Modeling and analysis on demand response for generalized load of power supply systems in industrial parks

doi:10.3969/j.issn.2097-0706.2024.01.002

Abstract

Abstract:

Given that energy supply systems of industrial parks always take multi-energy coupling and source‒network‒ load‒storage collaborative operation mode， the definition of power loads has been extended，and the generalized load is more comprehensive than the traditional concept. Considering the influence of electricity price signal on the response characteristics of active load， a generalized load model for the power supply system considering the demand response pricing is established. Typical load patterns are extracted from the model by hierarchical clustering method， and used to study the load demand response characteristics of the power supply system. To verify the effectiveness of the model， simulation was carried on a power supply system in an industrial park in south China， to summarize the response characteristics of active loads under real-time and time-of use electricity pricing mechanisms. The impact of proportion of active load in generalized load on its response characteristics analysed， and the contributions of uncontrollable load， transferable load， and interruptible load to generalized load demand response were quantitatively discussed. The results show that an appropriate electricity pricing mechanism will rationalize the peak load regulation of power supply systems，facilitate the construction of virtual power plants， and regulate interactions between source， grid and users.

Key words: industrial park power supply system, generalized load, demand response, load clustering, multi-energy coupling, source‒network‒load‒storage

CLC Number:

TK019

BAO Haibo, LIANG Junjie, LI Xiang. Modeling and analysis on demand response for generalized load of power supply systems in industrial parks[J]. Integrated Intelligent Energy, 2024, 46(1): 11-17.

Figures/Tables 12

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References 25

[1]	谷菁, 张海珍, 阮慧锋, 等. 园区供能企业向综合能源服务商转型思路探索[J]. 综合智慧能源, 2022, 44(12): 62-67. doi: 10.3969/j.issn.2097-0706.2022.12.009
	GU Jing, ZHANG Haizhen, RUAN Huifeng, et al. Exploration on the transformation of energy supply enterprises to integrated energy service providers in industrial parks[J]. Integrated Intelligent Energy, 2022, 44(12): 62-67. doi: 10.3969/j.issn.2097-0706.2022.12.009
[2]	吴鸣, 任学婧, 周丹, 等. 新电改背景下产业园区供电系统容量优化配置方法[J]. 电力系统自动化, 2018, 42(5): 2-8.
	WU Ming, REN Xuejing, ZHOU Dan, et al. Optimal allocation method for capacity of power supply system in industrial park under new electricity market reform[J]. Automation of Electric Power System, 2018, 42(5): 2-8.
[3]	张旭, 梁军, 贠志皓, 等. 广义负荷建模中纵横聚类策略研究[J]. 中国电机工程学报, 2015, 35(23): 6028-6038.
	ZHANG Xu, LIANG Jun, YUN Zhihao, et al. The longitudinal-horizontal clustering strategy study in the generalized load modeling[J]. Proceedings of the CSEE, 2015, 35(23): 6028-6038.
[4]	褚壮壮, 梁军, 张旭, 等. 基于仿射传播聚类算法的广义负荷稳态特性建模及其应用[J]. 电力自动化设备, 2016, 36(3):115-123.
	CHU Zhuangzhuang, LIANG Jun, ZHANG Xu, et al. Modeling of generalized load steady-state characteristics based on affinity propagation clustering algorithm and its application[J]. Electric Power Automation Equipment, 2016, 36(3):115-123.
[5]	TANG X, HASAN K N, MILANOVIC J V, et al. Estimation and validation of characteristic load profile through smart grid trials in a medium voltage distribution network[J]. IEEE Transactions on Power Systems, 2018, 33(99):1848-1859. doi: 10.1109/TPWRS.2017.2740563
[6]	孙国霞, 张剑, 吴海江. 包含多种分布式电源的广义负荷模型辨识与适应性研究[J]. 电力系统保护与控制, 2013, 41(4): 105-111.
	SUN Guoxia, ZHANG Jian, WU Haijiang. Research on the identification and adaptation of generalized load modeling based on distributed generators[J]. Power System Protection and Control, 2013, 41(4): 105-111.
[7]	PATERAKIS N G, ERDINÇ O, BAKIRTZIS A G, et al. Optimal household appliances scheduling under day-ahead pricing and load-shaping demand response strategies[J]. IEEE Transactions on Industrial Informatics, 2017, 11(6):1509-1519. doi: 10.1109/TII.2015.2438534
[8]	YANG J J, ZHAO J H, WEN F S, et al. A model of customizing electricity retail prices based on load profile clustering analysis[J]. IEEE Transactions on Smart Grid, 2019, 10(3): 3374-3386. doi: 10.1109/TSG.5165411
[9]	JIANG T, CAO Y, YU L, et al. Load shaping strategy based on energy storage and dynamic pricing in smart grid[J]. IEEE Transactions on Smart Grid, 2017, 5(6):2868-2876. doi: 10.1109/TSG.2014.2320261
[10]	ZHOU X, SHI J, KANG G. Optimal demand response aiming at enhancing the economy of high wind power penetration system[C]// The 6th International Conference on Renewable Power Generation. Wuhan, China, 2017.
[11]	XU Q, DING Y, YAN Q, et al. Day-ahead load peak shedding/shifting scheme based on potential load values utilization: Theory and practice of policy-driven demand response in China[J]. IEEE Access, 2017(99): 22892-22901.
[12]	刘会强, 郭裕, 邢华栋, 等. 基于前馈功率解耦控制的光伏电站系统稳定性研究[J]. 综合智慧能源, 2023, 45(3):17-23. doi: 10.3969/j.issn.2097-0706.2023.03.003
	LIU Huiqiang, GUO Yu, XING Huadong, et al. Study on system stability of photovoltaic power stations based on feedforward power decoupling control[J]. Integrated Intelligent Energy, 2023, 45(3): 17-23. doi: 10.3969/j.issn.2097-0706.2023.03.003
[13]	HASSAN M. Dynamic stability of an autonomous microgrid considering active load impact with new dedicated synchronization scheme[J]. IEEE Transactions on Power Systems, 2018, 33(5): 4994-5005. doi: 10.1109/TPWRS.59
[14]	LAI J G, LU X Q, YU X H, et al. Gossip-based distributed voltage control for active loads in low-voltage microgrids[C]// Chinese Automation Congress. 2017.
[15]	SHAO S N, PIPATTANASOMPORN M, RAHMAN S. Demand response as a load shaping tool in an intelligent grid with electric vehicles[J]. IEEE Transactions on Smart Grid, 2011, 2(4):624-631. doi: 10.1109/TSG.2011.2164583
[16]	PRUDENZI A, SILVESTRI A. Simulation of DSM actions impact prediction on residential daily load shape[C]// Universities' Power Engineering Conference.VDE, 2011:1-6.
[17]	REDDY S, PANWAR L, PANIGRAHI B K, et al. Investigating the impact of load profile attributes on demand response exchange[J]. IEEE Transactions on Industrial Informatics, 2017, 14(4):1382-1391. doi: 10.1109/TII.2017.2759186
[18]	ZHANG G, JIANG C W, WANG X, et al. Bidding strategy analysis of virtual power plant considering demand response and uncertainty of renewable energy[J]. IET Generation Transmission & Distribution, 2017, 11(13):3268-3277. doi: 10.1049/gtd2.v11.13
[19]	杜亮, 陈昱达, 田小蕾. 虚拟电厂在分布式光伏发电数字新基建示范区中的应用前景[J]. 东北电力技术, 2021, 42(12):5-9.
	DU Liang, CHEN Yuda, TIAN Xiaolei. Application prospects of virtual power plant on distributed photovoltaic power in demonstration area of digital new infrastructure construction[J]. Northeast Electric Power Technology, 2021, 42(12):5-9.
[20]	LI Y N, ZHANG J G. Research into probabilistic representation of electric vehicle's charging load and its effect to the load characteristics of the network[C]// International Conference on Electric Utility Deregulation and Restructuring and Power Technologies.IEEE, 2016:426-430.
[21]	SHAHIDINEJAD S, FILIZADEH S, BIBEAU E. Profile of charging load on the grid due to plug-in vehicles[J]. IEEE Transactions on Smart Grid, 2012, 3(1):135-141. doi: 10.1109/TSG.2011.2165227
[22]	屈星, 李欣然, 盛义发, 等. 面向广义负荷的光伏发电系统等效建模研究[J]. 电网技术, 2020, 44(6): 2143-2150.
	QU Xing, LI Xinran, SHENG Yifa, et al. Research on equivalent modeling of PV generation system for generalized load[J]. Power System Technology, 2020, 44(6): 2143-2150.
[23]	YING Y Q, WU Y J, SU Y Q, et al. Dispatching approach for active distribution network considering PV generation reliability and load predicting interval[C]// The 6th International Conference on Renewable Power Generation. Wuhan, China, 2017.
[24]	霍佳丽, 叶辛頔, 何卫斌, 等. 考虑广义负荷的配电网弹性电力电量平衡策略研究[J]. 浙江电力, 2022, 41(1):19-25.
	HUO Jiali, YE Xindi, HE Weibin, et al. Study of an elastic electric power and energy balance strategy for distribution networks considering generalized loads[J]. Zhejiang Electric Power, 2022, 41(1):19-25.
[25]	黄梅, 贺仁睦, 杨少兵. 模糊聚类在负荷实测建模中的应用[J]. 电网技术, 2006, 30(14):49-52.
	HUANG Mei, HE Renmu, YANG Shaobing. Application of fuzzy clustering in measurement-based load modeling[J]. Power System Technology, 2006, 30(14):49-52.

项目		最大负荷/MW	最小负荷/MW	峰谷差/MW	负荷率	P_MALD	P_MILI
响应前		531.29	143.89	387.4	0.59
响应后主动负荷占比/%	10	506.25	167.64	338.61	0.62	0.05	0.16
	30	448.88	190.33	258.54	0.69	0.16	0.32
	50	435.31	192.63	242.67	0.71	0.18	0.34
	70	478.96	180.7	298.26	0.64	0.09	0.26

项目		最大负荷/MW	最小负荷/MW	峰谷差/MW	负荷率	P_MALD	P_MILD
响应前		531.29	143.89	387.4	0.60
不同主动负荷占比下的响应/%	10	556.36	160.83	395.53	0.56	-0.05	-0.12
	30	548.39	187.91	360.47	0.57	-0.03	-0.31
	50	479.49	182.03	297.46	0.64	0.09	-0.26
	70	434.41	168.93	265.48	0.71	0.18	-0.17