基于决策实验室算法-对抗解释结构模型的电动汽车多场景需求响应策略分析

doi:10.12204/j.issn.1000-7229.2023.03.010

摘要/Abstract

摘要：

随着可再生能源渗透率的持续提升,电网波动性与随机性日益增加,需求侧可调资源将愈发重要。电动汽车(electric vehicle, EV)在需求侧资源中占比较大,但现有研究较少考虑电动汽车参与需求响应过程中个人与社会因素的多因素交互影响。因此,文章提出了一种基于决策实验室算法-对抗解释结构模型(decision-making trial and evaluation laboratory-adversarial interpretive structure modeling,DEMATEL-AISM)算法的EV多场景需求响应充电调度策略。首先,通过数据挖掘法分析多场景下充电站运行特性与电动汽车充能特性,构建电动汽车充电负荷特性模型;其次,使用DEMATEL-AISM算法对多场景下影响电动汽车充能行为的多因素耦合关系进行分析,挖掘主导因素;最后,基于多场景主导因素分析,制定多因素影响下的用户调控策略。通过仿真分析,验证了所提方法能有效平抑负荷峰谷水平,降低节点电压波动,提高电力系统需求侧的稳定性与经济性。

关键词: 电动汽车, 需求响应, 决策实验室算法-对抗解释结构模型(DEMATEL-AISM), 多因素影响度评估

Abstract:

With the continuous increase of renewable energy penetration, the volatility and randomness of the power grid are increasing, and demand-side resource supply will become more and more important. Electric vehicles (EVs) account for a relatively large share of demand-side resources, but existing studies have rarely considered the multi-factor interaction of individual and social factors in the process of EV participating in demand response. Therefore, an EV multi-scenario demand-response charging scheduling strategy based on the decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM) is constructed in this paper. Firstly, a data mining method is used to analyze the operating characteristics of charging stations and EV charging characteristics in multiple scenarios, and an EV charging load characteristic model is constructed. Secondly, the DEMATEL-AISM algorithm is used to analyze the multi-factor coupling relationship affecting EV charging behavior in multiple scenarios, and the dominant factors are explored. Finally, according to the analysis of the dominant factors in multiple scenarios, a user regulation strategy is formulated under the influence of multiple factors. Through simulation, it is verified that the method proposed in this paper can effectively smooth out the peak and valley levels of load, node voltage fluctuations is reduced, the stability and economy of the demand side of the power system is improved.

Key words: electric vehicles, demand response, decision-making trial and evaluation laboratory-adversarial interpretive structure modeling method (DEMATEL-AISM), multi-factor impact degree assessment

中图分类号:

TM73

姚寅, 朱烨冬, 李东东, 周波, 林顺富. 基于决策实验室算法-对抗解释结构模型的电动汽车多场景需求响应策略分析[J]. 电力建设, 2023, 44(3): 93-104.

YAO Yin, ZHU Yedong, LI Dongdong, ZHOU Bo, LIN Shunfu. Multi-scenario Demand Response Strategy Based on DEMATEL-AISM for Electric Vehicles[J]. ELECTRIC POWER CONSTRUCTION, 2023, 44(3): 93-104.

图/表 17

图1 DEMATEL-AISM计算流程

Fig.1 Calculation flow chart of DEMATEL-AISM

图2 不同区域EV电站负荷曲线

Fig.2 Load curves of EV charging station for different functional areas and dates

图3 UP型与DOWN型拓扑层级

Fig.3 Hierarchy diagram of UP and DOWN topology

图4 各因素在系统中的权重

Fig.4 The weights of factors in the integrated system affecting users' charging behavior

表1 重要因素间耦合权重分布

Table 1 Distribution of coupling weights among important factors

图5 不同策略下电网负荷曲线

Fig.5 Grid load curve under different DR strategies

图6 不同策略下峰谷差评估指标

Fig.6 Levels of load peak-to-valley differences and load fluctuation under different DR strategies

表2 不同策略下系统电压稳定性指标

Table 2 System voltage stability indicators under different DR strategies

表3 不同策略下各区域的电压稳定性指标

Table 3 Voltage stability indicators for each functional area under different DR strategies

图7 各策略下的成本及利润

Fig.7 Cost and profits under each strategy

表A1 具体用户参与需求响应可能性数值

Table A1 Possibility value of consumer participating in demand response

表A2 峰谷电价

Table A2 Peak and valley prices

表A3 各因素模糊影响矩阵

Table A3 Fuzzy influence matrix among factors

表A4 各因素间耦合权重值

Table A4 Coupling weight values among factors

图B1 各区域EV开始充电时刻

Fig.B1 Charging start time of EVs in different functional areas

图B2 各区域EV单次充电量

Fig.B2 Single-time charge capacity of EVs in different functional areas

图B3 IEEE 33节点系统

Fig.B3 IEEE 33-bus system

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