POWER DISTRIBUTION SYSTEM RELIABILITY AND RESILIENCY AGAINST EXTREME EVENTS

The objective of a power system is to provide electricity to its customers as economically as possible with an acceptable level of reliability while safeguarding the environment. Power system reliability has well-established quantitative metrics, regulatory standards, compliance incentives and jurisdictions of responsibilities. The increase in occurrence of extreme events like hurricane/tornadoes, floods, wildfires, storms, cyber-attacks etc. which are not considered in routine reliability evaluation has raised concern over the potential economic losses due to prolonged and large-scale power outages, and the overall sustainability and adaptability of power systems. This concern has motivated the utility planners, operators, and policy makers to acknowledge the importance of system resiliency against such events. However, power system resiliency evaluation is comparatively new, and lacks widely accepted standards, assessment methods and metrics. The thesis presents comparative review and analysis of power system resilience models, methodologies, and metrics in present literature and utility applications. It presents studies on two very different types of extreme events, (i) man-made and (ii) natural disaster, and analyzes their impacts on the resiliency of a distribution system. It draws conclusions on assessing and improving power system resiliency based on the impact of the extreme event, response from the distribution system, and effectiveness of the mitigating measures to tackle the extreme event. The advancement in technologies has seen an increasing integration of cyber and physical layer of the distribution system. The distribution system operators avails from the symbiotic relation of the cyber-physical layer, but the interdependency has also been its Achilles heel. The evolving infrastructure is being exposed to increase in cyber-attacks. It is of paramount importance to address the aforementioned issue by developing holistic approaches to comprehensibly upgrade the distribution system preventing huge financial loss and societal repercussions. The thesis models a type of cyber-attack using false data injection and evaluates its impact on the distribution system. It does so by developing a resilience assessment methodology accompanied by quantitative metrics. It also performs reliability evaluation to present the underlying principle and differences between reliability and resiliency. The thesis also introduces new indices to demonstrate the effectiveness of a bad-data detection strategy against such cyber-attacks. Extreme events like hurricane/tornadoes, floods, wildfires, storm, cyber-attack etc. are responsible for catastrophic damage to critical infrastructure and huge financial loss. Power distribution system is an important critical infrastructure driving the socio-economic growth of the country. High winds are one of the most common form of extreme events that are responsible for outages due to failure of poles, equipment damage etc. The thesis models effective extreme wind events with the help of fragility curves, and presents an analysis of their impacts on the distribution system. It also presents infrastructural and operational resiliency enhancement strategies and quantifies the effectiveness of the strategy with the metrics developed. It also demonstrates the dependency of resiliency of distribution system on the structural strength of transmission lines and presents measures to ensure the independency of the distribution system. The thesis presents effective resilience assessment methodology that can be valuable for distribution system utility planners, and operators to plan and ensure a resilient distribution system

Resilience Enhancement Strategies for Modern Power Systems

The frequency of extreme events (e.g., hurricanes, earthquakes, and floods) and man-made attacks (cyber and physical attacks) has increased dramatically in recent years. These events have severely impacted power systems ranging from long outage times to major equipment (e.g., substations, transmission lines, and power plants) destructions. Also, the massive integration of information and communication technology to power systems has evolved the power systems into what is known as cyber-physical power systems (CPPSs). Although advanced technologies in the cyber layer improve the operation and control of power systems, they introduce additional vulnerabilities to power system performance. This has motivated studying power system resilience evaluation and enhancements methods. Power system resilience can be defined as ``The ability of a system to prepare for, absorb, adapt to, and recover from disruptive events''. Assessing resilience enhancement strategies requires further and deeper investigation because of several reasons. First, enhancing the operational and planning resilience is a mathematically involved problem accompanied with many challenges related to modeling and computation methods. The complexities of the problem increases in CPPSs due to the large number and diverse behavior of system components. Second, a few studies have given attention to the stochastic behavior of extreme events and their accompanied impacts on the system resilience level yielding less realistic modeling and higher resilience level. Also, the correlation between both cyber and physical layers within the context of resilience enhancement require leveraging sophisticated modeling approaches which is still under investigation. Besides, the role of distributed energy resources in planning-based and operational-based resilience enhancements require further investigation. This calls for developing enhancement strategies to improve resilience of power grids against extreme events. This dissertation is divided into four parts as follows. Part I: Proactive strategies: utilizing the available system assets to prepare the power system prior to the occurrence of an extreme event to maintain an acceptable resilience level during a severe event. Various system generation and transmission constraints as well as the spatiotemporal behavior of extreme events should be properly modeled for a feasible proactive enhancement plan. In this part, two proactive strategies are proposed against weather-related extreme events and cyber-induced failure events. First, a generation redispatch strategy is formulated to reduce the amount of load curtailments in transmission systems against hurricanes and wildfires. Also, a defensive islanding strategy is studied to isolate vulnerable system components to cyber failures in distribution systems. Part II: Corrective strategies: remedial actions during an extreme event for improved performance. The negative impacts of extreme weather events can be mitigated, reduced, or even eliminated through corrective strategies. However, the high stochastic nature of resilience-based problem induces further complexities in modeling and providing feasible solutions. In this part, reinforcement learning approaches are leveraged to develop a control-based environment for improved resilience. Three corrective strategies are studied including distribution network reconfiguration, allocating and sizing of distributed energy resources, and dispatching reactive shunt compensators. Part III: Restorative strategies: retain the power service to curtailed loads in a fast and efficient means after a diverse event. In this part, a resilience enhancement strategy is formulated based on dispatching distributed generators for minimal load curtailments and improved restorative behavior. Part IV: Uncertainty quantification: Impacts of uncertainties on modeling and solution accuracy. Though there exist several sources of stochasticity in power systems, this part focuses on random behavior of extreme weather events and the associated impacts on system component failures. First, an assessment framework is studied to evaluate the impacts of ice storms on transmission systems and an evaluation method is developed to quantify the hurricane uncertainties for improved resilience. Additionally, the role of unavailable renewable energy resources on improved system resilience during extreme hurricane events is studied. The methodologies and results provided in this dissertation can be useful for system operators, utilities, and regulators towards enhancing resilience of CPPSs against weather-related and cyber-related extreme events. The work presented in this dissertation also provides potential pathways to leverage existing system assets and resources integrated with recent advanced computational technologies to achieve resilient CPPSs

Bridging Machine Learning for Smart Grid Applications

This dissertation proposes to develop, leverage, and apply machine learning algorithms on various smart grid applications including state estimation, false data injection attack detection, and reliability evaluation. The dissertation is divided into four parts as follows.. Part I: Power system state estimation (PSSE). The PSSE is commonly formulated as a weighted least-square (WLS) algorithm and solved using iterative methods such as Gauss-Newton methods. However, iterative methods have become more sensitive to system operating conditions than ever before due to the deployment of intermittent renewable energy sources, zero-emission technologies (e.g., electric vehicles), and demand response programs. Efficient approaches for PSSE are required to avoid pitfalls of the WLS-based PSSE computations for accurate prediction of operating conditions. The first part of this dissertation develops a data-driven real-time PSSE using a deep ensemble learning algorithm. In the proposed approach, the ensemble learning setup is formulated with dense residual neural networks as base-learners and a multivariate-linear regressor as a meta-learner. Historical measurements and states are utilized to train and test the model. The trained model can be used in real-time to estimate power system states (voltage magnitudes and phase angles) using real-time measurements. Most of current data-driven PSSE methods assume the availability of a complete set of measurements, which may not be the case in real power system data acquisition. This work adopts multivariate linear regression to forecast system states for instants of missing measurements to assist the proposed PSSE technique. Case studies are performed on various IEEE standard benchmark systems to validate the proposed approach. Part II: Cyber-attacks on Voltage Regulation. Several wired and wireless advanced communication technologies have been used for coordinated voltage regulation schemes in distribution systems. These technologies have been employed to both receive voltage measurements from field sensors and transmit control settings to voltage regulating devices (VRDs). Communication networks for voltage regulation can be susceptible to data falsification attacks, which can lead to voltage instability. In this context, an attacker can alter multiple field measurements in a coordinated manner to disturb voltage control algorithms. The second part of this dissertation develops a machine learning-based two-stage approach to detect, locate, and distinguish coordinated data falsification attacks on control systems of coordinated voltage regulation schemes in distribution systems with distributed generators. In the first stage (regression), historical voltage measurements along with current meteorological data (solar irradiance and ambient temperature) are provided to random forest regressor to forecast voltage magnitudes of a given current state. In the second stage, a logistic regression compares the forecasted voltage with the measured voltage (used to set VRDs) to detect, locate, and distinguish coordinated data falsification attacks in real-time. The proposed approach is validated through several case studies on a 240-node real distribution system (based in the USA) and the standard IEEE 123-node benchmark distribution system.Part III: Cyber-attacks on Distributed Generators. Part III of the dissertation proposes a deep learning-based multi-label classification approach to detect coordinated and simultaneously launched data falsification attacks on a large number of distributed generators (DGs). The proposed approach is developed to detect power output manipulation and falsification attacks on DGs including additive attacks, deductive attacks, and combination of additive and deductive attacks (attackers use the combination of additive and deductive attacks to camouflage their attacks). The proposed approach is demonstrated on several systems including the 240-node and IEEE 123-node distribution test system. Part IV: Composite System Reliability Evaluation. Traditional composite system reliability evaluation is computationally demanding and may become inapplicable to large integrated power grids due to the requirements of repetitively solving optimal power flow (OPF) for a large number of system states. Machine learning-based approaches have been used to avoid solving OPF in composite system reliability evaluation except in the training stage. However, current approaches have been utilized only to classify system states into success and failure states (i.e., up or down). In other words, they can be used to evaluate power system probability and frequency reliability indices, but they cannot be used to evaluate power and energy reliability indices unless OPF is solved for each failure state to determine minimum load curtailments. In the fourth part of this dissertation, a convolutional neural network (CNN)-based regression approach is proposed to determine the minimum amount of load curtailments of sampled states without solving OPF. Unavoidable load curtailments due to failures are then used to evaluate power and energy indices (e.g., expected demand not supplied) as well as to evaluate the probability and frequency indices. The proposed approach is applied on several systems including the IEEE Reliability Test System and Saskatchewan Power Corporation in Canada

Scenarios for the development of smart grids in the UK: literature review

Smart grids are expected to play a central role in any transition to a low-carbon energy future, and much research is currently underway on practically every area of smart grids. However, it is evident that even basic aspects such as theoretical and operational definitions, are yet to be agreed upon and be clearly defined. Some aspects (efficient management of supply, including intermittent supply, two-way communication between the producer and user of electricity, use of IT technology to respond to and manage demand, and ensuring safe and secure electricity distribution) are more commonly accepted than others (such as smart meters) in defining what comprises a smart grid. It is clear that smart grid developments enjoy political and financial support both at UK and EU levels, and from the majority of related industries. The reasons for this vary and include the hope that smart grids will facilitate the achievement of carbon reduction targets, create new employment opportunities, and reduce costs relevant to energy generation (fewer power stations) and distribution (fewer losses and better stability). However, smart grid development depends on additional factors, beyond the energy industry. These relate to issues of public acceptability of relevant technologies and associated risks (e.g. data safety, privacy, cyber security), pricing, competition, and regulation; implying the involvement of a wide range of players such as the industry, regulators and consumers. The above constitute a complex set of variables and actors, and interactions between them. In order to best explore ways of possible deployment of smart grids, the use of scenarios is most adequate, as they can incorporate several parameters and variables into a coherent storyline. Scenarios have been previously used in the context of smart grids, but have traditionally focused on factors such as economic growth or policy evolution. Important additional socio-technical aspects of smart grids emerge from the literature review in this report and therefore need to be incorporated in our scenarios. These can be grouped into four (interlinked) main categories: supply side aspects, demand side aspects, policy and regulation, and technical aspects.

Reliability Evaluation and Defense Strategy Development for Cyber-physical Power Systems

With the smart grid initiatives in recent years, the electric power grid is rapidly evolving into a complicated and interconnected cyber-physical system. Unfortunately, the wide deployment of cutting-edge communication, control and computer technologies in the power system, as well as the increasing terrorism activities, make the power system at great risk of attacks from both cyber and physical domains. It is pressing and meaningful to investigate the plausible attack scenarios and develop efficient methods for defending the power system against them. To defend the power grid, it is critical to first study how the attacks could happen and affect the power system, which are the basis for the defense strategy development. Thus, this dissertation quantifies the influence of several typical attacks on power system reliability. Specifically, three representative attack are considered, i.e., intrusion against substations, regional LR attack, and coordinated attacks. For the intrusion against substations, the occurrence frequency of the attack events is modeled based on statistical data and human dynamics; game-theoretical approaches are adopted to model induvial and consecutive attack cases; Monte Carlo simulation is deployed to obtain the desired reliability indices, which incorporates both the attacks and the random failures. For the false data injection attack, a practical regional load redistribution (LR) attack strategy is proposed; the man-in-the-middle (MITM) intrusion process is modeled with a semi-Markov process method; the reliability indices are obtained based on the regional LR attack strategy and the MITM intrusion process using Monte Carlo simulation. For the coordinated attacks, a few typical coordination strategies are proposed considering attacking the current-carrying elements as well as attacking the measurements; a bilevel optimization method is applied to develop the optimal coordination strategy. Further, efficient and effective defense strategies are proposed from the perspectives of power system operation strategy and identification of critical elements. Specially, a robustness-oriented power grid operation strategy is proposed considering the element random failures and the risk of man-made attacks. Using this operation strategy, the power system operation is robust, and can minimize the load loss in case of malicious man-made attacks. Also, a multiple-attack-scenario (MAS) defender-attack-defender model is proposed to identify the critical branches that should be defended when an attack is anticipated but the defender has uncertainty about the capability of the attacker. If those identified critical branches are protected, the expected load loss will be minimal