A new smart charging electric vehicle and optimal DG placement in active distribution networks with optimal operation of batteries

• Published in: Results in Engineering
• Main Authors: Bilal Naji Alhasnawi, Marek Zanker, Vladimír Bureš
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2025
• Subjects: IEEE 85-bus distributed network; IEEE 33-bus distribution network; Batteries; Electric vehicles; Optimization algorithms; Technology; T; Beluga; Beluga whale; Beluga*
• Online Access: https://doi.org/10.1016/j.rineng.2025.104521; https://doaj.org/article/e14e3ca9a29f44ac97c65489bbf4e331

The idea of Distribution Networks (DNs) is being developed to automate networks and better integrate renewable energy sources. To do this, the DNs integrate energy storage systems with Distributed Generating Units (DGs). This research report attempts to accomplish too many goals at once. In order to reduce MGs' reliance on the main grid, this study first proposes a smart charging method for PHEVs that maximizes the utilization of RERs and DERs while minimizing the amount of energy taken from the main grid. Second, the issue of how to best operate lithium-ion batteries to raise the technical, financial, and environmental indices of both independent and grid-connected distribution networks is addressed in this work. Thirdly, this paper proposes an optimization technique based on the Mountain Gazelle Optimizer (MGO), Improved Beluga Whale Optimization (IBWO), and Arithmetic Optimization Algorithm (AOA) for determining the optimal DGs in radial distribution systems. The effectiveness of the suggested framework is tested on IEEE 33-bus and IEEE 85-bus systems, and the findings demonstrate that, in spite of the complexity that arises from changing situations, the model offers an effective restoration solution. The proposed method finds reductions of about 6.83 % in power losses using AOA, reductions of about 17.92 % in power losses using IBWO, reductions of about 22.69 % in power losses and reductions of about 25.43 % in CO2 emissions using MGO, when compared to the benchmark case in the IEEE 33-bus network. whereas the proposed method finds reductions of about 1.31 % in power losses using AOA, reductions of about 15.85 % in power losses using IBWO, reductions of about 19.48 % in power losses and reductions of about 23.27 % in CO2 emissions using MGO, when compared to the benchmark case in the IEEE 85-bus network.