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.

能源系统的（全局）优化通常以高保真度和大规模复杂模型为特征，造成了巨大的挑战，部分原因是与导数计算相关的高噪声和/或计算费用。在存在多个相互冲突的目标的情况下，这种复杂性会进一步放大，其目标是生成权衡妥协解决方案，通常称为帕累托最优解决方案。我们之前介绍过 p-ARGONAUT 系统，即约束灰箱计算问题全局优化的并行算法，该算法旨在通过为所有未知方程假设准确且易于处理的代理公式来优化一般约束单目标灰箱问题。计算有效的方式。在这项工作中，我们将 p-ARGONAUT 扩展到多目标优化问题，并在许多等式约束下测试框架的准确性和一致性方面的性能。报告了许多基准多目标问题的计算结果以及商业建筑能源市场设计问题的案例研究，同时将该框架的性能与其他无导数优化求解器进行了比较。