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All sources NASA NTRS DOE OSTI

DOE OSTI2023

Towards an Energy Future with Ubiquitous Electric Vehicles: Barriers and Opportunities

The electrification of personal transportation holds great potential for lowering greenhouse gas emissions and reducing climate change. The promise of electric vehicles (EVs) to serve these goals has resulted in a broad range of supporting policies aimed at encouraging widespread EV adoption at both the state and federal levels in the United States and around the world. While the EV revolution and prospects of a world with ubiquitous EVs are impacting various industries and many aspects of daily life, strategic interactions between the power grid and EVs are crucial for a successful energy transition. However, managing the interplay between EVs and the power grid remains a challenge. Motivated by that tension, this paper surveys a variety of solutions, policies, and incentives that are focused on effectively managing EV charging behaviors. The paper’s objective is to explore these tools to ensure that EV owners have ultimate control over their personal vehicles while simultaneously allowing the power grid to mitigate adverse network impacts. Furthermore, this paper examines the role of charging infrastructure technology and its strategic placement in facilitating the seamless integration of EVs into the grid. Additionally, the paper highlights financial mechanisms associated with EV integration and discusses the consequences of these mechanisms.

25 ENERGY STORAGE↗

DOE OSTI2021

Passive high-power-density grid interface device

A hybrid switched capacitor power converter for high-power applications is provided. The converter has a transistor-switched input-stage boost converter followed by a capacitor-and-diode ladder circuit. The converter is adapted to produce an output voltage of at least 5 kV at a power level of at least 0.5 kW. The ladder circuit includes one or more multi-stage rails.

Brocato, Robert W.↗

DOE OSTI2024

Alternating Direction Decomposition with Strong Bounding and Convexification (ADDSBC) for Solving Security Constrained AC Unit Commitment Problems

This project aims to develop efficient and robust computational methods for solving the security-constrained unit commitment and alternating current optimal power flow problem (SC-UC-ACOPF). The SC-UC-ACOPF problem is at the center of the short-term operation of the U.S. Power Grid. It is solved every week, every day, and every 10 minutes to plan for the optimal action of electricity generation and consumption by minimizing the generation cost and maintaining power system reliability against potential disruptions of equipment failures. In mathematical terms, SC-UC-ACOPF is a challenging large-scale mixed-integer nonlinear optimization model. This means that the decisions involve both discrete variables, e.g. the turning on and off of generators and switching of transmission lines and transformers, and continuous decisions, e.g. the amount of energy generated by each generator and the power flows in the power grid. The physics of the power flow is described by nonlinear equations involving real and reactive power and bus voltages. Another key feature is the large number of contingencies, i.e. the system needs to stay reliable in face of failure of any one equipment, such as transmission lines and generators. The U.S. power grids are extremely complicated and large scale with more than 5,000 generators, 50,000 buses, and 100,000 high-voltage transmission lines, making the SC-UC-ACOPF a very large-scale computation challenge. The research developed in this project aims to solve the SC-UC-ACOPF problems in the three timescales, i.e. weekly, daily, and every 10-min. The proposed computational methods are built on a principled algorithmic approach of decomposition and penalization. More specifically, the algorithm develops spatial and temporal decomposition by exploiting the strong temporal coupling and weak spatial coupling of the UC problem and the complementary feature, i.e. weak temporal coupling and strong spatial coupling of the ACOPF problem. The algorithm also leverages recent progresses in strong convex relaxation of ACOPF. A unique feature of the proposed approach is that it generates a valid, global upper bound on the optimal maximum profit. In this way, a global optimality gap is available to measure the quality of the solution. To further speed up computation, the research team has developed a plethora of effective heuristics to strengthen the iterative penalty-based decomposition framework. For instance, a heuristic is developed to construct inner approximations of the time coupling constraints within the time decoupled problems. Contingencies are pre-screened and low-rank matrix computation is exploited to find the almost unique solution to each contingency. A novel heuristic for line switching is proposed and tested with positive impacts on instances where line switching is beneficial. Taking a systematic approach and carefully handling every detail of the problem pays off. The TIM-GO’s performance throughout the trials and the final event was stellar. TIM-GO garnered the second highest total prize money and is ranked in the top three positions across all categories of comparison.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Guest Editorial Special Section on Advanced Medium-Voltage Power Electronics for Grid Interactive Applications

Medium-voltage power electronics (MVPE) plays essential roles in power grid modernization and links the MV distribution grid with low-voltage consumers and prosumers. Various MVPE devices, such as solid-state transformers or circuit breakers, inverter-based resources, power flow controllers, etc., bring the benefits of voltage conversion and power regulation in small footprint, power quality and efficiency improvements, and enhancements of grid controllability, flexibility, stability, and resilience. The MVPE also makes it possible for sustainable energy systems, such as solar/wind farms and energy storage generating facilities, to directly access to MV grids without multistage conversions. With their intrinsic intelligence and communications, MVPE enables many new smart grid functions and applications, e.g., dc interconnections and electric vehicle charging, which were not envisioned by traditional power grids otherwise. In addition, the integration of physical power processing units with cyber components forms a cyber-physical system, which is essential for long-term sustainability, development, and environmental preservation. Nonetheless, technical challenges on MVPE device reliability, scalable and efficient converter topologies, control stability, large-scale modeling and simulation, to name a few, need to be addressed and advanced to the next level. In conclusion, this Special Section on Advanced MV Power Electronics for Grid Interactive Applications in IEEE Transactions on Power Electronics (TPEL) provides an insight on some of the recent advances in MVPE and emerging challenges and potential solutions.

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2021

Geomagnetically Induced Currents at Middle Latitudes: 1. Quiet-Time Variability

Geomagnetically induced currents (GICs) at middle latitudes have received increased attention after reported power grid disruptions due to geomagnetic disturbances. However, quantifying the risk to the electric power grid at middle latitudes is difficult without understanding how the GIC sensors respond to geomagnetic activity on a daily basis. Therefore, in this study the question “Do measured GICs have distinguishable and quantifiable long-period and short-period characteristics?” is addressed. The study focuses on the long-term variability of measured GIC, and establishes the extent to which the variability relates to quiet-time geomagnetic activity. GIC quiet-day curves (QDCs) are computed from measured data for each GIC node, covering all four seasons, and then compared with the seasonal variability of thermosphere-ionosphere-electrodynamics general circulation model (TIE- GCM)-simulated neutral wind and height-integrated current density. The results show strong evidence that the middle-latitude nodes routinely respond to the tidal-driven Sq variation, with a local time and seasonal dependence on the direction of the ionospheric currents, which is specific to each node. The strong dependence of GICs on the Sq currents demonstrates that the GIC QDCs may be employed as a robust baseline from which to quantify the significance of GICs during geomagnetically active times and to isolate those variations to study independently. The QDC-based significance score computed in this study provides power utilities with a node-specific measure of the geomagnetic significance of a given GIC observation. Finally, this study shows that the power grid acts as a giant sensor that may detect ionospheric current systems.

Adam C Kellerman↗

DOE OSTI2021

Precise Timing Based on Pulsar Observation for Grid Synchronization

While Global Positioning System signals are widely used as the synchronized timing Sources for wide-area measurement systems for power grid monitoring and control, they are vulnerable to failures and malicious attacks. To address this Problem, an alternative kind of timing sources, the millisecond pulsars, were proposed to serve the power grid as they are more precise and physically indestructible. Millisecond pulsars are a kind of neutron stars that emit radio pulse signals that have extremely stable periods of milliseconds. However, the observed pulse Signals from pulsars cannot be directly used for timing because of their low signal-to-noise ratio and the dispersion effects of interstellar medium. In this paper, the precise timing method based on pulsar observation data is proposed to interpret the pulsar observation data and therefrom to generate precise timing signal for the power grid synchronization. In our proposed method,. incoherent de-dispersion is used on the observation data to mitigate the effect of interstellar medium. dispersion. Then spectrum analysis and segment folding are used to precisely estimate the local pulsar period. Finally the standard pulse per second signal is generated from. the estimated pulsar period and its precision. is analyzed. Experiments with the observation data of the pulsar based timing signal can be more accurate compared to GPS timing signals.

Luo, Xiqian↗

DOE OSTI2026

Control Parameter Sensitivity Study for Inverter-Based-Resource Dominated Grids: A Small Signal Stability Approach and Framework

The growing adoption of renewable energy is driving the prevalence of inverter-based resources (IBRs) within power grids. Future power grids will integrate both grid-following IBRs (GFM-IBRs) and grid-forming IBRs (GFL-IBRs) alongside synchronous generators. Therefore, it is crucial to perform stability studies that account for all components and especially control interactions related to IBRs. Extensive research has performed to study the IBR-related stability, however, the sensitivity study of IBRs' control parameters on system stability has not been adequately studied yet, especially from a systematic way. Therefore, this paper conducts a small signal stability analysis for a generic grid with multiple types of resources and develops an analytical framework for assessing the sensitivity of control parameters affecting stability margins. To achieve that, the non-autonomous reduced-order non-linear dynamic model is developed for a generic power system with multiple synchronous generator-based resources (SGBRs), GFM-IBRs, and GFL-IBRs. Based on the analytic model, a systematic framework for parametric sensitivity on systems' asymptotic stability is developed. A parameter sensitivity analysis based on eigenvalue methods is proposed. The impact of the droop controllers of GFM-IBRs, PQ-dispatch and the PLL controller of GFL-IBR on the system asymptotic stability is discussed. This sensitivity study is aiming to provide deep insights on control parameters' impact on system stability, and gives direction for parameter tuning in case of instability.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

Enhancing Short-Term Flexibility through Characterization of HVDC Transmission Systems

Efficient power grid operation hinges on maintaining safety and reliability through adaptable system flexibility standards. However, in a future power grid characterized by extensive renewable energy integration, conventional system flexibility and predetermined requirements may falter under extreme conditions. This paper addresses the formidable challenge of sustaining secure and dependable power grid operations under these circumstances, emphasizing the role of controllable high voltage direct current (HVdc) transmission systems in amplifying overall system flexibility and tackling operational complexities. To quantitatively assess short-term flexibility and its associated economic benefits from HVdc within specific regions, we propose a Power Flow Distribution Factors (PTDFs) based approach. This methodology takes into account both dispatchable generators and HVdc systems, pinpointing their contributions. Through an analysis of the Western Electricity Coordinating Council (WECC) system, we demonstrate the practical application of this approach to scrutinize the impacts of existing and future HVdc lines.

Operational Flexibility, Multi-terminal HVdc, tran↗

DOE OSTI2020

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow

We develop, in this paper, a machine learning approach to optimize the real-time operation of electric power grids. In particular, we learn feasible solutions to the AC optimal power flow (OPF) problem with negligible optimality gaps. The AC OPF problem aims at identifying optimal operational conditions of the power grids that minimize power losses and/or generation costs. Due to the computational challenges with solving this nonconvex problem, many efforts have focused on linearizing or approximating the problem in order to solve the AC OPF on faster timescales. However, many of these approximations can be fairly poor representations of the actual system state and still require solving an optimization problem, which can be time consuming for large networks. In this work, we learn a mapping between the system loading and optimal generation values, enabling us to find near-optimal and feasible AC OPF solutions. This allows us to bypass solving the traditionally nonconvex AC OPF problem, resulting in a significant decrease in computational burden for grid operators.

machine learning↗

DOE OSTI2020

Learning Optimal Solutions for Extremely Fast AC Optimal Power Flow: Preprint

machine learning↗

DOE OSTI2021

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗

DOE OSTI2023

Autonomous Intelligence Measurements and Sensor Systems (AIMS): Gaussian Processes in Remote Sensing: Literature Review

Power grid resilience and reliability is crucial to supporting critical infrastructure. Service interruptions can have devastating consequences to communication, emergency, and transportation services, just to name a few. In addition to infrastructure, power services are crucial to everyday life. Nearly every element of modern-day living relies on a stable and functioning power grid - heating and cooling systems to lighting, refrigeration, telecommunications, and internet access. Interruptions in the power supply can range from minor inconveniences, such as the temporary loss of internet or television services, to more significant problems, like the inability to access medical equipment or emergency services during critical situations. As climate conditions worsen, the reliability of the power grid becomes even more significant. Extreme weather events, such as hurricanes, wildfires, or severe storms, can cause serious damage to power infrastructure, leading to prolonged power outages. Rising temperatures and changing weather patterns can strain the grid, resulting in increased demand for electricity, stressing transmission and distribution systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

OT Operational Anomaly Detection (OAD) T&D + DER

The growth of utility-scale renewable energy resources, distributed energy resources (DER), and transportation electrification has increased uncertainty and cybersecurity risks in power grids. The Purdue Enterprise Reference Architecture model which is widely adopted by the utility industry is now insufficient to protect the power grid against cyber-attacks. There is a need to identify what cybersecurity model is effective on Energy Management System (EMS), Advanced Distribution Management System (ADMS), and DER Management System (DERMS) to address the fundamental cybersecurity challenges in the age of increasing renewable energy and DER share as well as consumer participation in the electric energy industry. The next generation of cybersecurity model for OT network should be able to detect inside attackers, mitigate the cybersecurity risks arising from the new grid participants including DER aggregators, electric vehicle owners, and behind-the-meter consumers outside the utility company, and develop the strategy to trust consumer measurement data. This panel will discuss the challenges and pathways for the development of an ensemble cybersecurity model based on predictive state estimation to detect cybersecurity anomalies in OT network including EMS, ADMS, and DERMS.

cybersecurity↗

NASA NTRS2011

Grid-Tied Photovoltaic Power System

A grid-tied photovoltaic (PV) power system is connected directly to the utility distribution grid. Facility power can be obtained from the utility system as normal. The PV system is synchronized with the utility system to provide power for the facility, and excess power is provided to the utility. Operating costs of a PV power system are low compared to conventional power technologies. This method can displace the highest-cost electricity during times of peak demand in most climatic regions, and thus reduce grid loading. Net metering is often used, in which independent power producers such as PV power systems are connected to the utility grid via the customers main service panels and meters. When the PV power system is generating more power than required at that location, the excess power is provided to the utility grid. The customer pays the net of the power purchased when the on-site power demand is greater than the onsite power production, and the excess power is returned to the utility grid. Power generated by the PV system reduces utility demand, and the surplus power aids the community. Modern PV panels are readily available, reliable, efficient, and economical, with a life expectancy of at least 25 years. Modern electronics have been the enabling technology behind grid-tied power systems, making them safe, reliable, efficient, and economical with a life expectancy equal to the modern PV panels. The grid-tied PV power system was successfully designed and developed, and this served to validate the basic principles developed, and the theoretical work that was performed. Grid-tied PV power systems are reliable, maintenance- free, long-life power systems, and are of significant value to NASA and the community. Of particular value are the analytical tools and capabilities that have been successfully developed. Performance predictions can be made confidently for grid-tied PV systems of various scales. The work was done under the NASA Hybrid Power Management (HPM) Program, which is the integration of diverse power devices in an optimal configuration for space and terrestrial applications.

Eichenberg, Dennis J.↗

DOE OSTI2021

Grid Cyber-Security Strategy in an Attacker-Defender Model

The progression of cyber-attacks on the cyber-physical system is analyzed by the Probabilistic, Learning Attacker, and Dynamic Defender (PLADD) model. Although our research does apply to all cyber-physical systems, we focus on power grid infrastructure. The PLADD model evaluates the effectiveness of moving target defense (MTD) techniques. We consider the power grid attack scenarios in the AND configurations and OR configurations. In addition, we consider, for the first time ever, power grid attack scenarios involving both AND configurations and OR configurations simultaneously. Cyber-security managers can use the strategy introduced in this manuscript to optimize their defense strategies. Specifically, our research provides insight into when to reset access controls (such as passwords, internet protocol addresses, and session keys), to minimize the probability of a successful attack. Our mathematical proof for the OR configuration of multiple PLADD games shows that it is best if all access controls are reset simultaneously. For the AND configuration, our mathematical proof shows that it is best (in terms of minimizing the attacker's average probability of success) that the resets are equally spaced apart. We introduce a novel concept called hierarchical parallel PLADD system to cover additional attack scenarios that require combinations of AND and OR configurations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Artificial Intelligence Techniques in Smart Grid: A Survey

The smart grid is enabling the collection of massive amounts of high-dimensional and multi-type data about the electric power grid operations, by integrating advanced metering infrastructure, control technologies, and communication technologies. However, the traditional modeling, optimization, and control technologies have many limitations in processing the data; thus, the applications of artificial intelligence (AI) techniques in the smart grid are becoming more apparent. This survey presents a structured review of the existing research into some common AI techniques applied to load forecasting, power grid stability assessment, faults detection, and security problems in the smart grid and power systems. It also provides further research challenges for applying AI technologies to realize truly smart grid systems. Finally, this survey presents opportunities of applying AI to smart grid problems. The paper concludes that the applications of AI techniques can enhance and improve the reliability and resilience of smart grid systems.

energy systems↗

DOE OSTI2020

Correlation between Generator Trips and Locational Marginal Prices (LMPs)

Generally, disturbances in an interconnected network have an influence the stability and the economy of the power grid. Some phenomenon such as load shedding have proven to have a huge impact on real time electricity prices. Generation trip is considered one of the most common events in a power grid that may change the wholesale electricity market. In this paper, a locational marginal price (LMP) measure is used to notice how the electricity prices change with generator trip events at different locations of U.S. power grid. The study results help to find whether there is a clear correlation between generator trips and the changes in the electricity real-time market.

Alharbi, Abdulelah↗

DOE OSTI2023

Synchronous Wind: Evaluating the Grid Impact of Inverterless Grid-Forming Wind Power Plants

Grid-forming (GFM) control of Type-3 and Type-4 wind turbine generators has attracted substantial attention in power systems research; however, the limited over-current capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. This paper develops the generic model of synchronous wind power generators, also known as Type-5 wind turbines, for power system integration studies. The Type-5 wind turbine interfaces with the electric grid through a synchronous generator; hence, its operation and consequent grid impacts are similar to a conventional power plant. Based on the developed model, a Type-5 wind power plant is integrated into the IEEE 14-bus test bed to evaluate its control and operation with a bulk power system. Finally, the stability properties of Type-5 wind turbine generators are compared with Type-3 wind turbines in GFM control mode through impedance characterization.

grid strength↗