  Alternative current optimal power flow (ACOPF) problems have been studied for
over fifty years, and yet the development of an optimal algorithm to solve them
remains a hot and challenging topic for researchers because of their nonlinear
and nonconvex nature. A number of methods based on linearization and
convexification have been proposed to solve to ACOPF problems, which result in
near-optimal or local solutions, not optimal solutions. Nowadays, with the
prevalence of machine learning, some researchers have begun to utilize this
technology to solve ACOPF problems using the historical data generated by the
grid operators. The present paper reviews the research on solving ACOPF
problems using machine learning and neural networks and proposes future
studies. This body of research is at the beginning of this area, and further
exploration can be undertaken into the possibilities of solving ACOPF problems
using machine learning.
