IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i4p453-d94713.html
   My bibliography  Save this article

Energy Management with Support of PV Partial Shading Modelling in Micro Grid Environments

Author

Listed:
  • Marco Severini

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Emanuele Principi

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Marco Fagiani

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Stefano Squartini

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

  • Francesco Piazza

    (Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche 1, 60131 Ancona, Italy)

Abstract

Although photovoltaic power plants are suitable local energy sources in Micro Grid environments, when large plants are involved, partial shading and inaccurate modelling of the plant can affect both the design of the Micro Grid as well as the energy management process that allows for lowering the overall Micro Grid demand towards the main grid. To investigate the issue, a Photovoltaic Plant simulation model, based on a real life power plant, and an energy management system, based on a real life Micro Grid environment, have been integrated to evaluate the performance of a Micro Grid under partial shading conditions. Using a baseline energy production model as a reference, the energy demand of the Micro Grid has been computed in sunny and partial shading conditions. The experiments reveal that an estimation based on a simplified PV model can exceed by 65% the actual production. With regards to Micro Grid design, on sunny days, the expected costs, based on a simplified PV model, can be 5.5% lower than the cost based on the double inverter model. In single cloud scenarios, the underrating can reach 28.3%. With regard to the management process, if the energy yield is estimated by means of a simplified PV model, the actual cost can be from 17.1% to 21.5% higher than the theoretical cost expected at design time.

Suggested Citation

  • Marco Severini & Emanuele Principi & Marco Fagiani & Stefano Squartini & Francesco Piazza, 2017. "Energy Management with Support of PV Partial Shading Modelling in Micro Grid Environments," Energies, MDPI, vol. 10(4), pages 1-18, April.
  • Handle: RePEc:gam:jeners:v:10:y:2017:i:4:p:453-:d:94713
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/4/453/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/4/453/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Dolara, Alberto & Lazaroiu, George Cristian & Leva, Sonia & Manzolini, Giampaolo, 2013. "Experimental investigation of partial shading scenarios on PV (photovoltaic) modules," Energy, Elsevier, vol. 55(C), pages 466-475.
    2. Comodi, Gabriele & Giantomassi, Andrea & Severini, Marco & Squartini, Stefano & Ferracuti, Francesco & Fonti, Alessandro & Nardi Cesarini, Davide & Morodo, Matteo & Polonara, Fabio, 2015. "Multi-apartment residential microgrid with electrical and thermal storage devices: Experimental analysis and simulation of energy management strategies," Applied Energy, Elsevier, vol. 137(C), pages 854-866.
    3. Leva, S. & Dolara, A. & Grimaccia, F. & Mussetta, M. & Ogliari, E., 2017. "Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 131(C), pages 88-100.
    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. Su Su & Yong Hu & Tiantian Yang & Shidan Wang & Ziqi Liu & Xiangxiang Wei & Mingchao Xia & Yutaka Ota & Koji Yamashita, 2018. "Research on an Electric Vehicle Owner-Friendly Charging Strategy Using Photovoltaic Generation at Office Sites in Major Chinese Cities," Energies, MDPI, vol. 11(2), pages 1-19, February.

    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. Iolanda Saviuc & Herbert Peremans & Steven Van Passel & Kevin Milis, 2019. "Economic Performance of Using Batteries in European Residential Microgrids under the Net-Metering Scheme," Energies, MDPI, vol. 12(1), pages 1-28, January.
    2. Qiu, Rui & Zhang, Haoran & Wang, Guotao & Liang, Yongtu & Yan, Jinyue, 2023. "Green hydrogen-based energy storage service via power-to-gas technologies integrated with multi-energy microgrid," Applied Energy, Elsevier, vol. 350(C).
    3. Osorio, J.D. & Rivera-Alvarez, A. & Swain, M. & Ordonez, J.C., 2015. "Exergy analysis of discharging multi-tank thermal energy storage systems with constant heat extraction," Applied Energy, Elsevier, vol. 154(C), pages 333-343.
    4. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    5. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    6. Rodrigo, P. & Gutiérrez, S. & Velázquez, Ramiro & Fernández, Eduardo F. & Almonacid, F. & Pérez-Higueras, P.J., 2015. "A methodology for the electrical characterization of shaded high concentrator photovoltaic modules," Energy, Elsevier, vol. 89(C), pages 768-777.
    7. Abada, I. & Ehrenmann, A. & Lambin, X., 2017. "On the viability of energy communities," Cambridge Working Papers in Economics 1740, Faculty of Economics, University of Cambridge.
    8. Miguel Carpintero-Rentería & David Santos-Martín & Josep M. Guerrero, 2019. "Microgrids Literature Review through a Layers Structure," Energies, MDPI, vol. 12(22), pages 1-22, November.
    9. Omaji Samuel & Nadeem Javaid & Mahmood Ashraf & Farruh Ishmanov & Muhammad Khalil Afzal & Zahoor Ali Khan, 2018. "Jaya based Optimization Method with High Dispatchable Distributed Generation for Residential Microgrid," Energies, MDPI, vol. 11(6), pages 1-29, June.
    10. Hai Lan & Jinfeng Dai & Shuli Wen & Ying-Yi Hong & David C. Yu & Yifei Bai, 2015. "Optimal Tilt Angle of Photovoltaic Arrays and Economic Allocation of Energy Storage System on Large Oil Tanker Ship," Energies, MDPI, vol. 8(10), pages 1-16, October.
    11. Coelho, Vitor N. & Coelho, Igor M. & Coelho, Bruno N. & Cohen, Miri Weiss & Reis, Agnaldo J.R. & Silva, Sidelmo M. & Souza, Marcone J.F. & Fleming, Peter J. & Guimarães, Frederico G., 2016. "Multi-objective energy storage power dispatching using plug-in vehicles in a smart-microgrid," Renewable Energy, Elsevier, vol. 89(C), pages 730-742.
    12. Zhu, Kai & Li, Xueqiang & Campana, Pietro Elia & Li, Hailong & Yan, Jinyue, 2018. "Techno-economic feasibility of integrating energy storage systems in refrigerated warehouses," Applied Energy, Elsevier, vol. 216(C), pages 348-357.
    13. Dario Garozzo & Giuseppe Marco Tina, 2020. "Evaluation of the Effective Active Power Reserve for Fast Frequency Response of PV with BESS Inverters Considering Reactive Power Control," Energies, MDPI, vol. 13(13), pages 1-16, July.
    14. Koo, Choongwan & Hong, Taehoon & Jeong, Kwangbok & Ban, Cheolwoo & Oh, Jeongyoon, 2017. "Development of the smart photovoltaic system blind and its impact on net-zero energy solar buildings using technical-economic-political analyses," Energy, Elsevier, vol. 124(C), pages 382-396.
    15. Ramli, Makbul A.M. & Twaha, Ssennoga & Ishaque, Kashif & Al-Turki, Yusuf A., 2017. "A review on maximum power point tracking for photovoltaic systems with and without shading conditions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 67(C), pages 144-159.
    16. Dongkyu Lee & Jae-Weon Jeong & Guebin Choi, 2021. "Short Term Prediction of PV Power Output Generation Using Hierarchical Probabilistic Model," Energies, MDPI, vol. 14(10), pages 1-15, May.
    17. 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).
    18. Ahmad Khan, Aftab & Naeem, Muhammad & Iqbal, Muhammad & Qaisar, Saad & Anpalagan, Alagan, 2016. "A compendium of optimization objectives, constraints, tools and algorithms for energy management in microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1664-1683.
    19. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    20. McKenna, Russell & Merkel, Erik & Fichtner, Wolf, 2017. "Energy autonomy in residential buildings: A techno-economic model-based analysis of the scale effects," Applied Energy, Elsevier, vol. 189(C), pages 800-815.

    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:4:p:453-:d:94713. 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.