IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v508y2018icp710-725.html
   My bibliography  Save this article

A multi-layer agent-based model for the analysis of energy distribution networks in urban areas

Author

Listed:
  • Fichera, Alberto
  • Pluchino, Alessandro
  • Volpe, Rosaria

Abstract

Significant research contributions and Directives approach the issue of the insertion of renewable-based energy systems on urban territory in order to face with the growing energy needs of citizens. The introduction of such systems gives raise to installers to both satisfy their energy demands and distribute eventual energy excesses to close neighbours. This paper presents a multi-layer agent-based computational model that simulates multiple events of the energy distribution occurring within urban areas. The model runs on the NetLogo platform and aims at elaborating the most suitable strategy when dealing with the design of a network of energy distribution. Experimental data are discussed based on two main scenarios within an operating period of 24 h. Scenarios consider both the variation of the percentages of installers of renewable-based energy systems and the distance along which energy exchanges occur.

Suggested Citation

  • Fichera, Alberto & Pluchino, Alessandro & Volpe, Rosaria, 2018. "A multi-layer agent-based model for the analysis of energy distribution networks in urban areas," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 710-725.
    • Handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:710-725
    DOI: 10.1016/j.physa.2018.05.124
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S037843711830668X
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2018.05.124?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Bracco, Stefano & Dentici, Gabriele & Siri, Silvia, 2016. "DESOD: a mathematical programming tool to optimally design a distributed energy system," Energy, Elsevier, vol. 100(C), pages 298-309.
    2. Lopez-Rodriguez, I. & Hernandez-Tejera, M., 2015. "Infrastructure based on supernodes and software agents for the implementation of energy markets in demand-response programs," Applied Energy, Elsevier, vol. 158(C), pages 1-11.
    3. Weber, C. & Shah, N., 2011. "Optimisation based design of a district energy system for an eco-town in the United Kingdom," Energy, Elsevier, vol. 36(2), pages 1292-1308.
    4. Cedillos Alvarado, Dagoberto & Acha, Salvador & Shah, Nilay & Markides, Christos N., 2016. "A Technology Selection and Operation (TSO) optimisation model for distributed energy systems: Mathematical formulation and case study," Applied Energy, Elsevier, vol. 180(C), pages 491-503.
    5. Fichera, Alberto & Frasca, Mattia & Volpe, Rosaria, 2017. "Complex networks for the integration of distributed energy systems in urban areas," Applied Energy, Elsevier, vol. 193(C), pages 336-345.
    6. Mehleri, Eugenia D. & Sarimveis, Haralambos & Markatos, Nikolaos C. & Papageorgiou, Lazaros G., 2012. "A mathematical programming approach for optimal design of distributed energy systems at the neighbourhood level," Energy, Elsevier, vol. 44(1), pages 96-104.
    7. Mbodji, Abdoul K. & Ndiaye, Mamadou L. & Ndiaye, Papa A., 2016. "Decentralized control of the hybrid electrical system consumption: A multi-agent approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 972-978.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yang, Dong-xiao & Chen, Zi-yue & Yang, Yong-cong & Nie, Pu-yan, 2019. "Green financial policies and capital flows," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 522(C), pages 135-146.
    2. Alberto Fichera & Elisa Marrasso & Maurizio Sasso & Rosaria Volpe, 2020. "Energy, Environmental and Economic Performance of an Urban Community Hybrid Distributed Energy System," Energies, MDPI, vol. 13(10), pages 1-19, May.
    3. Nikolaos Koutsoukis & Pavlos Georgilakis, 2019. "A Chance-Constrained Multistage Planning Method for Active Distribution Networks," Energies, MDPI, vol. 12(21), pages 1-19, October.
    4. Fouladvand, Javanshir, 2022. "Behavioural attributes towards collective energy security in thermal energy communities: Environmental-friendly behaviour matters," Energy, Elsevier, vol. 261(PB).
    5. Davis, Natalie & Jarvis, Andrew & Polhill, J. Gareth, 2022. "Co-evolution of network structure and consumer inequality in a spatially explicit model of energetic resource acquisition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    6. Amtul Samie Maqbool & Jens Baetens & Sara Lotfi & Lieven Vandevelde & Greet Van Eetvelde, 2019. "Assessing Financial and Flexibility Incentives for Integrating Wind Energy in the Grid Via Agent-Based Modeling," Energies, MDPI, vol. 12(22), pages 1-32, November.
    7. Chen, Peipei & Wu, Yi & Zou, Lele, 2019. "Distributive PV trading market in China: A design of multi-agent-based model and its forecast analysis," Energy, Elsevier, vol. 185(C), pages 423-436.
    8. Sara Lumbreras & Sonja Wogrin & Guillermo Navarro & Ilaria Bertazzi & Maria Pereda, 2019. "A Decentralized Solution for Transmission Expansion Planning: Getting Inspiration from Nature," Energies, MDPI, vol. 12(23), pages 1-17, November.
    9. Nadia Giuffrida & Michela Le Pira & Giuseppe Inturri & Matteo Ignaccolo & Giovanni Calabrò & Blochin Cuius & Riccardo D’Angelo & Alessandro Pluchino, 2020. "On-Demand Flexible Transit in Fast-Growing Cities: The Case of Dubai," Sustainability, MDPI, vol. 12(11), pages 1-15, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Gabrielli, Paolo & Gazzani, Matteo & Martelli, Emanuele & Mazzotti, Marco, 2018. "Optimal design of multi-energy systems with seasonal storage," Applied Energy, Elsevier, vol. 219(C), pages 408-424.
    2. Alberto Fichera & Mattia Frasca & Rosaria Volpe, 2020. "A cost-based approach for evaluating the impact of a network of distributed energy systems on the centralized energy supply," Energy & Environment, , vol. 31(1), pages 77-87, February.
    3. Fleischhacker, Andreas & Lettner, Georg & Schwabeneder, Daniel & Auer, Hans, 2019. "Portfolio optimization of energy communities to meet reductions in costs and emissions," Energy, Elsevier, vol. 173(C), pages 1092-1105.
    4. Maximilian Hoffmann & Leander Kotzur & Detlef Stolten & Martin Robinius, 2020. "A Review on Time Series Aggregation Methods for Energy System Models," Energies, MDPI, vol. 13(3), pages 1-61, February.
    5. Karmellos, M. & Georgiou, P.N. & Mavrotas, G., 2019. "A comparison of methods for the optimal design of Distributed Energy Systems under uncertainty," Energy, Elsevier, vol. 178(C), pages 318-333.
    6. Jalil-Vega, Francisca & Hawkes, Adam D., 2018. "The effect of spatial resolution on outcomes from energy systems modelling of heat decarbonisation," Energy, Elsevier, vol. 155(C), pages 339-350.
    7. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    8. Fichera, Alberto & Frasca, Mattia & Volpe, Rosaria, 2017. "Complex networks for the integration of distributed energy systems in urban areas," Applied Energy, Elsevier, vol. 193(C), pages 336-345.
    9. Wu, Qiong & Ren, Hongbo & Gao, Weijun & Weng, Peifen & Ren, Jianxing, 2018. "Coupling optimization of urban spatial structure and neighborhood-scale distributed energy systems," Energy, Elsevier, vol. 144(C), pages 472-481.
    10. Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    11. Li, Ruonan & Mahalec, Vladimir, 2022. "Integrated design and operation of energy systems for residential buildings, commercial buildings, and light industries," Applied Energy, Elsevier, vol. 305(C).
    12. Capuder, Tomislav & Mancarella, Pierluigi, 2014. "Techno-economic and environmental modelling and optimization of flexible distributed multi-generation options," Energy, Elsevier, vol. 71(C), pages 516-533.
    13. Hoffmann, Maximilian & Priesmann, Jan & Nolting, Lars & Praktiknjo, Aaron & Kotzur, Leander & Stolten, Detlef, 2021. "Typical periods or typical time steps? A multi-model analysis to determine the optimal temporal aggregation for energy system models," Applied Energy, Elsevier, vol. 304(C).
    14. Marquant, Julien F. & Evins, Ralph & Bollinger, L. Andrew & Carmeliet, Jan, 2017. "A holarchic approach for multi-scale distributed energy system optimisation," Applied Energy, Elsevier, vol. 208(C), pages 935-953.
    15. Wouters, Carmen & Fraga, Eric S. & James, Adrian M., 2015. "An energy integrated, multi-microgrid, MILP (mixed-integer linear programming) approach for residential distributed energy system planning – A South Australian case-study," Energy, Elsevier, vol. 85(C), pages 30-44.
    16. Omu, Akomeno & Choudhary, Ruchi & Boies, Adam, 2013. "Distributed energy resource system optimisation using mixed integer linear programming," Energy Policy, Elsevier, vol. 61(C), pages 249-266.
    17. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
    18. Wakui, Tetsuya & Hashiguchi, Moe & Yokoyama, Ryohei, 2021. "Structural design of distributed energy networks by a hierarchical combination of variable- and constraint-based decomposition methods," Energy, Elsevier, vol. 224(C).
    19. Zwickl-Bernhard, Sebastian & Auer, Hans, 2021. "Open-source modeling of a low-carbon urban neighborhood with high shares of local renewable generation," Applied Energy, Elsevier, vol. 282(PA).
    20. Wakui, Tetsuya & Yokoyama, Ryohei, 2014. "Optimal structural design of residential cogeneration systems in consideration of their operating restrictions," Energy, Elsevier, vol. 64(C), pages 719-733.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:508:y:2018:i:c:p:710-725. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.