IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i13p3921-d585663.html
   My bibliography  Save this article

Field-Ready Implementation of Linear Economic Model Predictive Control for Microgrid Dispatch in Small and Medium Enterprises

Author

Listed:
  • Tobias Kull

    (Chair of Measurement and Control Systems, Center of Energy Technology (ZET), University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany)

  • Bernd Zeilmann

    (Richter R&W Steuerungstechnik GmbH, 95491 Ahorntal, Germany)

  • Gerhard Fischerauer

    (Chair of Measurement and Control Systems, Center of Energy Technology (ZET), University of Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany)

Abstract

The increasing share of distributed renewable energy resources (DER) in the grid entails a paradigm shift in energy system operation demanding more flexibility on the prosumer side. In this work we show an implementation of linear economic model predictive control (MPC) for flexible microgrid dispatch based on time-variable electricity prices. We focus on small and medium enterprises (SME) where information and communications technology (ICT) is available on an industrial level. Our implementation uses field devices and is evaluated in a hardware-in-the-loop (HiL) test bench to achieve high technological maturity. We use available forecasting techniques for power demand and renewable energy generation and evaluate their influence on energy system operation compared to optimal operation under perfect knowledge of the future and compared to a status-quo operation strategy without control. The evaluation scenarios are based on an extensive electricity price analysis to increase representativeness of the simulation results and are based on the use of historic real-world measurements in an existing production facility. Due to real-world restrictions (imperfect forecast knowledge, implementation on field hardware, power fluctuations), between 72.2% and 85.5% of the economic optimum (rather than 100%) is reached. Together with reduced operation cost, the economic MPC implementation on field-typical industrial ICT leads to an increased share of renewable energy demand.

Suggested Citation

  • Tobias Kull & Bernd Zeilmann & Gerhard Fischerauer, 2021. "Field-Ready Implementation of Linear Economic Model Predictive Control for Microgrid Dispatch in Small and Medium Enterprises," Energies, MDPI, vol. 14(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:13:p:3921-:d:585663
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/13/3921/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/13/3921/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alphonsus, Ephrem Ryan & Abdullah, Mohammad Omar, 2016. "A review on the applications of programmable logic controllers (PLCs)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 1185-1205.
    2. Auer, Hans & Haas, Reinhard, 2016. "On integrating large shares of variable renewables into the electricity system," Energy, Elsevier, vol. 115(P3), pages 1592-1601.
    3. De Lorenzi, Andrea & Gambarotta, Agostino & Morini, Mirko & Rossi, Michele & Saletti, Costanza, 2020. "Setup and testing of smart controllers for small-scale district heating networks: An integrated framework," Energy, Elsevier, vol. 205(C).
    4. Matteo Moncecchi & Claudio Brivio & Stefano Mandelli & Marco Merlo, 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria," Energies, MDPI, vol. 13(8), pages 1-18, April.
    5. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    6. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    Full references (including those not matched with items on IDEAS)

    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. Sinn, Hans-Werner, 2017. "Buffering volatility: A study on the limits of Germany's energy revolution," European Economic Review, Elsevier, vol. 99(C), pages 130-150.
    2. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    3. Albert Hiesl & Amela Ajanovic & Reinhard Haas, 2020. "On current and future economics of electricity storage," Greenhouse Gases: Science and Technology, Blackwell Publishing, vol. 10(6), pages 1176-1192, December.
    4. Quanhui Che & Suhua Lou & Yaowu Wu & Xiangcheng Zhang & Xuebin Wang, 2019. "Optimal Scheduling of a Multi-Energy Power System with Multiple Flexible Resources and Large-Scale Wind Power," Energies, MDPI, vol. 12(18), pages 1-14, September.
    5. Schipfer, F. & Mäki, E. & Schmieder, U. & Lange, N. & Schildhauer, T. & Hennig, C. & Thrän, D., 2022. "Status of and expectations for flexible bioenergy to support resource efficiency and to accelerate the energy transition," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    6. Tomi Thomasson & Kirsikka Kiviranta & Antton Tapani & Matti Tähtinen, 2021. "Flexibility from Combined Heat and Power: A Techno-Economic Study for Fully Renewable Åland Islands," Energies, MDPI, vol. 14(19), pages 1-19, October.
    7. Cruz, Marco R.M. & Fitiwi, Desta Z. & Santos, Sérgio F. & Catalão, João P.S., 2018. "A comprehensive survey of flexibility options for supporting the low-carbon energy future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 97(C), pages 338-353.
    8. Kristiansen, Martin & Korpås, Magnus & Svendsen, Harald G., 2018. "A generic framework for power system flexibility analysis using cooperative game theory," Applied Energy, Elsevier, vol. 212(C), pages 223-232.
    9. Sannamari Pilpola & Vahid Arabzadeh & Jani Mikkola & Peter D. Lund, 2019. "Analyzing National and Local Pathways to Carbon-Neutrality from Technology, Emissions, and Resilience Perspectives—Case of Finland," Energies, MDPI, vol. 12(5), pages 1-22, March.
    10. Davide Deltetto & Davide Coraci & Giuseppe Pinto & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Exploring the Potentialities of Deep Reinforcement Learning for Incentive-Based Demand Response in a Cluster of Small Commercial Buildings," Energies, MDPI, vol. 14(10), pages 1-25, May.
    11. Kohlhepp, Peter & Harb, Hassan & Wolisz, Henryk & Waczowicz, Simon & Müller, Dirk & Hagenmeyer, Veit, 2019. "Large-scale grid integration of residential thermal energy storages as demand-side flexibility resource: A review of international field studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 101(C), pages 527-547.
    12. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    13. Bruno Cárdenas & Lawrie Swinfen-Styles & James Rouse & Seamus D. Garvey, 2021. "Short-, Medium-, and Long-Duration Energy Storage in a 100% Renewable Electricity Grid: A UK Case Study," Energies, MDPI, vol. 14(24), pages 1-28, December.
    14. Jenkins, J.D. & Zhou, Z. & Ponciroli, R. & Vilim, R.B. & Ganda, F. & de Sisternes, F. & Botterud, A., 2018. "The benefits of nuclear flexibility in power system operations with renewable energy," Applied Energy, Elsevier, vol. 222(C), pages 872-884.
    15. Arjuna Nebel & Christine Krüger & Tomke Janßen & Mathieu Saurat & Sebastian Kiefer & Karin Arnold, 2020. "Comparison of the Effects of Industrial Demand Side Management and Other Flexibilities on the Performance of the Energy System," Energies, MDPI, vol. 13(17), pages 1-20, August.
    16. Soha, Tamás & Munkácsy, Béla & Harmat, Ádám & Csontos, Csaba & Horváth, Gergely & Tamás, László & Csüllög, Gábor & Daróczi, Henriett & Sáfián, Fanni & Szabó, Mária, 2017. "GIS-based assessment of the opportunities for small-scale pumped hydro energy storage in middle-mountain areas focusing on artificial landscape features," Energy, Elsevier, vol. 141(C), pages 1363-1373.
    17. Pusceddu, Elian & Zakeri, Behnam & Castagneto Gissey, Giorgio, 2021. "Synergies between energy arbitrage and fast frequency response for battery energy storage systems," Applied Energy, Elsevier, vol. 283(C).
    18. Polleux, Louis & Guerassimoff, Gilles & Marmorat, Jean-Paul & Sandoval-Moreno, John & Schuhler, Thierry, 2022. "An overview of the challenges of solar power integration in isolated industrial microgrids with reliability constraints," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    19. Mariia Kozlova & Alena Lohrmann, 2021. "Steering Renewable Energy Investments in Favor of Energy System Reliability: A Call for a Hybrid Model," Sustainability, MDPI, vol. 13(24), pages 1-18, December.
    20. Javed, Muhammad Shahzad & Jurasz, Jakub & McPherson, Madeleine & Dai, Yanjun & Ma, Tao, 2022. "Quantitative evaluation of renewable-energy-based remote microgrids: curtailment, load shifting, and reliability," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).

    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:gam:jeners:v:14:y:2021:i:13:p:3921-:d:585663. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.