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.
2021-01-01

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.
2021
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: https://hdl.handle.net/11365/1195995