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, numerous ESCOs, state energy offices, and others have contributed valuable information on energy-efficiency projects and ESCO industry activity. Review of US ESCO Industry Market Trends iv ABSTRACTThis article summarizes a comprehensive empirical analysis of U.S. energy service company (ESCO) industry trends and performance. We employ two parallel analytical approaches: a comprehensive survey of firms to estimate total industry size and a database of ~1500 ESCO projects, from which we report target markets and typical project characteristics, energy savings and customer economics. We estimate that industry investment for energy-efficiency related services reached US$2 billion in 2000 following a decade of strong growth. ESCO activity is concentrated in states with high economic activity and strong policy support. Typical projects save 150-200 MJ/m 2 /year and are cost-effective with median benefit/cost ratios of 1.6 and 2.1 for institutional and private sector projects. The median simple payback time is 7 years among institutional customers; 3 years is typical in the private sector. Reliance on DSM incentives has decreased since 1995. Preliminary evidence suggests that state enabling policies have boosted the industry in medium-sized states. ESCOs have proven resilient in the face of restructuring and will probably shift toward selling "energy solutions", with energy efficiency part of a package. We conclude that a private sector energy-efficiency services industry that targets large commercial and industrial customers is viable and selfsustaining with appropriate policy support -both financial and non-financial.

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This paper summarizes existing research on the relationship between energy efficiency and demand response. Using information gathered through interviews with program administrators, customers, and service providers, this paper discusses the coordination of energy efficiency and demand response programs, with a particular focus on current practices and opportunities. It also discusses barriers to coordinating these two types of programs. This paper was developed as a resource for a variety of audiences that may be interested in the nexus between energy efficiency and demand response. Intended audiences include policy-makers, program administrator staff, regulatory staff, service provider organizations, and stakeholders who provide input on the design and implementation of energy efficiency and demand response programs and tariffs.

Policymakers are encouraging the development of standardized and consistent methods to quantify the electric load impacts of demand response programs. For load impacts, an essential part of the analysis is the estimation of the baseline load profile. In this paper, we present a statistical evaluation of the performance of several different models used to calculate baselines for commercial buildings participating in a demand response program in California. In our approach, we use the model to estimate baseline loads for a large set of proxy event days for which the actual load data are also available. Measures of the accuracy and bias of different models, the importance of weather effects, and the effect of applying morning adjustment factors (which use data from the day of the event to adjust the estimated baseline) are presented. Our results suggest that (1) the accuracy of baseline load models can be improved substantially by applying a morning adjustment, (2) the characterization of building loads by variability and weather sensitivity is a useful indicator of which types of baseline models will perform well, and (3) models that incorporate temperature either improve the accuracy of the model fit or do not change it. Keywords:Demand response, Baseline load profile, Impacts estimation Introduction Both federal and state policymakers are encouraging the development of standardized and consistent approaches to quantify the load impacts of demand response programs. In their report to Congress on Demand Response and Advanced Metering, the Federal Energy Regulatory Commission identified the need for consistent and accurate measurement and verification of demand response as a key regulatory issue [1,2]. The California Public Utility Commission is currently overseeing a regulatory process to develop methods to estimate the load impacts of demand response (DR) programs, which will help to measure their cost-effectiveness, assist in resource planning and long-term forecasting exercises, and allow the California Independent System Operator to more effectively utilize DR as a resource.Policymakers are concerned that the methods used to estimate load reductions lead to fair and accurate compensation for DR program participants, and provide useful information to resource planners and system operators who wish to incorporate demand-side programs into the resource mix. Challenges in estimating load impacts include the diversity of customer loads and curtailment strategies, the heterogeneity in types of demand response programs and dynamic pricing tariffs, and variability in event characteristics such as timing, duration, and location [2]. Given the variability in the loads being modeled and the diversity of potential model applications, it is useful to have a general framework for evaluating the performance of different load impact estimation methods. This paper describes a new statistical analysis of the performance of different models used to calculate the baseline electric load for buildings participating in an ev...

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