Moth Flame Optimization for Optimal Sizing and Placement of Distributed Energy Systems

This paper proposed the application of moth flame optimization algorithm (MFO) for optimal sizing and allocation of multiple distributed generation (DG) units in electrical networks. Recently, there is significant increased penetration level of DGs in electrical networks in order to reduce power losses, improve voltage profile, security and stability of the power system. However, the optimal sizing and siting of the distributed generations are the most challenging task for most utilities. The advent of soft computing techniques has paved a way for optimal sizing and sitting of DGs in electrical networks. A lot of soft computing techniques have been proposed for sizing and sitting of multiple DGs in a distribution network, each with its merit, demerits and optimal solutions. Computational resources, objectives functions, constraints, DGs and transmission line capacities, and optimal solutions are some of the important factors considered by most researchers for selecting a particular soft computing technique. This research utilises the MFO algorithms for sizing and siting of multiple DGs in an electrical network. Active, reactive power and power factors are used in formulating the objective functions taking into cognisant power loss, DGs and transmission lines capacities. The proposed technique is applied on the IEEE 33-bus system for validation. The test results show that, the proposed (MFO) method produced an optimal solution, with less computational effort as compared to particle swamp optimization (PSO), harmony search algorithm, chaotic sine cosine algorithm, multi-objective Taguchi method and Bat Algorithm based techniques.