Optimal Design of Energy Systems Using Constrained Grey-Box Multi-Objective Optimization. Academic Article uri icon


  • 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.

published proceedings

  • Comput Chem Eng

altmetric score

  • 0.5

author list (cited authors)

  • Beykal, B., Boukouvala, F., Floudas, C. A., & Pistikopoulos, E. N.

citation count

  • 55

complete list of authors

  • Beykal, Burcu||Boukouvala, Fani||Floudas, Christodoulos A||Pistikopoulos, Efstratios N

publication date

  • August 2018