基于分区电价的跨区电力调度双层优化模型

Translated title of the contribution: Bi-level optimization model of inter-regional power dispatch based on regional electricity price
  • Liang Zhang
  • , Xiuli Wang
  • , Jianxue Wang
  • , Yijun Zheng
  • , Qian Ma
  • , Ziqiang Wang

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In order to fully use the inter-regional dispatch resource,a bi-level optimization model of inter-regional power dispatch based on regional electricity price is proposed. The equivalent generation cost curve is adopted to model the output of coal-fired units within the regions,and the chance constraint method based on integer programming is adopted to model the output of wind power and photovoltaic. A bi-level optimization model suitable for hierarchical dispatch mode is established. As the superior dispatch organization,the upper level model takes the inter-regional transmission power as the decision variables,and takes the maximum value of inter-regional transmission electricity as the goal. The lower level model realizes the economic dispatch within the regions. The bi-level model is transformed into a single-level model for solution according to Karush-Kuhn-Tucker(KKT) condition. The example results verify the effectiveness of the proposed model,which maintains a certain electricity price difference between the power sending region and power receiving region,and alleviates the power supply and demand contradiction in the power receiving region by transmitting a small amount of electricity.

Translated title of the contributionBi-level optimization model of inter-regional power dispatch based on regional electricity price
Original languageChinese (Traditional)
Pages (from-to)195-201
Number of pages7
JournalDianli Zidonghua Shebei/Electric Power Automation Equipment
Volume43
Issue number8
DOIs
StatePublished - Aug 2023

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

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