Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization
Burcu Beykal (1,2), Fani Boukouvala (3), Christodoulos A. Floudas (1,2), Efstratios N. Pistikopoulos (1,2)
1. Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA
2. Texas A&M Energy Institute, Texas A&M University, College Station, TX 77843, USA
3. School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
The (global) optimization of energy systems, commonly characterized by high-fidelity and large-scale complex models, poses a formidable challenge partially due to the high noise and/or computational expense associated with the calculation of derivatives. This complexity is further amplified in the presence of multiple conflicting objectives, for which the goal is to generate trade-off compromise solutions, commonly known as Pareto-optimal solutions. We have previously introduced the p-ARGONAUT system, parallel Algorithms for Global Optimization of constrained grey-box computational problems, which is designed to optimize general constrained single objective grey-box problems by postulating accurate and tractable surrogate formulations for all unknown equations in a computationally efficient manner. In this work, we extend p-ARGONAUT towards multi-objective optimization problems and test the performance of the framework, both in terms of accuracy and consistency, under many equality constraints. Computational results are reported for a number of benchmark multi-objective problems and a case study of an energy market design problem for a commercial building, while the performance of the framework is compared with other derivative-free optimization solvers.
Keywords: Derivative-free optimization; Grey/black-box optimization; Multi-objective optimization; Energy systems engineering