Dynamic Reserves dimensioning

The Challenge
Right dimensioning of operating reserves to ensure electricity system reliability.
With the deployment of intermittent renewable energy sources at large scale and the development of large HVDC cables, the system imbalance becomes larger, more volatile and complex to anticipate for a TSO. With such game changers, the dimensioning of the operating reserves is becoming increasingly challenging. To ensure the right reliability, the TSOs will need to adapt the sizing of the FRR requirements either : by increasing the

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 Solution
Advanced analytics to efficiently manage the imbalance risk.
The N-SIDE Dynamic Reserves Dimensioning enables the TSO to move towards a closer to real-time approach and leverage its data thanks to advanced analytics. The solution relies on machine learning to predict the imbalance risk (e.g. in day-ahead with hourly granularity) and the associated required FRR volume at the desired reliability target (e.g. 99%).

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.

The solution combines three key analytical modules:
  • 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)

 

The Results
Smarter dimensioning of reserves to realize the energy transition
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Adapt the volume of balancing reserves to the real risk faced by the system
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Improve the system imbalance management by reaching the reliability target at all times
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Avoid under and over procurement of balancing reserves
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Improve efficiency by reducing average FRR requirements and system operation costs 

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

— They trust us —

quote-testimonials

 

 

N-SIDE supports our business with advanced analytics tools which allows us to realize the energy transition. Their experts provided the knowledge to transform our reserve dimensioning to the state-of-the-art, ready to deal with future renewable power systems.

 

Kristof De Vos – System Services Manager : Ancillary services at Elia

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