阻塞结构与节点边际排放的超越界

中文摘要 AI 辅助

节点边际排放（LMEs）表示总运行碳排放对节点电力需求的敏感性。我们证明，在直流最优潮流下，这个包含n个条目的向量具有更小的内在维度。在固定的有效约束集内，LME向量位于均匀向量与阻塞线路的功率传输分配因子行的张成空间中。因此，其秩r至多比阻塞/拥塞线路数量多一。该结构使得r个独立的标量观测对于精确恢复是必要且充分的。在十个具有非零运行排放的系统中，从14到1354个节点，r的取值范围为2到15。例如，在一个300节点系统中，24次调度模拟即可恢复全部300个LME。我们还推导了在需求不确定情况下的排放超越界。该界将名义有效集不变时的变化、有效集变化的概率以及估计误差分离。数值结果表明，其可用预测误差范围取决于局部有效集的几何形状。

英文摘要

Locational marginal emissions (LMEs) give the sensitivity of total operating carbon emissions to nodal power demand. We show that this vector with $n$ entries has a much smaller intrinsic dimension under DC optimal power flow. Within a fixed active constraint set, the LME vector lies in the span of the uniform vector and the power transfer distribution factor rows of the binding lines. Its rank $r$ is therefore at most one more than the number of binding/congested lines. This structure makes $r$ independent scalar observations necessary and sufficient for exact recovery. Across ten systems with nonzero operating emissions, from 14 to 1,354 buses, $r$ ranges from 2 to 15. For instance, on a 300 bus system, 24 dispatch simulations recover all 300 LMEs. We also derive an emissions exceedance bound under uncertain demand. The bound separates variation while the nominal active set remains unchanged, the probability of an active set change, and estimation error. Numerical results show that its usable forecast error range depends on local active set geometry.

发表机构

• Georgia Institute of Technology（佐治亚理工学院）
• Harvard University（哈佛大学）
• University of Michigan（密歇根大学）

机构由 AI 辅助整理，请以论文原文为准。