Forecasting Extremes of Aggregated Production from a RES Virtual Power Plant

Simon Camal (1) , Andrea Michiorri (1) , Georges Kariniotakis (1)

1 PERSEE - Centre Procédés, Énergies Renouvelables, Systèmes Énergétiques

Transmission System Operators (TSOs) expect Renewable Energy Sources (RES) to participate to the provision of Ancillary Services (AS), in order to substitute conventional dispatchable power plants. A promising solution to ensure a sufficiently reliable provision of AS by weather-dependent RES is to aggregate dispersed plants into a Virtual Power Plant (VPP), so that the total production shows reduced uncertainty. Most AS markets operate at short-term horizon, typically for the day-ahead, and require that the service shall be provided with a minimal frequency of underfulfilment (i.e. reliability close to 100%). Therefore a Wind-PV-based AS offer must be based on an accurate forecast of the production uncertainty. A probabilistic forecasting model of the aggregated Wind-PV production based on machine learning has been developed in [1] and proved reliable down to the 1%-quantile of the production distribution. However for rare events (quantiles below 1%), the reliability of state-of-the-art machine learning models is known to deteriorate, mostly because of their lack of generalization on unobserved data [2]. We propose here two models specifically designed for a better forecast of the extremes of the distribution: the first model is based on the Extreme Value Theory (EVT) [3], and the second model is based on a quantile regression by a Deep Neural Network (DNN).

Mots clés: Virtual power plant; electricity markets; ancillary services; renewable energy; wind energy; photovoltaics; hydro power; smart grid; probabilistic forecasting; extreme value theory

Domaines: Energie électrique; Machine Learning [stat.ML]; Applications [stat.AP]; Optimisation et contrôle [math.OC]; Probabilités [math.PR]; Statistiques [math.ST]

Simon Camal, Andrea Michiorri, Georges Kariniotakis. Forecasting Extremes of Aggregated Production from a RES Virtual Power Plant. Proceedings of the Wind Energy Science Conference 2019, EAWE - European Academy of Wind Energy, Jun 2019, Cork, Ireland. ⟨hal-02158589⟩