PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

PC-ILP: A Fast and Intuitive Method to Place Electric Vehicle Charging Stations in Smart Cities

The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical re...

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Bibliographic Details
Published in:Smart cities (Basel) Vol. 6; no. 6; pp. 3060 - 3092
Main Authors: Bose, Mehul, Dutta, Bivas Ranjan, Shrivastava, Nivedita, Sarangi, Smruti R.
Format: Journal Article
Language:English
Published: Basel MDPI AG 01-11-2023
Subjects:
Accuracy
Algorithms
Approximation
Cities
Clustering
convolutional neural network
Electric vehicle charging
electric vehicle charging station placement
Electric vehicles
Heuristic methods
Integer programming
Linear programming
Optimization
persistent homology
Placement
Queueing
topological data analysis
Topology
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Summary:The widespread use of electric vehicles necessitates meticulous planning for the placement of charging stations (CSs) in already crowded cities so that they can efficiently meet the charging demand while adhering to various real-world constraints such as the total budget, queuing time, electrical regulations, etc. Many classical and metaheuristic-based approaches provide good solutions, but they are not intuitive, and they do not scale well for large cities and complex constraints. Many classical solution techniques often require prohibitive amounts of memory and their solutions are not easily explainable. We analyzed the layouts of the 50 most populous cities of the world and observed that any city can be represented as a composition of five basic primitive shapes (stretched to different extents). Based on this insight, we use results from classical topology to design a new charging station placement algorithm. The first step is a topological clustering algorithm to partition a large city into small clusters and then use precomputed solutions for each basic shape to arrive at a solution for each cluster. These cluster-level solutions are very intuitive and explainable. Then, the next step is to combine the small solutions to arrive at a full solution to the problem. Here, we use a surrogate function and repair-based technique to fix any resultant constraint violations (after all the solutions are combined). The third step is optional, where we show that the second step can be extended to incorporate complex constraints and secondary objective functions. Along with creating a full software suite, we perform an extensive evaluation of the top 50 cities and demonstrate that our method is not only 30 times faster but its solution quality is also 36.62% better than the gold standard in this area—an integer linear programming (ILP) approach with a practical timeout limit.
ISSN:2624-6511
2624-6511
DOI:10.3390/smartcities6060137