IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i10p1616-d115109.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/10/1616/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/10/10/1616/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Evelina Di Corso & Tania Cerquitelli & Daniele Apiletti, 2018. "METATECH: METeorological Data Analysis for Thermal Energy CHaracterization by Means of Self-Learning Transparent Models," Energies, MDPI, vol. 11(6), pages 1-24, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Jānis Krūmiņš & Māris Kļaviņš, 2023. "Investigating the Potential of Nuclear Energy in Achieving a Carbon-Free Energy Future," Energies, MDPI, vol. 16(9), pages 1-31, April.
    2. Alexandru Pîrjan & Simona-Vasilica Oprea & George Căruțașu & Dana-Mihaela Petroșanu & Adela Bâra & Cristina Coculescu, 2017. "Devising Hourly Forecasting Solutions Regarding Electricity Consumption in the Case of Commercial Center Type Consumers," Energies, MDPI, vol. 10(11), pages 1-36, October.
    3. Antoine Boche & Clément Foucher & Luiz Fernando Lavado Villa, 2022. "Understanding Microgrid Sustainability: A Systemic and Comprehensive Review," Energies, MDPI, vol. 15(8), pages 1-29, April.
    4. Zhijie Duan & Luo Zhang & Lili Feng & Shuguang Yu & Zengyou Jiang & Xiaoming Xu & Jichao Hong, 2021. "Research on Economic and Operating Characteristics of Hydrogen Fuel Cell Cars Based on Real Vehicle Tests," Energies, MDPI, vol. 14(23), pages 1-19, November.
    5. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    6. Stanek, Wojciech & Simla, Tomasz & Gazda, Wiesław, 2019. "Exergetic and thermo-ecological assessment of heat pump supported by electricity from renewable sources," Renewable Energy, Elsevier, vol. 131(C), pages 404-412.
    7. Yin, Rumeng & He, Jiang, 2023. "Design of a photovoltaic electric bike battery-sharing system in public transit stations," Applied Energy, Elsevier, vol. 332(C).
    8. Mohanraj, M. & Belyayev, Ye. & Jayaraj, S. & Kaltayev, A., 2018. "Research and developments on solar assisted compression heat pump systems – A comprehensive review (Part A: Modeling and modifications)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 90-123.
    9. James Hamilton & Michael Negnevitsky & Xiaolin Wang & Evgenii Semshchikov, 2020. "The Role of Low-Load Diesel in Improved Renewable Hosting Capacity within Isolated Power Systems," Energies, MDPI, vol. 13(16), pages 1-15, August.
    10. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
    11. Deshmukh, Swaraj Sanjay & Pearce, Joshua M., 2021. "Electric vehicle charging potential from retail parking lot solar photovoltaic awnings," Renewable Energy, Elsevier, vol. 169(C), pages 608-617.
    12. M. Rambabu & G. V. Nagesh Kumar & S. Sivanagaraju, 2019. "Optimal Power Flow of Integrated Renewable Energy System using a Thyristor Controlled SeriesCompensator and a Grey-Wolf Algorithm," Energies, MDPI, vol. 12(11), pages 1-18, June.
    13. Liu, Yanxin & Li, Huajiao & Guan, Jianhe & Liu, Xueyong & Guan, Qing & Sun, Qingru, 2019. "Influence of different factors on prices of upstream, middle and downstream products in China's whole steel industry chain: Based on Adaptive Neural Fuzzy Inference System," Resources Policy, Elsevier, vol. 60(C), pages 134-142.
    14. Wanjiru, Evan M. & Sichilalu, Sam M. & Xia, Xiaohua, 2017. "Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems," Applied Energy, Elsevier, vol. 204(C), pages 1333-1346.
    15. Abdulsalam S. Alghamdi & AbuBakr S. Bahaj & Luke S. Blunden & Yue Wu, 2019. "Dust Removal from Solar PV Modules by Automated Cleaning Systems," Energies, MDPI, vol. 12(15), pages 1-21, July.
    16. Wahiba Yaïci & Michela Longo & Evgueniy Entchev & Federica Foiadelli, 2017. "Simulation Study on the Effect of Reduced Inputs of Artificial Neural Networks on the Predictive Performance of the Solar Energy System," Sustainability, MDPI, vol. 9(8), pages 1-14, August.
    17. Dimri, Neha & Tiwari, Arvind & Tiwari, G.N., 2019. "Comparative study of photovoltaic thermal (PVT) integrated thermoelectric cooler (TEC) fluid collectors," Renewable Energy, Elsevier, vol. 134(C), pages 343-356.
    18. Reza Ahmadi Kordkheili & Seyyed Ali Pourmousavi & Mehdi Savaghebi & Josep M. Guerrero & Mohammad Hashem Nehrir, 2016. "Assessing the Potential of Plug-in Electric Vehicles in Active Distribution Networks," Energies, MDPI, vol. 9(1), pages 1-17, January.
    19. Kaiye Gao & Tianshi Wang & Chenjing Han & Jinhao Xie & Ye Ma & Rui Peng, 2021. "A Review of Optimization of Microgrid Operation," Energies, MDPI, vol. 14(10), pages 1-39, May.
    20. Chih-Lung Shen & You-Sheng Shen & Cheng-Tao Tsai, 2017. "Isolated DC-DC Converter for Bidirectional Power Flow Controlling with Soft-Switching Feature and High Step-Up/Down Voltage Conversion," Energies, MDPI, vol. 10(3), pages 1-23, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:10:p:1616-:d:115109. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.