Multi-objective coordinated EV charging strategy in distribution networks using an improved augmented epsilon-constrained method
Yunqi Wang, Hao Wang, Reza Razzaghi, Mahdi Jalili, Ariel Liebman
Abstract
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.