A Global Solution Algorithm for AC Optimal Power Flow through Linear Constrained Quadratic Programming

TL;DR

This paper introduces two algorithms, FSLP and FBB, for solving the NP-hard AC optimal power flow problem formulated as a linear constrained quadratic program, achieving global optimality efficiently.

Contribution

It presents novel algorithms that leverage convex relaxation, branch-and-bound, and linear programming to find globally optimal solutions for large-scale ACOPF problems.

Findings

FSLP and FBB can solve large-scale ACOPF instances globally.

Algorithms outperform existing methods in most PG-lib tests.

Achieve solutions within predefined epsilon-tolerance.

Abstract

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.