Learning-based short-time prediction of photovoltaic resources for pre-emptive excursion cancellation

There is a growing interest in using renewable energy resources (RES) such as wind, solar, geothermal and biomass in power systems. The main incentives for using renewable energy resources include the growing interest in sustainable and clean generation as well as reduced fuel cost. However, the challenge with using wind and solar resources is their indeterminacy which leads to voltage and frequency excursions. In this dissertation, first, the economic dispatch (ED) problem for a community microgrid is studied which explores a community energy market. As a result of this work, the importance of modeling and predicting renewable resources is understood. Hence, a new algorithm based on dictionary learning for prediction of solar production is introduced. In this method, a dictionary is trained to carry various behaviors of the system. Prediction is performed by reconstructing the tail of the upcoming signal using this dictionary. To improve the accuracy of prediction, a new approach based on a novel clustering-based Markov Switched Autoregressive Model is proposed that is capable of predicting short-term solar production. This method extracts autoregressive features of the training data and partitions them into multiple clusters. Later, it uses the representative feature of each cluster to predict the upcoming solar production level. Additionally, a Markov jump chain is added to improve the robustness of this scheme to noise. Lastly, a method to utilize these prediction mechanisms in a preemptive model predictive control is explored. By incorporating the expected production levels, a model predictive controller is designed to preemptively cancel the upcoming excursions --Abstract, page iv

Preemptive Control: A Paradigm in Supporting High Renewable Penetration Levels

This paper investigates a preemptive approach to cope with solar induced grid voltage intermittencies. To do so, this paper proposes a combination of a predictor and a voltage controller to eliminate the delays associated with conventional sample based voltage regulation. Unlike model predictive controllers, the proposed preemptive approach focuses on the input disturbances and does not neglect them as zero mean wiener processes. Additionally, due to the utilization of input predictors, the controller is no longer bound to causal response to the sampled data and can preemptively compensate for upcoming events. After introducing the proposed controller, simulation results are provided to compare the effectiveness of this approach in comparison with the existing voltage regulator schemes

Economic Dispatch for an Agent-Based Community Microgrid

In this paper, an economic dispatch (ED) problem for a community microgrid is studied. In this microgrid, each agent pursues an ED for its personal resources. In addition, each agent is capable of trading electricity with other agents through a local energy market. In this paper, an energy market operating in the presence of the grid is introduced. The proposed market is mainly developed for an experimental community microgrid at the Missouri University of Science and Technology, Rolla, MO, USA, and can be applied to other distribution level microgrids. To develop the algorithm, first, the microgrid is modeled and a dynamic ED algorithm for each agent is developed. Afterwards, an algorithm for handling the market is introduced. Lastly, simulation results are provided to demonstrate the proposed community market, and show the effectiveness of the market in reducing the operation costs of passive and active agents

Dictionary Learning for Short-term Prediction of Solar PV Production

Prediction of power generated from renewable energy resources such as solar photo-voltaic (PV) is a crucial task for stabilization of grids with high renewable penetration levels. Short-term prediction of these resources allow for preemptive regulation of injected power fluctuations. In this paper, a new algorithm based on dictionary learning for prediction of solar power fluctuations is introduced. This algorithm is effective on systems with structural regularities. In this method, a dictionary is trained to carry various behaviors of the system. Prediction is performed by reconstructing the tail of the upcoming signal using this dictionary. After introduction of the proposed algorithm, experimental results are provided to evaluate the prediction mechanism

Molecular eidemiology of carbapenem-resistant Enterobacter cloacae complex in a tertiary hospital in Shandong, China

Abstract Background The increasing incidence and prevalence of carbapenem-resistant Enterobacter cloacae complex (CREC) poses great challenges to infection prevention and disease treatment. However, much remains unknown about the clinical characteristics of CREC isolates. Our objective was to characterize antimicrobial resistance and, carbapenemase production in CREC with 36 CREC isolates collected from a tertiary hospital in Shandong, China. Results Three types of carbapenemases (NDM, IMP and VIM) were detected in these isolates. Among them, NDM carbapenemases were most prevalent, with a 61.2% (22/36) detection rate for NDM-1, 27.8% (10/36) for NDM-5 and 2.8% (1/36) for NDM-7. IMP-4 was found in two isolates and VIM-1 in only one isolate. The MLST analysis identified 12 different sequence types (STs), of which ST171 (27.8%) was the most prevalent, followed by ST418 (25.0%). ST171 isolates had significantly higher rates of resistance than other STs to gentamicin and tobramycin (Ps < 0.05), and lower rates of resistance to aztreonam than ST418 and other STs (Ps < 0.05). Among 17 carbapenemase-encoding genes, the bla NDM−5 gene was more frequently detected in ST171 than in ST418 and other isolates (Ps < 0.05). In contrast, the bla NDM−1 gene was more frequently seen in ST418 than in ST171 isolates. One novel ST (ST1965) was identified, which carried the bla NDM−1 gene. Conclusion NDM-5 produced by ST171 and NDM-1 carbapenemase produced by ST418 were the leading cause of CREC in this hospital. This study enhances the understanding of CREC strains and helps improve infection control and treatment in hospitals

Lactate Dehydrogenase B Is Associated with the Response to Neoadjuvant Chemotherapy in Oral Squamous Cell Carcinoma

<div><p>Oral squamous cell carcinoma (OSCC) comprises a subset of head and neck squamous cell carcinoma (HNSCC) with poor therapeutic outcomes and high glycolytic dependency. Neoadjuvant chemotherapy regimens of docetaxel, cisplatin and 5-fluorouracil (TPF) are currently accepted as standard regimens for HNSCC patients with a high risk of distant metastatic spread. However, the antitumor outcomes of TPF neoadjuvant chemotherapy in HNSCC remain controversial. This study investigated the role of lactate dehydrogenase B (LDHB), a key glycolytic enzyme catalyzing the inter-conversion between pyruvate and lactate, in determining chemotherapy response and prognosis in OSCC patients. We discovered that a high protein level of LDHB in OSCC patients was associated with a poor response to TPF regimen chemotherapy as well as poor overall survival and disease-free survival. Our in-depth study revealed that high LDHB expression conferred resistance to taxol but not 5-fluorouracil or cisplatin. LDHB deletion sensitized OSCC cell lines to taxol, whereas the introduction of LDHB decreased sensitivity to taxol treatment. Taxol induced a pronounced impact on LDHB-down-regulated OSCC cells in terms of apoptosis, G2/M phase cell cycle arrest and energy metabolism. In conclusion, our study highlighted the critical role of LDHB in OSCC and proposed that LDHB could be used as a biomarker for the stratification of patients for TPF neoadjuvant chemotherapy and the determination of prognosis in OSCC patients.</p></div

LDHB impacts the efficacy of taxol.

<p>(A, B) KB and HN12 cells were transfected with scrambled or LDHB siRNA. (A) After 72 h, LDHB expression was examined, and LDH activity was measured. (B) After 48 h, cells were washed with PBS twice followed by the addition of fresh serum-free media and extracellular lactate amount was measured at 12 h post-incubation. (C, D) KB and HN12 cells were transfected with scrambled or LDHB siRNA and then treated with DMSO or taxol for 72 h at the indicated concentrations, and cell viability was measured. (E) The LDHB stable-interference strains of KB cells were established and verified by western blotting; the clonogenic assay was conducted in shCON and shLDHB KB cells exposed to 0.5 nM taxol. (F) HN30 cells were transfected with empty vector or LDHB and treated with DMSO or taxol for 72 h at the indicated concentrations. *<i>P</i> < 0.05, compared to the control group. Mean ± SE (n = 3).</p

Combination of taxol and LDHB down-regulation exhibits a synergistic effect on cell metabolism.

<p>KB and HN12 cells were transfected with scramble or LDHB siRNA and then treated with DMSO or taxol of 10 nM for 24 h. (<b>A, B</b>) ECAR was measured by an XF96 analyzer. (<b>C, D</b>) OCR was measured by an XF96 analyzer. (<b>E, F</b>) Cell lysates were prepared, and the intracellular ATP level was determined. *<i>P</i> < 0.05, compared to the control group; ^#<i>P</i> < 0.05, compared to the taxol group. Mean ± SE (n = 3).</p

LDHB is associated with prognosis and TPF induction chemotherapy in OSCC patients.

<p>(A) Representative images of immunohistochemical staining for LDHB expression in OSCC patients. (B, C) The correlation of LDHB expression with overall survival and disease-free survival in OSCC patients (n = 107), as determined by Kaplan-Meier survival analysis. The <i>P</i> value was calculated using the log-rank test. (D) The correlation between LDHB expression and TPF induction chemotherapy efficacy in OSCC patients, as determined by cross-table analysis (n = 50).</p

Knockdown of LDHB induces the mitochondrion-dependent apoptosis pathway.

<p>KB cells were transfected with scrambled or LDHB siRNA and then treated with DMSO or 10 nM taxol for 24 h. (<b>A</b>) The mitochondria and the cytoplasm were separated and measured for cytochrome C by western blotting. COX IV was used as a marker for mitochondria. (<b>B</b>) Mitochondrial cytochrome C was measured by FACS analysis. (<b>C</b>) Cell lysates were prepared for western blotting. GAPDH was used as a loading control.</p