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

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Situational Intelligence for Improving Power System Operations Under High Penetration of Photovoltaics

Nowadays, power grid operators are experiencing challenges and pressures to balance the interconnected grid frequency with rapidly increasing photovoltaic (PV) power penetration levels. PV sources are variable and intermittent. To mitigate the effect of this intermittency, power system frequency is regulated towards its security limits. Under aforementioned stressed regimes, frequency oscillations are inevitable, especially during disturbances and may lead to costly consequences as brownout or blackout. Hence, the power system operations need to be improved to make the appropriate decision in time. Specifically, concurrent or beforehand power system precise frequencies simplified straightforward-to-comprehend power system visualizations and cooperated well-performed automatic generation controls (AGC) for multiple areas are needed for operation centers to enhance. The first study in this dissertation focuses on developing frequency prediction general structures for PV and phasor measurement units integrated electric grids to improve the situational awareness (SA) of the power system operation center in making normal and emergency decisions ahead of time. Thus, in this dissertation, a frequency situational intelligence (FSI) methodology capable of multi-bus type and multi-timescale prediction is presented based on the cellular computational network (CCN) structure with a multi-layer proception (MLP) and a generalized neuron (GN) algorithms. The results present that both CCMLPN and CCGNN can provide precise multi-timescale frequency predictions. Moreover, the CCGNN has a superior performance than the CCMLPN. The second study of this dissertation is to improve the SA of the operation centers by developing the online visualization tool based on the synchronous generator vulnerability index (GVI) and the corresponding power system vulnerability index (SVI) considering dynamic PV penetration. The GVI and SVI are developed by the coherency grouping results of synchronous generator using K-Harmonic Means Clustering (KHMC) algorithm. Furthermore, the CCGNN based FSI method has been implemented for the online coherency grouping procedure to achieve a faster-than-real-time grouping performance. Last but not the least, the multi-area AGCs under different PV integrated power system operating conditions are investigated on the multi-area multi-source interconnected testbed, especially with severe load disturbances. Furthermore, an onward asynchronous tuning method and a two-step (synchronous) tuning method utilizing particle swarm optimization algorithm are developed to refine the multi-area AGCs, which provide more opportunities for power system balancing authorities to interconnect freely and to utilize more PV power. In summary, a number of methods for improving the interconnected power system situational intelligence for a high level of PV power penetration have been presented in this dissertation

Cascading Outages Detection and Mitigation Tool to Prevent Major Blackouts

Due to a rise of deregulated electric market and deterioration of aged power system infrastructure, it become more difficult to deal with the grid operating contingencies. Several major blackouts in the last two decades has brought utilities to focus on development of Wide Area Monitoring, Protection and Control (WAMPAC) systems. Availability of common measurement time reference as the fundamental requirement of WAMPAC system is attained by introducing the Phasor Measurement Units, or PMUs that are taking synchronized measurements using the GPS clock signal. The PMUs can calculate time-synchronized phasor values of voltage and currents, frequency and rate of change of frequency. Such measurements, alternatively called synchrophasors, can be utilized in several applications including disturbance and islanding detection, and control schemes. In this dissertation, an integrated synchrophasor-based scheme is proposed to detect, mitigate and prevent cascading outages and severe blackouts. This integrated scheme consists of several modules. First, a fault detector based on electromechanical wave oscillations at buses equipped with PMUs is proposed. Second, a system-wide vulnerability index analysis module based on voltage and current synchrophasor measurements is proposed. Third, an islanding prediction module which utilizes an offline islanding database and an online pattern recognition neural network is proposed. Finally, as the last resort to interrupt series of cascade outages, a controlled islanding module is developed which uses spectral clustering algorithm along with power system state variable and generator coherency information

Hardware implementation of an automatic adaptive centralized under frequency load shedding

Due to the shortcomings of the conventional Under Frequency Load Shedding UFLS, most researches develop an adaptive UFLS schemes. With the aid of new technology hardware that enables the implementation of Wide Area Monitoring Protection and Control (WAMPC) application, this gives the opportunity for using industrial grade hardware to implement, evaluate these schemes and, therefore, to validate the recent research findings for industrial use. This study implements a centralized adaptive UFLS algorithm using industry grade hardware such as Phasor Measurement Units (PMUs), Synchrophasor Vector Processor (SVP) that resembles real time system operation. The study examines disturbance effects on the frequency at different disturbance locations. Each time the algorithm decisions are able to restore system frequency to a safe level and improve voltage level at the load buses. Besides, the algorithm is able to distribute the amount of power to be shed differently with the disturbance locations

Real-Time Event-Driven Load Shedding for Power System Transient Stability Control using Deep Learning Techniques

With the increasing integration of variational renewable energy and the more active demand side responses, there are more challenges in maintaining secure and reliable power system operation due to the escalated stochasticity and variations in the system. This can be experienced from recent rolling blackouts over the world. Event-driven load shedding (ELS) serves as a fast and effective stability control scheme for power system after a risky disturbance occurs, which can suppress grid oscillation, recover system stability, and prevent cascading failure. Unlike the response-driven control schemes, ELS executes the load shedding action immediately following the disturbance, which aims to control power system stability at an earlier stage with the minimum amount of control cost. The digitalized power systems deploy advanced measurement devices such as phasor measurement units and smart meters, which provides the adequate sensing infrastructure to implement real-time stability assessment and control. However, the conventional approaches for ELS rely on numerical simulations and iterative optimizations which are computationally burdensome and thus slow reactive to the real-time system variations. More recently, artificial intelligence (AI) techniques provide a new way to realize real-time ELS owing to their fast decision making capability. This research identifies the key issues in existing AI based ELS approaches and proposes a series of novel methodologies based on deep learning techniques to enhance overall ELS performance in practical situations. A deep neural network (DNN) model is first presented to improve the decision-making accuracy on ELS strategy. Moreover, considering the unbalanced control cost induced by an over- and under-estimated ELS amount, a risk-averse learning method for DNN is proposed to increase the likelihood of control success with negligible impairment on control cost. On top of those, a GraphSAGE-based ELS model is proposed to capture and embed the topological structure of power system into deep learning, which further improves the overall control performance of ELS. The proposed methodologies have been tested on New England 39 bus system and Nordic power system. The proposed deep learning methods have shown more exceptional control performance of ELS as compared to the existing methods