Introduction to Commercial Building Control Strategies and Techniques for Demand Response -- Appendices

Motegi, Naoya; Piette, Mary Ann; Watson, David S.; Kiliccote, Sila; Xu, Peng

doi:10.2172/1004169

Cited by 153 publications

(131 citation statements)

References 3 publications

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“…Examples of strategies include dimming or turning off non-critical lights, changing thermostat set-points, or turning off non-critical equipment. The site characteristics, selection and implementation of control strategies, and results of field tests have been extensively documented in reports available at the Demand Response Research Center web site [3][4][5]14]. The appendices of references [4,5,14] provide summary information for the sites included in this study.…”

Section: Introductionmentioning

confidence: 99%

Statistical analysis of baseline load models for non-residential buildings

Coughlin

Piette

Goldman

et al. 2009

Energy and Buildings

104

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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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Section: Introductionmentioning

confidence: 99%

Statistical analysis of baseline load models for non-residential buildings

Coughlin

Piette

Goldman

et al. 2009

Energy and Buildings

104

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“…Therefore, the implementation of DSM in this sector is easier than that of the residential sector. In [46] the authors propose several common methods to decrease the electricity load and DSM of commercial buildings and [47] provides a method for validating the DSM for commercial buildings.…”

Section: Classification Of Dsm Modelsmentioning

confidence: 99%

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Divshali

Choi

2016

Energies

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Rising demand, climate change, growing fuel costs, outdated power system infrastructures, and new power generation technologies have made renewable distribution generators very attractive in recent years. Because of the increasing penetration level of renewable energy sources in addition to the growth of new electrical demand sectors, such as electrical vehicles, the power system may face serious problems and challenges in the near future. A revolutionary new power grid system, called smart grid, has been developed as a solution to these problems. The smart grid, equipped with modern communication and computation infrastructures, can coordinate different parts of the power system to enhance energy efficiency, reliability, and quality, while decreasing the energy cost. Since conventional distribution networks lack smart infrastructures, much research has been recently done in the distribution part of the smart grid, called smart distribution grid (SDG). This paper surveys contemporary literature in SDG from the perspective of the electricity market in addition to power system considerations. For this purpose, this paper reviews current demand side management methods, supply side management methods, and electrical vehicle charging and discharging techniques in SDG and also discusses their drawbacks. We also present future research directions to tackle new and existing challenges in the SDG.

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“…Generally and miscellaneous equipment such as industrial process loads, cold storage, and irrigation water pumps [5]. There are several ways to control the demand on the customer's side.…”

Section: A Pgande's Critical Peak Pricing Programmentioning

confidence: 99%

Regression models for demand reduction based on cluster analysis of load profiles

Yamaguchi

Han

Ghatikar

et al. 2009

2009 IEEE PES/IAS Conference on Sustainable Alternative Energy (SAE)

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Abstract-This paper provides new regression models for demand reduction of Demand Response programs for the purpose of ex ante evaluation of the programs and screening for recruiting customer enrollment into the programs. The proposed regression models employ load sensitivity to outside air temperature and representative load pattern derived from cluster analysis of customer baseline load as explanatory variables.The proposed models examined their performances from the viewpoint of validity of explanatory variables and fitness of regressions, using actual load profile data of Pacific Gas and Electric Company's commercial and industrial customers who participated in the 2008 Critical Peak Pricing program including Manual and Automated Demand Response.

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Introduction to Commercial Building Control Strategies and Techniques for Demand Response -- Appendices

Cited by 153 publications

References 3 publications

Statistical analysis of baseline load models for non-residential buildings

Statistical analysis of baseline load models for non-residential buildings

Electrical Market Management Considering Power System Constraints in Smart Distribution Grids

Regression models for demand reduction based on cluster analysis of load profiles

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