reserve capacity level with a clear cost impact, or by adopting a real-time approach with a huge amount of data to cope with.
The underlying algorithm is able to identify periods of low or high risks based on systems conditions (e.g. wind, PV, load forecasts, time), generation plant schedules, and inter-connector forecasts.
This solution recommends FRR volumes adapted to the real underlying imbalance risk, with less balancing reserves for safe periods (no over procurement) and more reserves for risky periods (no under procurement), just to reach the desired reliability target and to secure the grid. The solution increases the efficiency of the TSO, in line with the Clean Energy Package, and relies on an approach approved by the CREG, the Belgian NRA.
- Forecast Errors Imbalance Risk Module estimates the imbalance risk linked to errors on forecasts (e.g. wind, PV, load,…) based on Machine Learning and more specifically on clustering methods. The algorithm combines efficiency and interpretability of results
- Rare Events Imbalance Risk Module estimates the imbalance risk linked to rare but impactful events such as forced outages of generation plants or inter-connectors, based on Monte Carlo simulations.
- N-1 Incident Management Module applies post-processing rules to cover additional requirements such as covering the largest possible incident (N-1)
M € of yearly reservation cost at Elia
MW of average FRR needs reduction at Elia
% reliability target reached at all times, not only on average
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