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IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level

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  • Scheller, Fabian
  • Johanning, Simon
  • Bruckner, Thomas

Abstract

Decision makers of municipal energy utilities responsible for future portfolio strategies are confronted with making informed decisions within the scope of continuously evolving systems. To cope with the increasing flexibility of customers, and their autonomous decision-making processes, determining newly established municipal energy-related infrastructure has become a challenge for utilities, which are struggling to develop suitable business models. Even though business portfolio decisions are already supported by energy system models, models only considering rational choices of economical drivers seem to be insufficient. Structural decisions of different market actors are often related to bounded rationality and thus are not fully rational. A combined analysis of sociological and technological dynamics might be necessary to evaluate new business models by providing insights into the interactions between the decision processes of market actors and the performance of the supply system. This research paper outlines a multi-model vision called IRPsim (Integrated Resource Planning and Simulation) including bounded and unbounded rationality modeling approaches. The techno-socio-economic model enables the determining of system impacts of behavior patterns of market actors on the business performance of the energy supply system. The mutual dependencies of the coupled models result in an interactive and dynamic energy model application for multi-year business portfolio assessment. The mixed-integer dynamic techno-economic optimization model IRPopt (Integrated Resource Planning and Optimization) represents an adequate starting point as a result of the novel actor-oriented multi-level framework. For the socioeconomic model IRPact (Integrated Resource Planning and Interaction), empirically grounded agent-based modeling turned out to be one of the most promising approaches as it allows for considering various influences on the adoption process on a micro level. Additionally, a large share of available applied research already deals with environmental and energy-related innovations.

Suggested Citation

  • Scheller, Fabian & Johanning, Simon & Bruckner, Thomas, 2018. "IRPsim: A techno-socio-economic energy system model vision for business strategy assessment at municipal level," Contributions of the Institute for Infrastructure and Resources Management 02/2018, University of Leipzig, Institute for Infrastructure and Resources Management.
  • Handle: RePEc:zbw:iirmco:022018
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    References listed on IDEAS

    as
    1. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2017. "Optimal operation, configuration and sizing of generation and storage technologies for residential heat pump systems in the spotlight of self-consumption of photovoltaic electricity," Applied Energy, Elsevier, vol. 188(C), pages 604-619.
    2. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2015. "Modelling market diffusion of electric vehicles with real world driving data – German market and policy options," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 95-112.
    3. Xiao, Yu & Han, Jingti, 2016. "Forecasting new product diffusion with agent-based models," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 167-178.
    4. Frank M. Bass, 1969. "A New Product Growth for Model Consumer Durables," Management Science, INFORMS, vol. 15(5), pages 215-227, January.
    5. Palmer, J. & Sorda, G. & Madlener, R., 2015. "Modeling the diffusion of residential photovoltaic systems in Italy: An agent-based simulation," Technological Forecasting and Social Change, Elsevier, vol. 99(C), pages 106-131.
    6. Davide Natalini & Giangiacomo Bravo, 2013. "Encouraging Sustainable Transport Choices in American Households: Results from an Empirically Grounded Agent-Based Model," Sustainability, MDPI, vol. 6(1), pages 1-20, December.
    7. Frederiks, Elisha R. & Stenner, Karen & Hobman, Elizabeth V., 2015. "Household energy use: Applying behavioural economics to understand consumer decision-making and behaviour," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 1385-1394.
    8. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data. Part I: Model structure and validation," Working Papers "Sustainability and Innovation" S4/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    9. Shafiei, Ehsan & Thorkelsson, Hedinn & Ásgeirsson, Eyjólfur Ingi & Davidsdottir, Brynhildur & Raberto, Marco & Stefansson, Hlynur, 2012. "An agent-based modeling approach to predict the evolution of market share of electric vehicles: A case study from Iceland," Technological Forecasting and Social Change, Elsevier, vol. 79(9), pages 1638-1653.
    10. Stummer, Christian & Kiesling, Elmar & Günther, Markus & Vetschera, Rudolf, 2015. "Innovation diffusion of repeat purchase products in a competitive market: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 245(1), pages 157-167.
    11. Kowalska-Pyzalska, Anna & Maciejowska, Katarzyna & Suszczyński, Karol & Sznajd-Weron, Katarzyna & Weron, Rafał, 2014. "Turning green: Agent-based modeling of the adoption of dynamic electricity tariffs," Energy Policy, Elsevier, vol. 72(C), pages 164-174.
    12. Elmar Kiesling & Markus Günther & Christian Stummer & Lea Wakolbinger, 2012. "Agent-based simulation of innovation diffusion: a review," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 20(2), pages 183-230, June.
    13. Sorda, G. & Sunak, Y. & Madlener, R., 2013. "An agent-based spatial simulation to evaluate the promotion of electricity from agricultural biogas plants in Germany," Ecological Economics, Elsevier, vol. 89(C), pages 43-60.
    14. Böttger, Diana & Götz, Mario & Theofilidi, Myrto & Bruckner, Thomas, 2015. "Control power provision with power-to-heat plants in systems with high shares of renewable energy sources – An illustrative analysis for Germany based on the use of electric boilers in district heatin," Energy, Elsevier, vol. 82(C), pages 157-167.
    15. Scheller, Fabian & Burgenmeister, Balthasar & Kondziella, Hendrik & Kühne, Stefan & Reichelt, David G. & Bruckner, Thomas, 2018. "Towards integrated multi-modal municipal energy systems: An actor-oriented optimization approach," Applied Energy, Elsevier, vol. 228(C), pages 2009-2023.
    16. Plötz, Patrick & Gnann, Till & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data — Part I: Model structure and validation," Ecological Economics, Elsevier, vol. 107(C), pages 411-421.
    17. Gnann, Till & Plötz, Patrick & Kühn, André & Wietschel, Martin, 2014. "Modelling market diffusion of electric vehicles with real world driving data: German market and policy options," Working Papers "Sustainability and Innovation" S12/2014, Fraunhofer Institute for Systems and Innovation Research (ISI).
    18. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    19. Eppstein, Margaret J. & Grover, David K. & Marshall, Jeffrey S. & Rizzo, Donna M., 2011. "An agent-based model to study market penetration of plug-in hybrid electric vehicles," Energy Policy, Elsevier, vol. 39(6), pages 3789-3802, June.
    20. Maya Sopha, Bertha & Klöckner, Christian A. & Hertwich, Edgar G., 2011. "Exploring policy options for a transition to sustainable heating system diffusion using an agent-based simulation," Energy Policy, Elsevier, vol. 39(5), pages 2722-2729, May.
    21. Jiang, Guoyin & Tadikamalla, Pandu R. & Shang, Jennifer & Zhao, Ling, 2016. "Impacts of knowledge on online brand success: an agent-based model for online market share enhancement," European Journal of Operational Research, Elsevier, vol. 248(3), pages 1093-1103.
    22. Delre, S.A. & Jager, W. & Bijmolt, T.H.A. & Janssen, M.A., 2007. "Targeting and timing promotional activities: An agent-based model for the takeoff of new products," Journal of Business Research, Elsevier, vol. 60(8), pages 826-835, August.
    23. Koirala, Binod Prasad & Koliou, Elta & Friege, Jonas & Hakvoort, Rudi A. & Herder, Paulien M., 2016. "Energetic communities for community energy: A review of key issues and trends shaping integrated community energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 722-744.
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    1. Fabian Scheller & Frauke Wiese & Jann Michael Weinand & Dominik Franjo Dominkovi'c & Russell McKenna, 2021. "An expert survey to assess the current status and future challenges of energy system analysis," Papers 2106.15518, arXiv.org.

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