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Machine learning prognosis for system imbalance volumes (IMPALA)


The costs that TSOs have related to mitigating imbalances and frequency deviations have increased steadily the last 10 years, especially in the Nordics but also in many other regions around the world. The drivers behind the increase in the Nordics (higher intermittent production from renewables and tighter integreation with surrounding countries) in costs are expected to become stronger going forward. In general, the shorter the time frame that a system operator has to respond to a system imbalance, the fewer resources are available for mitigating actions and correspondingly the more expensive it is for the system operator to take action. In addition, in extreme periods unexpected large imbalances can lead to system blackouts, which can have a very high societal cost. Being able to forecast system imbalance volumes therefore has a great value to system operators. Currently, there are no effective tools available in the market for providing such forecasts. Additionally, published literature on forecasting system imbalance volumes is very limited and of a quality that leaves great room for improvement. The question that this project is designed to address is thus: How can we design an algorithm that is fast enough to be used in a real-time environment while at the same time providing a highly reliable forecast of system imbalance that can easily be updated as new knowledge becomes known?

Project leader: Gavin Bell

Started: 2017

Ends: 2019

Category: Næringsliv

Sector: Næringsliv

Budget: 4070000


Address: Oslo