黏性电力用户参与需求侧响应的行为决策建模与分析

doi:10.3969/j.issn.2097-0706.2022.02.011

摘要/Abstract

摘要：

在推进落实“双碳”目标,构建新能源为主体的新型电力系统背景下,负荷侧电力用户的可调弹性负荷资源越来越受到重视。负荷用电具有随机性、趋利性及模仿其他电力负荷主体用电行为的主观性,很难高效、有序、公平地开展负荷侧市场运营。首先分析了电力用户黏性的影响因素并提出了黏性模型;然后对配网系统的黏性电力用户进行聚合建模,并提出了电力响应机制和效益评估模型;最后通过算例进行定量分析,验证了本文所设计的黏性电力用户电价刺激模型的正确性和效益优势,为负荷侧市场主体参与需求响应提供参考。

关键词: 碳中和, 新型电力系统, 负荷侧, 黏性电力用户, 需求响应, 电价刺激, 用电调整

Abstract:

With the implementation of the goals of carbon peak and carbon neutrality and the construction of a new energy-oriented new power system, the adjustable elastic load resources on power user side have drawn more and more attention. Power consumption is random and profit oriented, and the users tend to subjectively imitate the power consumption behaviours of other power users. It is difficult to keep the user-side market operating efficiently, orderly and fairly. The stickiness model is established based on the influence factor analysis of power user stickiness. Then, based on the aggregation modelling for the stickiness of power users in distribution networks, electric demand response mechanism and economic benefits evaluation model are established. Finally, quantitative analysis on cases verified the correctiveness and effectiveness of the pricing model stimulated by stickiness of users, which provides a reference for load-side market participants to participate in demand-side response.

Key words: carbon neutrality, new power system, load side, loyal power user, demand response, electricity price stimulation, electricity consumption adjustment

中图分类号:

TM71：F123.9

吉斌, 孙绘, 昌力, 张丹丹. 黏性电力用户参与需求侧响应的行为决策建模与分析[J]. 综合智慧能源, 2022, 44(2): 80-88.

JI Bin, SUN Hui, CHANG Li, ZHANG Dandan. Modeling and analysis on decision making behavior of loyal users participating in demand-side response[J]. Integrated Intelligent Energy, 2022, 44(2): 80-88.

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参考文献 21

[1]	吉斌, 昌力, 朱丽叶, 等. 区块链系统节点私钥泄露的电力数据防篡改方法与验证机制设计[J]. 电力自动化设备, 2021,41(12):87-94.
	JI Bin, CHANG Li, ZHU Liye, et al. Anti-tampering method and verification mechanism design of power data for private key leakage of node in blockchain system[J]. Electric Power Automation Equipment, 2021,41(12):87-94.
[2]	吉斌, 昌力, 陈振寰, 等. 基于区块链技术的电力碳排放权交易市场机制设计与应用[J]. 电力系统自动化, 2021,45(12):1-10.
	JI Bin, CHANG Li, CHEN Zhenhuan, et al. Blockchain technology based design and application of market mechanism for power carbon emission allowance trading[J]. Automation of Electric Power Systems, 2021,45(12):1-10.
[3]	王云泽, 王秋瑾, 马欣欣. 基于区块链技术的能源互联网交易方案设计[J]. 华电技术, 2020,42(8):83-89.
	WANG Yunze, WANG Qiujin, MA Xinxin. Design of energy internet trading system based on blockchain technology[J]. Huadian Technology, 2020,42(8):83-89.
[4]	张粒子, 刘方, 王帮灿, 等. 巴西电力市场研究:市场机制内在逻辑分析与对我国电力市场建设的启示[J]. 中国电机工程学报, 2020,40(10):3201-3214.
	ZHANG Lizi, LIU Fang, WANG Bangcan, et al. Brazil's electricity market research: The internal logic analysis of market mechanism and its enlightenment to China's power market construction[J]. Proceedings of the CSEE, 2020,40(10):3201-3214.
[5]	王晛, 王留晖, 张少华. 风电商与DR聚合商联营对电力市场竞争的影响[J]. 电网技术, 2018,42(1):110-116.
	WANG Xian, WANG Liuhui, ZHANG Shaohua. Impacts of cooperation between wind power producer and DR aggregator on electricity market equilibrium[J]. Power System Technology, 2018,42(1):110-116.
[6]	杨斌, 杜文娟, 王海风. 数据驱动下的虚拟同步发电机等效建模[J]. 电网技术, 2020,44(1):35-43.
	YANG Bin, DU Wenjuan, WANG Haifeng. Equivalent modeling of virtual synchronous generator based on data-driven method[J]. Power System Technology, 2020,44(1):35-43.
[7]	刘妍, 谭建成. 基于区块链技术的大用户直购电模式实现研究[J]. 电力信息与通信技术, 2018,16(7):94-100.
	LIU Yan, TAN Jiancheng. Application of blockchain technology in large consumers' direct purchase[J]. Electric Power Information and Communication Technology, 2018,16(7):94-100.
[8]	吉斌, 莫峻, 谭建成. 高比例光伏电能产消群电力需求响应机制设计[J]. 电网技术, 2018,42(10):3315-3323.
	JI Bin, MO Jun, TAN Jiancheng. Design of power demand response mechanism for high proportion of photovoltaic prosumer[J]. Power System Technology, 2018,42(10):3315-3323.
[9]	平健, 严正, 陈思捷, 等. 基于区块链的分布式能源交易市场信用风险管理方法[J]. 中国电机工程学报, 2019,39(24):7137-7145,7487.
	PING Jian, YAN Zheng, CHEN Sijie, et al. Credit risk management in distributed energy resource transactions based on blockchain[J]. Proceedings of the CSEE, 2019,39(24):7137-7145,7487.
[10]	冯志颖, 唐文虎, 吴青华, 等. 考虑负荷纵向随机性的用户用电行为聚类方法[J]. 电力自动化设备, 2018,38(9):39-44,53.
	FENG Zhiying, TANG Wenhu, WU Qinghua, et al. Users' consumption behavior clustering method considering longitudinal randomness of load[J]. Electric Power Automation Equipment, 2018,38(9):39-44,53.
[11]	邱起瑞, 李更丰, 潘雨晴. 基于用电行为综合指标的用户负荷分类研究[J]. 智慧电力, 2018,46(10):26-31.
	QIU Qirui, LI Gengfeng, PAN Yuqing. Research on user load classification based on synthetic index of electricity consumption behavior[J]. Smart Power, 2018,46(10):26-31.
[12]	CHARTRAND T L, BARGH J A. The chameleon effect: The perception-behavior link and social interaction[J]. Journal of Personality and Social Psychology, 1999,76(6):893-910. doi: 10.1037/0022-3514.76.6.893
[13]	肖白, 张小娜, 姜卓, 等. 考虑本位元胞接受能力和相邻元胞负荷影响的空间负荷预测[J]. 电力系统自动化, 2021,45(12):57-64.
	XIAO Bai, ZHANG Xiaona, JIANG Zhuo, et al. Spatial load forecasting considering the acceptance capacity of standard cells and the load of adjacent cells[J]. Automation of Electric Power Systems, 2021,45(12):57-64.
[14]	汪寅, 臧寅垠, 陈巍. 从“变色龙效应”到“镜像神经元”再到“模仿过多症”——作为社会交流产物的人类无意识模仿[J]. 心理科学进展, 2011,19(6):916-924.
	WANG Yin, ZANG Yinyin, CHEN Wei. From "chameleon effect" to "mirror neurons" and to "Echopraxia":Human mimicry comes from social interaction[J]. Advances in Psychological Science, 2011,19(6):916-924.
[15]	陈巍. 情绪对无意识模仿的影响:来自中国人“变色龙效应”的证据[C]//中国心理学会.心理学与创新能力提升——第十六届全国心理学学术会议论文集.中国心理学会, 2013.
[16]	LAKIN J L, JEFFERIS V E, CHENG C M, et al. The chameleon effect as social glue: Evidence for the evolutionary significance of nonconscious mimicry[J]. Journal of Nonverbal Behavior, 2003,27(3):145-162. doi: 10.1023/A:1025389814290
[17]	徐磊, 杨秀, 张美霞. 基于数据挖掘的工业用户用电行为分析[J]. 电测与仪表, 2017,54(16):68-74.
	XU Lei, YANG Xiu, ZHANG Meixia. Industrial users of electricity behavior analysis based on data mining[J]. Electrical Measurement ＆ Instrumentation, 2017,54(16):68-74.
[18]	吉斌, 刘妍, 朱丽叶, 等. 基于联盟区块链的电力碳权交易机制设计[J]. 华电技术, 2020,42(8):32-40.
	JI Bin, LIU Yan, ZHU Liye, et al. Design of carbon emission permit trading mechanism in power industry based on consortium blockchain[J]. Huadian Technology, 2020,42(8):32-40.
[19]	吉斌, 孙绘, 梁肖, 等. 面向“双碳”目标的碳电市场融合交易探讨[J]. 华电技术, 2021,43(6):33-40.
	JI Bin, SUN Hui, LIANG Xiao, et al. Discussion on convergent trading of the carbon and electricity market on the path to carbon peak and carbon neutrality[J]. Huadian Technology, 2021,43(6):33-40.
[20]	高亚静, 吕孟扩, 梁海峰, 等. 基于离散吸引力模型的用电需求价格弹性矩阵[J]. 电力系统自动化, 2014,38(13):103-107,144.
	GAO Yajing, LYU Mengkuo, LIANG Haifeng, et al. Electricity demand price elasticity matrix based on discrete attractive model[J]. Automation of Electric Power Systems, 2014,38(13):103-107,144.
[21]	周玲芳. 智能电网条件下的用户侧实时电价机制研究[D]. 北京:华北电力大学, 2015.

响应周期	响应电量/(kW·h)	弹性电价/[元·(kW·h)^-1]	实际电价/[元·(kW·h)^-1]	不平衡电量/(kW·h)	系统电量/(kW·h)
T₁	0	0	0.600 0	200.000	1 000.000
T₂	45.000	0.600 00	0.900 0	155.000	955.000
T₃	51.500	0.730 00	1.030 0	103.500	903.500
T₄	44.500	0.590 00	0.890 0	59.000	859.000
T₅	30.000	0.305 00	0.605 0	29.000	829.000
T₆	20.000	0.105 00	0.405 0	9.000	809.000
T₇	16.130	-0.022 50	0.277 5	-7.130	792.870
T₈	14.380	0.012 48	0.290 0	7.250	807.250
T₉	16.283	0.025 70	0.315 7	-9.033	790.970
T₁₀	14.250	0.015 90	0.315 9	5.750	805.750
T₁₁	15.560	0.001 13	0.298 9	-9.814	790.186
T₁₂	14.070	0.018 60	0.318 6	4.256	804.256
T₁₃	15.421	0.008 40	0.291 6	-11.165	788.835