AUTHOR=Jia Rui , Xia Xiangwu , Xuan Yi , Sun Zhiqing , Gao Yudong , Qin Shuo 

TITLE=Low-carbon planning of urban charging stations considering carbon emission evolution characteristics and dynamic demand

JOURNAL=Frontiers in Energy Research

VOLUME=Volume 12 - 2024

YEAR=2024

URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2024.1359824

DOI=10.3389/fenrg.2024.1359824

ISSN=2296-598X

ABSTRACT=As a new generation of transportation, electric vehicles play an important role in carbon peak targets.The development of electric vehicles needs the support of charging network, and improper planning of charging stations will result in a waste of resources. In order to expand the charging network of electric vehicles and give full play to the low-carbon and efficient characteristics of electric vehicles, this paper proposes a charging station planning method that considers the characteristics of carbon emission trends. This paper combined the long short-term memory (LSTM) network with STIRPAT model to predict the carbon emission trend, and quantified the correlation between the construction speed of charging station and the evolution characteristics of carbon emission by Pearson correlation coefficient. A multi-stage charging station planning model is established, which captures the dynamic characteristics of the charging demand of the transportation network, and determines the station deployment scheme with economic and low carbon benefits on the spatio-temporal scale. The Pareto frontier is solved by using the elitist nondominated sorting genetic algorithm. The model and solution algorithm are verified by the actual road network in a certain area of Shanghai. The results show that the proposed scheme can meet the charging demand of regional electric vehicles in the future, improve the utilization rate of charging facilities and reduce the carbon emission of transportation network.