Research on Real-time Demand Response Strategy Considering Renewable Energy Uncertainty

doi:10.3969/j.issn.1008-0198.2023.01.005

Abstract

Abstract: In order to solve the problem of uncertainty in renewable energy power supply, a method of establishing an optimal real-time demand response mechanism for power users managed by PMC is proposed. First, the electricity purchase model of a group of large scale electricity users managed by PMC is discussed, and its cost model and social welfare model are established. Then, with the goal of maximizing social welfare, an optimization algorithm based on real-time demand response is proposed to determine the PMC's day-ahead power and balance power purchasing decisions, as well as power users' real-time power consumption decisions. Finally, the proposed optimization algorithm is verified by simulation. The simulation results show that the algorithm can improve the social welfare of the power system while solving the uncertain power supply of renewable energy.

Key words: renewable energy, demand response, day-ahead power, balancing power, social welfare

CLC Number:

F426

HU Zhen, WANG Pan. Research on Real-time Demand Response Strategy Considering Renewable Energy Uncertainty[J]. Hunan Electric Power, 2023, 43(1): 30-34.

References 21

[1]	邹才能,熊波,薛华庆,等.新能源在碳中和中的地位与作用[J].石油勘探与开发,2021,48(2): 411-420.
[2]	卓振宇,张宁,谢小荣,等.高比例可再生能源电力系统关键技术及发展挑战[J].电力系统自动化,2021,45(9):171-191.
[3]	舒印彪,陈国平,贺静波,等.构建以新能源为主体的新型电力系统框架研究[J].中国工程科学,2021,23(6):61-69.
[4]	康重庆,杜尔顺,李姚旺,等.新型电力系统的“碳视角”:科学问题与研究框架[J].电网技术,2022,46(3):821-833.
[5]	S. N. Bragagnolo, J. C. Vaschetti, F. Magnago. On the scalability of supply cost for demand management in the smart grid[J]. IEEE Latin America Transactions, 2022, 20(4): 643-650.
[6]	颜宁,潘霄,马少华,等.基于用户需求侧管理的环型微电网群并网协调控制策略研究[J].太阳能学报,2021,42(12):368-374.
[7]	M. Franceschelli, A. Pilloni, A. Gasparri. Multi-Agent Co-ordination of Thermostatically Controlled Loads by Smart Power Sockets for Electric Demand Side Manage-ment[J].IEEE transactions on control systems technology, 2021,29(2):731-743.
[8]	杨军伟,杜露露,刘夏,等.高风电渗透率下考虑需求侧管理策略的智能微电网调度方法[J].智慧电力,2021,49(3):32-39,110.
[9]	朱兰,牛培源,唐陇军,等.考虑直接负荷控制不确定性的微能源网鲁棒优化运行[J].电网技术,2020,44(4):1400-1413.
[10]	郭新志,刘英新,李秋燕,等.基于智能负荷控制的分布式能源系统调控策略研究[J].智慧电力,2022,50(3):8-14.
[11]	胡国,颜云松,吴海,等.基于主配协同的配电网紧急负荷控制策略及终端实现[J].电力系统自动化,2022,46(2):180-187.
[12]	夏清,杨知方,赖晓文,等.基于分时容量电价的新型电力现货市场设计[J].电网技术,2022,46(5):1771-1779.
[13]	张效言,李先允.计及需求响应的峰谷分时电价研究[J].电气自动化,2022,44(3):25-27.
[14]	韩莹,于三川,李荦一,等.计及阶梯式碳交易的风光氢储微电网低碳经济配置方法[J].高电压技术,2022,48(7):2523-2533.
[15]	胡真.基于需求响应的高耗能企业发用电效益优化[D].湘潭:湘潭大学,2016.
[16]	Mohamed Ahmed A.S.,Day Dylan,Meintz Andrew,et al.Real-Time Implementation of Smart Wireless Charging of On-Demand Shuttle Service for Demand Charge Mitigation[J]. IEEE Transactions on Vehicular Technology, 2020,70(1): 59-68.
[17]	伍也凡,陈锦,张谜,等.湖南省面向大工业负荷需求侧响应研究[J].湖南电力,2020,40 (4):7-12.
[18]	潘宇航,王青松,陈力.应用于电网侧削峰填谷的储能系统配置及日出力优化策略[J].供用电,2022,39(7):9-16.
[19]	刘连德,何江,周家旭,等.含高比例风光发电的电力系统中抽蓄电站的优化控制策略[J].储能科学与技术,2022,11(7):2197-2205.
[20]	杨海柱,李梦龙,江昭阳,等.考虑需求侧电热气负荷响应的区域综合能源系统优化运行[J].电力系统保护与控制,2020,48(10):30-37.
[21]	Zhen Hu, Bin Duan, Yunke Xu, et al. Demand response optimization of power generation and consumption in energy intensive enterprise[C]//2015 IEEE Innovative Smart Grid Technologies-Asia(ISGT ASLA).Bangkok,Thailand:IEEE,2015,20:1-6.