Affiliation:
1. State Key Lab of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China
2. State Grid Tianjin Electric Power Research Institute Tianjin China
3. Key Laboratory of Smart Grid of Ministry of Education Tianjin University Tianjin China
Abstract
AbstractWith the rapid increase of electric vehicle (EV) ownership, the impact of EV charging load on the power grid is becoming more and more prominent. To reasonably guide EV charging/discharging to participate in Demand Response (DR) and help the power grid achieve peak cutting and valley filling, the charge‐discharge compensation pricing strategy of EV Aggregator (EVA) considering user response willingness from the perspective of Stackelberg game is proposed. Firstly, EVA, as the leader, provides charge‐discharge compensation price, to maximise its income within a day, taking into account user satisfaction constraints. Secondly, a user response willingness model is established. User engagement is used to describe the change in the number of EV responses with the change of the charge‐discharge compensation price by EVA and select the random EV set that accepts EVA charge‐discharge guidance. Finally, EV, as a follower, conducts charging/discharging behaviour to minimise the charging cost. By using the Karush–Kuhn–Tucker (KKT) condition, strong duality theory and iterative method, the strategy equilibrium solution is solved. The results show that considering the user response willingness can effectively reduce the decision risk when EVA participates in bidding. Although EVA income slightly decreases considering the response willingness, the average user satisfaction increases by 0.1.
Funder
National Natural Science Foundation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Computer Networks and Communications,Information Systems
Reference37 articles.
1. The total number of new energy vehicles in our country is 13.1 million showing rapid growth.http://www.gov.cn/xinwen/.2023‐01/11
2. Data driven optimization for electric vehicle charging station locating and sizing with charging satisfaction consideration in urban areas
3. Stackelberg game bidding model of electric vehicle agents participating in demand response;Cheng H.;New Technol. Electr. Eng. Electr. Energy,2020
4. Joint Optimization of bidding and pricing for electric vehicle dealers considering backup service;Yang S.;Electric Power Autom. Equip.,2018
5. Pricing Strategy and electric vehicle charging management of intelligent regional agents based on conditional var;Qiu D.;Syst. Eng. Theory Pract.,2018