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Energy storage for PV power plant dispatching

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  • Delfanti, Maurizio
  • Falabretti, Davide
  • Merlo, Marco

Abstract

Energy from the sun is weather-dependent. In modern electric grids that is a shortcoming; generation (and load) has to be regulated accordingly. This issue is a cornerstone for an effective transition to a renewable-based energy system. Weather forecast algorithms can predict photovoltaic production but, in real life conditions, their reliability is only partially effective with respect to the actual grid operation requirements. In the paper, Energy Storage Systems are adopted to compensate the mismatch between the injections of a photovoltaic power plant and the day-ahead market power schedule: the final goal is to achieve the full programmability of the photovoltaic resource by minimizing energy imbalances, as defined in the Italian regulatory framework, on an hourly basis. In particular, the optimal design of the storage apparatus (nominal power and capacity) is defined according to the regulating performances required. Moreover, three forecast models are tested to evaluate the impact of weather prediction accuracy on the ESS design. Finally, the benefit/cost ratio of the ESS application is assessed according to the main economic and technical parameters (ESS cost, round trip efficiency, lifespan). The analyses are performed on data measured on a real power plant, with hypotheses consistent with the actual Italian scenario.

Suggested Citation

  • Delfanti, Maurizio & Falabretti, Davide & Merlo, Marco, 2015. "Energy storage for PV power plant dispatching," Renewable Energy, Elsevier, vol. 80(C), pages 61-72.
  • Handle: RePEc:eee:renene:v:80:y:2015:i:c:p:61-72
    DOI: 10.1016/j.renene.2015.01.047
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    8. Bullich-Massagué, Eduard & Cifuentes-García, Francisco-Javier & Glenny-Crende, Ignacio & Cheah-Mañé, Marc & Aragüés-Peñalba, Mònica & Díaz-González, Francisco & Gomis-Bellmunt, Oriol, 2020. "A review of energy storage technologies for large scale photovoltaic power plants," Applied Energy, Elsevier, vol. 274(C).
    9. Wang, Guang Chao & Ratnam, Elizabeth & Haghi, Hamed Valizadeh & Kleissl, Jan, 2019. "Corrective receding horizon EV charge scheduling using short-term solar forecasting," Renewable Energy, Elsevier, vol. 130(C), pages 1146-1158.
    10. Pierro, Marco & Perez, Richard & Perez, Marc & Moser, David & Cornaro, Cristina, 2020. "Italian protocol for massive solar integration: Imbalance mitigation strategies," Renewable Energy, Elsevier, vol. 153(C), pages 725-739.
    11. Bracco, Stefano & Delfino, Federico & Pampararo, Fabio & Robba, Michela & Rossi, Mansueto, 2016. "A pilot facility for analysis and simulation of smart microgrids feeding smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 58(C), pages 1247-1255.
    12. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
    13. Parra, David & Swierczynski, Maciej & Stroe, Daniel I. & Norman, Stuart.A. & Abdon, Andreas & Worlitschek, Jörg & O’Doherty, Travis & Rodrigues, Lucelia & Gillott, Mark & Zhang, Xiaojin & Bauer, Chris, 2017. "An interdisciplinary review of energy storage for communities: Challenges and perspectives," Renewable and Sustainable Energy Reviews, Elsevier, vol. 79(C), pages 730-749.
    14. Polasek, Tomas & Čadík, Martin, 2023. "Predicting photovoltaic power production using high-uncertainty weather forecasts," Applied Energy, Elsevier, vol. 339(C).

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