Model-based control for a demand-driven biogas production to cover residual load rises

Demand-oriented biogas production to cover residual load of an electricity self-sufficient community using a simple kinetic model

Flexible biogas production can enable demand-oriented energy supply without the need for expensive gas storage expansions, but poses challenges to the stability of the anaerobic digestion (AD) process. In this work, biogas production of laboratory-scale AD of maize silage and sugar beets was optimized to cover the residual load of an electricity self-sufficient community using a simple process model based on first-order kinetics. Experiments show a good agreement between biogas demand, predicted, and measured biogas production. By optimizing biogas conversion schedules based on the measured gas production, a gas storage capacity of 7-8 h was identified for maximum flexibility, which corresponds to typical gas storage sizes at industrial biogas plants in Germany. Various stability indicators were continuously monitored and proved resilient process conditions. These results demonstrate that demand-oriented biogas production using model predictive control is a promising approach to enable existing biogas plants to provide balancing energy.

Biogas Plants in Renewable Energy Systems—A Systematic Review of Modeling Approaches of Biogas Production

Biogas production is a relevant component in renewable energy systems. The paper addresses modeling approaches from an energy system, as well as from a process optimization, point of view. Model approaches of biogas production show different levels of detail. They can be classified as white, gray, and black box, or bottom-up and top-down approaches. On the one hand, biogas modeling can supply dynamic information on the anaerobic digestion process, e.g., to predict biogas yields or to optimize the anaerobic digestion process. These models are characterized by a bottom-up approach with different levels of detail: the comprehensive ADM1 (white box), simplifications and abstractions of AD models (gray box), or highly simplified process descriptions (black box). On the other hand, biogas production is included in energy system models. These models usually supply aggregated information on regional biogas potentials and greenhouse gas emissions. They are characterized by a top-down approach with a low level of detail. Most energy system models reported in literature are based on black box approaches. Considering the strengths and weaknesses of the integration of detailed and deeply investigated process models in energy system models reveals the opportunity to develop dynamic and fluctuating business models of biogas usage.