Optimal Operation of Stationary and Mobile Batteries in Distribution Grids

TL;DR

This paper presents a scalable, convex optimization-based demand side management approach for integrating stationary and mobile batteries into distribution grids, effectively handling uncertainties and large system sizes.

Contribution

It introduces a stochastic DSM model that accounts for EV uncertainties, uses convex relaxation for efficiency, and employs a distributed algorithm for scalability.

Findings

The proposed DSM effectively manages EV and stationary battery integration.

Numerical results confirm the approach's accuracy and computational advantages.

Real-life EV data validates the method in a benchmark system.

Abstract

The trending integrations of Battery Energy Storage System (BESS, stationary battery) and Electric Vehicles (EV, mobile battery) to distribution grids call for advanced Demand Side Management (DSM) technique that addresses the scalability concerns of the system and stochastic availabilities of EVs. Towards this goal, a stochastic DSM is proposed to capture the uncertainties in EVs. Numerical approximation is then used to make the problem tractable. To accelerate the computational speed, the proposed DSM is tightly relaxed to a convex form using second-order cone programming. Furthermore, in light of the continuous increasing problem size, a distributed method with a guaranteed convergence is applied to shift the centralized computational burden to distributed controllers. To verify the proposed DSM, real-life EV data collected on UCLA campus is used to test the proposed DSM in an IEEE benchmark test system. Numerical results demonstrate the correctness and merits of the proposed approach.