4.6 Article

Management and coordination of LTC, SVR, shunt capacitor and energy storage with high PV penetration in power distribution system for voltage regulation and power loss minimization

Journal

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.ijepes.2018.02.015

Keywords

Voltage regulation; Power losses minimization; High PV penetration; Genetic algorithm optimization

Funding

  1. National Iranian Oil Company (NIOC)

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The optimal coordination of load tap changers (LTCs), step voltage regulators (SVRs), switched shunt capacitors (SCs) and energy storages (ESs) with high penetration of photovoltaic (PV) energy sources for simultaneously minimizing energy loss and improving voltage profile are performed using genetic algorithm (GA). The GA is developed to find out the optimal settings of the LTCs, SVRs and SCs, including the dispatch of energy storages. The influence of the PV penetration level on voltage profile and tap movement is to be analyzed. Five scenarios are involved in this case study. In the first scenario, regulator devices are disabled and voltage profile is monitored and recorded. When the LTC is enabled in the second scenario, the voltage profile is propped up yet still below the voltage limits. The voltage is regulated within the limits, when the SVRs are enabled in the third scenario. In the fourth scenario, switching 'on' the capacitor banks increases the voltage profile. This maintains the distribution system security as the voltage profile gets flattened off. Finally, the integration of the ES warrants voltage profile improvement, particularly when the voltage is low at night. The voltage deviation is substantially lowered as well from a 0.063 pu without the ES to a 0.018 pu with the ES. The proposed method is applied to the IEEE 123 test feeder, using time series analysis over a diurnal 24-h simulation period. Test results clearly show that the close coordination among the VAR control devices and energy storage scales back system energy losses and upholds voltage at customer terminals within statutory limits, under high penetration levels of solar-fueled generation. Comparison between proposed method with evolutionary algorithm (EA) and particle swarm optimization (PSO) are investigated that shows the proposed algorithm has better results than the other approaches.

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