Research On Distributed Charging Scheduling Method For Electric Vehicles Under Informational Network Factors

With the rapid development of electric vehicles(EVs),the charging scheduling problem of EVs becomes more and more important.In the EVs charging scheduling system,the determination of system price directly affects whether the charging scheduling system can achieve the scheduling of EVs.In order to determine the system price,each individual EV must reach a consensus on the charging price by interacting with each other through the communication network.However,with the continuous development of Internet and big data technology,the charging price interaction process faces many challenges,such as limited communication network resources,replay attacks,and privacy leakage.The existence of these factors can affect the correctness and privacy of charging price interaction,and thus affect the charging scheduling of EVs.Based on the above considerations,the main work of this paper is as follows:(1)To address the communication network in the presence of network resource limitation,we propose a distributed charging price interaction algorithm with an event-triggered mechanism.In the traditional periodic sampling interaction algorithm,each EV sends charging price information to other EVs at equal intervals.However,in the case of network congestion,this algorithm can result in a large amount of redundant information exchange,which increases the communication burden and latency.In order to solve this problem,we propose an charging price interaction algorithm based on event-triggered mechanism,which triggers the exchange of charging price information by error monitoring the change of individual charging price status of EVs,thereby avoiding unnecessary communication overhead.We have verified the effectiveness and reliability of this algorithm through numerical simulation experiments.(2)To address issues of replay attacks and limited communication network resources,we introduce an event-triggered resilient pricing interaction algorithm.In traditional pricing interaction algorithms,each EV sends pricing information to other EVs at predetermined time points.However,in the presence of replay attacks,this fixed interaction mode may result in tampered pricing information.To solve this problem,we use error detection mechanism and credibility level to isolate EVs subjected to replay attacks.We combine this with an event-triggered mechanism to address limited communication network resources,and propose an event-triggered resilient distributed charging pricing consensus algorithm.This algorithm dynamically adjusts the frequency of pricing information exchange to ensure efficient individual pricing consensus,while guaranteeing communication security.We verify the reliability and security of this algorithm in the face of replay attacks through simulation experiments.(3)In response to communication network with privacy leakage and limited communication network resources,we propose an event-triggered price interaction algorithm based on individual price decomposition mechanism.In the traditional price interaction algorithms,each EV has to fully share its price information to neighboring EVs,which may lead to the leakage of individual privacy.To solve this problem,we propose an event-triggered price interaction algorithm with individual price decomposition,which reduces the number of unnecessary price interactions and achieves only partial sharing of charging price information by decomposing each EV’s charging price into private price part and public price part,and interacting with neighboring EVs when the event-triggering condition is satisfied.We verify through simulation experiments that this algorithm can effectively protect the privacy of charging price in communication network while ensuring that individual prices achieve consensus.