陈哲,韦美佳,林达,李志浩,陈健
CHEN Zhe,WEI Meijia,LIN Da,LI Zhihao,CHEN Jian

• 1:国网浙江省电力有限公司电力科学研究院
• 2:电网智能化调度与控制教育部重点实验室(山东大学)

摘要(Abstract)：

电氢耦合系统中风光出力存在波动性，且电能与氢能调度时间尺度也具有差异性，这些因素给系统的经济、高效调度带来诸多挑战。为此，基于场景法和深度强化学习提出一种考虑风光不确定性的电氢耦合系统两阶段多时间尺度优化调度方法。首先分析储电与储氢等储能装置的工作特性，设计电氢耦合系统两阶段优化调度框架。然后考虑风光不确定性，构建长时间尺度和短时间尺度两阶段优化调度模型；长时间尺度优化模型以系统能量最大程度自平衡为目标，采用拉丁超立方场景生成和场景缩减得到典型风光出力场景，并进行优化求解；短时间尺度优化模型以系统运行经济性最优为目标，采用深度确定性策略梯度算法求解。最后，通过算例仿真表明所提优化调度方法能够实现氢能日间转移、有效平抑风光出力波动，验证了方法的有效性。
In electricity-hydrogen coupling systems, fluctuations in wind and solar power output, as well as the different timescales for electricity and hydrogen energy dispatch, pose significant challenges for economic and efficient system scheduling. To address these challenges, this paper, using scenario approach and deep reinforcement learning(DRL), proposes a two-stage multi-timescale optimal scheduling method for electricity-hydrogen coupling systems considering uncertainties of wind and solar power generation. First, the operational characteristics of energy storage devices, including electrical and hydrogen storage devices, are analyzed, and a two-stage optimal scheduling framework for electricity-hydrogen coupling systems is designed. Next, with the uncertainties of wind and solar power generation considered, long-term and short-term timescale optimal models are developed. The long-term timescale model aims to maximize the systems' energy self-balance by generating typical wind and solar output scenarios using Latin hypercube sampling(LHS) for scenario generation and reduction, followed by optimization. The short-term model focuses on minimizing the systems' operational costs and is solved using the deep deterministic policy gradient(DDPG) algorithm. Finally, case study simulations demonstrate that the proposed method effectively facilitates hydrogen energy shifting, smooths fluctuations in wind and solar output, verifying the method's effectiveness.

关键词(KeyWords)： 电氢耦合系统;多时间尺度优化调度;场景生成及削减;深度强化学习;风光不确定性
electricity-hydrogen coupling system;multi-timescale optimal scheduling;scenario generation and reduction;DRL;uncertainties of wind and solar power generation

Abstract:

Keywords:

基金项目(Foundation): 国网浙江省电力有限公司科技项目（5211DS230001）

作者(Author): 陈哲,韦美佳,林达,李志浩,陈健
CHEN Zhe,WEI Meijia,LIN Da,LI Zhihao,CHEN Jian

DOI: 10.19585/j.zjdl.202501006

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