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Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems

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  • Mingqi Wang

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, China)

  • Xinqiao Zheng

    (School of Energy Power and Mechanical Engineering, North China Electric Power University, Baoding 071000, China)

Abstract

Time interval and time length are two important indexes when analyzing the active output data of photovoltaic (PV) power stations. When the time interval is constant, the length of time is too small, and the included information is less, resulting in a lack and distortion of information; it the length of time is too large, the included information is redundant and complicated, resulting in unnecessary increases of storage capacity and calculation. Therefore, it is important to determine the appropriate length of data for the analysis of PV output data. In this paper, firstly, the output data of a PV power station is analyzed statistically, and the preliminary conclusions for time length selection are obtained by autocorrelation analysis. Based on the weather characteristics, clustering analysis methods and statistical principles are used to analyze the data and optimal sample capacity estimation, respectively, for different types of photovoltaic output data and determine the required data time length at the time of analyzing the PV power plant output data, the relationship between energy storage capacity demand and data length is investigated, the rationality of the length of the selected time is verified. Meanwhile, the energy storage system capacity configuration based on the optimal data time length is given. The results show that the requirement of data volume of energy storage system capacity configuration can be met when the time length of the PV output data is 23 days.

Suggested Citation

  • Mingqi Wang & Xinqiao Zheng, 2017. "Sensitivity Analysis of Time Length of Photovoltaic Output Power to Capacity Configuration of Energy Storage Systems," Energies, MDPI, vol. 10(10), pages 1-15, October.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1616-:d:115109
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    1. Ying-Yi Hong & Yuan-Ming Lai & Yung-Ruei Chang & Yih-Der Lee & Pang-Wei Liu, 2015. "Optimizing Capacities of Distributed Generation and Energy Storage in a Small Autonomous Power System Considering Uncertainty in Renewables," Energies, MDPI, vol. 8(4), pages 1-20, March.
    2. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Yue Wu, 2017. "Assessment of Large Scale Photovoltaic Power Generation from Carport Canopies," Energies, MDPI, vol. 10(5), pages 1-22, May.
    3. Han, Xiaojuan & Liu, Dahe & Liu, Jian & Kong, Lingda, 2017. "Sensitivity analysis of acquisition granularity of photovoltaic output power to capacity configuration of energy storage systems," Applied Energy, Elsevier, vol. 203(C), pages 794-807.
    4. Zárate-Miñano, Rafael & Milano, Federico, 2016. "Construction of SDE-based wind speed models with exponentially decaying autocorrelation," Renewable Energy, Elsevier, vol. 94(C), pages 186-196.
    5. Deger Saygin & Ruud Kempener & Nicholas Wagner & Maria Ayuso & Dolf Gielen, 2015. "The Implications for Renewable Energy Innovation of Doubling the Share of Renewables in the Global Energy Mix between 2010 and 2030," Energies, MDPI, vol. 8(6), pages 1-38, June.
    6. Sichilalu, Sam & Mathaba, Tebello & Xia, Xiaohua, 2017. "Optimal control of a wind–PV-hybrid powered heat pump water heater," Applied Energy, Elsevier, vol. 185(P2), pages 1173-1184.
    7. Yadav, Amit Kumar & Chandel, S.S., 2017. "Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using Artificial Neural Network and Multiple Linear Regression Models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 955-969.
    8. Rita Pinto & Sílvio Mariano & Maria Do Rosário Calado & José Felippe De Souza, 2016. "Impact of Rural Grid-Connected Photovoltaic Generation Systems on Power Quality," Energies, MDPI, vol. 9(9), pages 1-15, September.
    9. Morris Brenna & Alberto Dolara & Federica Foiadelli & George C. Lazaroiu & Sonia Leva, 2012. "Transient Analysis of Large Scale PV Systems with Floating DC Section," Energies, MDPI, vol. 5(10), pages 1-17, September.
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