Dynamic Incentive Design for Participation in Direct Load Scheduling Programs

TitleDynamic Incentive Design for Participation in Direct Load Scheduling Programs
Publication TypeJournal Article
Year of Publication2014
AuthorsMahnoosh Alizadeh, Yuanzhang Xiao, Anna Scaglione, Mihaela van der Schaar
JournalIEEE Journal of Selected Topics in Signal Processing
Pagination1111 - 1126
Date Published12/2014
Keywordsancillary services, CERTS, load scheduling, PEVs, RM11-007

Interruptible Load (IL) programs have long been an accepted measure to intelligently and reliably shed demand in case of contingencies in the power grid. However, the emerging market for Electric Vehicles (EV) and the notion of providing non-emergency ancillary services through the demand side have sparked new interest in designing direct load scheduling programs that manage the consumption of appliances on a day-to-day basis. In this paper, we define a mechanism for a Load Serving Entity (LSE) to strategically compensate customers that allow the LSE to directly schedule their consumption, every time they want to use an eligible appliance. We study how the LSE can compute such incentives by forecasting its profits from shifting the load of recruited appliances to hours when electricity is cheap, or by providing ancillary services, such as regulation and load following. To make the problem scalable and tractable we use a novel clustering approach to describe appliance load and laxity. In our model, customers choose to participate in this program strategically, in response to incentives posted by the LSE in publicly available menus. Since 1) appliances have different levels of demand flexibility; and 2) demand flexibility has a time-varying value to the LSE due to changing wholesale prices, we allow the incentives to vary dynamically with time and appliance cluster. We study the economic effects of the implementation of such program on a population of EVs, using real-world data for vehicle arrival and charge patterns.

Short TitleIEEE J. Sel. Top. Signal Process.