SOURCE-LINKED INTELLIGENCE
Dynamic Equivalencing using Machine Learning
Dynamic Equivalencing using Machine Learning Global renewable capacity is projected to increase by nearly 5,500 GW (about 75%) between 2024 and 2030. While this rapid expansion of decentralized generation brings opportunities, it also introduces significant challenges, such as reduced system inertia, stochastic power output, time-varying operating conditions, and stability concerns. A couple of power blackouts in recent years have highlighted that the stability of system operation is a critical issue. Together, these factors reduce grid flexibility and impose heavy computational demands for accurate analysis, estimation, and control. This proposal, dynamic equivalencing using machine learning (DEML), aims to address these challenges from a data-driven and physics-guided perspective. The objective is to accelerate computationally intense electromagnetic transient simulations (EMT) using deep learning-enabled model
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 276187.92
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T05:31:32.981Z. This is not the publication date.