As federal policy makers push for GHG regulation and transparent markets, the California experience shows what works and what doesn’t work.
New York Negawatts
Balancing risks and opportunities in efficiency investments.
customers’ willingness and ability to participate in programs.
Measure-performance risks are technical in nature and related to factors affecting how an energy-efficiency measure functions and delivers savings, such as poor quality, improper installation or operation, and measure interactions. These factors generally affect a program’s gross savings. Careful assessment of energy-efficiency technologies, commissioning protocols, and more rigorous measurement and verification improve measure performance. Still, as many energy-efficiency program evaluations have shown, they rarely, if ever, eliminate technical risks.
Finally, behavioral uncertainties mainly arise from potential free-ridership ( i.e., savings that would have occurred without the program) and rebound or take-back effects. The combined effects of these risk factors will determine the success of the New York’s six IOUs to achieve their energy-efficiency portfolio objectives and, ultimately, earn the incentives.
Given the range of uncertainties about the actual savings of energy-efficiency programs, it’s reasonable to analyze their outcomes in probabilistic (stochastic) terms, depending on the likely range of possible values of the risk factors. For this analysis, three variables—market acceptance, measure-savings realization rates, and net-to-gross ratios—were used to analyze the potential risks that might adversely affect a utility’s ability to reach its annual targets and earn an incentive. Figure 3 shows the assumed probability distributions for the three risk variables. For each of the three stochastic variables, the table shows five points on the variable’s probability distribution. The upper and lower bounds show the possible maximum and minimum values for each variable. The 25 percent, 50 percent, and 75 percent are, respectively, the first quartile, the median, and the third quartiles of distribution. For example, the value of 0.98 for market acceptance in the residential sector means there is a 25- percent probability a residential program will reach 98 percent of its expected participants.
The numbers in Figure 3 represent likely values for typical programs. They reflect the best knowledge about the variability of these factors, based on information available from evaluation reports on energy-efficiency programs being run in several states. Changes in these assumptions clearly will affect the results.
Performance of individual portfolios of the six IOUs was simulated 200 times using the Monte Carlo simulation. The procedure generated probability distributions for savings and potential incentive earnings for each utility, by customer sector and year, for each of the 200 iterations. Values for market acceptance, measure performance, and net-to-gross were drawn for each utility and iteration from distributions in Figure 1 . The resulting distributions were then aggregated across all utilities, and summarized in histograms by dividing the range of potential outcomes into bins, then counting the number of observations falling within each bin. In addition, smoothed probability density functions (in red) were generated by nonparametric kernel density estimation (see Figures 4 and 5) . For each distribution, the mean, standard deviation, median, first and third quartiles, and fifth and 95th percentiles of the distributions are shown in the insets.
Figure 4 shows the probability distribution of cumulative annual energy savings between 2009 and 2011, relative to the savings target (2,082 GWh) for the six utilities. As the inset in Figure 4 shows,