User Preference Learning and Response Optimization Based on Bayesian Inference

Weiqing SUN; Xiaonan LIU; Wei XIANG; Hongzhong LI

doi:10.7500/AEPS20200310002

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User Preference Learning and Response Optimization Based on Bayesian Inference

Basic Research | 更新时间：2025-06-16

- User Preference Learning and Response Optimization Based on Bayesian Inference
- Automation of Electric Power Systems Vol. 44, Issue 19, Pages: 92-100(2020)
- 作者机构：
  
  1.上海理工大学机械工程学院，上海市 200093
  2.上海电力大学电气工程学院，上海市 200090
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China
- DOI：10.7500/AEPS20200310002
  CLC：
- Received：10 March 2020，
  
  Revised：2020-03-23，
  
  Published Online：24 August 2020，
  
  Published：10 October 2020
- 稿件说明：
移动端阅览
孙伟卿,刘晓楠,向威等.基于贝叶斯推断的用户偏好学习与响应优化[J].电力系统自动化,2020,44(19):92-100.

SUN Weiqing,LIU Xiaonan,XIANG Wei,et al.User Preference Learning and Response Optimization Based on Bayesian Inference[J].Automation of Electric Power Systems,2020,44(19):92-100.
孙伟卿,刘晓楠,向威等.基于贝叶斯推断的用户偏好学习与响应优化[J].电力系统自动化,2020,44(19):92-100. DOI： 10.7500/AEPS20200310002.

SUN Weiqing,LIU Xiaonan,XIANG Wei,et al.User Preference Learning and Response Optimization Based on Bayesian Inference[J].Automation of Electric Power Systems,2020,44(19):92-100. DOI： 10.7500/AEPS20200310002.

摘要

灵活性负荷聚合商通过需求侧响应整合用户侧资源参与电力市场，提高系统可靠性和经济性并获得收益。但用户对用电方式和成本的偏好将对其响应电量带来不确定性，进而影响聚合商的电量申报精度和市场收益。文中将参与需求响应的负荷资源作为广义需求侧资源，构建用户响应偏好模型。进而利用贝叶斯推断对用户偏好进行学习，获得响应电量的概率性估计，生成最优响应计划。最后，建立基于偏差电量考核的聚合商市场收益模型，采用美国PJM市场用户报价及典型日交易数据进行算例仿真。仿真结果验证了贝叶斯推断对用户偏好学习的有效性，以及考虑用户响应偏好的广义需求侧资源响应优化的经济性。

Abstract

The flexible response aggregator (FRA) integrates user-side resources to participate in electricity market through demand response

which improves system reliability and economy and obtains revenue. However

consumer preference for electricity consumption modes and costs bring uncertainty to their response power quantity

which affects the accuracy of electricity declaration and market revenue of FRA. In this paper

the load resources involved in demand response are regarded as generalized demand side resources (GDSRs)

and the user response preference model is constructed. Then

Bayesian inference is used to learn the user preference

and the probabilistic estimation of the response power quantity is obtained to generate the optimal response plan. Finally

the market revenue model of aggregator based on energy deviation penalty is established

and the user quotation and typical daily transaction data of PJM market in the United States are used for simulation. The simulation results verify the effectiveness of Bayesian inference for user preference learning and the economy of GDSR response optimization considering user response preference.

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references

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