Memetic binary differential evolution to solve wind–thermal profit based unit commitment problem

Abstract

Recently, the utilization of renewable energy sources (RESs) has gained popularity all over the world due to its numerous benefits. The RESs are a good choice as a cleaner power producer with economic benefits. Power generation from wind energy is rapidly growing all over the world as compared to other RESs. Literature review reveals that a combinatorial, mixed integers (binary and real) optimization problem also known as profit based unit commitment problem (PBUCP) has been solved for only thermal generating units without using RESs. These days, the PBUCP with the integration of RESs has gained intention. In this paper, the PBUCP is solved with the integration of wind generating units in a single objective framework, wherein objective is profit maximization, as well as multi-objective framework, wherein objectives are profit maximization and emissions minimization. A memetic binary differential evolution (MBDE) algorithm is proposed to solve the wind–thermal​ PBUCP. The proposed MBDE algorithm is a blending of binary differential evolution (BDE) and binary search optimizer (BSO). The proposed hybridization improves the global searching capability of the BDE algorithm while maintaining the effective exploitation capability of the BSO. The proposed BSO makes binary perturbations to ameliorate the performance of the BDE algorithm. The achievement of the proposed algorithms has been tested on two hybrid power systems that comprise wind generating units and thermal generating units. In order to know the impacts of wind power generation on thermal power generation, the results of the wind–thermal PBUCP are compared with the results of the thermal PBUCP in the single and multi-objective framework. The best results of the proposed algorithms are collated with the best results of already applied well known algorithms and are found superior.