A Bi-level Optimisation Framework for Electric Vehicle Fleet Charging Management
Applied Energy, Vol. 184, pp. 1332-13422016The paper proposes a bi-level optimisation framework for electric vehicle (EV) fleet charging based on a realistic EV fleet model including a transport demand sub-model. The EV fleet is described by an aggregate battery model, which is parameterised by using recorded driving cycle data of a delivery vehicle fleet. The EV fleet model is used within the inner level of the bi-level optimisation framework, where the aggregate charging power is optimised by using the dynamic programming (DP) algorithm. At the superimposed optimisation level, the final state-of-charge (SoC) values of individual EVs being disconnected from the grid are optimised by using a multi-objective genetic algorithm-based optimisation. In each iteration of the bi-level optimisation algorithm, it is generally needed to recalculate the transport demand sub-model for the new set of final SoC values. In order to simplify this process, the transport demand is modelled by using a computationally efficient response surface method, which is based on naturalistic synthetic driving cycles and agent-based simulations of the EV model. When compared to the single-level charging optimisation approach, which assumes the final SoC values to be equal to 1 (full batteries on departure), the bi-level optimisation provides a degree of optimisation freedom more for more accurate techno-economic analyses of the integrated transport-energy system. The two approaches are compared through a simulation study of the particular delivery vehicle fleet transport-energy system. electric vehicle fleet; aggregate battery; modelling; charging optimisation; genetic algorithm; dynamic programming
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Applied Energy, Vol. 184, pp. 1332-1342
2016
Cited by 38
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[1] A review of mixed-integer linear formulations for framework-based energy system models🔗Advances in Applied Energy, 2024
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[2] Bi-level planning of electric vehicle charging station in coupled distribution-transportation networks🔗Electric power systems research, 2024
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[6] Comprehensive Evaluation of AC-DC Distribution Network in Photovoltaic-Energy Storage Charging Station Based on AHP-TOPSIS Method🔗2021 IEEE 5th Conference on Energy Internet and Energy System Integration (EI2), 2021
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[7] Fleet Management Approach for Manufacturers displayed at the Use Case of Battery Electric Vehicles🔗IEEE International Conference on Systems, Man and Cybernetics, 2021
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[8] E-Mobility: Transportation Sector in Transition🔗Handbook of Climate Change Mitigation and Adaptation, 2021
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[9] Optimization of electric vehicle recharge schedule and routing problem with time windows and partial recharge: A comparative study for an urban logistics fleet🔗Sustainable cities and society, 2021
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[13] Optimal fast charging station locations for electric ridesharing with vehicle-charging station assignment🔗Transportation Research Part D: Transport and Environment, 2020
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[15] Forecasting Recharging Demand to Integrate Electric Vehicle Fleets in Smart Grids🔗Advanced Communication and Control Methods for Future Smartgrids, 2019
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[16] Demand side energy management of EV charging stations by approximate dynamic programming🔗Energy Conversion and Management, 2019
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[20] Operational scheduling of a smart distribution system considering electric vehicles parking lot: A bi-level approach🔗International Journal of Electrical Power & Energy Systems, 2019
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[22] Range anxiety of electric vehicles in energy management of microgrids with controllable loads🔗Journal of Energy Storage, 2018
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[23] Advancements in sustainable development of energy, water and environment systems🔗Energy Conversion and Management, 2018
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[24] An integrated bi-level optimization model for air quality management of Beijing's energy system under uncertainty.🔗Journal of Hazardous Materials, 2018
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[29] Valuation of contract between power supplier and electric vehicle owner🔗2017 14th International Conference on the European Energy Market (EEM), 2017
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[34] Collaborative management for decarbonizing canada’s multi-regional electric power systems by 2050: A factorial non-deterministic multi-stage bi-level programming model🔗Energy Conversion and Management, 2025
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[35] Hierarchical Operation of Electric Vehicle Charging Station in Smart Grid Integration Applications — An Overview🔗International Journal of Electrical Power & Energy Systems, 2022
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[37] A Bilevel Model for Centralized Optimization of Charging Stops for EV on Highways🔗International Conference on Network Games, Control and Optimization, 2020