An Assessment Model for Energy Efficiency Program Planning in Electric Utilities: Case of the Pacific of Northwest U.S.A.

Abstract

Energy efficiency stands out with its potential to address a number of challenges that today's electric utilities face, including increasing and changing electricity demand, shrinking operating capacity, and decreasing system reliability and flexibility. Being the least cost and least risky alternative, the share of energy efficiency programs in utilities' energy portfolios has been on the rise since the 1980s, and their increasing importance is expected to continue in the future.Despite holding great promise, the ability to determine and invest in only the most promising program alternatives plays a key role in the successful use of energy efficiency as a utility-wide resource. This issue becomes even more significant considering the availability of a vast number of potential energy efficiency programs, the rapidly changing business environment, and the existence of multiple stakeholders.This dissertation introduces hierarchical decision modeling as the framework for energy efficiency program planning in electric utilities. The model focuses on the assessment of emerging energy efficiency programs and proposes to bridge the gap between technology screening and cost/benefit evaluation practices. This approach is expected to identify emerging technology alternatives which have the highest potential to pass cost/benefit ratio testing procedures and contribute to the effectiveness of decision practices in energy efficiency program planning. The model also incorporates rank order analysis and sensitivity analysis for testing the robustness of results from different stakeholder perspectives and ii future uncertainties in an attempt to enable more informed decision-making practices. The model was applied to the case of 13 high priority emerging energy efficiency program alternatives identified in the Pacific Northwest, U.S.A. The results of this study reveal that energy savings potential is the most important program management consideration in selecting emerging energy efficiency programs. Market dissemination potential and program development and implementation potential are the second and third most important, whereas ancillary benefits potential is the least important program management consideration. The results imply that program value considerations, comprised of energy savings potential and ancillary benefits potential; and program feasibility considerations, comprised of program development and implementation potential and market dissemination potential, have almost equal impacts on assessment of emerging energy efficiency programs. Considering the overwhelming number of value-focused studies and the few feasibility-focused studies in the literature, this finding clearly shows that feasibility-focused studies are greatly understudied. The hierarchical decision model developed in this dissertation is generalizable. Thus, other utilities or power systems can adopt the research steps employed in this study as guidelines and conduct similar assessment studies on emerging energy efficiency programs of their interes...

A Scoring Model to Assess Organizations' Technology Transfer Capabilities: The Case of a Power Utility in the Northwest USA

“…i.e., checking if the selected experts have any special reason or personal interest that would enhance the bias potential; how available or willing are the experts, i.e., not only the person should be an expert, but also he/she should be willing to fully participate in the study, as to spend enough time and attention taking care of their tasks as an expert [263], [266], [267].…”

“…A common method of measuring the disagreement between experts is to use the PCM group disagreement index, according to the formula below, which is adapted from [263], [265], [267]. In order to use this method, precedent has it that an acceptable disagreement level would be 10% or less [245], [263], [267], [268]. Hierarchical agglomerative clustering (HAC) has also been used in previous dissertations to complement the disagreement measurement and interpretation [267], [268].…”

“…In order to use this method, precedent has it that an acceptable disagreement level would be 10% or less [245], [263], [267], [268]. Hierarchical agglomerative clustering (HAC) has also been used in previous dissertations to complement the disagreement measurement and interpretation [267], [268]. [266].…”

“…The impacts of potential changes in the values on the different levels of a model is done by conducting a sensitivity analysis. This test is important because the preset priorities (or weights) of a model's components might change overtime [267], [268] This is especially true in the realm of technology, where changes occur extremely rapidly and constantly. Also, changes in the expert panels might bring new priorities (weights), and given the fact that these changes might occur, a sensitivity analysis would be appropriate [245].…”

“…The sensitivity analysis shows how strong the decisions and conclusions coming from the model are [263]. It is performed to test and assure the robustness of both the model and the results [267]. The test would also be helpful in understanding how each level of the model and its components relate to each other [266].…”