A Control Co-Design Framework to Achieve Solution Feasibility in Energy System Optimization Problems

TL;DR

This paper presents a framework to transform infeasible energy system control co-design problems into feasible ones by guiding constraint relaxation, demonstrated on microgrid battery design.

Contribution

It introduces a ranking-based procedure for constraint relaxation to efficiently achieve solution feasibility in CCD problems.

Findings

Framework reduces the number of iterations needed to find feasible solutions.

Applied to microgrid battery design, the method outperforms baseline approaches.

Guides selective constraint relaxation to improve problem solvability.

Abstract

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.