IDEAS home Printed from https://ideas.repec.org/a/eee/renene/v74y2015icp78-86.html
   My bibliography  Save this article

Impact of regulatory rules on economic performance of PV power plants

Author

Listed:
  • Cervone, A.
  • Santini, E.
  • Teodori, S.
  • Romito, Donatella Zaccagnini

Abstract

The rapid growth of Renewable Energy Sources (RES) power plants connected to the grid has introduced new problems related to the safe and reliable operation of the electricity network from transmission to distribution sectors. New regulatory rules can promote RES producers to make a commitment on the energy amount that is likely to be supplied to the network. The present paper concerns the analysis of the energy production of a PV power plant from the economic point of view, with reference to the presence of regulatory rules. Costs of penalty and value of energy are compared, in order to evaluate the economic efficiency of the plant. Use of auxiliary energy storage devices is investigated, with the aim to determine the relevant dimensions that increase the economic efficiency of the PV plant. A software instrument that implements these algorithms is described and applied to a case study.

Suggested Citation

  • Cervone, A. & Santini, E. & Teodori, S. & Romito, Donatella Zaccagnini, 2015. "Impact of regulatory rules on economic performance of PV power plants," Renewable Energy, Elsevier, vol. 74(C), pages 78-86.
    • Handle: RePEc:eee:renene:v:74:y:2015:i:c:p:78-86
    DOI: 10.1016/j.renene.2014.06.037
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960148114003772
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.renene.2014.06.037?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. Celik, Ali Naci & Acikgoz, NasIr, 2007. "Modelling and experimental verification of the operating current of mono-crystalline photovoltaic modules using four- and five-parameter models," Applied Energy, Elsevier, vol. 84(1), pages 1-15, January.
    2. Foley, Aoife M. & Leahy, Paul G. & Marvuglia, Antonino & McKeogh, Eamon J., 2012. "Current methods and advances in forecasting of wind power generation," Renewable Energy, Elsevier, vol. 37(1), pages 1-8.
    3. Mattei, M. & Notton, G. & Cristofari, C. & Muselli, M. & Poggi, P., 2006. "Calculation of the polycrystalline PV module temperature using a simple method of energy balance," Renewable Energy, Elsevier, vol. 31(4), pages 553-567.
    4. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
    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. Pflaum, Peter & Alamir, M. & Lamoudi, M.Y., 2017. "Battery sizing for PV power plants under regulations using randomized algorithms," Renewable Energy, Elsevier, vol. 113(C), pages 596-607.
    2. Lamedica, Regina & Santini, Ezio & Ruvio, Alessandro & Palagi, Laura & Rossetta, Irene, 2018. "A MILP methodology to optimize sizing of PV - Wind renewable energy systems," Energy, Elsevier, vol. 165(PB), pages 385-398.
    3. Cervone, A. & Carbone, G. & Santini, E. & Teodori, S., 2016. "Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach," Renewable Energy, Elsevier, vol. 85(C), pages 657-665.

    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. Àlex Alonso & Jordi de la Hoz & Helena Martín & Sergio Coronas & Pep Salas & José Matas, 2020. "A Comprehensive Model for the Design of a Microgrid under Regulatory Constraints Using Synthetical Data Generation and Stochastic Optimization," Energies, MDPI, vol. 13(21), pages 1-26, October.
    2. Da Liu & Kun Sun & Han Huang & Pingzhou Tang, 2018. "Monthly Load Forecasting Based on Economic Data by Decomposition Integration Theory," Sustainability, MDPI, vol. 10(9), pages 1-22, September.
    3. Reikard, Gordon & Hansen, Clifford, 2019. "Forecasting solar irradiance at short horizons: Frequency and time domain models," Renewable Energy, Elsevier, vol. 135(C), pages 1270-1290.
    4. Lan, Hai & Yin, He & Hong, Ying-Yi & Wen, Shuli & Yu, David C. & Cheng, Peng, 2018. "Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route," Applied Energy, Elsevier, vol. 211(C), pages 15-27.
    5. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
    6. Reikard, Gordon & Haupt, Sue Ellen & Jensen, Tara, 2017. "Forecasting ground-level irradiance over short horizons: Time series, meteorological, and time-varying parameter models," Renewable Energy, Elsevier, vol. 112(C), pages 474-485.
    7. Roberts, Justo José & Mendiburu Zevallos, Andrés A. & Cassula, Agnelo Marotta, 2017. "Assessment of photovoltaic performance models for system simulation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 1104-1123.
    8. Gu, Wenbo & Ma, Tao & Shen, Lu & Li, Meng & Zhang, Yijie & Zhang, Wenjie, 2019. "Coupled electrical-thermal modelling of photovoltaic modules under dynamic conditions," Energy, Elsevier, vol. 188(C).
    9. Kubik, M.L. & Coker, P.J. & Hunt, C., 2012. "The role of conventional generation in managing variability," Energy Policy, Elsevier, vol. 50(C), pages 253-261.
    10. Rehman, Shafiqur & El-Amin, Ibrahim, 2012. "Performance evaluation of an off-grid photovoltaic system in Saudi Arabia," Energy, Elsevier, vol. 46(1), pages 451-458.
    11. Javier Borquez & Hector Chavez & Karina A. Barbosa & Marcela Jamett & Rodrigo Acuna, 2020. "A Simple Distribution Energy Tariff under the Penetration of DG," Energies, MDPI, vol. 13(8), pages 1-17, April.
    12. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    13. John Boland & Sleiman Farah & Lei Bai, 2022. "Forecasting of Wind and Solar Farm Output in the Australian National Electricity Market: A Review," Energies, MDPI, vol. 15(1), pages 1-18, January.
    14. Yang, Mao & Wang, Da & Xu, Chuanyu & Dai, Bozhi & Ma, Miaomiao & Su, Xin, 2023. "Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting," Renewable Energy, Elsevier, vol. 211(C), pages 582-594.
    15. Mayer, Martin János & Yang, Dazhi & Szintai, Balázs, 2023. "Comparing global and regional downscaled NWP models for irradiance and photovoltaic power forecasting: ECMWF versus AROME," Applied Energy, Elsevier, vol. 352(C).
    16. Liu, Wen & Hu, Weihao & Lund, Henrik & Chen, Zhe, 2013. "Electric vehicles and large-scale integration of wind power – The case of Inner Mongolia in China," Applied Energy, Elsevier, vol. 104(C), pages 445-456.
    17. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    18. Pasta, Edoardo & Faedo, Nicolás & Mattiazzo, Giuliana & Ringwood, John V., 2023. "Towards data-driven and data-based control of wave energy systems: Classification, overview, and critical assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 188(C).
    19. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    20. Santamaría-Bonfil, G. & Reyes-Ballesteros, A. & Gershenson, C., 2016. "Wind speed forecasting for wind farms: A method based on support vector regression," Renewable Energy, Elsevier, vol. 85(C), pages 790-809.

    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:renene:v:74:y:2015:i:c:p:78-86. 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/renewable-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.