Uncertainty-aware nodal carbon intensity based on sliced Latin hypercube sampling

The variability of climate conditions leads to significant renewable uncertainty. With the large-scale integration of renewable energy, it becomes necessary to consider uncertainties in the nodal carbon intensity (NCI) of power systems. However, the renewable uncertainty though the power flow propagates to the carbon emission flow, making an unclear relationship between the renewable uncertainty and the carbon emission flow. In this paper, an uncertainty-aware nodal Carbon intensity method is proposed to handle this issue. An explicit matrix-form relationship linking renewable forecast errors, AGC-based generation dispatch, branch power flow variations, and NCI is established. In addition, a Jacobian-based local sensitivity expression is derived to characterize the local response of NCI to renewable forecast errors. Then, sliced Latin hypercube sampling (SLHS) is employed to estimate the statistical characteristics and probability distribution of NCI. Case studies performed on the PJM 5-bus and IEEE 300-bus systems demonstrate that the proposed method is capable of accurately accounting for the uncertainty in the nodal carbon intensity.