Unit commitment in thermal power generation dispatching with integration of PHEVs

Abstract

In this paper, presents a novel approach metaheuristics algorithm quasi oppositional passing vehicle search (QOPVS) algorithm for solve the unit commitment problem (UCP) of thermal units integrated with plug-in hybrid electric vehicles (PHEVs) in an electrical power system to optimize specific fuel consumption (FC) and emissions. Passing vehicle search (PVS) algorithm is a population based algorithm which mechanism is inspired by passing vehicles on two-lane rural highways. As algorithms are population based so enables to provide improved solution with integration of powerful techniques. A powerful technique named opposite based learning techniques (OBLT) is integrated with proposed PVS algorithm. OBLT provides enough strength to proposed PVS algorithm to gain a better approximation for both current and opposite population at the same time, as it provide a solution which is more nearer solution from optimal based from starting by checking both solutions. Thermal unit scheduling with PHEV problem is a nonlinear, nonconvex, discrete, complex and constrained optimization problem. To verify the effectiveness of the proposed QOPVS algorithm, an IEEE 10-unit test system is employed to investigate the impacts of PHEVs on generation scheduling. The results obtained from simulation analysis show a significant techno-economic saving.