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Evaluation of Building Mass Characterization for Energy Flexibility through Rule- and Schedule-Based Control: A Statistical Approach

Author

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  • Joscha Reber

    (Institute of Structural Mechanics and Design, Department of Civil and Environmental Engineering, Technical University of Darmstadt, 64287 Darmstadt, Germany)

  • Xenia Kirschstein

    (Institute of Structural Mechanics and Design, Department of Civil and Environmental Engineering, Technical University of Darmstadt, 64287 Darmstadt, Germany)

  • Nadja Bishara

    (Institute of Structural Mechanics and Design, Department of Civil and Environmental Engineering, Technical University of Darmstadt, 64287 Darmstadt, Germany)

Abstract

As renewables become more established in the electricity grid, the focus, and therefore adaptability, will need to shift from the generation side to the demand side. Since the building sector accounts for a large share of the energy demand, it will be strongly affected by this development. One possibility for adaptation is so-called demand side management (DSM). To assess the contribution of the building sector to energy flexibility, some key performance indicators (KPIs) have already been developed in previous work. In this study, we investigate and statistically compare two control strategies for temporarily raising the room temperature—one rule-based and one schedule-based—with regard to their influence on the characterization of the building mass as a type of thermal energy storage. In each case, we determine the thermal energy demand of a residential district based on a dynamic simulation that occurred for a period of one year. The rule-based control assigns in the median approximately 60% (mean: 41%) less capacity to the building mass than the schedule-based control for the same boundary conditions. The calculation of the time-independent heating load results in a median difference of 34% (mean: 36%). In addition, the establishment of energy-flexible control in the evening hours just before a night-time reduction in the room temperature has a negative impact on the efficiency of the thermal storage.

Suggested Citation

  • Joscha Reber & Xenia Kirschstein & Nadja Bishara, 2023. "Evaluation of Building Mass Characterization for Energy Flexibility through Rule- and Schedule-Based Control: A Statistical Approach," Energies, MDPI, vol. 16(19), pages 1-17, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:19:p:6878-:d:1250587
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    References listed on IDEAS

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    1. Lund, Henrik & Munster, Ebbe, 2006. "Integrated energy systems and local energy markets," Energy Policy, Elsevier, vol. 34(10), pages 1152-1160, July.
    2. Denholm, Paul & Hand, Maureen, 2011. "Grid flexibility and storage required to achieve very high penetration of variable renewable electricity," Energy Policy, Elsevier, vol. 39(3), pages 1817-1830, March.
    3. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, September.
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