Optimal Allocation of Fast Charging Station for Integrated Electric-Transportation System Using Multi-Objective Approach

The usage of Electric Vehicles (EVs) for transportation is expected to continue growing, which opens up new possibilities for creating new smart grids. It offers a large-scale penetration of Fast Charging Stations (FCE) in a local utility network. A severe voltage fluctuation and increased active power loss might result from the inappropriate placement of the FCE as it penetrates the Distribution System (DST). This paper proposes a multi-objective optimisation for the simultaneous optimal allocation of FCEs, Distributed Generators (DGs), and Shunted Capacitors (SCs). The proposed Pareto dominance-based hybrid methodology incorporates the advantages of the Grey Wolf Optimiser and Particle Swarm Optimisation algorithm to minimise the objectives on 118 bus radial distribution systems. The proposed method outperforms some other existing algorithms in terms of minimising (a) active power loss costs of the distribution system, (b) voltage deviations, (c) FCE development costs, (d) EV energy consumption costs, and (e) DG costs, as well as satisfying the number of FCEs and EVs in all zones based on transportation and the electrical network. The simulation results demonstrate that the simultaneous deployment technique yields better outcomes, such as the active power loss costs of the distribution system being reduced to 53.21%, voltage deviations being reduced to 68.99%, FCE development costs being reduced to 22.56%, EV energy consumption costs being reduced to 19.8%, and DG costs being reduced to 5.1%.

Automated summary

This paper proposes a multi-objective optimization method for the simultaneous optimal allocation of FCEs, DGs, and SCs. The proposed method outperforms other existing algorithms in terms of cost reduction, voltage stability, and meeting transportation requirements.