Abstract
Electric utilities have historically treated power demand as an uncontrollable input, requiring generation and transmission resources to maintain the supply-demand balance. In recent years, demand response (DR) has emerged as a means to manage customer loads to balance the grid. This paper presents analytic solutions to enable utilities to optimize DR programs to serve as operational resources for the grid. We developed two sets of analytics. First, we developed a clustering-based method to accurately estimate the load curtailments expected from customers during DR events. Then, we used an option value-based optimal DR event scheduling method to compute a dynamic threshold value that the utility can use to make daily decisions for triggering DR events. In extensive tests, the proposed methods show superior performance over existing approaches. We implemented these analytics in the General Electric (GE) PowerOn (TM) Precision Demand Response Management System, which GE offered from 2011 to 2015.