• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (12): 131-140.doi: 10.12204/j.issn.1000-7229.2022.12.014

• Energy Management and Scheduling • Previous Articles     Next Articles

Multi-Source Low-Carbon Peak-Shaving Transaction Optimization Model for Thermal Power-Energy Storage-Demand Response Considering the Uncertainty Information Gap of Wind Power

LI Peng1(), YU Xiaopeng1(), ZHOU Qingqing2(), TAN Zhongfu2, JU Liwei2(), QIAO Huiting3()   

  1. 1. Economic Research Institute, State Grid Henan Electric Power Company, Zhengzhou 450000, China
    2. School of Economics and Management, North China Electric Power University, Beijing 100026, China
    3. Technical and Economic Center, China Southern Power Grid Energy Development Research Institute Co., Ltd., Guangzhou 510530, China
  • Received:2022-04-06 Online:2022-12-01 Published:2022-12-06
  • Contact: ZHOU Qingqing E-mail:hdlp0830@163.com;xmli97@126.com;503609181@qq.com;183758841@qq.com;qiaohuiting@163.com
  • Supported by:
    Zhongyuan Science and Technology Innovation Leading Talent Project


This paper proposes to integrate carbon emission trading into peak-shaving trading, to account for the carbon variation effects produced by thermal power peak-shaving, and proposes a multi-source low-carbon peak-shaving cost accounting method. Aiming at the uncertainty of wind power, this paper uses the information gap decision theory (IGDT) to reflect the information gap between the predicted value and the actual value of wind power, and constructs an uncertainty multi-source low-carbon peak-shaving transaction optimization model. Finally, a local power grid in northwest China is selected as the simulation system to verify the correctness and validity of the proposed model. Results show that the proposed multi-source low-carbon peak-shaving transaction model can promote the integration of wind power generation, ensure all participants obtain the cooperation incremental benefits, and establish a peak-shaving transaction plan for decision makers with different risk attitudes.

Key words: low-carbon peak-shaving, energy storage, demand response, information intermittent, optimization model

CLC Number: