A Control Co-Design Framework to Achieve Solution Feasibility in Energy System Optimization Problems
For researchers working on CCD optimization of energy systems, this work provides a method to handle infeasibility, but the contribution is incremental as it adapts existing relaxation techniques to a specific class of problems.
This paper addresses infeasibility in control co-design (CCD) optimization for energy systems by proposing a framework that ranks metric bounds to guide constraint relaxation. Applied to a battery-in-microgrid CCD problem, the framework achieves solution feasibility with fewer iterations than a baseline approach.
This work explores methods to identify energy system designs for infeasible control co-design optimization problems. Control co-design, or CCD, has been recognized as a powerful tool to maximize energy system capabilities through simultaneous determination of plant and controller parameters. However, due to the inherent nonlinearities, complexity, and conflicting criteria of energy systems, CCD optimization problems are susceptible to infeasibility and can lack potential solutions. While transforming the optimization problem by relaxing constraints has been developed for optimal control infeasibility challenges, solution feasibility for CCD is relatively unexplored. This paper proposes a framework to convert infeasible optimization problems into solvable forms for a class of CCD problems. The framework introduces a procedure to rank metric bounds from least likely to most likely to cause infeasibility. This provides guidance to algorithmically relax a limited number of constraints, leaving others intact. The proposed framework is applied to a CCD problem for designing a battery within a microgrid. Comparison against a baseline approach for relaxing optimization problems shows the framework requires only a reduced number of iterations to determine a solution.