We describe a method to generate statistical models of electricity demand from Commercial and Industrial (C&I) facilities including their response to dynamic pricing signals. Models are built with historical electricity demand data. A facility model is the sum of a baseline demand model and a residual demand model; the latter quantifies deviations from the baseline model due to dynamic pricing signals from the utility. Three regression-based baseline computation methods were developed and analyzed. All methods performed similarly. To understand the diversity of facility responses to dynamic pricing signals, we have characterized the response of 44 C&I facilities participating in a Demand Response (DR) program using dynamic pricing in California (Pacific Gas & Electric’s Critical Peak Pricing Program). In most cases, facilities shed load during DR events but there is significant heterogeneity in facility responses. Modeling facility response to dynamic price signals is beneficial to the Independent System Operator for scheduling supply to meet demand, to the utility for improving dynamic pricing programs, and to the customer for minimizing energy costs.
Optimal Dynamic Pricing for Binary Demands in Smart Grids: A Fair and Privacy-Preserving Strategy
