IDEAS home Printed from https://ideas.repec.org/a/zna/indecs/v17y2019i3-bp546-585.html
   My bibliography  Save this article

Towards Agent-Based Model Specification of Smart Grid: A Cognitive Agent-Based Computing Approach

Author

Listed:
  • Waseem Akram

    (COMSATS University Islamabad, Computer Science Department, Islamabad, Pakistan)

  • Muaz Niazi

    (COMSATS University Islamabad, Computer Science Department, Islamabad, Pakistan)

  • Laszlo Barna Iantovics

    (Petru Maior University of Tirgu Mures, Informatics Department, Tirgu Mures, Romania)

  • Athanasios V. Vasilakos

    (Lulea University of Technology, Computer Science Department, Lulea, Sweden)

Abstract

A smart grid can be considered as a complex network where each node represents a generation unit or a consumer, whereas links can be used to represent transmission lines. One way to study complex systems is by using the agent-based modeling paradigm. The agent-based modeling is a way of representing a complex system of autonomous agents interacting with each other. Previously, a number of studies have been presented in the smart grid domain making use of the agent-based modeling paradigm. However, to the best of our knowledge, none of these studies have focused on the specification aspect of the model. The model specification is important not only for understanding but also for replication of the model. To fill this gap, this study focuses on specification methods for smart grid modeling. We adopt two specification methods named as Overview, design concept, and details and Descriptive agent-based modeling. By using specification methods, we provide tutorials and guidelines for model developing of smart grid starting from conceptual modeling to validated agent-based model through simulation. The specification study is exemplified through a case study from the smart grid domain. In the case study, we consider a large set of network, in which different consumers and power generation units are connected with each other through different configuration. In such a network, communication takes place between consumers and generating units for energy transmission and data routing. We demonstrate how to effectively model a complex system such as a smart grid using specification methods. We analyze these two specification approaches qualitatively as well as quantitatively. Extensive experiments demonstrate that Descriptive agent-based modeling is a more useful approach as compared with Overview, design concept, and details method for modeling as well as for replication of models for the smart grid.

Suggested Citation

  • Waseem Akram & Muaz Niazi & Laszlo Barna Iantovics & Athanasios V. Vasilakos, 2019. "Towards Agent-Based Model Specification of Smart Grid: A Cognitive Agent-Based Computing Approach," Interdisciplinary Description of Complex Systems - scientific journal, Croatian Interdisciplinary Society Provider Homepage: http://indecs.eu, vol. 17(3-B), pages 546-585.
    • Handle: RePEc:zna:indecs:v:17:y:2019:i:3-b:p:546-585
    as

    Download full text from publisher

    File URL: http://indecs.eu/2019/indecs2019-pp546-585.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    2. Chassin, David P. & Posse, Christian, 2005. "Evaluating North American electric grid reliability using the Barabási–Albert network model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 355(2), pages 667-677.
    3. Guan, Zhi-Hong & Chen, Long & Qian, Tong-Hui, 2011. "Routing in scale-free networks based on expanding betweenness centrality," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(6), pages 1131-1138.
    4. Barabási, Albert-László & Albert, Réka & Jeong, Hawoong, 1999. "Mean-field theory for scale-free random networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 272(1), pages 173-187.
    5. Grimm, Volker & Berger, Uta & DeAngelis, Donald L. & Polhill, J. Gary & Giske, Jarl & Railsback, Steven F., 2010. "The ODD protocol: A review and first update," Ecological Modelling, Elsevier, vol. 221(23), pages 2760-2768.
    6. Jiang, Zhong-Yuan & Ma, Jian-Feng & Shen, Yu-Long, 2017. "Check-in based routing strategy in scale-free networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 468(C), pages 205-211.
    7. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
    8. Ellabban, Omar & Abu-Rub, Haitham, 2016. "Smart grid customers' acceptance and engagement: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 65(C), pages 1285-1298.
    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. Minh Tran & Thanh Duong & Duc Pham-Hi & Marc Bui, 2020. "Detecting the Proportion of Traders in the Stock Market: An Agent-Based Approach," Mathematics, MDPI, vol. 8(2), pages 1-14, February.

    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. Lucas Cuadra & Miguel Del Pino & José Carlos Nieto-Borge & Sancho Salcedo-Sanz, 2017. "Optimizing the Structure of Distribution Smart Grids with Renewable Generation against Abnormal Conditions: A Complex Networks Approach with Evolutionary Algorithms," Energies, MDPI, vol. 10(8), pages 1-31, July.
    2. Mittal, Anuj & Krejci, Caroline C. & Dorneich, Michael C., 2019. "An agent-based approach to designing residential renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 112(C), pages 1008-1020.
    3. Lucas Cuadra & Sancho Salcedo-Sanz & Javier Del Ser & Silvia Jiménez-Fernández & Zong Woo Geem, 2015. "A Critical Review of Robustness in Power Grids Using Complex Networks Concepts," Energies, MDPI, vol. 8(9), pages 1-55, August.
    4. Tardy, Olivia & Lenglos, Christophe & Lai, Sandra & Berteaux, Dominique & Leighton, Patrick A., 2023. "Rabies transmission in the Arctic: An agent-based model reveals the effects of broad-scale movement strategies on contact risk between Arctic foxes," Ecological Modelling, Elsevier, vol. 476(C).
    5. Vimercati, Giovanni & Hui, Cang & Davies, Sarah J. & Measey, G. John, 2017. "Integrating age structured and landscape resistance models to disentangle invasion dynamics of a pond-breeding anuran," Ecological Modelling, Elsevier, vol. 356(C), pages 104-116.
    6. Liang, Wei & Shi, Yuming & Huang, Qiuling, 2014. "Modeling the Chinese language as an evolving network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 268-276.
    7. Nyong-Bassey, Bassey Etim & Giaouris, Damian & Patsios, Charalampos & Papadopoulou, Simira & Papadopoulos, Athanasios I. & Walker, Sara & Voutetakis, Spyros & Seferlis, Panos & Gadoue, Shady, 2020. "Reinforcement learning based adaptive power pinch analysis for energy management of stand-alone hybrid energy storage systems considering uncertainty," Energy, Elsevier, vol. 193(C).
    8. Shen, Lijuan & Cassottana, Beatrice & Tang, Loon Ching, 2018. "Statistical trend tests for resilience of power systems," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 138-147.
    9. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    10. Tianran Ding & Wouter Achten, 2023. "Coupling agent-based modeling with territorial LCA to support agricultural land-use planning," ULB Institutional Repository 2013/359527, ULB -- Universite Libre de Bruxelles.
    11. Yan Qiang & Bo Pei & Weili Wu & Juanjuan Zhao & Xiaolong Zhang & Yue Li & Lidong Wu, 2014. "Improvement of path analysis algorithm in social networks based on HBase," Journal of Combinatorial Optimization, Springer, vol. 28(3), pages 588-599, October.
    12. Crevier, Lucas Phillip & Salkeld, Joseph H & Marley, Jessa & Parrott, Lael, 2021. "Making the best possible choice: Using agent-based modelling to inform wildlife management in small communities," Ecological Modelling, Elsevier, vol. 446(C).
    13. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    14. Meli, Mattia & Auclerc, Apolline & Palmqvist, Annemette & Forbes, Valery E. & Grimm, Volker, 2013. "Population-level consequences of spatially heterogeneous exposure to heavy metals in soil: An individual-based model of springtails," Ecological Modelling, Elsevier, vol. 250(C), pages 338-351.
    15. Stephanie Rend'on de la Torre & Jaan Kalda & Robert Kitt & Juri Engelbrecht, 2016. "On the topologic structure of economic complex networks: Empirical evidence from large scale payment network of Estonia," Papers 1602.04352, arXiv.org.
    16. Hao Liang & Weihua Zhuang, 2014. "Stochastic Modeling and Optimization in a Microgrid: A Survey," Energies, MDPI, vol. 7(4), pages 1-24, March.
    17. Claudia Dislich & Elisabeth Hettig & Jan Salecker & Johannes Heinonen & Jann Lay & Katrin M Meyer & Kerstin Wiegand & Suria Tarigan, 2018. "Land-use change in oil palm dominated tropical landscapes—An agent-based model to explore ecological and socio-economic trade-offs," PLOS ONE, Public Library of Science, vol. 13(1), pages 1-20, January.
    18. Gabrielle Demange, 2012. "On the influence of a ranking system," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 39(2), pages 431-455, July.
    19. Dur, Gaël & Won, Eun-Ji & Han, Jeonghoon & Lee, Jae-Seong & Souissi, Sami, 2021. "An individual-based model for evaluating post-exposure effects of UV-B radiation on zooplankton reproduction," Ecological Modelling, Elsevier, vol. 441(C).
    20. Bauduin, Sarah & Grente, Oksana & Santostasi, Nina Luisa & Ciucci, Paolo & Duchamp, Christophe & Gimenez, Olivier, 2020. "An individual-based model to explore the impacts of lesser-known social dynamics on wolf populations," Ecological Modelling, Elsevier, vol. 433(C).

    More about this item

    Keywords

    agent-based modeling; cognitive agent-based computing; complex networks; smart grid;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    Statistics

    Access and download statistics

    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:zna:indecs:v:17:y:2019:i:3-b:p:546-585. 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: Josip Stepanic (email available below). General contact details of provider: .

    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.