Robust voltage control algorithm incorporating model uncertainty impacts. Issue 17 (7th August 2019)

• Record Type: Journal Article
• Authors: Bakhshideh Zad, Bashir; Toubeau, Jean‐François; Lobry, Jacques; Vallée, François
• Is Part Of: IET generation, transmission & distribution. Volume 13:Issue 17(2019)
• Page Start: 3921
• Page End: 3931
• Publication Date: 2019-08-07
• Subjects: voltage control -- robust control -- distributed power generation -- uncertain systems -- optimisation -- load flow -- power generation economics -- power distribution control -- Monte Carlo methods; incorporating model uncertainty impacts -- voltage control problem -- medium‐voltage distribution systems -- network model -- robust voltage control algorithm -- RVCA -- transformer models -- corrective solution -- network component models -- load flow calculations -- voltage constraints management -- LF calculations -- system voltages -- control variables -- network parameters -- neglecting model uncertainty -- studied uncertainties; Electric power production -- Periodicals; Electric power transmission -- Periodicals; Electric power distribution -- Periodicals; 621.3105
• DOI: 10.1049/iet-gtd.2018.6383
• Languages: English
• ISSNs: 1751-8687

Abstract

Abstract : This study addresses the voltage control problem of the medium‐voltage distribution systems under uncertainty of the network model. A robust voltage control algorithm (RVCA) is developed in order to manage the voltage constraints considering uncertainties associated with the parameters of load, line, and transformer models. The RVCA determines a corrective solution that remains immunised against any realisation of uncertainty associated with the parameters of the network model. To this end, prior to formulating the voltage control problem, Monte Carlo (MC) simulations are used to characterise uncertain parameters of the network component models and load flow (LF) calculations are carried out to evaluate their impacts. The voltage constraints management under the uncertain environment is then formulated as a robust optimisation (RO) problem. The latter is constructed based on the results obtained through the MC simulations and LF calculations. Once the RO is solved, in order to check the robustness of the solution, system voltages are evaluated using the LF calculations considering the new set‐points of control variables and uncertainty of network parameters. The simulation results reveal that neglecting model uncertainty in the voltage control problem can lead to infeasible solutions while the proposed RVCA, at an extra cost, determines a corrective solution which remains protected against the studied uncertainties.