As part of its triennial energy efficiency (EE) planning process, a mid-size utility client wondered how much savings it could capture, and at what cost, by focusing on its low-income and government, non-profit and institutional (GNI) markets. In effect, they wanted a deep dive into market characterization and a forecast of economic potential in seven specific segments that together comprise their low-income (residential) and GNI (commercial) markets. The utility engaged MCR to help them clarify objectives and specific questions, and to develop the characterization and forecast.
MCR brought a novel approach to the project. Whereas most market characterization and market potential work relies on the federal energy consumption surveys (i.e., the residential and commercial buildings energy consumption surveys, RECS and CBECS in this case) and Census data, MCR instead relied upon primary data from the utility’s own systems and staff. We interviewed 12 staff and worked with database analysts from multiple functional areas to develop a total of seven data queries. As a result of the interviews, we also identified or mined numerous local or service territory-specific secondary data resources. With primary and recommended secondary references in hand, MCR folded in analysis of the RECS, CBECS and Census data to develop basic descriptive statistics of the markets of interest. The outcome was a realistic grasp on the size and basic nature of the identified market segments that became the foundation for our work on three typical elements of market characterization and potential studies.
MCR used the most recent utility sales forecasts and FERC Form 1 data to develop baseline and forecast numbers of customers and loads by high-level customer class, and then decomposed the major customer classes into the identified segments and even more granular sub-segments (e.g., low income single family, low income multifamily, and mobile homes) using a combination of the utility-specific data and federal data as described above. We characterized the segments by applying staff interview insights, queries of the utility’s multiple database systems, and the RECS and CBECS microdata, that is the specific line-by-line survey responses, for 15 building types judged to represent all or most of the building stock in each identified segment. Rather than conduct a ground-up potential study that would require considerable investment of time and money, MCR conducted a meta study of recent potential studies in the client’s states and five neighboring states to develop annual and ten-year percentages of sales that could be cost-effectively saved by EE programs. We were then able to develop base, high, and low potential forecasts for each of the seven identified segments. Finally, we applied the acquisition cost ($/first year kWh saved) by high-level program from the approved EE plan to develop corresponding budgets.
The novelty and granularity of MCR’s approach, and the strength of a potential meta study were well-received by the client and the resulting information continues to provide insight for internal strategy and planning into the amount of EE headroom available in the historically hard to reach low-income and GNI markets, and the cost to achieve such savings.