A Load Aggregation Method for Electric Vehicle Standby Service Based on Multi-arm Learning Machine

Abstract

The invention discloses a load aggregation method for electric vehicle standby service based on a multi-arm learning machine, which belongs to the field of demand response of electric power systems. Firstly, according to the way electric vehicles participate in standby service, the behavior rocker for electric vehicle users to respond to load aggregation request signals is established. Model, and load aggregation objective function, and then establish the electric vehicle backup service load aggregation model; then consider the electric vehicle user behavior rocker model characteristics, based on risk aversion to weigh the load aggregation objective, and propose a risk aversion-based electric vehicle user selection algorithm, To learn the various response behaviors of each user and continuously update the estimation of user behavior, so as to select the appropriate users to participate in the backup service in an appropriate way, and complete the load aggregation goal. The invention can obtain more reliable load aggregation effect, lower load aggregation cost and higher user satisfaction, and provides an effective way for aggregators to balance load aggregation reliability and load aggregation cost.

Description

technical field [0001] The invention belongs to the field of power system demand response load aggregation control, and more specifically relates to a load aggregation method for electric vehicle backup service based on a multi-arm learning machine. Background technique [0002] At present, traditional low-efficiency coal-fired units are gradually withdrawing from the power system, and the proportion of new energy is gradually increasing. The output of new energy is highly random and uncertain, and large-scale new energy grid integration poses a huge challenge to the reliable operation and optimization of the power system. This requires the system to be equipped with more flexible reserve capacity to deal with the uncertainty risk caused by the forecast error of new energy output. However, the traditional thermal power units that can provide spinning backup are gradually decreasing, and new energy units do not have the ability to provide backup. Therefore, there is insuffi...

AI Technical Summary

Problems solved by technology

However, in the actual load aggregation process, there has always been the problem of uncertainty in the response behavior of electric vehicle users. Users may withdraw from demand response midway due to their own energy needs, travel arrangements, living habits, user fatigue, etc.

Unknown and uncertain user response behavior may cause extremely unreliable load aggregation effects. For example, if a large number of users quit demand response midway, the load aggregation amount will seriously deviate from the target amount, resulting in unreliable backup services provided by load aggregation.

At present, the research on the load aggregation method of electric vehicles focuses on the consideration of the aggregator's economic benefits, user satisfaction and other factors to formulate a demand response mechanism, but few studies start from exploring the characteristics of user behavior, and fail to essentially solve the problem of user behavior uncertainty. , there is a lack of reliable load aggregation methods for EV standby services

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