Incentive Compatibility, Scalability and Privacy in real time Demand Response

02/25/2019 ∙ by Georgios Tsaousoglou, et al. ∙ 0

The high penetration of Renewable Energy Sources in modern smart grids necessitated the development of Demand Response (DR) mechanisms as well as corresponding innovative services for the emerging flexibility markets. From a game theoretic perspective, the basic key performance indicators (KPIs) for such DR mechanisms are efficiency in terms of social welfare, practical applicability, and incentive guarantees, in the sense of making it a dominant strategy for each user to act truthfully according to his/her preferences, leaving no room for cheating. In this paper, we propose a DR architecture, including a mechanism based on Ausubel clinching auction and a communication protocol, that provably guarantee both efficiency and truthful user participation. Practicality/easiness of participation is enhanced via simple queries, while user privacy issues are addressed via a distributed implementation. Simulation results confirm the desired properties, while also showing that the truthfulness property becomes even more important in markets where participants are not particularly flexible



There are no comments yet.


page 1

page 2

page 3

page 4

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.