A machine learning proxy with a set aggregator and a feasible-projection module predicts solutions to a joint chance-constrained VPP dispatch problem in about 3 milliseconds, but at a roughly 10% higher objective cost than the solver-based baseline.
Power systems optimization under uncertainty: A review of methods and applications,
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Learning to Optimize Joint Chance-constrained Power Dispatch Problems
A machine learning proxy with a set aggregator and a feasible-projection module predicts solutions to a joint chance-constrained VPP dispatch problem in about 3 milliseconds, but at a roughly 10% higher objective cost than the solver-based baseline.