Decentralized Valley-fill Charging Control of Large-population Plug-in Electric Vehicles
Optimal charging control of large-population autonomous plug-in electric vehicles (PEVs) in power grid can be formulated as a class of constrained non-linear timevariant optimization problems. To overcome the computational complexity of this class of optimization problems, the author and his collaborators proposed a game-based decentralized control method such that individual agents update their best charging strategies simultaneously with respect to a common electricity price signal which is determined by the total demand in the grid. Due to the heterogeneity of individual PEVs, the game systems converge to a nearly valley-fill NE strategy with nontrivial deviation costs due to the heterogeneity property of individual PEV charging characteristics. In this paper the author proposed a novel algorithm to implement the optimal decentralized valley fill strategies for the charging problems of the PEV population which is composed of disjoint homogeneous subpopulations. The author introduces a cost which penalizes against the deviation of strategy of individual agent in a subpopulation from the average value of the subpopulation. It can be shown that in case that the update algorithm converges, the system reaches the optimal valley-fill equilibrium strategy where the introduced agent deviation cost vanishes. Simulation examples are used to illustrate the results developed in this paper.
Decentralized charging control Plug-in electric vehicles (PEVs) Valley-fill (VF) Nash equilibrium (NE)
Zhongjing Ma
School of Automation, Beijing Institute of Technology (BIT), and the Key Laboratory of Complex System Intelligent Control and Decision (BIT), Ministry of Education
国际会议
The 24th Chinese Control and Decision Conference (第24届中国控制与决策学术年会 2012 CCDC)
太原
英文
821-826
2012-05-23（万方平台首次上网日期，不代表论文的发表时间）