Enhancing static voltage stability of a power system in the presence of large-scale PV plants using a battery energy storage control scheme by the probabilistic technique.

This paper aims to investigate the impact of large-scale photovoltaic plant (LS-PVP) controllers on the voltage stability (VS) of a power system. Besides, a new control strategy is proposed for the large-scale battery energy storage system (LS-BESS) to enhance a power system integrated with LS-PVPs, especially VS. To this end, a probabilistic technique, based on the stochastic nature of LS-PVPs and loads, is employed to accurately evaluate the impact of LS-PVPs on the system. Next, a new coupling between the optimization algorithm and the probabilistic technique is proposed to determine the optimal number, location, and capacity of the LS-BESSs by the probabilistic particle swarm optimization (PPSO) algorithm. The objective function is then used to simultaneously maximize the total VS and minimize the total power losses, line loading, as well as investment costs of LS-BESSs. Furthermore, technical performance indices (TPIs) are used for a more comprehensive investigation to compare the proposed solution to the synchronous generators. Finally, several disturbances occurring in the Nordic system are used to validate the effectiveness of the proposed approach, using the Monte Carlo simulation (MCS) method. According to the results, when a fault occurs, a system integrated with LS-PVPs in the stressed area does not operate better than synchronous generators. Therefore, an advanced control method is required to reduce undesirable effects of LS-PVPs. Using the proposed solution, voltage instability was prevented, LS-BESS investment costs were minimized, and negative effects of LS-PVPs on the system were reduced. Besides, costs required for expanding the transmission network as well as communication between controllers and reactive power compensation devices were reduced. • A new advanced control scheme is presented to enhance static voltage stability. • Technical performance indices are applied to analyze the proposed solution. • A new coupling between optimization and probabilistic technique is proposed. • Calculating the optimal number, location, and capacity of the large-scale battery. • Minimizing the undesired effects of PV plants and investment costs simultaneously. [ABSTRACT FROM AUTHOR]