Strategic planning of distribution network integrated with EV charging stations using fuzzy pareto optimality for performance improvement and grid-side emission reduction benefits

The increasing penetration of electric vehicles (EVs) in distribution systems requires efficient charging load management techniques for delivering high-quality power to consumers. In this paper, we proposed a strategic planning approach for distribution systems integrated with EV charging stations, employing fuzzy Pareto optimality to enhance system performance and minimize carbon emissions. Our approach involves a fuzzy Pareto heuristic network reconfiguration to minimize real power loss and improve voltage profiles. Additionally, Time of Use (ToU) pricing-based EV charging load scheduling is developed to minimize the annual energy cost. These objectives aim to improve minimum node voltage, reduce power losses, and maintain branch current constraints. To simulate the impact of EV charging loads, we aggregate residential, commercial, and industrial loads, along with EV charging loads at charging stations based on customer choice time and ToU pricing methods. The proposed reconfiguration approach is tested on 33 and 69-bus distribution systems with integrated EV charging stations, distributed generations (DGs), and shunt capacitors (SCs). The simulation results demonstrate the ad-vantages of the reconfiguration algorithm in enhancing the performance of the distribution network in the presence of EV loads. Furthermore, we compare ToU pricing-based EV charging time zones with customer comfort-based EV charging, showcasing the advantages of the former in reducing emissions through real power loss reduction. Overall, our strategic planning approach not only improves distribution system performance but also offers economic benefits while minimizing carbon dioxide emissions.

Automated summary

This paper proposes a strategic planning approach for distribution systems integrated with EV charging stations, employing fuzzy Pareto optimality to enhance system performance and minimize carbon emissions. The approach includes fuzzy Pareto heuristic network reconfiguration and Time of Use (ToU) pricing-based EV charging load scheduling. Simulation results demonstrate the advantages of the proposed approach.