Robust Optimal Dispatch Strategy for Battery Energy Storage System Participating in User-Side Peak Load Shifting

doi:10.12204/j.issn.1000-7229.2022.10.007

Abstract

Abstract:

The existing robust optimization methods for energy storage dispatch in the field of user-side peak load shifting lack the research on robust optimal scheduling of energy storage considering nonlinear multi-objective optimization model. When the range of energy storage output is directly limited by the load uncertainty, the conventional column and constraint generation algorithm cannot solve this kind of model. Aiming at these problems, considering the user-side load, battery energy storage and other constraints, a robust optimization model of energy storage system dispatch with net load variance and user-side power expenditure as optimization objectives is established, while the uncertainty of user load and new energy output is also taken into consideration. Since the decision-making and optimization results of the second stage will affect the value range of the decision-making in the first stage, it is necessary to improve the generation algorithm of columns and constraints to solve this kind of robust optimization problem. With the sub-problem solving steps in each iteration of the original column and constraint generation algorithm expanded by considering the objective function and some constraints as multiple sub-problems, solving multiple sub-problems in each iteration can effectively broaden the application scope of the algorithm. In the end, the simulation results are presented to verify the effectiveness of the method, which can be successfully applied to nonlinear multi-objective robust optimization problems without losing the performance advantages of the conventional algorithm.

Key words: battery energy storage, robust optimization, multi-objective optimization, column and constraint generation

CLC Number:

TM732

CHEN Ruibin, LU Lingxia, BAO Zhejing, YU Miao. Robust Optimal Dispatch Strategy for Battery Energy Storage System Participating in User-Side Peak Load Shifting[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(10): 66-76.

Figures/Tables 14

Table 1 Residential electricity price in a typical city

Table 2 Parameters of battery energy storage system

Fig.1 Uncertainty of PV output

Fig.2 Uncertainty of load

Fig.3 Flow chart of robust optimization of two-objective energy storage scheduling

Fig.4 Charging and discharging power of battery energy storage system (two objectives,when ε1 =715)

Fig.5 SOC of battery energy storage system (two objectives,when ε1 =715)

Fig.6 User-side net load(in scenario most unfavorable to minimize net load variance)

Fig.7 User-side net load( in scenario most unfavorable to minimize total electricity expenditure)

Fig.8 Pareto frontier of economic cost and user-side net load variance

Fig.9 Flow chart of robust optimization of multi-objective energy storage scheduling

Fig.10 Charging and discharging power of battery energy storage system (three objectives, ε1=720、 ε2=92)

Fig.11 SOC of battery energy storage system (three objectives, ε1=720、 ε2=92)

Fig.12 User-side net load(in scenario most unfavorable to minimize net load peak-valley difference)

References 21

[1]	马锐, 李相俊, 李文启, 等. 可再生能源供电区域电网中储能系统协同调度策略[J]. 发电技术, 2021, 42(1):31-39.
	MA Rui, LI Xiangjun, LI Wenqi, et al. Cooperative scheduling strategy of energy storage systems for regional grid supplied by renewable energy[J]. Power Generation Technology, 2021, 42(1):31-39.
[2]	叶畅, 曹侃, 丁凯, 等. 基于广义储能的多能源系统不确定优化调度策略[J]. 电工技术学报, 2021, 36(17): 3753-3764.
	YE Chang, CAO Kan, DING Kai, et al. Uncertain optimal dispatch strategy based on generalized energy storage for multi-energy system[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3753-3764.
[3]	苏译, TEH Jiashen, 柏智, 等. 考虑不确定性扰动的含海量资源配电网弹性研究综述[J]. 电力系统自动化, 2022, 46(16):17-30..
	SU Yi, TEH Jiashen, BAI Zhi, et al. Review of research on resilience of distribution network containing massive resources considering uncertainty disturbance[J]. Automation of Electric Power Systems, 2022, 46(16):17-30.
[4]	李建林, 李雅欣, 陈光, 等. 退役动力电池健康状态特征提取及评估方法综述[J]. 中国电机工程学报, 2022, 42(4): 1332-1347.
	LI Jianlin, LI Yaxin, CHEN Guang, et al. Research on feature extraction and SOH evaluation methods for retired power battery[J]. Proceedings of the CSEE, 2022, 42(4): 1332-1347.
[5]	郑伟民, 王蕾, 孙可, 等. 考虑多能流广义储能作用的配电网协调规划[J]. 电力自动化设备, 2021, 41(7): 22-30.
	ZHENG Weimin, WANG Lei, SUN Ke, et al. Coordinated planning of distribution network considering function of multi-energy flow generalized energy storage[J]. Electric Power Automation Equipment, 2021, 41(7): 22-30.
[6]	周喜超, 孟凡强, 李娜, 等. 电池储能系统参与电网削峰填谷控制策略[J]. 热力发电, 2021, 50(4): 44-50.
	ZHOU Xichao, MENG Fanqiang, LI Na, et al. Control strategies of battery energy storage system participating in peak load regulation of power grid[J]. Thermal Power Generation, 2021, 50(4): 44-50.
[7]	王树东, 丁汀, 邱进亮, 等. 动力电池组新型充电均衡电路及控制策略[J]. 工业仪表与自动化装置, 2019(4): 46-49.
	WANG Shudong, DING Ting, QIU Jinliang, et al. New charging equalization circuit and control strategy for automobile power battery[J]. Industrial Instrumentation & Automation, 2019(4): 46-49.
[8]	史风栋, 宋大威, 熊慧, 等. 锂离子电池组均衡控制方法的研究进展[J]. 电池, 2019, 49(3): 251-254.
	SHI Fengdong, SONG Dawei, XIONG Hui, et al. Research progress in Li-ion battery equalization control methods[J]. Battery Bimonthly, 2019, 49(3): 251-254.
[9]	徐顺刚, 钟其水, 朱仁江. 动力电池均衡充电控制策略研究[J]. 电机与控制学报, 2012, 16(2): 62-65.
	XU Shungang, ZHONG Qishui, ZHU Renjiang. Research of equalizing charge control strategy for power battery[J]. Electric Machines and Control, 2012, 16(2): 62-65.
[10]	孙舟, 田贺平, 王伟贤, 等. 梯次利用电池储能系统参与用户侧削峰填谷的经济性研究[J]. 太阳能学报, 2021, 42(4): 95-100.
	SUN Zhou, TIAN Heping, WANG Weixian, et al. Research on economy of echelon utilization battery energy storage system for user-side peak load shifting[J]. Acta Energiae Solaris Sinica, 2021, 42(4): 95-100.
[11]	周健飞, 孙宇轩, 代军. 基于V2G技术的微电网调峰控制策略研究[J]. 电力与能源, 2019, 40(3): 351-353.
	ZHOU Jianfei, SUN Yuxuan, DAI Jun. Peak load control strategy of microgrid based on V2G[J]. Power & Energy, 2019, 40(3): 351-353.
[12]	林俐, 费宏运. 规模化分布式光伏并网条件下储能电站削峰填谷的优化调度方法[J]. 现代电力, 2019, 36(5): 54-61.
	LIN Li, FEI Hongyun. Optimal scheduling method for peak load shifting of energy storage station with large-scale distributed photovoltaic integration[J]. Modern Electric Power, 2019, 36(5): 54-61.
[13]	刘井军, 刘涛, 周旭, 等. 含不确定源荷虚拟电厂参与需求响应优化控制方法[J]. 电力系统及其自动化学报, 2021, 33(4): 84-89.
	LIU Jingjun, LIU Tao, ZHOU Xu, et al. VPP demand response control method considering uncertainties in source and load[J]. Proceedings of the CSU-EPSA, 2021, 33(4): 84-89.
[14]	彭春华, 陈婧, 郑聪. 基于机会约束高斯混合模型的含光热电站热电联供型微网鲁棒经济调度[J]. 电力自动化设备, 2021, 41(4): 77-84, 93.
	PENG Chunhua, CHEN Jing, ZHENG Cong. Robust economic dispatch of CSP-CHPMG based on chance constrained Gaussian mixture model[J]. Electric Power Automation Equipment, 2021, 41(4): 77-84, 93.
[15]	张敏, 王金浩, 常潇, 等. 考虑可再生能源不确定性的热-电耦合微能源系统多目标鲁棒规划方法[J]. 中国电力, 2021, 54(4): 119-129.
	ZHANG Min, WANG Jinhao, CHANG Xiao, et al. A multi-objective robust planning method for thermal-electrical coupling micro-energy system considering the uncertainty of renewable energy[J]. Electric Power, 2021, 54(4): 119-129.
[16]	耿博文. 储电与空调负荷联合削峰填谷优化方法研究[D]. 沈阳: 沈阳工业大学, 2019.
	GENG Bowen. Research on optimization method of peak load shifting for storage and air conditioning load[D]. Shenyang: Shenyang University of Technology, 2019.
[17]	李大祥, 张新松. 用户侧电池储能系统运行策略优化与效益分析[J]. 智慧电力, 2021, 49(12): 31-36.
	LI Daxiang, ZHANG Xinsong. Operation strategy optimization and benefits analysis of user side battery energy storage systems[J]. Smart Power, 2021, 49(12): 31-36.
[18]	陈晨, 李端超, 王海伟, 等. 考虑不确定性的综合能源系统日前经济调度[J]. 电力科学与技术学报, 2021, 36(2): 24-30.
	CHEN Chen, LI Duanchao, WANG Haiwei, et al. Study on day-ahead economic dispatch of integrated energy system considering uncertainty[J]. Journal of Electric Power Science and Technology, 2021, 36(2): 24-30.
[19]	ZENG B, ZHAO L. Solving two-stage robust optimization problems using a column-and-constraint generation method[J]. Operations Research Letters, 2013, 41(5): 457-461. doi: 10.1016/j.orl.2013.05.003 URL
[20]	刘战捷. 计及基站备用储能的电力系统经济调度[D]. 济南: 山东大学, 2018.
	LIU Zhanjie. The economic dispatch of power system considering base station standby energy storage[D]. Jinan: Shandong University, 2018.
[21]	钟士元, 张文锦, 罗路平, 等. 基于行业聚类电量曲线分解的中期负荷预测[J]. 电力建设, 2022, 43(2): 81-88. doi: 10.12204/j.issn.1000-7229.2022.02.010
	ZHONG Shiyuan, ZHANG Wenjin, LUO Luping, et al. Medium-term load forecasting based on industry clustering electricity curve decomposition[J]. Electric Power Construction, 2022, 43(2): 81-88. doi: 10.12204/j.issn.1000-7229.2022.02.010