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Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System

Author

Listed:
  • Ronny Gelleschus

    (Chair of Energy Storage Systems, Technische Universität Dresden, Helmholtzstr. 9, 01062 Dresden, Germany)

  • Michael Böttiger

    (Chair of Energy Storage Systems, Technische Universität Dresden, Helmholtzstr. 9, 01062 Dresden, Germany)

  • Thilo Bocklisch

    (Chair of Energy Storage Systems, Technische Universität Dresden, Helmholtzstr. 9, 01062 Dresden, Germany)

Abstract

The increasing share of renewable energies in the electricity sector promotes a more decentralized energy supply and the introduction of new flexibility options. These flexibility options provide degrees of freedom that should be used optimally. Therefore, in this paper, a model predictive control-based multi-objective optimizing energy management concept for a hybrid energy storage system, consisting of a photovoltaics (PV) plant, a battery, and a combined heat pump/heat storage device is presented. The concept’s objectives are minimal operation costs and reducing the power exchanged with the electrical grid while ensuring user comfort. In order to prove the concept to be viable and its objectives being fulfilled, investigations based on simulations of one year of operation have been carried out. Comparisons to a simple rule-based strategy and the same model predictive control scheme with ideal forecasts prove the concept’s viability while showing improvement potential in the treatment of nonlinear system behavior, caused by nonlinear battery losses, and of forecast uncertainties.

Suggested Citation

  • Ronny Gelleschus & Michael Böttiger & Thilo Bocklisch, 2019. "Optimization-Based Control Concept with Feed-in and Demand Peak Shaving for a PV Battery Heat Pump Heat Storage System," Energies, MDPI, vol. 12(11), pages 1-16, June.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:11:p:2098-:d:236303
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    References listed on IDEAS

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    1. Kondziella, Hendrik & Bruckner, Thomas, 2016. "Flexibility requirements of renewable energy based electricity systems – a review of research results and methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 10-22.
    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.
    3. Romero-Quete, David & Garcia, Javier Rosero, 2019. "An affine arithmetic-model predictive control approach for optimal economic dispatch of combined heat and power microgrids," Applied Energy, Elsevier, vol. 242(C), pages 1436-1447.
    4. 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.
    5. Akbar Maleki & Marc A. Rosen & Fathollah Pourfayaz, 2017. "Optimal Operation of a Grid-Connected Hybrid Renewable Energy System for Residential Applications," Sustainability, MDPI, vol. 9(8), pages 1-20, July.
    6. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
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    Cited by:

    1. Gerber, Daniel L. & Nordman, Bruce & Brown, Richard & Poon, Jason, 2023. "Cost analysis of distributed storage in AC and DC microgrids," Applied Energy, Elsevier, vol. 344(C).
    2. Sebastian Kuboth & Theresa Weith & Florian Heberle & Matthias Welzl & Dieter Brüggemann, 2020. "Experimental Long-Term Investigation of Model Predictive Heat Pump Control in Residential Buildings with Photovoltaic Power Generation," Energies, MDPI, vol. 13(22), pages 1-17, November.
    3. Alexander V. Klokov & Alexander S. Tutunin & Elizaveta S. Sharaborova & Aleksei A. Korshunov & Egor Y. Loktionov, 2023. "Inverter Heat Pumps as a Variable Load for Off-Grid Solar-Powered Systems," Energies, MDPI, vol. 16(16), pages 1-17, August.
    4. Manuel Kersic & Thilo Bocklisch & Michael Böttiger & Lisa Gerlach, 2020. "Coordination Mechanism for PV Battery Systems with Local Optimizing Energy Management," Energies, MDPI, vol. 13(3), pages 1-25, January.

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