计及源荷不确定性的综合能源系统近端策略优化调度
作者:
作者单位:

(1.广西大学电气工程学院,广西 南宁 530004;2.广西电网电力调度控制中心,广西 南宁 530023)

作者简介:

通讯作者:

姜爱华(1971—),女,博士,副教授,主要从事信息物理系统优化控制等研究;E?mail:1261153682@qq.com

中图分类号:

TM734

基金项目:

国家自然科学基金(51667004)


Proximal policy optimization dispatch of integrated energy system considering source‑load uncertainty
Author:
Affiliation:

(1.School of Electrical Engineering, Guangxi University, Nanning 530004, China;2.Power Dispatching Control Center, Guangxi Power Grid, Nanning 530023, China )

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 资源附件
  • |
  • 文章评论
    摘要:

    源荷的不确定性一直是综合能源系统优化调度中的难点问题。针对源荷不确定波动问题,提出一种基于数据深度强化学习的近端策略优化调度方法,实现在阶梯式碳交易下,满足用户需求的综合能源系统最优成本和降低碳排放总量的优化调度。首先,以阶梯式碳交易下计及碳交易费用的系统总成本为目标,建立多类型柔性负荷综合需求响应模型,提高需求响应的响应能力和调度灵活性;然后,在深度强化学习的框架下,设定了该模型的马尔可夫决策过程(Markov decision process, MDP);最后,对不确定性带来的数据变化,使用近端策略优化(proximal policy optimization, PPO)算法求解,引入小批量更新和重要性采样,将每次策略更新的幅度限制在一定范围内,从而保证策略更新的准确性。仿真结果表明,本方法可有效解决源荷不确定性带来的影响,有效降低碳排放总量和系统日平均运行成本。

    Abstract:

    In optimization dispatch of integrated energy systems, source?load uncertainty is always a difficult problem. Aiming at the source?load uncertainty fluctuation issue, a proximal policy optimization dispatching method is proposed based on data?depth reinforcement learning. Ensuring user requirements are satisfied, this method achieves the optimal cost for the integrated energy system while reducing the total carbon emissions under a tiered carbon trading framework. Firstly, with the objective of considering system overall cost, including carbon trading fees under a tiered carbon trading framework, a comprehensive demand response model for multiple types of flexible loads is established to enhance the responsiveness and scheduling flexibility of demand response. Then, within the framework of deep reinforcement learning, a Markov decision process (MDP) is defined for this model. Finally, to address the data variations caused by uncertainties, the proximal policy optimization (PPO) algorithm is employed to find solutions. This involves introducing mini?batch updates and importance sampling techniques, which limit the magnitude of policy updates within a certain range, ensuring the accuracy of policy updates in each iteration. The simulation results demonstrate that compared to the deep deterministic policy gradient (DDPG), this method can effectively mitigate the impact of source?load uncertainty while significantly reducing the total carbon emissions and the average daily operating cost of the system.

    参考文献
    相似文献
    引证文献
引用本文

雷嘉明,姜爱华,吴新飞,等.计及源荷不确定性的综合能源系统近端策略优化调度[J].电力科学与技术学报,2023,38(5):1-11.
LEI Jiaming, JIANG Aihua, WU Xinfei, et al. Proximal policy optimization dispatch of integrated energy system considering source‑load uncertainty[J]. Journal of Electric Power Science and Technology,2023,38(5):1-11.

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:
  • 最后修改日期:
  • 录用日期:
  • 在线发布日期: 2024-01-15
  • 出版日期:
文章二维码