Real-Time Welfare-Maximizing Regulation Allocation in Dynamic Aggregator-EVs System

TL;DR

This paper introduces a real-time algorithm for fair regulation allocation in a dynamic EV aggregator system, optimizing welfare while considering system constraints and outperforming simpler methods.

Contribution

The paper proposes the WMRA algorithm that handles system dynamics, battery constraints, and degradation costs without prior statistical knowledge, advancing regulation allocation methods.

Findings

WMRA achieves near-optimal welfare with an O(1/V) bound.

Simulation shows WMRA outperforms greedy algorithms.

Algorithm operates in real-time without prior system knowledge.

Abstract

The concept of vehicle-to-grid (V2G) has gained recent interest as more and more electric vehicles (EVs) are put to use. In this paper, we consider a dynamic aggregator-EVs system, where an aggregator centrally coordinates a large number of dynamic EVs to perform regulation service. We propose a Welfare-Maximizing Regulation Allocation (WMRA) algorithm for the aggregator to fairly allocate the regulation amount among its EVs. Compared to previous works, WMRA accommodates a wide spectrum of vital system characteristics, including dynamics of EV, limited EV battery size, EV battery degradation cost, and the cost of using external energy sources for the aggregator. The algorithm operates in real time and does not require any prior knowledge of the statistical information of the system. Theoretically, we demonstrate that WMRA is away from the optimum by O(1/V), where V is a controlling parameter depending on EV's battery size. In addition, our simulation results indicate that WMRA can substantially outperform a suboptimal greedy algorithm.