No abstract

Monitoring and actuation represent critical tasks for electric power utilities to maintain system stability and reliability. As such, the utility is highly dependent on a low latency communication infrastructure for receiving and transmitting measurement and control data to make accurate decisions. This dependency, however, can be exploited by an adversary to disrupt the integrity of the grid. We demonstrate that Denial of Service (DoS) attacks, even if perpetrated on a subset of cyber communication nodes, has the potential to succeed in disrupting the overall grid. One countermeasure to DoS attacks is enabling cyber elements to distributively reconfigure the system's routing topology so that malicious nodes are isolated. We propose a collaborative reputation-based topology configuration scheme and through game theoretic principles we prove that a lowlatency Nash Equilibrium routing topology always exists for the system. Numerical results indicate that during an attack on a subset of cyber nodes, the proposed algorithm effectively enables the remaining nodes to converge quickly to an equilibrium topology and maintain dynamical stability in the specific instance of an islanded microgrid system.

No abstract

Unlike prior work on demand management, which typically requires industrial loads to be turned off during peak times, this paper studies the potential to carry out demand response by modifying the elastic load components of common household appliances. Such a component can decrease its instantaneous power draw at the expense of increasing its duration of operation with no impact on the appliance's lifetime. We identify the elastic components of ten common household appliances. Assuming separate control of an appliance's elastic component, we quantify the relationship between the potential reduction in aggregate peak and the duration required to complete the operation of appliances in four geographic regions: Ontario, Quebec, France and India. We find that even with a small extension to the operation duration of appliances, peak demand can be significantly reduced in all four regions both during winter and summer. For example, during winter in Quebec, a nearly 125 MW reduction in peak demand can be obtained with just a 10% increase in appliance operation duration. We conclude that exploiting appliance elasticity to reduce peak power demand should be an important consideration for appliance manufacturers. From a policy perspective, our study gives regulators the ability to quantitatively assess the impact of requiring manufacturers to conform to "smart appliance" standards.

Distributed generators (DGs) are being widely deployed in today's power grid. These energy sources are highly variable posing practical challenges for deployment and grid management. In this paper, a novel scalable distributed power dispatch strategy is proposed to effectively manage DGs at the distribution substation level, capitalizing on the recent push to cyber-enable power grid operations. We demonstrate how the inherent separability of the power dispatch problem allows the use of dual decomposition that enables every participating DG to locally compute its dispatch strategy based on simple broadcast data by the utility. Results and comparisons indicate that the DGs are able to rapidly converge to an optimal economical dispatch vector with significantly less concentrated computational effort and communication overhead, promoting security and privacy.Index Terms-Cyber-physical systems, distributed algorithms, power generation dispatch, power system security, renewable energy sources.

With the advent of the advanced metering infrastructure, electricity usage data is being continuously generated at large volumes by smart meters vastly deployed across the modern power grid. Electric power utility companies and third party entities such as smart home management solution providers gain significant insights into these datasets via machine learning (ML) models. These are then utilized to perform active/passive power demand management that fosters economical and sustainable electricity usage. Although ML models are powerful, these remain vulnerable to adversarial attacks. A novel stealthy black-box attack construction model is proposed that targets deep learning models utilized to perform non-intrusive load monitoring based on smart meter data. These attacks are practical as there is no assumption of the knowledge of training data, internal parameters, and architecture of the targeted ML model. The profound impact of the proposed stealthy attack constructions on energy analytics and decision-making processes is shown through comprehensive theoretical, practical, and comparative analysis. This work sheds light on vulnerabilities of ML models in the smart grid context and provides valuable insights for securely accommodating increasing prevalence of artificial intelligence in the modern power grid.

Today's electricity grid is rapidly evolving to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous communication/computational capabilities in the grid and the internet of things paradigm that is steadily permeating modern society. Another trend is the recent resurgence of machine learning which is especially timely for smart grid applications. However, a major deterrent in effectively utilizing machine learning algorithms is the lack of labelled training data. We overcome this issue in the specific context of smart meter data by proposing a flexible framework for generating synthetic labelled load (e.g. appliance) patterns and usage habits via a non-intrusive novel data-driven approach. We leverage on recent developments in generative adversarial networks (GAN) and kernel density estimators (KDE) to eliminate model-based assumptions that otherwise result in biases. The ensuing synthetic datasets resemble real datasets and lend to rich and diverse training/testing platforms for developing effective machine learning algorithms pertaining to consumer-side energy applications. Theoretical and practical studies presented in this paper highlight the viability and superior performance of the proposed framework.

No abstract