A Banding Method for Power Transfer Limits of Critical Inter-Corridors Based on Scenario Clustering and Classification Hybrids

To secure power system operations, practical dispatches in industries place steady power transfer limit on critical inter-corridors rather than high-dimensional and strong nonlinear stability constraints. However, computational complexity leads to over-conservative transfer limit pre-settings, which further induce undesirable and nontechnical power transfer congestion. To conquer this barrier, a data-driven scenario-classification hybrids-based banding method is proposed. At the outset, cluster technique is adopted to separate similarities from historical and generated operating condition dataset. With a practical rule, transfer limits are approximated for each operational cluster. Then, towards an interpretable online transfer limit decision, cost-sensitive learning is applied to identify cluster affiliation, so that to assign transfer limit for a given operation. In this stage, critical variables that affect transfer limit are also picked out via mean impact value. This allows us to construct a low-complexity and dispatcher-friendly rules for fast transfer limit determination. The numerical case studies on the IEEE 39-bus system and a real-world regional power grid in China illustrate effectiveness and conservativeness of the proposed method.