Parallel Dispatch:A New Paradigm of Electrical Power System Dispatch

Modern power systems are evolving into sociotechnical systems with massive complexity, whose real-time operation and dispatch go beyond human capability. Thus,the need for developing and applying new intelligent power system dispatch tools are of great practical significance. In this paper, we introduce the overall business model of power system dispatch, the top level design approach of an intelligent dispatch system, and the parallel intelligent technology with its dispatch applications. We expect that a new dispatch paradigm,namely the parallel dispatch, can be established by incorporating various intelligent technologies, especially the parallel intelligent technology, to enable secure operation of complex power grids,extend system operators' capabilities, suggest optimal dispatch strategies, and to provide decision-making recommendations according to power system operational goals.

PARALLEL ALGORITHMS FOR VARIATIONAL INEQUALITIES BASED ON DOMAIN DECOMPOSITION

We present two parallel algorithms based on the domain decomposition methodfor solving a variational inequality over a closed convex cone.First,construct an opencovering {Ω_i}of the original domain Ω∶Ω=(?),where Ω_i,i=1,…,m,are overlapping.i.e.for each Ω_i there exists at least one Ω_j(j≠i)such that Ω_i∩Ω_i≠φ.Choosing an initial guessu^0 for the solution u,we solve parallelly the inequality in each subdomain Ω_i(i=1,…,m)to obtain m corrections.Take an appropriate average of these m corrections as a correctionover Ω and hence obtain a new approximation to u.In this paper we discuss the convergenceof the continuous problem and also the corresponding discrete problem which is obtained bythe finite element method.

Integrated in-process chatter monitoring and automatic suppression with adaptive pitch control in parallel turning

Simultaneous processes such as parallel turningor milling offer great opportunities for more efficientmanufacturing because of their higher material removalrates. To maximize their advantages, chatter suppressiontechnologies for simultaneous processes must be devel-oped. In this study, we constructed an automatic chattersuppression system with optimal pitch control for shared-surface parallel turning with rigid tools and a flexibleworkpiece, integrating in-process chatter monitoring basedon the cutting force estimation. The pitch angle betweentwo tools is tuned adaptively in a position control system inaccordance with the chatter frequency at a certain spindlespeed, in a similar manner as the design methodology forvariable-pitch cutters. The cutting force is estimatedwithout using an additional external sensor by employing amulti-encoder-based disturbance observer. In addition, thechatter frequency is measured during the process by per-forming a low-computational-load spectrum analysis at acertain frequency range, which makes it possible to cal-culate the power spectrum density in the control system ofthe machine tool. Thus, the constructed system for automatic chatter suppression does not require any addi-tional equipment.

Parallel-Data-Based Social Evolution Modeling

Abnormal or drastic changes in the natural environment may lead to unexpected events,such as tsunamis and earthquakes,which are becoming a major threat to national economy.Currently,no effective assessment approach can deduce a situation and determine the optimal response strategy when a natural disaster occurs.In this study,we propose a social evolution modeling approach and construct a deduction model for self-playing,self-learning,and self-upgrading on the basis of the idea of parallel data and reinforcement learning.The proposed approach can evaluate the impact of an event,deduce the situation,and provide optimal strategies for decisionmaking.Taking the breakage of a submarine cable caused by earthquake as an example,we find that the proposed modeling approach can obtain a higher reward compared with other existing methods.

Parallel Optimization of the Crystal-KMC on Tianhe-2

The Kinetic Monte Carlo(KMC)is one of the commonly used methods for simulating radiation damage of materials.Our team develops a parallel KMC software named Crystal-KMC,which supports the Embedded Atom Method(EAM)potential energy and utilizes the Message Passing Interface(MPI)technology to simulate the vacancy transition of the Copper(Cu)element under neutron radiation.To make better use of the computing power of modern supercomputers,we develop the parallel efficiency optimization model for the Crystal-KMC on Tianhe-2,to achieve a larger simulation of the damage process of materials under irradiation environment.Firstly,we analyze the performance bottleneck of the Crystal-KMC software and use the MIC offload statement to implement the operation of key modules of the software on the MIC coprocessor.We use Open MP to develop parallel optimization for the Crystal-KMC,combined with existing MPI inter-process communication optimization,finally achieving hybrid parallel optimization.The experimental results show that in the single-node CPU and MIC collaborative parallel mode,the speedup of the calculation hotspot reaches 30.1,and the speedup of the overall software reaches 7.43.

STOCHASTIC APPROXIMATION IN REAL TIME:A PIPE LINE APPROACH

new approach for stochastic approximation in real time is developed. A number of processors are simultaneously active to carry out a computing task. All processors work on the same system with different starting time. After each iteration, computed data are passed to the next processor on line. Interacting tasks and iterative instructions are carried through pipelining of computation and communication. Asymptotic properties of the algorithm are developed, and comparisons of the performance between the new algorithm and the classical one are made.

Implementation of 2D DomainDecomposition in the UCAN Gyrokinetic Particle-in-Cell Code and Resulting Performance of UCAN2

The massively parallel,nonlinear,three-dimensional(3D),toroidal,electrostatic,gyrokinetic,particle-in-cell(PIC),Cartesian geometry UCANcode,with particle ions and adiabatic electrons,has been successfully exercised to identify non-diffusive transport characteristics in present day tokamak discharges.The limitation in applying UCAN to larger scale discharges is the 1D domain decomposition in the toroidal(or z-)direction for massively parallel implementation using MPI which has restricted the calculations to a few hundred ion Larmor radii or gyroradii per plasma minor radius.To exceed these sizes,we have implemented 2D domain decomposition in UCANwith the addition of the y-direction to the processor mix.This has been facilitated by use of relevant components in the P2LIB library of field and particle management routines developed for UCLA’s UPIC Framework of conventional PIC codes.The gyroaveraging specific to gyrokinetic codes is simplified by the use of replicated arrays for efficient charge accumulation and force deposition.The 2D domain-decomposed UCAN2 code reproduces the original 1D domain nonlinear results within round-off.Benchmarks of UCAN2 on the Cray XC30 Edison at NERSC demonstrate ideal scaling when problem size is increased along with processor number up to the largest power of 2 available,namely 131,072 processors.These particle weak scaling benchmarks also indicate that the 1 nanosecond per particle per time step and 1 TFlops barriers are easily broken by UCAN2 with 1 billion particles or more and 2000 or more processors.