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An end-to-end generative framework for efficiently solving multi-period and multi-scenario stochastic model predictive control problems under nonlinear AC power-flow constraints is proposed, and a constraint-aware differentiable architecture is introduced.
The complex multi-energy coupling characteristics inherent to integrated energy system (IES) present unprecedented challenges for the implementation of low-carbon scheduling. Existing optimization methods often exhibit limitations in system scalability, algorithm adaptivity, and carbon reduction efficacy for complex IE...
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