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Impact of regulatory rules on economic performance of PV power plants

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  • 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
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    References listed on IDEAS

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    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.
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    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.

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