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Unit commitment using time-ahead priority list and heterogeneous comprehensive learning PSO

In power systems, Unit commitment problem (UCP) is mixed-integer nonlinear combinatorial optimization problem that gives the schedule of power generating units (binary on/off (0/1) and continuous power variables) while satisfying the system as well as unit constraints with the minimum production cost. In this paper, we propose a new hybrid method combining priority list (PL) and particle swarm optimization (PSO) to tackle the UCP. Firstly, we develop a new priority list method called time-ahead priority list (TPL) to determine binary on/off schedule of power generating units. Then a variant of PSO called heterogeneous comprehensive learning particle swarm optimization (HCLPSO) is applied to determine the amount of power to be generated with the minimum production cost. In addition, epsilon constraint handling method is implemented to handle the constraints of UCP. The performance of the proposed hybrid model, referred to as TPL_HCLPSO, is evaluated on the well-known benchmark power systems and compared with recent deterministic, stochastic and mainly with the hybrid approaches. The simulation results indicate that the proposed hybrid model achieves a remarkable cost reduction in power production and provides the lowest minimum production cost compared to the recent UCP approaches.