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Optimization of the battery size for PV systems under regulatory rules using a Markov-Chains approach

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  • Cervone, A.
  • Carbone, G.
  • Santini, E.
  • Teodori, S.

Abstract

In the last decade a high amount of photovoltaic and wind power generators have been connected to the electric grid, introducing operational problems for transmission and distribution system operators due to the variability and the non-programmability of solar radiation and wind. The paper concerns an analysis on the benefits in adopting storage systems to reduce the imbalance costs associated to renewable energy sources. An analysis on a photovoltaic system has been performed considering different battery technologies. Discrete-time Markov chains have been used to generate a 20 years' time series of irradiance, that has been used to calculate the PV power production. Markov simulation parameters have been deeply studied in order to optimize them and obtain reliable synthetic data of ground irradiance. This data was then used as input of a hybrid PV and storage model allowing to obtain realistic economic and technical results, improving thus the results respect to the methods based on probabilistic weather simulations. An imbalance tariff has been assumed and its cost has been analysed in relation to the storage system costs. An optimal size for the different battery technology has been investigated considering the reduction of variability of the photovoltaic production and the economic convenience of the hybrid system. The use of Markov chains for the optimization of the battery size can be considered as the major novelty of the proposed approach.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:657-665
    DOI: 10.1016/j.renene.2015.07.007
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    References listed on IDEAS

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    1. 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.
    2. Vernay, Christophe & Blanc, Philippe & Pitaval, Sébastien, 2013. "Characterizing measurements campaigns for an innovative calibration approach of the global horizontal irradiation estimated by HelioClim-3," Renewable Energy, Elsevier, vol. 57(C), pages 339-347.
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    Citations

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    Cited by:

    1. Parrado, C. & Girard, A. & Simon, F. & Fuentealba, E., 2016. "2050 LCOE (Levelized Cost of Energy) projection for a hybrid PV (photovoltaic)-CSP (concentrated solar power) plant in the Atacama Desert, Chile," Energy, Elsevier, vol. 94(C), pages 422-430.
    2. Yuqing Yang & Stephen Bremner & Chris Menictas & Merlinde Kay, 2019. "A Mixed Receding Horizon Control Strategy for Battery Energy Storage System Scheduling in a Hybrid PV and Wind Power Plant with Different Forecast Techniques," Energies, MDPI, vol. 12(12), pages 1-25, June.
    3. Wang, Zhongliang & Zhu, Hongyu & Zhang, Dongdong & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation," Applied Energy, Elsevier, vol. 352(C).
    4. Pavani Ponnaganti & Birgitte Bak-Jensen & Brian Vejrum Wæhrens & Jesper Asmussen, 2021. "Assessment of Energy Arbitrage Using Energy Storage Systems: A Wind Park’s Perspective," Energies, MDPI, vol. 14(16), pages 1-20, August.
    5. Feng, Ran & Wang, Kai & Xu, Xu & Yu, Zi-Tao & Lin, Qingyang, 2024. "Triple-layer optimization of distributed photovoltaic energy storage capacity for manufacturing enterprises considering carbon emissions and load management," Applied Energy, Elsevier, vol. 364(C).
    6. 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.
    7. Toopshekan, Ashkan & Abedian, Ali & Azizi, Arian & Ahmadi, Esmaeil & Vaziri Rad, Mohammad Amin, 2023. "Optimization of a CHP system using a forecasting dispatch and teaching-learning-based optimization algorithm," Energy, Elsevier, vol. 285(C).
    8. 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.
    9. Hina Fathima A & Kaliannan Palanisamy & Sanjeevikumar Padmanaban & Umashankar Subramaniam, 2018. "Intelligence-Based Battery Management and Economic Analysis of an Optimized Dual-Vanadium Redox Battery (VRB) for a Wind-PV Hybrid System," Energies, MDPI, vol. 11(10), pages 1-18, October.
    10. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Modelling and optimal energy management for battery energy storage systems in renewable energy systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    11. 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.
    12. Prasad, Abhnil Amtesh & Yang, Yuqing & Kay, Merlinde & Menictas, Chris & Bremner, Stephen, 2021. "Synergy of solar photovoltaics-wind-battery systems in Australia," Renewable and Sustainable Energy Reviews, Elsevier, vol. 152(C).
    13. Zamani, Ali Ghahgharaee & Zakariazadeh, Alireza & Jadid, Shahram, 2016. "Day-ahead resource scheduling of a renewable energy based virtual power plant," Applied Energy, Elsevier, vol. 169(C), pages 324-340.
    14. Mohammad Rayati & Pasquale De Falco & Daniela Proto & Mokhtar Bozorg & Mauro Carpita, 2021. "Generation Data of Synthetic High Frequency Solar Irradiance for Data-Driven Decision-Making in Electrical Distribution Grids," Energies, MDPI, vol. 14(16), pages 1-21, August.

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