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