Electric vehicles in smart grid: a survey on charging load modelling

Xiang, Yue; Hu, Shuai; Liu, Youbo; Zhang, Xin

doi:10.1049/iet-stg.2018.0053

Cited by 36 publications

(26 citation statements)

References 54 publications

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“…These various types of EMSs become essential components of the whole smart city [173]. Specifically, from the viewpoint of future popularization and controllability, EVs are expected to be used for power system operation including the Vehicle to Grid (V2G) scheme [174][175][176][177][178][179][180][181][182].…”

Section: Control Targets Of Emss An Objective Of Emssmentioning

confidence: 99%

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Fujimoto

Ishii

Hayashi

2020

IEEJ Transactions Elec Engng

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Smart cities will be an important component for the construction of a sustainable future society. This paper focuses on the functionalities of smart cities from the perspective of electric power systems and reviews recent works on the various types of energy management systems (EMSs), particularly those focusing on the interaction among systems distributed in a city. Furthermore, this paper describes the idea of a platform for evaluating cooperative EMS technologies distributed in a city. The related research and demonstration of the EMS technology in real cities that were evaluated using the proposed platform are presented. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Control Targets Of Emss An Objective Of Emssmentioning

confidence: 99%

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Fujimoto

Ishii

Hayashi

2020

IEEJ Transactions Elec Engng

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“…Besides, it has been shown that the uncertain characteristics of EVs charging would be adverse to power system reliability [21]. Thus, it is necessary to evaluate distribution network reliability with the integration of EVs.…”

Section: Introductionmentioning

confidence: 99%

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

et al. 2020

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Integration of large-scale cluster electric vehicles (EVs) and their spatial-temporal transfer randomness are likely to affect the safety and economic operation of the distribution network. This paper investigates the spatial-temporal distribution prediction of EVs' charging load and then evaluates the reliability of the distribution network penetrated with large-scale cluster EVs. To effectively predict the charging load, trip chain technology, Monte Carlo method, and Markov decision process (MDP) theory are employed. Moreover, a spatial-temporal transfer model of EVs is established, and based on which, an EV energy consumption model and a charging load prediction model are constructed with consideration of temperature, traffic condition and EV owner's subjective willingness in different scenarios. With the application of sequential Monte Carlo method, the paper further evaluates distribution network reliability in various charging scenarios. In the evaluation, indices including per unit value (PUV), fast voltage stability index (FVSI), loss of load probability (LOLP), system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), and expected energy not supplied (EENS) are incorporated. To validate the proposed prediction model and evaluation method, a series of numerical simulations are conducted on the basis of taking the traffic-distribution system of a typical city as an example. The result demonstrates that the proposed spatial-temporal transfer model is more practical in charging load prediction than the popularly used Dijkstra's shortest path algorithm. Moreover, high temperature, congestion and the increment of EV penetration rate will further weaken distribution network reliability. INDEX TERMS Electric vehicle, reliability evaluation, trip chain technology, Markov decision process, sequential Monte Carlo method, spatial-temporal transfer randomness NOMENCLATURE ABBREVIATIONS EV Electric vehicle MDP Markov decision process SOC State of charge TNN Traffic network node DNN Distribution network node DCP Dispersion of charging power PARAMETERS μ Mean of the beginning time of each trip  Variance of the beginning time of each trip v i,j max Maximum speed allowed at the road (i, j) x EV's position in the travel path D x Distance between the origin of the trip and x C EV Capacity of EV battery

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“…Ozturk et al used a ToU tariff within a self organising home energy network to reduce prices for the customer [5]. DSR can help to reduce peak demand through the coordinated control of electric vehicles (EVs), and PVs [6], [7]. Amongst the literature, there are papers which model specific appliances, for example Good et al considered DSR based on thermal energy storage in the form of hot water storage [8].…”

Section: Introductionmentioning

confidence: 99%

Smart Scheduling of Household Appliances to Decarbonise Domestic Energy Consumption

Stacpoole

Sun

Jiang

2019

2019 IEEE/CIC International Conference on Communications Workshops in China (ICCC Workshops)

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2019) 'Smart scheduling of household appliances to decarbonise domestic energy consumption.any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Additional information: Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details.Abstract-Demand side response (DSR) and the interconnectivity of smart technologies will be essential to transform and revolutionize the way consumers engage with the energy industry. The carbon intensity of electricity varies throughout the day as a result of emissions released during generation. These fluctuations in carbon intensity are predicted to increase due to increased penetration of variable generation sources. This paper proposes a novel insight into how reductions in domestic emissions can be achieved, through the scheduling of certain wet appliances to optimally manage low carbon electricity. An appliance detecting and scheduling algorithm is presented and results are generated using real demand data, electricity generation and carbon intensity values. Reductions were achieved from the variations in grid carbon intensity and the availability of solar generation from a household photovoltaic (PV) supply.

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Electric vehicles in smart grid: a survey on charging load modelling

Cited by 36 publications

References 54 publications

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Designing Sustainable Smart Cities: Cooperative Energy Management Systems and Applications

Charging Load Prediction and Distribution Network Reliability Evaluation Considering Electric Vehicles’ Spatial-Temporal Transfer Randomness

Smart Scheduling of Household Appliances to Decarbonise Domestic Energy Consumption

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