An approximate dynamic programming approach for coordinated charging control at vehicle-to-grid aggregator

Abstract

Abstract-A vehicle-to-grid (V2G) aggregator is an agent between the power grid and plug-in hybrid electrical vehicles (PHEVs). This paper studies the coordinated charging control of a V2G aggregator, which aims at minimizing the charging cost and reducing the power losses incurred by the fluctuating load. On one hand, a lower cost of charging gives the owners of PHEVs an incentive to cooperate. On the other hand, with an increasing popularity of PHEVs, the impact on the power grid such as power losses should be of concern to the aggregator. As an inherent property of a V2G aggregator, we enable bidirectional electric power flows between PHEVs and the power grid. Given the planned schedules of all the vehicles that are managed by an aggregator, we formulate the coordinated charging control as a dynamic programming problem. Due to the curse of dimensionality, we apply an approximate dynamic programming approach, which reduces the dimensionality of both state space and control space, to obtain the control sequences. We conduct simulations given the 24-hour schedules of 100 vehicles. Simulation results show that coordinated charging control can reduce both the total cost of charging and power losses significantly, compared with the scheme where each vehicle starts charging as soon as it is connected to the grid.

Smart Electric Vehicle Charging Through Cloud Monitoring and Management

“…An un coordinated charging infrastructure will create sudden demand upsurges, put constraints on the underlying power grid and at the worst case may lead to failures and outages [9]. The demand of electrical power will be different at different charging station according to number of xEVs deployed or routed per station.…”

“…In the same paper they also proposed a scalable local scheduling optimization problem which can achieve the performance close to that of global counterpart. A real-time approach to TCC is introduced in [9] and similarly real time PCC is addressed in [10] to minimize the power attenuation and shaping voltage profile during PHEV charging process. Request from vehicle or users are processed in [19] and [20] to devise hierarchical aggregator-based hybrid vehicles charging control mechanisms that may lower the variability of demand and distributed power generation through smart charging and discharging in real time.…”

“…Request from vehicle or users are processed in [19] and [20] to devise hierarchical aggregator-based hybrid vehicles charging control mechanisms that may lower the variability of demand and distributed power generation through smart charging and discharging in real time. Furthermore, [9] also classifies PHEV charging strategies into architecture based as well as control based. The former may be designed either in centralized or decentralized mode while there may be direct or incentive based control in the latter.…”

“…The former may be designed either in centralized or decentralized mode while there may be direct or incentive based control in the latter. A game theoretic paradigm is adopted in [9] to supervise the random crowd of vehicle fleet thereby safeguarding PHEV's privacy issues in terms of charging schedules. In [10], a fuzzy logic Fuzzy Charging Controller (FCC) is proposed that takes the States of Charge (SOC), the grid parameters represented in the system minimum voltage, and the hourly energy price as input parameters to keep the system threshold voltage within permissible limits.…”

Estimation of cost savings from participation of electric vehicles in vehicle to grid (V2G) schemes

“…They showed that pricing information is very useful for regulating user demand and balancing the network load. Further research work reported in [10][11][12][13] also investigated the potential use of EV aggregators in different electricity markets.…”