Resolving Micro-grid Energy Optimization Using Multi-objective Artificial Bee Colony Algorithm

Abstract

Aiming at the micro-grid energy scheduling, this paper proposes a bio-objective optimization model of the economic and emission dispatch, where two conflicting objectives of the fuel costs and pollution emission are constructed. In order to resolve this model, a multi-objective artificial bee colony (MOABC) algorithm is proposed by using the non-dominated sorting and best-candidate strategies. Especially, an external archive is used to preserve the non-dominated solutions and the best-candidate strategy is to select the best non-dominated solutions to maintain the diversity of Pareto optimal Solutions (PS). An experiment is conducted via comparing IBEA and BC-MOABC on a set of test instances. The results show that the BC-MOABC obtains better results in terms of the convergence on PF. Furthermore, we apply BC-MOABC to resolve the micro-grid energy scheduling optimization problems. Experimental results show that the proposed model and algorithm can reasonably arrange the distributed generations and reduce the system cost and gas emissions.