Robust Optimization Based Coordinated Scheduling Strategy for Integrated Demand Response in Micro Energy Grid

被引:0
作者
Chen L. [1 ,2 ]
Wu J. [1 ]
Zhang W. [3 ]
Tang H. [4 ]
Mao Y. [1 ]
机构
[1] School of Automation, Guangdong University of Technology, Guangzhou
[2] Department of Experiment Teaching, Guangdong University of Technology, Guangzhou
[3] Huizhou Power Supply Bureau of Guangdong Power Grid Co., Ltd., Huizhou
[4] School of Physics & Optoelectronic Engineering, Guangdong University of Technology, Guangzhou
来源
Dianli Xitong Zidonghua/Automation of Electric Power Systems | 2021年 / 45卷 / 21期
关键词
Column and constraint generation algorithm; Integrated demand response; Micro energy grid; Optimal scheduling; Two-stage robust optimization;
D O I
10.7500/AEPS20210201001
中图分类号
学科分类号
摘要
Aiming at the uncertainty of renewable energy output in the micro energy grid, based on the "multiple interaction" characteristics of integrated demand response (IDR), a strategy for IDR to smooth the fluctuation of renewable energy is proposed, and a two-stage robust optimization model for IDR day-ahead and intra-day collaborative scheduling in the micro energy grid is built. In the model, the uncertain budget parameters of wind and photovoltaic are introduced for regulating the economy and robustness of the system. In the day-ahead stage, the worst-case scenario is considered to determine the day-ahead scheduling scheme of the system. In the intra-day regulation stage, the regulation scheme in the worst-case scenario is optimized based on the optimization results of the day-ahead stage. The strong duality theory and column and constraint generation algorithm are used to transform and solve the robust optimization problem of min-max-min structure. The results show that the robust optimization method can improve the system ability to resist uncertain risks, and the IDR can improve the economy and self-sufficiency of the micro energy grid. © 2021 Automation of Electric Power Systems Press.
引用
收藏
页码:159 / 169
页数:10
相关论文
共 31 条
[21]  
ZHU Jiayuan, LIU Yang, XU Lixiong, Et al., Robust day-ahead economic dispatch of microgrid with combined heat and power system considering wind power accommodation, Automation of Electric Power Systems, 43, 4, pp. 40-48, (2019)
[22]  
ZHANG Jinhui, WANG Xu, JIANG Chuanwen, Et al., Two-stage robust optimization scheduling of distribution network with combined heat and power units in new-type towns, Automation of Electric Power Systems, 43, 23, pp. 155-163, (2019)
[23]  
EBRAHIMI M R, AMJADY N., Adaptive robust optimization framework for day-ahead microgrid scheduling, International Journal of Electrical Power & Energy Systems, 107, pp. 213-223, (2019)
[24]  
SHAMS M H, SHAHABI M, MANSOURLAKOURAJ M, Et al., Adjustable robust optimization approach for two-stage operation of energy hub-based microgrids, Energy, 1, (2021)
[25]  
CHEN Zhe, ZHANG Yining, MA Guang, Et al., Two-stage day-ahead and intra-day robust reserve optimization considering demand response, Automation of Electric Power Systems, 43, 24, pp. 67-76, (2019)
[26]  
ZOU Yunyang, YANG Li, LI Jiayong, Et al., Robust optimal dispatch of micro-energy grid with multi-energy complementation of cooling heating power and natural gas, Automation of Electric Power Systems, 43, 14, pp. 65-72, (2019)
[27]  
SOHEYLI S, MAYAM M H S, MEHRJOO M., Modeling a novel CCHP system including solar and wind renewable energy resources and sizing by a CC-MOPSO algorithm, Applied Energy, 184, pp. 375-395, (2016)
[28]  
XIONG Yan, WU Jiekang, WANG Qiang, Et al., An optimization coordination model and solution for combined cooling, heating and electric power systems with complimentary generation of wind, PV, gas and energy storage, Proceedings of the CSEE, 35, 14, pp. 3616-3625, (2015)
[29]  
LI M, MU H L, LI N, Et al., Optimal design and operation strategy for integrated evaluation of CCHP (combined cooling heating and power) system, Energy, 99, pp. 202-220, (2016)
[30]  
FANG F., A novel optimal operational strategy for the CCHP system based on two operating modes, IEEE Transactions on Power Systems, 27, 2, pp. 1032-1041, (2012)