Shuangshuang Jin

Computational Challenges for Power System Operation

As the power grid technology evolution and information technology revolution converge, power grids are witnessing a revolutionary transition, represented by emerging grid technologies and large scale deployment of new sensors and meters in networks. This transition brings opportunities, as well as computational challenges in the field of power grid analysis and operation. This paper presents some research outcomes in the areas of parallel state estimation using the preconditioned conjugated gradient method, parallel contingency analysis with a dynamic load balancing scheme and distributed system architecture. Based on this research, three types of computational challenges are identified: highly coupled applications, loosely coupled applications, and centralized and distributed applications. Recommendations for future work for power grid applications are also presented.

Predictive Dynamic Simulation for Large-Scale Power Systems through High-Performance Computing

Power system dynamic simulation solves a set of differential-algebraic equations to determine the time-series trajectory when the system is subject to disturbances such as a short-circuit fault, generator tripping, or line switching. Due to computational inefficiency, dynamic simulation, though widely used for off-line studies, has not been used in real-time operation. That limits the ability to operate a much-evolved power system with significant dynamic and stochastic behaviors introduced by the increasing penetration of renewable generation and the deployment of smart grid technologies. The need for performing dynamic simulation in real-time or faster than real-time for power grid operation becomes apparent. And such predictive dynamic simulation can enable many new power grid operation functions such as real-time path rating. To improve the computational efficiency of dynamic simulation requires parallel computing implementation of the solution methods, as computers no longer have only a single core. This paper examines the equations and implements a parallel version of power system dynamic simulation. The testing results clearly show a significant improvement in performance. Dynamic simulation of a largescale power system with a size equivalent to the Western U.S. power grid achieves a performance of three times faster than real time for the first time. This makes the simulation predictive in time. Applying such predictive dynamic simulation for real-time path rating is discussed as well.

Parallel implementation of power system dynamic simulation

Dynamic simulation of power system transient stability is important for planning, monitoring, operation, and control of electric power systems. However, modeling the system dynamics and network involves the computationally intensive time-domain solution of numerous differential and algebraic equations. This results in a transient stability simulation implementation that does not satisfy the real-time constraints of online dynamic security assessment. This paper presents a parallel implementation of the dynamic simulation on a high-performance computing platform using parallel simulation algorithms and architectures. It enables the simulation to run even faster than real time, enabling the “look-ahead” capability to study pending stability problems in the power grid.

Deriving optimal operational rules for mitigating inter-area oscillations

This paper introduces a new method for mitigating inter-area oscillations of a large scale interconnected power system by means of generation re-dispatch. The optimal mitigation procedures are derived by searching for the shortest distance from current operating condition to a targeted operating condition with the desired damping ratio of the oscillation mode. A sensitivity-based method is used to select the most effective generators for generation re-dispatch and decision tree is trained to approximate the security boundary in a space characterized by the selected generators. The optimal operational rules can be found by solving an optimization problem where the boundary constraints are provided by the decision tree rules. This method is tested on a Western Electricity Coordinating Council (WECC) 179-bus simplified model and simulation results have demonstrated the validity of the decision-tree-based method and shown promising application in real time operation.

Parallel state estimation assessment with practical data

This paper presents a parallel state estimation (PSE) implementation using a preconditioned conjugate gradient algorithm and an orthogonal decomposition-based algorithm. Preliminary tests against a commercial Energy Management System (EMS) State Estimation (SE) tool using real-world data are performed. The results show that while the preconditioned conjugate gradient algorithm can solve the SE problem faster with the help of parallel computing techniques, it might not be good for real-world data due to the large condition number of its gain matrix introduced by the wide range of measurement weights. With the help of PETSc package, the orthogonal decomposition-based PSE algorithm can achieve 5-20 times speedup comparing against the commercial EMS tool. It is very promising that the developed PSE can solve the SE problem for large power systems at the SCADA rate, to improve grid reliability.

A testbed for deploying distributed state estimation in power grid

With the increasing demand, scale, and data information of power systems, fast distributed applications are becoming more important in power system operation and control. This paper proposes a testbed for evaluating power system distributed applications, considering data exchange among distributed areas. A high-performance computing (HPC) version of distributed state estimation is implemented and used as an example distributed application. The IEEE 118-bus system is used to deploy the parallel distributed state estimation, and the MeDICi middleware is used for data communication. The performance of the testbed demonstrates its capability to evaluate parallel distributed state estimation by leveraging the HPC paradigm. This testbed can also be applied to evaluate other distributed applications.

Visual Analytics for Power Grid Contingency Analysis

Contingency analysis employs different measures to model scenarios, analyze them, and then derive the best response to any threats. A proposed visual-analytics pipeline for power grid management can transform approximately 100 million contingency scenarios to a manageable size and form. Grid operators can examine individual scenarios and devise preventive or mitigation strategies in a timely manner. Power grid engineers have applied the pipeline to a Western Electricity Coordinating Council power grid model.

Distributing Power Grid State Estimation on HPC Clusters - A System Architecture Prototype

The future power grid is expected to further expand with highly distributed energy sources and smart loads. The increased size and complexity lead to increased burden on existing computational resources in energy control centers. Thus the need to perform real-time assessment on such systems entails efficient means to distribute centralized functions such as state estimation in the power system. In this paper, we present our experience of prototyping a system architecture that connects distributed state estimators individually running parallel programs to solve non-linear estimation procedure. Through our experience, we highlight the needs of integrating the distributed state estimation algorithm with efficient partition and data communication tools so that distributed state estimation has low overhead compared to the centralized solution. We build a test case based on the IEEE 118 bus system and partition the state estimation of the whole system model to available HPC clusters. The measurement from the test bed demonstrates the low overhead of our solution.

Low Probability Tail Event Analysis and Mitigation in the BPA Control Area

This report investigated the uncertainties with the operations of the power system and their contributions to tail events, especially under high penetration of wind. A Bayesian network model is established to quantify the impact of these uncertainties on system imbalance. The framework is presented for a decision support tool, which can help system operators better estimate the need for balancing reserves and prepare for tail events.

GridPACK ™ : a framework for developing power grid simulations on high performance computing platforms

This paper describes the GridPACKTM framework, which is designed to help power grid engineers develop modeling software capable of running on high performance computers. The framework makes extensive use of software templates to provide high level functionality while at the same time allowing developers the freedom to express whatever models and algorithms they are using. GridPACKTM contains modules for setting up distributed power grid networks, assigning buses and branches with arbitrary behaviors to the network, creating distributed matrices and vectors and using parallel linear and non-linear solvers to solve algebraic equations. It also provides mappers to create matrices and vectors based on properties of the network and functionality to support IO and to manage errors. The goal of GridPACKTM is to substantially reduce the complexity of writing software for parallel computers while still providing efficient and scalable software solutions. The use of GridPACKTM is illustrated for a simple powerflow example and performance results for powerflow and dynamic simulation are discussed.