The uncertainties and intermittency associated with renewable generation sources, such as solar and wind, can pose significant overloading risks to power systems under N − k contingencies, potentially leading to cascading outages. Accurately quantifying these risks in independent system operator scale power systems, which may include tens of thousands of buses, remains a grand challenge. This paper proposes a computationally efficient, tail-distribution-aware approach for accurate overloading risk quantification in large-scale power systems. Specifically, a deep-kernel sparse vector-valued Gaussian process is developed and serves as a surrogate model. This model incorporates generation dispatch, predefined contingencies, and uncertain inputs, such as photovoltaic power and load demand, to predict their impacts on branch power flows, which are treated as the model outputs. To improve the fidelity of overloading risk assessment, we introduce an adaptive resampling mechanism based on power flow solver, which corrects biases in surrogate model predictions near the overloading threshold. Extensive results obtained on the realistic 21k+ bus New England power system demonstrate that the proposed method accelerates the risk assessment process by 22 times compared to the benchmark Monte Carlo sampling method, while maintaining high accuracy. Additionally, we validate the robustness of the approach across a wide range of distribution types and correlation scenarios between renewable generation and load demands. The study develops a computational method to assess power system overloading risks efficiently. It uses a surrogate model and resampling mechanism to enhance prediction accuracy near critical thresholds, demonstrating significant time savings and reliability on a large-scale power grid.
The aim of this study was to investigate the effects of antimony intoxicationon systemic hemodynamic parameters in rats against the background of hypercalcemia and melatonin administration. Material and methods. The experiment was conducted on 75 male Wistar rats divided into 7 groups. Over 30days, the animals were administered antimony chloride (3 mg/kg), vitamin D3 (3000 IU/100 g), and melatonin (10 mg/kg) both individually and in combination.Hypercalcemia was induced using vitamin D3. Key hemodynamic parameters, such as mean arterial pressure (MAP), heart rate (HR), cardiac index (CI), stroke index (SI), and total peripheral resistance (TPR), were measured using invasive methods. Results. The results showed that antimony intoxication caused a signifcant increase in MAP and TPR, as well as a decrease in CI and SI, indicating the development of a hypokinetic type of circulation. The administration of melatonin and vitamin D3 individually mitigated the toxic effects of antimony, reducing hypertensive effects and improving cardiac function. However, the most pronounced positive effect was observed with the combined use of melatonin and vitamin D3, which led to the normalization of MAP and signifcant restoration of cardiac function. Conclusion. The fndings confrm that melatonin and vitamin D3 can be effective in reducing the toxic effects of antimony on the cardiovascular system, particularly when used in combination. This opens prospects for further research into their protective mechanisms and potential clinical applications.
This study investigates the voltage ride-through responses of the distribution energy resources (DERs) with transmission-distribution co-simulation that involves study tools such as HELICS, PSS/E and GridLAB-D. Using realistic system planning model and data, the study aims at answering important questions of transmission planning such as the loss of distributed generation during transmission fault events. Key insights have been gained from this study. It has been found that the amount of tripped DER generation is related to DER penetration levels at each feeder and network phase unbalance, in addition to depressed substation voltages during fault events. The future efforts will include acquiring more DER and distribution network data, increasing the number of co-simulation study scenarios, and using the machine-learning regression models to make estimations of the percentage of lost generation.
Can private information or mediation change a sender's behavior and improve the receiver's expected utility in persuasive communication games? In a mediated Bayesian persuasion model, private information cannot improve the receiver's expected utility when the sender communicates it. When the intermediary communicates the private information, the receiver's expected utility improves only under sufficient accuracy of the intermediary's private information, as captured by a positive autarky value of the intermediary's private information (AVIPI). Finally, different classes of equilibria are analyzed to show that the sender's strategic behavior is generally affected by the intermediary's presence as he tries to persuade the intermediary to, in turn, persuade the receiver.
In recent years, weather events have increasingly impacted electric power production and delivery. To manage the uncertainties of weather variables, both analytical and data-driven approaches have been applied in power system operation and planning studies. This paper describes our project experience with using machine learning (ML) algorithms to predict transmission line outages. The lead ML algorithms are identified and implemented in the Online Weather Lookahead Study (OWLS), a web-based tool developed and deployed at the ISO New England. The main outcome of the ML algorithms are the transmission line outage probabilities for given local weather conditions, which are presented through graphical user interface to inform the system operator about the timeline, geographical distribution and severity of a developing weather system. The OWLS project is an important real-world application using ML techniques to assess the impact of weather events on the transmission system.
