US Power Grid Research Articles

Differentially private distributed estimation and learning

We study distributed estimation and learning problems in a networked environment where agents exchange information to estimate unknown statistical properties of random variables from their privately observed samples. The agents can collectively estimate the unknown quantities by exchanging information about their private observations, but they also face privacy risks. Our novel algorithms extend the existing distributed estimation literature and enable the participating agents to estimate the expected value of a complete sufficient statistic from private signals acquired offline or online over time and to preserve the privacy of their signals and network neighborhoods. This is achieved through linear aggregation schemes with adjusted randomization schemes that add noise to the exchanged estimates subject to differential privacy constraints, both in an offline and online manner. We provide convergence rate analysis and tight finite-time convergence bounds. We show that the noise that minimizes the convergence time to the best estimates is the Laplace noise, with parameters corresponding to each agent’s sensitivity to their signal and network characteristics. Our algorithms are amenable to dynamic topologies and balancing privacy and accuracy trade-offs. Finally, to supplement and validate our theoretical results, we run experiments on real-world data from the US Power Grid Network and electric consumption data from German Households to estimate the average power consumption of power stations and households under all privacy regimes and show that our method outperforms existing first-order, privacy-aware, distributed optimization methods.

A multi-node attack scheme based on community partitioning in large scale infrastructure networks

Node attack plays a vital role in evaluating the reliability of large scale infrastructure networks, which has attracted intensive research interests. Unfortunately, most of them focus on single node attack. Multi-node attack based on MBA (Module based Adaptive) is a promising research vector with great potential in improving efficiency. However, MBA-based node attack typically needs to repetitively consider community structures of large networks during the entire node attack process, which brings significant loss in network reliability and causes computational overheads. In light of this, we propose a novel scheme based on MBA to implement multi-node attack, which adopts the Multilevel community partitioning algorithm to overcome the above limitations. We evaluate our scheme on two artificial synthetic networks and four real-world networks with comparison to four widely used node attack schemes, and demonstrate the non-negligible superiority of our scheme over all benchmarks. Especially in the US Powergrid, our scheme can always achieve robustness around 10 times that of other schemes when deleting the same percentage of nodes. To the best of our knowledge, we are the first to achieve an MBA-based node attack scheme that only needs to execute the community partitioning algorithm once during the entire node deletion process.

Reduction in Emissions by Massive Solar Plant Integration in the US Power Grid

Fossil fuels, the predominant energy source in the United States, have been identified as major contributors to environmental pollution through the release of harmful emissions. As a countermeasure, there has been an increasing focus on the exploration and development of cleaner energy alternatives to alleviate the environmental degradation caused by fossil fuels and to satisfy the growing energy needs. This study conducted scenario analyses to evaluate the impact of integrating solar energy into specific US power grids on reducing carbon emissions. The analysis encompassed electrical systems within California, New England, New York, and the Southwest, utilizing datasets from the Energy Information Administration and National Renewable Energy Laboratory. The Energy Information Administration dataset includes information on net generation according to each source and carbon emissions according to fuel type, whereas the National Renewable Energy Laboratory dataset provides hourly projections for 6000 theoretical photovoltaic installations and detailed solar energy output data every five minutes over a year. Our findings indicated a notable decrease in carbon dioxide emissions following the introduction of solar power facilities. The most significant reductions were observed in the Southwest and California, attributed to solar plant integration. Conversely, New York and New England were identified as regions requiring additional policy measures and incentives to meet the emission reduction goals.

Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping

Quantifying the Power System Resilience of the US Power Grid Through Weather and Power Outage Data Mapping

A comprehensive environmental assessment of beef production and consumption in the United States

Life cycle assessments of have been completed documenting the environmental sustainability of beef, but these studies have often focused on specific cattle production systems with an emphasis on global warming potential. A need exists for a national-scale full life cycle assessment of beef production through consumption in the United States. Process level simulation of archetypical cattle production systems throughout the nation were combined with information gathered for harvest, processing, retail, and consumption of beef to provide inventory data for a cradle-to-grave life cycle assessment. A set of 18 environmental impact categories were quantified, and important sources of each were identified. In 13 of the categories, the major sources of impact were related to cattle production, and for 10 of these categories, cattle production and related upstream sources contributed more than half of the total impact. These categories were fine particulate matter, global warming, land use, mineral resource scarcity, ozone formation, stratospheric ozone depletion, terrestrial acidification, and water consumption. Categories where most of the impact occurred post farmgate were fossil resource scarcity, freshwater ecotoxicity, freshwater eutrophication, human carcinogenic toxicity, human non-carcinogenic toxicity, ionizing radiation, marine ecotoxicity, and terrestrial ecotoxicity. Mitigation strategies for reducing these environmental impacts are normally specific to the impact category. Because electricity use is an important contributor to many of the potential impacts throughout the full chain, reducing electricity use is an important mitigation strategy. We evaluated the sensitivity associated with greening of the electric grid in which the Northeast US power grid, which has a larger percentage of renewables, was used as the source of electricity for all systems. The impact remained constant or were reduced in 15 of the 18 impact categories including a 6% reduction in global warming and 22% reduction in particulate matter formation. Another major contributor to all impact categories was food loss and waste. A 50% reduction in food waste, primarily by the consumer, resulted in an across-the-board reduction of approximately 11% in each of the impact categories, which makes food waste reduction one of the most important strategies for improving the environmental sustainability of beef. This assessment provides a current baseline for evaluating mitigation strategies and measuring future improvements in sustainability for the U.S. beef industry.

Estimating Carbon Dioxide Emissions and Direct Power Consumption of Linear Accelerator–Based External Beam Radiation Therapy

Climate change is one of the direst health threats that humanity faces. We aim to estimate the carbon dioxide (CO2) emissions associated with the energy usage from linear accelerator (LINAC)-based external beam radiation therapy (EBRT) for the most common cancer diagnoses. We identified patients with the 4 most common cancer types treated with curative-intent EBRT. Beam-on time for each fraction was extracted from the treatment planning system and averaged over each site and treatment modality. The power was multiplied by the beam-on time in hours to yield kilowatt hours (kWh). Using the US Environmental Protection Agency Greenhouse Gas Equivalencies calculator, we converted the kWh into estimates of CO2-equivalent emissions for the average US power grid. Idle time of the LINAC was estimated via Varian Medical Systems. A total of 10 patients were included for each of the following modalities: conventionally fractionated for prostate cancer (28 fractions [fx]), prostate stereotactic body radiation therapy (SBRT) (5 fx), 15- and 5-fx regimens for early-stage breast cancer, 3- and 5-fx SBRT regimens for early-stage lung cancer, conventional EBRT (30 fx) for locally advanced lung cancer, and short- (5 fx) and long-course (25-28 fx) for rectal cancer. The modality with the lowest and highest carbon emissions per course, on average, was prostate SBRT (2.18 kg CO2; interquartile range, 1.92-2.30) and conventional treatment for prostate cancer (17.34 kg CO2; interquartile range, 10.26-23.79), respectively. This corresponds to CO2-equivalent emissions of driving an average of 5.4 miles and 41.2 miles in a standard vehicle, respectively. "Standby" mode for a LINAC TrueBeam and Clinac IX uses 112 kWh and 64.8 kWh per day, respectively. We have estimated CO2 emissions arising from direct energy usage of a LINAC for 4 common cancers treated with EBRT. "Standby" mode of a LINAC uses the most energy per day. Comprehensive studies are warranted to minimize the environmental effects of health and cancer care.

Method for spatiotemporal wind power generation profile under hurricanes: U.S.-Caribbean super grid proposition

Wind turbines hit by hurricanes produce high variability of power generation profile. This research proposes a method to estimate this variability and solutions to smooth it. The method calculates the hurricane wind-speed profile in each turbine by Holland's parametric equation in spatiotemporal steps of the hurricane trajectory. The calculated wind speed profile is converted into a power generation profile using wind turbine characteristic curves. Since the simulations show high variability of power profile, this research investigates two strategies to smooth such variability. The first one is systemic. A U.S.-Caribbean super grid interconnecting the Caribbean islands with the U.S. power grid by submarine high voltage direct current (HVDC) cables is proposed. This super grid shows technical feasibility since the proposed route of cables meets some installation criteria, such as a maximum water depth of 3 km and cable length not exceeding 879 km. The U.S.-Caribbean super grid reduces power profile variability by draining the wind power peak caused by hurricanes passing through the Caribbeans and transmitting it to the US power grid by the Florida-Bahamas HVDC interconnector. Another strategy, with special turbines, was tested to evaluate a potential reduction in power variability. However, the special turbines with a high cutout speed showed a limited effect on reducing the variability compared to the U.S.-Caribbean super grid. The proposed power profile estimation method is an essential tool for planning, preliminary design, and reinforcements of high voltage grids in hurricane-prone regions with wind power capacity.

Synchronization dynamics on power grids in Europe and the United States.

Dynamical simulation of the cascade failures on the Europe and United States (U.S.) high-voltage power grids has been done via solving the second-order Kuramoto equation. We show that synchronization transition happens by increasing the global coupling parameter K with metasatble states depending on the initial conditions so that hysteresis loops occur. We provide analytic results for the time dependence of frequency spread in the large-K approximation and by comparing it with numerics of d=2,3 lattices, we find agreement in the case of ordered initial conditions. However, different power-law (PL) tails occur, when the fluctuations are strong. After thermalizing the systems we allow a single line cut failure and follow the subsequent overloads with respect to threshold values T. The PDFs p(N_{f}) of the cascade failures exhibit PL tails near the synchronization transition point K_{c}. Near K_{c} the exponents of the PLs for the U.S. power grid vary with T as 1.4≤τ≤2.1, in agreement with the empirical blackout statistics, while on the Europe power grid we find somewhat steeper PLs characterized by 1.4≤τ≤2.4. Below K_{c}, we find signatures of T-dependent PLs, caused by frustrated synchronization, reminiscent of Griffiths effects. Here we also observe stability growth following the blackout cascades, similar to intentional islanding, but for K>K_{c} this does not happen. For T<T_{c}, bumps appear in the PDFs with large mean values, known as "dragon king" blackout events. We also analyze the delaying or stabilizing effects of instantaneous feedback or increased dissipation and show how local synchronization behaves on geographic maps.

Prevalence and scalable control of localized networks

The ability to control network dynamics is essential for ensuring desirable functionality of many technological, biological, and social systems. Such systems often consist of a large number of network elements, and controlling large-scale networks remains challenging because the computation and communication requirements increase prohibitively fast with network size. Here, we introduce a notion of network locality that can be exploited to make the control of networks scalable, even when the dynamics are nonlinear. We show that network locality is captured by an information metric and is almost universally observed across real and model networks. In localized networks, the optimal control actions and system responses are both shown to be necessarily concentrated in small neighborhoods induced by the information metric. This allows us to develop localized algorithms for determining network controllability and optimizing the placement of driver nodes. This also allows us to develop a localized algorithm for designing local feedback controllers that approach the performance of the corresponding best global controllers, while incurring a computational cost orders-of-magnitude lower. We validate the locality, performance, and efficiency of the algorithms in Kuramoto oscillator networks, as well as three large empirical networks: synchronization dynamics in the Eastern US power grid, epidemic spreading mediated by the global air-transportation network, and Alzheimer's disease dynamics in a human brain network. Taken together, our results establish that large networks can be controlled with computation and communication costs comparable to those for small networks.

Reading California’s Power Grid: A Metonymic Methodology

As climate change intensifies, the global imperative to minimize carbon emissions and move beyond oil deepens. Many visions for the future of energy imagine increased electrification as a solution to the transition away from fossil fuels that does not require major cultural change. In this imaginary, electricity acts as a bridge between the fossil-fueled twentieth century and technologically driven green energy futures. Not only does the vision of a green electrified future fail to address the unjust cultural and political power relations that surround energy systems, it also ignores the already disastrous impacts climate change has had on the US power grid. Examining the environmental entanglements of and the cultural imaginaries that shape electrification is necessary to imagining and enacting more just and transformative energy futures that do not use electrification as a means of prolonging the current extractive, colonial, and capitalist cultural approaches to energy. This essay takes as its starting point the tension between visions of green electric futures and the material reality of the grid. Focusing on California’s electricity grid, this essay analyzes electricity’s complicated web of power relations by triangulating three case studies that apply a metonymic methodology for close reading the region’s power grid. PG&amp;E’s smart meter upgrades are the focus of the first case study. The second case study examines California’s recent electricity equipment–sparked superfires. Finally, the essay concludes with a third case study focused on PG&amp;E’s public safety power shutoffs, the utility’s safety response intended to prevent additional fires. Ultimately, the three case studies, when juxtaposed, provide very different perspectives on the dominant narratives surrounding electrification. When triangulated, the case studies show how the imaginary of easily accessed limitless electricity, which fuels visions of increased electrification as a solution to climate change, relies on both an abstraction of the power grid and the continuation of unjust colonial practices.

Challenges to Improving Energy Security Abound: Part 1

The US power grid is an integral part of the clean energy transition and tied directly to US energy security. This author uses the framework of the four As, and the International Energy Agency's Model of Short‐term Energy Security (MOSE) to measure progress in achieving energy security. The four As of energy security include the availability, accessibility, affordability, and acceptability of energy sources as determined by each country. There are no international standards to guide countries. Instead, every government will prioritize the four As according to their needs. MOSES is a means to measure a country's energy security risks and resilience. The model recommends domestic and external actions that governments can take to reduce the risk of supply disruptions or grid failure and improve resilience.

Seven Myths About US Infrastructure

We focus here on traditional infrastructure, defined as capital-intensive physical assets that are vital to the functioning of an advanced society and that broadly enable economic activity: power and energy, transportation and transit, water supply and water treatment, and digital communications. This article questions seven myths about infrastructure investment in the United States: (1) the US government can unilaterally dictate where funds are spent for most infrastructure classes; (2) the federal government owns and controls interstate highways; (3) many infrastructure projects cannot get funding; (4) the United States disfavors public–private partnerships; (5) US airports should be privatized to achieve efficiencies and improved passenger service; (6) it is easy to transition the US power grid to renewable generation; and (7) only money matters for improving infrastructure. The vast stores of public and private capital earmarked for investments in infrastructure can be readily deployed if there is stakeholder alignment, political will, sufficient planning, timely regulatory approvals, and economic viability. In contrast, misalignment of costs and benefits, a lack of economic viability or political will, or opposition from key stakeholders creates project delivery challenges.

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Toward carbon-neutral electricity and mobility: Is the grid infrastructure ready?

Potential energy of complex networks: a quantum mechanical perspective

We propose a characterization of complex networks, based on the potential of an associated Schrödinger equation. The potential is designed so that the energy spectrum of the Schrödinger equation coincides with the graph spectrum of the normalized Laplacian. Crucial information is retained in the reconstructed potential, which provides a compact representation of the properties of the network structure. The median potential over several random network realizations, which we call ensemble potential, is fitted via a Landau-like function, and its length scale is found to diverge as the critical connection probability is approached from above. The ruggedness of the ensemble potential profile is quantified by using the Higuchi fractal dimension, which displays a maximum at the critical connection probability. This demonstrates that this technique can be successfully employed in the study of random networks, as an alternative indicator of the percolation phase transition. We apply the proposed approach to the investigation of real-world networks describing infrastructures (US power grid). Curiously, although no notion of phase transition can be given for such networks, the fractality of the ensemble potential displays signatures of criticality. We also show that standard techniques (such as the scaling features of the largest connected component) do not detect any signature or remnant of criticality.

Nonlinear model of cascade failure in weighted complex networks considering overloaded edges

Considering the elasticity of the real networks, the components in the network have a redundant capacity against the load, such as power grids, traffic networks and so on. Moreover, the interaction strength between nodes is often different. This paper proposes a novel nonlinear model of cascade failure in weighted complex networks considering overloaded edges to describe the redundant capacity for edges and capture the interaction strength of nodes. We fill this gap by studying a nonlinear weighted model of cascade failure with overloaded edges over synthetic and real weighted networks. The cascading failure model is constructed for the first time according to the overload coefficient, capacity parameter, weight coefficient, and distribution coefficient. Then through theoretical analysis, the conditions for stopping failure cascades are obtained, and the analysis shows the superiority of the constructed model. Finally, the cascading invulnerability is simulated in several typical network models and the US power grid. The results show that the model is a feasible and reasonable change of weight parameters, capacity coefficient, distribution coefficient, and overload coefficient can significantly improve the destructiveness of complex networks against cascade failure. Our methodology provides an efficacious reference for the control and prevention of cascading failures in many real networks.

Power-Law Distributions of Dynamic Cascade Failures in Power-Grid Models.

Power-law distributed cascade failures are well known in power-grid systems. Understanding this phenomena has been done by various DC threshold models, self-tuned at their critical point. Here, we attempt to describe it using an AC threshold model, with a second-order Kuramoto type equation of motion of the power-flow. We have focused on the exploration of network heterogeneity effects, starting from homogeneous two-dimensional (2D) square lattices to the US power-grid, possessing identical nodes and links, to a realistic electric power-grid obtained from the Hungarian electrical database. The last one exhibits node dependent parameters, topologically marginally on the verge of robust networks. We show that too weak quenched heterogeneity, coming solely from the probabilistic self-frequencies of nodes (2D square lattice), is not sufficient for finding power-law distributed cascades. On the other hand, too strong heterogeneity destroys the synchronization of the system. We found agreement with the empirically observed power-law failure size distributions on the US grid, as well as on the Hungarian networks near the synchronization transition point. We have also investigated the consequence of replacing the usual Gaussian self-frequencies to exponential distributed ones, describing renewable energy sources. We found a drop in the steady state synchronization averages, but the cascade size distribution, both for the US and Hungarian systems, remained insensitive and have kept the universal tails, being characterized by the exponent . We have also investigated the effect of an instantaneous feedback mechanism in case of the Hungarian power-grid.

A Novel Energy Storage-Based Net-Load Smoothing and Shifting Architecture for High Amount of Photovoltaics Integrated Power Distribution System

This article presents a novel ramp rate control and active power smoothing and shifting methodology for net-load profiles in large power distribution networks where high amount of photovoltaic (PV) penetration levels exist. The novelty is that the proposed methodology uses dynamic state-of-charge (SoC) management, energy storage optimal use of any given size and fast-Fourier transform-based reference curves that adequately fit energy storage system (ESS) active and reactive power capabilities. Then, a least square minimization-based optimization method is applied taking both ancillary applications into consideration and to maintain SoC within limits based on ESS capacity. The methodology is applied on a 8500 nodes US power grid model using CYMEDist simulation platform and applied on a real US power distribution system. Results show significant level of net-load ramp rate reduction as well as smoothing of fast fluctuations (up to 60%) due to both PV intermittency and load changes. The approach is also capable of breaking large systems into virtual local net-load locations where ESS's are installed to mitigate the severity of the anticipated shape of system net-loads.

Information source localization with protector diffusion in networks

Recently, the problem of detecting the information source in a network has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees if the number of infected nodes is sufficiently large. In this paper, we study the impact of an anti-information spreading on the original information source detection. We first show a negative result: the anti-information diffusion does not increase the detection probability under Maximum-Likelihood-Estimator (MLE) when the number of infected nodes are sufficiently large by passive diffusion that the anti-information starts to be spread by a special node, called the protector, after is reached by the original information. We next consider the case when the distance between the information source and the protector follows a certain type of distribution, but its parameter is hidden. Then, we propose the following learning algorithm: a) learn the distance distribution parameters under MLE, and b) detect the information source under Maximum-A-Posterior-Estimator (MAPE) based on the learnt parameters. We provide an analytic characterization of the source detection probability for regular trees under the proposed algorithm, where MAPE outperforms MLE by up to 50% for 3-regular trees and by up to 63% when the degree of the regular tree becomes large. We demonstrate our theoretical findings through numerical results, and further present the simulation results for general topologies (e.g., Facebook and US power grid networks) even without knowledge of the distance distribution, showing that under a simple protector placement algorithm, MAPE produces the detection probability much larger than that by MLE.

STC: Exposing Hidden Compromised Devices in Networked Sustainable Green Smart Computing Platforms by Partial Observation

Large-scale smart computing is generally more vulnerable to cyber attacks since their system devices are normally distributed as networked platforms and each device could be a target and get compromised. Due to resource constraints (i.e., Sustainable Computing demand) and cost-efficiency issues (i.e., Green Computing demand), we usually monitor only a few devices (i.e., partial observation) to ensure all operations across different platforms are under a secure environment. This leads to a critical problem for detecting compromised devices that are out of surveillance. To the best of our knowledge, this problem has not been solved so far. In this paper, we propose an unsupervised classifier based on source-tracing technique (STC in short) to expose hidden compromised devices with partial observation on the networked sustainable green smart computing platforms. STC mainly focuses on the cyber threats that can spread in the platform and compromise various system devices. To expose hidden compromised devices that are out-of-surveillance, STC first captures the spreading source by the reverse dissemination technique, and then relies on microscopic propagation modelling to probabilistically identify the most probable compromised devices. We carried out a series of experiments to validate the performance of our proposed method. The evaluations are based on three real networked platforms: Air Traffic Control system, AS-level Internet platform, and US Power Grid. The experiment results demonstrated that STC can accurately expose the hidden compromised devices in terms of following aspects: 1) Source-tracing (more than 80 percent runs got exact real source and 95 percent within two hops of real source); 2) Modelling (very close to the simulation results); 3) Exposing accuracy (almost all $>$> 90 percent); and 4) Comparison to baseline (superiority to three supervised and two unsupervised classifiers).

Corrosion Suppression with Redox Control in KCl-MgCl2 Under Flow

Solar power systems must be competitive with conventional energy sources in in terms of cost to make up a significant portion of the electric power grid. Solar power can likely more rapidly grow and make-up a larger portion of the US power grid if systems have energy storage capability that could continue to provide power for extended periods without sunlight and vary the power as needed (i.e. – be dispatchable). Concentrated solar power tower systems with thermal energy storage using advanced high operating temperature heat transfer fluids and advanced thermodynamic cycles can potentially meet the twin goals of low cost and high reliability while being dispatchable. Molten salts are a promising candidate for a high operating temperature heat transfer fluids. Due to the decomposition of molten nitrates at temperatures above about 550 °C, molten fluorides or chlorides are expected to be needed for higher temperature operation. KCl-MgCl2 is a promising heat transfer fluid due to its low cost and good thermal transport properties. Corrosion in molten chloride salts can be severe if salt chemistry is not well controlled. Research has shown that the main corrosion mechanism in Fe-Ni-Cr alloys exposed to molten salts is selective oxidation of Cr and that the corrosion mechanism is mass transfer limited. In the research to be presented, thermosiphons were operated at three different temperature ranges to add flow to corrosion tests and evaluate changed corrosion rates. Four alloys were tested in the thermosiphons with varying temperature and with and without Mg redox control. The most extreme case tested had the hot region at ~930 °C and cold region at ~880C. The research found that naturally convective flow could increase up to 5 times the corrosion compared to static tests for Haynes-230 without corrosion inhibition. Corrosion inhibition using Mg worked well to decrease corrosion and mass transfer reactions for Fe, Co, and Ni based superalloys.