Abstract:
With the emergence of the smart grid, the distribution network is facing various
problems, such as power limitations, voltage uncertainty, and many others. Apart from
the power sector, with the growth of electric vehicles (EVs), power demand is rising
day by day. These problems can potentially lead to blackouts. This paper presents three
meta-heuristic techniques: grey wolf optimization (GWO), whale optimization
algorithm (WOA), and dandelion optimizer (DO) for optimal allocation (sitting and
sizing) of solar photovoltaic (SPV), wind turbine generation (WTG), and electric
vehicle charging stations (EVCSs). The aim of implementing these techniques is to
optimum allocation of renewable energy distributed generation (RE-DG) for reducing
active power losses, reactive power losses, total voltage deviation and to improve the
voltage stability index in radial distribution networks (RDNs). MATLAB 2022 is used
for the simulation of meta-heuristic techniques. The proposed techniques are
implemented on IEEE 33-bus RDN for optimal allocation of RE-DGs and EVCSs
while considering seasonal variations and uncertainty modeling. The results validate
the efficiency of meta-heuristic techniques with a substantial reduction of active power
loss, reactive power loss, and improvement of voltage profile with optimal allocation
across all considered scenarios. The corelative analysis reveals that the proposed
techniques with existing literature validate the better execution of proposed
techniques.
