Battery Swapping Station as an Energy Storage for Capturing Distribution-Integrated Solar Variability

TL;DR

This paper presents a mixed-integer programming model for optimizing battery swapping stations to mitigate solar PV output fluctuations, enabling higher solar integration into distribution grids.

Contribution

It introduces a novel scheduling model for BSS to effectively manage solar variability, reducing operational costs and supporting increased solar penetration.

Findings

The model effectively captures solar variability constraints.

Numerical simulations show cost reductions and improved solar hosting capacity.

The approach demonstrates viability for utility grid applications.

Abstract

Managing the inherent variability of solar generation is a critical challenge for utility grid operators, particularly as the distribution grid-integrated solar generation is making fast inroads in power systems. This paper proposes to leverage Battery Swapping Station (BSS) as an energy storage for mitigating solar photovoltaic (PV) output fluctuations. Using mixed-integer programming, a model for the BSS optimal scheduling is proposed to capture solar generation variability. The proposed model aims at minimizing the BSS total operation cost, which represents the accumulated cost of exchanging power with the utility grid. The model is subject to four sets of constraints associated with the utility grid, the BSS system, individual batteries, and solar variability. Numerical simulations on a test BSS demonstrate the effectiveness of the proposed model and show its viability in helping the utility grids host a higher penetration of solar generation.