Scheduling of EV Battery Swapping, II: Distributed Solutions

TL;DR

This paper develops distributed algorithms for optimal EV battery swapping scheduling that coordinate multiple entities while preserving privacy, improving efficiency over centralized methods.

Contribution

It introduces two novel distributed solutions based on ADMM and dual decomposition for joint EV station scheduling and power flow optimization.

Findings

Distributed algorithms achieve near-optimal scheduling performance.

Privacy-preserving coordination enables effective multi-entity management.

Simulations demonstrate the algorithms' effectiveness and scalability.

Abstract

In Part I of this paper we formulate an optimal scheduling problem for battery swapping that assigns to each electric vehicle (EV) a best station to swap its depleted battery based on its current location and state of charge. The schedule aims to minimize a weighted sum of total travel distance and generation cost over both station assignments and power flow variables, subject to EV range constraints, grid operational constraints and AC power flow equations. We propose there a centralized solution based on the second-order cone programming (SOCP) relaxation of optimal power flow (OPF) and generalized Benders decomposition that is suitable when global information is available. In this paper we propose two distributed solutions based on the alternating direction method of multipliers (ADMM) and dual decomposition respectively that are suitable for cases where the distribution grid, battery stations and EVs are managed by separate entities. Our algorithms allow these entities to make individual decisions but coordinate through privacy-preserving information exchanges to jointly solve an approximate version of the joint battery swapping scheduling and OPF problem. We evaluate our algorithms through simulations.