How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach

Overview

EV-charging-impact

This repository contains the code that has been used for the Queue modelling for the paper "How will electric vehicles affect traffic congestion and energy consumption: an integrated modelling approach" by Artur Grigorev, Tuo Mao, Adam Berry, Joachim Tan, Loki Purushothaman, Adriana-Simona Mihaita. The paper has been published and presented during the IEEE ITSC 2021 conference. The preprint is available: https://arxiv.org/abs/2110.14064 .

You can find a working queue model in "queue_model.py" file.

This EV charging station queue simulation program reads file "Northern_Sydney_EV_charger_list.csv" and outputs queue simulation results into file "q2080_2016_seq.csv". It relies on multiprocessing package to perform parallel simulation.

Input parameters of the model:

  1. Duration of modeling (day, week, month)
  2. Number of plugs on EV stations
  3. Distribution of time intervals between arrivals
  4. Distribution of charging time: normaly distributed between 20% and 80%.
  5. Max queue size
  6. Power supply at EV charger: KW/h

Model output:

  • (O1) Mean queue length of an EV station [n]'] = HOURQUEUE[i]
  • (O2) Mean waiting time in queue at an EV station [hours]
  • (O3) Mean service time to charge at an EV station [hours]
  • (O4) Total time spent overall at an EV station [hours]
  • (O5) Total energy consumption of an EV station [kWh]
  • (O6) Maximum recorded queue length of an EV station [n]
  • (O7) Maximum waiting time in queue at an EV station [hours]
  • (O8) Maximum time spent overall at an EV station [hours]
  • (O9) Maximal energy consumption of an EV station [kW]
  • Consumed electricity by hour [kWh]
  • Total waiting time (minutes) by hour
  • Overall Mean Service time/day'

queue model

To perform calculations for specific OD traffic flow (2016, OD15, OD30) change the line: DICT['StationFlow'] = float(dt[dt.Name==N]['2016 volume']) at the "Setup" section (to 2016, 15 or 30).

The structure of the framework: framework

The code to produce lineplots is in "lineplots.ipynb":

lineplot

lineplot2

The code to produce supplementary animation is in "anim.ipynb": anim

Protect against subdomain takeover

domain-protect scans Amazon Route53 across an AWS Organization for domain records vulnerable to takeover deploy to security audit account scan your en

OVO Technology 0 Nov 17, 2022
[2021 MultiMedia] CONQUER: Contextual Query-aware Ranking for Video Corpus Moment Retrieval

CONQUER: Contexutal Query-aware Ranking for Video Corpus Moment Retreival PyTorch implementation of CONQUER: Contexutal Query-aware Ranking for Video

Hou zhijian 23 Dec 26, 2022
Home for cuQuantum Python & NVIDIA cuQuantum SDK C++ samples

Welcome to the cuQuantum repository! This public repository contains two sets of files related to the NVIDIA cuQuantum SDK: samples: All C/C++ sample

NVIDIA Corporation 147 Dec 27, 2022
Ian Covert 130 Jan 01, 2023
An Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering

PC-SOS-SDP: an Exact Solver for Semi-supervised Minimum Sum-of-Squares Clustering PC-SOS-SDP is an exact algorithm based on the branch-and-bound techn

Antonio M. Sudoso 1 Nov 13, 2022
Json2Xml tool will help you convert from json COCO format to VOC xml format in Object Detection Problem.

JSON 2 XML All codes assume running from root directory. Please update the sys path at the beginning of the codes before running. Over View Json2Xml t

Nguyễn Trường Lâu 6 Aug 22, 2022
Gray Zone Assessment

Gray Zone Assessment Get started Clone github repository git clone https://github.com/andreanne-lemay/gray_zone_assessment.git Build docker image dock

1 Jan 08, 2022
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data

Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data This is the official PyTorch implementation of the SeCo paper: @articl

ElementAI 101 Dec 12, 2022
Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand

Bidimensional Leaderboards: Generate and Evaluate Language Hand in Hand Introduction We propose a generalization of leaderboards, bidimensional leader

4 Dec 03, 2022
Code for Paper Predicting Osteoarthritis Progression via Unsupervised Adversarial Representation Learning

Predicting Osteoarthritis Progression via Unsupervised Adversarial Representation Learning (c) Tianyu Han and Daniel Truhn, RWTH Aachen University, 20

Tianyu Han 7 Nov 22, 2022
Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022)

Pop-Out Motion Pop-Out Motion: 3D-Aware Image Deformation via Learning the Shape Laplacian (CVPR 2022) Jihyun Lee*, Minhyuk Sung*, Hyunjin Kim, Tae-Ky

Jihyun Lee 88 Nov 22, 2022
Repo for 2021 SDD assessment task 2, by Felix, Anna, and James.

SoftwareTask2 Repo for 2021 SDD assessment task 2, by Felix, Anna, and James. File/folder structure: helloworld.py - demonstrates various map backgrou

3 Dec 13, 2022
Establishing Strong Baselines for TripClick Health Retrieval; ECIR 2022

TripClick Baselines with Improved Training Data Welcome 🙌 to the hub-repo of our paper: Establishing Strong Baselines for TripClick Health Retrieval

Sebastian Hofstätter 3 Nov 03, 2022
Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

Expressive Power of Invariant and Equivaraint Graph Neural Networks In this repository, we show how to use powerful GNN (2-FGNN) to solve a graph alig

Marc Lelarge 36 Dec 12, 2022
Implementation for Panoptic-PolarNet (CVPR 2021)

Panoptic-PolarNet This is the official implementation of Panoptic-PolarNet. [ArXiv paper] Introduction Panoptic-PolarNet is a fast and robust LiDAR po

Zixiang Zhou 126 Jan 01, 2023
🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗

🚗 INGI Dakar 2K21 - Be the first one on the finish line ! 🚗 This year's first semester Club Info challenge will put you at the head of a car racing

ClubINFO INGI (UCLouvain) 6 Dec 10, 2021
Composing methods for ML training efficiency

MosaicML Composer contains a library of methods, and ways to compose them together for more efficient ML training.

MosaicML 2.8k Jan 08, 2023
Fuzzy Overclustering (FOC)

Fuzzy Overclustering (FOC) In real-world datasets, we need consistent annotations between annotators to give a certain ground-truth label. However, in

2 Nov 08, 2022
Emotion Recognition from Facial Images

Reconhecimento de Emoções a partir de imagens faciais Este projeto implementa um classificador simples que utiliza técncias de deep learning e transfe

Gabriel 2 Feb 09, 2022