Optimal design of energy systems using constrained grey-box multi-objective optimization
Optimal design of energy systems using constrained grey-box multi-objective optimization
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DOI：
10.1016/j.compchemeng.2018.02.017
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发表时间：
2018-08-04
影响因子：
4.3
通讯作者：
Pistikopoulos, Efstratios N.
中科院分区：
文献类型：
--
作者：
Beykal, Burcu;Boukouvala, Fani;Pistikopoulos, Efstratios N.
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. (C) 2018 Elsevier Ltd. All rights reserved.
