Algorithmic Bidding for Utility-Scale Batteries: Regulating Algorithms for Efficient Dispatch on a Cleaner Grid
Utility-scale batteries are the linchpin of the clean energy transition—without them, intermittent wind and solar cannot reliably replace fossil generation. Yet the firms operating these batteries increasingly rely on auto-bidding algorithms (such as Tesla’s Megapack optimization tools) to decide when to charge and discharge. Whether these algorithms compete or tacitly collude has direct consequences for electricity prices, grid reliability, and the pace of decarbonization via investment incentives. At the same time, fast auto-bidding may improve dispatch by incorporating short-run information on demand, renewable output, and scarcity conditions more quickly into bids. This project uses granular five-minute bidding data from Australia’s electricity market, paired with information on battery technologies, to test whether battery bids are consistent with competitive conduct. We evaluate if algorithms help or hinder efficient dispatch of clean storage. The findings will inform regulators designing market rules for a grid where batteries—and the algorithms governing them—play an increasingly central role.
“Utility-scale batteries are essential for letting intermittent wind and solar replace fossil fuels. Firms operating these batteries increasingly rely on auto-bidding algorithms to decide when to charge and discharge. These algorithms can use high-frequency real-time data to improve dispatch, boost profitability, and encourage battery entry, supporting the energy transition. But policymakers also worry that automated bidding could facilitate tacit collusion, raise prices, and lead to inefficient dispatch. Using granular bidding data from Australia’s electricity market, this project studies firms’ algorithm choices and the real-world competitive effects of battery auto-bidding, with findings to inform regulators designing market rules.”
Eric Richert, Assistant Professor, Kenneth C. Griffin Department of Economics