IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i21p5761-d439515.html
   My bibliography  Save this article

Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System

Author

Listed:
  • Muthalagappan Narayanan

    (Technische Hochschule Ulm, Eberhard-Finckh-Strasse 11, 89075 Ulm, Germany)

  • Aline Ferreira de Lima

    (Graduate program in Neuroengineering—Edmond and Lily Safra International Institute of Neurosciences—IIN-ELS, Santos Dumont Institute—ISD, Av. Alberto Santos Dumont, 1560 Zona Rural, Macaiba 59280-000, Brazil)

  • André Felipe Oliveira de Azevedo Dantas

    (Graduate program in Neuroengineering—Edmond and Lily Safra International Institute of Neurosciences—IIN-ELS, Santos Dumont Institute—ISD, Av. Alberto Santos Dumont, 1560 Zona Rural, Macaiba 59280-000, Brazil)

  • Walter Commerell

    (Technische Hochschule Ulm, Eberhard-Finckh-Strasse 11, 89075 Ulm, Germany)

Abstract

An integrated electrical and thermal residential renewable energy system consisting of solar thermal collectors, gas boiler, fuel cell combined heat and power, a photovoltaic system with battery, inverter, and thermal storage for a single-family house of Sonnenhaus standard is investigated with a model predictive controller (MPC). The main focus of this article is to define a multi-objective mathematical function, develop a coupled simulation framework for the nonlinear time-varying deterministic discrete-time problem of the energy system using TRNSYS and MATLAB. With the developed methodology, a sensitivity analysis of maximum optimization time, swarm (or population or mesh) size of a typical spring day and a typical summer day assuming a 100% accurate weather and load forecast with three different algorithms: particle swarm optimization (PSO), genetic algorithm (GA) and global pattern search (GPS) are analyzed. Finally, the obtained results are compared with a status quo controller. Results show that the PSO algorithm optimizer performs the best in this MPC for such a complex and time-consuming MPC model in both the spring day and the summer day. The obtained results show that the PSO with swarm size 50 in the selected typical spring day and the PSO with swarm size 40 in the selected summer day reduces the objective function’s fitness value from 413 to −177 within 6 h optimization time and from 1396 to 1090 in 4 h optimization time respectively.

Suggested Citation

  • Muthalagappan Narayanan & Aline Ferreira de Lima & André Felipe Oliveira de Azevedo Dantas & Walter Commerell, 2020. "Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System," Energies, MDPI, vol. 13(21), pages 1-29, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:21:p:5761-:d:439515
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/21/5761/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/21/5761/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Menegon, Diego & Persson, Tomas & Haberl, Robert & Bales, Chris & Haller, Michel, 2020. "Direct characterisation of the annual performance of solar thermal and heat pump systems using a six-day whole system test," Renewable Energy, Elsevier, vol. 146(C), pages 1337-1353.
    2. Kuboth, Sebastian & Heberle, Florian & König-Haagen, Andreas & Brüggemann, Dieter, 2019. "Economic model predictive control of combined thermal and electric residential building energy systems," Applied Energy, Elsevier, vol. 240(C), pages 372-385.
    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. Karol Bot & Inoussa Laouali & António Ruano & Maria da Graça Ruano, 2021. "Home Energy Management Systems with Branch-and-Bound Model-Based Predictive Control Techniques," Energies, MDPI, vol. 14(18), pages 1-27, September.
    2. Martín Pensado-Mariño & Lara Febrero-Garrido & Estibaliz Pérez-Iribarren & Pablo Eguía Oller & Enrique Granada-Álvarez, 2021. "Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks," Energies, MDPI, vol. 14(16), pages 1-14, August.
    3. Paweł Ocłoń & Maciej Ławryńczuk & Marek Czamara, 2021. "A New Solar Assisted Heat Pump System with Underground Energy Storage: Modelling and Optimisation," Energies, MDPI, vol. 14(16), pages 1-15, August.
    4. Fran Torbarina & Kristian Lenic & Anica Trp, 2022. "Computational Model of Shell and Finned Tube Latent Thermal Energy Storage Developed as a New TRNSYS Type," Energies, MDPI, vol. 15(7), pages 1-26, March.
    5. Josef Meiers & Georg Frey, 2025. "Interfacing TRNSYS with MATLAB for Building Energy System Optimization," Energies, MDPI, vol. 18(2), pages 1-23, January.
    6. Tafone, Alessio & Raj Thangavelu, Sundar & Morita, Shigenori & Romagnoli, Alessandro, 2023. "Design optimization of a novel cryo-polygeneration demonstrator developed in Singapore – Techno-economic feasibility study for a cooling dominated tropical climate," Applied Energy, Elsevier, vol. 330(PB).
    7. Zheng, Zhihang & Xiao, Jian & Yang, Ying & Xu, Feng & Zhou, Jin & Liu, Hongcheng, 2024. "Optimization of exterior wall insulation in office buildings based on wall orientation: Economic, energy and carbon saving potential in China," Energy, Elsevier, vol. 290(C).

    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. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    2. Lu, Yakai & Tian, Zhe & Zhou, Ruoyu & Liu, Wenjing, 2021. "A general transfer learning-based framework for thermal load prediction in regional energy system," Energy, Elsevier, vol. 217(C).
    3. Wassim Salameh & Jalal Faraj & Elias Harika & Rabih Murr & Mahmoud Khaled, 2021. "On the Optimization of Electrical Water Heaters: Modelling Simulations and Experimentation," Energies, MDPI, vol. 14(13), pages 1-12, June.
    4. Tarragona, Joan & Pisello, Anna Laura & Fernández, Cèsar & Cabeza, Luisa F. & Payá, Jorge & Marchante-Avellaneda, Javier & de Gracia, Alvaro, 2022. "Analysis of thermal energy storage tanks and PV panels combinations in different buildings controlled through model predictive control," Energy, Elsevier, vol. 239(PC).
    5. Wu, Long & Yin, Xunyuan & Pan, Lei & Liu, Jinfeng, 2023. "Distributed economic predictive control of integrated energy systems for enhanced synergy and grid response: A decomposition and cooperation strategy," Applied Energy, Elsevier, vol. 349(C).
    6. Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    7. Golmohamadi, Hessam & Larsen, Kim Guldstrand & Jensen, Peter Gjøl & Hasrat, Imran Riaz, 2022. "Integration of flexibility potentials of district heating systems into electricity markets: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    8. Shin, Ji-Hyun & Cho, Young-Hum, 2024. "A variable water flow rate control method of hybrid geothermal heat pump systems," Renewable Energy, Elsevier, vol. 226(C).
    9. Chi, Fang'ai & Xu, Liming & Pan, Jiajie & Wang, Ruonan & Tao, Yekang & Guo, Yuang & Peng, Changhai, 2020. "Prediction of the total day-round thermal load for residential buildings at various scales based on weather forecast data," Applied Energy, Elsevier, vol. 280(C).
    10. Noor Muhammad Abd Rahman & Lim Chin Haw & Ahmad Fazlizan, 2021. "A Literature Review of Naturally Ventilated Public Hospital Wards in Tropical Climate Countries for Thermal Comfort and Energy Saving Improvements," Energies, MDPI, vol. 14(2), pages 1-22, January.
    11. Clara Ceccolini & Roozbeh Sangi, 2022. "Benchmarking Approaches for Assessing the Performance of Building Control Strategies: A Review," Energies, MDPI, vol. 15(4), pages 1-30, February.
    12. Michael Bachseitz & Muhammad Sheryar & David Schmitt & Thorsten Summ & Christoph Trinkl & Wilfried Zörner, 2024. "PV-Optimized Heat Pump Control in Multi-Family Buildings Using a Reinforcement Learning Approach," Energies, MDPI, vol. 17(8), pages 1-16, April.
    13. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    14. Wiesheu, Michael & Rutešić, Luka & Shukhobodskiy, Alexander Alexandrovich & Pogarskaia, Tatiana & Zaitcev, Aleksandr & Colantuono, Giuseppe, 2021. "RED WoLF hybrid storage system: Adaptation of algorithm and analysis of performance in residential dwellings," Renewable Energy, Elsevier, vol. 179(C), pages 1036-1048.
    15. Golmohamadi, Hessam, 2021. "Stochastic energy optimization of residential heat pumps in uncertain electricity markets," Applied Energy, Elsevier, vol. 303(C).
    16. Leroy Tomás & Manuel Lämmle & Jens Pfafferott, 2025. "Demonstration and Evaluation of Model Predictive Control (MPC) for a Real-World Heat Pump System in a Commercial Low-Energy Building for Cost Reduction and Enhanced Grid Support," Energies, MDPI, vol. 18(6), pages 1-25, March.
    17. Efkarpidis, Nikolaos A. & Vomva, Styliani A. & Christoforidis, Georgios C. & Papagiannis, Grigoris K., 2022. "Optimal day-to-day scheduling of multiple energy assets in residential buildings equipped with variable-speed heat pumps," Applied Energy, Elsevier, vol. 312(C).
    18. Sun, Xiaoyu & Wang, Zhichao & Li, Xiaofeng & Xu, Zhaowei & Yang, Qiang & Yang, Yingxia, 2021. "Seasonal heating performance prediction of air-to-water heat pumps based on short-term dynamic monitoring," Renewable Energy, Elsevier, vol. 180(C), pages 829-837.
    19. Zhang, Yichi & Johansson, Pär & Kalagasidis, Angela Sasic, 2021. "Techno-economic assessment of thermal energy storage technologies for demand-side management in low-temperature individual heating systems," Energy, Elsevier, vol. 236(C).
    20. Davide Fop & Ali Reza Yaghoubi & Alfonso Capozzoli, 2024. "Validation of a Model Predictive Control Strategy on a High Fidelity Building Emulator," Energies, MDPI, vol. 17(20), pages 1-20, October.

    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:13:y:2020:i:21:p:5761-:d:439515. 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.