Managing Distributed Flexibility Under Uncertainty by Combining Deep Learning With Duality
نویسندگان
چکیده
In modern power systems, small distributed energy resources (DERs) are considered a valuable source of flexibility towards accommodating high penetration Renewable Energy Sources (RES). this paper we consider an economic dispatch problem for community DERs, where management decisions made online and under uncertainty. We model multiple sources uncertainty such as RES, wholesale electricity prices well the arrival times needs set Electric Vehicles. The is formulated multi-agent Markov Decision Process. difficulties lie in curse dimensionality guaranteeing satisfaction constraints A novel method, that combines duality theory deep learning, proposed to tackle these challenges. particular, Neural Network (NN) trained return optimal dual variables problem. By training NN on instead primal, number output neurons dramatically reduced, which enhances performance reliability NN. Finally, by treating resulting prices, each agent can self-schedule, guarantees its constraints. As result, our simulations show scheme performs reliably efficiently.
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ژورنال
عنوان ژورنال: IEEE Transactions on Sustainable Energy
سال: 2021
ISSN: ['1949-3029', '1949-3037']
DOI: https://doi.org/10.1109/tste.2021.3086846