Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games With Demand Response Aggregators
提出一个Stackelberg博弈框架，用于需求响应聚合商与产消者之间的双向能源交易，并设计去中心化、保护隐私的算法来近似求解均衡，利用加州日前市场和校园建筑数据验证效果。
In the pathway to 2030 electricity generation decarbonization and 2050 net-zero economies, scalable integration of distributed load can support environmental goals and also help alleviate smart grid operational issues through its electricity market participation. In this work, a novel Stackelberg game theoretic framework is proposed for trading the energy bidirectionally between the demand-response (DR) aggregator and the prosumers (distributed load). 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 and the number of learning samples), the 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, the 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 the algorithm.