Optimizing Utility-Scale Batteries: Seeking Profit Under Uncertainty.
Kalkunte, Prithvi
Kalkunte, Prithvi
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Abstract
Utility-scale battery energy storage systems (BESS) can rely on energy arbitrage to generate profit. However, differences between day-ahead (predicted) and real-time (actual) electricity prices introduce uncertainty introduces risk. This work develops a stochastic optimization framework that explicitly accounts for this uncertainty to improve battery scheduling and profitability. Initial results deterministically optimize on the 2025 market data from the Midcontinent Independent System Operator (MISO). Multiple formulations�including nonlinear programming (NLP), mixed-integer linear programming (MILP), and relaxed linear models�are compared in terms of solution quality and computational performance. Results show that model formulation significantly impacts both runtime and profitability, with nonlinear approaches achieving higher profits at increased computational cost. Uncertainty is reflected by the scenario generation method based on Singular Value Decomposition (SVD). By decomposing historical error data into dominant patterns and reconstructing a reduced noise version, we identify the structure of forecast error. These reduced representations are clustered into distinct �day types,� which serve as representative uncertainty scenarios. This approach captures both typical operating conditions and rare, high-impact events while maintaining computational tractability. Clustering in reduced SVD space reveals a dominant baseline regime alongside several lower-probability, high-variance regimes, highlighting the structured nature of price uncertainty. This work demonstrates that combining stochastic optimization with structured, data-driven scenario generation can improve decision-making for battery arbitrage under uncertainty, with potential applications across electricity markets and future energy systems.
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Date
1/1/2026
Student Status
Graduate Student
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Poster
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Electrical Engineering
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College of Engineering and Mathematical Sciences
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Engineering