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Influence of Hydrogen-Based Storage Systems on Self-Consumption and Self-Sufficiency of Residential Photovoltaic Systems

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  • Christian Pötzinger

    (Lehrstuhl für Technische Thermodynamik und Transportprozesse, Zentrum für Energietechnik, Universität Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany)

  • Markus Preißinger

    (Lehrstuhl für Technische Thermodynamik und Transportprozesse, Zentrum für Energietechnik, Universität Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany)

  • Dieter Brüggemann

    (Lehrstuhl für Technische Thermodynamik und Transportprozesse, Zentrum für Energietechnik, Universität Bayreuth, Universitätsstraße 30, 95447 Bayreuth, Germany)

Abstract

This paper analyzes the behavior of residential solar-powered electrical energy storage systems. For this purpose, a simulation model based on MATLAB/Simulink is developed. Investigating both short-time and seasonal hydrogen-based storage systems, simulations on the basis of real weather data are processed on a timescale of 15 min for a consideration period of 3 years. A sensitivity analysis is conducted in order to identify the most important system parameters concerning the proportion of consumption and the degree of self-sufficiency. Therefore, the influences of storage capacity and of storage efficiencies are discussed. A short-time storage system can increase the proportion of consumption by up to 35 percentage points compared to a self-consumption system without storage. However, the seasonal storing system uses almost the entire energy produced by the photovoltaic (PV) system (nearly 100% self-consumption). Thereby, the energy drawn from the grid can be reduced and a degree of self-sufficiency of about 90% is achieved. Based on these findings, some scenarios to reach self-sufficiency are analyzed. The results show that full self-sufficiency will be possible with a seasonal hydrogen-based storage system if PV area and initial storage level are appropriate.

Suggested Citation

  • Christian Pötzinger & Markus Preißinger & Dieter Brüggemann, 2015. "Influence of Hydrogen-Based Storage Systems on Self-Consumption and Self-Sufficiency of Residential Photovoltaic Systems," Energies, MDPI, vol. 8(8), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:8:y:2015:i:8:p:8887-8907:d:54573
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    1. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Contreras, Javier, 2007. "Optimization of control strategies for stand-alone renewable energy systems with hydrogen storage," Renewable Energy, Elsevier, vol. 32(7), pages 1102-1126.
    2. Dufo-López, Rodolfo & Bernal-Agustín, José L. & Yusta-Loyo, José M. & Domínguez-Navarro, José A. & Ramírez-Rosado, Ignacio J. & Lujano, Juan & Aso, Ismael, 2011. "Multi-objective optimization minimizing cost and life cycle emissions of stand-alone PV–wind–diesel systems with batteries storage," Applied Energy, Elsevier, vol. 88(11), pages 4033-4041.
    3. Moshövel, Janina & Kairies, Kai-Philipp & Magnor, Dirk & Leuthold, Matthias & Bost, Mark & Gährs, Swantje & Szczechowicz, Eva & Cramer, Moritz & Sauer, Dirk Uwe, 2015. "Analysis of the maximal possible grid relief from PV-peak-power impacts by using storage systems for increased self-consumption," Applied Energy, Elsevier, vol. 137(C), pages 567-575.
    4. Wang, Xiaonan & Palazoglu, Ahmet & El-Farra, Nael H., 2015. "Operational optimization and demand response of hybrid renewable energy systems," Applied Energy, Elsevier, vol. 143(C), pages 324-335.
    5. Santos, João M. & Moura, Pedro S. & Almeida, Aníbal T. de, 2014. "Technical and economic impact of residential electricity storage at local and grid level for Portugal," Applied Energy, Elsevier, vol. 128(C), pages 254-264.
    6. Paola Caputo & Costa Gaia & Valentina Zanotto, 2013. "A Methodology for Defining Electricity Demand in Energy Simulations Referred to the Italian Context," Energies, MDPI, vol. 6(12), pages 1-19, December.
    7. Karellas, S. & Tzouganatos, N., 2014. "Comparison of the performance of compressed-air and hydrogen energy storage systems: Karpathos island case study," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 865-882.
    8. Masa-Bote, D. & Castillo-Cagigal, M. & Matallanas, E. & Caamaño-Martín, E. & Gutiérrez, A. & Monasterio-Huelín, F. & Jiménez-Leube, J., 2014. "Improving photovoltaics grid integration through short time forecasting and self-consumption," Applied Energy, Elsevier, vol. 125(C), pages 103-113.
    9. Luthander, Rasmus & Widén, Joakim & Nilsson, Daniel & Palm, Jenny, 2015. "Photovoltaic self-consumption in buildings: A review," Applied Energy, Elsevier, vol. 142(C), pages 80-94.
    10. Antimo Barbato & Antonio Capone, 2014. "Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey," Energies, MDPI, vol. 7(9), pages 1-38, September.
    11. Cao, Sunliang & Sirén, Kai, 2014. "Impact of simulation time-resolution on the matching of PV production and household electric demand," Applied Energy, Elsevier, vol. 128(C), pages 192-208.
    12. Bianchi, M. & Branchini, L. & Ferrari, C. & Melino, F., 2014. "Optimal sizing of grid-independent hybrid photovoltaic–battery power systems for household sector," Applied Energy, Elsevier, vol. 136(C), pages 805-816.
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