The framework models DER populations as a density evolving via PDE, convexifies the problem to a sparse LP via finite-volume discretization and flux-lifting for broadcast coordination, and uses data mixing plus Wasserstein relaxation for privacy and flexibility.
Scheduling HV AC loads to promote renewable generation integration with a learning-based joint chance-constrained approach
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A Convexified Eulerian Framework for Scalable Coordination of Massive DER Populations
The framework models DER populations as a density evolving via PDE, convexifies the problem to a sparse LP via finite-volume discretization and flux-lifting for broadcast coordination, and uses data mixing plus Wasserstein relaxation for privacy and flexibility.