  The multi-period dynamics of energy storage (ES), intermittent renewable
generation and uncontrollable power loads, make the optimization of power
system operation (PSO) challenging. A multi-period optimal PSO under
uncertainty is formulated using the chance-constrained optimization (CCO)
modeling paradigm, where the constraints include the nonlinear energy storage
and AC power flow models. Based on the emerging scenario optimization method
which does not rely on pre-known probability distribution functions, this paper
develops a novel solution method for this challenging CCO problem. The proposed
meth-od is computationally effective for mainly two reasons. First, the
original AC power flow constraints are approximated by a set of
learning-assisted quadratic convex inequalities based on a generalized least
absolute shrinkage and selection operator. Second, considering the physical
patterns of data and motived by learning-based sampling, the strategic sampling
method is developed to significantly reduce the required number of scenarios
through different sampling strategies. The simulation results on IEEE standard
systems indicate that 1) the proposed strategic sampling significantly improves
the computational efficiency of the scenario-based approach for solving the
chance-constrained optimal PSO problem, 2) the data-driven convex approximation
of power flow can be promising alternatives of nonlinear and nonconvex AC power
flow.
