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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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integrating local flexibility markets through distributed AI-based coordination, market mechanism design, and cloud-to-edge computing. It aims to develop scalable machine learning methods for coordinating grid
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energy system that is clean, reliable, and resilient to the challenges of the future. Due to its high-cost, green hydrogen currently constitutes less than 0.1 % of the total hydrogen produced and strong
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literature analysis, including works in both computer science, psychology and automated control. · Develop testing approaches and undertake extensive simulator studies. · Writing of research