Modeling an aggressive energy-efficiency scenario in long-range load forecasting for electric power transmission planning
Introduction
Analyzing and projecting consumers’ and firms’ consumption of electricity is one of the core applications of computational energy modeling. Over the past several decades, various methods for this problem of “load forecasting” have been applied by electric utilities, energy policy-makers, and other decision-making entities in the electric power system. Recently, new technological and policy developments related to electric power production, transmission, and consumption have placed new demands upon load forecasting methodologies. Expanded features including increasing levels of detail, longer time horizons, and the capacity to address an expanded set of policy and regulatory requirements, are being required in load forecasting models’ range of functionality.
Improving the representation of end-use energy efficiency, and of the effects of policies and programs to promote it, are among the priorities for enhancing load forecasting models and methods. Efficiency has become an important element of many utilities’ resource mix for meeting the demand for electrical energy services, as well as a key component of numerous policy portfolios for large-scale abatement of carbon dioxide emissions from the electric power system. Such developments necessitate new approaches, and extensions of existing approaches, to load forecasting.
This paper describes such an approach in application to an important example of emerging problems in load forecasting: Incorporating energy efficiency into long-run electricity transmission planning. The approach is a “hybrid” load forecasting and modeling framework combining econometric and technological elements, focusing on a higher level of aggregation than is commonly incorporated in standard efficiency potential studies, while still allowing for the representation of end-use technology detail.
Transmission planning itself is undergoing significant changes to meet new policy priorities and regulatory constraints, including the expanded deployment of renewable energy sources and increased requirements for system reliability. The analysis discussed here was undertaken in support of a multi-institution, multi-stakeholder initiative to improve transmission planning in the Western Electricity Coordinating Council (WECC), a federally-sanctioned transmission operations and planning organization. Projections of future load growth across the WECC and scenarios of expanded energy efficiency programs and policies were created in order to examine the implications of increased efficiency for the development of the transmission system. The purpose was to enable subsequent analysis, using transmission planning modeling, of how greater end-use efficiency, by reducing load growth, might affect requirements for future transmission capacity expansion. In this paper we describe the design and implementation of a scenario of aggressive efficiency or high “demand-side management (DSM)” programs, policies, and regulations that would significantly increase the deployment of efficient end-use technologies across the WECC and therefore substantially reduce load relative to the projected baseline.1
The paper is organized as follows. In the next section, we provide background on load forecasting, transmission planning and the WECC, and the genesis of the load forecast and aggressive efficiency scenario. Following this is a summary of the SAE framework and methodology, basic data inputs for this analysis, the representation of DSM impacts within the hybrid load forecasting framework, the treatment of uncertainty and model sensitivity, and a comparison to other approaches. Section 4 then presents the core content of the paper: the assumptions, structure, inputs, and results of the High DSM scenario. The paper ends with a discussion and concluding remarks.
Section snippets
Energy efficiency and methods of load forecasting
Load forecasting methods in general have been the subject of a number of relatively recent surveys, including [2], [3], [4], [5]. In this section we focus specifically on the issue of energy efficiency in the load forecasting context, and in Section 3.5 further discuss forecasting approaches.
Utilities increasingly treat efficiency as a resource analogous to conventional power generation, and are changing generation investment plans based on anticipated efficiency acquisitions. Indeed, DSM
The SAE load forecasting framework
The SAE analytic architecture is shown in Fig. 2. Historical values of various inputs (e.g., economic, weather, and building stock data) are used to estimate the individual models. Forecasts of those variables are then used to drive forecasts of monthly energy and peak demand, which are disaggregated into specific end-use categories.4
The core of the
A 20-year aggressive efficiency scenario for the WECC
Using the SAE framework, we developed an initial set of base case, 20-year load forecasts for all WECC BAs reflecting regional assumptions about end-use efficiency trends based primarily upon those of EIA/NEMS. These forecasts were then adjusted by incorporating state-specific assumptions about future DSM program savings under current policies and program plans. (For convenience, we refer to both the initial and adjusted forecasts as the “SAE baseline.”) Both forecasts were then used in
Discussion and concluding remarks
The results we have presented on an aggressive long-term energy efficiency scenario for the WECC illustrate the advantages of the hybrid methodology relative to the application of either of its component elements. On the one hand, in contrast to a strictly econometric approach, the inclusion of explicit end-use detail even at less than complete disaggregation enables us to tie technology-specific information directly to the projected loads in both the baseline and the High DSM cases. On the
Acknowledgements
The work described in this report was supported by the U.S. Department of Energy’s Office of Electricity Delivery and Energy Reliability under Contract No. DE-AC02-05CH11231. We would like to thank three anonymous referees for their invaluable comments.
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