Distributed Contingency Analysis over Wide Area Network among Dispatch Centers

Traditionally, a regional dispatch center uses the equivalent method to deal with external grids, which fails to reflect the interactions among regions. This paper proposes a distributed N-1 contingency analysis (DCA) solution, where dispatch centers join a coordinated computation using their private data and computing resources. A distributed screening method is presented to determine the Critical Contingency Set (DCCS) in DCA. Then, the distributed power flow is formulated as a set of boundary equations, which is solved by a Jacobi-Free Newton-GMRES (JFNG) method. During solving the distributed power flow, only boundary conditions are exchanged. Acceleration techniques are also introduced, including reusing preconditioners and optimal resource scheduling during parallel processing of multiple contingencies. The proposed method is implemented on a real EMS platform, where tests using the Southwest Regional Grid of China are carried out to validate its feasibility.Comment: 5 pages, 6 figures, 2017 IEEE PES General Meetin

On Fast-Converged Deep Reinforcement Learning for Optimal Dispatch of Large-Scale Power Systems under Transient Security Constraints

Power system optimal dispatch with transient security constraints is commonly represented as Transient Security-Constrained Optimal Power Flow (TSC-OPF). Deep Reinforcement Learning (DRL)-based TSC-OPF trains efficient decision-making agents that are adaptable to various scenarios and provide solution results quickly. However, due to the high dimensionality of the state space and action spaces, as well as the non-smoothness of dynamic constraints, existing DRL-based TSC-OPF solution methods face a significant challenge of the sparse reward problem. To address this issue, a fast-converged DRL method for TSC-OPF is proposed in this paper. The Markov Decision Process (MDP) modeling of TSC-OPF is improved by reducing the observation space and smoothing the reward design, thus facilitating agent training. An improved Deep Deterministic Policy Gradient algorithm with Curriculum learning, Parallel exploration, and Ensemble decision-making (DDPG-CPEn) is introduced to drastically enhance the efficiency of agent training and the accuracy of decision-making. The effectiveness, efficiency, and accuracy of the proposed method are demonstrated through experiments in the IEEE 39-bus system and a practical 710-bus regional power grid. The source code of the proposed method is made public on GitHub.Comment: 10 pages, 11 figure

Association of Heart Rate Variability in Taxi Drivers with Marked Changes in Particulate Air Pollution in Beijing in 2008

BACKGROUND: Heart rate variability (HRV), a marker of cardiac autonomic function, has been associated with particulate matter (PM) air pollution, especially in older patients and those with cardiovascular diseases. However, the effect of PM exposure on cardiac autonomic function in young, healthy adults has received less attention. OBJECTIVES: We evaluated the relationship between exposure to traffic-related PM with an aerodynamic diameters <= 2.5 mu m (PM(2.5)) and HRV in a highly exposed panel of taxi drivers. METHODS: Continuous measurements of personal exposure to PM(2.5) and ambulatory electrocardiogram monitoring were conducted on I I young healthy taxi drivers for a 12-hr work shift during their work time (0900-2100 hr) before, during, and after the Beijing 2008 Olympic Games. Mixed-effects regression models were used to estimate associations between PM(2.5) exposure and percent changes in 5-min HRV indices after combining data from the three time periods and controlling for potentially confounding variables. RESULTS: Personal exposures of taxi drivers to PM(2.5) changed markedly across the three time periods. The standard deviation of normal-to-normal (SDNN) intervals decreased by 2.2% [95% confidence interval (0), -3.8% to -0.6%] with an interquartile range (IQR; 69.5 mu g/m(3)) increase in the 30-min PM(2.5) moving average, whereas the low-frequency and high-frequency powers decreased by 4.2% (95% CI, -9.0% to 0.8%) and 6.2% (95% CI, -10.7% to -1.5%), respectively, in association with an IQR increase in the 2-hr PM(2.5) moving average. CONCLUSIONS: Marked changes in traffic-related PM(2.5) exposure were associated with altered cardiac autonomic function in young healthy adults.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000273292800029&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Environmental SciencesPublic, Environmental & Occupational HealthToxicologySCI(E)PubMed65ARTICLE187-9111

Si 3

Si3N4-SiCp composites reinforced by in situ catalytic formed nanofibers were prepared at a relatively low sintering temperature. The effects of catalyst Co on the phase compositions, microstructures, and physicochemical-mechanical properties of samples sintered at 1350°C–1450°C were investigated. The results showed that the catalyst Co enhanced the nitridation of Si. With the increase of Co addition (from 0 wt% to 2.0 wt.%), the apparent porosity of as-prepared refractories was initially decreased and subsequently increased, while the bulk density and the bending strength exhibited an opposite trend. The Si3N4-SiCp composites sintered at 1400°C had the highest strength of 60.2 MPa when the Co content was 0.5 wt.%. The catalyst Co facilitated the sintering of Si3N4-SiCp composites as well as the formation of Si3N4 nanofibers which exhibited network connection and could improve their strength