Ratio Analysis of Fast Charger in the Design of an EV Charging Station using MCMC Sampling

Keyword: Charging Infrastructure Electric Vehicle Fast Charger Markov Chain Monte Carlo Sampling Probabilistic Model

Mesh Keyword: Charging infrastructures Charging station Electric vehicle charging Electric vehicle charging infrastructures Fast chargers Growing demand Markov chain monte carlo samplings MCMC sampling Probabilistic models Ratio analysis

All Science Classification Codes (ASJC): Energy Engineering and Power Technology Nuclear Energy and Engineering Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering

Abstract: This study addresses the urgent challenge of expanding the electric vehicle (EV) charging infrastructure to meet the growing demand as EV adoption grows. The cost difference between fast and slow chargers is as much as sevenfold, and EV aggregators require assistance designing more cost-effective charging stations. The proposed method provides a method for determining the ratio of fast chargers in an EV charging station based on simulation analysis. The EV charging demand model is generated by MCMC sampling using Markov Chain characteristics, and a probability distribution based on actual data is applied. Ratio analysis of multiple charger combinations presents an insightful design for the EV aggregator.

URI: https://aurora.ajou.ac.kr/handle/2018.oak/37141
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85212677349&origin=inward

Funding: This research was supported by Energy AI Convergence Research & Development Program through the National IT Industry Promotion Agency of Korea(NIPA) funded by the Ministry of Science and ICT (No. S1601-20-1005)

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