• HOME
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • Chinese
Site search        
文章摘要
基于KL散度的含储能机组组合分布鲁棒优化方法
Distributionally robust optimization method for unit commitment with energy storage based on KL divergence
Received:August 06, 2021  Revised:August 10, 2021
DOI:10.19753/j.issn1001-1390.2022.07.015
中文关键词: 机组组合  分布鲁棒优化  混合整数凸规划  电池储能  KL散度
英文关键词: unit  commitment, distributionally  robust optimization, mixed-integer  convex programming, battery  energy storage  system, KL  divergence
基金项目:国网河南省电力公司科技项目(5217L0170010)
Author NameAffiliationE-mail
Xia Lei* Marketing Service Center Measurement Center of State Grid Henan,Zhenghzou, China zld2017@126.com 
Yang Lei Marketing Service Center Measurement Center of State Grid Henan,Zhenghzou, China zld2017@126.com 
Wang Guohui Marketing Service Center Measurement Center of State Grid Henan,Zhenghzou, China zld2017@126.com 
Liu Zhong Marketing Service Center Measurement Center of State Grid Henan,Zhenghzou, China zld2017@126.com 
Su Chenfei Xinxiang Power Supply Company of State Grid Henan Electric Power Company,Xinxiang zld2017@126.com 
Huo Gang Henan Marketing Service Center of XJ Electric Co,Ltd,Xuchang zld2017@126.com 
Hits: 1095
Download times: 351
中文摘要:
      为应对风电场出力的波动性和随机性给机组组合带来的问题,提出了基于KL散度的含储能机组组合的两阶段分布鲁棒优化模型。首先,在电池储能的运行模型的基础,将电池储能模型嵌入到传统的火电机组组合模型中,建立了含储能的机组组合两阶段优化模型;然后,基于KL散度构建了风电场出力的模糊集,形成了含储能机组组合的两阶段分布鲁棒优化模型,通过对偶变换和广义Benders分解将其转化成易于求解的混合整数凸优化模型进行求解。最后,通过IEEE RTS 24节点系统仿真结果表明,本文所提出的分布鲁棒优化方法保守性优于鲁棒优化方法,经济性接近随机优化方法,且随着KL散度增大,机组组合成本缓慢增加。
英文摘要:
      In order to solve the problem of unit commitment caused by the fluctuation and randomness of wind farm output, a two-stage distributionally robust optimization model of unit commitment with energy storage based on KL divergence is proposed. Firstly, based on the operation model of battery energy storage system, the battery energy storage system model is embedded into the traditional thermal power unit commitment model, and a two-stage optimization model of unit commitment with battery energy storage system is established; Then, the fuzzy set of wind farm output is constructed based on KL divergence, and a two-stage distributionally robust optimization model of unit commitment with battery energy storage system is formed. It is transformed into a mixed integer convex optimization model which is easy to solve by dual transformation and generalized Benders decomposition. Finally, the simulation results of IEEE RTS 24 bus system show that the proposed distributionally robust optimization method is more conservative than the robust optimization method, and its economy is close to the stochastic optimization method. With the increase of KL divergence, the unit commitment cost increases slowly.
View Full Text   View/Add Comment  Download reader
Close
  • Home
  • About Journal
    • Historical evolution
    • Journal Honors
  • Editorial Board
    • Members of Committee
    • Director of the Committee
    • President and Editor in chief
  • Submission Guide
    • Instructions for Authors
    • Manuscript Processing Flow
    • Model Text
    • Procedures for Submission
  • Academic Influence
  • Open Access
  • Ethics&Policies
    • Publication Ethics Statement
    • Peer Review Process
    • Academic Misconduct Identification and Treatment
    • Advertising and Marketing
    • Correction and Retraction
    • Conflict of Interest
    • Authorship & Copyright
  • Contact Us
  • 中文页面
Address: No.2000, Chuangxin Road, Songbei District, Harbin, China    Zip code: 150028
E-mail: dcyb@vip.163.com    Telephone: 0451-86611021
© 2012 Electrical Measurement & Instrumentation
黑ICP备11006624号-1
Support:Beijing Qinyun Technology Development Co., Ltd