Multi-objective LSF-MALO optimization for distributed generation placement in distribution systems considering EVCS integration

The rapid growth of Distributed Generation (DG) and Electric Vehicle Charging Stations (EVCS) in modern distribution networks has introduced technical and operational challenges that conventional planning and optimization approaches fail to address. While DG offers benefits such as loss reduction, improved voltage stability and enhanced sustainability, its performance is highly dependent on proper placement and sizing strategies. EVCS, on the other hand, creates voltage fluctuations, higher power losses, increased grid dependency and harmonic distortion, which significantly degrade network reliability. This research develops a new hybrid optimization technique, known as the Loss Sensitivity Factor - Mutated Ant Lion Optimizer (LSF-MALO), to address these issues. The technique combines the computational efficiency of the LSF Analytical with the global search capability of the MALO algorithm, offering both accuracy and robustness. The proposed algorithm is first validated for single objective optimization to minimize losses and improve voltage profiles and later extended into a multi-objective framework that simultaneously incorporates technical, economic and power quality aspects. A systematic assessment of distribution systems with existing charging infrastructures is conducted, explicitly incorporating both Grid-to-Vehicle (G2V) and Vehicle-to-Grid (V2G) operational modes to capture the real impact of EVCS under various loading and charging conditions. The algorithm is then applied to standard IEEE-33 bus and 69-bus radial distribution systems to evaluate its effectiveness. Results demonstrate that LSF-MALO outperforms existing analytical and metaheuristic approaches by achieving up to 63.75% reduction in real power losses and up to 77.5% in annual cost savings. Additionally, the proposed methodology consistently maintains Total Harmonic Distortion (THD) below the 5% standard limit, significantly minimizing grid dependency where other algorithms failed. Furthermore, the method ensures scalability and adaptability across diverse scenarios, providing a balanced trade-off between technical performance and economic feasibility. This research makes a substantial contribution by bridging methodological gaps and delivering a comprehensive optimization framework that supports reliable, efficient and sustainable distribution system planning in the era of increasing electric vehicle (EV) and renewable energy integration.