Installation of new energy sources as redundant black-start (BS) units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the smart grid performance during both restoration and normal conditions. To this end, there will be contradictory objective functions to be minimized. Therefore, a multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called crow search algorithm (CSA). A typical test system is used for validation of the proposed method. The simulation results reveal that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. It also provides the optimal start-up sequences of non-blackstart (NBS) units with the optimal transmission paths during the restoration process.

Multi-Objective Model for Allocation of Gas Turbines with the Aim of Black-Start Capability Enhancement in Smart Grids

R. Faranda;H. Hafezi;
2019-01-01

Abstract

Installation of new energy sources as redundant black-start (BS) units is an efficient way to enhance the speed of power system restoration, especially when there is a high risk that the available power plants considered as BS units fail to operate. In this regard, this paper provides a new optimal design for the placement of the Gas Turbine (GT) as the redundant energy source to improve the smart grid performance during both restoration and normal conditions. To this end, there will be contradictory objective functions to be minimized. Therefore, a multi-objective problem (MOP), as a mixed integer linear programming (MILP), is defined. The Pareto optimal solutions of the MOP are obtained by using a new population-based meta-heuristic technique, called crow search algorithm (CSA). A typical test system is used for validation of the proposed method. The simulation results reveal that the system can benefit from this method not only to increase the capability of black-start generation, but also to improve the power system performance in normal conditions. It also provides the optimal start-up sequences of non-blackstart (NBS) units with the optimal transmission paths during the restoration process.
2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe), ISGT 2019
power system restoration, black-start units, crow search algorithm, multi objective design, smart grid
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1118735
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