The application of floating photovoltaics (PVs) in hydropower plants has gained increasing interest in forming hybrid energy systems (HESs). It
enhances the operational benefits of the existing hydropower plants. However,
uncertainties of PV and load powers can present great challenges to scheduling
HESs. To address these uncertainties, this paper proposes a novel two-stage
optimization approach that combines distributionally robust chance-constrained (DRCC)
and robust-stochastic optimization (RSO) approaches to minimize the operational cost of an HES. In the first
stage, the scheduling of each device is obtained via the DRCC approach
considering the PV power and load forecast errors. The second stage provides a
robust near real time energy dispatch according to different scenarios of PV
power and load demand. The solution of the RSO problem is obtained via a novel
double-layer particle swarm optimization algorithm. The performance of the
proposed approach is compared to the traditional stochastic and
robust-stochastic approaches. Simulation results de- monstrate the superiority of the proposed two-stage
approach and its solution method in terms of operational costand
execution time.
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