A central challenge in using price signals to coordinate the electricity consumption of a group of users is the operator's lack of knowledge of the users due to privacy concerns. In this paper, we develop a two-time-scale incentive mechanism that alternately updates between the users and a system operator. As long as the users can optimize their own consumption subject to a given price, the operator does not need to know or attempt to learn any private information of the users for price design. Users adjust their consumption following the price and the system redesigns the price based on the users' consumption. We show that under mild assumptions, this iterative process converges to the social welfare solution. In particular, the cost of the users need not always be convex and its consumption can be the output of a machine learning-based load control algorithm.

翻译：使用价格信号协调一组用户的电力消耗所面临的核心挑战在于，由于隐私问题，运营商缺乏对用户的了解。在本文中，我们开发了一种双时间尺度激励机制，该机制在用户与系统运营商之间交替更新。只要用户能够根据给定的价格优化自身的消费行为，运营商就无需知道或尝试学习用户的任何隐私信息来进行价格设计。用户根据价格调整其消费，而系统则基于用户的消费重新设计价格。我们证明，在宽松假设下，这一迭代过程能够收敛到社会福利最优解。具体而言，用户的成本函数不必总是凸的，其消费可以是基于机器学习的负荷控制算法的输出结果。