Real-time scheduling of electric vehicles for ancillary services

Abstract

Abstract-This work is part of a project that aims to demonstrate the concept of Vehicle-to-Grid (V2G) with an operational fleet. A fleet of electric vehicles is operated with the objective of providing regulation services to the grid. The focus of this paper is on the real-time operation of the fleet. Specifically, given an optimal trajectory for the vehicle state of charge, schemes for distributing the regulation power commands among the vehicles are tested. A scheme based on a convex optimization problem is proposed. Several numerical illustrations and simulations show the effectiveness of the scheme respect to common scheduling heuristics in terms of accuracy.

Real-Time Charging Strategies for an Electric Vehicle Aggregator to Provide Ancillary Services

“…This paper develops a framework for designing real-time charging controllers to operate an EVs fleet participating in the AS market. We extend and refine an earlier conference paper [30] to improve regulation capacity and accuracy in following AGC signals 1 as compared with simpler approaches. A set of different controllers is designed and tested, from which the more complex approaches achieve better performance (accuracy) and less cycling.…”

“…The limits that guarantee that no upper or lower boundaries are violated for task i at time step k are denoted ik and ik , respectively. These limits include all the information needed to ensure that the SoC stays within physical limits and is able to fulfill the task's minimum energy requirements [30].…”

“…The data set used in our numerical simulations is the same as in [30], with a fleet of 18 EVs, with a maximum regulation capacity of 15 kilowatts [kW] per vehicle, and a run time of two days with time steps every five minutes, during which the number of available vehicles changes according to a fixed task schedule. TFMPC used a forecast horizon of N 10.…”

“…This has led to the consideration of heuristic algorithms such as Earliest Deadline First (EDF) or Least Laxity First (LLF) for this purpose. However, these heuristics can negatively impact the performance of the batteries, especially when compared to alternative algorithms based on convex optimization formulations, as discussed in [30].…”

“…Figure 1 depicts a schematic diagram of the control hierarchy for the LAAFB V2G project, showing the interaction of the real-time distribution developed for that project with the rest of the project. In previous work [30], a real-time controller based on convex optimization was described, and it was shown that better results can be achieved with that controller as compared to some benchmarks. This control algorithm is currently integrated into the EV fleet management platform developed by Kisensum, Inc. [34] for the project.…”