Development of Charging/Discharging Scheduling Algorithm for Economical and Energy-Efficient Operation of Multi-EV Charging Station
Source abstract
Abstract
As the number of electric vehicles (EVs) significantly increases, the excessive charging demand of parked EVs in the charging station may incur an instability problem to the electricity network during peak hours. For the charging station to take a microgrid (MG) structure, an economical and energy-efficient power management scheme is required for the power provision of EVs while considering the local load demand of the MG. For these purposes, this study presents the power management scheme of interdependent MG and EV fleets aided by a novel EV charging/discharging scheduling algorithm. In this algorithm, the maximum amount of discharging power from parked EVs is determined based on the difference between local load demand and photovoltaic (PV) power production to alleviate imbalances occurred between them. For the power management of the MG with charging/discharging scheduling of parked EVs in the PV-based charging station, multi-objective optimization is performed to minimize the operating cost and grid dependency. In addition, the proposed scheme maximizes the utilization of EV charging/discharging while satisfying the charging requirements of parked EVs. Moreover, a more economical and energy-efficient PV-based charging station is established using the future trends of local load demand and PV power production predicted by a gated recurrent unit (GRU) network. With the proposed EV charging/discharging scheduling algorithm, the operating cost of PV-based charging station is decreased by 167.71% and 28.85% compared with the EV charging scheduling algorithm and the conventional EV charging/discharging scheduling algorithm, respectively. It is obvious that the economical and energy-efficient operation of PV-based charging station can be accomplished by applying the power management scheme with the proposed EV charging/discharging scheduling strategy.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Advanced Battery Technologies Research · Smart Grid Energy Management
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Cho Chun Shik Graduate School of Mobility, Korea Advanced Institute of Science and Technology (KAIST), 291, Daehak-ro, Yuseong-gu, Daejeon 34141, Korea
Environment ICT Research Section, Electronics and Telecommunications Research Institute (ETRI), 218, Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Funding
Ministry of Science, ICT
References (68)
Show source-provided references
1. Ehsani, M., Gao, Y., Longo, S., and Ebrahimi, K.M. (2018). Modern Electric, Hybrid Electric, and Fuel Cell Vehicles, CRC Press.10.1201/9781420054002 ↗
2. Electric vehicle charging choices: Modelling and implications for smart charging services10.1016/j.trc.2017.05.006 ↗
3. A scalable three-step approach for demand side management of plug-in hybrid vehicles10.1109/TSG.2012.2213847 ↗
4. Peak load reduction in a smart building integrating microgrid and V2B-based demand response scheme10.1109/JSYST.2018.2880864 ↗
5. Costs and gains of smart charging electric vehicles to provide regulation services10.1016/j.procs.2012.06.110 ↗
6. Plug-in electric vehicle to home (V2H) operation under a grid outage10.1109/TSG.2016.2603502 ↗
7. A multi-objective optimization for energy management in a renewable micro-grid system: A data mining approach10.1063/1.4873997 ↗
8. Optimal charging scheduling by pricing for EV charging station with dual charging modes10.1109/TITS.2018.2876287 ↗
9. Socially optimal charging strategies for electric vehicles10.1109/TAC.2014.2346089 ↗
10. Game-theoretic control of PHEV charging with power flow analysis10.3934/energy.2016.2.379 ↗
11. Multi-period planning for electric car charging station locations: A case of Korean Expressways10.1016/j.ejor.2014.10.029 ↗
12. Electric vehicle charging station placement: Formulation, complexity, and solutions10.1109/TSG.2014.2344684 ↗
13. As one falls, another rises? Residential peak load reduction through electricity rate structures10.1016/j.scs.2020.102191 ↗
14. Dominguez, J.A., Dante, A.W., Agbossou, K., Henao, N., Campillo, J., Cardenas, A., and Kelouwani, S. (2020, January 17–19). Optimal Charging Scheduling of Electric Vehicles based on Principal Component Analysis and Convex Optimization. Proceedings of the 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, The Netherlands.10.1109/ISIE45063.2020.9152292 ↗
15. McCarthy, D., and Wolfs, P. (2010, January 5–8). The HV system impacts of large scale electric vehicle deployments in a metropolitan area. Proceedings of the 2010 20th Australasian Universities Power Engineering Conference, Christchurch, New Zealand.
16. Putrus, G., Suwanapingkarl, P., Johnston, D., Bentley, E., and Narayana, M. (2009, January 7–10). Impact of electric vehicles on power distribution networks. Proceedings of the 2009 IEEE Vehicle Power and Propulsion Conference, Dearborn, MI, USA.10.1109/VPPC.2009.5289760 ↗
17. An optimization approach for efficient management of EV parking lots with batteries recharging facilities10.1007/s12652-013-0174-y ↗
18. Hüls, J., and Remke, A. (2016, January 25–28). Coordinated charging strategies for plug-in electric vehicles to ensure a robust charging process. Proceedings of the 10th EAI International Conference on Performance Evaluation Methodologies and Tools on 10th EAI International Conference on Performance Evaluation Methodologies and Tools, Taormina, Italy.10.4108/eai.25-10-2016.2266997 ↗
19. Multi-objective optimization technique for the operation of grid tied PV powered EV charging station10.1016/j.epsr.2018.08.004 ↗
20. Economic and environmental impacts of a PV powered workplace parking garage charging station10.1016/j.apenergy.2013.02.068 ↗
21. Integration of PV power into future low-carbon smart electricity systems with EV and HP in Kansai Area, Japan10.1016/j.renene.2012.01.003 ↗
22. Co-benefits of large scale plug-in hybrid electric vehicle and solar PV deployment10.1016/j.jpowsour.2012.10.007 ↗
23. Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables10.1016/j.energy.2016.11.118 ↗
24. Kempton, W., Tomic, J., Letendre, S., Brooks, A., and Lipman, T. (2001). Vehicle-to-Grid Power: Battery, Hybrid, and Fuel Cell Vehicles as Resources for Distributed Electric Power in California, UC Davis Institute of Transportation Studies.
25. Smart charging for electric vehicles: A survey from the algorithmic perspective10.1109/COMST.2016.2518628 ↗
26. Displacing natural gas with electric vehicles for grid stabilization10.1016/j.energy.2017.09.064 ↗
27. Marano, V., and Rizzoni, G. (2008, January 22–24). Energy and economic evaluation of PHEVs and their interaction with renewable energy sources and the power grid. Proceedings of the 2008 IEEE International Conference on Vehicular Electronics and Safety, Columbus, OH, USA.10.1109/ICVES.2008.4640909 ↗
28. Vehicle-to-grid power implementation: From stabilizing the grid to supporting large-scale renewable energy10.1016/j.jpowsour.2004.12.022 ↗
29. Optimal operation planning of V2G-equipped Microgrid in the presence of EV aggregator10.1016/j.epsr.2017.07.015 ↗
30. Intelligent unit commitment with vehicle-to-grid—A cost-emission optimization10.1016/j.jpowsour.2009.08.035 ↗
31. Assessment of electric vehicles concerning impacts, charging infrastructure with unidirectional and bidirectional chargers, and power flow comparisons10.1002/er.4033 ↗
32. Lambert, G., Lavoie, S., Lecourtois, É., Giumento, A., Lagace, M., Dupré, J.-L., Patault, L.-A., Boudjerida, N., Zaghib, K., and Perreault, É. (2017). Bidirectional Charging System for Electric Vehicle. (20170050529A1), U.S. Patent.
33. Kaiser, A., Nguyen, A., Pham, R., Granados, M., and Le, H.T. (2018, January 13–15). Efficient Interfacing Electric Vehicles with Grid using Bi-directional Smart Inverter. Proceedings of the 2018 IEEE Transportation Electrification Conference and Expo (ITEC), Long Beach, CA, USA.10.1109/ITEC.2018.8450152 ↗
34. Zhang, R., Cheng, X., and Yang, L. (2017, January 24–27). Stable matching based cooperative V2V charging mechanism for electric vehicles. Proceedings of the 2017 IEEE 86th Vehicular Technology Conference (VTC-Fall), Toronto, ON, Canada.10.1109/VTCFall.2017.8288322 ↗
35. Wang, M., Ismail, M., Zhang, R., Shen, X.S., Serpedin, E., and Qaraqe, K. (2014, January 8–12). A semi-distributed V2V fast charging strategy based on price control. Proceedings of the 2014 IEEE Global Communications Conference, Austin, TX, USA.10.1109/GLOCOM.2014.7037525 ↗
36. Design algorithm for optimum capacity of ESS connected with PVs under the RPS program10.1109/ACCESS.2018.2865744 ↗
37. Venayagamoorthy, G.K., Mitra, P., Corzine, K., and Huston, C. (2009, January 20–24). Real-time modeling of distributed plug-in vehicles for V2G transactions. Proceedings of the 2009 IEEE Energy Conversion Congress and Exposition, San Jose, CA, USA.10.1109/ECCE.2009.5316210 ↗
38. MobQoS: Mobility-aware and QoS-driven SDN framework for autonomous vehicles10.1109/MWC.2019.1800521 ↗
39. SDN-based secure and privacy-preserving scheme for vehicular networks: A 5G perspective10.1109/TVT.2019.2917776 ↗
40. Implementing vehicle-to-grid (V2G) technology with IEC 61850-7-42010.1109/TSG.2012.2227515 ↗
41. Optimal scheduling for charging and discharging of electric vehicles10.1109/TSG.2011.2173507 ↗
42. Integration of electric vehicles and management in the internet of energy10.1016/j.rser.2017.11.004 ↗
43. Saltanovs, R., Krivchenkov, A., and Krainyukov, A. (2017, January 12–13). Analysis of effective wireless communications for V2G applications and mobile objects. Proceedings of the 2017 IEEE 58th International Scientific Conference on Power and Electrical Engineering of Riga Technical University (RTUCON), Riga, Latvia.10.1109/RTUCON.2017.8124831 ↗
44. Schmutzler, J., Wietfeld, C., and Andersen, C.A. (2012, January 9–12). Distributed energy resource management for electric vehicles using IEC 61850 and ISO/IEC 15118. Proceedings of the 2012 IEEE Vehicle Power and Propulsion Conference, Seoul, Korea.10.1109/VPPC.2012.6422683 ↗
45. Long term load forecasting accuracy in electric utility integrated resource planning10.1016/j.enpol.2018.04.060 ↗
46. Brownlee, J. (2018). Deep Learning for Time Series Forecasting: Predict the Future with MLPs, CNNs and LSTMs in Python, Machine Learning Mastery.
47. Deep learning method based on gated recurrent unit and variational mode decomposition for short-term wind power interval prediction10.1109/TNNLS.2019.2946414 ↗
48. Short-term residential load forecasting based on LSTM recurrent neural network10.1109/TSG.2017.2753802 ↗
49. Accurate photovoltaic power forecasting models using deep LSTM-RNN10.1007/s00521-017-3225-z ↗
50. Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network10.1007/s12065-018-00196-0 ↗
51. A Robust Auto Encoder-Gated Recurrent Unit (AE-GRU) Based Deep Learning Approach for Short Term Solar Power Forecasting10.1016/j.ijleo.2021.168515 ↗
52. Optimal power management for nanogrids based on technical information of electric appliances10.1016/j.enbuild.2019.03.026 ↗
53. Charging distributed sensor nodes exploiting clustering and energy trading10.1109/JSEN.2016.2629027 ↗
54. Ferrite position identification system operating with wireless power transfer for intelligent train position detection10.1109/TITS.2018.2797991 ↗
55. A real-time EV charging scheduling for parking lots with PV system and energy store system10.1109/ACCESS.2019.2925559 ↗
56. Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles10.1109/TPEL.2012.2212917 ↗
57. Aggregated impact of plug-in hybrid electric vehicles on electricity demand profile10.1109/TSTE.2011.2158123 ↗
58. Pasaoglu, G., Fiorello, D., Martino, A., Scarcella, G., Alemanno, A., Zubaryeva, A., and Thiel, C. (2012). Driving and Parking Patterns of European Car Drivers—A Mobility Survey, European Commission Joint Research Centre.
59. Qdr, Q. (2006). Benefits of Demand Response in Electricity Markets and Recommendations for Achieving Them.
60. A critical review of Korea’s long-term contract for renewable energy auctions: The relationship between the import price of liquefied natural gas and system marginal price10.1016/j.jup.2020.101132 ↗
61. Machine Learning for Advanced Wireless Sensor Networks: A Review10.1109/JSEN.2020.3035846 ↗
62. Deep learning for household load forecasting—A novel pooling deep RNN10.1109/TSG.2017.2686012 ↗
63. Natural language understanding approaches based on joint task of intent detection and slot filling for IoT voice interaction10.1007/s00521-020-04805-x ↗
64. Optimum photovoltaic array size for a hybrid wind/PV system10.1109/60.326466 ↗
65. Lithium-ion battery cell degradation resulting from realistic vehicle and vehicle-to-grid utilization10.1016/j.jpowsour.2009.10.010 ↗
66. Nengroo, S.H., Ali, M.U., Zafar, D.A., Hussain, S., Murtaza, T., Alvi, M.J., Raghavendra, K.V.G., and Kim, H.J. (2019). An Optimized Methodology for a Hybrid Photo-Voltaic and Energy Storage System Connected to a Low-Voltage Grid. Electronics, 8.10.3390/electronics8020176 ↗
67. Liu, C.-Y., Zou, C.-M., and Wu, P. (2014, January 24–27). A task scheduling algorithm based on genetic algorithm and ant colony optimization in cloud computing. Proceedings of the 2014 13th International Symposium on Distributed Computing and Applications to Business, Engineering and Science, Xi’an, China.10.1109/DCABES.2014.18 ↗
68. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization10.1016/j.rser.2020.109792 ↗