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Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”

Author

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  • Christoph Bader

    (Agricultural Systems Engineering, TUM School of Life Sciences, Technical University of Munich, Dürnast 10, 85354 Freising, Germany)

  • Jörn Stumpenhausen

    (Faculty of Sustainable Agricultural and Energy Systems, University of Applied Sciences Weihenstephan-Triesdorf, Am Staudengarten 1, 85354 Freising, Germany)

  • Heinz Bernhardt

    (Agricultural Systems Engineering, TUM School of Life Sciences, Technical University of Munich, Dürnast 10, 85354 Freising, Germany)

Abstract

In view of rising global demand, energy is becoming a significant cost factor in industry and society. In addition to the global players China, India, and the USA, Africa will also become a driver of the world’s primary energy demand in the future due to the rapidly growing developing countries. In addition to the armed conflicts in Ukraine and the Middle East, global energy markets are tense and volatile due to inflation and higher borrowing costs. Because of society’s desire to phase out the use of fossil fuels, the use of renewable energies is increasingly taking center stage worldwide and especially in Germany. Rural areas and agriculture, especially energy-intensive livestock farms, are particularly affected by this development and are therefore faced with additional economic challenges. Additional energy can be generated by using photovoltaic systems on the roofs of farm buildings or by utilizing the liquid manure from livestock farming in biogas plants. For these farms, such alternative sources of energy could open previously untapped potential and additional synergies for using their own inexpensive energy on the farm or supplying surplus electricity directly to the public grid as a market participant. Agriculture could thus serve as an actor in a decentralized energy supply and thus build up regional energy networks. However, intelligent electricity storage concepts and a corresponding energy management system (EMS) are essential to be able to utilize the potential for renewable energy generation at all, to coordinate both internal production processes and the varying energy demand and supply on the electricity grid. As agricultural production processes differ greatly from farm to farm and region to region, the introduction of an energy management system is strongly dependent on user acceptance. The purpose of this study is to use the web-based software tool ADOPT (CSIRO 2018) to predict the level of acceptance and the duration of the market launch of an EMS based on the region of Bavaria. Individual important influencing factors for the subsequent regional marketing concept are also identified.

Suggested Citation

  • Christoph Bader & Jörn Stumpenhausen & Heinz Bernhardt, 2024. "Prediction of the Spatial and Temporal Adoption of an Energy Management System in Automated Dairy Cattle Barns in Bavaria—“CowEnergySystem”," Energies, MDPI, vol. 17(2), pages 1-13, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:435-:d:1320010
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    References listed on IDEAS

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    1. Höwer, Daniel & Oberst, Christian A. & Madlener, Reinhard, 2019. "General regionalization heuristic to map spatial heterogeneity of macroeconomic impacts: The case of the green energy transition in NRW," Utilities Policy, Elsevier, vol. 58(C), pages 166-174.
    2. Bartholdsen, Hans-Karl & Eidens, Anna & Löffler, Konstantin & Seehaus, Frederik & Wejda, Felix & Burandt, Thorsten & Oei, Pao-Yu & Kemfert, Claudia & Hirschhausen, Christian von, 2019. "Pathways for Germany's Low-Carbon Energy Transformation Towards 2050," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 12(15), pages 1-33.
    3. Kuehne, Geoff & Llewellyn, Rick & Pannell, David J. & Wilkinson, Roger & Dolling, Perry & Ouzman, Jackie & Ewing, Mike, 2017. "Predicting farmer uptake of new agricultural practices: A tool for research, extension and policy," Agricultural Systems, Elsevier, vol. 156(C), pages 115-125.
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