A Multi-core High Performance Computing Framework for Distribution Power Flow

There is an enormous growth in performance capability of computing platform in the last decade. The parallelism becomes an inevitable trend for future computing hardware / software design. Motivated by the practical computation performance demands in power system, especially distribution system, and the advances in modern computing platform, we developed a high performance parallel distribution power flow solver for Monte Carlo styled application. From computer architecture and programming point of view, we show that by applying various performance tuning techniques and parallelization, our distribution power flow solver is able to achieve 50% of a CPU's theoretical peak performance. That is 50x speedup comparing to an already fully compiler-optimized C++ implementation.</p

A Multi-core High Performance Computing Framework for Probabilistic Solutions of Distribution Systems

<p>Multi-core CPUs with multiple levels of parallelism and deep memory hierarchies have become the mainstream computing platform. In this paper we developed a generally applicable high performance computing framework for Monte Carlo simulation (MCS) type applications in distribution systems, taking advantage of performance-enhancing features of multi-core CPUs. The application in this paper is to solve the probabilistic load flow (PLF) in real time, in order to cope with the uncertainties caused by the integration of renewable energy resources. By applying various performance optimizations and multi-level parallelization, the optimized MCS solver is able to achieve more than 50% of a CPU's theoretical peak performance and the performance is scalable with the hardware parallelism. We tested the MCS solver on the IEEE 37-bus test feeder using a new Intel Sandy Bridge multi-core CPU. The optimized MCS solver is able to solve millions of load flow cases within a second, enabling the real-time Monte Carlo solution of the PLF.</p

A Quasi-Monte Carlo Approach for Radial Distribution System Probabilistic Load Flow

<p>Monte Carlo simulation (MCS) is a numerical method to solve the probabilistic load flow (PLF) problem. Comparing to analytical methods, MCS for PLF has advantages such as flexibility, general purpose, able to deal with large nonlinearity and large variances, and embarrassingly parallelizable. However, MCS also suffers from low convergence speed and high computational burden, especially for problems with multiple random variables. In this paper, we proposed a Quasi-Monte Carlo (QMC) based method to solve the PLF for radial distribution network. QMC uses samples from low-discrepancy sequence intended to cover the high dimension random sample space as uniformly as possible. The QMC method is particularly suitable for the high dimension problems with low effective dimensions, and has been successfully used to solve large scale problems in econometrics and statistical circuit design. In this paper, we showed that the PLF for radial distribution system has the similar properties and can be a good candidate for QMC method. The proposed method possesses the advantage of MCS method and significantly increases the convergence rate and overall speed. Numerical experiment results on IEEE test feeders have shown the effectiveness of the proposed method.</p

Power System Probabilistic and Security Analysis on Commodity High Performance Computing Systems

<p>Large scale integration of stochastic energy resources in power systems requires probabilistic analysis approaches for comprehensive system analysis. The large-varying grid condition on the aging and stressed power system infrastructures also requires merging of offline security analyses into online operation. Meanwhile in computing, the recent rapid hardware performance growth comes from the more and more complicated architecture. Fully utilizing the computing power for specific applications becomes very difficult. Given the challenges and opportunities in both the power system and the computing fields, this paper presents the unique commodity high performance computing system solutions to the following fundamental tools for power system probabilistic and security analysis: 1) a high performance Monte Carlo simulation (MCS) based distribution probabilistic load flow solver for real-time distribution feeder probabilistic solutions. 2) A high performance MCS based transmission probabilistic load flow solver for transmission grid probabilistic analysis. 3) A SIMD accelerated AC contingency calculation solver based on Woodbury matrix identity on multi-core CPUs. By aggressive algorithm level and computer architecture level performance optimizations including optimized data structures, optimization for superscalar out-of-order execution, SIMDization, and multi-core scheduling, our software fully utilizes the modern commodity computing systems, makes the critical and computational intensive power system probabilistic and security analysis problems solvable in real-time on commodity computing systems.</p

Privacy Preserving Smart Metering System Based Retail Level Electricity Market

<p>Smart metering systems in distribution networks provide near real-time, two-way information exchange between end users and utilities, enabling many advanced smart grid technologies. However, the fine grained real-time data as well as the various market functionalities also pose great risks to customer privacy. In this work we propose a secure multi-party computation (SMC) based privacy preserving smart metering system. Using the proposed SMC protocol, a utility is able to perform advanced market based demand management algorithms without knowing the actual values of private end user consumption and configuration data. Using homomorphic encryption, billing is secure and verifiable. We implemented a demonstration system that includes a graphical user interface and simulates real-world network communication of the proposed SMC-enabled smart meters. The demonstration shows the feasibility of our proposed privacy preserving protocol for advanced smart grid technologies which includes load management and retail level electricity market support.</p

Secure Multiparty Computation Based Privacy Preserving Smart Metering System

Smart metering systems provide high resolution, realtime end user power consumption data for utilities to better monitor and control the system, and for end users to better manage their energy usage and bills. However, the high resolution realtime power consumption data can also be used to extract end user activity details, which could pose a great threat to user privacy. In this work, we propose a secure multi-party computation (SMC) based privacy preserving protocol for smart meter based load management. Using SMC and a proper designed electricity plan, the utility is able to perform real time demand management with individual users, without knowing the actual value of each user's consumption data. Using homomorphic encryption, the billing is secure and verifiable. We have further implemented a demonstration system which includes a graphical user interface and simulates network communication. The demonstration shows that the proposed privacy preserving protocol is feasible for implementation on commodity IT systems.</p

Accelerated AC Contingency Calculation on Commodity Multi-core SIMD CPUs

<p>Multi-core CPUs with multiple levels of parallelism (i.e. data level, instruction level and task/core level) have become the mainstream CPUs for commodity computing systems. Based on the multi-coreCPUs, in this paper we developed a high performance computing framework for AC contingencycalculation (ACCC) to fully utilize the computing power of commodity systems for online and real time applications. Using Woodbury matrix identity based compensation method, we transform and pack multiple contingency cases of different outages into a fine grained vectorized data parallel programming model. We implement the data parallel programming model using SIMD instruction extension on x86CPUs, therefore, fully taking advantages of the CPU core with SIMD floating point capability. We also implement a thread pool scheduler for ACCC on multi-core CPUs which automatically balances the computing loads across CPU cores to fully utilize the multi-core capability. We test the ACCC solver on the IEEE test systems and on the Polish 3000-bus system using a quad-core Intel Sandy Bridge CPU. The optimized ACCC solver achieves close to linear speedup (SIMD width multiply core numbers) comparing to scalar implementation and is able to solve a complete N-1 line outage AC contingencycalculation of the Polish grid within one second on a commodity CPU. It enables the complete ACCC as a real-time application on commodity computing systems.</p