Where to Store Electricity? Optimizing Locations of Batteries in a Grid with Interpretable Machine Learning

The project aims to optimize the placement of grid-scale battery storage by analyzing and simulating price volatility and location-based profitability in the electricity grid, particularly focusing on renewable energy impacts and market dynamics in California. The methodology includes developing machine learning models to map network structures and predict price effects based on renewable generation and electricity demand changes. This research will provide actionable insights for system operators, renewable energy developers, and policymakers to enhance grid management and storage deployment strategies.

Grant Result

What if batteries could move to where the grid needs them most? This research shows how mobile energy storage can boost profits, ease congestion, and accelerate renewables—if policy evolves to unlock geospatial flexibility.

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Serguei Netessine

Senior Vice Dean of Innovation & Global Initiatives, Wharton

Serguei Netessine is Senior Vice Dean for Innovation and Global Initiatives and Dhirubhai Ambani Professor of Innovation and Entrepreneurship at Penn’s Wharton School.

Vishrut Rana

Doctoral Candidate, Operations, Information and Decisions Department

Vishrut Rana is a doctroal canidate in Operations, Information and Decisions Department in the Wharton School. He is interested in addressing research questions related to renewable energy integration.

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