Coordinated clustering algorithms to support charging infrastructure design for electric vehicles

Abstract

The confluence of several developments has created an opportune moment for energy system modernization. In the past decade, smart grids have attracted many research activities in different domains. To realize the next generation of smart grids, we must have a comprehensive understanding of interdependent networks and processes. Next-generation energy systems networks cannot be effectively designed, analyzed, and controlled in isolation from the social, economic, sensing, and control contexts in which they operate. In this paper, we develop coordinated clustering techniques to work with network models of urban environments to aid in placement of charging stations for an electrical vehicle deployment scenario. We demonstrate the multiple factors that can be simultaneously leveraged in our framework in order to achieve practical urban deployment. Our ultimate goal is to help realize sustainable energy system management in urban electrical infrastructure by modeling and analyzing networks of interactions between electric systems and urban populations.

Electric Vehicles: A Data Science Perspective Review

“…Yes Machine Learning XGBoost, Clustering [36] Clustering [97,98] Optimization Greedy, Genetic [99] Mathematical programming [52,53,100] No Optimization Genetic [101][102][103] Mathematical programming [104][105][106][107] Simulation Queuing theory [51] Agent-based modelling [108,109] He et al [104] proposed a mathematical framework for the macroscopic deployment of charging stations taking into account the equilibrium between demand and supply of energy. User's desire to choose a destination was formulated based on: time, price, and availability of chargers.…”

“…Momtazpour et al [98] used a synthetic dataset because of the lack of real-world data. Authors take into consideration the duration of charging and decided to place chargers in locations that people visit for an extended period of time.…”

A Conceptual Framework to Integrate Electric Vehicles Charging Infrastructure into the Electric Grid

“…The authors argued that an optimal solution for placing the charging infrastructure is based on mean trip times of electric vehicles. Additionally, Momtazpour et al [15] proposed another approach to determine the location of EV charging stations. This approach is urban computing with clustering.…”

Optimization models for placement of an energy-aware electric vehicle charging infrastructure

“…Several other methods have also be investigated to improve the charging station placement strategies, such as grid partition method (Ge et al (2011)), clustering methods (Ip et al (2010) and Momtazpour et al (2012)), Voronoi diagrams for equilibrium arrangements (Koyanagi and Yokoyama (2010), Koyanagi et al (2001) and Feng et al (2012)), simulation-based methods (Ma et al (2014) and Hess et al (2012)), dynamic spatial and temporal models (Wirges et al (2012)), equilibrium modeling frameworks (He et al (2013)), continuous facility location models (Sathaye and Kelley (2013)) and flow-refueling location models (Hodgson (1990), Kuby and Lim (2005), Wang and Lin (2013), Chung and Kwon (2015)). All these methods have provided important contributions from different viewpoints on the optimal charging station placement problem.…”