COMANDO: A Next-Generation Open-Source Framework for Energy Systems Optimization

TL;DR

COMANDO is an open-source Python framework enabling flexible, component-oriented modeling and optimization of complex energy systems, supporting nonlinear, dynamic, and stochastic problem formulations with customizable solution routines.

Contribution

It introduces COMANDO, a novel open-source framework that extends energy systems modeling beyond linear programming to include nonlinear, dynamic, and stochastic optimization capabilities.

Findings

Supports nonlinear, dynamic, and discrete system components

Enables automated linearization and stochastic programming

Incorporates neural network surrogate models for global optimization

Abstract

Existing open-source modeling frameworks dedicated to energy systems optimization typically utilize (mixed-integer) linear programming ((MI)LP) formulations, which lack modeling freedom 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 as surrogate models in a reduced-space formulation for deterministic global optimization.