Battery sizing for PV power plants under regulations using randomized algorithms

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

Renewable Energy, 2017, vol. 113, issue C, 596-607

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.

Keywords: PV plant; Battery sizing; Regulations; Randomized algorithm; Optimization; PV forecast uncertainty (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:113:y:2017:i:c:p:596-607

DOI: 10.1016/j.renene.2017.05.091

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