A Framework for Analyzing the Impact of Data Integrity/Quality on Electricity Market Operations

This dissertation examines the impact of data integrity/quality in the supervisory control and data acquisition (SCADA) system on real-time locational marginal price (LMP) in electricity market operations. Measurement noise and/or manipulated sensor errors in a SCADA system may mislead system operators about real- time conditions in a power system, which, in turn, may impact the price signals in real-time power markets. This dissertation serves as a first attempt to analytically investigate the impact of bad/malicious data on electric power market operations. In future power system operations, which will probably involve many more sensors, the impact of sensor data integrity/quality on grid operations will become increasingly important. The first part of this dissertation studies from a market participant’s perspective a new class of malicious data attacks on state estimation, which subsequently influences the result of the newly emerging look-ahead dispatch models in the real-time power market. In comparison with prior work of cyber-attack on static dispatch where no inter-temporal ramping constraint is considered, we propose a novel attack strategy, named ramp-induced data (RID) attack, with which the attacker can manipulate the limits of ramp constraints of generators in look-ahead dispatch. It is demonstrated that the proposed attack can lead to financial profits via malicious capacity withholding of selected generators, while being undetected by the existing bad data detection algorithm embedded in today’s state estimation software. In the second part, we investigate from a system operator’s perspective the sensitivity of locational marginal price (LMP) with respect to data corruption-induced state estimation error in real-time power market. Two data corruption scenarios are considered, in which corrupted continuous data (e.g., the power injection/flow and voltage magnitude) falsify power flow estimate whereas corrupted discrete data (e.g., the on/off status of a circuit breaker) do network topology estimate, thus leading to the distortion of LMP. We present an analytical framework to quantify real-time LMP sensitivity subject to continuous and discrete data corruption via state estimation. The proposed framework offers system operators an analytical tool to identify economically sensitive buses and transmission lines to data corruption as well as find sensors that impact LMP changes significantly. This dissertation serves as a first step towards rigorous understanding of the fundamental coupling among cyber, physical and economical layers of operations in future smart grid

Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts

Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the Internet of Things devices (such as smart metres and intelligent electronic devices). While the convergence of a significant number of cyber-physical elements has enabled the Smart Grid to be far more efficient and competitive in addressing the growing global energy challenges, it has also introduced a large number of vulnerabilities culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical cyberattacks, and appears to be a focal point of interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the FDI attacks, with particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts in the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid. Related literature are analysed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack research is identified, and a number of future research directions is recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl

On an Information and Control Architecture for Future Electric Energy Systems

This paper presents considerations towards an information and control architecture for future electric energy systems driven by massive changes resulting from the societal goals of decarbonization and electrification. This paper describes the new requirements and challenges of an extended information and control architecture that need to be addressed for continued reliable delivery of electricity. It identifies several new actionable information and control loops, along with their spatial and temporal scales of operation, which can together meet the needs of future grids and enable deep decarbonization of the electricity sector. The present architecture of electric power grids designed in a different era is thereby extensible to allow the incorporation of increased renewables and other emerging electric loads.Comment: This paper is accepted, to appear in the Proceedings of the IEE

Risk Aware Robust Decision Making in Power Systems with Renewable Resources

The increasing penetration of renewable generation poses significant risks to the reliable operation of power systems, mainly due to the variable and uncertain nature of the output of wind and solar resources. This dissertation presents a robust optimization based decision making framework in future power systems with high penetration of variable renewable resources. The first part of this dissertation involves the modeling and analysis of a robust optimization based bidding strategy for the combination of a wind farm and an energy storage device participating in a deregulated electricity market. The selection of the uncertainty set for the robust optimization problem, based on the decision maker’s risk preference, is also discussed. From the market participant’s point of view improved utilization of the renewable resource, through storage enabled energy arbitrage, can lead to better economic performance. The storage device can provide firming power to the output of the wind farm, enabling the renewable resource to participate in the electricity market. The robust optimization based approach is compared to a deterministic optimization based approach through a numerical example. The second part of this dissertation investigates the metric and the dispatch method needed for a more robust real-time market operation. A novel metric for evaluating system-wide ramp flexibility is proposed. A robust framework to ensure the reliable dispatch of generators is presented and analyzed. The robust model is compared to both the conventional economic dispatch as well as a proposed industry approach to managing system flexibility called the look-ahead dispatch. Furthermore, the formulation for a robust multi-zonal dispatch model is presented. The proposed robust model and flexibility index is demonstrated through a numerical on a modified IEEE 24 Bus Reliability Test System

Power Market Cybersecurity and Profit-targeting Cyberattacks

The COVID-19 pandemic has forced many companies and business to operate through remote platforms, which has made everyday life and everyone more digitally connected than ever before. The cybersecurity has become a bigger priority in all aspects of life. A few real-world cases have demonstrated the current capability of cyberattacks as in [1], [2], and [3]. These cases invalidate the traditional belief that cyberattacks are unable to penetrate real-world industrial systems. Beyond the physical damage, some attackers target financial arbitrage advantages brought by false data injection attacks (FDIAs) [4]. Malicious breaches into power market operations could induce catastrophic consequences on fair financial settlements and reliable transmission services. In this dissertation, an in-depth study is conducted to investigate power market cybersecurity and profit-targeting cyberattacks. In the first work, we demonstrate the importance of market-level behavior in defending cyberattacks and designing cyberattacks. A market-level defense analysis is developed to help operators identify cyberattacks, and an LMP-disguising attack strategy is developed to disguise the abnormal LMPs, which can bypass both the bad data detection and market-level detection. In the second work, we propose a comprehensive CVA model for delivering a detailed analysis of four aspects of vulnerability: highly probable cyberattack targets, devastating attack targets, risky load levels, and mitigation ability under different degrees of defense. In the third work, we identify that revenue adequacy, a fundamental power market operation criterion, has not been analyzed under the context of cybersecurity, and we explore the impact of FDIAs targeting real-time (RT) market operations on ISO revenue adequacy analytically and numerically. In the last work, we extend the power system cybersecurity analysis to multi-energy system (MES) framework. An optimally coordinated (OC-FDIA) targeting MES is proposed. Then, we show that the OC-FDIA cause much more severe damages than single-system FDIA and uncoordinated FDIAs. Further, an effective countermeasure is developed against the proposed OCFDIA based on deep learning technique (DL)

A fast state-estimation-based data integrity threat detection approach for combined AC-DC bulk power systems

The challenges encountered in modern power system operations have increased as electricity grids have become more geographically widespread and complex. Some modern bulk grids now combine AC and DC subsystems to effectively serve their loads. Communications and controls for such combined grids, in particular, have increasingly become more challenging. System disturbances in such combined grids have the potential to cascade and affect much larger portions of the grid. These challenges have only been exacerbated by the deepening penetrations of renewable energy resources, such as wind and solar. A key operational concern of such combined grids is the ability of the system operators to continually maintain situational awareness in their operations. The response times within which corrective control actions must be dispatched under contingencies have shortened considerably. In an attempt to ensure the intact transmission of an ever larger amount of information for combined AC-DC grids so as to maintain situational awareness and promptly dispatch control actions, the electric power industry has increased the deployment of cyberphysical, microprocessor-based devices in system monitoring and control. These devices provide system operational information to the system operator over digital channels with latencies much lower than those using conventional copper wire analog signals. But, with this more intense reliance in power system monitoring and control on communication channels, cybersecurity has become an additional concern. The possibility that entities/individuals with malicious intent can gain access to these communication channels and are able to alter operational commands is a fact of life. The types of cyber attacks that threat agents can perform are varied and include false data injection and data integrity attacks, spoofing and denial of service. While it is advisable to include information technology-based intrusion detection/prevention techniques to parse and verify the syntax of protocol messages, effective use of the physical characteristics of the power grid provides alternative, physics-based detection methods. So far, physics-based detection methods have mostly focused on AC system applications. Some investigations have been conducted on combined AC-DC systems, which have focused primarily on microgrids so as to restrict the applications to low- and medium-voltage systems. In this report, we propose and investigate a physics-based approach to threat detection in bulk combined AC-DC grids via the use of a rapid, approximate state estimation scheme. We specifically investigate data integrity attacks, which aim to corrupt the active power dispatch commands on the HVDC lines in these combined bulk AC-DC grids. The state-estimation based scheme we propose requires the determination of the system state estimates at sufficiently frequent time intervals to allow the performance of consistency checks between the approximate injections computed from the estimate of the state with respect to that of the power flow that corresponds to the true power order transmitted for implementation. We obtain gains in computational speed in the proposed approximate state-estimation-based approach to make it capable to track the changes in state with adequate accuracy for detection purposes. For this purpose, we use the power transfer distribution factors (PTDFs) as the criterion for measurement prioritization to produce a reduced subset of prioritized measurement. In addition, we impose a judiciously specified limit on the number of iterations in the state estimation to meet the time response requirements. These two modifications, combined with effectively implemented sparsity-techniques, result in a robust approach for the detection of the class of cyber threats considered in this report. The contribution of this report lies in the use of the widely used power system state estimation tool to develop a simple, practical physics-based approach to data integrity attack detection specifically for use in combined bulk AC-DC grids. We advantageously use the incorporation of this PTDF-based measurement prioritization feature into the conventional AC state estimation extensively deployed in modern EMSs to create a detection scheme for the cyber threats considered in this report. We demonstrate the effective deployment of this state-estimation-based approach with results from case studies on a representative 2470-bus synthetic combined AC-DC test system that is based on the U.S. part of the WECC interconnection with the California-Oregon Pacific DC intertie. In our simulation studies, we are able to detect the corruption of a 920 MW power order command to within 5 % of its true value. The implementation of this corrupted power order is detected within a 30-second time period with the prioritized measurement subset to contain the measurements associated with less than 2 % of the total number of lines in the system. The results discussed have provided insights into the performance of the heuristic procedures and a basis for the appropriate choices of the tunable parameters of the state estimation scheme. We discuss the computational and accuracy aspects and provide bounds on the extent to which an attacker can corrupt power orders that the scheme successfully detects. We observe, for instance, that the accuracy of the approach is more sensitive to our choice of the prioritized measurements than the limit on the number of iterations. We also share our insights on the deployment aspects of this approach by a system operator of a physical combined AC-DC bulk power grid

A World-Class University-Industry Consortium for Wind Energy Research, Education, and Workforce Development: Final Technical Report

During the two-year project period, the consortium members have developed control algorithms for enhancing the reliability of wind turbine components. The consortium members have developed advanced operation and planning tools for accommodating the high penetration of variable wind energy. The consortium members have developed extensive education and research programs for educating the stakeholders on critical issues related to the wind energy research and development. In summary, The Consortium procured one utility-grade wind unit and two small wind units. Specifically, the Consortium procured a 1.5MW GE wind unit by working with the world leading wind energy developer, Invenergy, which is headquartered in Chicago, in September 2010. The Consortium also installed advanced instrumentation on the turbine and performed relevant turbine reliability studies. The site for the wind unit is InvenergyÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs Grand Ridge wind farmin Illinois. The Consortium, by working with Viryd Technologies, installed an 8kW Viryd wind unit (the Lab Unit) at an engineering lab at IIT in September 2010 and an 8kW Viryd wind unit (the Field Unit) at the Stuart Field on IITÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs main campus in July 2011, and performed relevant turbine reliability studies. The operation of the Field Unit is also monitored by the Phasor Measurement Unit (PMU) in the nearby Stuart Building. The Consortium commemorated the installations at the July 20, 2011 ribbon-cutting ceremony. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on turbine reliability included (1) Predictive Analytics to Improve Wind Turbine Reliability; (2) Improve Wind Turbine Power Output and Reduce Dynamic Stress Loading Through Advanced Wind Sensing Technology; (3) Use High Magnetic Density Turbine Generator as Non-rare Earth Power Dense Alternative; (4) Survivable Operation of Three Phase AC Drives in Wind Generator Systems; (5) Localization of Wind Turbine Noise Sources Using a Compact Microphone Array; (6) Wind Turbine Acoustics - Numerical Studies; and (7) Performance of Wind Turbines in Rainy Conditions. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs researches on wind integration included (1) Analysis of 2030 Large-Scale Wind Energy Integration in the Eastern Interconnection; (2) Large-scale Analysis of 2018 Wind Energy Integration in the Eastern U.S. Interconnection; (3) Integration of Non-dispatchable Resources in Electricity Markets; (4) Integration of Wind Unit with Microgrid. The ConsortiumÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂÃÂs education and outreach activities on wind energy included (1) Wind Energy Training Facility Development; (2) Wind Energy Course Development; (3) Wind Energy Outreach

The Resilience Of Smart Energy Systems Against Adversarial Attacks, Operational Degradation And Variabilities

The presented research investigates selected topics concerning resilience of critical energy infrastructures against certain types of operational disturbances and/or failures whether natural or man-made. A system is made resilient through the deployment of physical devices enabling real-time monitoring, strong feedback control system, advanced system security and protection strategies or through prompt and accurate man-made actions or both. Our work seeks to develop well-planned strategies that act as a foundation for such resiliency enabling techniques.The research conducted thus far addresses three attributes of a resilient system, namely security, efficiency, and robustness, for three types of systems associated with current or future energy infrastructures. First (chapter 1), we study the security aspect of cyber-physical systems which integrate physical system dynamics with digital cyberinfrastructure. The smart electricity grid is a common example of this system type. In this work, an abstract theoretical framework is proposed to study data injection/modification attacks on Markov modeled dynamical systems from the perspective of an adversary. The adversary is capable of modifying a temporal sequence of data and the physical controller is equipped with prior statistical knowledge about the data arrival process to detect the presence of an adversary. The goal of the adversary is to modify the arrivals to minimize a utility function of the controller while minimizing the detectability of his presence as measured by the K-L divergence between the prior and posterior distribution of the arriving data. The trade-off between these two metrics– controller utility and the detectability cost is studied analytically for different underlying dynamics.Our second study (chapter 2) reviews the state of the art ocean wave generation technologies along with system level modeling while providing an initial study of the impacts of integration on a typical electrical grid network as compared to the closest related technology, wind energy extraction. In particular, wave power is computed from high resolution measured raw wave data to evaluate the effects of integrating wave generation into a small power network model. The system with no renewable energy sources and the system with comparable wind generation have been used as a reference for evaluation. Simulations show that wave power integration has good prospects in reducing the requirements of capacity and ramp reserves, thus bringing the overall cost of generation down.Our third study(chapter 3) addresses the robustness of resilient ocean wave generation systems. As an early-stage but rapidly developing technology, wave power extraction systems must have strong resilience requirements in harsh, corrosive ocean environments while enabling economic operation throughput their lifetime. Such systems are comprised of Wave Energy Converters (WECs) that are deployed offshore and that derive power from rolling ocean waves. The Levelized Cost of Electricity (LCOE) for WECs is high and one important way to reduce this cost is to employ strategies that minimize the cost of maintenance of WECs in a wave farm. In this work, an optimal maintenance strategy is proposed for a group of WECs, resulting in an adaptive scheduling of the time of repair, based on the state of the entire farm. The state-based maintenance strategy seeks to find an optimal trade-off between the moderate revenue generated from a farm with some devices being in a deteriorated or failed state and the high repair cost that typifies ocean wave farm maintenance practices. The formulation uses a Markov Decision Process (MDP) approach to devise an optimal policy which is based on the count of WECs in different operational states.Our fourth study (chapter 4) focuses on enabling resilient electricity grids with Grid Scale Storage (GSS). GSS offers resilient operations to power grids where the generation, transmission, distribution and consumption of electricity has traditionally been ``just in time . GSS offers the ability to buffer generated energy and dispatch it for consumption later, e.g., during generation outage and shortages. Our research addresses how to operate GSS to generate revenue efficiency in frequency regulation markets. Operation of GSS in frequency regulation markets is desirable due to its fast response capabilities and the corresponding revenues. However, GSS health is strongly dependent on its operation and understanding the trade-offs between revenues and degradation factors is essential. This study answers whether or not operating GSS at high efficiency regularly reduces its long-term performance (and thereby its offered resilience to the power grid).Our fifth study (chapter 5) focuses on the resilience of Wide Area Measurement Systems (WAMS) which is an integral part of modern electrical grid infrastructure. The problem of the global positioning system (GPS) spoofing attacks on smart grid endowed with phasor measurement units (PMUs) is addressed, taking into account the dynamical behavior of the states of the system. It is shown how GPS spoofing introduces a timing synchronization error in the phasor readings recorded by the PMU and alters the measurement matrix of the dynamical model. A generalized likelihood ratio-based hypotheses testing procedure is devised to detect changes in the measurement matrix when the system is subjected to a spoofing attack. Monte Carlo simulations are performed on the 9-bus, 3-machine test grid to demonstrate the implication of the spoofing attack on dynamic state estimation and to analyze the performance of the proposed hypotheses test. Asymptotic performance analysis of the proposed test, which can be used for large-scale smart grid networks, is also presented

Future Challenges and Mitigation Methods for High Photovoltaic Penetration: A Survey

: Integration of high volume (high penetration) of photovoltaic (PV) generation with power grids consequently leads to some technical challenges that are mainly due to the intermittent nature of solar energy, the volume of data involved in the smart grid architecture, and the impact power electronic-based smart inverters. These challenges include reverse power flow, voltage fluctuations, power quality issues, dynamic stability, big data challenges and others. This paper investigates the existing challenges with the current level of PV penetration and looks into the challenges with high PV penetration in future scenarios such as smart cities, transactive energy, proliferation of plug-in hybrid electric vehicles (PHEVs), possible eclipse events, big data issues and environmental impacts. Within the context of these future scenarios, this paper reviewed the existing solutions and provides insights to new and future solutions that could be explored to ultimately address these issues and improve the smart grid’s security, reliability and resilienc