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Battery sizing for PV power plants under regulations using randomized algorithms

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  • Pflaum, Peter
  • Alamir, M.
  • Lamoudi, M.Y.

Abstract

The increasing amount of PV (photo-voltaic) power plants comes along with an increased instability in the power grid due to the high uncertainty of the PV power production. As a stabilizing measure, grid operators introduce regulations on the injected power profiles comprising the obligation to declare in advance the predicted power production as well as penalties which apply in case these previously declared production profiles were not respected. In order to meet these regulations power plant owners are forced to invest into expensive storage capacities. In this work an algorithm is proposed which allows to determine the optimal battery size that maximizes the to-be-expected revenue of such an installation for a given regulative framework. Moreover the scheme explicitly takes into account the uncertainty in the PV power production and it provides guaranteed lower bounds on the to-be-expected revenue at a configurable probability. The underlying method allowing to achieve these objectives is a randomized algorithm. The principle of this method is to compute probabilistic guarantees for respecting a binary constraint, considering only a limited number of uncertainty scenarios.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:113:y:2017:i:c:p:596-607
    DOI: 10.1016/j.renene.2017.05.091
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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. Hoppmann, Joern & Volland, Jonas & Schmidt, Tobias S. & Hoffmann, Volker H., 2014. "The economic viability of battery storage for residential solar photovoltaic systems – A review and a simulation model," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 1101-1118.
    3. 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.
    4. 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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    Cited by:

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    3. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Zhang, Jian & Cho, Heejin & Luck, Rogelio & Mago, Pedro J., 2018. "Integrated photovoltaic and battery energy storage (PV-BES) systems: An analysis of existing financial incentive policies in the US," Applied Energy, Elsevier, vol. 212(C), pages 895-908.
    5. Liu, Jiangyang & Liu, Zhongbing & Wu, Yaling & Chen, Xi & Xiao, Hui & Zhang, Ling, 2022. "Impact of climate on photovoltaic battery energy storage system optimization," Renewable Energy, Elsevier, vol. 191(C), pages 625-638.

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