Analysis on the Effect of Time-of-Use Electricity Price on Electricity Cost Based on Difference-in-Differences Model

doi:10.11930/j.issn.1004-9649.202311104

Abstract

Abstract:

An effective time-of-use (TOU) electricity pricing mechanism is essential to stimulate customers to provide load flexibility by price signals, and can directly determine users' electricity cost. TOU electricity price has been adjusted in many provinces in China recently. This paper introduces and analyzes the new round of adjustment. Based on the difference-in-differences (DiD) method, the TOU effect on customers' electricity cost is calculated. Furthermore, enabled by a multiple regression model, the effect of TOU mechanism on electricity cost is revealed by fitting the decoupled coefficient obtained by DiD. Based on the real electricity cost of TOU and non-TOU customers of specific provinces, types, and voltage levels in July 2022 and July 2023, the TOU effects and general trends are obtained. Then, the accuracy of the regression model is verified. Finally, the TOU effect on the cost per kilowatt hour of commercial customers under different price ratios is analyzed. It is found that there is an optimal critical peak and peak ratio to minimize the electricity cost for commercial customers.

Key words: time-of-use electricity price, cost per kilowatt hour of electricity, difference-in-differences method, multiple regression model, elasticity of demand

Junlong LI, Peipei YOU, Chao ZHANG, Lurui FANG, Wenzhe ZHANG. Analysis on the Effect of Time-of-Use Electricity Price on Electricity Cost Based on Difference-in-Differences Model[J]. Electric Power, 2024, 57(11): 173-182.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.202311104

https://www.electricpower.com.cn/EN/Y2024/V57/I11/173

Figures/Tables 9

Fig.1 External and TOU effects on cost per kW·h of the electricity of residential customers from different provinces, and related peak-valley ratios of TOU prices

Fig.2 TOU effects of large industrial electricity customers with different voltage levels from typical provinces

Table 1 External and TOU effects of general industrial and commercial electricity customers with different voltage levels

用户类别	电压等级/kV	度电成本变化/(元·(MW·h)^–1)
用户类别	电压等级/kV	外因变化	政策效果
非、普通工业	≤0.4	27.82	–54.17
	1~10	4.39	–36.79
	35（含20）	13.62	–20.70
	110（含66）	16.43	–39.15
非居民照明	≤0.4	–61.97	–48.68
	1~10	3.96	–20.07
	35（含20）	14.05	–49.73
	110（含66）	43.27	26.65
	220	44.92	–27.21
商业	≤0.4	41.89	–3.83
	1~10	44.69	–12.03
	35（含20）	26.11	–11.17

Fig.3 External and TOU effects of commercial electricity customers of 0.4 kV and below in different provinces with related peak ratios of TOU prices

Table 2 Correlation test results of various regression models for residential customers (large industrial electricity customers)

回归模型类型	r²检验值
	居民	大工业			商业
	居民	1~ 10 kV	110 kV	220 kV	商业
$ {\boldsymbol{\varOmega }}_{slv}{\boldsymbol{X}}_{slv}+{C}_{slv} $	0.62	0.75	0.26	0.31	0.78
$ {\boldsymbol{\varOmega }}_{slv}{\boldsymbol{X}}_{slv}+{\boldsymbol{\varPsi }}_{slv}{\hat{\boldsymbol{X}}}_{slv}+{C}_{slv} $	0.85	0.91	0.64	0.65	0.90
$ {\boldsymbol{\varOmega }}_{slv}{\boldsymbol{X}}_{slv}+{\boldsymbol{\varPsi }}_{slv}{\hat{\boldsymbol{X}}}_{slv}+{\boldsymbol{\varPhi }}_{slv}{\stackrel{~}{\boldsymbol{X}}}_{slv}+{C}_{slv} $	0.95	0.99	0.98	0.99	0.99

Table 3 Fitting results of coefficients in regression models (commercial electricity customers of 0.4 kV and below)

变量	回归系数	变量	回归系数
$ {C}_{slv} $	–2.28	$ {{x}_{4}^{2}} $	4.58
$ {x}_{1} $	0.01	$ {x}_{12} $	3.35
$ {x}_{2} $	3.57	$ {x}_{13} $	–0.07
$ {x}_{3} $	–5.02	$ {x}_{14} $	–10.08
$ {x}_{4} $	–9.54	$ {x}_{23} $	–2.84
$ {{x}_{1}^{2}} $	–8.80	$ {x}_{24} $	–1.20
$ {{x}_{2}^{2}} $	0.44	$ {x}_{34} $	5.60
$ {{x}_{3}^{2}} $	2.48

Fig.4 Decision variables and peak ratios of commercial electricity customers of 0.4 kV and below in different provinces

Fig.5 Correlation between TOU effect on electricity cost and critical peak , peak and valley ratio of TOU for commercial electricity customers of 0.4 kV and below in Anhui province

Fig.6 Correlation among TOU effect on electricity cost, critical peak ratio and peak ratio for commercial electricity customers of 0.4 kV and below in Anhui province

References 21

1	李东东, 汪露璐, 王维等. 考虑源荷互动的综合能源系统多目标双层规划[J]. 电网技术, 2024, 48 (2): 527- 542.
	LI Dongdong, WANG Luwei, WANG Wei, et al. Multi-objective bi-level planning for integrated energy systems considering source-load interaction[J]. Power System Technology, 2024, 48 (2): 527- 542.
2	万灿, 宋永华. 新能源电力系统概率预测理论与方法及其应用[J]. 电力系统自动化, 2021, 45 (1): 2- 16. DOI
	WAN Can, SONG Yonghua. Theories, methodologies and applications of probabilistic forecasting for power systems with renewable energy sources[J]. Automation of Electric Power Systems, 2021, 45 (1): 2- 16. DOI
3	柳启俊. 面向智能电网的需求侧响应及其居民电价研究[D]. 北京: 华北电力大学, 2016.
	LIU Qijun. Electrical demand response in smart grid and its research of residential electrovalency[D]. Beijing: North China Electric Power University, 2016.
4	欧华钰. 需求响应背景下分时电价研究综述[J]. 电气开关, 2023, 61 (5): 6- 11, 17. DOI
	OU Huayu. Summary of time-of-use research in a demand response environment[J]. Electric Switchgear, 2023, 61 (5): 6- 11, 17. DOI
5	黄剑平. 基于用户响应行为的峰谷分时电价优化策略研究[D]. 广州: 华南理工大学, 2022.
	HUANG Jianping. Research on the optimization strategy for time-of-use prices based on customer response behavior[D]. Guangzhou: South China University of Technology, 2022.
6	黄刚. V2G模式下峰谷电价方案评估模型研究[D]. 宜昌: 三峡大学, 2018.
	HUANG Gang. Study on the evaluation model of peak valley electricity price scheme under V2G model[D]. Yichang: China Three Gorges University, 2018.
7	付强. 考虑峰谷分时电价影响的电网规划及其经济效益分析研究[D]. 长沙: 湖南大学, 2018.
	FU Qiang. Research on power grid planning considering influence of peak-valley time-of-use power price and its economic benefits analysis[D]. Changsha: Hunan University, 2018.
8	李国荣. 基于用户需求响应的分时电价优化研究[D]. 南京: 南京邮电大学, 2023.
	LI Guorong. Research on time-of-use price optimization based on user's demand response[D]. Nanjing: Nanjing University of Posts and Telecommunications, 2023.
9	汪磊, 杨贺钧, 马英浩, 等. 考虑多重评价指标的多时段分时电价优化模型[J]. 中国电力, 2019, 52 (6): 54- 59.
	WANG Lei, YANG Hejun, MA Yinghao, et al. Optimization model of multi-period time of use strategy considering multiple assessment indices[J]. Electric Power, 2019, 52 (6): 54- 59.
10	王星华, 刘升伟, 陈豪君, 等. 考虑用户差异性的售电公司需求响应电价模型[J]. 电力建设, 2019, 40 (9): 116- 123. DOI
	WANG Xinghua, LIU Shengwei, CHEN Haojun, et al. Demand response pricing model for power sales companies considering user differences[J]. Electric Power Construction, 2019, 40 (9): 116- 123. DOI
11	谈金晶, 王蓓蓓, 李扬. 基于多智能体的用户分时电价响应模型[J]. 电网技术, 2012, 36 (2): 257- 263.
	TAN Jinjing, WANG Beibei, LI Yang. Modeling of user response to time-of-use price based on multi-agent technology[J]. Power System Technology, 2012, 36 (2): 257- 263.
12	石宇航. 降低企业用电成本案例分析[J]. 中国电力企业管理, 2018, (13): 44- 45. DOI
13	方彬楠, 冉黎黎. 引导工商业错峰用电北京分时电价进阶[N]. 北京商报, 2023-08-22(002).
14	张延伟, 高昌辉. 中国省域间碳排放权交易机制检验——基于双重差分法[J]. 时代经贸, 2023, 20 (9): 5- 9. DOI
15	杨新梅, 周瑞辉. 设立国家高新区能否驱动城市绿色发展: 基于双重差分法的经验数据[J]. 中国环境管理, 2023, 15 (3): 51- 61.
	YANG Xinmei, ZHOU Ruihui. Can national high-tech zones drive urban green development? —empirical data based on DID[J]. Chinese Journal of Environmental Management, 2023, 15 (3): 51- 61.
16	张科, 熊子怡, 黄细嘉, 等. 红色旅游发展与革命老区城乡收入差距——基于双重差分法的经验分析[J]. 中国农村经济, 2023, (5): 103- 121.
	ZHANG Ke, XIONG Ziyi, HUANG Xijia, et al. Red tourism and urban-rural income gap in old revolutionary base areas: an empirical analysis based on the difference-in-differences method[J]. Chinese Rural Economy, 2023, (5): 103- 121.
17	赖玲玲, 程跃. 战略性新兴产业政策对企业创新绩效的影响研究——基于双重差分法的准自然实验[J]. 现代管理科学, 2022, (5): 98- 108. DOI
18	翟玉鹏, 曹俊文. 基于双重差分法的我国差别电价政策对电力消费强度影响研究[J]. 电力与能源, 2018, 39 (6): 725- 732.
	ZHAI Yupeng, CAO Junwen. Influence of China's differential price policy on electricity consumption intensity based on difference-in-difference method[J]. Power & Energy, 2018, 39 (6): 725- 732.
19	刘力铭. 分时电价项目下负荷削峰效果的影响因子识别与分析研究[D]. 保定: 华北电力大学(保定), 2019.
	LIU Liming. Research on identification and analysis of impact factors for peak demand reduction under the time of use programs [D]. Baoding: North China Electric Power University, 2019.
20	唐文升, 王阳, 张煜等. 基于多维价格弹性系数的分时电价对负荷特性影响机理[J]. 中国电力, 2024, 57 (2): 202- 211.
	TANG Wensheng, WANG Yang, ZHANG Yu, et al. Influence mechanism of time-of-use electricity prices on industry load characteristics based on multi-dimensional price elasticity coefficient matrix[J]. Electric Power, 2024, 57 (2): 202- 211.
21	田佳垚, 冯自平, 胡亚飞等. 基于多元回归模型的燃气热泵系统制热性能分析[J]. 热能动力工程, 2023, 38 (6): 129- 136.
	TIAN Jiayao, FENG Ziping, HU Yafei, et al. Analysis of heating performance of gas engine-driven heat pump system based on multiple regression model[J]. Journal of Engineering for Thermal Energy and Power, 2023, 38 (6): 129- 136.

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