This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the 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 Liège University (ULiège), Belgium.

Dumas, J., Cornelusse, B., Fettweis, X., Giannitrapani, A., Paoletti, S., & Vicino, A. (2021). Probabilistic forecasting for sizing in the capacity firming framework. In 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings (pp.1-6). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/PowerTech46648.2021.9494947].

Probabilistic forecasting for sizing in the capacity firming framework

Giannitrapani A.;Paoletti S.;Vicino A.
2021

Abstract

This paper proposes a strategy to size a grid-connected photovoltaic plant coupled with a battery energy storage device within the 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 Liège University (ULiège), Belgium.
978-1-6654-3597-0
Dumas, J., Cornelusse, B., Fettweis, X., Giannitrapani, A., Paoletti, S., & Vicino, A. (2021). Probabilistic forecasting for sizing in the capacity firming framework. In 2021 IEEE Madrid PowerTech, PowerTech 2021 - Conference Proceedings (pp.1-6). New York : Institute of Electrical and Electronics Engineers Inc. [10.1109/PowerTech46648.2021.9494947].
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11365/1195995