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1. Voltage Stability Based Placement of Distributed Generation Against Extreme Events

   Öner, A ; Abur, A. ( January 2020 , 2020 Power Systems Computation Conference (PSCC))

   This paper is concerned about improving the resilience of power grids against extreme events which may lead to the line and generator outages and subsequent voltage stability problems and blackouts. The reported study investigates ways of eliminating or substantially reducing the chances of having such voltage stability problems during expected extreme events, by strategically placing a few distributed generators in the system. The problem is addressed in two stages, where a reasonably inclusive list of credible contingencies are individually considered first. A minimum number of distributed generators are selected and placed in order to maintain voltage stability under each considered contingency. In the second stage, the number of generators is minimized by the strategic selection of locations to reach a solution that ensures voltage stability under all considered contingencies in the system. Effectiveness and computational performance of the developed strategy are illustrated by simulating several outage scenarios using the IEEE 118-bus system. 

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2. Attack Context Embedded Data Driven Trust Diagnostics in Smart Metering Infrastructure

   https://doi.org/10.1145/3426739  

   Bhattacharjee, Shameek ; Madhavarapu, Venkata Praveen ; Silvestri, Simone ; Das, Sajal K. ( February 2021 , ACM Transactions on Privacy and Security)

   null (Ed.)

   Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains. 

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3. N-k Interdiction Modeling for Natural Gas Networks

   Ahumada-Paras, Mareldi ; Sundar, Kaarthik ; Bent, Russel ; Zlotnik, Anatoly ( January 2020 , 21st Power Systems Computation Conference (PSCC))

   Although electricity transmission systems are typically very robust, the impacts that arise when they are disrupted motivate methods for analyzing outage risk. For example, N-k interdiction models were developed to characterize disruptions by identifying the sets of k power system components whose failure results in “worst case” outages. While such models have advanced considerably, they generally neglect how failures outside the power system can cause large-scale outages. Specifically, failures in natural gas pipeline networks that provide fuel for gas-fired generators can affect the function of the power grid. In this study, we extend N-k interdiction modeling to gas pipeline networks. We use recently developed convex relaxations for natural gas flow equations to yield tractable formulations for identifying sets of k components whose failure can cause curtailment of natural gas delivery. We then present a novel cutting-plane algorithm to solve these problems. Finally, we use test instances to analyze the performance of the approach in conjunction with simulations of outage effects on electrical power grids. 

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4. Bayesian Optimization and Hierarchical Forecasting of Non-Weather-Related Electric Power Outages

   https://doi.org/10.3390/en15061958  

   Owolabi, Olukunle O. ; Sunter, Deborah A. ( March 2022 , Energies)

   Power outage prediction is important for planning electric power system response, restoration, and maintenance efforts. It is important for utility managers to understand the impact of outages on the local distribution infrastructure in order to develop appropriate maintenance and resilience measures. Power outage prediction models in literature are often limited in scope, typically tailored to model extreme weather related outage events. While these models are sufficient in predicting widespread outages from adverse weather events, they may fail to capture more frequent, non-weather related outages (NWO). In this study, we explore time series models of NWO by incorporating state-of-the-art techniques that leverage the Prophet model in Bayesian optimization and hierarchical forecasting. After defining a robust metric for NWO (non-weather outage count index, NWOCI), time series forecasting models that leverage advanced preprocessing and forecasting techniques in Kats and Prophet, respectively, were built and tested using six years of daily state- and county-level outage data in Massachusetts (MA). We develop a Prophet model with Bayesian True Parzen Estimator optimization (Prophet-TPE) using state-level outage data and a hierarchical Prophet-Bottom-Up model using county-level data. We find that these forecasting models outperform other Bayesian and hierarchical model combinations of Prophet and Seasonal Autoregressive Integrated Moving Average (SARIMA) models in predicting NWOCI at both county and state levels. Our time series trend decomposition reveals a concerning trend in the growth of NWO in MA. We conclude with a discussion of these observations and possible recommendations for mitigating NWO. 

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5. Detection and Forensics against Stealthy Data Falsification in Smart Metering Infrastructure

   https://doi.org/10.1109/TDSC.2018.2889729  

   Bhattacharjee, Shameek ; Das, Sajal K. ( October 2019 , IEEE Transactions on Dependable and Secure Computing)

   False power consumption data injected from compromised smart meters in Advanced Metering Infrastructure (AMI) of smart grids is a threat that negatively affects both customers and utilities. In particular, organized and stealthy adversaries can launch various types of data falsification attacks from multiple meters using smart or persistent strategies. In this paper, we propose a real time, two tier attack detection scheme to detect orchestrated data falsification under a sophisticated threat model in decentralized micro-grids. The first detection tier monitors whether the Harmonic to Arithmetic Mean Ratio of aggregated daily power consumption data is outside a normal range known as safe margin. To confirm whether discrepancies in the first detection tier is indeed an attack, the second detection tier monitors the sum of the residuals (difference) between the proposed ratio metric and the safe margin over a frame of multiple days. If the sum of residuals is beyond a standard limit range, the presence of a data falsification attack is confirmed. Both the ‘safe margins’ and the ‘standard limits’ are designed through a ‘system identification phase’, where the signature of proposed metrics under normal conditions are studied using real AMI micro-grid data sets from two different countries over multiple years. Subsequently, we show how the proposed metrics trigger unique signatures under various attacks which aids in attack reconstruction and also limit the impact of persistent attacks. Unlike metrics such as CUSUM or EWMA, the stability of the proposed metrics under normal conditions allows successful real time detection of various stealthy attacks with ultra-low false alarms. 

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