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


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


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

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    References listed on IDEAS

    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. 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.
    5. 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.
    6. 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.
    7. 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.
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    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. Nikolaos Koutsoukis & Pavlos Georgilakis, 2019. "A Chance-Constrained Multistage Planning Method for Active Distribution Networks," Energies, MDPI, vol. 12(21), pages 1-19, October.
    7. Fouladvand, Javanshir, 2022. "Behavioural attributes towards collective energy security in thermal energy communities: Environmental-friendly behaviour matters," Energy, Elsevier, vol. 261(PB).
    8. 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).
    9. 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.

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