In order to effectively cope with the adverse effects of uncertainty in demand response on the economy and decision-making reliability of power grid demand planning, a robust dispatch strategy based on load voltage sensitivity for distribution network demand response is proposed. Firstly, the deficiencies of traditional demand response day ahead scheduling in decision economy and model uncertainty are studied and analyzed; Secondly, in view of the drawbacks of the traditional model, the load pressure sensitivity is introduced to participate in demand response scheduling to improve the economy of decision-making, and the reliability of the model under uncertainty is strengthened by using distributed robust optimization to build an improved demand response day ahead scheduling model; Then, the improved model is transformed into a two-stage three-layer optimization problem and the C&CG algorithm is used to solve the proposed model; Finally, the economic and reliability of the proposed strategy were verified through simulation examples of IEEE 33 nodes.

错误:搜索内容不能为空,请输入英文关键词
错误:关键词超出字数限制,请精简
高级检索

Day-Ahead Scheduling of Demand Response in Distribution Networks Based on Load Voltage Sensitivity and Distributionally Robust Optimization

  • Changqing Ye,
  • Zhipeng Xu,
  • Ziyin Lin,
  • Yongjun Zhang

摘要

In order to effectively cope with the adverse effects of uncertainty in demand response on the economy and decision-making reliability of power grid demand planning, a robust dispatch strategy based on load voltage sensitivity for distribution network demand response is proposed. Firstly, the deficiencies of traditional demand response day ahead scheduling in decision economy and model uncertainty are studied and analyzed; Secondly, in view of the drawbacks of the traditional model, the load pressure sensitivity is introduced to participate in demand response scheduling to improve the economy of decision-making, and the reliability of the model under uncertainty is strengthened by using distributed robust optimization to build an improved demand response day ahead scheduling model; Then, the improved model is transformed into a two-stage three-layer optimization problem and the C&CG algorithm is used to solve the proposed model; Finally, the economic and reliability of the proposed strategy were verified through simulation examples of IEEE 33 nodes.