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1. 电网智能化调度与控制教育部重点实验室(山东大学),山东省 济南市 250061
2. 国网山东省电力公司经济技术研究院,山东省 济南市 250021
Received:18 November 2019,
Revised:2019-12-27,
Published Online:22 May 2020,
Published:10 July 2020
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徐广达,张利,梁军等.基于设备用电特征的居民电力需求价格弹性评估[J].电力系统自动化,2020,44(13):48-55.
XU Guangda,ZHANG Li,LIANG Jun,et al.Estimation of Price Elasticity for Residential Electricity Demand Based on Electricity Consumption Features of Appliances[J].Automation of Electric Power Systems,2020,44(13):48-55.
徐广达,张利,梁军等.基于设备用电特征的居民电力需求价格弹性评估[J].电力系统自动化,2020,44(13):48-55. DOI: 10.7500/AEPS20191118003.
XU Guangda,ZHANG Li,LIANG Jun,et al.Estimation of Price Elasticity for Residential Electricity Demand Based on Electricity Consumption Features of Appliances[J].Automation of Electric Power Systems,2020,44(13):48-55. DOI: 10.7500/AEPS20191118003.
能源互联网建设可实现居民用电设备的状态感知,为需求价格弹性的评估提供全新的数据支持。以此为前提,提出一种基于设备用电特征的居民电力需求价格弹性评估方法。首先
根据需求价格弹性的定义,分析居民用电设备的使用特征与自弹性、交叉弹性的关联性,揭示用电设备可调整性对居民用户电价响应行为的客观约束。其次,针对居民用户因数量庞大且主观响应意愿存在差异而导致的设备用电调整不确定性,结合与用电特征关联的设备可调整性分类,构建基于设备用电调整模糊推理的居民电价响应行为分析模型。最后,将响应结果对应到需求价格弹性矩阵计算,完成居民电力需求价格弹性评估。算例分析验证了所提的方法的合理性与有效性。
Energy Internet construction can realize the state awareness of residential appliances
and provide new data support for the estimation of price elasticity for demand. On this premise
an estimation method of price elasticity for residential electricity demand is proposed based on the electricity consumption features of appliances. Firstly
the correlation between the features and self-elasticity as well as cross-elasticity of residential appliances is analyzed according to the definition of price elasticity for demand. The objective constraint on behaviors of residential price response subjected to adjustability of appliances is revealed. Secondly
considering that the adjustment of appliances is uncertain due to the large number of residents and subjective differentiation of their response willingness
an analysis model based on fuzzy reasoning of electricity consumption adjustment of appliances is constructed to simulate the price response behavior of residents
which takes advantages of the classification of the adjustable features for appliances. Finally
the response results are used to calculate the price elasticity matrix of demand
and the residential price elasticity of electricity demand is estimated. Simulation results show the rationality and effectiveness of the proposed method.
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