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N-SIDE uses machine learning to produce a variety of forecasts for system operators, including system imbalance forecast, reserve needs forecast and congestion risk forecast.
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This robust market clearing algorithm matches supply and demand bids in a closed-gate auction fashion, considering potential congestion and other constraints.
Outage Planning Optimization
Leverage advanced analytics to determine the optimal strategy and daily schedule for grid interventions and related outages.
Dynamic Reserves Dimensioning
TSOs: dynamically adapt reserves volume based on the real imbalance risk faced by the system using machine learning and advanced analytics.
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The latest insights from N-SIDE
Pauline Martin
On the 30th of October, Elia published the results of a new study performed in collaboration with N-SIDE, on the Dynamic dimensioning of reserves. Based on accurate Machine Learning technologies, this new methodology will allow Elia to size the balancing reserves needs closer to real time based on day-ahead predicted system conditions.
Why is such a new methodology key in the Energy Market ?
With the deployment of intermittent Renewable Energy sources at large scale and the development of large HVDC cables, the level of system imbalance risk faced by the system is less driven by large baseload units and more by the punctual system conditions (forecast of PV and wind production, risk of storm, HVDC cable and power plants dispatch). This supports the idea of doing the sizing and the procurement of the system reserves in a dynamic way (e.g. in D-1): it allows to leverage the system conditions forecasts thanks to advanced analytics such as machine learning, to predict the imbalance risk and size the reserve needs accordingly.
This methodology will ensure a more constant reliability to the system and reduce on average the needs for reserve.
N-SIDE is really pleased to be part of the game and to sustain Elia in building the Energy Market of tomorrow.
Read more about Dynamic dimensioning of reserves in Elia’s report.