Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality

Electric utilities deploy Conservation Voltage Reduction (CVR) and Volt-VAR Optimization (VVO) programs to reduce energy consumption and peak demand by lowering the voltage on the distribution system. These programs offer a cost-effective way to improve system-wide energy efficiency and to provide b...

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Autores principales:	Zohreh S. Hosseini, Mohsen Mahoor, Amin Khodaei, Md Shakawat Hossan, Wen Fan, Paul Pabst, E. Aleksi Paaso
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Conservation voltage reduction data anomaly distribution network energy savings ZIP load modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/ad3fbab275204365bd0b9fa1ccf462de
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spelling	oai:doaj.org-article:ad3fbab275204365bd0b9fa1ccf462de2021-12-02T00:00:21ZSensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality2169-353610.1109/ACCESS.2021.3128950https://doaj.org/article/ad3fbab275204365bd0b9fa1ccf462de2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9618970/https://doaj.org/toc/2169-3536Electric utilities deploy Conservation Voltage Reduction (CVR) and Volt-VAR Optimization (VVO) programs to reduce energy consumption and peak demand by lowering the voltage on the distribution system. These programs offer a cost-effective way to improve system-wide energy efficiency and to provide benefits to customers. This paper focuses on conducting a comprehensive study, modeling, simulation, and comparison to identify the sensitivity of various CVR Measurement and Verification (M&V) methodologies to various data anomaly issues. A major challenge in evaluating the results of CVR M&V methodologies is the lack of benchmark load consumption measurement when CVR is active. Therefore, a benchmark test system is created in this paper to allow access to pre-CVR measurements and enable analyses on the impact of various data anomaly issues. This benchmark is created based on real utility data (considered as pre-CVR data), and through a detailed ZIP load modeling and post-CVR data generation. The studies show that a time-varying ZIP load model, accompanied by a constrained and bounded Sequential Least-Squares Quadratic Programming (SLSQP) method for parameter identification, is suitable for precise load modeling. In this paper, SCADA data is used as it shows higher accuracy in load modeling compared to its corresponding AMI data. Consequently, the sensitivity of multiple commonly used CVR M&V methodologies, including regression-based, comparison-based, and constant CVR factor, against data anomaly issues is examined using this benchmark system. The simulation results advocate that regardless of the methodologies utilized, data anomaly issues cause divergence of the results from their original values, however, with various degrees of sensitivity.Zohreh S. HosseiniMohsen MahoorAmin KhodaeiMd Shakawat HossanWen FanPaul PabstE. Aleksi PaasoIEEEarticleConservation voltage reductiondata anomalydistribution networkenergy savingsZIP load modelingElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 157203-157214 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Conservation voltage reduction data anomaly distribution network energy savings ZIP load modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Conservation voltage reduction data anomaly distribution network energy savings ZIP load modeling Electrical engineering. Electronics. Nuclear engineering TK1-9971 Zohreh S. Hosseini Mohsen Mahoor Amin Khodaei Md Shakawat Hossan Wen Fan Paul Pabst E. Aleksi Paaso Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
description	Electric utilities deploy Conservation Voltage Reduction (CVR) and Volt-VAR Optimization (VVO) programs to reduce energy consumption and peak demand by lowering the voltage on the distribution system. These programs offer a cost-effective way to improve system-wide energy efficiency and to provide benefits to customers. This paper focuses on conducting a comprehensive study, modeling, simulation, and comparison to identify the sensitivity of various CVR Measurement and Verification (M&V) methodologies to various data anomaly issues. A major challenge in evaluating the results of CVR M&V methodologies is the lack of benchmark load consumption measurement when CVR is active. Therefore, a benchmark test system is created in this paper to allow access to pre-CVR measurements and enable analyses on the impact of various data anomaly issues. This benchmark is created based on real utility data (considered as pre-CVR data), and through a detailed ZIP load modeling and post-CVR data generation. The studies show that a time-varying ZIP load model, accompanied by a constrained and bounded Sequential Least-Squares Quadratic Programming (SLSQP) method for parameter identification, is suitable for precise load modeling. In this paper, SCADA data is used as it shows higher accuracy in load modeling compared to its corresponding AMI data. Consequently, the sensitivity of multiple commonly used CVR M&V methodologies, including regression-based, comparison-based, and constant CVR factor, against data anomaly issues is examined using this benchmark system. The simulation results advocate that regardless of the methodologies utilized, data anomaly issues cause divergence of the results from their original values, however, with various degrees of sensitivity.
format	article
author	Zohreh S. Hosseini Mohsen Mahoor Amin Khodaei Md Shakawat Hossan Wen Fan Paul Pabst E. Aleksi Paaso
author_facet	Zohreh S. Hosseini Mohsen Mahoor Amin Khodaei Md Shakawat Hossan Wen Fan Paul Pabst E. Aleksi Paaso
author_sort	Zohreh S. Hosseini
title	Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
title_short	Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
title_full	Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
title_fullStr	Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
title_full_unstemmed	Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality
title_sort	sensitivity analyses of cvr measurement and verification methodologies to data availability and quality
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/ad3fbab275204365bd0b9fa1ccf462de
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Sensitivity Analyses of CVR Measurement and Verification Methodologies to Data Availability and Quality

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