Biogas Power Plant Operation Modeling based on Statistical and Grey-box Methods

Biogas plants have been the subject of modeling and research in recent years. Such a plant is a system consisting of several components, the most important of which are the combined heat and power (CHP) unit that produces electricity and heat, the organic feed receiving area, the digester and the gas storage. All of these components have separate and detailed models developed for different purposes. A missing system approach that provides a sufficient level of detail and correlation between feeding, biogas production rate, storage level and CHP output was identified. Therefore, this work presents the concept of an approach that uses a deeper system understanding combined with processing of big amount of data rather than the popular fully machine learning based models. It
outlines the methodology for establishing a relationship between CHP power and gas storage level in both forward and backward coupling. Possible physics based models are presented, which should serve as a predictor between the storage level of the biogas power plant and the feedstock. Finally, the individual steps are linked to a system-level approach that captures the fact that the power generated affects the state of the system and vice versa. The models presented are under development and use a sophisticated mix of statistical, data-driven and physical modelling methods. As input, they require recorded data that is minimally in detail and generally available for all biogas power plants. Together with the presented approach, the developed model should be able to serve as a digital twin of an arbitrary power plant that can be integrated into energy system models. Additionally, it should deliver quality indicators for the biogas produced at the respective plant. As such, the proposed methodology offers added value over existing approaches, which are often unidirectional or focused solely on individual components.