湖南电力 ›› 2022, Vol. 42 ›› Issue (6): 76-82.doi: 10.3969/j.issn.1008-0198.2022.06.013

• 新技术及应用 • 上一篇    下一篇

火电多市场联合运行策略研究

王淑强1, 刘世件1, 黄超1, 梁家豪2   

  1. 1.五凌电力有限公司,湖南 长沙 410004;
    2.华北电力大学经济与管理学院,北京 102206
  • 收稿日期:2022-08-25 修回日期:2022-10-10 出版日期:2022-12-25 发布日期:2023-01-13
  • 作者简介:王淑强(1983),男,工程师,研究方向为电力辅助服务。刘世件(1981),男,工程师,研究方向为电力辅助服务。黄超(1990),男,工程师,研究方向为电力辅助服务。梁家豪(1997),男,硕士研究生,通信作者,研究方向为电力市场。
  • 基金资助:
    五凌电力辅助服务市场竞争策略研究项目(19ZDA081)

Research on Thermal Power Multi-Market Joint Operation Strategy

WANG Shuqiang1, LIU Shijian1, HUANG Chao1, LIANG Jiahao2   

  1. 1. Wuling Power Corporation Limited,Changsha 410004, China;
    2. School of Economy and Management, North China Electric Power University, Beijing 102206, China
  • Received:2022-08-25 Revised:2022-10-10 Online:2022-12-25 Published:2023-01-13

摘要: 为保证新型电力系统中火电厂商在多个市场的市场竞争力,基于电能量市场和辅助服务市场的发展情况分析火电厂商的市场竞争力,考虑最优化火电厂商的市场效益,构建基于电能量市场和辅助服务市场的火电机组出力曲线优化模型;结合火电厂商并网博弈、分时段电力电量价值的基本情况,构建基于A2C的火电机组出力优化求解模型。结合仿真算例对所提模型对火电厂商市场效益的提升情况进行验证,结果证明了该模型的经济性和有效性。

关键词: 深度调峰, 电能量市场, 深度强化学习, 竞争策略, 电力交易

Abstract: In order to ensure the market competitiveness of thermal power manufacturers in the new power system in multiple markets, the market competitiveness of thermal power manufacturers based on the development of electricity market and auxiliary service market is analyzed. Considering the market benefit of the optimized thermal power manufacturer, the optimization model of thermal power unit output curve considering the electricity energy market and auxiliary service market is constructed. Combined with the game of multiple thermal power manufacturers connected to the grid and the basic situation of time-segment power quantity value, the output optimization model of thermal power unit based on A2C is constructed. Combined with a simulation example, the model proposed is verified to improve the market benefit of thermal power plant, and the results show that the model is economic and effective.

Key words: deep load changing, main and auxiliary union, deep reinforcement learning, competitive strategy, power transaction

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