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Electric Vehicle Scenario Simulator Tool for Smart Grid Operators

Author

Listed:
  • João Soares

    (GECAD, Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Bruno Canizes

    (GECAD, Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Cristina Lobo

    (GECAD, Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD, Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

  • Hugo Morais

    (GECAD, Knowledge Engineering and Decision-Support Research Center, Institute of Engineering, Polytechnic of Porto (ISEP/IPP), Rua Dr. António Bernardino de Almeida, 431, 4200-072 Porto, Portugal)

Abstract

This paper presents a simulator for electric vehicles in the context of smart grids and distribution networks. It aims to support network operators’ planning and operations but can be used by other entities for related studies. The paper describes the parameters supported by the current version of the Electric Vehicle Scenario Simulator (EVeSSi) tool and its current algorithm. EVeSSi enables the definition of electric vehicles scenarios on distribution networks using a built-in movement engine. The scenarios created with EVeSSi can be used by external tools (e.g., power flow) for specific analysis, for instance grid impacts. Two scenarios are briefly presented for illustration of the simulator capabilities.

Suggested Citation

  • João Soares & Bruno Canizes & Cristina Lobo & Zita Vale & Hugo Morais, 2012. "Electric Vehicle Scenario Simulator Tool for Smart Grid Operators," Energies, MDPI, vol. 5(6), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:5:y:2012:i:6:p:1881-1899:d:18368
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    References listed on IDEAS

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    1. Marianne Hatzopoulou & Jiang Hao & Eric Miller, 2011. "Simulating the impacts of household travel on greenhouse gas emissions, urban air quality, and population exposure," Transportation, Springer, vol. 38(6), pages 871-887, November.
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    Cited by:

    1. Soares, João & Ghazvini, Mohammad Ali Fotouhi & Borges, Nuno & Vale, Zita, 2017. "Dynamic electricity pricing for electric vehicles using stochastic programming," Energy, Elsevier, vol. 122(C), pages 111-127.
    2. Junjie Hu & Hugo Morais & Tiago Sousa & Shi You & Reinhilde D’hulst, 2017. "Integration of Electric Vehicles into the Power Distribution Network with a Modified Capacity Allocation Mechanism," Energies, MDPI, vol. 10(2), pages 1-20, February.
    3. João Soares & Nuno Borges & Zita Vale & P.B. De Moura Oliveira, 2016. "Enhanced Multi-Objective Energy Optimization by a Signaling Method," Energies, MDPI, vol. 9(10), pages 1-23, October.
    4. Bruno Canizes & João Soares & Angelo Costa & Tiago Pinto & Fernando Lezama & Paulo Novais & Zita Vale, 2019. "Electric Vehicles’ User Charging Behaviour Simulator for a Smart City," Energies, MDPI, vol. 12(8), pages 1-20, April.
    5. Da Xie & Haoxiang Chu & Yupu Lu & Chenghong Gu & Furong Li & Yu Zhang, 2015. "The Concept of EV’s Intelligent Integrated Station and Its Energy Flow," Energies, MDPI, vol. 8(5), pages 1-28, May.
    6. Thomas J.T. Van der Wardt & Amro M. Farid, 2017. "A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification," Energies, MDPI, vol. 10(5), pages 1-25, May.
    7. Sousa, Tiago & Vale, Zita & Carvalho, Joao Paulo & Pinto, Tiago & Morais, Hugo, 2014. "A hybrid simulated annealing approach to handle energy resource management considering an intensive use of electric vehicles," Energy, Elsevier, vol. 67(C), pages 81-96.
    8. Tobias Rösch & Peter Treffinger, 2019. "Cluster Analysis of Distribution Grids in Baden-Württemberg," Energies, MDPI, vol. 12(20), pages 1-25, October.
    9. Wojciech Sałabun & Krzysztof Palczewski & Jarosław Wątróbski, 2019. "Multicriteria Approach to Sustainable Transport Evaluation under Incomplete Knowledge: Electric Bikes Case Study," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    10. Monica Alonso & Hortensia Amaris & Jean Gardy Germain & Juan Manuel Galan, 2014. "Optimal Charging Scheduling of Electric Vehicles in Smart Grids by Heuristic Algorithms," Energies, MDPI, vol. 7(4), pages 1-27, April.
    11. Pol Olivella-Rosell & Roberto Villafafila-Robles & Andreas Sumper & Joan Bergas-Jané, 2015. "Probabilistic Agent-Based Model of Electric Vehicle Charging Demand to Analyse the Impact on Distribution Networks," Energies, MDPI, vol. 8(5), pages 1-28, May.
    12. Seog-Chan Oh & Alfred J. Hildreth, 2013. "Decisions on Energy Demand Response Option Contracts in Smart Grids Based on Activity-Based Costing and Stochastic Programming," Energies, MDPI, vol. 6(1), pages 1-19, January.
    13. Hu, Junjie & Morais, Hugo & Sousa, Tiago & Lind, Morten, 2016. "Electric vehicle fleet management in smart grids: A review of services, optimization and control aspects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1207-1226.
    14. Sousa, Tiago & Morais, Hugo & Vale, Zita & Castro, Rui, 2015. "A multi-objective optimization of the active and reactive resource scheduling at a distribution level in a smart grid context," Energy, Elsevier, vol. 85(C), pages 236-250.
    15. Andrii Shekhovtsov & Volodymyr Kozlov & Viktor Nosov & Wojciech Sałabun, 2020. "Efficiency of Methods for Determining the Relevance of Criteria in Sustainable Transport Problems: A Comparative Case Study," Sustainability, MDPI, vol. 12(19), pages 1-23, September.
    16. Vaclav Kaczmarczyk & Zdenek Bradac & Petr Fiedler, 2017. "A Heuristic Algorithm to Compute Multimodal Criterial Function Weights for Demand Management in Residential Areas," Energies, MDPI, vol. 10(7), pages 1-28, July.
    17. Andrea Pirisi & Francesco Grimaccia & Marco Mussetta & Riccardo E. Zich, 2012. "Novel Speed Bumps Design and Optimization for Vehicles' Energy Recovery in Smart Cities," Energies, MDPI, vol. 5(11), pages 1-19, November.
    18. Julian Garcia-Guarin & David Alvarez & Arturo Bretas & Sergio Rivera, 2020. "Schedule Optimization in a Smart Microgrid Considering Demand Response Constraints," Energies, MDPI, vol. 13(17), pages 1-19, September.
    19. Soares, João & Fotouhi Ghazvini, Mohammad Ali & Vale, Zita & de Moura Oliveira, P.B., 2016. "A multi-objective model for the day-ahead energy resource scheduling of a smart grid with high penetration of sensitive loads," Applied Energy, Elsevier, vol. 162(C), pages 1074-1088.
    20. Yang, Zhile & Li, Kang & Foley, Aoife, 2015. "Computational scheduling methods for integrating plug-in electric vehicles with power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 396-416.

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