Optimization of Multi-time-scale Peak Shaving Considering Controllable Margin of Flexible Load

doi:10.12204/j.issn.1000-7229.2021.04.008

Abstract

Abstract:

How to guide the massive distributed load-side resources to participate in the regulation and operation of the power grid has become a new challenge for the power system. However, there is still a lack of in-depth research on collaborative control methods for massive heterogeneous loads. For this reason, a control method of heterogeneous load cluster considering controllable margin of flexible load is proposed. Firstly, a generalized energy storage aggregation model through air conditioning load, electric vehicle charging load, and distributed energy storage is established. Secondly, at the aggregator-load equipment level, aiming at the heterogeneous characteristics of multiple types of loads, a general state-serialization control strategy based on controllable margin indicators is proposed. At the dispatch center-aggregator level, the optimization models of day-ahead and intra-day peak-regulation are established. In the day-ahead stage, considering the start and stop plan of conventional units, electric vehicle charging plan and air-conditioning interruption plan, a scheduling model with the goal of optimal day-ahead system operation economy is established. In the intra-day stage, distributed energy storage of aggregators further copes with the uncertainty of wind power and load forecasting. Finally, a case study is used to realize the peak shaving and valley filling of the system while reducing the operating cost, which verifies the feasibility and effectiveness of the control strategy and peak shaving optimization method.

Key words: flexible load, multiple time scales, peak shaving, generalized energy storage, aggregator

CLC Number:

TM73

CHEN Ke, GAO Shan, LIU Yu. Optimization of Multi-time-scale Peak Shaving Considering Controllable Margin of Flexible Load[J]. ELECTRIC POWER CONSTRUCTION, 2021, 42(4): 69-78.

Figures/Tables 17

Fig.1 Hierarchical coordinated control architecture for flexible load clusters

Fig.2 Charging curve of an electric vehicle

Table 1 State queue algorithm for various loads

Fig.3 Day-ahead forecast power of wind power and load

Table 2 Quantity allocation of aggregators

Table 3 Parameter configuration of air conditioning load

Table 4 Parameter configuration of electric vehicle

Table 5 Parameter configuration of distributed energy storage

Table 6 Cost coefficient of resource aggregators

Fig.4 Day-ahead scheduling results

Fig.5 Comparison of load curves before and after flexible resource controlled

Table 7 Comparison of peak valley values before and after aggregator participation

Fig.6 Overall load response of load aggregators on day-ahead scale

Fig.7 Intra-day scheduling results

Fig.8 Comparison of load curves before and after aggregator participating in control

Fig.9 Overall load response of load aggregators on intra-day scale

Table 8 Scheduling cost of each scenario

References 33

[1]	吕凯, 唐昊, 王珂, 等. 计及源荷双侧不确定性的跨区互联电网源网荷协同调度[J]. 电力系统自动化, 2019,43(22):38-45.
	LYU Kai, TANG Hao, WANG Ke, et al. Coordinated dispatching of source-grid-load for inter-regional power grid considering uncertainties of both source and load sides[J]. Automation of Electric Power Systems, 2019,43(22):38-45.
[2]	许洪强, 姚建国, 於益军, 等. 支撑一体化大电网的调度控制系统架构及关键技术[J]. 电力系统自动化, 2018,42(6):1-8.
	XU Hongqiang, YAO Jianguo, YU Yijun, et al. Architecture and key technologies of dispatch and control system supporting integrated bulk power grids[J]. Automation of Electric Power Systems, 2018,42(6):1-8.
[3]	董蓓, 毛文博, 李峰, 等. 考虑多类型柔性负荷的日前优化调度技术[J]. 电力工程技术, 2018,37(6):97-102.
	DONG Bei, MAO Wenbo, LI Feng, et al. The technique of day-ahead optimized scheduling with multi-type of flexible loads[J]. Electric Power Engineering Technology, 2018,37(6):97-102.
[4]	刘文颖, 文晶, 谢昶, 等. 考虑风电消纳的电力系统源荷协调多目标优化方法[J]. 中国电机工程学报, 2015(5):1079-1088.
	LIU Wenying, WENJING, XIE Chang, et al. Multi-objective optimal method considering wind power accommodation based on source-load coordination[J]. Proceedings of the CSEE, 2015(5):1079-1088.
[5]	赵冬梅, 宋原, 王云龙, 等. 考虑柔性负荷响应不确定性的多时间尺度协调调度模型[J]. 电力系统自动化, 2019,43(22):21-30.
	ZHAO Dongmei, SONG Yuan, WANG Yunlong, et al. Coordinated scheduling model with multiple time scales considering response uncertainty of flexible load[J]. Automation of Electric Power Systems, 2019,43(22):21-30.
[6]	LI Z H, WU W C, ZHANG B M, et al. Capacity guaranteed control method for air conditioning cluster joining power grid frequency regulation[J]. The Journal of Engineering, 2018(17):1884-1888. doi: 10.1049/tje2.v2018.17 URL
[7]	HUI H X, DING Y, ZHENG M L. Equivalent modeling of inverter air conditioners for providing frequency regulation service[J]. IEEE Transactions on Industrial Electronics, 2019,66(2):1413-1423. doi: 10.1109/TIE.2018.2831192 URL
[8]	CUI W Q, DING Y, HUI H X, et al. Evaluation and sequential dispatch of operating reserve provided by air conditioners considering lead-lag rebound effect[J]. IEEE Transactions on Power Systems, 2018,33(6):6935-6950. doi: 10.1109/TPWRS.59 URL
[9]	戚野白, 王丹, 贾宏杰, 等. 基于归一化温度延伸裕度控制策略的温控设备需求响应方法研究[J]. 中国电机工程学报, 2015,35(21):5455-5464.
	QI Yebai, WANG Dan, JIA Hongjie, et al. Research on demand response for thermostatically controlled appliances based on normalized temperature extension margin control strategy[J]. Proceedings of the CSEE, 2015,35(21):5455-5464.
[10]	王珂, 姚建国, 姚良忠, 等. 电力柔性负荷调度研究综述[J]. 电力系统自动化, 2014,38(20):127-135.
	WANG Ke, YAO Jianguo, YAO Liangzhong, et al. Survey of research on flexible loads scheduling technologies[J]. Automation of Electric Power Systems, 2014,38(20):127-135.
[11]	CALLAWAY D S, HISKENS I A. Achieving controllability of electric loads[J]. Proceedings of the IEEE, 2011,99(1):184-199. doi: 10.1109/JPROC.2010.2081652 URL
[12]	黄一诺, 郭创新, 王力成, 等. 考虑用户满意度的电动汽车分群调度策略[J]. 电力系统自动化, 2015,39(17):183-191.
	HUANG Yinuo, GUO Chuangxin, WANG Licheng, et al. A cluster-based dispatch strategy for electric vehicles considering user satisfaction[J]. Automation of Electric Power Systems, 2015,39(17):183-191.
[13]	黄亚峰, 朱玉杰, 穆钢, 等. 基于温度预报的户用电采暖负荷可调节能力评估[J]. 电网技术, 2018,42(8):2487-2493.
	HUANG Yafeng, ZHU Yujie, MU Gang, et al. Evaluation of adjustable capacity of household electrical heating load based on temperature forecast[J]. Power System Technology, 2018,42(8):2487-2493.
[14]	SHAD M, MOMENI A, ERROUISSI R, et al. Identification and estimation for electric water heaters in direct load control programs[J]. IEEE Transactions on Smart Grid, 2017,8(2):947-955.
[15]	王怡岚, 童亦斌, 黄梅, 等. 基于需求侧响应的空调负荷虚拟储能模型研究[J]. 电网技术, 2017,41(2):394-401.
	WANG Yilan, TONG Yibin, HUANG Mei, et al. Research on virtual energy storage model of air conditioning loads based on demand response[J]. Power System Technology, 2017,41(2):394-401.
[16]	于大洋, 宋曙光, 张波, 等. 区域电网电动汽车充电与风电协同调度的分析[J]. 电力系统自动化, 2011,35(14):24-29.
	YU Dayang, SONG Shuguang, ZHANG Bo, et al. Synergistic dispatch of PEVs charging and wind power in Chinese regional power grids[J]. Automation of Electric Power Systems, 2011,35(14):24-29.
[17]	李佳佳, 胡林献. 基于二级热网电锅炉调峰的消纳弃风方案研究[J]. 电网技术, 2015,39(11):3286-3291.
	LI Jiajia, HU Linxian. Research on accommodation scheme of curtailed wind power based on peak-shaving electric boiler in secondary heat supply network[J]. Power System Technology, 2015,39(11):3286-3291.
[18]	文刚, 翁维华, 赵岩, 等. 考虑负荷聚集商参与的源荷互动双层优化模型[J]. 电网技术, 2017,41(12):3956-3963.
	WEN Gang, WENG Weihua, ZHAO Yan, et al. A bi-level optimal dispatching model considering source-load interaction integrated with load aggregator[J]. Power System Technology, 2017,41(12):3956-3963.
[19]	杨胜春, 刘建涛, 姚建国, 等. 多时间尺度协调的柔性负荷互动响应调度模型与策略[J]. 中国电机工程学报, 2014,34(22):3664-3673.
	YANG Shengchun, LIU Jiantao, YAO Jianguo, et al. Model and strategy for multi-time scale coordinated flexible load interactive scheduling[J]. Proceedings of the CSEE, 2014,34(22):3664-3673.
[20]	许鹏. 面向可再生能源消纳的负荷态势感知及调控策略研究[D]. 北京: 华北电力大学, 2019.
	XU Peng. Renewable energy consumption oriented load situation awareness and control strategy research[D]. Beijing: North China Electric Power University, 2019.
[21]	LIU Z, WANG D, JIA H J, et al. Power system operation risk analysis considering charging load self-management of plug-in hybrid electric vehicles[J]. Applied Energy, 2014,136:662-670. doi: 10.1016/j.apenergy.2014.09.069 URL
[22]	高赐威, 李倩玉, 李扬. 基于DLC的空调负荷双层优化调度和控制策略[J]. 中国电机工程学报, 2014,34(10):1546-1555.
	GAO Ciwei, LI Qianyu, LI Yang. Bi-level optimal dispatch and control strategy for air-conditioning load based on direct load control[J]. Proceedings of the CSEE, 2014,34(10):1546-1555.
[23]	HAN X X, ZOU H M, WU J, et al. Investigation on the heating performance of the heat pump with waste heat recovery for the electric bus[J]. Renewable Energy, 2020,152:835-848. doi: 10.1016/j.renene.2020.01.075 URL
[24]	张谦, 周林, 周雒维, 等. 计及电动汽车充放电静态频率特性的负荷频率控制[J]. 电力系统自动化, 2014,38(16):74-80.
	ZHANG Qian, ZHOU Lin, ZHOU Luowei, et al. Load frequency control considering charging and discharging static frequency characteristics of electric vehicles[J]. Automation of Electric Power Systems, 2014,38(16):74-80.
[25]	孙强, 许方园, 唐佳, 等. 基于需求响应的电动汽车集群充电负荷建模及容量边界控制策略[J]. 电网技术, 2016,40(9):2638-2645.
	SUN Qiang, XU Fangyuan, TANG Jia, et al. Study on modeling of aggregated charging load of electric vehicles and control strategy by adjusting capacity boundaries based on demand response[J]. Power System Technology, 2016,40(9):2638-2645.
[26]	FARIA P, VALE Z. Demand response in electrical energy supply: An optimal real time pricing approach[J]. Energy, 2011,36(8):5374-5384. doi: 10.1016/j.energy.2011.06.049 URL
[27]	RAHIMI F, IPAKCHI A. Demand response as a market resource under the smart grid paradigm[J]. IEEE Transactions on Smart Grid, 2010,1(1):82-88. doi: 10.1109/TSG.2010.2045906 URL
[28]	王冉, 王丹, 贾宏杰, 等. 基于参数序列化技术的电动汽车集群响应控制策略[J]. 电力系统自动化, 2015,39(20):47-53,134.
	WANG Ran, WANG Dan, JIA Hongjie, et al. A control strategy for electric vehicle cluster response based on parameter-serialization technique[J]. Automation of Electric Power Systems, 2015,39(20):47-53,134.
[29]	王栋, 徐青山, 陈亮, 等. 参与调峰控制的空调负荷建模仿真研究[J]. 电力工程技术, 2018,37(6):80-86.
	WANG Dong, XU Qingshan, CHEN Liang, et al. Air conditioning load modeling and simulation of peak load regulation[J]. Electric Power Engineering Technology, 2018,37(6):80-86.
[30]	王冉, 王丹, 贾宏杰, 等. 一种平抑微网联络线功率波动的电池及虚拟储能协调控制策略[J]. 中国电机工程学报, 2015,35(20):5124-5134.
	WANG Ran, WANG Dan, JIA Hongjie, et al. A coordination control strategy of battery and virtual energy storage to smooth the micro-grid tie-line power fluctuations[J]. Proceedings of the CSEE, 2015,35(20):5124-5134.
[31]	贾雨龙, 米增强, 王俊杰, 等. 柔性负荷聚合商参与电力系统的多时间尺度协同优化调度策略[J]. 华北电力大学学报(自然科学版), 2019,46(6):17-26.
	JIA Yulong, MI Zengqiang, WANG Junjie, et al. Mutli-time scale collaborative optimal scheduling strategy with flexible load aggregator participating in power system[J]. Journal of North China Electric Power University (Natural Science Edition), 2019,46(6):17-26.
[32]	MA H, SHAHIDEHPOUR S M. Unit commitment with transmission security and voltage constraints[J]. IEEE Transactions on Power Systems, 1999,14(2):757-764. doi: 10.1109/59.761909 URL
[33]	陈长升. 风电的系统成本分析及分段价值发现研究[D]. 南京: 东南大学, 2015.
	CHEN Changsheng. Research on system cost and block value of wind power[D]. Nanjing: Southeast University, 2015.