1. A Robust Two-Stage Planning Model for the Charging Station Placement Problem Considering Road Traffic Uncertainty
- Author
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Karuna Kalita, Sam Cross, Kari Tammi, Xiao-Zhi Gao, Sanchari Deb, Pinakeswar Mahanta, VTT Technical Research Centre of Finland, Mechatronics, University of Eastern Finland, Indian Institute of Technology Guwahati, Department of Mechanical Engineering, Aalto-yliopisto, and Aalto University
- Subjects
optimization ,Mathematical optimization ,business.product_category ,Computer science ,Mechanical Engineering ,Reliability (computer networking) ,congestion ,electric vehicle ,Environmental pollution ,Grid ,Multi-objective optimization ,Hybrid algorithm ,SDG 11 - Sustainable Cities and Communities ,Computer Science Applications ,Charging station ,CSO TLBO ,Bayesian network ,Automotive Engineering ,Electric vehicle ,Vehicle routing problem ,charging station ,business ,SDG 12 - Responsible Consumption and Production - Abstract
The current critical global concerns regarding fossil fuel exhaustion and environmental pollution have been driving advancements in transportation electrification and related battery technologies. In turn, the resultant growing popularity of electric vehicles (EVs) calls for the development of a well-designed charging infrastructure. However, an inappropriate placement of charging stations might hamper smooth operation of the power grid and be inconvenient to EV drivers. Thus, the present work proposes a novel two-stage planning model for charging station placement. The candidate locations for the placement of charging stations are first determined by fuzzy inference considering distance, road traffic, and grid stability. The randomness in road traffic is modelled by applying a Bayesian network (BN). Then, the charging station placement problem is represented in a multi-objective framework with cost, voltage stability reliability power loss (VRP) index, accessibility index, and waiting time as objective functions. A hybrid algorithm combining chicken swarm optimization and the teaching-learning-based optimization (CSO TLBO) algorithm is used to obtain the Pareto front. Further, fuzzy decision making is used to compare the Pareto optimal solutions. The proposed planning model is validated on a superimposed IEEE 33-bus and 25-node test network and on a practical network in Tianjin, China. Simulation results validate the efficacy of the proposed model.
- Published
- 2022
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