Joint optimization of electric bus charging and energy storage system scheduling

Abstract

The widespread use of energy storage systems in electric bus transit centers presents new opportunities and challenges for bus charging and transit center energy management. A unified optimization model is proposed to jointly optimize the bus charging plan and energy storage system power profile. The model optimizes overall costs by considering battery aging, time-of-use tariffs, and capacity service charges. The model is linearized by a series of relaxations of the nonlinear constraints. This means that we can obtain the exact solution of the model quickly with a commercial solver that is fully adapted to the time scale of day-ahead scheduling. The numerical simulations demonstrate that the proposed method can optimize the bus charging time, charging power, and power profile of energy storage systems in seconds. Monte Carlo simulations reveal that the proposed method significantly reduces the cost and has sufficient robustness to uncertain fluctuations in photovoltaics and office loads.

Graphical abstract

Keywords

electric vehicle / energy storage / mixed integer nonlinear programming / Monte Carlo simulations / public transit.

References

[1] Abdelwahed A, van den Berg P L, Brandt T, Collins J, Ketter W, (2020). Evaluating and optimizing opportunity fast-charging schedules in transit battery electric bus networks. Transportation Science, 54( 6): 1601–1615

[2] Ansariyar A, Tahmasebi M, (2022). Investigating the effects of gradual deployment of market penetration rates (MPR) of connected vehicles on delay time and fuel consumption. Journal of Intelligent and Connected Vehicles, 5( 3): 188–198

[3] Attard M, (2022). Active travel and sustainable transport. Communications in Transportation Research, 2: 100059

[4] ChengYTaoJ (2018). Optimization of a micro energy network integrated with electric bus battery swapping station and distributed PV. In: 2018 2nd IEEE Conference on Energy Internet and Energy System Integration

[5] Ding H, Hu Z, Song Y, (2015). Value of the energy storage system in an electric bus fast charging station. Applied Energy, 157: 630–639

[6] Fang L, Guan Z, Wang T, Gong J, Du F, (2022). Collision avoidance model and its validation for intelligent vehicles based on deep learning LSTM. Journal of Automotive Safety and Energy, 13: 104–111

[7] Fathabadi H, (2017). Novel solar powered electric vehicle charging station with the capability of vehicle-to-grid. Solar Energy, 142: 136–143

[8] He J, Yan N, Zhang J, Yu Y, Wang T, (2022). Battery electric buses charging schedule optimization considering time-of-use electricity price. Journal of Intelligent and Connected Vehicles, 5( 2): 138–145

[9] He Y, Liu Z, Song Z, (2020). Optimal charging scheduling and management for a fast-charging battery electric bus system. Transportation Research Part E, Logistics and Transportation Review, 142: 102056

[10] He Y, Song Z, Liu Z, (2019). Fast-charging station deployment for battery electric bus systems considering electricity demand charges. Sustainable Cities and Society, 48: 101530

[11] Houbbadi A, Trigui R, Pelissier S, Redondo-Iglesias E, Bouton T, (2019). Optimal scheduling to manage an electric bus fleet overnight charging. Energies, 12( 14): 2727

[12] Huang D, Wang Y, Jia S, Liu Z, Wang S, (2022). A Lagrangian relaxation approach for the electric bus charging scheduling optimisation problem. Transportmetrica A: Transport Science, 19( 2): 1–24

[13] IEA (2022). Global EV outlook 2022 securing supplies for an electric future

[14] Jahic A, Eskander M, Schulz D, (2019). Charging schedule for load peak minimization on large-scale electric bus depots. Applied Sciences, 9( 9): 1748

[15] Ji J, Bie Y, Zeng Z, Wang L, (2022). Trip energy consumption estimation for electric buses. Communications in Transportation Research, 2: 100069

[16] Kersey J, Popovich N D, Phadke A A, (2022). Rapid battery cost declines accelerate the prospects of all-electric interregional container shipping. Nature Energy, 7( 7): 664–674

[17] Leou R C, Hung J J, (2017). Optimal charging schedule planning and economic analysis for electric bus charging stations. Energies, 10( 4): 483

[18] Li P, Jin S, Hu W, Gao L, Che Y, Tan Z, (2022). Complexity evaluation of vehicle-vehicle accident scenarios for autonomous driving simulation tests. Journal of Automotive Safety and Energy, 13: 697–704

[19] Li X, Wang T, Li L, Feng F, Wang W, Cheng C, (2020). Joint optimization of regular charging electric bus transit network schedule and stationary charger deployment considering partial charging policy and time-of-use electricity prices. Journal of Advanced Transportation, 2020: 8863905

[20] Lin B, Ghaddar B, Nathwani J, (2021). Electric vehicle routing with charging/discharging under time-variant electricity prices. Transportation Research Part C, Emerging Technologies, 130: 103285

[21] Lin H, Yan Y, Cheng Q, (2023). Future role of artificial intelligence in advancing transportation electrification. Journal of Intelligent and Connected Vehicles, 6( 3): 183–186

[22] Liu K, Gao H, Liang Z, Zhao M, Li C, (2021). Optimal charging strategy for large-scale electric buses considering resource constraints. Transportation Research Part D, Transport and Environment, 99: 103009

[23] Liu Y, Francis A, Hollauer C, Lawson M C, Shaikh O, Cotsman A, Bhardwaj K, Banboukian A, Li M, Webb A, Asensio O I, (2023a). Reliability of electric vehicle charging infrastructure: A cross-lingual deep learning approach. Communications in Transportation Research, 3: 100095

[24] Liu Y, Liang H, (2021). A three-layer stochastic energy management approach for electric bus transit centers with pv and energy storage systems. IEEE Transactions on Smart Grid, 12( 2): 1346–1357

[25] Liu Y, Wang L, Zeng Z, Bie Y, (2022). Optimal charging plan for electric bus considering time-of-day electricity tariff. Journal of Intelligent and Connected Vehicles, 5( 2): 123–137

[26] Liu Y, Wu F, Liu Z, Wang K, Wang F, Qu X, (2023b). Can language models be used for real-world urban-delivery route optimization?. Innovation, 4: 100520

[27] Manzolli J A, Trovão J P, Antunes C H, (2022). A review of electric bus vehicles research topics – Methods and trends. Renewable & Sustainable Energy Reviews, 159: 112211

[28] Messaoudi B, Oulamara A, (2019). Electric bus scheduling and optimal charging. ICCL 2019. Lecture Notes in Computer Science, 11756: 233–247

[29] Nykvist B, Nilsson M, (2015). Rapidly falling costs of battery packs for electric vehicles. Nature Climate Change, 5( 4): 329–332

[30] Pei M, Zhu H, Ling J, Hu Y, Yao H, Zhong L, (2023). Empowering highway network: Optimal deployment and strategy for dynamic wireless charging lanes. Communications in Transportation Research, 3: 100106

[31] Qin 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

[32] Qu X, Lin H, Liu Y, (2023). Envisioning the future of transportation: Inspiration of ChatGPT and large models. Communications in Transportation Research, 3: 100103

[33] Qu X, Liu Y, Chen Y, Bie Y, (2022a). Urban electric bus operation management: Review and outlook. Journal of Automotive Safety and Energy, 13: 407–420

[34] Qu X, Wang S, Niemeier D, (2022b). On the urban-rural bus transit system with passenger-freight mixed flow. Communications in Transportation Research, 2: 100054

[35] Rinaldi M, Picarelli E, D’Ariano A, Viti F, (2020). Mixed-fleet single-terminal bus scheduling problem: Modelling, solution scheme and potential applications. Omega, 96: 102070

[36] Rodrigues A L P, Seixas S R C, (2022). Battery-electric buses and their implementation barriers: Analysis and prospects for sustainability. Sustainable Energy Technologies and Assessments, 51: 101896

[37] Schoch J, Gaerttner J, Schuller A, Setzer T, (2018). Enhancing electric vehicle sustainability through battery life optimal charging. Transportation Research Part B: Methodological, 112: 1–18

[38] Schoenberg S, Buse D S, Dressler F, (2023). Siting and sizing charging infrastructure for electric vehicles with coordinated recharging. IEEE Transactions on Intelligent Vehicles, 8( 2): 1425–1438

[39] Schoenberg S, Dressler F, (2023). Reducing waiting times at charging stations with adaptive electric vehicle route planning. IEEE Transactions on Intelligent Vehicles, 8( 1): 95–107

[40] Shin M, Choi D H, Kim J, (2020). Cooperative management for pv/ess-enabled electric vehicle charging stations: A multiagent deep reinforcement learning approach. IEEE Transactions on Industrial Informatics, 16( 5): 3493–3503

[41] Sun C, Liu B, Sun F, (2022). Review of energy-saving planning and control technology for new energy vehicles. Journal of Automotive Safety and Energy, 13: 593–616

[42] Torreglosa J P, García-Triviño P, Fernández-Ramirez L M, Jurado F, (2016). Decentralized energy management strategy based on predictive controllers for a medium voltage direct current photovoltaic electric vehicle charging station. Energy Conversion and Management, 108: 1–13

[43] Tran V T, Islam M R, Muttaqi K M, Sutanto D, (2019). An efficient energy management approach for a solar-powered EV battery charging facility to support distribution grids. IEEE Transactions on Industry Applications, 55( 6): 6517–6526

[44] WangHJiangJHuangMZhangWBaoY (2017). Hierarchical energy storage configuration method for pure electric vehicle fast charging station. In: 2017 IEEE Transportation Electrification Conference and Expo, Asia-Pacific, ITEC Asia-Pacific

[45] Wang H, Zhao D, Cai Y, Meng Q, Ong G P, (2019). A trajectory-based energy consumption estimation method considering battery degradation for an urban electric vehicle network. Transportation Research Part D, Transport and Environment, 74: 142–153

[46] Wang K, Xiao Y, He Y, (2023). Charting the future: Intelligent and connected vehicles reshaping the bus system. Journal of Intelligent and Connected Vehicles, 6( 3): 113–115

[47] Wei Z, Li Y, Cai L, (2018). Electric vehicle charging scheme for a park-and-charge system considering battery degradation costs. IEEE Transactions on Intelligent Vehicles, 3( 3): 361–373

[48] WirasantiPSrirattanawichaikulWPremrudeepreechachamS (2018). Online SOC and battery life estimation: Results from filed test of electric bus transit. In: 2018 21st International Conference on Electrical Machines and Systems (ICEMS)

[49] Yan Q, Zhang B, Kezunovic M, (2019). Optimized operational cost reduction for an EV charging station integrated with battery energy storage and PV generation. IEEE Transactions on Smart Grid, 10( 2): 2096–2106

[50] Zeng Z, Qu X, (2023). What’s next for battery-electric bus charging systems. Communications in Transportation Research, 3: 100094

[51] Zeng Z, Wang S, Qu X, (2022). On the role of battery degradation in en-route charge scheduling for an electric bus system. Transportation Research Part E, Logistics and Transportation Review, 161: 102727

[52] Zeng Z, Wang S, Qu X, (2023). Consolidating bus charger deployment and fleet management for public transit electrification: A life-cycle cost analysis framework. Engineering, 21: 45–60

[53] Zhang J, Cheng M, Luo X, Li H, Luo L, Cheng X, (2022). Current status of the research on key technologies of vehicle fuel cell stack. Journal of Automotive Safety and Energy, 13: 1–28

[54] Zhang L, Han Y, Peng J, Wang Y, (2023). Vehicle and charging scheduling of electric bus fleets: A comprehensive review. Journal of Intelligent and Connected Vehicles, 6( 3): 116–124

[55] Zhang L, Zeng Z, Qu X, (2021). On the role of battery capacity fading mechanism in the lifecycle cost of electric bus fleet. IEEE Transactions on Intelligent Transportation Systems, 22( 4): 2371–2380

[56] Zhou Y, Meng Q, Ong G P, (2022a). Electric bus charging scheduling for a single public transport route considering nonlinear charging profile and battery degradation effect. Transportation Research Part B: Methodological, 159: 49–75

[57] Zhou Y, Wang H, Wang Y, Li R, (2022b). Robust optimization for integrated planning of electric-bus charger deployment and charging scheduling. Transportation Research Part D, Transport and Environment, 110: 103410

RIGHTS & PERMISSIONS

Higher Education Press