Using fast-responding resources to control frequency in a power system

Frequency control is one of the major concerns of power system operators. Frequency varies as the result of a supply-demand mismatch. Due to possible destructive outcomes of large frequency variations, several mechanisms are in operation to keep supply and demand in balance. Increasing penetration of non-dispatchable intermittent generation resources may increase power supply volatility, which makes frequency control more challenging. Emerging utility-scale storage technologies with reasonable cost have participated in electricity markets in recent years. Because of fast-ramping capabilities of these resources, one of their attractive applications is providing frequency regulation service. However, the amount of energy they can produce or consume is limited due to their restricted storage capabilities. Thus, in spite of their fast response to a deployment signal, their duration of response is bounded. In this thesis, we focus on using fast-responding resources to control frequency in power systems. In this research, the first question is if the participation of these resources in the regulation market have any adverse effect on the frequency control performance of the system. If the answer is yes, the next question is what is the best strategy to not only prevent the negative consequences but also improve the benefits of using fast-responding resources for frequency control. For this research, the system of Electric Reliability Council of Texas (ERCOT) is selected. All power system studies related to frequency control require an appropriate dynamic model. In this dissertation, a simplified model is constructed, which represents the ERCOT system frequency response during a short period of time after a contingency. The model is validated and tuned against system frequency measured by Phasor Measurement Units. Especially in situations of not having information about system individual units, this simplified model is highly advantageous. However, to study system frequency during normal conditions, a more comprehensive model is essential. Thus, we develop ERCOT Frequency Modeling and Analysis Tool (EFMAT), which has the required level of details and accuracy to simulate system frequency. All proposed approaches of modeling and parameter tuning in this research are also applicable to other power systems. In order to answer our research questions, we start with investigation of ERCOT Fast-Responding Regulation Service (FRRS). For selected historic days, conventional regulation providers are replaced by a storage system providing FRRS. For various capacities of the storage system, frequency is simulated using EFMAT and a system frequency control performance index is calculated. Comparing calculated index of different simulations can reveal the effect of FRRS capacity on the system performance. The simulations are repeated for several FRRS deployment strategies similar to the strategies of other North America power markets along with our proposed modifications. Three different storage systems are assumed in the simulations: one with unlimited stored energy, one with 6 minutes energy duration, and one with 15 minutes energy duration. Finally, FRRS optimal capacity and equivalency ratio between FRRS and conventional regulation are defined and calculated for the best deployment strategy.Electrical and Computer Engineerin

A Dynamic Response Recovery Framework Using Ambient Synchrophasor Data

Wide-area dynamic studies are of paramount importance to ensure the stability and reliability of power grids. The rising deployment synchrophasor and other sensing technologies has made data-driven modeling and analysis possible using the synchronized fast-rate dynamic measurements. This paper presents a general model-free framework of inferring the grid dynamic responses using the ubiquitous ambient data collected during normal grid operations. Building upon the second-order dynamic model, we have established the connection from the cross-correlation of various types of angle, frequency, and line flow data at any two locations, to their corresponding dynamic responses. The theoretical results enabled a fully data-driven framework for estimating the latter using real-time ambient data. Numerical results using the WSCC 9-bus system and a synthetic 2000-bus Texas system have demonstrated the effectiveness of proposed approaches for dynamic modeling of realistic power systems

Synchronized measurement data conditioning and real-time applications

Phasor measurement units (PMU), measuring voltage and current phasor with synchronized timestamps, is the fundamental component in wide-area monitoring systems (WAMS) and reveals complex dynamic behaviors of large power systems. The synchronized measurements collected from power grid may degrade due to many factors and impacts of the distorted synchronized measurement data are significant to WAMS. This dissertation focus on developing and improving applications with distorted synchronized measurements from power grid. The contributions of this dissertation are summarized below. In Chapter 2, synchronized frequency measurements of 13 power grids over the world, including both mainland and island systems, are retrieved from Frequency Monitoring Network (FNET/GridEye) and the statistical analysis of the typical power grids are presented. The probability functions of the power grid frequency based on the measurements are calculated and categorized. Developments of generation trip/load shedding and line outage events detection and localization based on high-density PMU measurements are investigated in Chapters 3 and 4 respectively. Four different types of abnormal synchronized measurements are identified from the PMU measurements of a power grid. The impacts of the abnormal synchronized measurements on generation trip/load shedding events detection and localization are evaluated. A line outage localization method based on power flow measurements is proposed to improve the accuracy of line outage events location estimation. A deep learning model is developed to detect abnormal synchronized measurements in Chapter 5. The performance of the model is evaluated with abnormal synchronized measurements from a power grid under normal operation status. Some types of abnormal synchronized measurements in the testing cases are recently observed and reported. An extensive study of hyper-parameters in the model is conducted and evaluation metrics of the model performance are presented. A non-contact synchronized measurements study using electric field strength is investigated in Chapter 6. The theoretical foundation and equation derivations are presented. The calculation process for a single circuit AC transmission line and a double circuit AC transmission line are derived. The derived method is implemented with Matlab and tested in simulation cases

Power System Frequency Measurement Based Data Analytics and Situational Awareness

This dissertation presents several measurement-based research from power system wide-area dynamics data analytics to real-time situational awareness application development. All the research are grounded on the power system phasor measurements provided by wide-area Frequency Monitoring Network (FNET/GridEye), which collects the Global Positioning System (GPS) signal synchronized power system phasor measurements at distribution networks. The synchronized frequency measurement at FNET/GridEye enables real-time monitoring of bulk power systems (BPSs) and allows the dynamics interpretation of power system disturbances. Research on both the dynamic and ambient frequency measurements are conducted in this dissertation.The dynamics refer to the frequency measurement when the system is experiencing sudden contingencies. This dissertation focuses on two types of contingency: generation trip and oscillation and conducts both data analytics and corresponding real-time applications. Historical generation trip events in North America are analyzed in purpose to develop a frequency measurement based indicator of power systems low inertia events. Then the frequency response study is extended to bulk power systems worldwide to derive its association with system capacity size. As an essential parameter involved in the frequency response, the magnitude of the power imbalances is estimated based on multiple linear regression for improved accuracy. With respect to situational awareness, a real-time FNET/GridEye generation trip detection tool is developed for PMU use at power utilities and ISOs, which overcomes several challenges brought by different data situations.Regarding the oscillation dynamics, statistical analysis is accomplished on power system inter-area oscillations demonstrating the yearly trend of low-frequency oscillations and the association with system load. A novel real-time application is developed to detect power systems sustained oscillation in large area. The application would significantly facilitate the power grid situational awareness enhancement and system resiliency improvement.Furthermore, an additional project is executed on the ambient frequency measurement at FNET/GridEye. This project discloses the correlation between power system frequency and the electric clock time drift. In practice, this technique serves to track the time drifts in traffic signal systems

Data Mining and Machine Learning Applications of Wide-Area Measurement Data in Electric Power Systems

Wide-area measurement systems (WAMS) are quickly becoming an important part of modern power system operation. By utilizing the Global Positioning System, WAMS offer highly accurate time-synchronized measurements that can reveal previously unobtainable insights into the grid’s status. An example WAMS is the Frequency Monitoring Network (FNET), which utilizes a large number of Internet-connected low-cost Frequency Disturbance Recorders (FDRs) that are installed at the distribution level. The large amounts of data collected by FNET and other WAMS present unique opportunities for data mining and machine learning applications, yet these techniques have only recently been applied in this domain. The research presented here explores some additional applications that may prove useful once WAMS are fully integrated into the power system. Chapter 1 provides a brief overview of the FNET system that supplies the data used for this research. Chapter 2 reviews recent research efforts in the application of data mining and machine learning techniques to wide-area measurement data. In Chapter 3, patterns in frequency extrema in the Eastern and Western Interconnections are explored using cluster analysis. In Chapter 4, an artificial neural network (ANN)-based classifier is presented that can reliably distinguish between different types of power system disturbances based solely on their frequency signatures. Chapter 5 presents a technique for constructing electromechanical transient speed maps for large power systems using FNET data from previously detected events. Chapter 6 describes an object-oriented software framework useful for developing FNET data analysis applications. In the United States, recent environmental regulations will likely result in the removal of nearly 30 GW of oil and coal-fired generation from the grid, mostly in the Eastern Interconnection (EI). The effects of this transition on voltage stability and transmission line flows have previously not been studied from a system-wide perspective. Chapter 7 discusses the results of power flow studies designed to simulate the evolution of the EI over the next few years as traditional generation sources are replaced with greener ones such as natural gas and wind. Conclusions, a summary of the main contributions of this work, and a discussion of possible future research topics are given in Chapter 8

A New Solution for Improving Transmission Line Distance Protection Security During System-Wide Cascading Failures

Protection misoperation is responsible for a large portion of all cascading failures. These cascading failures can lead to blackouts that have tremendous social impacts. This dissertation proposes a new method that uses local distance relay instantaneous three-phase currents to enhance the security of distance protection of transmission lines during wide-area cascading events. The method incorporates advanced signal processing techniques and pattern recognition approaches to prevent zone 3 distance protection misoperation. Prevention of misoperation is done through three major stages. The first stage is fault detection. In this first stage, the proposed method merely recognizes that a fault exists somewhere in the transmission system. The second stage determines whether this fault is within the distance relay’s protective reach. The last stage detects whether this fault has been cleared. If the second stage determines that the fault is outside the zone 3 reach of the relay, a blocking signal will be sent to the relay to prevent operation even if the impedance falls within the operating characteristics of the relay. Alternatively, if the second stage determines that the fault is indeed within zone 3 protection reach of the relay, a permissive trip signal will be sent to the relay only if the third stage determines that the fault has not been cleared yet. The first and second stages use three different k-nearest neighbor classifiers that are trained using level 3 detail coefficients of discrete wavelet transform of the aerial mode currents. The third stage uses the current fundamental to detect fault clearing. Several wide area cascading scenarios were simulated, and various performance metrics were analyzed to study the effectiveness of the proposed methodology

An Assessment of PIER Electric Grid Research 2003-2014 White Paper

This white paper describes the circumstances in California around the turn of the 21st century that led the California Energy Commission (CEC) to direct additional Public Interest Energy Research funds to address critical electric grid issues, especially those arising from integrating high penetrations of variable renewable generation with the electric grid. It contains an assessment of the beneficial science and technology advances of the resultant portfolio of electric grid research projects administered under the direction of the CEC by a competitively selected contractor, the University of California’s California Institute for Energy and the Environment, from 2003-2014

Optimization-based Fast-frequency Support in Low Inertia Power Systems

The future electrical energy demand will largely be met by non-synchronous renewable energy sources (RESs) in the form of photovoltaics and wind energy. The lack of inertial response from these non-synchronous, inverter-based generation in microgrids makes the system vulnerable to large rate-of-change-of-frequency (ROCOF) and frequency excursions. This can trigger under frequency load shedding and cause cascaded outages which may ultimately lead to total blackouts. To limit the ROCOF and the frequency excursions, fast-frequency support can be provided through appropriate control of energy storage systems (ESSs). For proper deployment of such fast-frequency control strategies, accurate information regarding the inertial response of the microgrid is required. In this dissertation, a moving horizon estimation (MHE)-based approach is first proposed for online estimation of inertia and damping constants of a low-inertia microgrid. The MHE also provides real estimates of the noisy frequency and ROCOF measurements. The estimates are employed by a model predictive control (MPC) algorithm that computes control actions to provide fast-frequency support by solving a finite-horizon, online optimization problem. The combined MHE-MPC framework allows an ESS operator to provide near-optimal fast-frequency support as a service. The framework maintains the desired quality-of-service (limiting the ROCOF and frequency) while taking into account the ESS lifetime and physical limits. Additionally, this approach avoids oscillatory behavior induced by delays that are common when using low pass filter and traditional derivative-based (virtual inertia) controllers with high gains. Through simulation results, it has been shown that the proposed framework can provide near-optimal fast-frequency support while incorporating the physical limits of the ESS. The MHE estimator provides accurate state and parameter estimates that help in improving the dynamic performance of the controller compared to traditional derivative-based controllers. Furthermore, the flexibility of the proposed approach to achieve desired system dynamics based on the desired quality-of-service has also been demonstrated

Wide-Area Measurement Application and Power System Dynamics

Frequency monitoring network (FNET) is a GPS-synchronized distribution-level phasor measurement system. It is a powerful synchronized monitoring network for large-area power systems that provides significant information and data for power system situational awareness, real time and post-event analysis, and other important aspects of bulk systems. This work explored FNET measurements and utilized them for different applications and power system analysis. An island system was built and validated with FNET measurements to study the stability of the OTEC integration. FNET measurements were also used to validate a large system model like the U.S. Eastern Interconnection. It tries to match the simulation result and frequency measurement of a real event by adjusting the simulation model. The system model is tuned with the combination of different impact factors for different confirmed actual events, and some general rules and specific tuning quantities were concluded from the model validation process. This work also investigated the behavior of the power system frequency during large-scale, synchronous societal events, like the World Cup, Super Bowl and Royal Wedding. It is apparent that large groups of people engaging in the same event at roughly the same time can have significant impacts on the power grid frequency. The systematic analysis of the accumulating and statistical FNET frequency data presents an incisive point of view on the power grid frequency behavior during such events. To better understanding of system events recorded by FNET, a visualization tool was developed to visualize major events that occurred in the North American power grid. The measurement plot combined with the geographical contour map provides intuitive visualization of the event. Finally, the EI system was simplified and clustered into four groups based on FNET measurements and simulation results of generator trip cases. The generation and load capacity of each cluster was calculated based on the clustering result and simulation model, and a flow diagram of this simplified EI system was demonstrated with clusters and power flow between them

Power System Digital Twins and Real-Time Simulations in Modern Grids

Power systems are in a state of constant change with new hardware, software and applications affecting their planning, operation, and maintenance. Power system control centers are also evolving through new technologies and functionalities to adapt to current needs. System control rooms have moved from fully manual to automated operations, from analog to digital, and have become an embedded and complex information, communication, computation and control system. Digital twins are virtual representations of physical systems, assets and/or processes. They are enabled through software, hardware and data integration, and allow real-time monitoring, controlling, prediction, optimization, and improved decision-making. Consequently, digital twins arise as a technology capable of incorporating existing control systems along with new ones to collect, classify, store, retrieve and disseminate data for the future generation of control centers. Power system digital twins (PSDTs) can uplift how data from power grids and their equipment is processed, providing operators new ways to visualize and understand the information. Nevertheless, complexity and size of modern power systems narrow the scope a current digital twin can have. Furthermore, the services provided are limited to only certain phenomena and/or applications. This thesis addresses the need for a flexible and versatile solution that is also robust and adaptable for monitoring, operating and planning future power systems. The modular design for implementation of the next generation of PSDTs is proposed based on grid applications and/or services they can provide. From a modeling perspective, this thesis also distinguishes how real-time simulations enable the design, development, and operation of a PSDT. First, the need for enhanced power system modeling and simulation techniques is established. Moreover, the necessity of expanding to a more complete and varied open-source library of power system models is identified. The thesis continues by designing, developing, and testing models of inverter-based resources that can be used by the industry and researchers when developing PSDTs. Furthermore, the first-of-its-kind synthetic grid with a longitudinal structure, the S-NEM2300-bus benchmark model, based on the Australian National Electricity Market is created. The synthetic grid is, finally, used to illustrate the first steps towards implementing a practical PSDT