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Assessment of Technical and Economic Impacts of EV User Behavior on EV Aggregator smart charging

Journal of Modern Power Systems and Clean Energy 2020年第2期8卷 356-366页

作者： Jean-Michel Clairand Javier Rodriguez-Garcia Carlosalvarez-Bel the Facultad de Ingenieria y Ciencias Agropecuarias Universidad de las Americas-Ecuador170122 QuitoEcuador the Institute for Energy Engineering Universitat Politecnica de Valencia46022 ValenciaSpain

The increase in global electricity consumption has made energy efficiency a priority for ***,there has been a focus on the efficient integration of a massive penetration of electric vehicles(EVs)into energy *** study presents an assessment of various strategies for EV *** this analysis,the smart charging methodology proposed in a previous study is *** smart charging technique employs charging power rate modulation and considers user *** adopt several strategies,this study simulates the effect of these actions in a case study of a distribution system from the city of Quito,*** actions are simulated,and the EV aggregator costs and technical conditions are evaluated.

关键词： Electric vehicle(EV) smart grid aggregator smart charging charging power modulation charging strategies

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Data-driven smart charging for heterogeneous electric vehicle fleets

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能源与人工智能(英文) 2020年第1期1卷 74-86页

作者： Oliver Frendo Jerome Graf Nadine Gaertner Heiner Stuckenschmidt SAP SE Dietmar-Hopp-Allee 16Walldorf 69190Germany University of Mannheim Schloss68131 Mannheim

The ongoing electrification of mobility comes with the challenge of charging electric vehicles(EVs)sufficiently while charging infrastructure capacities are *** charging algorithms produce charge plans for indi-vidual EVs and aim to assign charging capacities fairly and efficiently between vehicles in a *** practice,EV charging processes follow nonlinear charge profiles such as constant-current,constant-voltage(CCCV).smart charging must consider charge profiles in order to avoid gaps between charge plans and actual EV power *** valid models of charge profiles and their parameters for a diverse set of EVs are not publicly *** this work we propose a data-driven approach for integrating a machine learning model to pre-dict arbitrary charge profiles into a smart charging *** train machine learning models with a dataset consisting of charging processes from the workplace gathered in 2016–2018 from a heterogeneous EV fleet of 1001 EVs with 18 unique *** charging process includes the time series of charging *** pre-processing,the dataset contains 10.595 charging processes leading to 1.2 million data points in *** then compare different machine learning models for charge profile predictions finding that XGBoost yields the most accurate predictions with a mean absolute error(MAE)of 126W and a relative MAE of *** show that smart charging with the integrated XGBoost model leads to a more effective infrastructure usage with up to 21%more energy charged compared to smart charging without considering charge ***,an ablation study on regression model features shows the EV’s model is not a necessary attribute for accurate charge profile ***,charging features are required including the number of phases used for charging.

关键词： smart charging Electric vehicles Data-driven approach Machine learning

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Review on Optimization of Forecasting and Coordination Strategies for Electric Vehicle charging

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Journal of Modern Power Systems and Clean Energy 2023年第2期11卷 389-400页

作者： Zixuan Jia Jianing Li Xiao-Ping Zhang Ray Zhang Department of Electronic Electrical and Systems EngineeringSchool of EngineeringUniversity of BirminghamBirminghamUnited Kingdom

The rapid development of electric vehicles(EVs)has benefited from the fact that more and more countries or regions have begun to attach importance to clean energy and environmental *** paper focuses on the optimization of EV charging,which cannot be ignored in the rapid development of *** increase in the penetration of EVs will generate new electrical loads during the charging process,which will bring new challenges to local power ***,the uncoordinated charging of EVs may increase the peak-to-valley difference in the load,aggravate harmonic distortions,and affect auxiliary *** stabilize the operations of power grids,many studies have been carried out to optimize EV *** paper reviews these studies from two aspects:EV charging forecasting and coordinated EV charging *** analyses are carried out to identify the advantages and disadvantages of different methods or *** the end of this paper,recommendations are given to address the challenges of EV charging and associated charging strategies.

关键词： Electric vehicle(EV) forecasting aggregator coordination strategy smart charging

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A Review on Electric Vehicle charging Infrastructure Development in the UK

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Journal of Modern Power Systems and Clean Energy 2020年第2期8卷 193-205页

作者： Tianjin Chen Xiao-Ping Zhang Jianji Wang Jianing Li Cong Wu Mingzhu Hu Huiping Bian XU JI Power Co. Ltd.XuchangChina Department of Electronic Electrical and Systems EngineeringUniversity of BirminghamBirminghamUK State Grid Shandong Electric Power Company LinyiChina

This paper focuses on the development of electric vehicle(EV)charging infrastructure in the UK,which is a vital part of the delivering ultra-low-emission vehicle(ULEV)and will transition into low emission energy systems in the near *** a brief introduction to global landscape of EV and its infrastructure,this paper presents the EV development in the *** then unveils the government policy in recent years,charging equipment protocols or standards,and existing EV charging *** topologies of charging infrastructure are ***,three important factors to be considered in a typical site,i.e.,design,location and cost,are discussed in ***,the management and operation of charging infrastructure including different types of business models are *** but not least,challenges and future trends are discussed.

关键词： Electric vehicle(EV) charging point charging infrastructure smart charging

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A hybrid policy gradient and rule-based control framework for electric vehicle charging

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能源与人工智能(英文) 2021年第2期4卷 1-15页

作者： Brida V.Mbuwir Lennert Vanmunster Klaas Thoelen Geert Deconinck EnergyVille Thor Park 8310Genk 3600Belgium ESAT-Electa KU LeuvenKasteelpark Arenberg 10 bus 2445Leuven 3001Belgium

Recent years have seen a significant increase in the adoption of electric vehicles,and investments in electric vehicle charging infrastructure and rooftop photo-voltaic *** ability to delay electric vehicle charging provides inherent flexibility that can be used to compensate for the intermittency of photo-voltaic generation and optimize against fluctuating electricity *** this flexibility,however,requires smart control algorithms capable of handling uncertainties from photo-voltaic generation,electric vehicle energy demand and user’s *** paper proposes a control framework combining the advantages of reinforcement learning and rule-based control to coordinate the charging of a fleet of electric vehicles in an office *** control objective is to maximize self-consumption of locally generated electricity and consequently,minimize the electricity cost of electric vehicle *** performance of the proposed framework is evaluated on a real-world data set from EnergyVille,a Belgian research *** results show that the proposed control framework achieves a 62.5%electricity cost reduction compared to a business-as-usual or passive charging *** addition,only a 5%performance gap is achieved in comparison to a theoretical near-optimal strategy that assumes perfect knowledge on the required energy and user behaviour of each electric vehicle.

关键词： Electric vehicles smart charging Proximal policy optimization Reinforcement learning

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