To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,comp...To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.展开更多
基金supported in part by State Grid Corporation of China Project“Research on high penetrated renewable energy oriented intelligent identification for curtailment impacts and aid decision-making for promoting consumption in regional power grids”(No.5108-202135035A-0-0-00).
文摘To secure power system operations,practical dispatches in industries place a steady power transfer limit on critical inter-corridors,rather than high-dimensional and strong nonlinear stability constraints.However,computational complexities lead to over-conservative pre-settings of transfer limit,which further induce undesirable and non-technical congestion of power transfer.To conquer this barrier,a scenario-classification hybrid-based banding method is proposed.A cluster technique is adopted to separate similarities from historical and generated operating condition dataset.With a practical rule,transfer limits are approximated for each operating cluster.Then,toward an interpretable online transfer limit decision,costsensitive learning is applied to identify cluster affiliation to assign a transfer limit for a given operation.In this stage,critical variables that affect the transfer limit are also picked out via mean impact value.This enables us to construct low-complexity and dispatcher-friendly rules for fast determination of transfer limit.The numerical case studies on the IEEE 39-bus system and a real-world regional power system in China illustrate the effectiveness and conservativeness of the proposed method.
