Combined Physics- and Data-Driven Modeling for the Design and Operation Optimization of an Energy Concept Including a Storage System

The industrial sector accounts for a huge amount of energy- and process-related CO 2 emissions. One decarbonization measure is to build an energy concept that provides electricity and heat for industrial processes using a combination of different renewable energy sources, such as photovoltaic, wind turbine, and solar thermal collector systems, integrating also energy conversion power-to-heat components such as heat pumps, electric boilers, and thermal energy storage. The challenge for the industries is the economic aspect of the decarbonization, as industries require a cost-efficient solution. Minimizing cost and emissions together is a complex problem, which requires two major tasks: (I) modeling of components and (II) multi-objective coupled design and operation optimization of the energy concept. The optimal design and capacity of the components and optimal system operation depend majorly on component modeling, which is either physics-driven or data-driven. This paper shows different types of physics- and data-driven modeling of energy components for multi-objective coupled optimization in order to minimize costs and emissions of a specific industrial process as a case study. Several modeling techniques and their influence on the optimization are compared in terms of computational effort, solution accuracy, and optimal capacity of components. The results show that the combination of physics- and data-driven models has a computational time reduction of up to 37% for an energy concept without thermal energy storage and 29% for that with thermal energy storage, both with high-accuracy solutions compared to complete physics-driven models for the considered case study.