Research on Multiple Time Scales Optimal Dispatching Strategy for User Side Energy Storage Participating in Demand Response

doi:10.11985/2021.03.016

Abstract

Abstract:

In order to solve the problem of scheduling power fluctuation when user-side energy storage participates in demand response, the day-ahead and real-time multi-time scale optimal scheduling scheme based on model predictive control method is proposed. At the aim of minimizing the user purchasing electric power cost, an day-ahead scheduling model is established, which considers the time-of-use(TOU) electricity prices, the subsidy revenue of demand response and the loss cost of energy storage battery. Furhermore, considering the impact of the load forecasting deviation, the real-time scheduling model is benchmarked on the result of day-ahead scheduling and based on model predictive control method to minimize the deviation of net load power and energy storage stage of charge(SOC). Finally, the feasibility and effectiveness of the proposed scheduling scheme are verified by examples.

Key words: User side energy storage, demand response, model predictive control, multiple time scales, optimal dispatching

CLC Number:

TM73

WANG Jianbo, WANG Chunliang, WEI Qiang, ZHOU Baozhong, ZHANG Jiguang, ZHU Yeyang, FAN Ziwei. Research on Multiple Time Scales Optimal Dispatching Strategy for User Side Energy Storage Participating in Demand Response[J]. Journal of Electrical Engineering, 2021, 16(3): 115-122.

Figures/Tables 14

References 16

[1]	訾振宁, 何永君, 赵东元, 等. 新一代电力系统灵活柔性特征研究[J]. 电气工程学报, 2019, 14(3):1-8.
	ZI Zhenning, HE Yongjun, ZHAO Dongyuan, et al. Research on flexibility of new generation power system[J]. Journal of Electrical Engineering, 2019, 14(3):1-8.
[2]	邵振, 邹晓松, 袁旭峰, 等. 柔性配电网背景下的储能应用研究评述[J]. 电测与仪表, 2020, 57(3):86-93.
	SHAO Zhen, ZOU Xiaosong, YUAN Xufeng, et al. Review on energy storage application research in the context of flexible distribution network[J]. Electrical Measurement & Instrumentation, 2020, 57(3):86-93.
[3]	王开科, 南东亮, 李勇, 等. 虚拟电厂参与大规模新能源系统的储能侧双层优化运行策略[J]. 电气工程学报, 2020, 15(2):24-33.
	WANG Kaike, NAN Dongliang, LI Yong, et al. Bi-level optimal operation strategy of energy storage with large-scale new energy system under the participation of virtual power plant[J]. Journal of Electrical Engineering, 2020, 15(2):24-33.
[4]	谢东亮, 薛峰, 宋晓芳. 基于最优价值网络的储能系统调度优化及仿真[J]. 电力系统自动化, 2016, 40(24):42-48.
	XIE Dongliang, XUE Feng, SONG Xiaofang. Optimization and simulation for dispatch of energy storage systems based on optimal value networks[J]. Automation of Electric Power Systems, 2016, 40(24):42-48.
[5]	陈浩, 贾燕冰, 郑晋, 等. 规模化储能调频辅助服务市场机制及调度策略研究[J]. 电网技术, 2019, 43(10):3607-3615.
	CHEN Hao, JIA Yanbing, ZHENG Jin, et al. Research on market mechanism and scheduling strategy of frequency regulation auxiliary service of large-scale energy strorage[J]. Power System Technology, 2019, 43(10):3607-3615.
[6]	薛琰, 殷文倩, 杨志豪, 等. 电力市场环境下独立储能电站的运行策略研究[J]. 电力需求侧管理, 2018, 20(6):12-15.
	XUE Yan, YIN Wenqian, YANG Zhihao, et al. Study on operation strategy of independent energy storage power station in electricity market[J]. Power Demand Side Management, 2018, 20(6):12-15.
[7]	LUO F, DONG Z Y, CHEN G, et al. Flexible transmission expansion planning associated with large-scale wind farms integration considering demand response[J]. IET Generation,Transmission & Distribution, 2015, 9(15):2276-2283. doi: 10.1049/gtd2.v9.15
[8]	别朝红, 胡国伟, 谢海鹏, 等. 考虑需求响应的含风电电力系统的优化调度[J]. 电力系统自动化, 2014, 38(13):115-120.
	BIE Zhaohong, HU Guowei, XIE Haipeng, et al. Optimal dispatch for wind power integrated systems considering demand response[J]. Automation of Electric Power Systems, 2014, 38(13):115-120.
[9]	李文启, 李朝晖, 杨海晶, 等. 需求响应下分布式储能协调运行策略研究[J]. 电力需求侧管理, 2018, 20(3):11-14.
	LI Wenqi, LI Zhaohui, YANG Haijing, et al. Research on coordinated strategy of distributed battery energy storage in demand response[J]. Power Demand Side Management, 2018, 20(3):11-14.
[10]	任佳依, 顾伟, 王勇, 等. 基于模型预测控制的主动配电网多时间尺度有功无功协调调度[J]. 中国电机工程学报, 2018, 38(5):1397-1407.
	REN Jiayi, GU Wei, WANG Yong, et al. Multi time scale active and reactive power coordinated dispatch of active distribution network based on model predictive control[J]. Proceedings of the CSEE, 2018, 38(5):1397-1407.
[11]	吴成辉, 林声宏, 夏成军, 等. 基于模型预测控制的微电网群分布式优化调度[J]. 电网技术, 2019, 44(2):530-538.
	WU Chenghui, LIN Shenghong, XIA Chengjun, et al. Distributed optimal dispatch of microgrid cluster based on model predictive control[J]. Power System Technology, 2019, 44(2):530-538.
[12]	王尉杰, 刘皓明, 杨志豪, 等. 基于需求响应的家庭能效管理优化调度[J]. 电力需求侧管理, 2017, 19(3):15-20.
	WANG Weijie, LIU Haoming, YANG Zhihao, et al. Household energy efficiency management and optimal dispatch base on demand response[J]. Power Demand Side Management, 2017, 19(3):15-20.
[13]	陈科彬, 邱晓燕, 史光耀, 等. 含可再生能源的微电网分时间尺度储能方案[J]. 电测与仪表, 2018, 55(6):30-34,41.
	CHEN Kebin, QIU Xiaoyan, SHI Guangyao, et al. Time- scale decomposition of energy storage in micro-grid with renewable energy[J]. Electrical Measurement & Instrumentation, 2018, 55(6):30-34,41.
[14]	李柯江, 宋天昊, 韩肖清, 等. 计及电价不确定性和损耗成本的储能竞价策略[J]. 电力系统自动化, 2020, 44(17):52-62.
	LI Kejiang, SONG Tianhao, HAN Xiaoqing, et al. Bidding strategy of energy storage considering electricity price uncertainty and loss cost[J]. Automation of Electric Power Systems, 2020, 44(17):52-62.
[15]	景江帆. 计及可再生能源消纳的日前电力市场出清优化研究[D]. 太原:山西大学, 2020.
	JING Jiangfan. The study on optimization of day-ahead electricity market clearing with renewavle energy consumption[D]. Taiyuan:Shanxi University, 2020.
[16]	LIU G, TOMSOVIC K. Quantifying spinning reserve in systems with significant wind power penetration[J]. IEEE Transactions on Power Systems, 2012, 27(4):2385-2393. doi: 10.1109/TPWRS.2012.2207465

参数	数值	参数	数值
额定容量/(kW·h)	200	初始SOC	0.4
额定充放电功率/kW	30	自放电率	0.001
SOC最大值	0.85	充放电效率(%)	85
SOC最小值	0.4	N_a/次	5 000
C_w/[元/(MW·h)]	100 000	${D_{{\text{dod}}}}$	0.8

峰平谷	时段	电价/[元/(kW·h)]	峰平谷	时段	电价/[元/(kW·h)]
高峰	10:00~15:00	0.8	平段	15:00~17:00	0.6
	17:00~22:00	0.8	平段	22:00~24:00	0.6
	7:00~10:00	0.6	低谷	00:00~7:00	0.3

方案	1	2	3	4	5
储能蓄电池	×	√	×	√	√
可转移负荷	×	×	√	√	×

场景	储能损耗成本/元	用户不满意费用/元	购电成本/元	总成本/元
1	0	0	5 871.3	5 871.3
2	102.2	0	5 514.5	5 616.7
3	0	170.4	5 620.1	5 790.5
4	63.0	90.5	5 428.8	5 582.3
5	110.1	0	5 603.3	5 713.4