A comprehensive overview of electric vehicle charging using renewable energy

The integration of PV with the electric vehicle (EV) charging system has been on the rise due to several factors, namely continuous reduction in the price of PV modules, rapid growth in EV and concern over the effects of greenhouse gases. Over the years, numerous papers have been published on EV charging using the standard utility (grid) electrical supply; however, there seems to be an absence of a comprehensive overview using PV as one of the components for the charger. With the growing interest in this topic, it is timely to review, summarize and update all the related works on PV charging, and to present it as a single reference. For the benefit of a wider audience, the paper also includes the bries description on EV charging stations, background of EV, as well as a brief description of PV systems. Some of the main features of battery management system (BMS) for EV battery are also presented. It is envisaged that the information gathered in this paper will be a valuable one–stop source of information for researchers working in this topic

Desain Pengisian Optimal Kendaraan Listrik Berdasarkan Kebutuhan Daya Grid dan Kondisi Grid pada Stasiun Pengisian Kendaraan Listrik Menggunakan Kontroler Logika Fuzzy

Electric vehicles has become one of alternatives in addressing energy crisis in the field of transportation. Increasing the number of electric vehicles that are not accompanied by a proper charging station management would give negative impact either to the distribution system on the network such as voltage fluctuation, voltage drop, voltage stress, lack of continuity of the power system, and even cause the blackout. Energy management design is needed for electric vehicles charging stations to obtain optimal power flow model between charging station and grid. This research will be designed an analysis and design of optimal charging by considering estimated power flow between charging station with the grid and load conditions on the grid (off-peak / peak) using fuzzy logic controller. This charging management uses the concept vehicle to vehicle (V2V), vehicle to grid (V2G), and the grid to vehicle (G2V) which adjust by charging index and charging rate results from rule fuzzy scoring result. The simulation results show that the fuzzy-based system can flatten the load curve peak of electric vehicle, reducing the impact of peak load to the grid, and can provide cost advantages in the form of cost saving

Optimal Scheduling of Home Energy Management System with Plug-in Electric Vehicles Using Model Predictive Control

abstract: With the growing penetration of plug-in electric vehicles (PEVs), the impact of the PEV charging brought to the utility grid draws more and more attention. This thesis focused on the optimization of a home energy management system (HEMS) with the presence of PEVs. For a household microgrid with photovoltaic (PV) panels and PEVs, a HEMS using model predictive control (MPC) is designed to achieve the optimal PEV charging. Soft electric loads and an energy storage system (ESS) are also considered in the optimization of PEV charging in the MPC framework. The MPC is solved through mixed-integer linear programming (MILP) by considering the relationship of energy flows in the optimization problem. Through the simulation results, the performance of optimization results under various electricity price plans is evaluated. The influences of PV capacities on the optimization results of electricity cost are also discussed. Furthermore, the hardware development of a microgrid prototype is also described in this thesis.Dissertation/ThesisMasters Thesis Engineering 201

Mejora de la estabilidad en sistemas eléctricos de distribución mediante el uso de autos eléctricos como fuentes de inyección de energía

stability in Electrical Distribution Systems (DS) through the use of Electric Vehicle (EV) batteries charging and discharging systems as sources of energy injection that replace the energy produced by Thermal Generators, to satisfy the needs of the demand, maximizing the efficiency and minimizing the use of resources in peak hours. This is done through the application of the Fuzzy Logic (FL) that defines the possible states of the operation, determining an optimal scenario for: loading times of the EV, times of use of the EV and loading a DS of Injection. The study is supported by mathematical simulation in the software MATLAB and its fuzzy toolbox, allowing to analyze and subsequently optimize the delivery of electrical energy to DS, supporting the case studies discussed in this paper, testing the results of stability and feasibility in the system.El presente documento tiene como finalidad la mejora de la estabilidad en Sistemas Eléctricos de Distribución (DS) mediante la utilización de Sistemas Coordinados de carga y descarga de las baterías de los Vehículos Eléctricos (EV) como fuentes de inyección de energía reemplazando a la energía producida por las Generadoras Térmicas, para satisfacer las necesidades de la demanda, maximizando la eficiencia y minimizando la utilización de recursos energéticos en horas consideradas picos. Esto se lo realiza mediante la aplicación de la Lógica Difusa (FL) definiendo los posibles estados de operación del DS, determinando un Escenario Optimo para: tiempos de carga de los EV, tiempos de uso de los EV, carga a Inyectarse al DS. El estudio se respalda mediante la simulación matemática en el software MATLAB y su Fuzzy Toolbox permitiendo analizar y posteriormente optimizar la entrega de energía eléctrica al DS, sustentando los casos de estudio tratados en el presente documento, probando los resultados de estabilidad y factibilidad en el sistema

Desain Pengisian Optimal Kendaraan Listrik Berdasarkan Kebutuhan Daya Grid dan Kondisi Grid pada Stasiun Pengisian Kendaraan Listrik Menggunakan Kontroler Logika Fuzzy

Electric vehicles has become one of alternatives in addressing energy crisis in the field of transportation. Increasing the number of electric vehicles that are not accompanied by a proper charging station management would give negative impact either to the distribution system on the network such as voltage fluctuation, voltage drop, voltage stress, lack of continuity of the power system, and even cause the blackout. Energy management design is needed for electric vehicles charging stations to obtain optimal power flow model between charging station and grid. This research will be designed an analysis and design of optimal charging by considering estimated power flow between charging station with the grid and load conditions on the grid (off-peak / peak) using fuzzy logic controller. This charging management uses the concept vehicle to vehicle (V2V), vehicle to grid (V2G), and the grid to vehicle (G2V) which adjust by charging index and charging rate results from rule fuzzy scoring result. The simulation results show that the fuzzy-based system can flatten the load curve peak of electric vehicle, reducing the impact of peak load to the grid, and can provide cost advantages in the form of cost saving

A multi-agent based scheduling algorithm for adaptive electric vehicles charging

This paper presents a decentralized scheduling algorithm for electric vehicles charging. The charging control model follows the architecture of a Multi-Agent System (MAS). The MAS consists of an Electric Vehicle (EV)/Distributed Generation (DG) aggregator agent and “Responsive” or “Unresponsive” EV agents. The EV/DG aggregator agent is responsible to maximize the aggregator’s profit by designing the appropriate virtual pricing policy according to accurate power demand and generation forecasts. “Responsive” EV agents are the ones that respond rationally to the virtual pricing signals, whereas “Unresponsive” EV agents define their charging schedule regardless the virtual cost. The performance of the control model is experimentally demonstrated through different case studies at the micro-grid laboratory of the National Technical University of Athens (NTUA) using Real Time Digital Simulator. The results highlighted the adaptive behaviour of “Responsive” EV agents and proved their ability to charge preferentially from renewable energy sources

Perspectiva del transformador de distribución en redes eléctricas con alta penetración de generación distribuida y vehículos eléctricos

New challenges in the operation, management, and design of the distribution transformer are required due to the modernization of electricity networks. These challenges depend among other things on particular conditions as network topology, distributed generation, and penetration rates of the electric mobility technologies. In this paper, a state of the art of the potential impacts that distributed generation and electric vehicles may generate on the operation of the distribution transformer is developed. Major strategies used to mitigate potential negative impacts are alsopresented.La modernización de las redes eléctricas es una realidad que genera nuevos desafíos en el diseño, operación y gestión del transformador de distribución, los cuales dependerán, entre otras cosas, de condiciones particulares como la topología de la red y los índices de penetración de tecnologías de movilidad eléctrica y generación distribuida (GD). Este artículo revisa aspectos generales de la bibliografía internacional frente al impacto que puede tener la generación distribuida y los vehículos eléctricos en el transformador en el marco de las Smart Grid. También se presentan las principales estrategias aplicadas para mitigar los posibles impactos negativos

An energy-aware algorithm for electric vehicle infrastructures in smart cities

[EN] The deployment of a charging infrastructure to cover the increasing demand of electric vehicles (EVs) has become a crucial problem in smart cities. Additionally, the penetration of the EV will increase once the users can have enough charging stations. In this work, we tackle the problem of locating a set of charging stations in a smart city considering heterogeneous data sources such as open data city portals, geo-located social network data, and energy transformer substations. We use a multi-objective genetic algorithm to optimize the charging station locations by maximizing the utility and minimizing the cost. Our proposal is validated through a case study and several experimental results.This work was partially supported by MINECO/FEDER, Spain RTI2018-095390-B-C31 project of the Spanish government. Jaume Jordan and Vicent Botti are funded by UPV, Spain PAID-06-18 project. Jaume Jordan is also funded by grant APOSTD/2018/010 of Generalitat Valenciana -Fondo Social Europeo, Spain.Palanca Cámara, J.; Jordán, J.; Bajo, J.; Botti Navarro, VJ. (2020). An energy-aware algorithm for electric vehicle infrastructures in smart cities. Future Generation Computer Systems. 108:454-466. https://doi.org/10.1016/j.future.2020.03.001S454466108Gan, L., Topcu, U., & Low, S. H. (2013). Optimal decentralized protocol for electric vehicle charging. IEEE Transactions on Power Systems, 28(2), 940-951. doi:10.1109/tpwrs.2012.2210288Ma, T., & Mohammed, O. A. (2014). Optimal Charging of Plug-in Electric Vehicles for a Car-Park Infrastructure. IEEE Transactions on Industry Applications, 50(4), 2323-2330. doi:10.1109/tia.2013.2296620Needell, Z. A., McNerney, J., Chang, M. T., & Trancik, J. E. (2016). Potential for widespread electrification of personal vehicle travel in the United States. Nature Energy, 1(9). doi:10.1038/nenergy.2016.112Franke, T., & Krems, J. F. (2013). Understanding charging behaviour of electric vehicle users. Transportation Research Part F: Traffic Psychology and Behaviour, 21, 75-89. doi:10.1016/j.trf.2013.09.002Shukla, A., Pekny, J., & Venkatasubramanian, V. (2011). An optimization framework for cost effective design of refueling station infrastructure for alternative fuel vehicles. Computers & Chemical Engineering, 35(8), 1431-1438. doi:10.1016/j.compchemeng.2011.03.018Nie, Y. (Marco), & Ghamami, M. (2013). A corridor-centric approach to planning electric vehicle charging infrastructure. Transportation Research Part B: Methodological, 57, 172-190. doi:10.1016/j.trb.2013.08.010Tu, W., Li, Q., Fang, Z., Shaw, S., Zhou, B., & Chang, X. (2016). Optimizing the locations of electric taxi charging stations: A spatial–temporal demand coverage approach. Transportation Research Part C: Emerging Technologies, 65, 172-189. doi:10.1016/j.trc.2015.10.004Dong, J., Liu, C., & Lin, Z. (2014). Charging infrastructure planning for promoting battery electric vehicles: An activity-based approach using multiday travel data. Transportation Research Part C: Emerging Technologies, 38, 44-55. doi:10.1016/j.trc.2013.11.001He, J., Yang, H., Tang, T.-Q., & Huang, H.-J. (2018). An optimal charging station location model with the consideration of electric vehicle’s driving range. Transportation Research Part C: Emerging Technologies, 86, 641-654. doi:10.1016/j.trc.2017.11.026Jordán, J., Palanca, J., del Val, E., Julian, V., & Botti, V. (2018). A Multi-Agent System for the Dynamic Emplacement of Electric Vehicle Charging Stations. Applied Sciences, 8(2), 313. doi:10.3390/app8020313Jurdak, R., Zhao, K., Liu, J., AbouJaoude, M., Cameron, M., & Newth, D. (2015). Understanding Human Mobility from Twitter. PLOS ONE, 10(7), e0131469. doi:10.1371/journal.pone.0131469Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182-197. doi:10.1109/4235.996017Coello Coello, C. A. (2002). Theoretical and numerical constraint-handling techniques used with evolutionary algorithms: a survey of the state of the art. Computer Methods in Applied Mechanics and Engineering, 191(11-12), 1245-1287. doi:10.1016/s0045-7825(01)00323-

A Case Study on Grid Impacts of Electric Vehicles on New York City Power Grid

The U.S. electric power industry is anticipating a huge increase in electricity demand in the future due to reformation of the transportation industry. In this work, we focus on electric cars and their impact on the transportation industry as well as the electric grid. The increase in number of electric cars over the years and their growing number indicates that in the future, transportation means are going to largely depend upon electricity to achieve cost and environmental benefits. In other words, in future, the transportation will be impacting the electric grid and vice versa. The surge in electric vehicles on streets particularly in New York City requires the charging infrastructure to support the electricity demand, and mandates proper planning for the electric power grids to keep them upgraded and resilient enough to support the growth in electric vehicles load. In this thesis, the historical minimum and peak load demand data of areas served by Consolidated Edison, an energy company, in New York City have been collected, processed and analyzed to analyze the performance of the future electric grid. The available data of electric vehicles and parking spaces in different regions of New York City were analyzed to study the impact of electric vehicles on future power infrastructure in the City. Also, the charging and parking behavior of vehicles in New York City was analyzed to determine how the time of charging will impact the electricity demand at the different hours of the day at different locations in New York City, and to find ways to minimize the demand in areas where the electric grid network is congested. It was also concluded that in the future, after phasing out of a portion of the gasoline fleets, electric vehicles will be able help to reduce the greenhouse gasses and conserve the environment and help the City and State of New York’s initiatives to achieve their goals of sustainability. Last but not least, more than eighty percent of the New York City electric infrastructure is underground, which means when the demand in peak hours of summer requires upgrading and increasing the feeder capacity, it will require massive capital investment. So, electric vehicles can be used as Energy Storage Resources and can be utilized if needed to shave the peak demand. The data has been analyzed to find out the kind of infrastructure required and the potential locations in different areas of New York City to use the electric vehicles as distributed energy storage resources