计算机科学

计算机科学 ›› 2024, Vol. 51 ›› Issue (11A): 240300013-7.doi: 10.11896/jsjkx.240300013

• 交叉&应用 • 上一篇    下一篇

基于双层优化的电动车与电网实时协同定价机制

王琼1, 卢钺2, 刘顺2, 李清涛2, 刘洋2, 王洪彪1, 刘卫亮3   

  1. 1 国网北京市电力公司 北京 100032
    2 国网北京海淀供电公司 北京 100080
    3 华北电力大学自动化系 河北 保定 071003
  • 出版日期:2024-11-16 发布日期:2024-11-13
  • 通讯作者: 刘卫亮(15933579831@163.com)
  • 作者简介:(coycathy@163.com)
  • 基金资助:
    国网北京市电力公司科技项目:电动汽车充放电站 V2G/S2G 车网互动及智慧集群调控技术研究及示范 (520204220008)

Real-time Collaborative Pricing Mechanism of Between Vehicle and Power Grid Based on Bi-levelOptimization

WANG Qiong1, LU Yue2, LIU Shun2, LI Qingtao2, LIU Yang2, WANG Hongbiao1, LIU Weiliang3   

  1. 1 State Grid Beijing Electric Power Company,Beijing 100032,China
    2 State Grid Beijing Haidian Power Supply Company,Beijing 100080,China
    3 Department of Automation,North China Electric Power University,Baoding,Hebei 071003,China
  • Online:2024-11-16 Published:2024-11-13
  • About author:WANG Qiong,borin in 1983,Ph.D candidate,engineer.Her main research interests include development of software and hardware for charging stations and power-side infrastructure,alongside the integration of advanced security initiatives in vehicle networking platforms.
    LIU Weiliang,born in 1983,Ph.D,associate professor.His main research interests include modeling,simulation and optimal control of integrated energy system.
  • Supported by:
    State Grid Beijing Electric Power Company Technology Project:Research and Demonstration of V2G/S2G Vehicle Network Interaction and Intelligent Cluster Control Technology for Electric Vehicle Charging and Discharging Stations(520204220008).

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摘要： 电动车充电行为的非完全竞争性和不完全信息性,以及电力系统的非线性和不确定性,导致电网实时定价问题的建模和求解及其复杂。现有解决方案往往将其建模为一个带约束的优化问题,并且认为效用函数对于电网是已知的,忽略了现实中存在的信息不完全性。为了克服这一局限,在效用函数参数未知的情况下,提出了一种基于双层优化的电动车与电网实时协同定价机制。该机制的创新性在于能够更好地反映电动车充电市场的真实动态;同时,引入电网的潮流方程来反映电网的实时负载。在该模型中,上层模型最大化电网供电商的收益,同时尽可能减小电网的负载压力;下层模型优化电动车充电行为,每一辆电动车的目标是最小化自身的充电成本。通过与固定电价以及峰谷电价情况进行对比,实验仿真数据揭示了所提机制能够更好地平衡电网以及电动车的收益并且增加两者总收益,同时减小电网的负载。

关键词: 智能电网, 实时定价, 双层优化, 优化算法, 功率流

Abstract: Due to the incomplete competition and information on the behavior of electric vehicles,as well as the nonlinearity and uncertainty of power systems,the modeling and solving of real-time pricing problems are highly complex. Existing solutions typically model this as a constrained optimization problem,assuming that the utility function,which is a quantitative representation of the electricity network's economic benefits,is known to the network operators.This overlooks the incomplete information prevalent in actual scenarios.To overcome this limitation,this paper proposes an innovative real-time pricing mechanism for the vehicle-to-grid problem based on a bi-level optimization approach under the condition of unknown utility function parameters.Meanwhile,it considers the power flow equation to reflect the distributed grid's real-time load.This mechanism more accurately reflects the market's real dynamics.In this bi-level model,the upper level represents the optimization problem of the market operators,aiming to maximize their own welfare and minimize the load of the distributed grid.In contrast,the lower level represents the optimization problem of electric vehicles,aiming to maximize their profits or minimize their cost.Through comparative experiment simulations with the fixed pricing and peak-valley pricing methods,the experimental simulation data demonstrates the effectiveness of improving the profit of the grid and vehicles.At the same time,the load of the power grid is reduced.

Key words: Smart grid, Real-time pricing, Bi-level optimization, Optimization algorithm, Powerflow

中图分类号: 

  • TP311
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