Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gaussian Mixture Model Based Distributionally Robust Optimal Power Flow
  With CVaR Constraints   This paper proposes a distributionally robust optimal power flow (OPF) model
for transmission grids with wind power generation. The model uses the
conditional value-at-risk (CVaR) constraints to control the reserve and branch
flow limit violations caused by wind power forecast errors. Meanwhile, the
Gaussian mixture model (GMM) is integrated into the CVaR constraints to guard
against the non-Gaussian forecast error distributions. Unlike the previous
studies considering the GMM with fixed parameters, this paper allows the GMM
parameters to be variable within some credible regions and develops a
data-driven GMM-based ambiguity set to achieve the distributional robustness.
Also, rather than using the traditional sample-based approximation of CVaR with
high computational burden, this paper designs a scalable cutting-plane
algorithm to handle the distributionally robust CVaR constraints. Case studies
on the IEEE 2736-bus system show the effectiveness and scalability of the
proposed OPF model.

21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
3
The categories of this paper are: physics
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
28
The categories of this paper are: alg-geom
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
13
The categories of this paper are: quant-ph
15
The categories of this paper are: nucl-ex