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基于深度强化学习的风电场储能系统预测决策一体化调度
作者:
作者单位:

1.山东大学电网智能化调度与控制教育部重点实验室,山东省济南市 250061;2.国网陕西省电力公司电力科学研究院,陕西省西安市 710054

摘要:

风电场储能系统的优化控制可以提高风电场作为发电商在电力市场中的竞争力。文中提出基于深度强化学习的储能系统预测决策一体化调度方法,令高维度的风电场原始测量数据直接驱动储能系统。与预测、决策相分离的传统调度模式相比,预测决策一体化调度模式将风电功率预测与储能系统动作决策相融合,避免了预测阶段中有效决策信息的损失,使风电的随机规律无须通过数学假设等方式被人为刻画,避免了建模误差。其次,引入深度强化学习Rainbow算法优化风电场测量数据与储能系统动作指令之间端到端的控制策略,该策略具备动态统筹多时段系统收益的能力。最后,基于风电场历史数据的算例分析,验证了所提一体化调度模式的优越性和深度强化学习应对不确定性问题的有效性。

关键词:

基金项目:

国家电网公司科技项目(52060018000X)。

通信作者:

作者简介:

于一潇(1993—),女,硕士,主要研究方向:新能源功率预测、机器学习。E-mail:challenger_yu@163.com
杨佳峻(1994—),男,硕士,主要研究方向:人工智能在电力系统中的应用。E-mail:yangjj201714179@163.com
杨明(1980—),男,通信作者,教授,博士生导师,主要研究方向:电力系统分析与控制。E-mail:myang@sdu.edu.cn


Prediction and Decision Integrated Scheduling of Energy Storage System in Wind Farm Based on Deep Reinforcement Learning
Author:
Affiliation:

1.Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education (Shandong University), Jinan 250061, China;2.Electric Power Research Institute of State Grid Shaanxi Electric Power Corporation, Xi’an 710054, China

Abstract:

The optimal control of the energy storage system (ESS) in the wind farm can improve the competitiveness of the wind farm, which is a power producer in the electricity market. In this paper, a prediction and decision integrated scheduling method of ESS of the wind farm is proposed based on deep reinforcement learning, in order to make the original high-dimensional measurement data of the wind farm directly drive the ESS. Compared with the traditional scheduling model that separates prediction and decision, the prediction and decision integrated scheduling model combines the wind power prediction and ESS operation decision together, to avoid the loss of effective decision-making information in the prediction period. Stochastic laws of wind power do not need to be artificially portrayed through mathematical assumptions and modeling errors are avoided. Then, Rainbow algorithm, a deep reinforcement learning algorithm, is introduced to optimize the end-to-end control strategy between the wind farm measurement data and the ESS operation instruction, which can dynamically coordinate multi-period system profits. Finally, by analyzing case studies based on the wind farm historical data, the superiority of the proposed integrated scheduling mode and the effectiveness of deep reinforcement learning in dealing with the uncertainty problems are verified.

Keywords:

Foundation:
This work is supported by State Grid Corporation of China (No. 52060018000X).
引用本文
[1]于一潇,杨佳峻,杨明,等.基于深度强化学习的风电场储能系统预测决策一体化调度[J].电力系统自动化,2021,45(1):132-140. DOI:10.7500/AEPS20200226003.
YU Yixiao, YANG Jiajun, YANG Ming, et al. Prediction and Decision Integrated Scheduling of Energy Storage System in Wind Farm Based on Deep Reinforcement Learning[J]. Automation of Electric Power Systems, 2021, 45(1):132-140. DOI:10.7500/AEPS20200226003.
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  • 收稿日期:2020-02-26
  • 最后修改日期:2020-06-01
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  • 在线发布日期: 2021-01-05
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