Incentives to Manipulate Demand Response Baselines With Uncertain Event Schedules

Abstract

We study baseline-based demand response (DR) programs. In such programs, customers get rebates based on how much they reduce electricity consumption during DR events relative to a “baseline,” where this baseline is determined by their consumption during previous non-event days. Customers, or automated controls working on their behalf, can achieve higher DR payments by decreasing consumption during DR events (desired behavior), and by increasing consumption during non-event times (baseline manipulation). Importantly, the customers have imperfect knowledge of when future demand response events will occur. To understand customers' incentives for baseline manipulation, we present a novel multi-stage stochastic dynamic programming model that optimizes customer actions for maximum expected rewards under uncertain event schedules. Analytical results for special cases show fundamental drivers of customer incentives. Simulation results reveal incentives to manipulate baselines and impacts to program performance for a realistic baseline-based demand response program, and how program and customer parameters affect incentives.