The impact of domestic plug-in hybrid electric vehicles on power distribution system loads

The market for Plug-in Hybrid Electric Vehicle (PHEVs) is expected to grow significantly over the next few years and a number of new products are soon to come onto the market, such as the Toyota Prius plug-in version, . The charging demand of wide-scale use of PHEVs may have a significant impact on domestic electricity loads and could risk of overloading the power system if appropriate charging strategies not applied to prevent this. A Monte Carlo Simulation (MCS) model of domestic PHEV use and availability has been developed based on probabilistic characterisations obtained from UKTUS and quantifies charging demand of PHEVs as a function of time of day. The MCS model has been developed in order to simulate the impact on the electricity distribution system. This article also discusses the potential for responsive battery charging load from PHEVs

Impact of plug in electric vehicle battery charging on a distribution system

Plug-in Electric Vehicles (PEV) battery chargers are mostly connected to the low-voltage grid for charging, hence their increased penetration coupled with uncoordinated charging could impact the distribution system in terms of voltage unbalance and transformer overloading. Although PEV battery charging is increasing, impact on the distribution system is not fully understood. This study focuses on voltage unbalance caused by uneven distribution of PEV penetration among the phases. Using real data provided by utility, a distribution system has been modeled and tested using MATLAB-SIMULINK. PEV penetration level at 10-80% is studied, voltage unbalance is calculated and transformer overloading is analyzed. In the simulations conducted without PEV penetration, the real data at intellirupters of the system were close to simulated system voltages and currents. As PEV adoption is expected to increase, the impact on the distribution system will increase. Coordinated or smart charging of PEVs will be essential for consumers and utilities

Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies

[EN] The market for electric vehicles (EVs) has grown with each year, and EVs are considered to be a proper solution for the mitigation of urban pollution. So far, not much attention has been devoted to the use of EVs for public transportation, such as taxis and buses. However, a massive introduction of electric taxis (ETs) and electric buses (EBs) could generate issues in the grid. The challenges are different from those of private EVs, as their required load is much higher and the related time constraints must be considered with much more attention. These issues have begun to be studied within the last few years. This paper presents a review of the different approaches that have been proposed by various authors, to mitigate the impact of EBs and ETs on the future smart grid. Furthermore, some projects with regard to the integration of ETs and EBs around the world are presented. Some guidelines for future works are also proposed.This research was funded by the project SIS.JCG.19.03 of Universidad de las Americas, Ecuador.Clairand-Gómez, J.; Guerra-Terán, P.; Serrano-Guerrero, JX.; González-Rodríguez, M.; Escrivá-Escrivá, G. (2019). Electric Vehicles for Public Transportation in Power Systems: A Review of Methodologies. Energies. 12(16):1-22. https://doi.org/10.3390/en12163114S1221216Emadi, A. (2011). Transportation 2.0. IEEE Power and Energy Magazine, 9(4), 18-29. doi:10.1109/mpe.2011.941320Fahimi, B., Kwasinski, A., Davoudi, A., Balog, R., & Kiani, M. (2011). Charge It! IEEE Power and Energy Magazine, 9(4), 54-64. doi:10.1109/mpe.2011.941321Yilmaz, M., & Krein, P. T. (2013). Review of Battery Charger Topologies, Charging Power Levels, and Infrastructure for Plug-In Electric and Hybrid Vehicles. IEEE Transactions on Power Electronics, 28(5), 2151-2169. doi:10.1109/tpel.2012.2212917Tagliaferri, C., Evangelisti, S., Acconcia, F., Domenech, T., Ekins, P., Barletta, D., & Lettieri, P. (2016). Life cycle assessment of future electric and hybrid vehicles: A cradle-to-grave systems engineering approach. Chemical Engineering Research and Design, 112, 298-309. doi:10.1016/j.cherd.2016.07.003Zackrisson, M., Fransson, K., Hildenbrand, J., Lampic, G., & O’Dwyer, C. (2016). Life cycle assessment of lithium-air battery cells. Journal of Cleaner Production, 135, 299-311. doi:10.1016/j.jclepro.2016.06.104Wu, Y., Yang, Z., Lin, B., Liu, H., Wang, R., Zhou, B., & Hao, J. (2012). Energy consumption and CO2 emission impacts of vehicle electrification in three developed regions of China. Energy Policy, 48, 537-550. doi:10.1016/j.enpol.2012.05.060Shen, W., Han, W., Chock, D., Chai, Q., & Zhang, A. (2012). Well-to-wheels life-cycle analysis of alternative fuels and vehicle technologies in China. Energy Policy, 49, 296-307. doi:10.1016/j.enpol.2012.06.038Wang, R., Wu, Y., Ke, W., Zhang, S., Zhou, B., & Hao, J. (2015). Can propulsion and fuel diversity for the bus fleet achieve the win–win strategy of energy conservation and environmental protection? Applied Energy, 147, 92-103. doi:10.1016/j.apenergy.2015.01.107Clement-Nyns, K., Haesen, E., & Driesen, J. (2010). The Impact of Charging Plug-In Hybrid Electric Vehicles on a Residential Distribution Grid. IEEE Transactions on Power Systems, 25(1), 371-380. doi:10.1109/tpwrs.2009.2036481Shafiee, S., Fotuhi-Firuzabad, M., & Rastegar, M. (2013). Investigating the Impacts of Plug-in Hybrid Electric Vehicles on Power Distribution Systems. IEEE Transactions on Smart Grid, 4(3), 1351-1360. doi:10.1109/tsg.2013.2251483Pieltain Fernandez, L., Gomez San Roman, T., Cossent, R., Mateo Domingo, C., & Frias, P. (2011). Assessment of the Impact of Plug-in Electric Vehicles on Distribution Networks. IEEE Transactions on Power Systems, 26(1), 206-213. doi:10.1109/tpwrs.2010.2049133Lucas, A., Bonavitacola, F., Kotsakis, E., & Fulli, G. (2015). Grid harmonic impact of multiple electric vehicle fast charging. Electric Power Systems Research, 127, 13-21. doi:10.1016/j.epsr.2015.05.012Turker, H., Bacha, S., Chatroux, D., & Hably, A. (2012). Low-Voltage Transformer Loss-of-Life Assessments for a High Penetration of Plug-In Hybrid Electric Vehicles (PHEVs). IEEE Transactions on Power Delivery, 27(3), 1323-1331. doi:10.1109/tpwrd.2012.2193423Kempton, W., & Tomić, J. (2005). Vehicle-to-grid power fundamentals: Calculating capacity and net revenue. Journal of Power Sources, 144(1), 268-279. doi:10.1016/j.jpowsour.2004.12.025Guille, C., & Gross, G. (2009). A conceptual framework for the vehicle-to-grid (V2G) implementation. Energy Policy, 37(11), 4379-4390. doi:10.1016/j.enpol.2009.05.053Geng, Z., Conejo, A. J., Chen, Q., Xia, Q., & Kang, C. (2017). Electricity production scheduling under uncertainty: Max social welfare vs. min emission vs. max renewable production. Applied Energy, 193, 540-549. doi:10.1016/j.apenergy.2017.02.051Verbruggen, A., Fischedick, M., Moomaw, W., Weir, T., Nadaï, A., Nilsson, L. J., … Sathaye, J. (2010). Renewable energy costs, potentials, barriers: Conceptual issues. Energy Policy, 38(2), 850-861. doi:10.1016/j.enpol.2009.10.036Oda, T., Aziz, M., Mitani, T., Watanabe, Y., & Kashiwagi, T. (2018). Mitigation of congestion related to quick charging of electric vehicles based on waiting time and cost–benefit analyses: A japanese case study. Sustainable Cities and Society, 36, 99-106. doi:10.1016/j.scs.2017.10.024Arkin, E. M., Carmi, P., Katz, M. J., Mitchell, J. S. B., & Segal, M. (2019). Locating battery charging stations to facilitate almost shortest paths. Discrete Applied Mathematics, 254, 10-16. doi:10.1016/j.dam.2018.07.019Gallardo-Lozano, J., Milanés-Montero, M. I., Guerrero-Martínez, M. A., & Romero-Cadaval, E. (2012). Electric vehicle battery charger for smart grids. Electric Power Systems Research, 90, 18-29. doi:10.1016/j.epsr.2012.03.015Aziz, M., Oda, T., & Ito, M. (2016). Battery-assisted charging system for simultaneous charging of electric vehicles. Energy, 100, 82-90. doi:10.1016/j.energy.2016.01.069Mehboob, N., Restrepo, M., Canizares, C. A., Rosenberg, C., & Kazerani, M. (2019). Smart Operation of Electric Vehicles With Four-Quadrant Chargers Considering Uncertainties. IEEE Transactions on Smart Grid, 10(3), 2999-3009. doi:10.1109/tsg.2018.2816404García-Villalobos, J., Zamora, I., San Martín, J. I., Asensio, F. J., & Aperribay, V. (2014). Plug-in electric vehicles in electric distribution networks: A review of smart charging approaches. Renewable and Sustainable Energy Reviews, 38, 717-731. doi:10.1016/j.rser.2014.07.040Richardson, D. B. (2013). Electric vehicles and the electric grid: A review of modeling approaches, Impacts, and renewable energy integration. Renewable and Sustainable Energy Reviews, 19, 247-254. doi:10.1016/j.rser.2012.11.042Haidar, A. M. A., Muttaqi, K. M., & Sutanto, D. (2014). Technical challenges for electric power industries due to grid-integrated electric vehicles in low voltage distributions: A review. Energy Conversion and Management, 86, 689-700. doi:10.1016/j.enconman.2014.06.025Mwasilu, F., Justo, J. J., Kim, E.-K., Do, T. D., & Jung, J.-W. (2014). Electric vehicles and smart grid interaction: A review on vehicle to grid and renewable energy sources integration. Renewable and Sustainable Energy Reviews, 34, 501-516. doi:10.1016/j.rser.2014.03.031Habib, S., Kamran, M., & Rashid, U. (2015). Impact analysis of vehicle-to-grid technology and charging strategies of electric vehicles on distribution networks – A review. Journal of Power Sources, 277, 205-214. doi:10.1016/j.jpowsour.2014.12.020Tan, K. M., Ramachandaramurthy, V. K., & Yong, J. Y. (2016). Integration of electric vehicles in smart grid: A review on vehicle to grid technologies and optimization techniques. Renewable and Sustainable Energy Reviews, 53, 720-732. doi:10.1016/j.rser.2015.09.012Raslavičius, L., Azzopardi, B., Keršys, A., Starevičius, M., Bazaras, Ž., & Makaras, R. (2015). Electric vehicles challenges and opportunities: Lithuanian review. Renewable and Sustainable Energy Reviews, 42, 786-800. doi:10.1016/j.rser.2014.10.076Rahman, I., Vasant, P. M., Singh, B. S. M., Abdullah-Al-Wadud, M., & Adnan, N. (2016). Review of recent trends in optimization techniques for plug-in hybrid, and electric vehicle charging infrastructures. Renewable and Sustainable Energy Reviews, 58, 1039-1047. doi:10.1016/j.rser.2015.12.353Faddel, S., Al-Awami, A., & Mohammed, O. (2018). Charge Control and Operation of Electric Vehicles in Power Grids: A Review. Energies, 11(4), 701. doi:10.3390/en11040701Ercan, T., Onat, N. C., & Tatari, O. (2016). Investigating carbon footprint reduction potential of public transportation in United States: A system dynamics approach. Journal of Cleaner Production, 133, 1260-1276. doi:10.1016/j.jclepro.2016.06.051Kwan, S. C., & Hashim, J. H. (2016). A review on co-benefits of mass public transportation in climate change mitigation. Sustainable Cities and Society, 22, 11-18. doi:10.1016/j.scs.2016.01.004Kolbe, K. (2019). Mitigating urban heat island effect and carbon dioxide emissions through different mobility concepts: Comparison of conventional vehicles with electric vehicles, hydrogen vehicles and public transportation. Transport Policy, 80, 1-11. doi:10.1016/j.tranpol.2019.05.007Zalakeviciute, R., Rybarczyk, Y., López-Villada, J., & Diaz Suarez, M. V. (2018). Quantifying decade-long effects of fuel and traffic regulations on urban ambient PM 2.5 pollution in a mid-size South American city. Atmospheric Pollution Research, 9(1), 66-75. doi:10.1016/j.apr.2017.07.001Dell’ Olio, L., Ibeas, A., & Cecin, P. (2011). The quality of service desired by public transport users. Transport Policy, 18(1), 217-227. doi:10.1016/j.tranpol.2010.08.005Mahmoud, M., Garnett, R., Ferguson, M., & Kanaroglou, P. (2016). Electric buses: A review of alternative powertrains. Renewable and Sustainable Energy Reviews, 62, 673-684. doi:10.1016/j.rser.2016.05.019Nissan Leafhttps://www.nissan.co.uk/vehicles/new-vehicles/leaf/range-charging.htmlIntroducing the Fully Charged 2020 Kia Soul EVhttps://www.kia.com/us/en/content/vehicles/upcoming-vehicles/2020-soul-eve6https://en.byd.com/wp-content/uploads/2017/06/e6_cutsheet.pdfTesla Model Shttps://www.tesla.com/modelsBushttps://en.byd.com/bus/40-electric-motor-coach/Urbino Electrichttps://www.solarisbus.com/en/vehicles/zero-emissions/urbino-electricVolvo 7900 Electrichttps://www.volvobuses.co.uk/en-gb/our-offering/buses/volvo-7900-electric/specifications.htmlCollin, R., Miao, Y., Yokochi, A., Enjeti, P., & von Jouanne, A. (2019). Advanced Electric Vehicle Fast-Charging Technologies. Energies, 12(10), 1839. doi:10.3390/en12101839Yang, Y., El Baghdadi, M., Lan, Y., Benomar, Y., Van Mierlo, J., & Hegazy, O. (2018). Design Methodology, Modeling, and Comparative Study of Wireless Power Transfer Systems for Electric Vehicles. Energies, 11(7), 1716. doi:10.3390/en11071716Bi, Z., Song, L., De Kleine, R., Mi, C. C., & Keoleian, G. A. (2015). Plug-in vs. wireless charging: Life cycle energy and greenhouse gas emissions for an electric bus system. Applied Energy, 146, 11-19. doi:10.1016/j.apenergy.2015.02.031Siqi Li, & Mi, C. C. (2015). Wireless Power Transfer for Electric Vehicle Applications. IEEE Journal of Emerging and Selected Topics in Power Electronics, 3(1), 4-17. doi:10.1109/jestpe.2014.2319453Musavi, F., & Eberle, W. (2014). Overview of wireless power transfer technologies for electric vehicle battery charging. IET Power Electronics, 7(1), 60-66. doi:10.1049/iet-pel.2013.0047Wang, Z., Wei, X., & Dai, H. (2015). Design and Control of a 3 kW Wireless Power Transfer System for Electric Vehicles. Energies, 9(1), 10. doi:10.3390/en9010010Sarker, M. R., Pandzic, H., & Ortega-Vazquez, M. A. (2015). Optimal Operation and Services Scheduling for an Electric Vehicle Battery Swapping Station. IEEE Transactions on Power Systems, 30(2), 901-910. doi:10.1109/tpwrs.2014.2331560Adegbohun, F., von Jouanne, A., & Lee, K. (2019). Autonomous Battery Swapping System and Methodologies of Electric Vehicles. Energies, 12(4), 667. doi:10.3390/en12040667OPPChargeCommon Interface for Automated Charging of Hybrid Electric and Electric Commercial Vehicleshttps://www.oppcharge.org/dok/OPPCharge Specification 2nd edition 20190421.pdfFast Charging of Electric Vehicleshttps://www.oppcharge.orgJiang, C. X., Jing, Z. X., Cui, X. R., Ji, T. Y., & Wu, Q. H. (2018). Multiple agents and reinforcement learning for modelling charging loads of electric taxis. Applied Energy, 222, 158-168. doi:10.1016/j.apenergy.2018.03.164Fraile-Ardanuy, J., Castano-Solis, S., Álvaro-Hermana, R., Merino, J., & Castillo, Á. (2018). Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet. Energy Conversion and Management, 157, 59-70. doi:10.1016/j.enconman.2017.11.070Rao, R., Cai, H., & Xu, M. (2018). Modeling electric taxis’ charging behavior using real-world data. International Journal of Sustainable Transportation, 12(6), 452-460. doi:10.1080/15568318.2017.1388887Litzlbauer, M. (2015). Technische Machbarkeitsanalyse einer rein elektrisch betriebenen Taxiflotte. e & i Elektrotechnik und Informationstechnik, 132(3), 172-177. doi:10.1007/s00502-015-0296-3Liao, B., Li, L., Li, B., Mao, J., Yang, J., Wen, F., & Salam, M. A. (2016). Load modeling for electric taxi battery charging and swapping stations: Comparison studies. 2016 IEEE 2nd Annual Southern Power Electronics Conference (SPEC). doi:10.1109/spec.2016.7846135Zou, Y., Wei, S., Sun, F., Hu, X., & Shiao, Y. (2016). Large-scale deployment of electric taxis in Beijing: A real-world analysis. Energy, 100, 25-39. doi:10.1016/j.energy.2016.01.062Asamer, J., Reinthaler, M., Ruthmair, M., Straub, M., & Puchinger, J. (2016). Optimizing charging station locations for urban taxi providers. Transportation Research Part A: Policy and Practice, 85, 233-246. doi:10.1016/j.tra.2016.01.014Yang, J., Dong, J., & Hu, L. (2017). A data-driven optimization-based approach for siting and sizing of electric taxi charging stations. Transportation Research Part C: Emerging Technologies, 77, 462-477. doi:10.1016/j.trc.2017.02.014Jiang, C., Jing, Z., Ji, T., & Wu, Q. (2018). Optimal location of PEVCSs using MAS and ER approach. IET Generation, Transmission & Distribution, 12(20), 4377-4387. doi:10.1049/iet-gtd.2017.1907Pan, A., Zhao, T., Yu, H., & Zhang, Y. (2019). Deploying Public Charging Stations for Electric Taxis: A Charging Demand Simulation Embedded Approach. IEEE Access, 7, 17412-17424. doi:10.1109/access.2019.2894780Chen Lianfu, Zhang, W., Huang, Y., & Zhang, D. (2014). Research on the charging station service radius of electric taxis. 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). doi:10.1109/itec-ap.2014.6941081Yang, Y., Zhang, W., Niu, L., & Jiang, J. (2015). Coordinated Charging Strategy for Electric Taxis in Temporal and Spatial Scale. Energies, 8(2), 1256-1272. doi:10.3390/en8021256Niu, L., & Zhang, D. (2015). Charging Guidance of Electric Taxis Based on Adaptive Particle Swarm Optimization. The Scientific World Journal, 2015, 1-9. doi:10.1155/2015/354952Yang, Z., Guo, T., You, P., Hou, Y., & Qin, S. J. (2019). Distributed Approach for Temporal–Spatial Charging Coordination of Plug-in Electric Taxi Fleet. IEEE Transactions on Industrial Informatics, 15(6), 3185-3195. doi:10.1109/tii.2018.2879515Rossi, F., Iglesias, R., Alizadeh, M., & Pavone, M. (2020). On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms. IEEE Transactions on Control of Network Systems, 7(1), 384-397. doi:10.1109/tcns.2019.2923384Liang, Y., Zhang, X., Xie, J., & Liu, W. (2017). An Optimal Operation Model and Ordered Charging/Discharging Strategy for Battery Swapping Stations. Sustainability, 9(5), 700. doi:10.3390/su9050700XU, X., YAO, L., ZENG, P., LIU, Y., & CAI, T. (2015). Architecture and performance analysis of a smart battery charging and swapping operation service network for electric vehicles in China. Journal of Modern Power Systems and Clean Energy, 3(2), 259-268. doi:10.1007/s40565-015-0118-yJing, Z., Fang, L., Lin, S., & Shao, W. (2014). Modeling for electric taxi load and optimization model for charging/swapping facilities of electric taxi. 2014 IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific). doi:10.1109/itec-ap.2014.6941160Wang, Y., Ding, W., Huang, L., Wei, Z., Liu, H., & Stankovic, J. A. (2018). Toward Urban Electric Taxi Systems in Smart Cities: The Battery Swapping Challenge. IEEE Transactions on Vehicular Technology, 67(3), 1946-1960. doi:10.1109/tvt.2017.2774447You, P., Yang, Z., Zhang, Y., Low, S. H., & Sun, Y. (2016). Optimal Charging Schedule for a Battery Switching Station Serving Electric Buses. IEEE Transactions on Power Systems, 31(5), 3473-3483. doi:10.1109/tpwrs.2015.2487273Yang, Z., Sun, L., Chen, J., Yang, Q., Chen, X., & Xing, K. (2014). Profit Maximization for Plug-In Electric Taxi With Uncertain Future Electricity Prices. IEEE Transactions on Power Systems, 29(6), 3058-3068. doi:10.1109/tpwrs.2014.2311120Yang, Z., Sun, L., Ke, M., Shi, Z., & Chen, J. (2014). Optimal Charging Strategy for Plug-In Electric Taxi With Time-Varying Profits. IEEE Transactions on Smart Grid, 5(6), 2787-2797. doi:10.1109/tsg.2014.2354473Yang, J., Xu, Y., & Yang, Z. (2017). Regulating the Collective Charging Load of Electric Taxi Fleet via Real-Time Pricing. IEEE Transactions on Power Systems, 32(5), 3694-3703. doi:10.1109/tpwrs.2016.2643685Du, R., Liao, G., Zhang, E., & Wang, J. (2018). Battery charge or change, which is better? A case from Beijing, China. Journal of Cleaner Production, 192, 698-711. doi:10.1016/j.jclepro.2018.05.021Yang, J., Dong, J., Lin, Z., & Hu, L. (2016). Predicting market potential and environmental benefits of deploying electric taxis in Nanjing, China. Transportation Research Part D: Transport and Environment, 49, 68-81. doi:10.1016/j.trd.2016.08.037You, P., Low, S. H., Yang, Z., Zhang, Y., & Lingkun Fu. (2016). Real-time recommendation algorithm of battery swapping stations for electric taxis. 2016 IEEE Power and Energy Society General Meeting (PESGM). doi:10.1109/pesgm.2016.7741620Dai, Q., Cai, T., Duan, S., & Zhao, F. (2014). Stochastic Modeling and Forecasting of Load Demand for Electric Bus Battery-Swap Station. IEEE Transactions on Power Delivery, 29(4), 1909-1917. doi:10.1109/tpwrd.2014.2308990Mohamed, M., Farag, H., El-Taweel, N., & Ferguson, M. (2017). Simulation of electric buses on a full transit network: Operational feasibility and grid impact analysis. Electric Power Systems Research, 142, 163-175. doi:10.1016/j.epsr.2016.09.032Zhang, X. (2018). Short-Term Load Forecasting for Electric Bus Charging Stations Based on Fuzzy Clustering and Least Squares Support Vector Machine Optimized by Wolf Pack Algorithm. Energies, 11(6), 1449. doi:10.3390/en11061449Ding, H., Hu, Z., & Song, Y. (2015). Value of the energy storage system in an electric bus fast charging station. Applied Energy, 157, 630-639. doi:10.1016/j.apenergy.2015.01.058Qin, N., Gusrialdi, A., Paul Brooker, R., & T-Raissi, A. (2016). Numerical analysis of electric bus fast charging strategies for demand charge reduction. Transportation Research Part A: Policy and Practice, 94, 386-396. doi:10.1016/j.tra.2016.09.014Huimiao Chen, Zechun Hu, Zhiwei Xu, Jiayi Li, Honggang Zhang, Xue Xia, … Mingwei Peng. (2016). Coordinated charging strategies for electric bus fast charging stations. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). doi:10.1109/appeec.2016.7779677Chen, H., Hu, Z., Zhang, H., & Luo, H. (2018). Coordinated charging and discharging strategies for plug-in electric bus fast charging station with energy storage system. IET Generation, Transmission & Distribution, 12(9), 2019-2028. doi:10.1049/iet-gtd.2017.0636Gao, Y., Guo, S., Ren, J., Zhao, Z., Ehsan, A., & Zheng, Y. (2018). An Electric Bus Power Consumption Model and Optimization of Charging Scheduling Concerning Multi-External Factors. Energies, 11(8), 2060. doi:10.3390/en11082060Cheng, Y., & Tao, J. (2018). Optimization of A Micro Energy Network Integrated with Electric Bus Battery Swapping Station and Distributed PV. 2018 2nd IEEE Conference on Energy Internet and Energy System Integration (EI2). doi:10.1109/ei2.2018.8582236Sebastiani, M. T., Luders, R., & Fonseca, K. V. O. (2016). Evaluating Electric Bus Operation for a Real-World BRT Public Transportation Using Simulation Optimization. IEEE Transactions on Intelligent Transportation Systems, 17(10), 2777-2786. doi:10.1109/tits.2016.2525800Wang, Y., Huang, Y., Xu, J., & Barclay, N. (2017). Optimal recharging scheduling for urban electric buses: A case study in Davis. Transportation Research Part E: Logistics and Transportation Review, 100, 115-132. doi:10.1016/j.tre.2017.01.001Liu, Z., Song, Z., & He, Y. (2018). Planning of Fast-Charging Stations for a Battery Electric Bus System under Energy Consumption Uncertainty. Transportation Research Record: Journal of the Transportation Research Board, 2672(8), 96-107. doi:10.1177/0361198118772953Leou, R.-C., & Hung, J.-J. (2017). Optimal Charging Schedule Planning and Economic Analysis for Electric Bus Charging Stations. Energies, 10(4), 483. doi:10.3390/en10040483Bak, D.-B., Bak, J.-S., & Kim, S.-Y. (2018). Strategies for Implementing Public Service Electric Bus Lines by Charging Type in Daegu Metropolitan City, South Korea. Sustainability, 10(10), 3386. doi:10.3390/su10103386Chen, Z., Yin, Y., & Song, Z. (2018). A cost-competitiveness analysis of charging infrastructure for electric bus operations. Transportation Research Part C: Emerging Technologies, 93, 351-366. doi:10.1016/j.trc.2018.06.006Cheng, Y., Wang, W., Ding, Z., & He, Z. (2019). Electric bus fast charging station resource planning consid

Comparison of intelligent charging algorithms for electric vehicles to reduce peak load and demand variability in a distribution grid

A potential breakthrough of the electrification of the vehicle fleet will incur a steep rise in the load on the electrical power grid. To avoid huge grid investments, coordinated charging of those vehicles is a must. In this paper, we assess algorithms to schedule charging of plug-in (hybrid) electric vehicles as to minimize the additional peak load they might cause. We first introduce two approaches, one based on a classical optimization approach using quadratic programming, and a second one, market based coordination, which is a multi-agent system that uses bidding on a virtual market to reach an equilibrium, price that matches demand and supply. We benchmark these two methods against each other, as well as to a baseline scenario of uncontrolled charging. Our simulation results covering a residential area with 63 households show that controlled charging reduces peak load, load variability, and deviations from the nominal grid voltage

Charging ahead on the transition to electric vehicles with standard 120 v wall outlets

Electrification of transportation is needed soon and at significant scale to meet climate goals, but electric vehicle adoption has been slow and there has been little systematic analysis to show that today's electric vehicles meet the needs of drivers. We apply detailed physics-based models of electric vehicles with data on how drivers use their cars on a daily basis. We show that the energy storage limits of today's electric vehicles are outweighed by their high efficiency and the fact that driving in the United States seldom exceeds 100 km of daily travel. When accounting for these factors, we show that the normal daily travel of 85-89% of drivers in the United States can be satisfied with electric vehicles charging with standard 120 V wall outlets at home only. Further, we show that 77-79% of drivers on their normal daily driving will have over 60 km of buffer range for unexpected trips. We quantify the sensitivities to terrain, high ancillary power draw, and battery degradation and show that an extreme case with all trips on a 3% uphill grade still shows the daily travel of 70% of drivers being satisfied with electric vehicles. These findings show that today's electric vehicles can satisfy the daily driving needs of a significant majority of drivers using only 120 V wall outlets that are already the standard across the United States

Forecasting the state of health of electric vehicle batteries to evaluate the viability of car sharing practices

Car sharing practices are introducing electric vehicles into their fleet. However, literature suggests that at this point shared electric vehicle systems are failing to reach satisfactory commercial viability. Potential reason for this is the effect of higher vehicle usage which is characteristic for car sharing, and the implication on the battery state of health. In this paper, we forecast state of health for two identical electric vehicles shared by two different car sharing practices. For this purpose, we use real life transaction data from charging stations and different electric vehicles’ sensors. The results indicate that insight into users’ driving and charging behaviour can provide valuable point of reference for car sharing system designers. In particular, the forecasting results show that the moment when electric vehicle battery reaches its theoretical end of life can differ in as much as ¼ of time when vehicles are shared under different conditions

Open-Source, Open-Architecture SoftwarePlatform for Plug-InElectric Vehicle SmartCharging in California

This interdisciplinary eXtensible Building Operating System–Vehicles project focuses on controlling plug-in electric vehicle charging at residential and small commercial settings using a novel and flexible open-source, open-architecture charge communication and control platform. The platform provides smart charging functionalities and benefits to the utility, homes, and businesses.This project investigates four important areas of vehicle-grid integration research, integrating technical as well as social and behavioral dimensions: smart charging user needs assessment, advanced load control platform development and testing, smart charging impacts, benefits to the power grid, and smart charging ratepayer benefits