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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    Cited by:

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    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. 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).
    6. 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).

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