SOURCE-LINKED INTELLIGENCE

Data-DrIVen Solutions for Efficient PoweR DiStribution Network OpErations

CORDIS · observation · Publication date unknown

velops an AI-powered framework to optimize power distribution networks, supporting robust RES integration while tackling key issues such as variability and grid reconfiguration. By combining advanced machine learning, deep learning, and adaptive grid technologies, DIVERSE introduces predictive analytics, near real-time optimization, and consumer-side flexibility to enhance grid efficiency and scalability. The project's objectives include: _Developing AI-driven forecasting and regulation strategies for RES integration _Enhancing operational flexibility and efficiency through adaptive control mechanisms _Ensuring grid resilience across different network configurations _Optimizing multi-energy system coordination DIVERSE's innovative approach incorporates resilience-focused design into distributed energy resource (DER) coordination, ensuring effective grid operation under dynamic scenarios. Through simulations on RTDS-based platforms, the project optimizes adaptive response strategies, enhancing long-term grid flexibility, reliability, and multi-energy integration. The consortium uni

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recordType

award

status

SIGNED

region

EU

value

801600

unit

EUR