The traditional approaches for implementing event-based Demand Response (DR) have been static, and do not involve feedback to the consumers regardless of their perfor-mance during the DR event. This may however lead to anincomplete system-wide response, thereby forcing the utility toemploy direct load control to achieve the required response,or buy additional generation reserve from the spot market. To mitigate this inefficiency, this paper proposes closing the loopthrough an incentive control for residential DR participantsenabled using event stream monitoring. By realizing the latter inan adaptive and distributed manner, the data communication and computation overhead involved in the decision makingprocess is reduced. With simple assumptions, it is demonstratedthat methods for event stream processing can ensure that thescheduled DR is achieved completely. Therefore, the proposed implementation allows for scalable, effective, privacy-preserving,and robust implementation of incentive-based residential DR thatensures full overall compliance to the DR task.
related publications
Gururaghav Raman, Jimmy Chih-Hsien Peng, Bo Zhao, Matthias Weidlich: Dynamic Decision Making for Demand Response through Adaptive Event Stream Monitoring In Proc. of 2019 IEEE Power & Energy Society General Meeting (PESGM), Atlanta, GA, USA. IEEE, August 2019.