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Efficiency and Demand Response: Twins, Siblings, or Cousins?
Analyzing the conservation effects of demand response programs.
Does demand response increase or decrease overall electricity usage?
During the 1980s and 1990s, as conservation evolved into the concept of efficiency, load management evolved into a new concept and discipline-demand response. In recent years, demand response programs and activities have grown rapidly in the United States, driven by the desire or need to address peak capacity shortages and reliability issues, or to use demand response as a price-modulating tool in competitive markets. Energy conservation has not been a design objective of this demand response activity, and some might say that the tradition of efficiency and demand response as the "twin pillars" of demand-side management has been lost. Our study seeks to determine whether or not this is the case.
Given that demand response programs have not been designed to focus on energy consumption, it is not surprising that the evaluations of these activities, where they have been performed, have not focused on determining consumption impacts. Much of the measurement is based on criteria such as load reduced as a percentage of enrolled load, or as a percentage of baseline usage, or the percentage of load reduced versus that which was promised to be reduced.
An extensive review of demand response programs and their conservation effect, which we define as the change in total monthly or annual energy consumption attributable to the program, shows that although the primary intended effect of demand response programs is to reduce electricity use during times of peak load, the vast majority of demand response programs also yields a small conservation effect. 1
We used a broad definition of demand response, organizing our findings into three main program types: dynamic-pricing programs, reliability programs, and information/feedback programs. Dynamic-pricing programs produce an average conservation effect of 4 percent. Reliability programs, which operate less than 100 hours per year and typically include automated controls (, air conditioner cyclers or energy management systems), reduce total consumption by 0.2 percent. Feedback programs, such as Internet-based or in-home usage displays, have been found to reduce total consumption by an average of 11 percent.
The conservation effect varies significantly among more than 100 programs with available data, from a -5 percent (i.e., increased consumption) effect in one program to more than 20 percent in others. With respect to a single variable, such as the difference between peak and off-peak prices, there is no clear correlation between the variable and the level of conservation effect. In contrast, however, our analysis of the data revealed a very strong correlation among three factors: dynamic pricing, automated controls, and feedback programs or devices. Any combination of these factors produces a greater conservation effect than a single factor alone, with the combination of all three factors producing the greatest effect. While a precise estimate of this synergistic effect is not possible from the literature, the data suggests the effects are often largely additive. 2
Conservation Effect of Dynamic Pricing
Dynamic pricing includes pricing that varies by time of day to reflect the higher cost of generating