Multi-objective coordinated EV charging strategy in distribution networks using an improved augmented epsilon-constrained method

The surging adoption of electric vehicles (EVs) poses significant challenges for distribution networks (DNs) due to EV charging impact. This paper presents a multi-objective optimization (MOO) model that coordinates EV charging in DNs, aiming to address the interests of different stakeholders, such as the distribution network operator (DNO) and EV owners and achieve a balanced outcome. Specifically, our model’s objectives include minimizing the operation costs for the DNO, power loss in the DN, and EV owners’ charging expenses, emphasizing the delicate trade-off between these objectives. An innovative improved-augmented epsilon-constrained (I-AUGMENCON) method is proposed to tackle the complex trade-offs by solving the MOO effectively and efficiently. A case study on an modified IEEE 33-bus DN attests to the strategy’s efficacy, showcasing a range of solutions that coordinate DNO’s costs, DN power loss, and EV charging costs. Furthermore, our I-AUGMENCON outperforms other prevalent MOO solution methods, such as the weighted-sum and Non-dominated Sorting Genetic Algorithm II (NSGA-II), in determining non-dominated solutions and obtaining a Pareto-efficient solution set for the MOO to characterize an effective trade-off between three key objectives. Our model coordinates EV charging to optimize economic and technical objectives, reducing power losses from 6% to around 2% and enhancing voltage stability. By balancing cost savings with power quality, the strategy improves operational efficiency and grid resilience, marking a significant advancement in complex distribution network management.