IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v35y2010i12p5082-5092.html
   My bibliography  Save this article

A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems

Author

Listed:
  • Tan, Chee Wei
  • Green, Tim C.
  • Hernandez-Aramburo, Carlos A.

Abstract

This paper presents a stochastic simulation using Monte Carlo technique to size a battery to meet dual objectives of demand shift at peak electricity cost times and outage protection in BIPV (building integrated photovoltaic) systems. Both functions require battery storage and the sizing of battery using numerical optimization is popularly used. However, the weather conditions, outage events and demand peaks are not deterministic in nature. Therefore, the sizing of battery storage capacity should also be based on a probabilistic approach. The Monte Carlo simulation is a rigorous method to sizing BIPV system as it takes into account a real building load profiles, the weather information and the local historical outage distribution. The simulation is split into seasonal basis for the analysis of demand shifting and outage events in order to match the seasonal weather conditions and load profiles. Five configurations of PV (photovoltaic) are assessed that cover different areas and orientations. The simulation output includes the predicted PV energy yield, the amount of energy required for demand management and outage event. Therefore, consumers can base sizing decisions on the historical data and local risk of outage statistics and the success rate of meeting the demand shift required. Finally, the economic evaluations together with the sensitivity analysis and the assessment of customers’ outage cost are discussed.

Suggested Citation

  • Tan, Chee Wei & Green, Tim C. & Hernandez-Aramburo, Carlos A., 2010. "A stochastic method for battery sizing with uninterruptible-power and demand shift capabilities in PV (photovoltaic) systems," Energy, Elsevier, vol. 35(12), pages 5082-5092.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:12:p:5082-5092
    DOI: 10.1016/j.energy.2010.08.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544210004342
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2010.08.007?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Diaf, S. & Diaf, D. & Belhamel, M. & Haddadi, M. & Louche, A., 2007. "A methodology for optimal sizing of autonomous hybrid PV/wind system," Energy Policy, Elsevier, vol. 35(11), pages 5708-5718, November.
    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. Schroeder, Andreas, 2011. "Modeling storage and demand management in power distribution grids," Applied Energy, Elsevier, vol. 88(12), pages 4700-4712.
    2. Sukumar, Shivashankar & Mokhlis, Hazlie & Mekhilef, Saad & Naidu, Kanendra & Karimi, Mazaher, 2017. "Mix-mode energy management strategy and battery sizing for economic operation of grid-tied microgrid," Energy, Elsevier, vol. 118(C), pages 1322-1333.
    3. Yin, Cong & Gao, Yan & Guo, Shaoyun & Tang, Hao, 2014. "A coupled three dimensional model of vanadium redox flow battery for flow field designs," Energy, Elsevier, vol. 74(C), pages 886-895.
    4. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
    5. Al-Yasiri, Mohammed & Park, Jonghyun, 2018. "A novel cell design of vanadium redox flow batteries for enhancing energy and power performance," Applied Energy, Elsevier, vol. 222(C), pages 530-539.
    6. Tsianikas, Stamatis & Zhou, Jian & Birnie, Dunbar P. & Coit, David W., 2019. "Economic trends and comparisons for optimizing grid-outage resilient photovoltaic and battery systems," Applied Energy, Elsevier, vol. 256(C).
    7. Mehrabankhomartash, Mahmoud & Rayati, Mohammad & Sheikhi, Aras & Ranjbar, Ali Mohammad, 2017. "Practical battery size optimization of a PV system by considering individual customer damage function," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 36-50.
    8. Khezri, Rahmat & Mahmoudi, Amin & Aki, Hirohisa, 2022. "Optimal planning of solar photovoltaic and battery storage systems for grid-connected residential sector: Review, challenges and new perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    9. Zhou, Yuekuan, 2023. "Sustainable energy sharing districts with electrochemical battery degradation in design, planning, operation and multi-objective optimisation," Renewable Energy, Elsevier, vol. 202(C), pages 1324-1341.
    10. Mohammad Rozali, Nor Erniza & Wan Alwi, Sharifah Rafidah & Manan, Zainuddin Abdul & Klemeš, Jiří Jaromír, 2015. "Peak-off-peak load shifting for hybrid power systems based on Power Pinch Analysis," Energy, Elsevier, vol. 90(P1), pages 128-136.
    11. Virulkar, Vasudeo & Aware, Mohan & Kolhe, Mohan, 2011. "Integrated battery controller for distributed energy system," Energy, Elsevier, vol. 36(5), pages 2392-2398.
    12. Tareen, Wajahat Ullah & Mekhilef, Saad, 2016. "Transformer-less 3P3W SAPF (three-phase three-wire shunt active power filter) with line-interactive UPS (uninterruptible power supply) and battery energy storage stage," Energy, Elsevier, vol. 109(C), pages 525-536.
    13. Zahraee, S.M. & Khalaji Assadi, M. & Saidur, R., 2016. "Application of Artificial Intelligence Methods for Hybrid Energy System Optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 617-630.
    14. Musolino, V. & Pievatolo, A. & Tironi, E., 2011. "A statistical approach to electrical storage sizing with application to the recovery of braking energy," Energy, Elsevier, vol. 36(11), pages 6697-6704.
    15. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
    16. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2018. "Battery energy storage system size determination in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 109-125.
    17. Carlo Bianca, 2022. "On the Modeling of Energy-Multisource Networks by the Thermostatted Kinetic Theory Approach: A Review with Research Perspectives," Energies, MDPI, vol. 15(21), pages 1-22, October.
    18. Kaplani, E. & Kaplanis, S., 2012. "A stochastic simulation model for reliable PV system sizing providing for solar radiation fluctuations," Applied Energy, Elsevier, vol. 97(C), pages 970-981.
    19. Erdinc, O. & Uzunoglu, M., 2012. "Optimum design of hybrid renewable energy systems: Overview of different approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(3), pages 1412-1425.
    20. Sultan, Ali J. & Ingham, Derek B. & Hughes, Kevin J. & Ma, Lin & Pourkashanian, Mohamed, 2021. "Optimization and performance enhancement of concentrating solar power in a hot and arid desert environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    21. Li, Jianwei & Yang, Qingqing & Robinson, Francis. & Liang, Fei & Zhang, Min & Yuan, Weijia, 2017. "Design and test of a new droop control algorithm for a SMES/battery hybrid energy storage system," Energy, Elsevier, vol. 118(C), pages 1110-1122.
    22. Spiliotis, Konstantinos & Gonçalves, Juliana E. & Saelens, Dirk & Baert, Kris & Driesen, Johan, 2020. "Electrical system architectures for building-integrated photovoltaics: A comparative analysis using a modelling framework in Modelica," Applied Energy, Elsevier, vol. 261(C).

    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. Rajanna, S. & Saini, R.P., 2016. "Modeling of integrated renewable energy system for electrification of a remote area in India," Renewable Energy, Elsevier, vol. 90(C), pages 175-187.
    2. Kamjoo, Azadeh & Maheri, Alireza & Putrus, Ghanim A., 2014. "Chance constrained programming using non-Gaussian joint distribution function in design of standalone hybrid renewable energy systems," Energy, Elsevier, vol. 66(C), pages 677-688.
    3. Wissem, Zghal & Gueorgui, Kantchev & Hédi, Kchaou, 2012. "Modeling and technical–economic optimization of an autonomous photovoltaic system," Energy, Elsevier, vol. 37(1), pages 263-272.
    4. Jiang, X.S. & Jing, Z.X. & Li, Y.Z. & Wu, Q.H. & Tang, W.H., 2014. "Modelling and operation optimization of an integrated energy based direct district water-heating system," Energy, Elsevier, vol. 64(C), pages 375-388.
    5. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2014. "Technical feasibility study on a standalone hybrid solar-wind system with pumped hydro storage for a remote island in Hong Kong," Renewable Energy, Elsevier, vol. 69(C), pages 7-15.
    6. Paliwal, Priyanka & Patidar, N.P. & Nema, R.K., 2014. "Determination of reliability constrained optimal resource mix for an autonomous hybrid power system using Particle Swarm Optimization," Renewable Energy, Elsevier, vol. 63(C), pages 194-204.
    7. Rezzouk, H. & Mellit, A., 2015. "Feasibility study and sensitivity analysis of a stand-alone photovoltaic–diesel–battery hybrid energy system in the north of Algeria," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1134-1150.
    8. Sawle, Yashwant & Gupta, S.C. & Bohre, Aashish Kumar, 2018. "Socio-techno-economic design of hybrid renewable energy system using optimization techniques," Renewable Energy, Elsevier, vol. 119(C), pages 459-472.
    9. Liu, F. & Tait, S. & Schellart, A. & Mayfield, M. & Boxall, J., 2020. "Reducing carbon emissions by integrating urban water systems and renewable energy sources at a community scale," Renewable and Sustainable Energy Reviews, Elsevier, vol. 123(C).
    10. Ma, Tao & Yang, Hongxing & Lu, Lin & Peng, Jinqing, 2015. "Optimal design of an autonomous solar–wind-pumped storage power supply system," Applied Energy, Elsevier, vol. 160(C), pages 728-736.
    11. Perera, A.T.D. & Wickramasinghe, P.U. & Nik, Vahid M. & Scartezzini, Jean-Louis, 2019. "Machine learning methods to assist energy system optimization," Applied Energy, Elsevier, vol. 243(C), pages 191-205.
    12. Tezer, Tuba & Yaman, Ramazan & Yaman, Gülşen, 2017. "Evaluation of approaches used for optimization of stand-alone hybrid renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 840-853.
    13. Prodan, Ionela & Zio, Enrico & Stoican, Florin, 2015. "Fault tolerant predictive control design for reliable microgrid energy management under uncertainties," Energy, Elsevier, vol. 91(C), pages 20-34.
    14. Jebaselvi, G.D. Anbarasi & Paramasivam, S., 2013. "Analysis on renewable energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 625-634.
    15. Diaf, S. & Notton, G. & Belhamel, M. & Haddadi, M. & Louche, A., 2008. "Design and techno-economical optimization for hybrid PV/wind system under various meteorological conditions," Applied Energy, Elsevier, vol. 85(10), pages 968-987, October.
    16. Belouda, M. & Jaafar, A. & Sareni, B. & Roboam, X. & Belhadj, J., 2013. "Integrated optimal design and sensitivity analysis of a stand alone wind turbine system with storage for rural electrification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 616-624.
    17. Andreas Schröder & Jan Siegmeier & Murk Creusen, 2011. "Modeling Storage and Demand Management in Electricity Distribution Grids," Discussion Papers of DIW Berlin 1110, DIW Berlin, German Institute for Economic Research.
    18. Adoum Abdoulaye, Mahamat & Waita, Sebastian & Wabuge Wekesa, Cyrus & Mwabora, Julius Mwakondo, 2024. "Optimal sizing of an off-grid and grid-connected hybrid photovoltaic-wind system with battery and fuel cell storage system: A techno-economic, environmental, and social assessment," Applied Energy, Elsevier, vol. 365(C).
    19. Matteo Moncecchi & Claudio Brivio & Stefano Mandelli & Marco Merlo, 2020. "Battery Energy Storage Systems in Microgrids: Modeling and Design Criteria," Energies, MDPI, vol. 13(8), pages 1-18, April.
    20. Kalantari, Hosein & Sasmito, Agus P. & Ghoreishi-Madiseh, Seyed Ali, 2021. "An overview of directions for decarbonization of energy systems in cold climate remote mines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).

    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:eee:energy:v:35:y:2010:i:12:p:5082-5092. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.