GridPACK Application Concept

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This document describes the GridPACK application concept for the various solution methods available <seq 1 label="Figure"/>

<seq 1/> - Protocol and data model layers of GridPACK

GridPACK provide a family of libraries that are necessary to implement power flow applications. In general, these libraries are packaged use a set of protocols and data models that transform the problem from a concrete power model to an abstract numerical solution, as shown in <seq 1 last/>.

Steady State Powerflow

Steady state powerflow analysis is method of solving a general power system problem where the currents and voltages on the busses (nodes) and branches (lines) of an electric network are computed based on the impedances, current and power injections at nodes (if any), the boundary voltages (if any) and topology of the network.

Power system applications provide this information to a solver in the form of a matrix of bus admittance (called Y), bus injections (called S) and boundary conditions (e.g., V).

Because the problem is essentially non-linear, various iterative solution methods are employed to obtain the solution to this flow problem. The include

Gauss-Seidel (GS) 
This method is the simplest to describe and implement, is guaranteed to converge for any network for which a solution exists, but converges quite slowly. It is sometimes used to initialize other faster solution methods that sometimes can converge reliably (such as Newton-Raphson). The method is implicitly parallel.
Newton-Raphson (NR) 
This method requires the a Jacobian be computed and maintained for the current system. It is not guaranteed to converge but when it does converge it is quite fast. The method is not readily parallelized.
Forward-Backsweep (FBS) 
This method works only on radial flow models but is extremely fast and can be readily parallelized for more networks.
Conjugate-Gradient (CG) 
Template:TODO Yousu to summarize this method.

Dynamic Simulation

Template:TODO

State Estimation

Template:TODO

State Prediction

(Included from GridPACK Application Concept (State Prediction))

Title: A Statistical State Prediction Methodology to Improve Reliability and Efficiency of Power System Operation

Team: N Zhou, DJ Haglin, FK Tuffner, Y Chen, TA Ferryman, G Lin, J Yin, M Vlachopoulou

This project is motivated by the challenges of increasing uncertainty and variation brought in by the high penetration of renewable generation to power grid operation. A state estimator is an essential tool for power grid operation. Due to the delays from communications and computations, current state estimators can only provide power grid status in the past. This delay has typically been on the order of 2 to 5 minutes, and recent developments may reduce this delay to 30 to 45 seconds. Even with the relatively fast update on the state estimates, the power grid has to be operated based on its past states. Scheduling interchanges and dispatching generation is currently handled through forecasting of the system demand. This kind of practice is acceptable when a power grid does not change very much or does not deviate from the forecasting model significantly. However, with high level penetration of renewable generation (e.g., wind and solar), the North American power grid is going to experience significant levels of variation and uncertainty in power flow. With quick changes and large uncertainty brought in by renewable generation, operations based on the past deterministic states can lower the reliability and efficiency of power grid operation.

The study will result in a power system state predictor, which cannot only provide prediction of power system states, but also quantify prediction errors (or uncertainty) on those estimates. The prediction method can generate power system state estimates for the current and future time. Combined with past states from a traditional state estimator, the state predictor can provide a whole picture of the power system, in the past, current and future with uncertainty quantification. It is expected that the whole picture can improve operator’s situational awareness of the power grid and risks, enable the proactive operation of the power grid, and thus help improve operational efficiency and reliability.

The technologies that have been proposed in the same power grid initiative can be leveraged to bring out more benefits from the proposed state prediction. Power system modeling efforts (proposed by Shuai Lu) can increase the modeling accuracy, which, in turn, helps increase accuracy of the simulation and prediction in this project. To further shorten the computational time, the state prediction can be implemented on a high performance computer platform. The state estimation procedure can be solved effectively by leveraging the study of “linear algebra solver” for the high performance computation (proposed by Barry Lee). With only partial data needed through smart sampling study, the data can be processed locally and only condensed information needs to be passed. Thus the distribution computation technology (proposed by Jenny Liu) can be leveraged to further reduce the communication and computation time. The scalable middleware study (proposed by Jian Yin) can further reduce the communication delay by providing a unified scalable data management service. To guarantee that the computation be finished within the prediction lead time, the state prediction can be implemented on the real time operating system for high performance computers (proposed by Peter Hui).

In this research, we plan to build the power system state prediction methodology from three perspectives:

  1. the prediction method, which forecasts the values of power grid variables in the next time instant;
  2. the prediction error (or uncertainty) quantification method, which gives the confidence interval of the forecast;
  3. the error propagation method, which calculate the prediction errors associated with the derived variables or states.

In addition, the developed state prediction methodology can be evaluated with simulation data and validated with field measurement data.

The short term state forecast consists of many components, such as generation dispatch schedule, load forecast, weather condition, and schedule interchanges. To generate optimal prediction, all the components should be taken into consideration properly. Due to the size of the problem and limited resources available, the project is not to duplicate the studies which have already been carried out in the area of load forecast, variable generation forecast, and generation scheduling. Instead, the focus of this project is placed on three perspectives:

  1. line flow forecast ,
  2. net interchange forecast,
  3. state prediction with uncertainty quantification and propagation.

Transmission lines play a key role in power grid operations. Each transmission line has its capacity limits determined by thermal and/or stability constraints under normal and contingency scenarios. A balancing authority (BA) must make sure that these limits are not exceeded. Because power flow is distributed among different transmission lines according to the Ohm’s law, it takes time to re-route power through dispatch. The prediction of transmission line flow can give operators additional reaction time to avoid violating these constraints. The net interchange forecast complements the existing forecast methods in that it forecasts how neighbor behaviors influence the operation of a local BA. Because an integrated power grid is managed by many BAs, each BA must maintain the balance between load and generation within its territory while considering the behaviors of its neighbors. The net interchange schedule (NIS) is the sum of the transactions (in MW) between a local BA and its neighboring BAs, and it determines the real power to be exchanged in the future. NIS is generally used by the local BA to determine the economic dispatch for the next several hours and therefore directly influence the future states of the local BA. An accurate forecast of NIS can lower the cost while maintaining the reliability through helping generate a close-to-optimal dispatch. The state prediction with uncertainty quantification and error propagation summarizes the results from all the forecast components and gives a comprehensive vision about future system status. In this study, we plan to perform the following studies:

  • Prediction Methods
  • Prediction Error (Uncertainty) Quantification Methods
  • Error Propagation Methods
  • Evaluation and Validation

See Also

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