707 research outputs found

    Large Scale Integration of Electric Vehicles into the Power Grid and Its Potential Effects on Power System Reliability

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    In this thesis, the potential effects of large scale integration of electric vehicles into the power grid are discussed in both the beneficial and detrimental aspects. The literature review gives a comprehensive introduction about the existing smart charging algorithms. According to the system structure and market mechanism, the smart charging algorithms can be divided into centralized and distributed method. With the knowledge of driving patterns and charging characteristics of electric vehicles, both the centralized and decentralized smart charging algorithms are studied in this research. Based on the smart charging pricing and sequential price update mechanism, a multi-agent based distributed smart charging algorithm is used in this research to flatten the load curve and therefore mitigate the potential detrimental effects caused by uncoordinated charging. Each EV agent has some extent of intelligence to solve its own charging scheduling problem. The optimization method used in this research is the binary hybrid GSA-PSO algorithm, which combines the merits of particle swarm optimization (PSO) and gravitational search algorithm (GSA), and has very good exploration and exploitation abilities. A V2G enabled centralized smart charging algorithm is also introduced in this thesis, each EV can earn revenues by discharging power into the grid. The dominant search matrix is used to resolve the \u27\u27curse of dimensionality\u27\u27 problem existing in the centralized optimization problems. Numerical case studies show both the distributed and V2G enabled smart charging algorithms can effectively transfer the charging load from the peak load period to the load valley hours. Because of the limited integration ratio of electric vehicles, most power system reliability methods do not evaluate the charging load of EVs separately in their analytical procedures. However, with a fast increasing integration level, the potential effects of large scale integration of EVs on the power system reliability should be comprehensively evaluated. The effects of EV charging on power system reliability in the planning phase is analyzed in this research based on the RBTS. The results show the uncontrolled charging will deteriorate the reliability level while the smart charging can effectively decrease the detrimental effect. The potential application of aggregated EV providing operating reserve to the grid as a kind of ancillary service is also discussed, and the related effects on power system reliability in operating phase are calculated using the modified PJM method. The case study shows the unit commitment risk of the system can decrease to a very low level with the additional operating reserve capacity provided by aggregated EVs, which can not only improve the system\u27s reliability level but also save the cost

    Normalization of large-scale behavioural data collected from zebrafish

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    Many contemporary neuroscience experiments utilize high-throughput approaches to simultaneously collect behavioural data from many animals. The resulting data are often complex in structure and are subjected to systematic biases, which require new approaches for analysis and normalization. This study addressed the normalization need by establishing an approach based on linear-regression modeling. The model was established using a dataset of visual motor response (VMR) obtained from several strains of wild-type (WT) zebrafish collected at multiple stages of development. The VMR is a locomotor response triggered by drastic light change, and is commonly measured repeatedly from multiple larvae arrayed in 96-well plates. This assay is subjected to several systematic variations. For example, the light emitted by the machine varies slightly from well to well. In addition to the light-intensity variation, biological replication also created batch-batch variation. These systematic variations may result in differences in the VMR and must be normalized. Our normalization approach explicitly modeled the effect of these systematic variations on VMR. It also normalized the activity profiles of different conditions to a common baseline. Our approach is versatile, as it can incorporate different normalization needs as separate factors. The versatility was demonstrated by an integrated normalization of three factors: light-intensity variation, batch-batch variation and baseline. After normalization, new biological insights were revealed from the data. For example, we found larvae of TL strain at 6 days post-fertilization (dpf) responded to light onset much stronger than the 9-dpf larvae, whereas previous analysis without normalization shows that their responses were relatively comparable. By removing systematic variations, our model-based normalization can facilitate downstream statistical comparisons and aid detecting true biological differences in high-throughput studies of neurobehaviour

    Analysis of the Impact of the Fed’s Interest Rate Hike Policy Based on the Triple Exponential Smoothing Method

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    The US has been employing quantitative easing and printing money for more than a decade, fueling inflation while lifting government debt. To deal with the high CPI, the Federal Reserve (Fed) has resorted to a policy of interest rate hikes, especially in the past two years, when the US interest rates have been on an upward trend. This has led to divergence in the economic trends and policy directions of countries, with China facing a more complex and severe external environment. In this connection, first of all, this paper gathers the response policy of each country during the Fed’s interest rate hikes, makes qualitative analysis of those response policies, and provides pertinent suggestions for China’s economic activities based on its national realities. Then, this paper collects data on the US interest rates, CPI, changes in nonfarm payrolls, monthly rate of retail sales, ISM manufacturing index, and ISM non-manufacturing index starting from January 2022 to December 2023, builds a model using the Triple Exponential Smoothing Method and applying statistical analysis, and solves it with python programming. Finally, the paper analyzes changes in the macro-indicators of the results and forecasts data on each indicator for the next six months to derive the trend of the data on indicators concerning future interest rate hikes. By predicting the impact of future events on China’s economy, investment, foreign trade and other aspects, the author expects the country to make corresponding regulatory plans and policy reserves

    Expression profiling of the retina of pde6c, a zebrafish model of retinal degeneration

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    Retinal degeneration often affects the whole retina even though the disease-causing gene is specifically expressed in the light-sensitive photoreceptors. The molecular basis of the retinal defect can potentially be determined by gene-expression profiling of the whole retina. In this study, we measured the gene-expression profile of retinas microdissected from a zebrafish pde6cw59 (pde6c) mutant. This retinal-degeneration model not only displays cone degeneration caused by a cone-specific mutation, but also other secondary cellular changes starting from 4 days postfertilization (dpf). To capture the underlying molecular changes, we subjected pde6c and wild-type (WT) retinas at 5 dpf/ 120 h postfertilization (hpf) to RNA sequencing (RNA-Seq) on the Illumina HiSeq 2,000 platform. We also validated the RNA-Seq results by Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) of seven phototransduction genes. Our analyses indicate that the RNA-Seq dataset was of high quality, and effectively captured the molecular changes in the whole pde6c retina. This dataset will facilitate the characterization of the molecular defects in the pde6c retina at the initial stage of retinal degeneration

    MPCPA: Multi-Center Privacy Computing with Predictions Aggregation based on Denoising Diffusion Probabilistic Model

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    Privacy-preserving computing is crucial for multi-center machine learning in many applications such as healthcare and finance. In this paper a Multi-center Privacy Computing framework with Predictions Aggregation (MPCPA) based on denoising diffusion probabilistic model (DDPM) is proposed, in which conditional diffusion model training, DDPM data generation, a classifier, and strategy of prediction aggregation are included. Compared to federated learning, this framework necessitates fewer communications and leverages high-quality generated data to support robust privacy computing. Experimental validation across multiple datasets demonstrates that the proposed framework outperforms classic federated learning and approaches the performance of centralized learning with original data. Moreover, our approach demonstrates robust security, effectively addressing challenges such as image memorization and membership inference attacks. Our experiments underscore the efficacy of the proposed framework in the realm of privacy computing, with the code set to be released soon
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