Study on Demand Response of Users in Photovoltaic Station Area Applying Evolutionary Game Model in Social Network

doi:10.12204/j.issn.1000-7229.2022.08.015

Abstract

Abstract:

In the "double carbon target" background, the coordinated operation of the new power system by implementing demand-side response is the main means to solve the uncertainty of new energy output. However, in reality, customers have bounded rationality, and their response to tariffs or incentives is heterogeneous and uncertain. In view of this, considering the information interaction and strategy learning characteristics of bounded rational users in the response process of electricity price or incentive measures, this paper proposes a user-side demand response analysis model based on evolutionary game model in social network. Firstly, considering that the real economic man has information interaction and strategy learning and updating in decision-making, and that the information interaction is a complex community system, the information interaction relationship between users is described by constructing a social network. Secondly, according to the electricity price theory and the balance between power supply and demand, the response decision of a single user will affect the interests of other users. The game model is used to describe the decision-making process of group users. Finally, considering the bounded rationality of users’ information processing, the evolutionary game model in social network is used to describe the response process of users to electricity price or incentive measures. The effects of different social network structures and electricity prices or incentives on user response are simulated and analyzed. The results show that the enhancement of the small world attribute of users’ social network will improve users’ response, and the increase of price coefficient will reduce users’ willingness to use electricity.

Key words: bounded rationality, demand response, social network, new power system, evolutionary game

CLC Number:

TM769

FAN Hui, LUO Peng, WANG Hongli, LIANG Jifeng, LI Qian, YANG Jun, WU Fuzhang. Study on Demand Response of Users in Photovoltaic Station Area Applying Evolutionary Game Model in Social Network[J]. ELECTRIC POWER CONSTRUCTION, 2022, 43(8): 150-158.

Figures/Tables 11

Fig.1 The impact of changes in demand on market equilibrium and prices

Fig.2 Algorithm for solving evolutionary game models on social networks

Fig.3 Topology of social network structure in photovoltaic station area

Fig.4 Convergence process of the proportion of users with different participation

Fig.5 Convergence process of average participation in photovoltaic station area

Fig.6 Strategy evolution process on social networks

Fig.7 Evolution process of 10-node strategy

Fig.8 Convergence process of average power consumption with different clustering coefficients

Table 1 Different electricity-price setting strategies

Fig.9 Convergence process of average power consumption under different parameter combinations

Fig.10 Comparison of simulation results between the model in this paper and the other methods

References 26

[1]	张涛, 郭玥彤, 李逸鸿, 等. 计及电气热综合需求响应的区域综合能源系统优化调度[J]. 电力系统保护与控制, 2021, 49(1): 52-61.
	ZHANG Tao, GUO Yuetong, LI Yihong, et al. Optimization scheduling of regional integrated energy systems based on electric-thermal-gas integrated demand response[J]. Power System Protection and Control, 2021, 49(1): 52-61.
[2]	吴勇, 吕林, 许立雄, 等. 考虑电/热/气耦合需求响应的多能微网多种储能容量综合优化配置[J]. 电力系统保护与控制, 2020, 48(16): 1-10.
	WU Yong, LÜ Lin, XU Lixiong, et al. Optimized allocation of various energy storage capacities in a multi-energy micro-grid considering electrical/thermal/gas coupling demand response[J]. Power System Protection and Control, 2020, 48(16): 1-10.
[3]	李彬, 陈京生, 李德智, 等. 我国实施大规模需求响应的关键问题剖析与展望[J]. 电网技术, 2019, 43(2): 694-704.
	LI Bin, CHEN Jingsheng, LI Dezhi, et al. Analysis and prospect of key issues in China’s demand response for further large scale implementation[J]. Power System Technology, 2019, 43(2): 694-704.
[4]	梁宁, 邓长虹, 谭津, 等. 计及电量电价弹性的主动配电网多时间尺度优化调度[J]. 电力系统自动化, 2018, 42(12): 44-50.
	LIANG Ning, DENG Changhong, TAN Jin, et al. Optimization scheduling with multiple time scale for active distribution network considering electricity price elasticity[J]. Automation of Electric Power Systems, 2018, 42(12): 44-50.
[5]	王剑晓, 钟海旺, 夏清, 等. 基于成本—效益分析的温控负荷需求响应模型与方法[J]. 电力系统自动化, 2016, 40(5): 45-53.
	WANG Jianxiao, ZHONG Haiwang, XIA Qing, et al. Model and method of demand response for thermostatically-controlled loads based on cost-benefit analysis[J]. Automation of Electric Power Systems, 2016, 40(5): 45-53.
[6]	李丹, 刘俊勇, 刘友波, 等. 考虑风储参与的电力市场联动博弈分析[J]. 电网技术, 2015, 39(4): 1001-1008.
	LI Dan, LIU Junyong, LIU Youbo, et al. Analysis on electricity market linkage game considering participation of wind power and energy storage[J]. Power System Technology, 2015, 39(4): 1001-1008.
[7]	王程, 刘念, 成敏杨, 等. 基于Stackelberg博弈的光伏用户群优化定价模型[J]. 电力系统自动化, 2017, 41(12): 146-153.
	WANG Cheng, LIU Nian, CHENG Minyang, et al. Stackelberg game based optimal pricing model for photovoltaic prosumer cluster[J]. Automation of Electric Power Systems, 2017, 41(12): 146-153.
[8]	李章溢, 马昕, 裴玮, 等. 含用户聚合代理的工业园区需求响应主从博弈机制与策略[J]. 中国电力, 2020, 53(8): 40-49.
	LI Zhangyi, MA Xin, PEI Wei, et al. Leader-follower game mechanism and strategy of industrial park demand response with user aggregator[J]. Electric Power, 2020, 53(8): 40-49.
[9]	包佳瑞琦. 计及有限理性决策的微网容量多策略集演化博弈规划[D]. 吉林: 东北电力大学, 2020.
	BAO Jiaruiqi. Capacity planning of microgrid based on multi-strategy set evolution game of bounded rationality decision[D]. Jilin: Northeast Dianli University, 2020.
[10]	梅生伟, 刘锋, 魏韡. 工程博弈论基础及电力系统应用[M]. 北京: 科学出版社, 2016.
[11]	程乐峰, 余涛. 发电市场长期竞价均衡自发形成过程中的一般多策略演化博弈决策行为研究[J]. 中国电机工程学报, 2020, 40(21): 6936-6956.
	CHENG Lefeng, YU Tao. Decision-making behavior investigation for general multi-strategy evolutionary games in the spontaneous formation of long-term bidding equilibria of a power generation market[J]. Proceedings of the CSEE, 2020, 40(21): 6936-6956.
[12]	刘念, 赵璟, 王杰, 等. 基于合作博弈论的光伏微电网群交易模型[J]. 电工技术学报, 2018, 33(8): 1903-1910.
	LIU Nian, ZHAO Jing, WANG Jie, et al. A trading model of PV microgrid cluster based on cooperative game theory[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1903-1910.
[13]	代业明, 高岩, 高红伟, 等. 智能住宅小区的需求响应主从博弈模型[J]. 电力系统自动化, 2017, 41(15): 88-94.
	DAI Yeming, GAO Yan, GAO Hongwei, et al. Leader-follower game model for demand response in smart residential grid[J]. Automation of Electric Power Systems, 2017, 41(15): 88-94.
[14]	JIN X, WANG Y H. Research on social network structure and public opinions dissemination of micro-blog based on complex network analysis[J]. Journal of Networks, 2013, 8(7): 1543-1550.
[15]	BANDYOPADHYAY A, KAR S. Coevolution of cooperation and network structure in social dilemmas in evolutionary dynamic complex network[J]. Applied Mathematics and Computation, 2018, 320: 710-730. doi: 10.1016/j.amc.2017.09.048 URL
[16]	SANTOS F C, PACHECO J M. Scale-free networks provide a unifying framework for the emergence of cooperation[J]. Physical Review Letters, 2005, 95(9): 098104. doi: 10.1103/PhysRevLett.95.098104 URL
[17]	NEWMAN M E J, GIRVAN M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113. doi: 10.1103/PhysRevE.69.026113 URL
[18]	LI C G, MAINI P K. An evolving network model with community structure[J]. Journal of Physics A: Mathematical and General, 2005, 38(45): 9741-9749. doi: 10.1088/0305-4470/38/45/002 URL
[19]	蒋国华. 基于博弈模型的智能电网需求响应管理及定价策略[D]. 杭州: 浙江工业大学, 2013.
	JIANG Guohua. Game theory model based demand response management and pricing strategy in smart grid[D]. Hangzhou: Zhejiang University of Technology, 2013.
[20]	YANG Z J, WANG L. Demand response management for multiple utility companies and multi-type users in smart grid[C]// 2016 35th Chinese Control Conference (CCC). Chengdu, China: IEEE, 2016: 10051-10055.
[21]	朱振宇. 智能需求响应下的居民用电行为与演化博弈[D]. 南京: 东南大学, 2018.
	ZHU Zhenyu. Residents’ electricity consumption behavior and evolutionary game under intelligent demand response[D]. Nanjing: Southeast University, 2018.
[22]	王锡凡, 王秀丽, 陈皓勇. 电力市场基础[M]. 西安: 西安交通大学出版社, 2003.
[23]	韩伟吉. 需求响应特性分析方法研究[D]. 济南: 山东大学, 2012.
	HAN Weiji. Studies on analysis methods of demand response characteristics[D]. Ji’nan: Shandong University, 2012.
[24]	彭春华, 钱锟, 闫俊丽. 新能源并网环境下发电侧微分演化博弈竞价策略[J]. 电网技术, 2019, 43(6): 2002-2010.
	PENG Chunhua, QIAN Kun, YAN Junli. A bidding strategy based on differential evolution game for generation side in power grid integrated with renewable energy resources[J]. Power System Technology, 2019, 43(6): 2002-2010.
[25]	韩玉莹. 基于演化博弈和仿真分析的节能产品补贴策略研究[D]. 哈尔滨: 哈尔滨工业大学, 2019.
	HAN Yuying. Research on subsidy strategy of energy-saving products based on evolutionary game and simulation analysis[D]. Harbin: Harbin Institute of Technology, 2019.
[26]	刘小兰, 杨军, 王清蓉. 碳排放交易机制下政府补贴的供应链减排博弈分析[J]. 昆明理工大学学报(社会科学版), 2017, 17(2): 73-82.
	LIU Xiaolan, YANG Jun, WANG Qingrong. Game analysis on carbon emission reduction on supply chain considering government subsidies under carbon cap-and-trade system[J]. Journal of Kunming University of Science and Technology (Social Science Edition), 2017, 17(2): 73-82.