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1. Enhanced solar and wind potential during widespread temperature extremes across the U.S. interconnected energy grids

   https://doi.org/10.1088/1748-9326/ad2e72  

   Singh, Deepti ; Bekris, Yianna S ; Rogers, Cassandra D ; Doss-Gollin, James ; Coffel, Ethan D ; Kalashnikov, Dmitri A ( March 2024 , Environmental Research Letters)

   Several recent widespread temperature extremes across the United States (U.S.) have been associated with power outages, disrupting access to electricity at times that are critical for the health and well-being of communities. Building resilience to such extremes in our energy infrastructure needs a comprehensive understanding of their spatial and temporal characteristics. In this study, we systematically quantify the frequency, extent, duration, and intensity of widespread temperature extremes and their associated energy demand in the six North American Electric Reliability Corporation regions using ERA5 reanalysis data. We show that every region has experienced hot or cold extremes that affected nearly their entire extent and such events were associated with substantially higher energy demand, resulting in simultaneous stress across the entire electric gird. The western U.S. experienced significant increases in the frequency (123%), extent (32%), duration (55%) and intensity (29%) of hot extremes and Texas experienced significant increases in the frequency (132%) of hot extremes. The frequency of cold extremes has decreased across most regions without substantial changes in other characteristics. Using power outage data, we show that recent widespread extremes in nearly every region have coincided with power outages, and such outages account for between 12%–52% of all weather-related outages in the past decade depending on the region. Importantly, we find that solar potential is significantly higher during widespread hot extremes in all six regions and during widespread cold extremes in five of the six regions. Further, wind potential is significantly higher during widespread hot or cold extremes in at least three regions. Our findings indicate that increased solar and wind capacity could be leveraged to meet the higher demand for energy during such widespread extremes, improving the resilience and reliability of our energy systems in addition to limiting carbon emissions.

    

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2. An Iterative Algorithm for Accurate Estimation of Power System Forced Oscillation Parameters

   https://doi.org/10.1109/PESGM52003.2023.10253231  

   Dosiek, Luke ; Hosur, Sanjay ( July 2023 , 2023 IEEE Power & Energy Society General Meeting (PESGM))

   This paper proposes an iterative method of estimating power system forced oscillation (FO) amplitude, frequency, phase, and start/stop times from measured data. It combines three algorithms with favorable asymptotic statistical properties: a periodogram-based iterative frequency estimator, a Discrete-Time Fourier Transform (DTFT)-based method of estimating amplitude and phase, and a changepoint detection (CPD) method for estimating the FO start and stop samples. Each of these have been shown in the literature to be approximate maximum likelihood estimators (MLE), meaning that for large enough sample size or signal-to-noise ratio (SNR), they can be unbiased and reach the Cramer-Rao Lower Bound in variance. The proposed method is shown through Monte Carlo simulations of a low-order model of the Western Electricity Coordinating Council (WECC) power system to achieve statistical efficiency for low SNR values. The proposed method is validated with data measured from the January 11, 2019 US Eastern Interconnection (EI) FO event. It is shown to accurately extract the FO parameters and remove electromechanical mode meter bias, even with a time-varying FO amplitude. 

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3. Modeling and tracking Covid-19 cases using Big Data analytics on HPCC system platform

   https://doi.org/10.1186/s40537-021-00423-z  

   Villanustre, Flavio ; Chala, Arjuna ; Dev, Roger ; Xu, Lili ; LexisNexis, Jesse Shaw ; Furht, Borko ; Khoshgoftaar, Taghi ( December 2021 , Journal of Big Data)

   Abstract This project is funded by the US National Science Foundation (NSF) through their NSF RAPID program under the title “Modeling Corona Spread Using Big Data Analytics.” The project is a joint effort between the Department of Computer & Electrical Engineering and Computer Science at FAU and a research group from LexisNexis Risk Solutions. The novel coronavirus Covid-19 originated in China in early December 2019 and has rapidly spread to many countries around the globe, with the number of confirmed cases increasing every day. Covid-19 is officially a pandemic. It is a novel infection with serious clinical manifestations, including death, and it has reached at least 124 countries and territories. Although the ultimate course and impact of Covid-19 are uncertain, it is not merely possible but likely that the disease will produce enough severe illness to overwhelm the worldwide health care infrastructure. Emerging viral pandemics can place extraordinary and sustained demands on public health and health systems and on providers of essential community services. Modeling the Covid-19 pandemic spread is challenging. But there are data that can be used to project resource demands. Estimates of the reproductive number (R) of SARS-CoV-2 show that at the beginning of the epidemic, each infected person spreads the virus to at least two others, on average (Emanuel et al. in N Engl J Med. 2020, Livingston and Bucher in JAMA 323(14):1335, 2020). A conservatively low estimate is that 5 % of the population could become infected within 3 months. Preliminary data from China and Italy regarding the distribution of case severity and fatality vary widely (Wu and McGoogan in JAMA 323(13):1239–42, 2020). A recent large-scale analysis from China suggests that 80 % of those infected either are asymptomatic or have mild symptoms; a finding that implies that demand for advanced medical services might apply to only 20 % of the total infected. Of patients infected with Covid-19, about 15 % have severe illness and 5 % have critical illness (Emanuel et al. in N Engl J Med. 2020). Overall, mortality ranges from 0.25 % to as high as 3.0 % (Emanuel et al. in N Engl J Med. 2020, Wilson et al. in Emerg Infect Dis 26(6):1339, 2020). Case fatality rates are much higher for vulnerable populations, such as persons over the age of 80 years (> 14 %) and those with coexisting conditions (10 % for those with cardiovascular disease and 7 % for those with diabetes) (Emanuel et al. in N Engl J Med. 2020). Overall, Covid-19 is substantially deadlier than seasonal influenza, which has a mortality of roughly 0.1 %. Public health efforts depend heavily on predicting how diseases such as those caused by Covid-19 spread across the globe. During the early days of a new outbreak, when reliable data are still scarce, researchers turn to mathematical models that can predict where people who could be infected are going and how likely they are to bring the disease with them. These computational methods use known statistical equations that calculate the probability of individuals transmitting the illness. Modern computational power allows these models to quickly incorporate multiple inputs, such as a given disease’s ability to pass from person to person and the movement patterns of potentially infected people traveling by air and land. This process sometimes involves making assumptions about unknown factors, such as an individual’s exact travel pattern. By plugging in different possible versions of each input, however, researchers can update the models as new information becomes available and compare their results to observed patterns for the illness. In this paper we describe the development a model of Corona spread by using innovative big data analytics techniques and tools. We leveraged our experience from research in modeling Ebola spread (Shaw et al. Modeling Ebola Spread and Using HPCC/KEL System. In: Big Data Technologies and Applications 2016 (pp. 347-385). Springer, Cham) to successfully model Corona spread, we will obtain new results, and help in reducing the number of Corona patients. We closely collaborated with LexisNexis, which is a leading US data analytics company and a member of our NSF I/UCRC for Advanced Knowledge Enablement. The lack of a comprehensive view and informative analysis of the status of the pandemic can also cause panic and instability within society. Our work proposes the HPCC Systems Covid-19 tracker, which provides a multi-level view of the pandemic with the informative virus spreading indicators in a timely manner. The system embeds a classical epidemiological model known as SIR and spreading indicators based on causal model. The data solution of the tracker is built on top of the Big Data processing platform HPCC Systems, from ingesting and tracking of various data sources to fast delivery of the data to the public. The HPCC Systems Covid-19 tracker presents the Covid-19 data on a daily, weekly, and cumulative basis up to global-level and down to the county-level. It also provides statistical analysis for each level such as new cases per 100,000 population. The primary analysis such as Contagion Risk and Infection State is based on causal model with a seven-day sliding window. Our work has been released as a publicly available website to the world and attracted a great volume of traffic. The project is open-sourced and available on GitHub. The system was developed on the LexisNexis HPCC Systems, which is briefly described in the paper. 

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4. Coordinated Wide-Area Control of Multiple Controllers in a Power System Embedded with HVDC Lines

   https://doi.org/10.1109/TPWRS.2020.3016354  

   Gupta, Pooja ; Pal, Anamitra ; Vittal, Vijay ( August 2020 , IEEE Transactions on Power Systems)

   This paper assesses the stability improvements that can be achieved through the coordinated wide-area control of power system stabilizers (PSSs), static VAr compensators (SVCs), and supplementary damping controllers (SDCs) for damping low frequency oscillations (LFOs) in a power system embedded with multiple high voltage DC (HVDC) lines. The improved damping is achieved by designing a coordinated widearea damping controller (CWADC) that employs partial state feedback. The design methodology uses a linear matrix inequality (LMI)-based mixed H2=H1 robust control for multiple operating scenarios. To reduce the high computational burden, an enhanced version of selective modal analysis (SMA) is employed that not only reduces the number of required wide-area feedback signals, but also identifies alternate feedback signals. Additionally, the impact of delays on the performance of the control design is investigated. The studies are performed on a 29 machine, 127 bus equivalent model of the Western Electricity Coordinating Council (WECC) system-embedded with three HVDC lines and two wind farms. 

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5. The Impact of Neglecting Climate Change and Variability on ERCOT’s Forecasts of Electricity Demand in Texas

   https://doi.org/10.1175/WCAS-D-21-0140.1  

   Lee, Jangho ; Dessler, Andrew E. ( April 2022 , Weather, Climate, and Society)

   The Electric Reliability Council of Texas (ERCOT) manages the electric power across most of Texas. They make short-term assessments of electricity demand on the basis of historical weather over the last two decades, thereby ignoring the effects of climate change and the possibility of weather variability outside the recent historical range. In this paper, we develop an empirical method to predict the impact of weather on energy demand. We use that with a large ensemble of climate model runs to construct a probability distribution of power demand on the ERCOT grid for summer and winter 2021. We find that the most severe weather events will use 100% of available power—if anything goes wrong, as it did during the 2021 winter, there will not be sufficient available power. More quantitatively, we estimate a 5% chance that maximum power demand would be within 4.3 and 7.9 GW of ERCOT’s estimate of best-case available resources during summer and winter 2021, respectively, and a 20% chance it would be within 7.1 and 17 GW. The shortage of power on the ERCOT grid is partially hidden by the fact that ERCOTs seasonal assessments, which are based entirely on historical weather, are too low. Prior to the 2021 winter blackout, ERCOT forecast an extreme peak load of 67 GW. In reality, we estimate hourly peak demand was 82 GW, 22% above ERCOT’s most extreme forecast and about equal to the best-case available power. Given the high stakes, ERCOT should develop probabilistic estimates using modern scientific tools to predict the range of power demand more accurately.

    

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