[BETA] Fortnightly

Demand response—temporary changes to electric loads in reaction to conditions in the grid—has grown to become an important part of today’s power systems and a central component of the smart grid of the future. In the United States, the aggregate impact of demand response (DR) is estimated at 58 GW, or 7.6 percent of the peak demand, ¹ up 42 percent from two years ago. ² The Federal Energy Regulatory Commission (FERC) views the potential for further cost-effective DR to reach as much as 20 percent of the system peak. ³ Today, the cost-benefit analysis for DR is underway for electric systems around the world, with proponents pointing to cost savings, environmental benefits and increased reliability as reasons to invest in a more responsive electric system. ⁴

While demand response has gained traction among stakeholders in the electricity industry— e.g., utilities, regulators, customers and service providers—the adoption of demand response has varied significantly across geographies. Some regions have embraced the concept and show remarkable results; one electric cooperative in Minnesota reports that nearly half of its 700,000 residential customers are enrolled in a program that allows utilities to remotely cycle off their air conditioners during peak events. ⁵ In contrast, other areas have developed little or no demand response.

Why has demand response has taken strong hold in some places and been absent in others? Analyzing a set of potential drivers and the levels of DR achieved sheds some light on the historical evolution of demand response in recent years, and serves efforts to characterize the factors that will influence demand response in the future.

The analysis presented here utilizes publicly available data to assess relationships between demand response participation and potential DR drivers (see Figure 1) . This work isn’t a rigorous and comprehensive statistical analysis. Rather, the objective is to identify possible drivers in a qualitative way and test their impact by examining basic patterns in the data. Further, many of the parameters are related and can’t be interpreted as independent drivers of DR. For example, a state might have low electricity prices because it has a high reserve margin and few reliability concerns, possibly because of a large share of base-load generation capacity. Although these characteristics are assessed separately in the analysis, it’s clear they are dependent upon each other and must be considered together to