Constrained unit commitment based power generation dispatching with integration of PHEVs

In this paper, presents a metaheuristics technique quasi reflected oppositional heat transfer search (QROHTS) algorithm for solve the generation scheduling of thermal units integrated with plug-in hybrid electric vehicles (PHEVs) in an electrical power system to optimize fuel cost (FC) and emissions. Heat transfer search algorithm's mechanism is inspired by thermal equilibrium can be used to derive effective mechanisms for searching in a high-dimensional solution space. As algorithms are population based so enables to provide improved solution with integration of powerful techniques. In this paper, such a powerful technique named opposite based learning techniques (OBLT) is integrated with HTS algorithm. OBLT provides enough strength to HTS algorithm to gain a better approximation for both current and opposite population at the same time, as it provides a solution which is 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 QROHTS algorithm is applied to IEEE 10 thermal unit test systems in a 24-hour load scheduling horizon. To investigate the impacts of plug-in hybrid electric vehicles on generation scheduling. The results obtained from simulation analysis show a significant techno-economic saving.