DOI: https://doi.org/10.14710/ijred.2021.34241

Configuring the Objective Function of A Model Predictive Controller for An Integrated Thermal-Electrical Decentral Renewable Energy System

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

Received: 18 Nov 2020; Revised: 30 Dec 2020; Accepted: 4 Jan 2021; Available online: 12 Jan 2021; Published: 1 May 2021.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2021 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

With the increasing integration of decentral renewable energy systems in the residential sector, the opportunity to enhance the control via model predictive control is available. In this article, the main focus is to investigate the objective function of the model predictive controller (MPC) of an integrated thermal-electrical renewable energy system consisting of photovoltaics, solar thermal collectors, fuel cell along with auxiliary gas boiler and electricity grid using electrical and thermal storage in a single-family house. The mathematical definition of the objective function and the depth of detailing the objectives are the prime focus of this particular article. Four different objective functions are defined and are investigated on a day-to-day basis in the selected six representative days of the whole year for the single-family house in Ehingen, Germany with a white-box simulation model simulated using TRNSYS and MATLAB. Using the clustering technique then the six representative days are weighted extrapolated to a whole year and the outcomes of the whole year MPC implementation are estimated. The results show that the detailing of the mathematical model, even though is time and personnel consuming, does have its advantages. With the detailed objective function, 9% more solar thermal fraction; 32% less power-to-heat at an expense of 32% more gas boiler usage; 6% more thermal system effectiveness along with 10% increased total self-consumption fraction with 16% decrease in space heating demand, 492 kWh more battery usage and 66% reduced fuel cell production is achieved by the MPC in comparison to the status quo controller. Except for the effectiveness of the thermal system with increased gas boiler usage, which occurs due to less power-to-heat, the detailed objective function in comparison to the simple mathematical definition does evidently increase the smartness of the MPC.

Fulltext View|Download
Keywords: Energy optimization; TRNSYS; MATLAB; home energy management; self-consumption; single-family house; whitebox MPC;

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Analytical and Numerical Solution for H-type Darrieus Wind Turbine Performance at the Tip Speed Ratio of Below One Modeling and Analysis of the Dynamic Response of an Off-Grid Synchronous Generator Driven Micro Hydro Power System Energy Use of Mediterranean Forest Biomass in Sustainable Public Heating Systems and its Effects on Climate Change – Case of Study Evaluation of a PV-TEG Hybrid System Configuration for an Improved Energy Output: A Review Effect of Fluid Flow Direction on Charging of Multitube Thermal Energy Storage for Flat Plate Solar Collectors More recent articles
Most cited articles
Design and Economic Analysis of a Photovoltaic System: A Case Study Thermal and Ash Characterization of Indonesian Bamboo and Its Potential for Solid Fuel and Waste Valorization Study of Fabricated Solar Dryer of Tomato Slices under Jordan Climate Condition Integration of 5G Technologies in Smart Grid Communication-A Short Survey A Review of a Successful Unsubsidized Market-Based Rural Solar Development Initiative in Laikipia District, Central Kenya More cited articles
  1. Acatech, Leopoldina, & Akademienunion. (2020). Centralized and decentralized components in the energy system. The right mix for ensuring a stable and sustainable supply. https://en.acatech.de/publication/centralized-and-decentralized-components-in-the-energy-system/
  2. Alibabaei, N., Fung, A. S., & Raahemifar, K. (2016). Development of Matlab-TRNSYS co-simulator for applying predictive strategy planning models on residential house HVAC system. Energy and Buildings, 128, 81–98. https://doi.org/10.1016/j.enbuild.2016.05.084
  3. Badanjak, D., & Bogdanović, M. (2019). Model Predictive Control Energy Management In Building With Battery And Thermal Storage
  4. Bundesministerium für Wirtschaft und Energie (BMWi). (2015). Energieeffizienzstrategie Gebäude, Wege zu einem nahezu klimaneutralen Gebäudebestand. https://www.bmwi.de/Redaktion/DE/Downloads/E/energieeffizienzstrategie-gebaeude-kurzfassung.pdf?__blob=publicationFile&v=15
  5. Bundesministerium für Wirtschaft und Energie (BMWi). (2019). Zweiter Fortschrittsbericht zur Energiewende, Die Energie der Zukunft. https://www.bmwi.de/Redaktion/DE/Publikationen/Energie/fortschrittsbericht-monitoring-energiewende-kurzfassung.pdf?__blob=publicationFile&v=18
  6. Deutsche Energie Agentur, DENA. (2019). dena—Gebäudereport Kompakt 2019, Statistiken und Analysen zur Energieeffizienz im Gebäudebestand. https://www.dena.de/fileadmin/dena/Publikationen/PDFs/2019/dena-GEBAEUDEREPORT_KOMPAKT_2019.pdf
  7. Görtler, G., & Beigelböck, B. (2010). Simulation einer prädiktiven Raumtemperaturregelung unter Verwendung einer idealen Wettervorhersage. Bauphysik, 32(6), 405–413. https://doi.org/10.1002/bapi.201010047
  8. Julia 1-3—Dammann-Haus. (2018, June 8). https://www.dammann-haus.de/julia-1-3.html?articles=julia-1-3
  9. Jungwirth, J. (2014). Lastmanagement in Gebäuden: Entwicklung einer modellprädiktiven Regelung mit einem adaptiven Gebäudemodell zur Flexibilisierung der Wärme- und Kälteversorgung von Gebäuden. München, Techn. Univ
  10. Khakimova, A., Kusatayeva, A., Shamshimova, A., Sharipova, D., Bemporad, A., Familiant, Y., Shintemirov, A., Ten, V., & Rubagotti, M. (2017). Optimal energy management of a small-size building via hybrid model predictive control. Energy and Buildings, 140, 1–8. https://doi.org/10.1016/j.enbuild.2017.01.045
  11. Kuboth, S., Heberle, F., König-Haagen, A., & Brüggemann, D. (2019). Economic model predictive control of combined thermal and electric residential building energy systems. Applied Energy, 240, 372–385. https://doi.org/10.1016/j.apenergy.2019.01.097
  12. Laustsen, J. (2008). Energy efficiency requirements in building codes, energy efficiency policies for new buildings. International Energy Agency (IEA), 477–488
  13. Narayanan, M. (2020). Annual evaluation of a model predictive controller in an integrated thermal-electrical renewable energy system using clustering technique. 21
  14. Narayanan, M., Aline Ferreira, de L., André Felipe Oliveira, de A. D., & Walter, C. (2020). Development of a coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of integrated electrical and thermal Residential Renewable Energy System. 29
  15. Nocedal, J., & Wright, S. J. (Eds.). (2006). Penalty and Augmented Lagrangian Methods. In Numerical Optimization (pp. 497–528). Springer. https://doi.org/10.1007/978-0-387-40065-5_17
  16. Ostadijafari, M., Dubey, A., Liu, Y., Shi, J., & Yu, N. (2019). Smart Building Energy Management using Nonlinear Economic Model Predictive Control. ArXiv:1906.00362 [Cs]. http://arxiv.org/abs/1906.00362
  17. Pichler, M., Heinz, A., & Rieberer, R. (2017). Model Predictive Heat Pump-and Building Control to Maximize PV-Power On Site Use. 12th IEA Heat Pump Conference
  18. Pichler, M. F., Schranzhofer, H., Heinz, A., & Heimrath, R. (2014). Potential Performance Enhancement of a Solar Combisystem with an Intelligent Controller. Journal of Technology Innovations in Renewable Energy, 3, 107–119. http://dx.doi.org/10.6000/1929-6002.2014.03.03.4
  19. Pichler, Martin Felix, Lerch, W., Heinz, A., Goertler, G., Schranzhofer, H., & Rieberer, R. (2014). A novel linear predictive control approach for auxiliary energy supply to a solar thermal combistorage. Solar Energy, 101, 203–219. https://doi.org/10.1016/j.solener.2013.12.015
  20. Pintaldi, S., Li, J., Sethuvenkatraman, S., White, S., & Rosengarten, G. (2019). Model predictive control of a high efficiency solar thermal cooling system with thermal storage. Energy and Buildings, 196, 214–226. https://doi.org/10.1016/j.enbuild.2019.05.008
  21. Prognos AG, Fraunhofer ISI, GWS, & iinas. (2020). Energiewirtschaftliche Projektionen und Folgeabschätzungen 2030/2050. https://www.bmwi.de/Redaktion/DE/Publikationen/Wirtschaft/klimagutachten.pdf?__blob=publicationFile&v=8
  22. Reynders, G., Nuytten, T., & Saelens, D. (2013). Potential of structural thermal mass for demand-side management in dwellings. Building and Environment, 64, 187–199. https://doi.org/10.1016/j.buildenv.2013.03.010
  23. Serale, G., Fiorentini, M., Capozzoli, A., Cooper, P., & Perino, M. (2018). Formulation of a model predictive control algorithm to enhance the performance of a latent heat solar thermal system. Energy Conversion and Management, 173, 438–449. https://doi.org/10.1016/j.enconman.2018.07.099
  24. Sonnenhauskriterien für Wohngebäude. (2014, June). http://www.sonnenhaus-institut.de/wp-content/uploads/1-Sonnenhauskriterien-2014.pdf
  25. Sturzenegger, D., Gyalistras, D., Morari, M., & Smith, R. S. (2016). Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis. IEEE Transactions on Control Systems Technology, 24(1), 1–12. https://doi.org/10.1109/TCST.2015.2415411
  26. Thieblemont, H., Haghighat, F., Ooka, R., & Moreau, A. (2017). Predictive Control Strategies based on Weather Forecast in Buildings with Energy Storage System: A Review of the State-of-the Art. Energy and Buildings. https://doi.org/10.1016/j.enbuild.2017.08.010
  27. Yu, Z. (Jerry), Huang, G., Haghighat, F., Li, H., & Zhang, G. (2015). Control strategies for integration of thermal energy storage into buildings: State-of-the-art review. Energy and Buildings, 106, 203–215. https://doi.org/10.1016/j.enbuild.2015.05.038
  28. Zakula, T. (2013). Model Predictive Control for Energy Efficient Cooling and Dehumidification. 170
  29. Zemann, C., Deutsch, M., Zlabinger, S., Hofmeister, G., Gölles, M., & Horn, M. (2020). Optimal operation of residential heating systems with logwood boiler, buffer storage and solar thermal collector. Biomass and Bioenergy, 140, 105622. https://doi.org/10.1016/j.biombioe.2020.105622

Last update:

  1. Adaptiveness of a model predictive controller for a thermal-electrical renewable energy system in four different German single-family house energy standards

    Muthalagappan Narayanan, Gerhard Mengedoht, Walter Commerell. Case Studies in Thermal Engineering, 26 , 2021. doi: 10.1016/j.csite.2021.101118

  2. Evaluation of SOFC-CHP’s ability to integrate thermal and electrical energy system decentrally in a single-family house with model predictive controller

    Muthalagappan Narayanan, Gerhard Mengedoht, Walter Commerell. Sustainable Energy Technologies and Assessments, 48 , 2021. doi: 10.1016/j.seta.2021.101643

Last update: 2024-12-26 23:26:45

No citation recorded.