Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack granularity for technical system design and operation. We present COMANDO, an open-source Python package for component-oriented modeling and optimization for nonlinear design and operation of integrated energy systems. COMANDO allows to assemble system models from component models including nonlinear, dynamic and discrete characteristics. Based on a single system model, different deterministic and stochastic problem formulations can be obtained by varying objective function and underlying data, and by applying automatic or manual reformulations. The flexible open-source implementation allows for the integration of customized routines required to solve challenging problems, e.g., initialization, problem decomposition, or sequential solution strategies. We demonstrate features of COMANDO via case studies, including automated linearization, dynamic optimization, stochastic programming, and the use of nonlinear artificial neural networks (ANNs) as surrogate models in a reduced-space formulation for deterministic global optimization.

现有的专门用于能源系统优化的开源建模框架通常使用（混合整数）线性规划 ((MI)LP) 公式，缺乏技术系统设计和操作的粒度。我们本COMANDO，一个开源Python包为Ç omponent- ö riented米odeling和optimiz一个用于灰Ñ庞晓虹d ESIGN和ö综合能源系统的运行。COMANDO 允许从组件模型组装系统模型，包括非线性、动态和离散特性。基于单个系统模型，可以通过改变目标函数和基础数据，以及应用自动或手动重构来获得不同的确定性和随机问题公式。灵活的开源实现允许集成解决挑战性问题所需的定制例程，例如初始化、问题分解或顺序解决策略。我们通过案例研究展示了 COMANDO 的功能，包括自动线性化、动态优化、随机编程、