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...
Municipal water reuse can contribute to a circular water economy in different contexts and with various treatment trains. This study synthesized information regarding the current technological and regulatory statuses of municipal reuse. It provides process-level information on cost and energy metrics for three potable reuse and one nonpotable reuse case studies using the new Water Techno-economic Assessment Pipe-Parity Platform (WaterTAP3). WaterTAP3 enabled comparisons of cost and energy metrics for different treatment trains and for different alternative water sources consistently with a common platform. A carbon-based treatment train has both a lower calculated levelized cost of water (LCOW) ($0.40/m3) and electricity intensity (0.30 kWh/m3) than a reverse osmosis (RO)-based treatment train ($0.54/m3 and 0.84 kWh/m3). In comparing LCOW and energy intensity for water production from municipal reuse, brackish water, and seawater based on the largest facilities of each type in the United States, municipal reuse had a lower LCOW and electricity than seawater but higher values than for production from brackish water. For a small (2.0 million gallon per day) inland RO-based municipal reuse facility, WaterTAP3 evaluated different deep well injection and zero liquid discharge (ZLD) scenarios for management of RO concentrate. Adding ZLD to a facility that currently allows surface discharge of concentrate would approximately double the LCOW. For all four case studies, LCOW is most sensitive to changes in weighted average cost of capital, on-stream capacity, and plant life. Baseline assessments, pipe parity metrics, and scenario analyses can inform greater observability and understanding of reuse adoption and the potential for cost-effective and energy-efficient reuse.
This paper presents the results of a survey and analysis of electricity tariffs and marginal electricity prices for commercial buildings. The tariff data come from a survey of 90 utilities and 250 tariffs for non-residential customers collected in 2004 as part of the Tariff Analysis Project at LBNL [2]. The goals of this analysis are to provide useful summary data on the marginal electricity prices commercial customers actually see, and insight into the factors that are most important in determining prices under different circumstances. We provide a new, empirically-based definition of several marginal prices: the effective marginal price, and energy-only and demand-only prices, and derive a simple formula that expresses the dependence of the effective marginal price on the marginal load factor. The latter is a variable that can be used to characterize the load impacts of a particular end-use or efficiency measure. We calculate all these prices for eleven regions within the continental U.S. The methodology developed here can be adapted to any particular customer or utility sub-sample that may be of interest.
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