Planning of EV Charging Station With Distribution Network Expansion Considering Traffic Congestion and Uncertainties

• Published in: IEEE transactions on industry applications 2023-05, Vol.59 (3), p.1-15
• Main Authors: Pal, Arnab, Bhattacharya, Aniruddha, Chakraborty, Ajoy Kumar
• Format: Article
• Language: English
• Subjects: Costs; Distributed generation; Distribution network; Distribution networks; Electric vehicle charging; electric vehicle charging station; Electric vehicle charging stations; Electrical loads; Energy consumption; Energy distribution; expansion planning; Heuristic methods; Indexes; Integer programming; Linear programming; optimal allocation; Planning; Resource management; Substations; Traffic congestion; Uncertainty; Voltage
• ISSN: 0093-9994; 1939-9367
• DOI: 10.1109/TIA.2023.3237650

Proper planning for electric vehicle (EV) charging stations along with the required expansion of the distribution network is essential for the continuous growth of EV and conventional loads. This paper proposes a realistic and sustainable solution for deciding the location and capacity of the EV fast-charging stations along with the optimal multistage expansion of the distribution network to cope with future load growth. An overlaid traffic network with a distribution network is taken for the realistic approach. EV user convenience factor and user happiness factor are proposed along with the distribution network energy loss for better planning of fast-charging stations. To get charged, the appropriate fast-charging station and its best route are decided through minimum energy consumption by the EVs, considering traffic congestion and distances. Up-gradation of lines, up-gradation of the substation, addition of new fast-charging station, allocation of DSTATCOM and allocation of renewable-based distributed generation are performed for distribution network expansion planning. The whole work is formulated in three layers with a sub-layer. Uncertainties of EV driving patterns, energy requirement, traffic congestion, load and renewable generation are undertaken by the 2m point estimation method. The solution is carried out along with validation using different metaheuristic methods and integer linear programming.