Data-Driven Continuous-Time Framework for Frequency-Constrained Unit Commitment

The conventional approach to solving the unit commitment problem involves discrete intervals at an hourly scale, particularly when integrating frequency dynamics to formulate a frequency-constrained unit commitment. To overcome this limitation, a novel continuous-time frequency-constrained unit commitment framework is proposed in this paper. In this approach, Bernstein polynomials represent continuous variables in the unit commitment problem and enable the calculation of frequency response-related metrics such as the rate of change of frequency, quasi-steady-state frequency, and frequency nadir. Notably, startup and shut-down trajectories are meticulously considered, transforming the formulation into a fully continuous-time model and simplifying constraints related to variable continuity. To address the complexities associated with integrating the obtained non-linear frequency nadir constraint into a mixed-integer linear problem, an alternative data-driven frequency nadir constraint is proposed, which accurately constrains frequency nadir deviations throughout the time interval. To validate the proposed model, it is applied to the real-life network of the Spanish Island of La Palma. The results demonstrate the effectiveness of the proposed formulation, indicating that the model is solved timely while mitigating the impact of intra-hour real-time power fluctuations on system frequency

Unit commitment with analytical underfrequency load-shedding constraints for island power systems

This letter presents a corrective frequency-constrained UC (C-FCUC) for island power systems implementing analytical constraints on underfrequency load shedding (UFLS). Since UFLS is inevitable for sufficiently large disturbances, one can argue that less spinning reserve could be held back since UFLS takes place anyway. Congruently, the reserve criterion should consider UFLS likely to occur under disturbances. The C-FCUC can be converted into a preventive frequency-constrained UC (P-FCUC) or the standard unit commitment (UC) and the C-FCUC is thus a generalization. The proposed formulation is successfully applied to a Spanish island power system

Data-driven Estimation of Under Frequency Load Shedding after Outages in Small Power Systems

This paper presents a data-driven methodology for estimating Under Frequency Load Shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold following loss of generation. Using a dynamic System Frequency Response (SFR) model we generate different values of UFLS (i.e., labels) predicated on a set of carefully selected operating conditions (i.e., features). Machine Learning (ML) algorithms are then applied to learn the relationship between chosen features and the UFLS labels. A novel regression tree and the Tobit model are suggested for this purpose and we show how the resulting non-linear model can be directly incorporated into a Mixed Integer Linear Programming (MILP) problem. The trained model can be used to estimate UFLS in security-constrained operational planning problems, improving frequency response, optimizing reserve allocation, and reducing costs. The methodology is applied to the La Palma island power system, demonstrating its accuracy and effectiveness. The results confirm that the amount of UFLS can be estimated with the Mean Absolute Error (MAE) as small as 0.213 MW for the whole process, with a model that is representable as a MILP for use in scheduling problems such as unit commitment among others

SmarTEG: An autonomous wireless sensor node for high accuracy accelerometer-based monitoring

We report on a self-sustainable, wireless accelerometer-based system for wear detection in a band saw blade. Due to the combination of low power hardware design, thermal energy harvesting with a small thermoelectric generator (TEG), an ultra-low power wake-up radio, power management and the low complexity algorithm implemented, our solution works perpetually while also achieving high accuracy. The onboard algorithm processes sensor data, extracts features, performs the classification needed for the blade’s wear detection, and sends the report wirelessly. Experimental results in a real-world deployment scenario demonstrate that its accuracy is comparable to state-of-the-art algorithms executed on a PC and show the energy-neutrality of the solution using a small thermoelectric generator to harvest energy. The impact of various low-power techniques implemented on the node is analyzed, highlighting the benefits of onboard processing, the nano-power wake-up radio, and the combination of harvesting and low power design. Finally, accurate in-field energy intake measurements, coupled with simulations, demonstrate that the proposed approach is energy autonomous and can work perpetually

Data-driven estimation of the amount of under frequency load shedding in small power systems

This paper presents a data-driven methodology for estimating under frequency load shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified threshold following loss of generation. Using a dynamic system frequency response (SFR) model we generate different values of UFLS (i.e., labels) predicated on a set of carefully selected operating conditions (i.e., features). Machine learning (ML) algorithms are then applied to learn the relationship between chosen features and the UFLS labels. A novel regression tree and the Tobit model are suggested for this purpose and we show how the resulting non-linear model can be directly incorporated into a MILP problem. The trained model can be used to estimate UFLS in security-constrained operational planning problems, improving frequency response, optimizing reserve allocation, and reducing costs. The methodology is applied to the La Palma island power system, demonstrating its accuracy and effectiveness. The results confirm that the amount of UFLS can be estimated with the mean absolute error (MAE) as small as 0.213 megawatts for the whole process, with a model that is representable as a mixed integer linear programming (MILP) for use in scheduling problems such as unit commitment among others

Improvement of rat islet viability during transplantation: validation of pharmacological approach to induce VEGF overexpression:

Delayed and insufficient revascularization during islet transplantation deprives islets of oxygen and nutrients, resulting in graft failure. Vascular endothelial growth factor (VEGF) could play a critical role in islet revascularization. We aimed to develop pharmacological strategies for VEGF overexpression in pancreatic islets using the iron chelator deferoxamine (DFO), thus avoiding obstacles or safety risks associated with gene therapy. Rat pancreatic islets were infected in vivo using an adenovirus (ADE) encoding human VEGF gene (4.10(8) pfu/pancreas) or were incubated in the presence of DFO (10 mumol/L). In vitro viability, functionality, and the secretion of VEGF were evaluated in islets 1 and 3 days after treatment. Infected islets or islets incubated with DFO were transplanted into the liver of syngenic diabetic rats and the graft efficiency was estimated in vivo by measuring body weight, glycemia, C-peptide secretion, and animal survival over a period of 2 months. DFO induced transient VEGF overexpression over 3 days, whereas infection with ADE resulted in prolonged VEGF overexpression lasting 14 days; however, this was toxic and decreased islet viability and functionality. The in vivo study showed a decrease in rat deaths after the transplantation of islets treated with DFO or ADE compared with the sham and control group. ADE treatment improved body weight and C-peptide levels. Gene therapy and DFO improved metabolic control in diabetic rats after transplantation, but this effect was limited in the presence of DFO. The pharmacological approach is an interesting strategy for improving graft efficiency during transplantation, but this approach needs to be improved with drugs that are more specific

A robust cell cycle control mechanism limits E2F-induced proliferation of terminally differentiated cells in vivo

Overexpression of both CycE and E2F is necessary to trigger cell cycle reentry and overproliferation of terminally differentiated wing cells

The SIB Swiss Institute of Bioinformatics' resources: focus on curated databases

The SIB Swiss Institute of Bioinformatics (www.isb-sib.ch) provides world-class bioinformatics databases, software tools, services and training to the international life science community in academia and industry. These solutions allow life scientists to turn the exponentially growing amount of data into knowledge. Here, we provide an overview of SIB's resources and competence areas, with a strong focus on curated databases and SIB's most popular and widely used resources. In particular, SIB's Bioinformatics resource portal ExPASy features over 150 resources, including UniProtKB/Swiss-Prot, ENZYME, PROSITE, neXtProt, STRING, UniCarbKB, SugarBindDB, SwissRegulon, EPD, arrayMap, Bgee, SWISS-MODEL Repository, OMA, OrthoDB and other databases, which are briefly described in this article