A Dynamic Framework for Multiobjective Mixed-Integer Optimal Power Flow Analyses

This paper proposes a reliable and computationally efficient framework for solving multiobjective mixed-integer Optimal Power Flow problems. The main idea is to apply the interior point theory and the goal-attainment method to recast a generic OPF problem with both real and integer decision variables by an equivalent scalar optimization problem with equality constraints. Then, thanks to the adoption of the Lyapunov theory, an asymptotically stable dynamic system is designed such that its equilibrium points coincide with the stationary points of the Lagrangian function of the equivalent problem. Thanks to this approach, the OPF solutions can be promptly and reliably obtained by solving a set of ordinary differential equations, rather than using an iterative Newton-based scheme, which can fail to converge due to several numerical issues. Detailed numerical results are presented and discussed in order to prove the effectiveness of the proposed framework in solving real world problems.