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In this work, a novel Stackelberg game theoretic framework is proposed fortrading energy bidirectionally between the demand-response (DR) aggregator andthe prosumers. This formulation allows for flexible energy arbitrage andadditional monetary rewards while ensuring that the prosumers' desired dailyenergy demand is met. Then, a scalable (linear with the number of prosumers),decentralized, privacy-preserving algorithm is proposed to find approximateequilibria with online sampling and learning of the prosumers' cumulative bestresponse, which finds applications beyond this energy game. Moreover, costbounds are provided on the quality of the approximate equilibrium solution.Finally, real data from the California day-ahead market and the UC Davis campusbuilding energy demands are utilized to demonstrate the efficacy of theproposed framework and algorithm.

Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators