Long Duration Battery Sizing, Siting, and Operation Under Wildfire Risk Using Progressive Hedging

TL;DR

This work tackles the problem of optimally sizing, siting, and operating utility-scale batteries under both normal conditions and wildfire-induced PSPS events over a full year. It introduces a linear DC-OPF-based model with battery SOC dynamics and SIC constraints, while treating line energization as a wildfire-risk parameter. To achieve scalability across thousands of hourly scenarios, the authors develop a temporal progressive hedging approach that couples first-stage placement with SOC at subproblem boundaries, implemented with mpi-sppy on a WECC-240 test system. The results show that the PH-based decomposition yields near-extensive-form quality within about 70 minutes and reveals year-round, location-diverse battery placements that adapt to seasonal wildfire risk, enabling practical long-horizon planning for grid resilience. The methodology extends to rolling budgets and multiple risk profiles, offering a scalable path for incorporating long-horizon infrastructure decisions under uncertainty in power systems.

Abstract

Battery sizing and siting problems are computationally challenging due to the need to make long-term planning decisions that are cognizant of short-term operational decisions. This paper considers sizing, siting, and operating batteries in a power grid to maximize their benefits, including price arbitrage and load shed mitigation, during both normal operations and periods with high wildfire ignition risk. We formulate a multi-scenario optimization problem for long duration battery storage while considering the possibility of load shedding during Public Safety Power Shutoff (PSPS) events that de-energize lines to mitigate severe wildfire ignition risk. To enable a computationally scalable solution of this problem with many scenarios of wildfire risk and power injection variability, we develop a customized temporal decomposition method based on a progressive hedging framework. Extending traditional progressive hedging techniques, we consider coupling in both placement variables across all scenarios and state-of-charge variables at temporal boundaries. This enforces consistency across scenarios while enabling parallel computations despite both spatial and temporal coupling. The proposed decomposition facilitates efficient and scalable modeling of a full year of hourly operational decisions to inform the sizing and siting of batteries. With this decomposition, we model a year of hourly operational decisions to inform optimal battery placement for a 240-bus WECC model in under 70 minutes of wall-clock time.

Figures (8)

• Figure 1: Two-stage stochastic program representation
• Figure 2: Time-period decomposition structure of battery sizing, siting, and operation model.
• Figure 3: Battery state-of-charge decomposition structure in the battery sizing, siting, and operation model.
• Figure 4: Geo-location of the WECC-240 system network.
• Figure 5: Optimal battery placements for the WECC network in April 2021 (top) and in June 2021 (bottom). Red circles are sized proportionally to the number of batteries placed at that bus with the largest circle representing 4 batteries.