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arXiv · 2106.02323

Probabilistic forecasting for sizing in the capacity firming framework

Jonathan Dumas·Bertrand Cornélusse·Xavier Fettweis·Antonello Giannitrapani·Simone Paoletti·Antonio Vicino
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Abstract

This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the \textit{capacity firming} specifications of the French Energy Regulatory Commission. In this context, the sizing problem is challenging due to the two-phase engagement control with a day-ahead nomination and an intraday control to minimize deviations from the planning. The two-phase engagement control is modeled with deterministic and stochastic approaches. The optimization problems are formulated as mixed-integer quadratic problems, using a Gaussian copula methodology to generate PV scenarios, to approximate the mixed-integer non-linear problem of the capacity firming. Then, a grid search is conducted to approximate the optimal sizing for a given selling price using both the deterministic and stochastic approaches. The case study is composed of PV production monitored on-site at the Li\`ege University (ULi\`ege), Belgium.

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Jonathan Dumas, Bertrand Cornélusse, Xavier Fettweis, Antonello Giannitrapani, Simone Paoletti, Antonio Vicino. 2021-06-04. Probabilistic forecasting for sizing in the capacity firming framework. https://doi.org/10.1109/powertech46648.2021.9494947

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