Power Capacity Optimization in a Photovoltaics-Based Microgrid Using the Improved Artificial Bee Colony Algorithm  
[PERSON]  
1 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Z.X. )1 State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China; [EMAIL_ADDRESS] (H.Z. ); [EMAIL_ADDRESS] (Z.X. )1 Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung 41170, Taiwan 1117, 1117, 1117, 1117, 1117,  
of carbon dioxide emission and total system cost was established using theoptimization software (CPLEX). The capacity configuration was simulated using Energy Plus software.The above two schemes improved the capacity allocation optimization model, but there was no sufficient clarification on the weight of the multi-objective function. Prior studies usingthe optimization methods have made some progress in this field. For instance, [6] proposed a hybrid solution to meet the residential energy demand, which was composed of micro-CHP units and solar power units. Alternatively, [7] evaluated the performance of several designs ofhybrid CHP systems composed of solar thermal collectors, photovoltaic panels and natural gas internal combustion engines. Compared with the traditional mathematical methods, meta-heuristic algorithms had the advantages of fast solving speed and high accuracy in dealing with the highly coupled problems [8; 9]. The genetic algorithm (GA) was proposed to optimize the capacity of configuration for the energy consumption, operation cost, and pollutant emission index in the CCHP system [10]. Ref. [11] established a microgrid system mixed with a photovoltaic power generation system and CCHP. It wasa multi-objective function based on energy, environmental protection and economy. The particle swarm optimization (PSO) algorithm was adopted to minimize the value of the objective function. However, typical GA, PSO, DE and other algorithms may be easily trapped intoa local convergenceand get unsatisfactory results. For this reason, a new CCHP microgrid model based on a multi-objective optimization and hybrid multi-objective intelligent algorithm(C-NSGA-II) wasproposed [12]. It was focused on environmental benefit, energy saving rate and security. In [13], the chaos-mutation-while optimization algorithm achieved an optimization solution of a CCHP system in a shorter number of iterations. On the other hand, ahybrid algorithm that combined DE and PSO algorithms was proposed to optimize the CCHP systems [14]. The artificialbee colony (ABC)algorithm is often used to optimize the weighting factors of a multi-objective function [15]. Therefore, a new optimizationapproach based on the ABC algorithm was developed to determine the optimal DG-unit's size, power factor, and location for minimizing the total system real power loss [16]. In [17], a multi-period ABC optimization algorithm wasproposed foreconomic dispatch, considering generation, storage and responsiveload offers. However, the convergence rate may decrease when it is applied to a multi-dimensional multi-objective function model. To sum up, the optimization of capacity allocation in the CCHP microgrid still requires further improvement as follows: (1) A new energy power generation hybrid system should be given more attention because of the greatchallenge of the recent energy crisis and environmental problems. (2) The multi-objective function constructed by taking the economic, energy and environmental factors should find an appropriate weight for the capacity allocation. (3) PSO, GA, DE and other typical algorithms were widely applied to find the optimization solutionfor the power capacity allocation, but they may be trapped into the local minima solution, which will affect the optimization performance. In this paper, mathematical model that considers the hybrid microgrid based on the photovoltaic and CCHP systemis built.