Optimal Sizing, Siting, and Operation of Long-Duration Battery Storage Systems to Mitigate Wildfire Risk in Power Grids

Core Concepts

Optimal placement and operation of long-duration battery storage systems in power grids to mitigate the impact of wildfire-related Public Safety Power Shutoff (PSPS) events while also maximizing benefits during normal operations.

Abstract

The paper presents a method for optimally sizing, siting, and operating long-duration utility-scale battery storage systems in power grids, considering both normal operations and periods of high wildfire risk.
Key highlights:

Wildfire risk poses a growing threat to power system infrastructure, leading to Public Safety Power Shutoff (PSPS) events that can result in load shedding.
Utility-scale batteries can help mitigate the impact of PSPS events, but their placement and operation must be optimized to balance benefits during both normal and high-risk conditions.
The authors formulate a multi-scenario optimization problem to determine the optimal battery sizing, siting, and operation, considering the possibility of load shedding during PSPS events.
To enable a computationally scalable solution, the authors develop a customized temporal decomposition method based on a progressive hedging framework.
This decomposition approach allows for efficient and scalable modeling of a full year of hourly operational decisions to inform the optimal battery placement for a 240-bus WECC model in under 70 minutes of wall-clock time.
The results show that the optimal battery placement can vary significantly between normal operations and high-risk periods, highlighting the importance of considering the full year of operations.

Stats

"Battery parameters used in the numerical tests include a capacity of 1.0 p.u. (100 MWh), a charge/discharge efficiency of 95%, and an hourly carryover rate to model a 0.1% daily self-discharge loss."
"The optimization model contains over 1.2 million constraints, 1.9 million variables, and 5 million nonzero values for a one-month simulation in June 2021."
"The decomposed, one-year horizon battery sizing, siting, and operating problem for the WECC-240 network was solved in under 70 minutes with a relative optimality gap of 0.023% and an absolute gap of 0.367."

Quotes

"Utility-scale battery energy storage systems [9], [10] are another investment that can help mitigate the impact of PSPS events on customers during wildfire season [11], [12]."
"Finding optimal locations to place utility-scale batteries on a large power grid is a computationally challenging problem [13]–[15]."
"To enable computational tractability, we employ a progressive hedging (PH) algorithm, which is a scenario-based decomposition technique originally developed for stochastic programming [18]."

Key Insights Distilled From

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

by Ryan Piansky... at arxiv.org 04-19-2024

https://arxiv.org/pdf/2404.12296.pdf

Deeper Inquiries

How could the proposed optimization framework be extended to consider uncertainties in load and renewable generation within each time period, rather than just across the full year

To extend the optimization framework to consider uncertainties in load and renewable generation within each time period, we can introduce scenario-specific variables for these uncertainties. Each scenario would represent a different realization of load and renewable generation profiles for that specific time period. By incorporating these scenario-specific variables into the optimization model, we can capture the variability and uncertainty in load and renewable generation within each time period. This would allow the model to make more robust decisions that account for the dynamic nature of these factors throughout the year.

What other types of infrastructure investments, such as microgrids or covered conductors, could be incorporated into the optimization model alongside the battery storage systems

In addition to battery storage systems, the optimization model could be expanded to incorporate other types of infrastructure investments such as microgrids and covered conductors. Microgrids can provide localized energy solutions and enhance grid resilience, especially during wildfire events or other emergencies. By including microgrids in the optimization model, the placement and sizing of microgrids can be optimized to complement the battery storage systems and improve overall system reliability. Covered conductors can also be integrated into the model to reduce the risk of power outages caused by wildfires or extreme weather events. By optimizing the placement of covered conductors along with battery storage systems, the model can enhance grid resilience and mitigate wildfire risks more effectively.

How could the optimization approach be adapted to consider the potential for battery degradation and replacement over the lifetime of the system

To adapt the optimization approach to consider battery degradation and replacement over the system's lifetime, we can introduce variables and constraints related to battery health and degradation rates. The optimization model can include parameters that capture the degradation characteristics of the batteries, such as degradation rates, cycle life, and capacity fade over time. By incorporating these factors into the model, the optimization algorithm can make decisions that account for the long-term performance and degradation of the battery storage systems. Additionally, constraints can be added to limit the depth of discharge or the number of charge-discharge cycles to prolong the battery's lifespan and optimize the timing of replacements to minimize costs and ensure system reliability.