Power System Resilience Enhancement Using Artificial Intelligence

Extreme weather events and natural disasters are the major cause of power outages in the United States. An accurate forecast of component outages and the resultant load curtailment in response to extreme events is an essential task in pre- and post-event planning, recovery and hardening of power systems. Power system resilience improvement is investigated in this work from component outage prediction to identifying the potential power outages in the system to estimating probable load curtailment due to these outages and offering methods for grid hardening. Initially, two machine learning based prediction methods are proposed to determine the potential outage of power grid components in response to an imminent hurricane, namely a second order logistic regression model and a three-dimensional Support Vector Machine (SVM). The logistic regression model defines the decision boundary, which partitions the components\u27 states into two sets of damaged and operational. Two metrics are examined to validate the performance of the obtained decision boundary in efficiently predicting component outages. The proposed three-dimensional SVM furthermore leverages its accuracy-uncertainty tradeoff to achieve highly accurate results, which can be further used to schedule system resources in a predictive manner with the objective of maximizing its resilience. The performance of the model is tested through numerical simulations and validated based on well-defined and commonly-used performance measures. After training the outage estimation model, the predicted component outages are plugged into a load curtailment minimization model to estimate the nodal load curtailments in the system. The standard IEEE 30-bus system with a combination of hurricane path and intensity scenarios are used to study the model where the results demonstrate that the proposed modelling framework is capable of effectively capturing the dynamics of load curtailment estimation in response to extreme events. Furthermore, a machine learning based grid hardening model is proposed with the objective of improving power grid resilience. The predictions from previous stages are fed into the proposed grid hardening model, which determines strategic locations for placement of distributed generation (DG) units. In contrast to existing literature in hardening and resilience enhancement, this work co-optimizes grid economic and resilience objectives by considering the intricate dependencies of the two. The numerical simulations on the standard IEEE 118-bus test system illustrate the merits and applicability of the proposed model. The results further indicate that the proposed hardening model through decentralized and distributed local energy resources can produce a more robust solution that can protect the system significantly against multiple component outages. Finally, a probabilistic load curtailment estimation model is proposed through a three-step sequential method. At first, to determine a deterministic outage state of the grid components in response to a forecasted hurricane, a machine learning model based on TWSVM is proposed. Then, to convert the deterministic results into probabilistic outage states, a posterior probability sigmoid model is trained on the obtained results from the previous step. Finally, the obtained component outages are integrated into a load curtailment estimation model to determine the potential load curtailments in the system. The simulation results on a standard test system illustrate the high accuracy performance of the proposed method

Component Outage Estimation based on Support Vector Machine

Predicting power system component outages in response to an imminent hurricane plays a major role in preevent planning and post-event recovery of the power system. An exact prediction of components states, however, is a challenging task and cannot be easily performed. In this paper, a Support Vector Machine (SVM) based method is proposed to help estimate the components states in response to anticipated path and intensity of an imminent hurricane. Components states are categorized into three classes of damaged, operational, and uncertain. The damaged components along with the components in uncertain class are then considered in multiple contingency scenarios of a proposed Event-driven Security-Constrained Unit Commitment (E-SCUC), which considers the simultaneous outage of multiple components under an N-m-u reliability criterion. Experimental results on the IEEE 118-bus test system show the merits and the effectiveness of the proposed SVM classifier and the E-SCUC model in improving power system resilience in response to extreme events

Power Grid Management in Response to Extreme Events

Power system management in response to extreme events is one the most important operational aspects of power systems. In this thesis, a novel Event-driven Security Constrained Unit Commitment (E-SCUC) model and a statistical method, based on regression and data mining to estimate the system components outages, are proposed. The proposed models help consider the simultaneous outage of several system components represented by an N-1-m reliability criterion and accordingly determine the proper system response. In addition, an optimal microgrid placement model with the objective of minimizing the cost of unserved energy to enhance power system resilience is proposed. The numerical simulations on the standard IEEE 30-bus and IEEE 118-bus test systems exhibit the merits and applicability of the proposed E-SCUC model, as well as the advantages of the data mining approach in estimating component outage, and the effectiveness of the optimal microgrid placement in ensuring an economic operation under normal conditions and a resilient operation under contingency cases

Impact Analysis of Seismic Events On Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems

Dynamic Pricing, Advanced Metering, and Demand Response in Electricity Markets

Presents an overview and analysis of the possible approaches to bringing an active demand side into electricity markets. Part of a series of research reports that examines energy issues facing California

Measurement-Based Monitoring and Control in Power Systems with High Renewable Penetrations

Power systems are experiencing rapid changes in their generation mixes because of the increasing integration of inverter-based resources (IBRs) and the retirement of traditional generations. This opens opportunities for a cleaner energy outlook but also poses challenges to the safe operation of the power networks. Enhanced monitoring and control based on the increasingly available measurements are essential in assisting stable operation and effective planning for these evolving systems. First, awareness of the evolving dynamic characteristics is quintessential for secure operation and corrective planning. A quantified monitoring study that keeps track of the inertial response and primary frequency response is conducted on the Eastern Interconnection (EI) for the past decade with field data. Whereas the inertia declined by at least 10%, the primary frequency response experienced an unexpected increase. The findings unveiled in the trending analysis also led to an improved event MW size estimation method, as well as discussions about regional dynamics. Experiencing a faster and deeper renewable integration, the Continental Europe Synchronous Area (CESA) system has been threatened by more frequent occurrences of inter-area oscillations during light-load high-renewable periods. A measurement-based oscillation damping control scheme is proposed for CESA with reduced reliance on system models. The design, implementation, and hardware-in-the-loop (HIL) testing of the controller are discussed in detail. Despite the challenges, the increasing presence of IBRs also brings opportunities for fast and efficient controls. Together with synchronized measurement, IBRs have the potential to flexibly complement traditional frequency and voltage control schemes for improved frequency and voltage recovery. The design, implementation, and HIL testing of the measurement-based frequency and voltage control for the New York State Grid are presented. In addition to the transmission level development, IBRs deployed in distribution networks can also be valuable assets in emergency islanding situations if controlled properly. A power management module is proposed to take advantage of measurements and automatically control the electric boundaries of islanded microgrids for maximized power utilization and improved frequency regulation. The module is designed to be adaptive to arbitrary non-meshed topologies with multiple source locations for increased flexibility, expedited deployment, and reduced cost