Demand response reduces overall energy usage, but the magnitude of the reduction depends on whether the technologies are developed and deployed with efficiency in mind.
Planning for Efficiency
Forecasting the geographic distribution of demand reductions. Copyright © 2011 Consolidated Edison Company of New York, Inc.
overestimate the savings in commercial, daytime-peaking networks. Similarly, a network that peaks at 10 p.m. will realize much less savings from lighting programs than a network peaking at 8 p.m. (Compare Figures 3 and 4). Thus, it’s important to allocate energy savings first, and then use the appropriate demand conversion for the network in question. This allows Con Edison to use a different peaking time for each network when constructing the independent network peak forecast, or use the same time of day when constructing the coincident (system peak) forecast.
A major weakness in the current implementation is the fact that market research data for regrouping the service classes is currently available only down to the borough level. Clearly, the demographic composition of networks within a borough varies with geography (see Figure 2) . For instance, all of the outer boroughs become more residential as one moves away from the city center. Even Manhattan isn’t homogeneous; there are areas with more large commercial property (downtown and Midtown) and areas that are more residential and small commercial (the Village, Harlem, and the Upper East and Upper West sides). The company is working to generate service class consumption allocations for each network in order to improve the accuracy of the forecasting process.
Finally, we note that we don’t currently attempt to forecast DSM impacts below the network level, because the random variability typically becomes overwhelming for such circuits. In many cases, a secondary feeder might serve only a handful of customers or a single building. However, Con Edison does attempt to proactively target secondary circuits. That is, we market efficiency programs specifically to customers on highly loaded secondary circuits in order to relieve these circuits or keep them from becoming overloaded.
Integrating the expected demand reductions from energy efficiency programs into a utility’s T&D planning process involves significant and underappreciated forecasting challenges. In particular, the inherent geographic variability of DSM achievements requires consideration of the standard deviation of DSM savings and not just calculation of the expectation value. Nevertheless, more regulators are likely to require the inclusion of DSM in demand forecasts in order to control T&D spending and squeeze more benefits from energy efficiency programs.
Con Edison has a long track record of including DSM in its peak demand forecast and capital planning process. The company’s methodology for forecasting the future geographic distribution of DSM from non-targeted energy efficiency programs among its distribution networks involves first allocating the expected energy savings to the networks using the previous year’s consumption by market segment in that network as a mapping function; then converting these energy savings to coincident demand savings at the corresponding network peak; and finally reducing the resulting expectation value to get a DSM reduction that will distribute into that network with 95-percent confidence.
Con Edison intends to validate the accuracy of this model and quantify the geographic variability of each efficiency program’s achievements as actual performance data from its efficiency programs becomes available.
1. Order Establishing Energy Efficiency Portfolio Standard and Approving Programs,” June 23, 2008, New York Public