Voltage regulation methods for active distribution networks considering the reactive power optimization of substations

Wei Ma, Wei Wang, Zhe Chen, Xuezhi Wu, Ruonan Hu, Fen Tang and Weige Zhang

Applied Energy, 2021, vol. 284, issue C, No S0306261920317293

Abstract: Due to the increasing penetration of photovoltaic (PV) power systems in active distribution networks (ADNs), PV power fluctuations may result in significant voltage variations of ADNs. Therefore, this paper proposes a voltage regulation method for ADNs to minimize the operational losses while keeping the nodal voltages within the limit with the reduced PV power curtailment and the reduced switching numbers of on-load tap changers (OLTCs) and capacitor banks (CBs). Meanwhile, the proposed voltage regulation method also aims to minimize the reactive power flowing through OLTCs, and to minimize the switching numbers of substation CBs. In this study, the centralized voltage regulation is performed based on the worst voltage variation scenarios of ADNs, where a multi-objective mixed integer nonlinear programming (MINP) model with time-varying decision variables is established. The MINP model is solved using the non-dominated sorting genetic algorithm II (NSGA-II), and a practical decision-making algorithm is developed to select the best solution from the Pareto optimal set. Moreover, the decentralized voltage regulation aims at mitigating real-time nodal voltage variations via adjusting the real-time active and reactive power of each PV plant. Several simulations and comparisons are carried out on a modified IEEE 33-node system to verify the effectiveness of the proposed methods, and to compare with some previous voltage regulation methods. Simulation results show that the proposed voltage regulation methods can not only effectively control voltage variations of ADNs but also improve the economics of ADNs, substations, and PV plants.

Keywords: Active distribution network; Voltage regulation; Photovoltaic power fluctuations; Voltage variations; Multi-objective mixed integer nonlinear programming; Non-dominated sorting genetic algorithm II (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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DOI: 10.1016/j.apenergy.2020.116347

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