Abstract
A key motivation to fasten roll-out of electric vehicles (EVs) to the market is to implement Vehicle-to-Grid (V2G) functionalities. With V2G in place, EV owners can have extra freedom to interact their battery energy with power grids, namely by selling their energy to the grid when their EVs are not in use. On the other hand, EV aggregators and utility companies can leverage the flexibility of the collected energy to implement various ancillary services to the grids, which may significantly reduce costs of, for instance, running spinning reserve of traditional power plants on the grid side. However, this extra freedom also poses practical challenges in terms of how to devise a discharging strategy for a group of EVs that is fair and in some sense optimal. In this paper, we present a new design of EV discharging strategy in a typical V2G energy trading framework whilst leveraging the whale optimization algorithm in a decentralized manner, a metaheuristic algorithm that has been shown effective in solving large-scale centralized optimization problems. We demonstrate that by using simple ideas of data shuffling and aggregation, one can design an EV discharging strategy in a fair, optimal and privacy-aware manner, where the privacy refers to the fact that no critical information of EVs should be exchanged with the EV aggregator, and vice versa. The fairness implies that a common discharge rate needs to be sought for all EVs so that no one gets better benefits than others in the same V2G programme. Simulation results are presented to illustrate the efficacy of our proposed system.
Highlights
I N RECENT years, there has been an increasing interest in providing vehicle-to-grid (V2G) as a service to users of electric vehicles (EVs) [1]–[3]
Similar to what we have modeled for the EV cost functions, we consider two main factors for modeling the net cost incurred by an EV aggregator, including 1) the potential benefits that the collected V2G power can be leveraged by the EV aggregator in the energy market and 2) the monetary cost of an EV aggregator for sourcing the V2G power from EV users
We modified the whale optimization algorithm (WOA) to its decentralized form, decentralized whale optimization algorithm (DWOA), to solve an optimal consensus problem while leveraging simple ideas of data shuffling and aggregation to address the privacy concerns when multiusers need to be involved in the joint computing process
Summary
I N RECENT years, there has been an increasing interest in providing vehicle-to-grid (V2G) as a service to users of electric vehicles (EVs) [1]–[3]. We note that some works have a strong focus on the privacy-preserving perspective in V2G, where the main idea was to not reveal any sensitive information during the information processing, coordination, and communication exchange between EVs and a central computing node, e.g., an EV aggregator, using V2G; typically, these pieces of information may include an EV user’s personal ID, an EV’s location information, as well as payment and billing information [19] To address these issues, decentralized approaches have been more preferable in V2G practices; see [20]–[22] for some recent use cases.
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