Understanding the Impact of Data Anomalies on Conservation Voltage Reduction Measurement and Verification - A Practical Study

Abstract

Conservation Voltage Reduction (CVR) and Volt-VAR Optimization (VVO) are becoming significant programs in grid modernization. These programs support electric utilities in reducing energy and peak demand at the distribution grid without requiring active customer participation. However, identifying their impact, which is carried out through various CVR measurement and verification (M&V) methodologies, depends heavily on the quality of the data collected from the field. This paper focuses on this critical problem for electric utilities and conducts a comprehensive study to identify the sensitivity of various M&V methodologies to common data anomaly issues, in particular, load shifts and outliers. Numerical simulations on three real-world feeders demonstrate that, regardless of the methodology employed, data anomalies cause divergence of the results from the results of using original data; however, this may be more apparent in some methods than the others.