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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&#x0026;V) methodologies to various data anomaly issues. A major challenge in evaluating the results of CVR M&#x0026;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&#x0026;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.

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