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

Artificial Intelligence for Resilience in Smart Grid Operations

Today, the electric power grid is transforming into a highly interconnected network of advanced technologies, equipment, and controls to enable a smarter grid. The growing complexity of smart grid requires resilient operation and control. Power system resilience is defined as the ability to harden the system against and quickly recover from high-impact, low-frequency events. The introduction of two-way flows of information and electricity in the smart grid raises concerns of cyber-physical attacks. Proliferated penetration of renewable energy sources such as solar photovoltaic (PV) and wind power introduce challenges due to the high variability and uncertainty in generation. Unintentional disruptions and power system component outages have become a threat to real-time power system operations. Recent extreme weather events and natural disasters such as hurricanes, storms, and wildfires demonstrate the importance of resilience in the power system. It is essential to find solutions to overcome these challenges in maintaining resilience in smart grid. In this dissertation, artificial intelligence (AI) based approaches have been developed to enhance resilience in smart grid. Methods for optimal automatic generation control (AGC) have been developed for multi-area multi-machine power systems. Reliable AI models have been developed for predicting solar irradiance, PV power generation, and power system frequencies. The proposed short-horizon AI prediction models ranging from few seconds to a minute plus, outperform the state-of-art persistence models. The AI prediction models have been applied to provide situational intelligence for power system operations. An enhanced tie-line bias control in a multi-area power system for variable and uncertain environments has been developed with predicted PV power and bus frequencies. A distributed and parallel security-constrained optimal power flow (SCOPF) algorithm has been developed to overcome the challenges in solving SCOPF problem for large power networks. The methods have been developed and tested on an experimental laboratory platform consisting of real-time digital simulators, hardware/software phasor measurement units, and a real-time weather station

Data-Driven Power System Stability Analysis and Control

In recent years, with the expansion of power system size, the increase of interconnection and the use of large-scale renewable energy, power system stability and safe operations have become more prominent, causing challenges to the normal operation of power grid. Traditional analysis rely on detailed models of the system. But for real power systems, the operating state of the system is variable, and the model-based analysis methods may not accurately reflect the real operating state of the system. Therefore, this dissertation is focused on data-driven stability analysis and control. First, a method for locating the oscillation source of multi-machine systems is proposed. The electromagnetic torque expressions of various generators in a multi-machine system are deduced, and it is found that in each oscillation mode, the electromagnetic torque can be decomposed into a damping torque and a synchronous torque. Based on this development, an oscillation source positioning scheme based on decoupling mode is proposed. Then, a transfer and CNN-LSTM-based method is developed to accelerate and improve the accuracy of the dynamic frequency prediction process. The proposed method exploits system spatial-temporal information and mines the local features of inputs, which highly improves the performance compared with other machine learning methods. Next, a Distributional Soft Actor-Critic (DSAC) method is developed to solve the emergency frequency control problem. The frequency control is formulated as a MDP problem and solved through a novel distributional deep reinforcement learning method. Further, high penetration renewable energy source increase the system uncertainties and impact the cyber security. We propose a detection method based on Bayesian GAN. It can successfully distinguish between securely operating measurements and those that have been attacked with imbalanced training data. Simulation results of this dissertation show the effectiveness of the proposed methods

Ohio State University Bulletin

Classes available for students to enroll in during the 1986-1987 academic year for The Ohio State University

Ohio State University Bulletin

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Ohio State University Bulletin

Classes available for students to enroll in during the 1987-1988 academic year for The Ohio State University

Ohio State University Bulletin

Classes available for students to enroll in during the 1990-1991 academic year for The Ohio State University