Planning and operation of EV charging stations by chicken swarm optimization driven heuristics

Sachan, Sulabh; Deb, Sanchari; Singh, S. N.; Singh, Pravin Prakash; Sharma, Desh Deepak

doi:10.1049/enc2.12030

Cited by 21 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Charging station placement is a specialization of the generic facility location problem that is known to be NP-hard. Along with bespoke algorithms, there are many specialized approximation algorithms for facility location that are tailored towards charging station placement [13][14][15][16]. This area is still far from saturation because existing algorithms are still quite slow and many produce non-intuitive solutions.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, a rich set of metaheuristics has evolved to solve such problems quickly with an acceptable quality (with respect to ILP). These approaches include methods that use particle swarm optimization [24], genetic algorithms [5], ant colony optimization [15], chicken swarm optimization [14], gray wolf optimization [25], and bee colony optimization [16]. Furthermore, there are approximation algorithms as well as intuitive heuristics that are, in essence, smart greedy algorithms that intelligently combine local and global information [8].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Bose,

Dutta,

Shrivastava

et al. 2023

Smart Cities

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Bose,

Dutta,

Shrivastava

et al. 2023

Smart Cities

View full text Add to dashboard Cite

show abstract

“…The optimization issue was handled by a surrogate-assisted optimization approach. In [45], a novel CSO-driven metaheuristic was proposed for the solving, planning and operation of charging stations. Meanwhile, in [46], a novel teaching-learning-based CSO is used for the charger placement problem.…”

Section: Introductionmentioning

confidence: 99%

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

et al. 2021

Self Cite

View full text Add to dashboard Cite

The electrification of transportation is necessary due to the expanded fuel cost and change in climate. The management of charging stations and their easy accessibility are the main concerns for receipting and accepting Electric Vehicles (EVs). The distribution network reliability, voltage stability and power loss are the main factors in designing the optimum placement and management strategy of a charging station. The planning of a charging stations is a complicated problem involving roads and power grids. The Gradient-based optimizer (GBO) used for solving the charger placement problem is tested in this work. A good balance between exploitation and exploration is achieved by the GBO. Furthermore, the likelihood of becoming stuck in premature convergence and local optima is rare in a GBO. Simulation results establish the efficacy and robustness of the GBO in solving the charger placement problem as compared to other metaheuristics such as a genetic algorithm, differential evaluation and practical swarm optimizer.

show abstract

“…The increased load due to uncoordinated charging in the charging stations may hamper hassle free operation of the power system, thereby, causing voltage instability, harmonics, power losses, as well as degradation of the reliability (Khalid et al, 2019; Manríquez et al, 2020; Sachan, 2018; Sachan et al, 2020, 2021). However, in recent years, vehicle grid integration (VGI) has become a feasible option to manage and control the load by either discharging (vehicle to grid [V2G]) or by adjusting the charging rate of the EVs.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive review of standards and best practices for utility grid integration with electric vehicle charging stations

Sachan

Deb

Singh

et al. 2021

WIREs Energy & Environment

Self Cite

View full text Add to dashboard Cite

The present work presents a comprehensive state-of-the-art bibliographical review of standards related to utility grid integration and best practices of the electric vehicle (EV) charging stations. The presence of a robust tuning method is essential for successful utility grid integration with the charging stations. The lack of system standardization may hamper the EV uptake as well as successful utility grid integration with the charging stations. The distributed energy resources (DER) and vehicle to grid (V2G) are going to play a vital role in the power system operation and control. The applicability of criterions within the utility grid integration with charging station area is important to the grid operators, charging service providers, manufacturers, fleet operators, and so forth to ensure safety, dependability, and interoperability. Hence, this work tries to deliver a comprehensive and systematic review of standards and best practices for utility grid interaction with charging stations. It will help the specialists of power as well as transport sectors to track down every one of the norms and best practices, which are accessible at one stage to compare different guidelines.

show abstract

Planning and operation of EV charging stations by chicken swarm optimization driven heuristics

Cited by 21 publications

References 33 publications

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

Performance of Gradient-Based Optimizer on Charging Station Placement Problem

A comprehensive review of standards and best practices for utility grid integration with electric vehicle charging stations

Contact Info

Product

Resources

About