Non-traditional competitors may pose a threat to investor-owned utilities. New research shows that real competition is coming from brick-and-mortar retailers, cable and phone companies, and online...
Planning for Efficiency
Forecasting the geographic distribution of demand reductions. Copyright © 2011 Consolidated Edison Company of New York, Inc.
While the various EEPS programs weren’t designed along service class lines, for the most part they were designed to target non-overlapping market sectors that can be roughly matched to parts of service classes. For instance, all SC-8 (multi-family buildings) fall into either Con Edison or NYSERDA multi-family programs, but there are also a number of SC-1 customers (individual residential accounts) who live in apartment buildings that are eligible for multi-family programs. Thus, while all of the SC-8 consumption could be allocated to the multi-family sector, SC-1 consumption had to be somehow divided among one- to four-family residences, five-plus (multi-) family residences, and small commercial accounts (mostly houses of worship).
Internal market research data developed during the design of the various EEPS programs allowed service class energy consumption to be allocated among the EEPS programs, but only down to a regional level. That is, we knew the approximate split of SC-1 consumption among the various housing stocks for each borough (Manhattan, Brooklyn, Queens, Bronx, and Staten Island) and for Westchester County, but not among different networks within each region (see Figure 2) . Similarly, the boundaries between large and small commercial accounts established for EEPS programs are different from those established for service classes, and so our SC-9 (large commercial) had to be apportioned between these two groups.
Using the market research data we were able to regroup the consumption within each region into the following six market sectors: 1) single family; 2) multi-family; 3) small commercial; 4) large commercial; 5) NYPA; and 6) electric heating.
The resulting matrix (M*) had 91 rows but only 6 columns. The first four sectors correspond to various Con Edison and NYSERDA EEPS programs. No EEPS achievements were mapped to the last two sectors, as NYPA customers aren’t eligible to participate in EEPS programs and electric heating measures don’t constitute a significant part of any current EEPS program.
The mapping matrix (M*) allowed program targets (expressed as annual energy savings in MWh) to be allocated among Con Edison’s 91 networks by the following formula, where G is a 6 x 6 diagonal matrix of the annual energy savings expected for each market segment and S is the 91 x 6 matrix of annual energy savings expected in each network from each segment:
The next step was to convert these annual energy savings for each program into a corresponding demand reduction at each network’s peak. To do this, it was necessary to construct a composite 8,760 (hours in a year) load curve for each segment (program) and pick out the largest (summer weekday) load reduction corresponding to each network’s peak (Figure 3) .
The 8,760 load curves were created using the Cadmus Portfolio Pro tool, which was the same model used to design each program. This tool combined the load curves for each program measure—in proportion to their expected contribution to the program goal—to determine each program’s composite load curve normalized to a per-MWh basis. For each network, this load curve was sampled at the corresponding peaking hour to build a 91 x 6 matrix