IDEAS home Printed from https://ideas.repec.org/a/jas/jasssj/2018-49-4.html
   My bibliography  Save this article

Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis

Author

Listed:

Abstract

This paper tries to show the various roles agent-based modeling and simulation (ABMS) can play in technology and policy assessment of energy systems. We examine the advantages of ABMS methods using three case studies of electricity market models as example (AMIRIS, EMLab-Generation and PowerACE). In particular, we argue why ABMS might serve as framework for many future energy system models that integrate many different algorithms. We then discuss practical and theoretical problems in the development, validation and assessment of energy-system-analytical ABMS and conclude with an outlook and recommendations for energy system modellers who consider incorporating ABMS into their modelling toolbox.

Suggested Citation

  • Martin Klein & Ulrich J. Frey & Matthias Reeg, 2019. "Models Within Models – Agent-Based Modelling and Simulation in Energy Systems Analysis," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 22(4), pages 1-6.
    • Handle: RePEc:jas:jasssj:2018-49-4
    as

    Download full text from publisher

    File URL: https://www.jasss.org/22/4/6/6.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    2. Axelrod, Robert, 2006. "Agent-based Modeling as a Bridge Between Disciplines," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 33, pages 1565-1584, Elsevier.
    3. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    4. Tesfatsion, Leigh, 2006. "Agent-Based Computational Economics: A Constructive Approach to Economic Theory," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 16, pages 831-880, Elsevier.
    5. Trutnevyte, Evelina, 2016. "Does cost optimization approximate the real-world energy transition?," Energy, Elsevier, vol. 106(C), pages 182-193.
    6. Yousefi, Shaghayegh & Moghaddam, Mohsen Parsa & Majd, Vahid Johari, 2011. "Optimal real time pricing in an agent-based retail market using a comprehensive demand response model," Energy, Elsevier, vol. 36(9), pages 5716-5727.
    7. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    8. Nigel Gilbert & Petra Ahrweiler & Pete Barbrook-Johnson & Kavin Preethi Narasimhan & Helen Wilkinson, 2018. "Computational Modelling of Public Policy: Reflections on Practice," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 21(1), pages 1-14.
    9. Arthur, W. Brian, 2006. "Out-of-Equilibrium Economics and Agent-Based Modeling," Handbook of Computational Economics, in: Leigh Tesfatsion & Kenneth L. Judd (ed.), Handbook of Computational Economics, edition 1, volume 2, chapter 32, pages 1551-1564, Elsevier.
    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. Glismann, Samuel, 2021. "Ancillary Services Acquisition Model: Considering market interactions in policy design," Applied Energy, Elsevier, vol. 304(C).
    2. Amir Ali Safaei Pirooz & Mohammad J. Sanjari & Young-Jin Kim & Stuart Moore & Richard Turner & Wayne W. Weaver & Dipti Srinivasan & Josep M. Guerrero & Mohammad Shahidehpour, 2023. "Adaptation of High Spatio-Temporal Resolution Weather/Load Forecast in Real-World Distributed Energy-System Operation," Energies, MDPI, vol. 16(8), pages 1-16, April.
    3. Abba, Z.Y.I. & Balta-Ozkan, N. & Hart, P., 2022. "A holistic risk management framework for renewable energy investments," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    4. Ulrich J. Frey & Martin Klein & Kristina Nienhaus & Christoph Schimeczek, 2020. "Self-Reinforcing Electricity Price Dynamics under the Variable Market Premium Scheme," Energies, MDPI, vol. 13(20), pages 1-19, October.
    5. Georg Holtz & Christian Schnülle & Malcolm Yadack & Jonas Friege & Thorben Jensen & Pablo Thier & Peter Viebahn & Émile J. L. Chappin, 2020. "Using Agent-Based Models to Generate Transformation Knowledge for the German Energiewende—Potentials and Challenges Derived from Four Case Studies," Energies, MDPI, vol. 13(22), pages 1-26, November.

    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. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    2. Jonathan F. Cogliano & Roberto Veneziani & Naoki Yoshihara, 2022. "Computational methods and classical‐Marxian economics," Journal of Economic Surveys, Wiley Blackwell, vol. 36(2), pages 310-349, April.
    3. Marc Deissenroth & Martin Klein & Kristina Nienhaus & Matthias Reeg, 2017. "Assessing the Plurality of Actors and Policy Interactions: Agent-Based Modelling of Renewable Energy Market Integration," Complexity, Hindawi, vol. 2017, pages 1-24, December.
    4. Nigar Hashimzade & Gareth Myles, 2017. "Risk-based Audits in a Behavioral Model," Public Finance Review, , vol. 45(1), pages 140-165, January.
    5. Seppecher, Pascal, 2012. "Flexibility Of Wages And Macroeconomic Instability In An Agent-Based Computational Model With Endogenous Money," Macroeconomic Dynamics, Cambridge University Press, vol. 16(S2), pages 284-297, September.
    6. Delli Gatti,Domenico & Fagiolo,Giorgio & Gallegati,Mauro & Richiardi,Matteo & Russo,Alberto (ed.), 2018. "Agent-Based Models in Economics," Cambridge Books, Cambridge University Press, number 9781108400046.
    7. Nan Lu, 2018. "La modélisation de l’indice CAC 40 avec un modèle basé agent," Erudite Ph.D Dissertations, Erudite, number ph18-02 edited by François Legendre, February.
    8. Gaujal, Bruno & Gulyas, Laszlo & Mansury, Yuri & Thierry, Eric, 2014. "Validating an agent-based model of the Zipf׳s Law: A discrete Markov-chain approach," Journal of Economic Dynamics and Control, Elsevier, vol. 41(C), pages 38-49.
    9. Asjad Naqvi & Franziska Gaupp & Stefan Hochrainer-Stigler, 2020. "The risk and consequences of multiple breadbasket failures: an integrated copula and multilayer agent-based modeling approach," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 42(3), pages 727-754, September.
    10. Tatiana Filatova & Dawn C. Parker & Anne van der Veen, 2009. "Agent-Based Urban Land Markets: Agent's Pricing Behavior, Land Prices and Urban Land Use Change," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 12(1), pages 1-3.
    11. Kaye-Blake, William & Schilling, Chris & Monaghan, Ross & Vibart, Ronaldo & Dennis, Samuel & Post, Elizabeth, 2019. "Quantification of environmental-economic trade-offs in nutrient management policies," Agricultural Systems, Elsevier, vol. 173(C), pages 458-468.
    12. Kirill S. Glavatskiy & Mikhail Prokopenko & Adrian Carro & Paul Ormerod & Michael Harré, 2021. "Explaining herding and volatility in the cyclical price dynamics of urban housing markets using a large-scale agent-based model," SN Business & Economics, Springer, vol. 1(6), pages 1-21, June.
    13. Chad Seagren, 2011. "Examining social processes with agent-based models," The Review of Austrian Economics, Springer;Society for the Development of Austrian Economics, vol. 24(1), pages 1-17, March.
    14. Curto, José Dias & Quinaz, Pedro Miguel Mateus Dias, 2016. "Prudential regulation in an artificial banking system," Economics Discussion Papers 2016-27, Kiel Institute for the World Economy (IfW Kiel).
    15. Tesfatsion, Leigh, 2006. "Agent-Based Computational Modeling And Macroeconomics," Staff General Research Papers Archive 12402, Iowa State University, Department of Economics.
    16. Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022. "Machine Learning Simulates Agent-Based Model Towards Policy," Papers 2203.02576, arXiv.org, revised Nov 2022.
    17. Leopoldo S'anchez-Cant'u & Carlos Arturo Soto-Campos & Andriy Kryvko, 2016. "Evidence of Self-Organization in Time Series of Capital Markets," Papers 1604.03996, arXiv.org, revised Mar 2017.
    18. Michael S. Harr'e, 2018. "Multi-agent Economics and the Emergence of Critical Markets," Papers 1809.01332, arXiv.org.
    19. Klaus Jaffe, 2015. "Agent based simulations visualize Adam Smith's invisible hand by solving Friedrich Hayek's Economic Calculus," Papers 1509.04264, arXiv.org, revised Nov 2015.
    20. Francesco Lamperti & Giovanni Dosi & Mauro Napoletano & Andrea Roventini & Alessandro Sapio, 2018. "And then he wasn't a she : Climate change and green transitions in an agent-based integrated assessment model," Working Papers hal-03443464, HAL.

    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:jas:jasssj:2018-49-4. 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: Francesco Renzini (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.