Optimal location of EV charging stations in a neighborhood considering a multi-objective approach

Bitencourt, Leonardo; Abud, Tiago P.; Dias, Bruno H.; Borba, Bruno Soares Moreira César; Maciel, Renan Silva; Quirós-Tortós, Jairo

doi:10.1016/j.epsr.2021.107391

Cited by 48 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, in addition to attitudes and opinions, one of the obstacles is infrastructure investment as well as investment in the purchase of autonomous electric vehicles [114][115][116]. The use of such vehicles requires an adapted infrastructure in terms of the availability of electric charging stations in the city and the suitability of electric chargers themselves as well as changing user habits since charging stations are not available in all places [117][118][119]. In other words, planning and the need to analyze the current situation as well as analyzing potential places where the charging points themselves can be located are again highlighted here, which can be a significant problem when it comes to populated urban areas.…”

Section: Discussionmentioning

confidence: 99%

New Paradigm of Sustainable Urban Mobility: Electric and Autonomous Vehicles—A Review and Bibliometric Analysis

2022

View full text Add to dashboard Cite

The growing relevance of sustainability, as well as the necessity to replace traditional forms of transportation with sustainable ones, has made sustainable urban mobility an imperative. In order to respond to the ever-increasing need to develop sustainable modes of transport, the importance of electric, autonomous, and electric autonomous vehicles is increasingly emphasized. In addition, as trends of growth and development in electric autonomous vehicle technology are increasing, one of the questions that has appeared is whether autonomous electric vehicles represent one of the mechanisms that will be used to increase the sustainability of urban mobility. With this in mind, the results of a systematic analysis of existing research in the WOS and Scopus databases using the keywords “urban mobility”, “electric vehicles”, and “autonomous vehicles” was carried out to identify research trends in the use of autonomous electric vehicles in urban areas. The research showed that authors focus on the advantages and disadvantages of autonomous electric vehicles and their usage in the urban mobility system, but an insufficient number of authors consider and define the need to plan the transition towards incorporating autonomous electric vehicles into the urban system. The results of this research also indicate an insufficient number of papers that research and describe the application of autonomous electric vehicles in distribution logistics. This paper provides an overview of existing research related to autonomous electric vehicles and the challenges of transition in the context of infrastructure and the development of a culture of sustainability among urban residents.

show abstract

Section: Discussionmentioning

confidence: 99%

New Paradigm of Sustainable Urban Mobility: Electric and Autonomous Vehicles—A Review and Bibliometric Analysis

2022

View full text Add to dashboard Cite

show abstract

“…However, this approach relies heavily on expert knowledge and intuition, which may lead to inconsistencies or biases if decision-makers' assumptions or knowledge are incorrect or incomplete. Similarly, [45] used hierarchical clustering to define charging station service zones based on EV owners' locations. However, this method lacks a straightforward method to determine the optimal number of clusters, which requires empirical decisions that may affect the accuracy of the resulting clusters.…”

Section: Weight Coefficients 𝛽 𝑣1mentioning

confidence: 99%

Optimal Siting and Sizing of EV Charging Station Using Stochastic Power Flow Analysis for Voltage Stability

Jin

Acquah

Seo

et al. 2024

IEEE Trans. Transp. Electrific.

View full text Add to dashboard Cite

Existing literature on planning for electric vehicle charging stations (EVCS) fails to consider uncertain factors in power systems, such as load fluctuations and the impact of EV integration. Consequently, using deterministic power flow (DPF) algorithms for EVCS planning is unreliable. To address this, we propose a probabilistic model for EV charging loads and introduce a novel dynamic system voltage stability (DSVS) index. We then present an effective optimization model for EVCS site and size planning using stochastic power flow (SPF). Our model aims to maximize capital gains on investment costs of EVCS, minimize yearly EV users' average wait time and distance to charge costs and minimize the DSVS index. To simplify the problem, we use the super-efficiency data envelopment analysis (SEDEA) method to determine objective weights and transform the multi-objective optimization problem into a single-objective one. Finally, we jointly solve the model using the voronoi diagram and adaptive differential evolution optimization algorithm (ADEOA). We verify the effectiveness of our proposed method using a case study with the IEEE 33-node distribution network topology diagram and a planning area diagram.

show abstract

“…In the the field of deep learning and convolutional neural networks, the convolution operation is a core mathematical operation that centres on feature extraction from input data by means of a sliding convolution kernel. In this process, a series of key parameters and computational steps are involved 19 .…”

Section: Modelling and Solvingmentioning

confidence: 99%

Studying computer communication networks to identify errors using enhanced neural network algorithms

zhang

2024

Second International Conference on Physics, Photonics, and Optical Engineering (ICPPOE 2023)

View full text Add to dashboard Cite

The stability and reliability of computer communication networks are crucial for the proper functioning of modern society. However, network faults are inevitable, so efficient fault detection systems are needed to quickly identify and solve problems. This study aims to improve the performance of network fault detection by introducing improved neural network algorithms and applying deep learning techniques to this field. First, we studied in detail the various types of faults that may occur in computer communication networks and constructed the corresponding fault datasets. Then, we introduced improved neural network structures, including convolutional neural networks and recurrent neural networks, to better capture the spatio-temporal characteristics of network data. Experimental results show that the improved neural network algorithm exhibits excellent performance in network fault detection. Compared with traditional methods, our model achieves significant improvements in detection rate and accuracy. In addition, the model demonstrates adaptability to different fault types, including traditional faults and emerging network threats. In summary, this study provides an efficient and reliable solution for fault detection in computer communication networks by introducing an improved neural network algorithm. This approach is expected to play an important role in real network operations and improve network stability and security, thus providing more reliable support for information communication in modern society.

show abstract

Optimal location of EV charging stations in a neighborhood considering a multi-objective approach

Cited by 48 publications

References 28 publications

New Paradigm of Sustainable Urban Mobility: Electric and Autonomous Vehicles—A Review and Bibliometric Analysis

New Paradigm of Sustainable Urban Mobility: Electric and Autonomous Vehicles—A Review and Bibliometric Analysis

Optimal Siting and Sizing of EV Charging Station Using Stochastic Power Flow Analysis for Voltage Stability

Studying computer communication networks to identify errors using enhanced neural network algorithms

Contact Info

Product

Resources

About