LAUSR

The conservation voltage reduction (CVR) technique is a well-established technique for peak demand reduction and energy savings in the presence of voltage-sensitive loads. However, intermittent and uncertain nature of renewable energy sources such as photovoltaic generation may produce some operational issues under real-time CVR operation, which results in major problems such as undervoltage as well as increase in power losses in an active distribution network. Advanced power-electronics-based devices such as inverter-based distributed generators (IDGs) and soft open point (SOP) devices emerge as potential solutions for the aforementioned issues by providing the volt–ampere reactive (VAR) support. However, an efficient CVR methodology is much needed for proper coordination of multiple traditional volt/VAR control devices and advanced power-electronics-based devices. Therefore, this article introduces a multistage coordinated CVR methodology incorporating IDG and SOP devices for offline (i.e., day-ahead scheduling) and online (i.e., real-time dispatch) studies. For day-ahead scheduling, stochastic CVR formulation has been developed considering intermittency and uncertainties in power loads and generations. In contrast, online simulations have been performed in two stages for near-real-time operation using an inner-day rolling control model based on model-predictive control and real-time control through modified volt/VAR droop (VVD) control by optimal VVD curve selection. To validate the proposed scheme, a real-time cosimulation platform using a real-time digital simulator and a MATLAB–GAMS interface has been built. The efficacy of the proposed method has been validated on various case studies and also tested on sudden external disturbances such as sudden cloud transient, cloud cover, and substation voltage changes. Furthermore, the robustness of the proposed method has also been checked under faulty conditions. The test results demonstrate the significance of the proposed methodology on voltage violation mitigation, energy loss, energy consumption minimization, and service restoration.