Research on Optimization of Demand Response Characteristics Based on MCMC Sampling and Considering User Production Characteristics

2021 6th Asia Conference on Power and Electrical Engineering (ACPEE) Pub Date : 2021-04-01 DOI:10.1109/ACPEE51499.2021.9436984

Li Bingjie

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Abstract

Mobilizing users to participate in Demand Side Response (DSR) is an inevitable requirement for the construction of the Energy Internet. The user's demand response characteristics shape the user's ability to reduce the total amount of electricity bills and control the risk of electricity bill fluctuations in an environment of uncertain electricity price fluctuations. Different users have different risk preferences, and because different users have different production characteristics, the difficulty of adjusting their electricity consumption behaviors in each time period is also different. Based on the aforementioned two reasons, users need personalized demand response characteristics to maximize their own utility. Based on the modeling of user response behavior and the simulation of electricity price risk environment based on MCMC sampling method, this paper designs a method that can optimize the demand response characteristics of different users according to their risk preferences and production characteristics with the help of Genetic Algorithm.

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基于MCMC抽样并考虑用户生产特性的需求响应特性优化研究

动员用户参与需求侧响应(DSR)是能源互联网建设的必然要求。用户的需求响应特征塑造了用户在电价波动不确定的环境下降低电费总量和控制电费波动风险的能力。不同的用户具有不同的风险偏好，由于不同的用户具有不同的生产特征，因此每个时间段调整其用电量行为的难度也不同。基于上述两个原因，用户需要个性化的需求响应特征来最大化自身的效用。本文在用户响应行为建模和基于MCMC抽样法的电价风险环境模拟的基础上，设计了一种利用遗传算法根据不同用户的风险偏好和生产特征优化不同用户需求响应特征的方法。

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2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)

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