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The uncertainty of multiple power loads and renewable energy generations(PLREG) in power systems increases the complexity of power flow analysis fordecision-makers. The chance-constrained method can be applied to model theoptimization problems of power flow under uncertainty. This paper develops anovel solution approach for chance-constrained AC optimal power flow (CCACOPF)problem based on the data-driven convexification of power flow and a fastalgorithm for scenario technique (FAST). This method is computationallyeffective for mainly two reasons. First, the original nonconvex AC power flow(ACPF) constraints are approximated by a set of learning-based quadratic convexones. Second, FAST is an advanced scenario-based solution method (SSM) thatdoesn't rely on the pre-assumed probability distribution, using far lessscenarios than the conventional SSM. Eventually, the CCACOPF is converted intoa computationally tractable convex optimization problem. The simulation resultson IEEE test cases indicate that 1) the proposed solution method can outperformthe conventional SSM in computational efficiency, 2) the data-drivenconvexification of power flow is effective in approximating original complex ACpower flow.

Efficient Solution Strategy for Chance-Constrained Optimal Power Flow based on FAST and Data-driven Convexification