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Multi-level interactive unit commitment of regional power system

international JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2021年 125卷 106464-106464页

作者： Ji, Xingquan Zhang, Yumin Han, Xueshan Ye, Pingfeng Xu, Bo Yu, Yongjin Shandong Univ Sci & Technol Coll Elect Engn & Automat Qingdao 266590 Peoples R China Shandong Univ Minist Educ Key Lab Power Syst Intelligent Dispatch & Control Jinan 250061 Peoples R China State Grid Energy Res Inst Co Beijing 102209 Peoples R China

distributed energy resource (DER) including wind power, solar energy and energy storage system (ESS) are connected to the active distribution network (ADN) in various combination ways, which makes the distribution network have interaction. As a bridge connecting the transmission grid (TG) and micro grid (MG), ADN breaks the traditional operation pattern of TG + ADN + MG. Considering the physical connections and shared information among TG, ADN and MG, this paper proposes a decentralized and parallel analytical target cascading (ATC) algorithm for interactive unit commitment (UC) implementation in regional power systems. To explore the synergistic ability of the TG + ADN + MG coping with uncertainties of DER, i.e., wind power, the primary and secondary frequency regulation of TG are implemented to cope with uncertainties. Furthermore, the distributional uncertainty of wind power is well modeled by data driven, which is proposed in our previous work (Zhang et al., 2019) [1]. Both the startup/shutdown variables of the thermal units and the variables in TG + ADN + MG are integrated into the multi-level interactive UC model to optimize simultaneously, thus realizing the optimal goal of the whole network, resources complementary and optimal allocation of power system. An improved 6-bus system is used to test the proposed model, the numerical results show that the proposed decentralized algorithm is a fully parallelized procedure. And it also demonstrates the parallel implementation significantly enhances computations efficiency of the ATC algorithm.

关键词： Analytical target cascading algorithm distributed parallel computing Primary and secondary frequency regulation Unit commitment

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PARMA-CC: parallel Multiphase Approximate Cluster Combining 20

PARMA-CC: Parallel Multiphase Approximate Cluster Combining

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21st international conference on distributed computing and Networking (ICDCN)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450377515

Clustering is a common component in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g. rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g. detecting critical situations, that are known to be accuracy-dependent), demand for novel approaches that respond faster while coping with large data sets. The latter is the challenge we address in this paper. We propose an algorithm, PARMA-CC, that complements existing density-based and distance-based clustering methods. PARMA-CC, is based on approximate, data parallel cluster combining, where parallel threads can compute summaries of clusters of data (sub)sets and, through combining, together construct a comprehensive summary of the sets of clusters. By approximating clusters with their respective geometrical summaries, our technique scales well with increased data volumes, and, by computing and efficiently combining the summaries in parallel, it enables latency improvements. PARMA-CC combines the summaries using special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC can complement and outperform well-established methods, with significantly better scalability, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour. In the paper we also describe how PARMA-CC can facilitate time-critical applications through appropriate use of the summaries.

关键词： approximate parallel clustering cluster combining model fitting

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Image Classification Algorithm for Transmission Line Defects Based on Dual-Channel Feature Fusion 6th

Image Classification Algorithm for Transmission Line Defects...

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6th international conference on Human Centered computing, HCC 2020

作者： Liao, Yongli He, Jinqiang Zhu, Dengjie Fan, Xujuan Zhang, Xiancong Electric Power Research Institute CSG Guangzhou Guangdong510663 China Guangzhou Power Supply Bureau of Guangdong Power Grid Limited Liabilities Company Guangzhou Guangdong510000 China

ISBN: (纸本)9783030706258

The power system is of great significance to the normal production of society and the daily life of the people, so regular inspection of transmission lines is essential. However, transmission lines are usually exposed to the outdoors, and the surrounding terrain and environment are complex, which may lead to problems such as structural aging and mechanical strength reduction, which in turn may lead to large area power outages and cause huge economic losses. In this paper, a two-channel feature fusion classification method is proposed to address the transmission line image classification problem. Using a two-channel parallel network structure, a neural network model is constructed to fuse the overall and local feature information, and then determine whether there are defects in the transmission line images. The experimental results show that the classification accuracy of the two-channel parallel convolutional neural network based on ResNet32 is 82.24% and 77.87% for the bird’s nest defect and insulator burst defect, respectively, on the actual transmission line image dataset, which exceeds the classification accuracy of other CNN models. This indicates that the classification accuracy can be effectively improved by fusing feature information. © 2021, Springer Nature Switzerland AG.

关键词： Losses

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distributed load shedding algorithm for islanded microgrid using fog computing paradigm 6

Distributed load shedding algorithm for islanded microgrid u...

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6th IEEE international Energy conference, ENERGYCon 2020

作者： Rwegasira, Diana Dhaou, Imed Ben Kakakhel, Syed Westerlund, Tomi Tenhunen, Hannu University of Dar Es Salaam Tanzania United Republic of Royal Instittute of Technology Sweden Universty of Monastir Tunisia University of Turku Finland

ISBN: (纸本)9781728129563

Demand Side Management, DSM, is a program supported by the smart-grid which aims at matching the energy consumption and production. Several techniques for demand-side management have been proposed, including load-shedding, time of use pricing, real-time pricing, and critical peak pricing. In this work, we propose a distributed load-shedding algorithm using the multi-agent system. The agents in residential areas collaborate to reduce the energy demands using various forecasting techniques. The computational distributed framework is provided via fog computing to minimize power consumption, costs, and latency when designed using LoRaWAN protocol. © 2020 IEEE.

关键词： Energy utilization

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Proceedings - 2020 IEEE international conference on Joint Cloud computing, JCC 2020

Proceedings - 2020 IEEE International Conference on Joint Cl...

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2020 IEEE international conference on Joint Cloud computing, JCC 2020

ISBN: (纸本)9781728169811

The proceedings contain 18 papers. The topics discussed include: identifying the end-to-end backbone of information diffusion;honeypot contract risk warning on ethereum smart contracts;GraphLib: a parallel graph mining library for joint cloud computing;a cost-efficient and low-latency data hosting scheme in JointCloud storage;Jupiter: a modern federated learning platform for regional medical care;communication-efficient collaborative learning of geo-distributed JointCloud from heterogeneous datasets;a distributed computing framework based on variance reduction method to accelerate training machine learning models;and cross-domain workloads performance prediction via runtime metrics transferring.

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parallel simulation of stochastic reward nets using theatre 21

Parallel simulation of stochastic reward nets using theatre

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Proceedings of the 2021 IEEE/ACM 25th international Symposium on distributed Simulation and Real Time Applications

作者： Franco Cicirelli Libero Nigro CNR - National Research Council of Italy Italy University of Calabria Italy

ISBN: (纸本)9781665433266

This work aims at the development of tools for supporting modelling and analysis of timed systems by Stochastic Reward Nets (SRN). In a first approach it was proposed and experimented a formal reduction of SRN over Timed Automata (TA) in the context of the Uppaal popular toolbox. The reduction has the merit to allow both exhaustive model checking of an SRN model, useful for the assessment of qualitative properties (e.g., absence of deadlocks, occurrence of particular event sequences etc.), and quantitative analysis through the statistical model checker, which is based on simulations. However, although Uppaal enabled formal reasoning on the semantics of SRN, its practical usage suffers of scalability problems, that is it can introduce severe limitations in time and space when studying complex models. To cope with this problem, this paper describes a Java implementation of the SRN operational core engine, using the lock-free and efficient Theatre actor system which permits the parallel simulation of large models. The realization can be used for functional property checking on an untimed version of a source SRN model, and quantitative estimation of measurables through simulations. The paper discusses the design and implementation of the core engine of SRN on top of Theatre, together with supported intuitive configuration process of an SRN model, and reports some experimental results using a scalable grid computing model. The experiments confirm Theatre/SRN are capable of exploiting the potential of modern multi-core machines and can deliver good execution performances on large models.

关键词： theatre Java stochastic reward nets actors high-performance computing performability analysis

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High Power Density parallel LC-Link PV Inverter for Stand-alone and grid Mode of Operation 1st

High Power Density Parallel LC-Link PV Inverter for Stand-al...

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1st international conference on Intelligent computing Techniques for Smart Energy Systems (ICTSES)

作者： Athikamsetti, Rudra Santhosh Kumar Gudey, Satish Kumar Gayatri Vidya Parishad Coll Engn Autonomous Dept Elect & Elect Engn Visakhapatnam Andhra Pradesh India

ISBN: (纸本)9789811502149;9789811502132

A parallel LC-link PV inverter is presented for grid-tied and grid-independent operation. This topology is a single-stage system. This system produces desired sinusoidal voltage and current in autonomous and grid connection operation. In this topology inductor and capacitor are connected in parallel between unidirectional switches and bidirectional switches. The inductor is an energy storage element at the link and it charges and discharges through the unidirectional and bidirectional active switches, respectively. The capacitor connected in across the link inductor and it provides resonance. Hence the active switches are turned-on at zero voltage which reduces the switching losses and voltage effect on the switches. In this work parallel LC-link is used in place of electrolytic capacitor because it is an unreliable component and is very sensitive to temperature. Most of the inverters fail because of electrolytic capacitors, particularly at high temperature. Hence an LC pair is used in this work which replaces an electrolytic capacitor to improve the reliability of this PV inverter. The performance and operation are observed in various modes at high frequency as a result of high frequency operation make inductor and capacitor size compact. The modes are obtained through charging and discharging of the inductor. This type of inverter is highly efficient, reliable with high power density. Simulation is done by considering a 30 kW PV operation in grid interconnection and stand-alone modes. The frequency of link is considered to be 6 kHz. PSCAD/EMTDC v4.6 is used for simulation work.

关键词： LC-link Inverters Zero voltage switching

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Scalable Knowledge Graph Analytics at 136 Petaflop/s

Scalable Knowledge Graph Analytics at 136 Petaflop/s

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international conference on High Performance computing, Networking, Storage and Analysis (SC)

作者： Kannan, Ramakrishnan Sao, Piyush Lu, Hao Herrmannova, Drahomira Thakkar, Vijay Patton, Robert Vuduc, Richard Potok, Thomas Oak Ridge Natl Lab Oak Ridge TN 37830 USA Georgia Inst Technol Atlanta GA 30332 USA

ISBN: (纸本)9781728199986

We are motivated by newly proposed methods for data mining large-scale corpora of scholarly publications, such as the full biomedical literature, which may consist of tens of millions of papers spanning decades of research. In this setting, analysts seek to discover how concepts relate to one another. They construct graph representations from annotated text databases and then formulate the relationship -mining problem as one of computing all -pairs shortest paths (APSP), which becomes a significant bottleneck. In this context, we present a new high-performance algorithm and implementation of the FloydWarshall algorithm for distributed -memory parallel computers accelerated by GPUs, which we call DSNAPSHOT (distributed Accelerated Semiring All -Pairs Shortest Path). For our largest experiments, we ran DSNAPSHOT on a connected input graph with millions of vertices using 4,096 nodes (24,576 GPUs) of the Oak Ridge National Laboratory's Summit supercomputer system. We find DSNAPSHOT achieves a sustained performance of 136 x 1015 floating-point operations per second (136 petaflop/s) at a parallel efficiency of 90% under weak scaling and, in absolute speed, 70 % of the best possible performance given our computation (in the single -precision tropical semiring or "min -plus" algebra). Looking forward, we believe this novel capability will enable the mining of scholarly knowledge corpora when embedded and integrated into artificial intelligence -driven natural language processing workflows at scale.

关键词： Shortest path problem High Performance computing parallel Algorithms

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Supercomputing conference

作者： Giuseppe M. J. Barca Calum Snowdon Jorge L. Galvez Vallejo Fazeleh Kazemian Alistair P. Rendell Mark S. Gordon School of Computing Australian National University Canberra Australia Department of Chemistry Iowa State University Ames IA United States College of Science and Engineering Flinders University Adelaide Australia

ISBN: (纸本)9781665454452

Correlated electronic structure calculations enable an accurate prediction of the physicochemical properties of complex molecular systems; however, the scale of these calculations is limited by their extremely high computational cost. The Fragment Molecular Orbital (FMO) method is arguably one of the most effective ways to lower this computational cost while retaining predictive accuracy. In this paper, a novel distributed many-GPU algorithm and implementation of the FMO method are presented. When applied in tandem with the Hartree-Fock and RI-MP2 methods, the new implementation enables correlated calculations on 623,016 electrons and 146,592 atoms in less than 45 minutes using 99.8% of the Summit supercomputer (27,600 GPUs). The implementation demonstrates remarkable speedups with respect to other current GPU and CPU codes, and excellent strong scalability on Summit achieving 94.6 % parallel efficiency on 4600 nodes. This work makes feasible correlated quantum chemistry calculations on significantly larger molecular systems than before and with higher accuracy.

关键词： Technological innovation Orbital calculations Codes Scalability Quantum chemistry Graphics processing units Prediction algorithms

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Scalable Graph Convolutional Network based Link Prediction on a distributed Graph Database Server 13

Scalable Graph Convolutional Network based Link Prediction o...

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IEEE 13th international conference on Cloud computing (CLOUD)

作者： Karunarathna, Anuradha Senarath, Dinika Madhushanki, Shalika Weerakkody, Chinthaka Dayarathna, Miyuru Jayasena, Sanath Suzumura, Toyotaro Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka WSO2 Inc Mountain View CA USA IBM TJ Watson Res Ctr New York NY USA MIT IBM Watson AI Lab Cambridge MA USA Barcelona Supercomp Ctr Barcelona Spain

ISBN: (纸本)9781728187808

Graph Convolutional Networks (GCN) have become a popular means of performing link prediction due to the high accuracy offered by them. However, scaling such link prediction into large graphs of billions of vertices and edges with rich types of attributes is a significant issue to be addressed due to the storage and computation limitations of the machines. In this paper we present a scalable link prediction approach which conducts GCN training and link prediction on top of a distributed graph database server called JasmineGraph. We partition graph data and persist them in multiple workers. We implement parallel graph node embedding generation using GraphSAGE algorithm in multiple workers. Our approach avoids facing performance bottlenecks in GCN training using an intelligent scheduling algorithm. We show our approach scales well with an increasing number of partitions (2,4,8, and 16) using four real world data sets called Twitter, Amazon, Reddit, and DBLP-V11. JasmineGraph was able to train a GCN from the largest dataset DBLP-V11 (> 9.3GB) in 11 hours and 40 minutes time using 16 workers on a single server while the original GraphSAGE implementation could not process it at all. The original GraphSAGE implementation processed the second largest dataset Reddit in 238 minutes while JasmineGraph took only 100 minutes on the same hardware with 16 workers leading to 2.4 times improved performance.

关键词： Machine Learning Graph databases Database management distributed databases Graph theory Graph Convolutional Neural Networks Deep learning Link Prediction

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