Moth Flame Algorithm to Optimize the Size and Location of Energy Storage Units in a Radial Distribution Network to mitigate Power Loss

Abstract

The ideal placements and capacities for Battery Energy Storage Systems (BESS) sources in a distributed generation (DG) environment have an impact on power losses in the distribution network. Therefore, engineers are quite concerned with DG and BESS. The output from DG is, however, typically unpredictable and inconsistent. The secure and stable functioning of the power system will be impacted to varying degrees when it is connected to various sites, capabilities, and power grids. To provide a safer, more stable, more dependable, as well as more effective power grid operation, power grid planners must take the impact of capacity, type, and location into account while choosing the best BESS accessibility method. The objective is to choose bus stops with the right potential and minimal loss. This study demonstrates how to use a Moth Flame Optimization Algorithm (MFOA) to establish the appropriate battery energy storage system (BESS) sizing in a hybrid solar and wind energy conversion system (WECS) generation integrated in a radial distribution network. This research investigates the optimal capacities and allotment of the BESSs within a 33-bus and 69-bus radial network of distribution for the maximum power dissipation reduction. This research also provides a framework for carrying out conservation voltage reduction (CVR) when distributed energy resources like solar photovoltaic (PV) systems, WECS, and energy storage systems (ESSs) are connected into distribution networks. The simulation has been conducted using Matlab code and the suggested method's output is compared with Firefly Algorithm (FA), Genetic Algorithm (GA) and Ant Colony Algorithms (ACA) and evaluated against other results.