Day-ahead stochastic game and real-time adaptive time scale optimization method for multiple virtual power plants

被引：0
|
作者：
Ge X. [1 ]
Cao X. [1 ]
Li Y. [1 ]
机构：
[1] College of Electrical Engineering, Shanghai University of Electric Power, Shanghai
来源：
Dianli Zidonghua Shebei/Electric Power Automation Equipment | 2023年 / 43卷 / 11期
基金：
中国国家自然科学基金;
关键词：
cooperative game; coordination optimization; demand response; multi-time scale; virtual power plants;
D O I：
10.16081/j.epae.202301003
中图分类号：
学科分类号：
摘要：
In order to solve the problems of multiple information risks and limited scheduling flexibility in the operation performance of virtual power plant （VPP），a day-ahead stochastic game and the real-time adaptive time scale optimization method for multiple VPPs considering network constraints is proposed. In order to cope with various risks faced by the VPP in the day-ahead operation，the risk utility model is established by comprehensively considering the probability distribution and the adjustment level of the random prediction information in VPPs，and the risk level of all components in the VPP and the whole VPP are quantitatively characterized during all time periods. Considering the cross-regional characteristics，the network-related constraints and the dynamic coupling constraints between the transmission fee and the bidding price are constructed，more feasible day-ahead VPP power energy transaction model is established. In view of the update and fluctuation of real-time forecast information，considering the game between the scheduling deviation reduction rate and the comprehensive cost increase rate，a real-time adaptive time scale optimization model is established. The simulative results show that the proposed model can effectively adapt to various uncertain operation scenarios，and improve the power curve tracking ability while ensuring economy. © 2023 Electric Power Automation Equipment Press. All rights reserved.
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收藏
页码：150 / 157
页数：7
相关论文
共 19 条
  • [1] LIN Yujun, MIAO Shihong, YANG Weichen, Et al., Day-ahead optimal scheduling strategy of virtual power plant for environment with multiple uncertainties[J], Electric Power Automation Equipment, 41, 12, pp. 143-150, (2021)
  • [2] LIU Siyuan, AI Qian, ZHENG Jianping, Et al., Bi-level coordination mechanism and operation strategy of multi-time scale multiple virtual power plants[J], Proceedings of the CSEE, 38, 3, pp. 753-761, (2018)
  • [3] LI Jiamei, AI Qian, Operation mode of virtual power plant considering peak regulation auxiliary service[J], Electric Power Automation Equipment, 41, 6, pp. 1-13, (2021)
  • [4] Gao ZHANG, JIANG Chuanwen, WANG Xu, Et al., Risk assessment and bi-level optimization dispatch of virtual power plants considering renewable energy uncertainty[J], IEEJ Transactions on Electrical and Electronic Engineering, 12, 4, pp. 510-518, (2017)
  • [5] FAN Songli, AI Qian, HE Xing, Risk analysis on dispatch of virtual power plant based on chance constrained programming [J], Proceedings of the CSEE, 35, 16, pp. 4025-4034, (2015)
  • [6] TAN Zhongfu, WANG Guan, Liwei JU, Et al., Application of CVaR risk aversion approach in the dynamical scheduling optimization model for virtual power plant connected with wind-photovoltaic-energy storage system with uncertainties and demand response[J], Energy, 124, pp. 198-213, (2017)
  • [7] WU Chenshuo, Multi-agent collaborative optimization method of virtual power plant based on Bayesian game, (2021)
  • [8] ZHOU Buxiang, ZHANG Yue, ZANG Tianlei, Et al., Blockchain-based stackelberg game optimal operation of multiple virtual power plants[J], Automation of Electric Power Systems, 46, 1, pp. 155-163, (2022)
  • [9] ZHANG Bingying, Qiqiang LI, WANG Luhao, Et al., Robust optimization for energy transactions in multi-microgrids under uncertainty[J], Applied Energy, 217, pp. 346-360, (2018)
  • [10] ZHOU Bo, GAO Hongjun, Et al., Robust day-ahead trading strategy for multiple virtual power plants[J], Power System Technology, 42, 8, pp. 2694-2703, (2018)
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