Probabilistic CVR Assessment in Distribution Networks using Synthetic Consumption Database of Household Appliances

Abstract

The operational behavior of modern active distribution networks differs greatly from their passive predecessors. Active network management schemes, such as Conservation Voltage Reduction (CVR), are being widely explored to enhance energy conservation and network resiliency. CVR can potentially decrease energy consumption in distribution networks by reducing the voltage at load terminals. However, accurately assessing CVR suitability for distribution feeders is paramount, as it exploits the voltage dependency of load. Most CVR assessment schemes require extensive data from smart meters, micro-pmus, and detailed network topology information. This paper presents a novel synthetic data-based approach for probabilistic CVR assessment, which uses customer categorization to generate realistic synthetic data for probabilistic assessment, taking into account the voltage dependency of common household appliances. Additionally, an extended version of the probabilistic synthetic-data-based approach is introduced to enhance the accuracy of CVR assessment using limited distribution network information. MATLAB was used for data handling and customer categorization, while OpenDSS was used for power flow analysis to evaluate CVR effectiveness. The proposed CVR assessment methodologies are tested and validated in detail for the TOPI distribution network in KPK, Pakistan. The study’s findings shows that CVR can significantly reduce energy and cost, with a 10.13% reduction in energy use and approximately 4 million PKR in cost