摘要
激励型需求响应是一种通过补贴信号灵活调度需求侧能源的手段,对于维持电力系统供需平衡,提升经济效益有巨大潜力。在面向居民用户的激励型需求响应中,电力运营商选择削减电量潜力大的居民用户并向其提供补贴价格,以激励其削减电量。然而,站在电力运营商的角度,面对居民用户未知且不确定的用电行为,识别并选择削减电量潜力大的居民用户以及如何动态制定补贴价格是关键挑战。为了解决这一问题,本文提出基于在线学习的激励型需求响应算法(IDR-OL),利用多臂赌博机框架在线学习居民用户削减电量潜力,建立电力运营商运营成本优化模型选择最优参与需求响应的居民用户并动态制定补贴价格。仿真结果表明,本文提出的IDR-OL算法能够在实现供需平衡的同时更大限度地降低电力运营商运营成本。
Incentive demand response is a means of flexibly dispatching demand-side energy through subsidy signals,which has great potential for maintaining the balance of supply and demand in the power system and improving economic benefits.In the incentive demand response for residential users,power operators choose residential users with high power reduction potential and provide them with subsidized prices to incentivize them to cut power to reduce electricity.However,from the perspective of power operators,in the face of unknown and uncertain electricity consumption behaviors of residential users,how to identify and select residential users with great power reduction potential and formulate the dynamic subsidy price are the key challenges.In order to solve this problem,this paper proposes an incentive demand response algorithm based on online learning.Based on the multi-armed bandits,it learns the load reduction potential of residential users online,and establishes an optimization model for the operation cost of electric power operators to select the optimal consumers participating in demand response and formulate subsidized prices dynamically.According to the simulation results,the algorithm can achieve a balance between supply and demand while reducing the operating cost of power operators to a greater extent.
作者
姜昊
王玉峰
JIANG Hao;WANG Yufeng(School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China)
出处
《电工电能新技术》
CSCD
北大核心
2023年第6期23-33,共11页
Advanced Technology of Electrical Engineering and Energy
关键词
需求响应
多臂赌博机
削减电量潜力
动态定价
居民用户选择
demand response(DR)
multi-armed bandits(MAB)
load reduction potential
dynamic pricing
residential users selection
