Energy Efficiency in Western Utility Resource Plans: Impacts onRegional Resources Assessment and Support for WGA Policies
Bibliographic record
Abstract
In the aftermath of the consumer price shocks and short-term power shortages of the 2000-01 electricity crisis, policymakers and regulators in Western states are placing increased emphasis on integrated resource planning (IRP), resource adequacy and assessment and a diversified portfolio of resources to meet the needs of electricity consumers. In some states, this has led to a resurgence in state and utility commitments to energy efficiency. Increasing interest in acquiring energy efficiency as a power-system resource is also driven by the desire to dampen high growth rates in electricity demand in some Western states, rapid increases in natural gas prices, concerns about the environmental impacts of electricity generation (e.g. water consumption by power plants, air quality), and the potential of energy efficiency to provide utility bill savings for households and businesses (WGA CDEAC 2006). Recognizing the cost-competitiveness and environmental benefits of energy efficiency, the Western Governor's Association (WGA) has set a high priority for energy efficiency, establishing a goal of reducing projected electricity demand by 20% across the West by 2020 in a policy resolution on Clean and Diversified Energy for the West (WGA 2004). Nationally, the need for improved tracking of demand-side resources in load forecasting is formalized in the North American Electric Reliability Council (NERC)'s recently adopted reliability standards, which utilities and regional reliability organizations will need to comply with (NERC 2005a and 2005b). In this study, we examine the treatment of energy efficiency in recent resource plans issued by fourteen investor-owned utilities (IOUs) in the Western United States and Canada. The goals of this study are to: (1) summarize energy-efficiency resources as represented in a large sample of recent resource plans prepared by Western utilities and identify key issues; (2) evaluate the extent to which the information provided in current resource plans can be used to support region-wide resource assessment and tracking of state/utility progress in meeting the WGA's energy-efficiency goals (WGA 2004); and (3) offer recommendations on information and documentation of energy-efficiency resources that should be included in future resource plans to facilitate comparative review and regional coordination. The scope of this report covers projected electric end-use efficiency investments reported in all Western utility resource plans that were publicly available as of February 2006. While a few utilities included additional demand-side resources, such as demand response, in their plans, we do not report that information. However, many of the issues and recommendations in reference to energy efficiency in this report are relevant to other demand-side resources as well. This report is organized as follows. Section 2 outlines the data sources and approach used in this study and conceptualizes methods and metrics for tracking energy-efficiency resources over time. Section 3 presents results from the review of the utility resource plans. Important issues encountered in reviewing the resource plans are discussed in section 4. Finally, section 5 concludes with recommendations for improving the tracking and reporting of energy efficiency in forthcoming resource plans.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".