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山东大学学报 (工学版) ›› 2020, Vol. 50 ›› Issue (4): 90-97.doi: 10.6040/j.issn.1672-3961.0.2019.341

• 电气工程 • 上一篇    

基于学习理论的含光储联合系统的输电网双层规划

孙东磊1,赵龙1,秦敬涛2*,韩学山2,杨明2,王明强2   

  1. 1. 国网山东省电力公司经济技术研究院, 山东 济南 250021;2. 电网智能化调度与控制教育部重点实验室(山东大学), 山东 济南 250061
  • 发布日期:2020-08-13
  • 作者简介:孙东磊(1988— ),男,山东济宁人,博士,高级工程师,主要研究方向为电力系统规划. E-mail:sdusdlei@sina.com. *通信作者简介:秦敬涛(1995— ),男,山东青州人,硕士研究生,主要研究方向为深度强化学习算法在可再生能源消纳中的应用. E-mail:201814272@mail.sdu.edu.cn
  • 基金资助:
    国网山东省电力公司科技资助项目

Bi-level planning of transmission network with solar-storage combination system based on learning theory

SUN Donglei1, ZHAO Long1, QIN Jingtao2*, HAN Xueshan2, YANG Ming2, WANG Mingqiang2   

  1. 1. Economic &
    Technology Research Institute, State Grid Shandong Electric Power Company, Jinan 250021, Shandong, China;
    2. Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education(Shandong Uniersity), Jinan 250061, Shandong, China
  • Published:2020-08-13

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摘要： 针对传统输电网规划中对光伏出力不确定性处理中存在的问题,提出一种基于学习理论的含光储联合系统的输电网双层规划模型。下层基于学习理论对光储联合系统进行优化,目标为光伏电站长期运行收益最大与计划功率不确定性最小。将下层优化求解得到的光储联合系统计划功率代入上层的输电网规划模型,以线路投资成本、运行成本和弃光成本最小为目标进行规划。最后用改进的IEEE118节点算例验证了光储联合系统可以减小计划功率的不确定性,提高规划结果的可信度。本研究建立的Q学习控制器具有良好的在线学习能力,通过大量数据的学习后能对光储联合系统的计划出力进行有效的指导。

关键词: 学习理论, Q学习算法, 输电网规划, 光储联合系统, 不确定性

Abstract: In order to address the solar power output uncertainty in transmission network planning, a bi-level planning model of transmission network was proposed in which the solar-storage combination system was modeled by learning theory. In the lower level, the scheduled power of solar-storage combination system submitted to the large power system was optimized by maximizing the long-term profit of the solar-storage combination system and minimizing the uncertainty of the planned power. Substituting the planned power of the solar-storage combination system obtained by the lower layer optimization into the upper transmission network planning model, then we minimized the transmission line investment cost, power system operating cost, and solar-shedding cost. The modified IEEE-118 bus system experimental results verified that the solar-storage combination system could reduce the uncertainty of the planned power and enhanced the credibility of planning result. The Q-learning controller established in this paper had good online learning ability and could effectively guide the planned output of the solar-storage combination system after learning a large amount of data.

Key words: learning theory, Q learning algorithm, planning of transmission network, solar-storage combination system, uncertainty

中图分类号: 

  • TM71
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