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The multi-period dynamics of energy storage (ES), intermittent renewablegeneration and uncontrollable power loads, make the optimization of powersystem operation (PSO) challenging. A multi-period optimal PSO underuncertainty is formulated using the chance-constrained optimization (CCO)modeling paradigm, where the constraints include the nonlinear energy storageand AC power flow models. Based on the emerging scenario optimization methodwhich does not rely on pre-known probability distribution functions, this paperdevelops a novel solution method for this challenging CCO problem. The proposedmeth-od is computationally effective for mainly two reasons. First, theoriginal AC power flow constraints are approximated by a set oflearning-assisted quadratic convex inequalities based on a generalized leastabsolute shrinkage and selection operator. Second, considering the physicalpatterns of data and motived by learning-based sampling, the strategic samplingmethod is developed to significantly reduce the required number of scenariosthrough different sampling strategies. The simulation results on IEEE standardsystems indicate that 1) the proposed strategic sampling significantly improvesthe computational efficiency of the scenario-based approach for solving thechance-constrained optimal PSO problem, 2) the data-driven convex approximationof power flow can be promising alternatives of nonlinear and nonconvex AC powerflow.

Optimal Operation of Power Systems with Energy Storage under Uncertainty: A Scenario-based Method with Strategic Sampling