The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide-area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi-objective problem (MOP) is proposed as a mixed-integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced.

Power system observability enhancement for parallel restoration of subsystems considering renewable energy resources

Siano P.
2020-01-01

Abstract

The observability of power systems during the parallel restoration of subsystems is one of the most important issues for system operators to accomplish the restoration task as quick as possible. Thus, this article proposes a coordinated optimal plan to solve the observability and sectionalizing problems by determining the locations of phasor measurement units (PMUs) and subsystems. Also, the impact of renewable energy resources on power system sectionalizing and the reliability value of power generation are taken into account in the proposed model. The objective functions that are considered in the optimization problem are the cost of wide-area measurement system (WAMS), the worst observability index among all subsystems and the lowest value of quality among all subsystems based on the reliability of subsystems. Since there are three contradictory objective functions, a multi-objective problem (MOP) is proposed as a mixed-integer nonlinear problem (MINLP). The Pareto curve of the proposed MOP is extracted by using a particle swarm optimization (PSO) algorithm. Two standard power grids are considered to validate the suggested technique. The outcomes of simulations confirm that the observability value of all sections is enhanced during the parallel restoration of the system. Also, the results show that the quality of subsystems in the presence of renewable energy resources is enhanced.
2020
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4757689
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