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Optimal day-to-day scheduling of multiple energy assets in residential buildings equipped with variable-speed heat pumps

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  • Efkarpidis, Nikolaos A.
  • Vomva, Styliani A.
  • Christoforidis, Georgios C.
  • Papagiannis, Grigoris K.

Abstract

The largest parts of energy demand within the residential sector are used for space heating (SH), domestic hot water (DHW) and space cooling (SC). The thermal and electrical needs can be mainly covered by energy systems that consist of Photovoltaics (PVs), solar thermal collectors (STCs), heat pumps (HPs) and flexibility sources, e.g. battery energy storage (BES) and thermal energy storage (TES). In this paper, various control strategies for the optimal day-to-day operation of residential energy systems that consist of a variable-speed HP, STCs, PVs, as well as BES and TES units are examined. The proposed strategies are based on the concepts of self-consumption maximization (SCM) and time-of-use arbitrage (ToUA) considering priorities on BES and TES charging. A typical Greek single-family building is assumed examining multiple criteria for the assessment of the rule-based approaches. The results demonstrate that priority on TES charging can lead to higher system performance than on BES charging. Regardless of the number of ToU tariff zones, during off/mid-peak periods the upper energy needs of the SH/SC trajectory should be fulfilled only in case of excess PV power, otherwise the cost of imported energy increases significantly. Both SCM and ToUA strategies can lead to rates of self-consumption and self-sufficiency that deviate in average per about 4% from the optimal values. Moreover, the ToUA-based strategies can approach the minimum cost of imported energy with a relative increase of 22% compared to the optimum. However, the minimum thermal discomfort due to space temperature cannot be achieved without the prediction of the daily SH/SC demand. Furthermore, the proposed strategies can reach the minimum thermal discomfort due to DHW losses, while leading to lower BES degradation rates than the optimization. Finally, from the multi-objective optimization for the first year of system operation, it is verified that the optimal dimensioning of PV, BES and TES is considerably affected by the type of rule-based strategy selected.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:312:y:2022:i:c:s0306261922001635
    DOI: 10.1016/j.apenergy.2022.118702
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    References listed on IDEAS

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    1. Beck, T. & Kondziella, H. & Huard, G. & Bruckner, T., 2017. "Optimal operation, configuration and sizing of generation and storage technologies for residential heat pump systems in the spotlight of self-consumption of photovoltaic electricity," Applied Energy, Elsevier, vol. 188(C), pages 604-619.
    2. Ashouri, Araz & Fux, Samuel S. & Benz, Michael J. & Guzzella, Lino, 2013. "Optimal design and operation of building services using mixed-integer linear programming techniques," Energy, Elsevier, vol. 59(C), pages 365-376.
    3. Terlouw, Tom & AlSkaif, Tarek & Bauer, Christian & van Sark, Wilfried, 2019. "Optimal energy management in all-electric residential energy systems with heat and electricity storage," Applied Energy, Elsevier, vol. 254(C).
    4. Mancarella, Pierluigi, 2014. "MES (multi-energy systems): An overview of concepts and evaluation models," Energy, Elsevier, vol. 65(C), pages 1-17.
    5. Wolisz, Henryk & Schütz, Thomas & Blanke, Tobias & Hagenkamp, Markus & Kohrn, Markus & Wesseling, Mark & Müller, Dirk, 2017. "Cost optimal sizing of smart buildings' energy system components considering changing end-consumer electricity markets," Energy, Elsevier, vol. 137(C), pages 715-728.
    6. Jacob, Ammu Susanna & Banerjee, Rangan & Ghosh, Prakash C., 2018. "Sizing of hybrid energy storage system for a PV based microgrid through design space approach," Applied Energy, Elsevier, vol. 212(C), pages 640-653.
    7. Langer, Lissy & Volling, Thomas, 2020. "An optimal home energy management system for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 278(C).
    8. Psimopoulos, Emmanouil & Bee, Elena & Widén, Joakim & Bales, Chris, 2019. "Techno-economic analysis of control algorithms for an exhaust air heat pump system for detached houses coupled to a photovoltaic system," Applied Energy, Elsevier, vol. 249(C), pages 355-367.
    9. 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.
    10. Hurtado, L.A. & Rhodes, J.D. & Nguyen, P.H. & Kamphuis, I.G. & Webber, M.E., 2017. "Quantifying demand flexibility based on structural thermal storage and comfort management of non-residential buildings: A comparison between hot and cold climate zones," Applied Energy, Elsevier, vol. 195(C), pages 1047-1054.
    11. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
    12. Bianchini, Gianni & Casini, Marco & Pepe, Daniele & Vicino, Antonio & Zanvettor, Giovanni Gino, 2019. "An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings," Applied Energy, Elsevier, vol. 240(C), pages 327-340.
    13. Salpakari, Jyri & Lund, Peter, 2016. "Optimal and rule-based control strategies for energy flexibility in buildings with PV," Applied Energy, Elsevier, vol. 161(C), pages 425-436.
    14. Fischer, David & Bernhardt, Josef & Madani, Hatef & Wittwer, Christof, 2017. "Comparison of control approaches for variable speed air source heat pumps considering time variable electricity prices and PV," Applied Energy, Elsevier, vol. 204(C), pages 93-105.
    15. Steen, David & Stadler, Michael & Cardoso, Gonçalo & Groissböck, Markus & DeForest, Nicholas & Marnay, Chris, 2015. "Modeling of thermal storage systems in MILP distributed energy resource models," Applied Energy, Elsevier, vol. 137(C), pages 782-792.
    16. Fischer, David & Madani, Hatef, 2017. "On heat pumps in smart grids: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 342-357.
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    2. Víctor Sanz i López & Ramon Costa-Castelló & Carles Batlle, 2022. "Literature Review of Energy Management in Combined Heat and Power Systems Based on High-Temperature Proton Exchange Membrane Fuel Cells for Residential Comfort Applications," Energies, MDPI, vol. 15(17), pages 1-22, September.
    3. Stef Jacobs & Margot De Pauw & Senne Van Minnebruggen & Sara Ghane & Thomas Huybrechts & Peter Hellinckx & Ivan Verhaert, 2023. "Grouped Charging of Decentralised Storage to Efficiently Control Collective Heating Systems: Limitations and Opportunities," Energies, MDPI, vol. 16(8), pages 1-28, April.
    4. Maier, Laura & Schönegge, Marius & Henn, Sarah & Hering, Dominik & Müller, Dirk, 2022. "Assessing mixed-integer-based heat pump modeling approaches for model predictive control applications in buildings," Applied Energy, Elsevier, vol. 326(C).
    5. Brudermueller, Tobias & Kreft, Markus & Fleisch, Elgar & Staake, Thorsten, 2023. "Large-scale monitoring of residential heat pump cycling using smart meter data," Applied Energy, Elsevier, vol. 350(C).
    6. Chen, Minghao & Xie, Zhiyuan & Sun, Yi & Zheng, Shunlin, 2023. "The predictive management in campus heating system based on deep reinforcement learning and probabilistic heat demands forecasting," Applied Energy, Elsevier, vol. 350(C).

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