Reinforcement Learning Scheduler for Vehicle-to-Vehicle Communications Outside Coverage

Radio resources in vehicle-to-vehicle (V2V) communication can be scheduled either by a centralized scheduler residing in the network (e.g., a base station in case of cellular systems) or a distributed scheduler, where the resources are autonomously selected by the vehicles. The former approach yields a considerably higher resource utilization in case the network coverage is uninterrupted. However, in case of intermittent or out-of-coverage, due to not having input from centralized scheduler, vehicles need to revert to distributed scheduling. Motivated by recent advances in reinforcement learning (RL), we investigate whether a centralized learning scheduler can be taught to efficiently pre-assign the resources to vehicles for out-of-coverage V2V communication. Specifically, we use the actor-critic RL algorithm to train the centralized scheduler to provide non-interfering resources to vehicles before they enter the out-of-coverage area. Our initial results show that a RL-based scheduler can achieve performance as good as or better than the state-of-art distributed scheduler, often outperforming it. Furthermore, the learning process completes within a reasonable time (ranging from a few hundred to a few thousand epochs), thus making the RL-based scheduler a promising solution for V2V communications with intermittent network coverage.Comment: Article published in IEEE VNC 201

A Generation Prioritization Method for Power System Restoration with Renewable Resources

The electrical grid is a key component of the Nation's critical infrastructure. Its continuous and reliable operation is of vital importance; any system-wide disruption would have a debilitating impact on crucial services, public health and safety, the economy, and the national security of the United States. High-impact low-frequency events pose the greatest threat to the grid due to their severity and unpredictability; these may be caused by naturally occurring hazards (e.g., earthquakes, solar storms, extreme weather conditions) or acts of human volition (e.g., malicious cyber-, or coordinated set of physical attacks). To improve the resilience of the grid against such threats and enhance the protection of the critical infrastructure, this research examines ways in which power system restoration processes are considerably accelerated. A new generation prioritization method is proposed for time-sensitive system restorations, which significantly shortens the total restoration times. To achieve this, available renewable generation is leveraged and an algorithm is developed that plans the optimal operational schedule of the system close to real time. Benchmarking and validation experiments are performed on test systems modeled after the electrical grid of the U.S. Pacific Northwest, and prove that this convenient toolset supports a quick recovery, faster than other common approaches

Real-Time Implementation of Dynamic State Estimation for Microgrid Load Bus Protection

Inverter-interfaced microgrids, owing to the lack of fault current, cannot be protected using traditional over-current protections, while admittance or differential relaying protection schemes are not practical to be implemented. Dynamic state estimation can track and predict power system transients and has been extensively investigated for setting-less protection. A novel real-time application of dynamic state estimation for protection is proposed in this paper, wherein parameter estimation and parallel processing is used to identify the state of the system. The implementation scheme has low process complexity and employs a data acquisition device and estimator that run on a general-purpose computer. This proposed implementation extends the state-of-the-art, under short-circuit conditions, to a real-time implementation with a lumped-load radial microgrid and a grid-forming inverter with current-limiting behavior.Comment: Preprint. Accepted by the 2023 IEEE Kansas Power and Energy Conferenc

Dynamic State Estimation for Load Bus Protection on Inverter-Interfaced Microgrids

Inverter-interfaced microgrids results in challenges when designing protection systems. Traditional time-overcurrent, admittance, and differential protection methods are unsuitable on account of lack of fault current, excessively short lines, or a prohibitive number of protective devices needing to be installed. Current practice is to force all inverters to shut down during fault conditions, weakening resilience and reducing reliability. Dynamic state estimation (DSE), which has been explored for both line protection and load bus protection before, is a potential solution to these challenges to create widely utilizable, highly reliable protection systems. However, it has only been tested for load protection with ideal voltage sources, which do not capture the short-circuit behavior of inverter-interfaced generation, notably low fault current and unbalanced output voltage. This paper aims to extend the state-of-the-art on DSE load protection: the performance of DSE during short-circuit conditions with a grid-forming inverter with current-limiting behavior during fault conditions is investigated.Comment: 5 pages. 3 figures. 1 table

Relevance of CYP2C9 Function in Valproate Therapy

Genetic polymorphisms of drug metabolizing enzymes can substantially modify the pharmacokinetics of a drug and eventually its efficacy or toxicity; however, inferring a patient's drug metabolizing capacity merely from his or her genotype can lead to false prediction. Non-genetic host factors (age, sex, disease states) and environmental factors (nutrition, co-medication) can transiently alter the enzyme expression and activities resulting in genotype-phenotype mismatch. Although valproic acid is a well-tolerated anticonvulsant, pediatric patients are particularly vulnerable to valproate injury that can be partly attributed to the age-related differences in metabolic pathways. CYP2C9 mediated oxidation of valproate, which is the minor metabolic pathway in adults, appears to become the principal route in children. Genetic and non-genetic variations in CYP2C9 activity can result in significant inter- and intra-individual differences in valproate pharmacokinetics and valproate induced adverse reactions. The loss-of-function alleles, CYP2C9*2 or CYP2C9*3, display significant reduction in valproate metabolism in children; furthermore, low CYP2C9 expression in patients with CYP2C9*1/*1 genotype also leads to a decrease in valproate metabolizing capacity. Due to phenoconversion, the homozygous wild genotype, expected to be translated to CYP2C9 enzyme with normal activity, is transiently switched into poor (or extensive) metabolizer phenotype. Novel strategy for valproate therapy adjusted to CYP2C9-status (CYP2C9 genotype and CYP2C9 expression) is strongly recommended in childhood. The early knowledge of pediatric patients' CYP2C9-status facilitates the optimization of valproate dosing which contributes to the avoidance of misdosing induced adverse reactions, such as abnormal blood levels of ammonia and alkaline phosphatase, and improves the safety of children's anticonvulsant therapy. 

What is the role of activism in air pollution politics? Understanding policy change in Poland

There has been growing awareness across the world of the negative health effects of air pollution. Poland is the European country that is worst affected by this problem, and the Polish government has in recent years adopted a number of measures designed to reduce coal use. This paper explores the role of civil society activism in this shift, investigating the extent to which local activists played a catalytic role in shaping popular awareness of air pollution and accounting for policy developments in this area. We draw on individual-level data from two Eurobarometer surveys together with qualitative data from a series of original elite interviews and the analysis of related policy documents, and we find little evidence that activism was a driver of variations in local popular awareness of air pollution, but support for the supposition that activism played a major role in shaping policy change at local level

Activism in the era of democratic backsliding: explaining the efficacy of the clean-air campaigns in Poland

Recent scholarship on popular mobilization and activism in Central and East Europe suggests a shift from institutionalized civil society organizations towards grassroots mobilization. Whilst the emergence of such citizen-led activism across the region can be traced back to the anti-neoliberal urban movements that arose in the 2010s in the immediate post EU-accession period, the so-called “illiberal turn” and the legal restrictions placed on formal civil society organizations by radical right and conservative politicians have arguably exacerbated the shift and momentum. In Poland, the reaction of political elites to air pollution activism and the apparent responsiveness of policymakers is particularly puzzling given the “green conservatism” bordering on “environmental nativism” of the Law and Justice government (2015-2023). Building on semi-structured interviews conducted with 30 policymakers and activists involved in the clean air campaign in Poland, we contend that their success in terms of increased public awareness and positive government response is a consequence of the concurrence of (i) a particular (health) framing of air pollution, (ii) the devolution of power and responsibility for managing air quality to regional government, (iii) the circulation of new information and data, and (iv) the emergence of new actors and activist strategies

Measuring and Analyzing Effects of HEMP Simulation on Synthetic Power Grids

There is significant uncertainty about the potential effects of a high-altitude electromagnetic pulse (HEMP) detonation on the bulk electric system. This study attempts to account for such uncertainty, in using Monte-Carlo methods to account for speculated range of effect of HEMP contingency. Through task parallelism and asynchronous processing techniques implemented throughout simulation, this study measure the effects of 700 large-scale HEMP simulations on a 7173 bus synthetic power grid. Analysis explores how contingency severity varies, depending on initial contingency parameters. Severity indices were captured throughout simulation to measure and quantify the cascading nature of an HEMP event. Further development of HEMP simulation modeling is explored as well, which could augment forecasts of potential contingency events as well.Comment: 6 pages. 13 figure