Savasci, AlperCeylan, OǧuzhanPaudyal, Sumit2025-09-252025-09-25202497814673272759781538677032979835038183297814799130399781665405072978146738040997815090416889781728119816978172815508197814799641541944-99251944-9933https://doi.org/10.1109/PESGM51994.2024.10760279https://hdl.handle.net/20.500.12573/3561This study presents machine learning-based dispatch strategies for legacy voltage regulation devices, i.e., onload tap changers (OLTCs), step-voltage regulators (SVRs), and switched-capacitors (SCs) in modern distribution networks. The proposed approach utilizes k-nearest neighbor (KNN), random forest (RF), and neural networks (NN) to map nodal net active and reactive injections to the optimal legacy controls and resulting voltage magnitudes. To implement these strategies, first, an efficient optimal power flow (OPF) is formulated as a mixed-integer linear program that obtains optimal decisions of tap positions for OLTCs, SVRs, and on/off status of SCs. Then, training and testing datasets are generated by solving the OPF model for daily horizons with 1-hr resolution for varying loading and photovoltaic (PV) generation profile. Case studies on the 33-node feeder demonstrate high-accuracy mapping between the input feature and the output vector, which is promising for integrated Volt/VAr control schemes. © 2024 Elsevier B.V., All rights reserved.eninfo:eu-repo/semantics/closedAccessDistribution GridMachine LearningOptimal Power FlowVoltage ControlInteger ProgrammingLoad Flow ControlLoad Flow OptimizationMixed-Integer Linear ProgrammingNearest Neighbor SearchPower Distribution NetworksRandom ForestsControl DeviceData-Driven MethodsDispatch StrategyDistribution GridMachine-LearningOnload Tap ChangerOptimal Power FlowsOptimal SettingStep Voltage RegulatorsSwitched CapacitorDecision TreesConference Object10.1109/PESGM51994.2024.107602792-s2.0-85212398232