Optimal BESS Sizing and Placement for Mitigating EV-Induced Voltage Violations: A Scalable Spatio-Temporal Adaptive Targeting Strategy

TL;DR

This paper presents a scalable, adaptive strategy for optimally sizing and placing battery energy storage systems to mitigate voltage violations caused by increasing electric vehicle loads in distribution networks.

Contribution

It introduces a novel spatio-temporal adaptive targeting strategy that reduces computational complexity in BESS planning for EV integration.

Findings

Effective voltage violation mitigation demonstrated on 33-, 69-, and 240-bus systems.

Significant cost savings in electricity purchases achieved.

Scalable approach suitable for large distribution networks.

Abstract

The escalating adoption of electric vehicles (EVs) and the growing demand for charging solutions are driving a surge in EV charger installations in distribution networks. However, this rising EV load strains the distribution grid, causing severe voltage drops, particularly at feeder extremities. This study proposes a proactive voltage management (PVM) framework that can integrate Monte Carlo-based simulations of varying EV charging loads to (i) identify potential voltage violations through a voltage violation analysis (VVA) model, and (ii) then mitigate those violations with optimally-invested battery energy storage systems (BESS) through an optimal expansion planning (OEP) model. A novel spatio-temporal adaptive targeting (STAT) strategy is proposed to alleviate the computational complexity of the OEP model by defining a targeted OEP (T-OEP) model, solved by applying the OEP model to (i) a reduced set of representative critical time periods and (ii) candidate BESS installation nodes. The efficacy and scalability of the proposed approach are validated on 33-bus, 69-bus, and a large-scale 240-bus system. Results demonstrate that the strategic sizing and placement of BESS not only effectively mitigate voltage violations but also yield substantial cost savings on electricity purchases under time-of-use tariffs. This research offers a cost-effective and scalable solution for integrating high penetrations of EVs, providing crucial insights for future distribution network planning.