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.

翻译：本文提出了一种新颖的Stackelberg博弈理论框架，用于需求响应聚合商与产消者之间的双向能源交易。该框架在保障产消者每日所需能源需求得到满足的同时，实现了灵活能源套利与额外货币奖励。随后，提出了一种可扩展（复杂度与产消者数量呈线性关系）、去中心化且隐私保护的算法，通过在线采样与学习产消者的累积最优响应来寻找近似均衡解，该方法的应用范围超越了本能源博弈场景。此外，我们提供了近似均衡解质量的成本界限。最后，利用加州日前市场的实际数据与加州大学戴维斯分校校园建筑能源需求数据，验证了所提框架与算法的有效性。