Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability

Authors

  • Wen Wang State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China
  • Ye Yang State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China
  • Fangqiu Xu Beijing Information Science and Technology University, Beijing 102206, China
  • Yulu Zhong State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China
  • Chunhua Jin Beijing Information Science and Technology University, Beijing 102206, China
  • Xinye Zhong Beijing Information Science and Technology University, Beijing 102206, China
  • Jian Qin State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China
  • Mingcai Wang State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3927

Keywords:

Electric vehicle cluster, operation optimization, electricity spot market, V2G, scheduling capability

Abstract

The widespread use of electric vehicles (EV) has put a strain on the stable operation of power grid. Therefore, the potential of EV cluster power load regulation has been paid attention. In the cluster, the electric vehicle aggregator (EVA) can gather a large number of EVs and participate in the electricity spot market by optimizing the charging/discharging power. In this study, a bi-objective optimization model for V2G enabled EV cluster operation is proposed to determine the optimal load of EV cluster considering the electricity spot market. First, the scheduling capability of EVs is modelled and aggregated considering the EV user willingness. Then, the demand response and electricity spot trade for EVA are analyzed. Based on the capability constraints and the market rules, an optimization model is established with two objectives of maximizing EVA profits and EV user satisfaction. Finally, a case study in Beijing, China is implemented to prove the feasibility of the proposed model. The results show that the EV user willingness for orderly charging/discharging is distributed in the range of 0.26 and 0.94 with an average value of 0.85. In addition, the proposed EV cluster operation strategy can improve the EVA daily profits by 81.27% and increase the EV user satisfaction by 70% compared with normal charging strategy.

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Author Biographies

Wen Wang, State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

Wen Wang, PhD, senior engineer, deputy general manager of State Grid Smart Internet of Vehicles Co., LTD., Director of Beijing Electric Vehicle mobile Energy Storage Cluster Control Technology Engineering Center. He has been engaged in the research of power grid dispatching, electric vehicle charging and replacement, information security and other fields.

Ye Yang, State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

Ye Yang, senior engineer in State Grid Smart Internet of Vehicles Co., LTD. In 2014, he received his PhD degree in Florida State University, majoring in Electronic Engineering. In 2010, he graduated from Texas Tech University with a master’s degree in electronic engineering. In 2008, he received his bachelor’s degree in Information engineering from Tianjin University. He has been engaged in the research and development of power electronics, intelligent micro-grid, vehicle network interaction and other fields.

Fangqiu Xu, Beijing Information Science and Technology University, Beijing 102206, China

Fangqiu Xu received her M.S. and PhD degrees in North China Electric Power University majoring in management science and engineering. She is currently working as an associate professor in Beijing Information Science and Technology University. Her research interests include intelligent optimization of energy system, electric vehicle regulation and intelligent decision making.

Yulu Zhong, State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

Yulu Zhong received her master’s degree in Signal and Information Processing from the School of Mechanical and Electrical Engineering, China University of Mining and Technology. From 2011 to 2019, she worked as a teacher in Beijing Polytechnic, teaching digital circuit, analog circuit and other courses. Since 2019, she have been engaged in science and technology project management in State Grid Smart Internet of Vehicles Co., LTD. Her research interests include charging and replacing technology and interactive technology of vehicle network.

Chunhua Jin, Beijing Information Science and Technology University, Beijing 102206, China

Chunhua Jin received his PhD degree in Beijing science and technology university. He is currently working as a professor in Beijing Information Science and Technology University. His research interests include the intelligent decision making, management innovation and optimization.

Xinye Zhong, Beijing Information Science and Technology University, Beijing 102206, China

Xinye Zhong received the B.S. degrees in computer science and technology from Beijing Information Science and Technology University, Beijing, China, in 2023. She is now studying in the same university for an M.S. degree in industrial engineering and management. Her research interests include the intelligent decision making and optimization of energy system.

Jian Qin, State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

Jian Qin graduated from Hohai University majoring in computer science and technology, focusing on the research of electric vehicle charging and replacement technology and operation mode direction. He is currently working as an engineer in State Grid Smart Internet of Vehicles Co., LTD.

Mingcai Wang, State Grid Smart Internet of Vehicles Co., Ltd, Beijing 100032, China

Mingcai Wang, received his B.S. and M.S. degrees in electrical engineering from Beijing Jiaotong University, Beijing, China,in 2008 and 2010. He is currently working as senior manager at State Grid Smart Internet of Vehicles Company, Ltd. His research field mainly covers electric vehicle charging and exchanging business field (V2G), interaction between electric vehicle and power grid.

References

Zhou Y. Worldwide carbon neutrality transition? Energy efficiency, renewable, carbon trading and advanced energy policies[J/OL]. Energy Reviews, 2023, 2(2): 100026. https://doi.org/10.1016/j.enrev.2023.100026.

Siddhartha P, Sujeeth T, Shiva B, et al. Integration Of Renewable Energy Sources With Power Management Strategy For Effective Bidirectional Vehicle To Grid Power Transfer[J/OL]. Procedia Computer Science, 2023, 218: 9–23. https://doi.org/10.1016/j.procs.2022.12.397.

Jithin S, Rajeev T. Flywheel Energy Storage Supported Adaptive Energy Management Strategy for Solar-powered Electric Vehicle Charging Station[J/OL]. Distributed Generation and Alternative Energy Journal, 2023, 38(2): 669–690. https://doi.org/10.13052/dgaej2156-3306.38213.

Wei H, Zhang Y, Wang Y, et al. Planning integrated energy systems coupling V2G as a flexible storage[J/OL]. Energy, 2022, 239: 122215. https://doi.org/10.1016/j.energy.2021.122215.

Bibak B, Tekiner-Mogulkoc H. Influences of vehicle to grid (V2G) on power grid: An analysis by considering associated stochastic parameters explicitly[J/OL]. Sustainable Energy, Grids and Networks, 2021, 26: 100429. https://doi.org/10.1016/j.segan.2020.100429.

Hannan M A, Mollik M S, Al-Shetwi A Q, et al. Vehicle to grid connected technologies and charging strategies: Operation, control, issues and recommendations[J/OL]. Journal of Cleaner Production, 2022, 339: 130587. https://doi.org/10.1016/j.jclepro.2022.130587.

Tian X, Cheng B, Liu H. V2G optimized power control strategy based on time-of-use electricity price and comprehensive load cost[J/OL]. Energy Reports, 2023, 10: 1467–1473. https://doi.org/10.1016/j.egyr.2023.08.017.

Shi R, Li S, Zhang P, et al. Integration of renewable energy sources and electric vehicles in V2G network with adjustable robust optimization[J/OL]. Renewable Energy, 2020, 153: 1067–1080. https://doi.org/10.1016/j.renene.2020.02.027.

Singirikonda S, Obulesu Y P, Kannan R, et al. Adaptive control-based Isolated bi-directional converter for G2V& V2G charging with integration of the renewable energy source[J/OL]. Energy Reports, 2022, 8: 11416–11428. https://doi.org/10.1016/j.egyr.2022.08.223.

Yang Z, Gao T, Liu Y, et al. A two-stage pricing strategy for electric vehicles participating in emergency power supply for important loads[J/OL]. Electric Power Systems Research, 2023, 218: 109239. https://doi.org/10.1016/j.epsr.2023.109239.

Shi L, Guo M. An economic evaluation of electric vehicles balancing grid load fluctuation, new perspective on electrochemical energy storage alternative[J/OL]. Journal of Energy Storage, 2023, 68: 107801. https://doi.org/10.1016/j.est.2023.107801.

Huang B, Meijssen A G, Annema J A, et al. Are electric vehicle drivers willing to participate in vehicle-to-grid contracts? A context-dependent stated choice experiment[J/OL]. Energy Policy, 2021, 156: 112410. https://doi.org/10.1016/j.enpol.2021.112410.

Visaria A A, Jensen A F, Thorhauge M, et al. User preferences for EV charging, pricing schemes, and charging infrastructure[J/OL]. Transportation Research Part A: Policy and Practice, 2022, 165: 120–143. https://doi.org/10.1016/j.tra.2022.08.013.

Jabalameli N, Ghosh A. Online Centralized Coordination of Charging and Phase Switching of PEVs in Unbalanced LV Networks With High PV Penetrations[J/OL]. IEEE Systems Journal, 2021, 15(1): 1015–1025. https://doi.org/10.1109/JSYST.2020.3000504.

Roman L F A, Gondim P R L, Lloret J. Pairing-based authentication protocol for V2G networks in smart grid[J/OL]. Ad Hoc Networks, 2019, 90: 101745. https://doi.org/10.1016/j.adhoc.2018.08.015.

Itoo S, Som L K, Ahmad M, et al. A robust ECC-based authentication framework for energy internet (EI)-based vehicle to grid communication system[J/OL]. Vehicular Communications, 2023, 41: 100612. https://doi.org/10.1016/j.vehcom.2023.100612.

Oprea S V, Bâra A. Devising a trading mechanism with a joint price adjustment for local electricity markets using blockchain. Insights for policy makers[J/OL]. Energy Policy, 2021, 152: 112237. https://doi.org/10.1016/j.enpol.2021.112237.

Vignali R, Falsone A, Ruiz F, et al. Towards a comprehensive framework for V2G optimal operation in presence of uncertainty[J/OL]. Sustainable Energy, Grids and Networks, 2022, 31: 100740. https://doi.org/10.1016/j.segan.2022.100740.

Huang P, Munkhammar J, Fachrizal R, et al. Comparative studies of EV fleet smart charging approaches for demand response in solar-powered building communities[J/OL]. Sustainable Cities and Society, 2022, 85: 104094. https://doi.org/10.1016/j.scs.2022.104094.

Ray S, Kasturi K, Patnaik S, et al. Review of electric vehicles integration impacts in distribution networks: Placement, charging/discharging strategies, objectives and optimisation models[J/OL]. Journal of Energy Storage, 2023, 72: 108672. https://doi.org/10.1016/j.est.2023.108672.

Zhang L, Sun C, Cai G, et al. Charging and discharging optimization strategy for electric vehicles considering elasticity demand response[J/OL]. eTransportation, 2023, 18: 100262. https://doi.org/10.1016/j.etran.2023.100262.

Zheng Y, Wang Y, Yang Q. Bidding strategy design for electric vehicle aggregators in the day-ahead electricity market considering price volatility: A risk-averse approach[J/OL]. Energy, 2023: 129138. https://doi.org/10.1016/j.energy.2023.129138.

Huang P, Lovati M, Zhang X, et al. A coordinated control to improve performance for a building cluster with energy storage, electric vehicles, and energy sharing considered[J/OL]. Applied Energy, 2020, 268: 114983. https://doi.org/10.1016/j.apenergy.2020.114983.

Mastoi M S, Zhuang S, Munir H M, et al. A study of charging-dispatch strategies and vehicle-to-grid technologies for electric vehicles in distribution networks[J/OL]. Energy Reports, 2023, 9: 1777–1806. https://doi.org/10.1016/j.egyr.2022.12.139.

Tian J, Lv Y, Zhao Q, et al. A charging plan generation method for EV clusters considering the operation of regional distribution network[J/OL]. Energy Reports, 2022, 8: 135–143. https://doi.org/10.1016/j.egyr.2022.10.117.

Wu J, Zhang M, Xu T, et al. A review of key technologies in relation to large-scale clusters of electric vehicles supporting a new power system[J/OL]. Renewable and Sustainable Energy Reviews, 2023, 182: 113351. https://doi.org/10.1016/j.rser.2023.113351.

Zhang Q, Wu X, Deng X, et al. Bidding strategy for wind power and Large-scale electric vehicles participating in Day-ahead energy and frequency regulation market[J/OL]. Applied Energy, 2023, 341: 121063. https://doi.org/10.1016/j.apenergy.2023.121063.

Guo S, Qiu Z, Xiao C, et al. A multi-level vehicle-to-grid optimal scheduling approach with EV economic dispatching model [J/OL]. Energy Reports, 2021, 7: 22–37. https://doi.org/10.1016/j.egyr.2021.10.058.

Zhang S, Li X, Li Y, et al. A green-fitting dispatching model of station cluster for battery swapping under charging-discharging mode [J/OL]. Energy, 2023, 276: 127600. https://doi.org/10.1016/j.energy.2023.127600.

Hu J, Cao J, Rutkowski L, et al. Hierarchical interactive demand response power profile tracking optimization and control of multiple EV aggregators[J/OL]. Electric Power Systems Research, 2022, 208: 107894. https://doi.org/10.1016/j.epsr.2022.107894.

Shinde P, Kouveliotis-Lysikatos I, Amelin M, et al. A Modified Progressive Hedging Approach for Multistage Intraday Trade of EV aggregators[J/OL]. Electric Power Systems Research, 2022, 212: 108518. https://doi.org/10.1016/j.epsr.2022.108518.

Hou L, Herrera O E, Mérida W. Charging scheduling and energy management for mobile chargers in a grid-interactive transportation system[J/OL]. Journal of Energy Storage, 2023, 65: 107305. https://doi.org/10.1016/j.est.2023.107305.

Datta J, Das D. Energy management of multi-microgrids with renewables and electric vehicles considering price-elasticity based demand response: A bi-level hybrid optimization approach[J/OL]. Sustainable Cities and Society, 2023, 99: 104908. https://doi.org/10.1016/j.scs.2023.104908.

Xu J, Huang Y. The short-term optimal resource allocation approach for electric vehicles and V2G service stations[J/OL]. Applied Energy, 2022, 319: 119200. https://doi.org/10.1016/j.apenergy.2022.119200.

Li C, Zhang H, Zhou H, et al. Double-layer optimized configuration of distributed energy storage and transformer capacity in distribution network[J/OL]. International Journal of Electrical Power & Energy Systems, 2023, 147: 108834. https://doi.org/10.1016/j.ijepes.2022.108834.

Bhushan Sahay K, Abourehab M A S, Mehbodniya A, et al. Computation of electrical vehicle charging station (evcs) with coordinate computation based on meta-heuristics optimization model with effective management strategy for optimal charging and energy saving[J/OL]. Sustainable Energy Technologies and Assessments, 2022, 53: 102439. https://doi.org/10.1016/j.seta.2022.102439.

Wang Y, Yao E, Pan L. Electric vehicle drivers’ charging behavior analysis considering heterogeneity and satisfaction[J/OL]. Journal of Cleaner Production, 2021, 286: 124982. https://doi.org/10.1016/j.jclepro.2020.124982.

Qin Y, Dai Y, Huang J, et al. Charging patterns analysis and multiscale infrastructure deployment: based on the real trajectories and battery data of the plug-in electric vehicles in Shanghai[J/OL]. Journal of Cleaner Production, 2023: 138847. https://doi.org/10.1016/j.jclepro.2023.138847.

Sikder S K, Nagarajan M, Mustafee N. Augmenting EV charging infrastructure towards transformative sustainable cities: An equity-based approach[J/OL]. Technological Forecasting and Social Change, 2023, 196: 122829. https://doi.org/10.1016/j.techfore.2023.122829.

Jenn A, Highleyman J. Distribution grid impacts of electric vehicles: A California case study[J/OL]. iScience, 2022, 25(1): 103686. https://doi.org/10.1016/j.isci.2021.103686.

Barman P, Dutta L, Bordoloi S, et al. Renewable energy integration with electric vehicle technology: A review of the existing smart charging approaches[J/OL]. Renewable and Sustainable Energy Reviews, 2023, 183: 113518. https://doi.org/10.1016/j.rser.2023.113518.

Lin H, Dang J, Zheng H, et al. Two-stage electric vehicle charging optimization model considering dynamic virtual price-based demand response and a hierarchical non-cooperative game[J/OL]. Sustainable Cities and Society, 2023, 97: 104715. https://doi.org/10.1016/j.scs.2023.104715.

Ahmad T, Madonski R, Zhang D, et al. Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm[J/OL]. Renewable and Sustainable Energy Reviews, 2022, 160: 112128. https://doi.org/10.1016/j.rser.2022.112128.

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Published

2024-02-03

How to Cite

Wang, W. ., Yang, Y. ., Xu, F. ., Zhong, Y. ., Jin, C. ., Zhong, X. ., Qin, J. ., & Wang, M. . (2024). Optimal Operation Strategy of Electric Vehicle Cluster in the Electricity Spot Market Considering Scheduling Capability. Distributed Generation &Amp; Alternative Energy Journal, 39(02), 369–402. https://doi.org/10.13052/dgaej2156-3306.3927

Issue

2024: Vol 39 Iss 2

Section

Renewable Power & Energy Systems