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Cost optimal sizing of smart buildings' energy system components considering changing end-consumer electricity markets

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  • Wolisz, Henryk
  • Schütz, Thomas
  • Blanke, Tobias
  • Hagenkamp, Markus
  • Kohrn, Markus
  • Wesseling, Mark
  • Müller, Dirk

Abstract

Managing the electricity system becomes increasingly challenging, calling for modifications of the current electricity market. High fluctuations in power generation could make the introduction of dynamic end-consumer electricity pricing reasonable. Furthermore, the prediction of end-consumers’ power consumption would get easier when charging the maximum power capacity, instead of the consumed energy. Thus, this paper discusses the capability of smart buildings to cope with such market models and evaluates how the design of the electrical and thermal energy system of a modern German building is affected. Therefore, cost optimal sizing of the main supply system components is carried out based on a hybrid MILP and a heuristic optimization algorithm. The results indicate that local photovoltaic generation is beneficial in almost all market conditions, while except for the capacity market, batteries are only economical if prices decrease by more than 60%. The identified electricity price dynamics are too low to incentivize investments into load shifting capable supply or storage systems. Nevertheless, if an installed heat pump and the associated thermal storage have smart home capabilities, they support the maximization of PV self-consumption and reduce electricity cost.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:137:y:2017:i:c:p:715-728
    DOI: 10.1016/j.energy.2017.06.025
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    4. Urban, Kristof L. & Scheller, Fabian & Bruckner, Thomas, 2021. "Suitability assessment of models in the industrial energy system design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    5. Wakui, Tetsuya & Sawada, Kento & Yokoyama, Ryohei & Aki, Hirohisa, 2019. "Predictive management for energy supply networks using photovoltaics, heat pumps, and battery by two-stage stochastic programming and rule-based control," Energy, Elsevier, vol. 179(C), pages 1302-1319.
    6. Fernando Salinas-Herrera & Ali Moeini & Innocent Kamwa, 2022. "Survey of Simulation Tools to Assess Techno-Economic Benefits of Smart Grid Technology in Integrated T&D Systems," Sustainability, MDPI, vol. 14(13), pages 1-36, July.
    7. Schütz, Thomas & Schraven, Markus Hans & Remy, Sebastian & Granacher, Julia & Kemetmüller, Dominik & Fuchs, Marcus & Müller, Dirk, 2017. "Optimal design of energy conversion units for residential buildings considering German market conditions," Energy, Elsevier, vol. 139(C), pages 895-915.
    8. Troitzsch, Sebastian & Sreepathi, Bhargava Krishna & Huynh, Thanh Phong & Moine, Aurelie & Hanif, Sarmad & Fonseca, Jimeno & Hamacher, Thomas, 2020. "Optimal electric-distribution-grid planning considering the demand-side flexibility of thermal building systems for a test case in Singapore," Applied Energy, Elsevier, vol. 273(C).
    9. Coppitters, Diederik & De Paepe, Ward & Contino, Francesco, 2021. "Robust design optimization of a photovoltaic-battery-heat pump system with thermal storage under aleatory and epistemic uncertainty," Energy, Elsevier, vol. 229(C).
    10. 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).
    11. Gomez-Gonzalez, M. & Hernandez, J.C. & Vera, D. & Jurado, F., 2020. "Optimal sizing and power schedule in PV household-prosumers for improving PV self-consumption and providing frequency containment reserve," Energy, Elsevier, vol. 191(C).
    12. Wakui, Tetsuya & Hashiguchi, Moe & Sawada, Kento & Yokoyama, Ryohei, 2019. "Two-stage design optimization based on artificial immune system and mixed-integer linear programming for energy supply networks," Energy, Elsevier, vol. 170(C), pages 1228-1248.
    13. 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.

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