Adaptive clustering of subproblems in Benders decomposition yields grouped cuts that outperform standard multi-cut formulations for energy CEMs under weak inter-temporal coupling, with gains largest in big systems with short horizons.
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A projected stochastic gradient descent algorithm with primal-dual framework solves two-stage power system planning under uncertainty and yields lower simulated costs than perfect-foresight models.
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Clustering-enhanced adaptive Benders decomposition for energy systems planning optimization
Adaptive clustering of subproblems in Benders decomposition yields grouped cuts that outperform standard multi-cut formulations for energy CEMs under weak inter-temporal coupling, with gains largest in big systems with short horizons.
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Uncertainty-aware Power System Planning via Gradient Descent
A projected stochastic gradient descent algorithm with primal-dual framework solves two-stage power system planning under uncertainty and yields lower simulated costs than perfect-foresight models.