Method for Planning, Optimizing, and Regulating EV Charging Infrastructure

Author:

Chowdhury AmorORCID,Klampfer Saša,Sredenšek KlemenORCID,Seme SebastijanORCID,Hadžiselimović Miralem,Štumberger Bojan

Abstract

The paper presents and solves the problems of modeling and designing the required EV charging service capacity for systems with a slow dynamic component. This includes possible bursts within a peak hour interval. A simulation tool with a newly implemented capacity planning method has been developed and implemented for these needs. The method can be used for different system simulations and simultaneously for systems with high, medium, and low service dynamics. The proposed method is based on a normal distribution, a primary mechanism that describes events within a daily interval (24 h) or a peak hour interval (rush hour). The goal of the presented approach, including the proposed method, is to increase the level and quality of the EV charging service system. The near-optimal solution with the presented method can be found manually by changing the service capacity parameter concerning the criterion function. Manual settings limit the number of rejected events, the time spent in the queue, and other service system performance parameters. In addition to manual search for near-optimal solutions, the method also provides automatic search by using the automation procedure of simulation runs and increasing/decreasing the service capacity parameter by a specifically calculated amount.

Publisher

MDPI AG

Subject

Energy (miscellaneous),Energy Engineering and Power Technology,Renewable Energy, Sustainability and the Environment,Electrical and Electronic Engineering,Control and Optimization,Engineering (miscellaneous),Building and Construction

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review;Energies;2023-07-17

2. Research on Intelligent Charging Management of New Energy Vehicles Based on Big Data;Proceedings of the 2023 International Conference on Frontiers of Artificial Intelligence and Machine Learning;2023-04-14

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