To enable a sustainable supply of chemicals, novel biotechnological solutions are required that replace the reliance on fossil resources. One potential solution is to utilize tailored biosynthetic modules for the metabolic conversion of CO2 or organic waste to chemicals and fuel by microorganisms. Currently, it is challenging to commercialize biotechnological processes for renewable chemical biomanufacturing because of a lack of highly active and specific biocatalysts. As experimental methods to engineer biocatalysts are time- and cost-intensive, it is important to establish efficient and reliable computational tools that can speed up the identification or optimization of selective, highly active, and stable enzyme variants for utilization in the biotechnological industry. Here, we review and suggest combinations of effective state-of-the-art software and online tools available for computational enzyme engineering pipelines to optimize metabolic pathways for the biosynthesis of renewable chemicals. Using examples relevant for biotechnology, we explain the underlying principles of enzyme engineering and design and illuminate future directions for automated optimization of biocatalysts for the assembly of synthetic metabolic pathways.
This paper analyzes how demand-response aggregators, which provide active power reserves, affect frequency control. A rebound effect can occur when the modulation of energy consumption leads to an increased consumption after activation of the reserve. The technical basics are presented together with a model incorporating Continental European control structures including demand-response aggregation, both implemented in SIMULINK. The focus is placed on thermostatically controlled loads, which are typical for real-life demand-response aggregation for the provision of ancillary services. We investigate scenarios with forced outages, ramping behavior, and historical reserve activations. The results imply a negligible impact of the rebound effect in normal operation. Under worst-case assumptions, however, the rebound effect causes power oscillations leading to instabilities in the entire power system.
This paper focuses on designing a framework for the activation of tertiary control reserves in order to improve frequency control performance. Considering the fact that tertiary control reserves are not immediately available and, therefore, must be requested in advance, a pattern is proposed based on a Model Predictive Control (MPC) scheme. Linear regression models based on a robust Maximum Likelihood Estimate (MLE) are employed to predict the control signal. The proposed optimization technique is simulated using measurements from the Swiss power system to illustrate technical and economic aspects.
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