Efficient processing of system scenarios in statistical and machine learning studies for power system operational and investment planning

Power System security assessment and the associated planning studies are becoming more and more complex with ever increasing uncertainties in all time horizons. An effective means of performing operational and investment planning studies of network limitations associated with static or dynamic post-disturbance performance problems has been to take a Monte Carlo simulation based approach. The approach harnesses computing power to develop a database of post-contingency response over a wide range of different operating conditions, and then apply statistical or machine learning methods to extract useful planning and operational information from the database. Key to the machine learning based planning approach is the manner in which the different operating conditions are sampled to generate a training database. This work develops an efficient sampling procedure that maximizes information content in the training database while minimizing computing requirements to generate it, by finding the most influential region in the sampling state space and sampling operating conditions from it according to their relative likelihood. The Monte-Carlo variance-reduction methods are used to construct the proposed sampling approach, which is envisioned to allow market-oriented industries to operate the system according to economic rule. The dissertation also develops a comprehensive methodology to perform decision tree based security assessment for multiple contingencies. The system security limits and associated operating rules depend on the set of contingencies considered for planning. Considering the probabilistic nature of the power system, this work develops a risk based contingency ranking method that helps in screening the most critical contingencies from a contingency list. The developed contingency risk estimation method gives realistic risk indices since it takes into account the non-parametric nature of operating condition distribution, and it also saves tremendous computational cost since it uses linear sensitivities to estimate the risk. Finally, a contingency grouping method is proposed that guides in generating common operating rules for every group that performs well for all the contingencies in that respective group, thereby providing system operators the benefit of dealing with lesser number of rules. The contingency grouping is based on newly devised metric called progressive entropy that helps in finding similarities among contingencies based on their consequences on the operating conditions along all the load ranges, and not just their proximity in the grid. The proposed methods are implemented in the west France, Brittany region of RTE-France\u27s test system to derive decision rules for multiple contingencies against voltage stability problems

Coordinated static and dynamic reactive power planning against power system voltage stability related problems

Power System, over the many years, has undergone dramatic revolution both in technological as well as structural aspects. With the ongoing growth of the electric utility industry, including deregulation in many countries; numerous changes are continuously being introduced to a once predictable system. In an attempt to maximally use the transmission system capacities for economic transfers, transmission systems are being pushed closer to their stability and thermal limits, with voltage instability becoming a major limiting factor. Insufficient reactive power support affects the reliable operation of electric power systems leading to voltage collapses as observed by the recent 2003 blackout. Among the many available solution options, installation of reactive power control devices such as MSCs, FACTS devices etc seem more viable. This is a typical long term planning problem that needs to consider both steady state as well dynamic condition of the power system after severe contingencies and use better informative indices for the planning process.;A mixed integer programming based algorithm is made use of in this work to develop a comprehensive tool to perform a coordinated planning of static and dynamic reactive power control devices while satisfying the performance requirements of voltage stability margin and transient voltage dip. The systematic planning procedure is illustrated on a large scale case study. The effectiveness of the planning algorithm is demonstrated using two separate planning problems, one where steady state planning is done exclusively against static voltage stability problems, and the other where a coordinated steady state and dynamic Var planning problem is solved.;The results of this work show the effectiveness of the developed planning tool to find a low cost optimal reactive power allocation solution to enable higher real power transfers and improve voltage stability. We envision the method developed will be a research grade tool for planning reactive control devices against voltage instability and will provide system planners a proper guide to find viable and economical planning solutions

Astrocytic S100B, Blood-Brain Barrier and Neurodegenerative Diseases

Increased life span and expectations of a better quality of life have resulted in a spotlight on neurodegenerative and cardiovascular diseases generally associated with aging. The drive toward evidence-based medicine has necessitated a constant search for objective biomarkers to assay disease onset, progress, and outcomes to make the best clinical decisions. Enhancement of their use depends on the mechanistic understanding of the biomarker’s role in the disease process itself. This chapter focuses on S100B. It is a calcium sensor protein that is primarily astrocytic. While it plays a complex, interlinked role in signaling, serum levels of S100B as a biomarker for clinical decisions is also an area of intense investigation. Both aspects are presented, with an emphasis on the role of S100B in in maintaining a blood-brain barrier, especially in the context of suggesting a unified mechanism for the onset and progression of neurodegenerative diseases

Data on Final Calcium Concentration in Native Gel Reagents Determined Accurately Through Inductively Coupled Plasma Measurements

In this article we present data on the concentration of calcium as determined by Inductively Coupled Plasma (ICP) measurements. Calcium was estimated in the reagents used for native gel electrophoresis of Neuronal Calcium Sensor (NCS) proteins. NCS proteins exhibit calcium-dependent mobility shift in native gels. The sensitivity of this shift to calcium necessitated a precise determination of calcium concentrations in all reagents used. We determined the calcium concentrations in different components used along with the samples in the native gel experiments. These were: 20 mM Tris pH 7.5, loading dye and running buffer, with distilled water as reference. Calcium determinations were through ICP measurements. It was found that the running buffer contained calcium (244 nM) over the blank

Effect of glucagon-like peptide 1 receptor agonists on albuminuria in adult patients with type 2 diabetes mellitus: A systematic review and meta-analysis

Aims: To determine the effect of glucagon-like peptide 1 receptor agonists (GLP-1RAs) on albuminuria in adult patients with type 2 diabetes mellitus (T2DM). Methods: Medline Ovid, Scopus, Web of Science, EMCARE and CINAHL databases from database inception until 27 January 2022. Studies were eligible for inclusion if they were randomized controlled trials that involved treatment with a GLP-1RA in adult patients with T2DM and assessed the effect on albuminuria in each treatment arm. Data extraction was conducted independently by three individual reviewers. The PRISMA guidelines were followed regarding data extraction and quality assessment. Data were pooled using a random effects inverse variance model and all analysis was carried out with RevMan 5.4 software. The Jadad scoring tool was employed to assess the quality of evidence and risk of bias in the randomized controlled trials. Results: The initial search revealed 2419 articles, of which 19 were included in this study. An additional three articles were identified from hand-searching references of included reviews. Therefore, in total, 22 articles comprising 39 714 patients were included. Meta-analysis suggested that use of GLP1-RAs was associated with a reduction in albuminuria in patients with T2DM (weighted mean difference −16.14%, 95% CI −18.42 to −13.86%; p < .0001) compared with controls. Conclusions: This meta-analysis indicates that GLP-1RAs are associated with a significant reduction in albuminuria in adult patients with T2DM when compared with placebo