Design of a V2G aggregator to optimize PHEV charging and frequency regulation control

Ran Wang, Yifan Li, Ping Wang, D. Niyato
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引用次数: 35

Abstract

The key elements in an indirect V2G system architecture are aggregators. They act as an interface between the grid and a group of plug-in hybrid electric vehicles (PHEVs). In this paper, we design an optimal vehicle to grid (V2G) aggregator to control the charging and frequency regulation processes of a group of PHEVs. We consider a problem that an aggregator has to minimize the overall cost of PHEV fleet in a multiple time slot horizon and meet the required battery level when PHEVs plug out. We adopt summation of PHEVs' expenditure in a finite number of time slots as our objective function, which is a quadratic optimization problem. A model predictive control based (MPC-based) PHEV charging and regulation algorithm is proposed to schedule the charging and regulation processes. Through the numerical experiments, we obtain the optimal charging and frequency regulation sequences for each PHEV, the effect of price prediction error on PHEV's cost as well as the impact of penalty factor to plug-out State of Charge (SOC). It is also shown that by taking the optimal control sequences, the PHEV owner can reduce his cost and depart with desired SOC.
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基于插电式混合动力充电和频率调节控制的V2G聚合器设计
间接V2G系统架构中的关键元素是聚合器。它们作为电网和一组插电式混合动力汽车(phev)之间的接口。在本文中,我们设计了一个最优车辆到电网(V2G)聚合器来控制一组插电式混合动力车的充电和频率调节过程。我们考虑一个问题,即聚合器必须在多个时间段内最小化插电式混合动力汽车车队的总成本,并在插电式混合动力汽车插电时满足所需的电池电量。我们以插电式混合动力汽车在有限时段的总支出作为目标函数,这是一个二次优化问题。提出了一种基于模型预测控制(mpc)的插电式混合动力汽车充电调节算法,对充电调节过程进行调度。通过数值实验,得到了各插电式混合动力汽车的最优充电和频率调节顺序、价格预测误差对插电式混合动力汽车成本的影响以及惩罚因子对插电式混合动力汽车SOC的影响。采用最优控制序列,PHEV车主可以降低成本,以期望的SOC出发。
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