Opportunities for Price Manipulation by Aggregators in Electricity Markets

Aggregators are playing an increasingly crucial role in the integration of renewable generation in power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators difficult to monitor and regulate, raising concerns about potential market manipulation by aggregators. In this paper, we study this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, efficient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit are possible via strategic curtailment in practical settings

Financially Motivated LMP Manipulation By Aggregators in Power Markets

Renewable energy accounts for a sizeable share within modern power systems and aggregators of renewable generators play an important role in the electricity market. However, because renewable generators produce power intermittently, it is hard to monitor and supervise the behavior of the aggregators. There is a chance for aggregators to manipulate the locational marginal prices (LMPs) in the power market by curtailing generation in order to increase their profits. In this thesis we propose a tri-level model that can quantify aggregators’ potential profits. This model is based on both a real-time optimal dispatch and an LMP clearing procedure. With this model, the relationship between curtailment of generation and profits of aggregators was studied by using different backup generators in an IEEE 14-bus power system. At the same time, we found the most profitable point at which aggregators curtail generation. We also used the same IEEE 14-bus power system to devise a resilience strategy to keep LMPs steady throughout the whole power system. This resilience strategy led to a decline in aggregators’ motivation to manipulate LMPs in power markets. In the study, we show that the aggregators can increase their profits through the curtailment of generation and this behavior can lead to significant LMP changes in the whole power system. The profit of aggregators can be different when the independent system operators (ISOs) use different generators to make up the financially motivated curtailment. Further, this thesis shows that aggregators have the potential to conduct financially motivated LMP manipulation in the power market and it can push ISOs to improve the related management rules

Opportunities for Price Manipulation by Aggregators in Electricity Markets

Aggregators of distributed generation are playing an increasingly crucial role in the integration of renewable energy in power systems. However, the intermittent nature of renewable generation makes market interactions of aggregators difficult to monitor and regulate, raising concerns about potential market manipulation by aggregators. In this paper, we study this issue by quantifying the profit an aggregator can obtain through strategic curtailment of generation in an electricity market. We show that, while the problem of maximizing the benefit from curtailment is hard in general, efficient algorithms exist when the topology of the network is radial (acyclic). Further, we highlight that significant increases in profit are possible via strategic curtailment in practical settings

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

Real-Time Machine Learning Models To Detect Cyber And Physical Anomalies In Power Systems

A Smart Grid is a cyber-physical system (CPS) that tightly integrates computation and networking with physical processes to provide reliable two-way communication between electricity companies and customers. However, the grid availability and integrity are constantly threatened by both physical faults and cyber-attacks which may have a detrimental socio-economic impact. The frequency of the faults and attacks is increasing every year due to the extreme weather events and strong reliance on the open internet architecture that is vulnerable to cyber-attacks. In May 2021, for instance, Colonial Pipeline, one of the largest pipeline operators in the U.S., transports refined gasoline and jet fuel from Texas up the East Coast to New York was forced to shut down after being attacked by ransomware, causing prices to rise at gasoline pumps across the country. Enhancing situational awareness within the grid can alleviate these risks and avoid their adverse consequences. As part of this process, the phasor measurement units (PMU) are among the suitable assets since they collect time-synchronized measurements of grid status (30-120 samples/s), enabling the operators to react rapidly to potential anomalies. However, it is still challenging to process and analyze the open-ended source of PMU data as there are more than 2500 PMU distributed across the U.S. and Canada, where each of which generates more than 1.5 TB/month of streamed data. Further, the offline machine learning algorithms cannot be used in this scenario, as they require loading and scanning the entire dataset before processing. The ultimate objective of this dissertation is to develop early detection of cyber and physical anomalies in a real-time streaming environment setting by mining multi-variate large-scale synchrophasor data. To accomplish this objective, we start by investigating the cyber and physical anomalies, analyzing their impact, and critically reviewing the current detection approaches. Then, multiple machine learning models were designed to identify physical and cyber anomalies; the first one is an artificial neural network-based approach for detecting the False Data Injection (FDI) attack. This attack was specifically selected as it poses a serious risk to the integrity and availability of the grid; Secondly, we extend this approach by developing a Random Forest Regressor-based model which not only detects anomalies, but also identifies their location and duration; Lastly, we develop a real-time hoeffding tree-based model for detecting anomalies in steaming networks, and explicitly handling concept drifts. These models have been tested and the experimental results confirmed their superiority over the state-of-the-art models in terms of detection accuracy, false-positive rate, and processing time, making them potential candidates for strengthening the grid\u27s security

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)

State of the art of cyber-physical systems security: An automatic control perspective

Cyber-physical systems are integrations of computation, networking, and physical processes. Due to the tight cyber-physical coupling and to the potentially disrupting consequences of failures, security here is one of the primary concerns. Our systematic mapping study sheds light on how security is actually addressed when dealing with cyber-physical systems from an automatic control perspective. The provided map of 138 selected studies is defined empirically and is based on, for instance, application fields, various system components, related algorithms and models, attacks characteristics and defense strategies. It presents a powerful comparison framework for existing and future research on this hot topic, important for both industry and academia