  We formulate the Alternating Current Optimal Power Flow Problem (ACOPF) as a
Linear Constrained Quadratic Program (LCQP) with many negative eigenvalues
($r$) and linear constraints, making it NP-hard. We propose two algorithms,
Feasible Successive Linear Programming (FSLP) and Feasible Branch-and-Bound
(FBB), for a global optimal solution. These use optimization strategies like
bounded successive linear programming, convex relaxation, initialization, and
branch-and-bound to find a globally optimal solution within a predefined
$\epsilon$-tolerance. The complexity of FSLP and FBB is $\mathcal{O}\left(N
\prod_{i=1}^r\left\lceil\frac{\sqrt{r}(t_u^i-t_l^i)}{2
\sqrt{\epsilon}}\right\rceil\right)$, where $N$ is the complexity of solving
subproblems at each FBB node. Variables $t_l$ and $t_u$ are the lower and upper
bounds of $t$, respectively, and $-|t|^2$ is the negative quadratic component
in the ACOPF objective function. We use penalized semidefinite modeling, convex
relaxation, and line search to design a globally feasible branch-and-bound
algorithm for the LCQP form of ACOPF, finding an optimal solution within
$\epsilon$-tolerance. Initial results show FSLP and FBB can find global optimal
solutions for large-scale ACOPF instances, even with large $r$, and outperform
other methods in most PG-lib tests.
