
| Abstract: |
| To enhance the efficiency of renewable energy utilization and minimize operational costs within the source- grid-load-storage system with electro-hydrogen hybrid energy storage, this paper presents an optimal operation method of electro- hydrogen hybrid energy storage system considering an SOC optimization setting to realize the day-ahead real-time optimal scheduling of the system. First, a method for SOC optimization setting of high-capacity energy storage systems is proposed to determine the day-ahead SOC optimization settings at the start and end of each day for the energy storage system. Subsequently, based on a twin delayed deep deterministic policy gradient algorithm, a day-ahead real-time optimal scheduling model training method is proposed. A real-time model for source-network-load energy storage system is established based on the optimized set points of energy storage SOCs and day-ahead operation data to achieve day-ahead real-time integrated optimal scheduling. Finally, the effectiveness of the proposed method is validated through case study results. The result indicates that the method proves to be efficient in enhancing system revenue, while the day-ahead real-time optimal scheduling model mitigates the impact of prediction errors. |
| Key words: hybrid energy storage hydrogen energy storage system SOC optimization setting deep reinforcement learning day-ahead real-time scheduling |
