White Shark Optimizer for Multi-Objective Optimal Allocation of Photovoltaic Distribution Generation in Electrical Distribution Networks Considering Different Kinds of Load Models and Penetration Levels

Abstract

In recent times, reduction of greenhouse gas (GHG) emission in conventional power grids becomes most important towards global sustainability. Thus, adaption of renewable energy (RE) sources in electricity sector has become alternative solution. Photovoltaic (PV) technology has become most adapted RE technology even at small scale consumers levels due to their clean and noiseless operation, low investment and operational cost and easy to install and maintenance. In this paper, a new heuristic approach white shark optimizer (WSO) is introduced for solving the PV based distribution generation (DG) in electrical distribution networks (EDNs). Loss minimization, voltage profile improvement and reduction in GHG emission are considered for formulating the proposed multiobjective function. Simulation studies are performed on IEEE 33-bus EDN for different kinds of load penetrations. The computational efficiency of WSO is quantified using 50 independent simulations and compared with various other recent algorithms such as pathfinder algorithm (PFA), mayfly optimization algorithm (MOA), coyote optimization algorithm (COA), and future search algorithm (FSA). In various case studies, WSO has resulted for best solution than compared algorithms. A similar kind of overall improvement is observed even for increased load penetration levels along with improved voltage profiles by having PV based DGs in EDN optimally.

NeSOS Based GA Operator for Optimal Single-Phase DG in Unbalanced and Harmonic Polluted Distribution Network

“…In the past twenty years, various studies have explored the advantages of integrating DG into the power system [4,5]. Those studies generally focused on linear loads, assuming the distribution system is in a balanced state.…”