Stochastic distributionally robust unit commitment with deep scenario clustering

The increasing penetration of intermittent renewable generation in power systems allows for more available historical data at hand. However, it is challenging for traditional distributionally robust optimization to capture the difference and heterogeneity in historical samples which makes the solution remain highly conservative. To comprehensively characterize the underlying factors of uncertainty, we proposed a data-driven stochastic distributionally robust optimization model for unit commitment via group-wise ambiguity set constructed by deep representation clustering method. The expectation of the worst-case distribution under a group of scenarios is calculated in the model, with assuming that the scenarios belong to different groups which are ambiguity. A tractable approximation of the model is derived to avoid computational burden and we analyze the optimality conditions of the approximate formulation. Simulations on a modified IEEE-118 illustrate that the proposed approach can make a trade-off between stochastic optimization and distributionally robust optimization and has benefits in reducing the operation costs with the capability to hedge against the perturbation of unrelated samples.

Automated summary

With the increasing penetration of intermittent renewable generation in power systems, more historical data is available. However, traditional distributionally robust optimization struggles to capture the difference and heterogeneity in historical samples, resulting in highly conservative solutions. To comprehensively characterize uncertainty, a data-driven stochastic distributionally robust optimization model for unit commitment is proposed using deep representation clustering method. Simulation results demonstrate the proposed approach's ability to strike a balance between stochastic optimization and distributionally robust optimization, reducing operation costs and hedging against perturbations.