  The Gaussian Process (GP) based Chance-Constrained Optimal Power Flow
(CC-OPF) is an open-source Python code developed for solving economic dispatch
(ED) problem in modern power grids. In recent years, integrating a significant
amount of renewables into a power grid causes high fluctuations and thus brings
a lot of uncertainty to power grid operations. This fact makes the conventional
model-based CC-OPF problem non-convex and computationally complex to solve. The
developed tool presents a novel data-driven approach based on the GP regression
model for solving the CC-OPF problem with a trade-off between complexity and
accuracy. The proposed approach and developed software can help system
operators to effectively perform ED optimization in the presence of large
uncertainties in the power grid.
