Ruisheng Diao

Centralized and decentralized control for demand response

Demand response has been recognized as an essential element of the smart grid. Frequency response, regulation and contingency reserve functions performed traditionally by generators are now starting to involve demand side resources. Additional benefits from demand response include peak reduction and load shifting, which will defer new infrastructure investment and improve generator operation efficiency. Technical approaches designed to realize these functionalities can be categorized into centralized control and decentralized control, depending on where the response decision is made. This paper discusses these two control philosophies and compares their response performances in terms of delay time and predictability. A distribution system model with detailed household loads and controls is built to demonstrate the characteristics of the two approaches. The conclusion is that the promptness and reliability of decentralized control should be combined with the controllability and predictability of centralized control to achieve the best performance of the smart grid.

Modeling of Electric Water Heaters for Demand Response: A Baseline PDE Model

Demand response (DR) control can effectively relieve balancing and frequency regulation burdens on conventional generators, facilitate integrating more renewable energy, and reduce generation and transmission investments needed to meet peak demands. Electric water heaters (EWHs) have a great potential in implementing DR control strategies because: a) the EWH power consumption has a high correlation with daily load patterns; b) they constitute a significant percentage of domestic electrical load; c) the heating element is a resistor, without reactive power consumption; and d) they can be used as energy storage devices when needed. Accurately modeling the dynamic behavior of EWHs is essential for successfully designing DR controls. In this paper, a new partial differential equation (PDE) physics-based model is developed to capture the detailed temperature profiles at different tank locations, which is validated against field test data for more than 10 days. The developed PDE model is compared with the one-mass and two-mass models, and shows better performance in capturing water thermal dynamics for benchmark testing purposes.

A comparison of forecast error generators for modeling wind and load uncertainty

This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.

Integration of uncertainty information into power system operations

Contemporary power systems face uncertainties coming from multiple sources, including forecast errors of load, wind and solar generation, uninstructed deviation and forced outage of traditional generators, and unscheduled loss of transmission lines. With increasing amounts of wind and solar generation being integrated into the system, these uncertainties have been growing significantly. It is critically important to build the knowledge of major sources of uncertainties, learn how to model them, and then incorporate this information into decision-making processes and power system operations, for better reliability and efficiency. This paper gives a comprehensive overview on the sources of uncertainties in power systems, their important characteristics and models, and approaches for integrating uncertainty information into system operations. It is primarily based on previous works conducted at the Pacific Northwest National Laboratory (PNNL).

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.

MANGO – Modal Analysis for Grid Operation: A Method for Damping Improvement through Operating Point Adjustment

Small signal stability problems are one of the major threats to grid stability and reliability in the U.S. power grid. An undamped mode can cause large-amplitude oscillations and may result in system breakups and large-scale blackouts. There have been several incidents of system-wide oscillations. Of those incidents, the most notable is the August 10, 1996 western system breakup, a result of undamped system-wide oscillations. Significant efforts have been devoted to monitoring system oscillatory behaviors from measurements in the past 20 years. The deployment of phasor measurement units (PMU) provides high-precision, time-synchronized data needed for detecting oscillation modes. Measurement-based modal analysis, also known as ModeMeter, uses real-time phasor measurements to identify system oscillation modes and their damping. Low damping indicates potential system stability issues. Modal analysis has been demonstrated with phasor measurements to have the capability of estimating system modes from both oscillation signals and ambient data. With more and more phasor measurements available and ModeMeter techniques maturing, there is yet a need for methods to bring modal analysis from monitoring to actions. The methods should be able to associate low damping with grid operating conditions, so operators or automated operation schemes can respond when low damping is observed. The work presented in this report aims to develop such a method and establish a Modal Analysis for Grid Operation (MANGO) procedure to aid grid operation decision making to increase inter-area modal damping. The procedure can provide operation suggestions (such as increasing generation or decreasing load) for mitigating inter-area oscillations.

Calibrating multi-machine power system parameters with the extended Kalman filter

Large-scale renewable resources and novel smart-grid technologies continue to increase the complexity of power systems. As power systems continue to become more complex, accurate modeling for planning and operation becomes a necessity. Inaccurate system models would result in an unreliable assessment of system security conditions and could cause large-scale blackouts. This motivates the need for model parameter calibration, since some or all of the model parameters could either be unknown or inaccurate. In this paper, the extended Kalman filter is used to calibrate the parameters of a multi-machine power system in the presence of faults. The calibration performance is tested under varying fault locations, parameter errors, and measurement noise giving an insight into how many generators and which generators could be difficult to calibrate.

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.

Distributed dynamic state estimation with extended Kalman filter

Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.

The influence of topology changes on inter-area oscillation modes and mode shapes

The topology of a power grid network is a piece of critical information for power grid operations. Different power grid topologies can change grid characteristics, inter-area oscillation modes, mode shapes, and even the robustness of the power system. This paper presents some preliminary study results, based on an approved WECC operating case and a modified low damping WECC system, to show the impact of topology changes resulting from N-1 contingencies on power system modes and mode shapes. The results show that topology changes can have very different impact on modal properties in a power system: some result in an unstable situation, while others can improve small signal stability. For the former, the studies show about a 4.5% damping reduction, so a 5% damping margin would be required to ensure the system can sustain the contingencies. For the latter, those topology changes could be used as a control method to improve small signal stability. Mode shapes normally do not change when there is an N-1 topology change. These observations suggest that the inclusion of topological information is useful for improving the accuracy and effectiveness of power system control schemes.