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随着智能电网和电动汽车产业的快速发展，电动车接入电网的规模不断增大，这给充电站资源分配、电网负荷平衡及用户充放电决策带来了挑战。因此，提出了一种基于Stackelberg博弈的智能充放电调度模型。该模型利用物联网传感器采集车辆电池状态、用户行为和充电站数据，并通过车联网(IoVs)实现车辆与充电桩、交通管理系统之间的高效通信。通过动态优化充放电策略，框架能够平衡用户需求与电网约束，有效提升充电站资源利用率，优化电网负荷分布，减少高峰时段的充电拥堵。利用真实地图及真实电价数据对模型进行了全面的分析和评估，研究结果表明，该调度模型能够在不同时间段和不同车辆数下，有效优化充放电调度完成时间、车辆开销和充电站开销，并取得显著的改进。
Abstract：With the rapid development of the smart grid and electric vehicle industry, the increasing scale of electric vehicles connected to the grid has posed challenges to charging station resource allocation, grid load balancing, and user charging and discharging decisions. Therefore, an intelligent charging and discharging scheduling model based on Stackelberg game theory has been proposed. This model utilizes internet of things(IoT) sensors to collect data on vehicle battery status, user behavior, and charging station information, and employs the internet of vehicles(IoVs) to enable efficient communication among vehicles, charging piles, and traffic management systems. By dynamically optimizing charging and discharging strategies, the framework balances user demands with grid constraints, effectively improves charging station resource utilization, optimizes grid load distribution, and reduces charging congestion during peak hours. The model was comprehensively analyzed and evaluated using real-world maps and electricity price data. The research results demonstrate that this scheduling model can effectively optimize the completion time of charging and discharging scheduling, vehicle costs, and charging station costs under different time periods and varying numbers of vehicles, achieving significant improvements.
[1] ZHANG X,CHAN K W,LI H,et al.Deep-learning-based probabilistic forecasting of electric vehicle charging load with a novel queuing model[J].IEEE Transactions on Cybernetics,2020,51(6):3157-3170.
[2] JEMBER A G,XU W,PAN C,et al.Game and contract theory-based energy transaction management for internet of electric vehicle[J].IEEE Access,2020,8:203478-203487.
[3] BORGHETTI F,LONGO M,MIRAFTABZADEH S M,et al.A quantitative method to assess the vehicle-to-grid feasibility of a local public transport company[J].IEEE Access,2023,11:55644-55656.
[4] ZHANG S,LEUNG K C.A smart cross-system framework for joint allocation and scheduling with vehicle-to-grid regulation service[J].IEEE Transactions on Vehicular Technology,2022,71(6):6019-6031.
[5] 李咸善，鄂璇.分档电价和碳配额激励的含电动汽车微电网优化调度策略[J].科学技术与工程，2022,22(16):6537-6546.
[6] CHEN P,HAN L,XIN G,et al.Game theory based optimal pricing strategy for V2G participating in demand response[J].IEEE Transactions on Industry Applications,2023,59(4):4673-4683.
[7] YANG Q,LI D,AN D,et al.Towards incentive for electrical vehicles demand response with location privacy guaranteeing in microgrids[J].IEEE Transactions on Dependable and Secure Computing,2020,19(1):131-148.
[8] HAO L,JIN J,XU Y.Laxity differentiated pricing and deadline differentiated threshold scheduling for a public electric vehicle charging station[J].IEEE Transactions on Industrial Informatics,2022,18(9):6192-6202.
[9] 胡志勇，郭雪丽，王爽，等.考虑响应意愿的电动汽车群-空调集群需求响应策略研究[J].电力系统保护与控制，2023,51(15):109-119.
[10] 王海鑫，袁佳慧，陈哲，等.智慧城市车-站-网一体化运行关键技术研究综述及展望[J].电工技术学报，2022,37(1):112-132.
[11] DEGLA A,CHIKH M,CHOUDER A,et al.Update battery model for photovoltaic application based on comparative analysis and parameter identification of lead-acid battery models behaviour[J].IET Renewable Power Generation,2018,12(4):484-493.
[12] HUANG Q,WANG X,FAN J,et al.V2G optimal scheduling of multiple EV aggregator based on TOU electricity price[C]//2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe,2019:1-6.
[13] REN H,ZHANG A,WANG F,et al.Optimal scheduling of an EV aggregator for demand response considering triple level benefits of three-parties[J].International Journal of Electrical Power & Energy Systems,2021,125:106447.
[14] 戴朝华，杨帅，叶圣永，等.供需双方博弈视角下的V2G优化策略[J].西南交通大学学报，2023,60(1):166-174.
[15] 胡超，彭文河，方支剑.基于光储充电站的电动汽车分层优化调度[J].综合智慧能源，2023,45(9):11-17.
[16] ZHU W Q,YU Z,YUAN X F,et al.Electric Vehicle Charging Path Planning Based on Real-time Traffic Information[C]//2022 IEEE 6th Conference on Energy Internet and Energy System Integration (EI2),2022:768-774.
[17] SCHOENBERG S,DRESSLER F.Reducing waiting times at charging stations with adaptive electric vehicle route planning[J].IEEE Transactions on Intelligent Vehicles,2022,8(1):95-107.
[18] TANG M,LI B,LIU H,et al.Energy-oriented routing strategy of electric vehicle:An end-to-end reinforcement learning approach[C]//2022 6th CAA International Conference on Vehicular Control and Intelligence,2022:1-7.
[19] ZHANG Y,LI M,CHEN Y,et al.A constraint-based routing and charging methodology for battery electric vehicles with deep reinforcement learning[J].IEEE Transactions on Smart Grid,2022,14(3):2446-2459.
基本信息:
中图分类号:U491.8;TM73
引用信息:
[1]董晓堃,汪淑娟.基于Stackelberg博弈的动态电量定价和充放电调度[J].陕西理工大学学报(自然科学版),2025,41(06):53-65+78.
基金信息:
国家自然科学基金项目(62562043,61962032); 云南省科技厅重大科技专项计划项目(202302AD080006-3,202302AQ370003-4)