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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

A Nonlinear Algebraic Multigrid Framework for the Power Flow Equations

Multigrid is a highly scalable class of methods most often used for solving large linear systems. In this paper we develop a nonlinear algebraic multigrid framework for the power flow equations, a complex quadratic system of the form ${diag}({v})\overline{Y{v}}={s}$, where $Y$ is approximately a complex scalar rotation of a real graph Laplacian. This is a standard problem that needs to be solved repeatedly during power grid simulations. A key difference between our multigrid framework and typical multigrid approaches is the use of a novel multiplicative coarse-grid correction to enable a dynamic multigrid hierarchy. We also develop a new type of smoother that allows one to coarsen together the different types of nodes that appear in power grid simulations. In developing a specific multigrid method, one must make a number of choices that can significantly affect the method's performance, such as how to construct the restriction and interpolation operators, what smoother to use, and how aggressively to coarsen. In this paper, we make simple but reasonable choices that result in a scalable and robust power flow solver. Experiments demonstrate this scalability and show that it is significantly more robust to poor initial guesses than current state-of-the-art solvers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

The increased penetration of renewable energy has significantly changed the conditions and the operational timing of the electricity grid. More flexible, faster ramping resources are needed to compensate for the uncertainty and variability introduced by renewable energy. Distributed energy resources (DERs) such as distributed generators, energy storage, and controllable loads could help manage the power grid in terms of both economic efficiency and operational reliability. In order to realize the benefits of DERs, coordination and control approaches must be designed to enable seamless integration of DERs into the power grid. Transactive coordination and control is a new approach for DER integration, where individual resources are automated and engaged through market interaction. Transactive approaches use economic signals—prices or incentives—to engage DERs. These economic signals must reflect the true value of the DER contributions, so that they seamlessly and equitably compete for the opportunities that today are only available to grid-owned assets. Value signals must be communicated to the DERs in near-real time, the assets must be imbued with new forms of distributed intelligence and control to take advantage of the opportunities presented by these signals, and they must be capable of negotiating and transacting a range of market-driven energy services. The concepts of transactive energy systems are not new, but build upon evolutionary economic changes in financial and electric power markets. These concepts also recognize the different regional structures of wholesale power markets, electricity delivery markets, retail markets, and vertically integrated service provider markets. Although transactive energy systems are not revolutionary, they will be transformational in their ability to provide flexibility and operational efficiency. A main goal of this research is to establish useful foundation for analysis of transactive energy systems and to facilitate new transactive energy system design with demonstrable guarantees on stability and performance. Specifically, the goals are to (1) establish a theoretical basis for evaluating the performance of different transactive systems, (2) devise tools to address canonical problems that exemplify challenges and scenarios of transactive systems, and (3) provide guidelines for design of future transactive systems. This report, Part 1 of a two part series, advances the above-listed research objectives by reviewing existing transactive systems and identifying a theoretical foundation that integrates payoff functions, control decisions, information privacy, and mathematical solution concepts.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

Transactive System: Part I: Theoretical Underpinnings of Payoff Functions, Control Decisions, Information Privacy, and Solution Concepts

The increased penetration of renewable energy has significantly changed the conditions and the operational timing of the electricity grid. More flexible, faster ramping resources are needed to compensate for the uncertainty and variability introduced by renewable energy. Distributed energy resources (DERs) such as distributed generators, energy storage, and controllable loads could help manage the power grid in terms of both economic efficiency and operational reliability. In order to realize the benefits of DERs, coordination and control approaches must be designed to enable seamless integration of DERs into the power grid. Transactive coordination and control is a new approach for DER integration, where individual resources are automated and engaged through market interaction. Transactive approaches use economic signals—prices or incentives—to engage DERs. These economic signals must reflect the true value of the DER contributions, so that they seamlessly and equitably compete for the opportunities that today are only available to grid-owned assets. Value signals must be communicated to the DERs in near-real time, the assets must be imbued with new forms of distributed intelligence and control to take advantage of the opportunities presented by these signals, and they must be capable of negotiating and transacting a range of market-driven energy services. The concepts of transactive energy systems are not new, but build upon evolutionary economic changes in financial and electric power markets. These concepts also recognize the different regional structures of wholesale power markets, electricity delivery markets, retail markets, and vertically integrated service provider markets. Although transactive energy systems are not revolutionary, they will be transformational in their ability to provide flexibility and operational efficiency. A main goal of this research is to establish useful foundation for analysis of transactive energy systems and to facilitate new transactive energy system design with demonstrable guarantees on stability and performance. Specifically, the goals are to (1) establish a theoretical basis for evaluating the performance of different transactive systems, (2) devise tools to address canonical problems that exemplify challenges and scenarios of transactive systems, and (3) provide guidelines for design of future transactive systems. This report, Part 1 of a two part series, advances the above-listed research objectives by reviewing existing transactive systems and identifying a theoretical foundation that integrates payoff functions, control decisions, information privacy, and mathematical solution concepts.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2016

Virtual interconnection platform initiative scoping study

Due to security and liability concerns, the research community has limited access to realistic large-scale power grid models to test and validate new operation and control methodologies. It is also difficult for industry to evaluate the relative value of competing new tools without a common platform for comparison. This report proposes to develop a large-scale virtual power grid model that retains basic features and represents future trends of major U.S. electric interconnections. This model will include realistic power flow and dynamics information as well as a relevant geospatial distribution of assets. This model will be made widely available to the research community for various power system stability and control studies and can be used as a common platform for comparing the efficacies of various new technologies.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗
DOE OSTI2013

New Methods of Uncertainty Quantification for Mixed Discrete-Continuous Variable Models

The scale and complexity of problems such as designing power grids or planning for climate change is growing rapidly, driving the development of complicated computer models. More complex models have longer run times and incorporate larger numbers of inputs, both continuous and discrete. For example, a detailed physics model may have continuous variables such as temperature, height or pressure along with discrete variables that indicate the choice of a material for a particular piece or the model to be used to calculate air flow. A power grid design model may have continuous variables such as generation capacity, power flow or demand along with discrete variables such as number of generators, number of transmission lines or binary variables to indicate whether or not a node is chosen for generation expansion. A growing awareness of uncertainty and the desire to make risk-informed decisions is causing uncertainty quantification (UQ) to be more routine and often required. UQ provides the underpinnings necessary to establish confidence in models and their use; therefore, much time and effort is being invested in creating efficient approaches for UQ. However, these efforts have been focused on models that take continuous variables as inputs. When discrete inputs are thrown into the mix, the basic approach is to repeat the UQ analysis for each combination of discrete inputs or some subset thereof; this rapidly becomes intractable. Because of the computational complexity inherent in mixed discrete-continuous models, researchers will focus on the uncertainty in their particular problem finding ways to take advantage of symmetries, simplifications or structures. For example, uncertainty propagation in certain dynamical systems can be efficiently carried out after various decomposition steps or uncertainty propagation in stochastic programming is confined to scenario generation. Unfortunately models are not always available for such machinations: models may be embedded in legacy codes, may utilize commercial off the shelf codes or may be created by stringing a series of codes together. It is also time consuming to start each problem from scratch; worse there may not be any simplifications or symmetries to take advantage of. For these situations a UQ method developed for any black box function is necessary. This report documents a new conceptual model for performing UQ for mixed discrete-continuous models which not only applies to any simulator function, but allows the use of the efficient UQ methods that have been developed for continuous inputs only. The conceptual model is presented and an estimation procedure is fleshed out for one class of problems. This is applied to variations of a mixed discrete-continuous optimization test problem. This procedure provides comparable results to a benchmark solution with fewer function evaluations.

Bauman, Lara E.↗
DOE OSTI2017

Experience in the Implementation of an Automatic System for Stabilizing the Mode of a Power District in Terms of Voltage and Reactive Power

Questions regarding the creation of a power district two-level control system for voltage and reactive power overflows are examined. Objectives and tasks are designated for the control system, and resolution of this makes it possible to increase the power efficiency of the power grid company. A methodology for controlling equipment at the substation (SS) level is briefly described, as well as expansion and test results on a dynamic model of the power grid.

42 ENGINEERING↗
DOE OSTI2024

Bilevel Nodal Behind-the-meter Solar Disaggregation Under Unexpected Extreme Weather Conditions

As the power grid undergoes significant paradigm shift due to the increasing penetration of renewable generation, the ever-growing installation of behind-the-meter (BTM) solar generation in the power grid also has a significant impact on nodal loads, posing challenges on transmission operators. Furthermore, increasing frequent and severe extreme weather events intertwine with ubiquitous BTM solar generations and have amplified the challenges of accurately model nodal load profiles, especially under the lack of ground-truth information for verification. To tackle these challenges, this paper introduces a bilevel model that utilizes year-long data (e.g., proxy solar, zonal load, and individual node load profiles) to disaggregate metered profiles into actual demand and BTM solar generation at each transmission node. The proxy solar not only scales the BTM solar generation of individual nodes but also create a compensation term for enhancing performance on days with unexpected extreme weather events. The proposed algorithm is validated with real-world PJM Interconnection data during unexpected events like the recent Winter Storm Elliott. For quantitative evaluations, a novel Score error is introduced, which is based on mean percentages and load scales and offers a universal assessment method suitable for all nodes and different data formats (e.g., normalized or raw values).

behind-the-meter solar, load disaggregations, load↗
NASA NTRS2001

Latency Hiding in Dynamic Partitioning and Load Balancing of Grid Computing Applications

The Information Power Grid (IPG) concept developed by NASA is aimed to provide a metacomputing platform for large-scale distributed computations, by hiding the intricacies of highly heterogeneous environment and yet maintaining adequate security. In this paper, we propose a latency-tolerant partitioning scheme that dynamically balances processor workloads on the.IPG, and minimizes data movement and runtime communication. By simulating an unsteady adaptive mesh application on a wide area network, we study the performance of our load balancer under the Globus environment. The number of IPG nodes, the number of processors per node, and the interconnected speeds are parameterized to derive conditions under which the IPG would be suitable for parallel distributed processing of such applications. Experimental results demonstrate that effective solution are achieved when the IPG nodes are connected by a high-speed asynchronous interconnection network.

Das, Sajal K.↗
DOE OSTI2021

Adding power of artificial intelligence to situational awareness of large interconnections dominated by inverter‐based resources

Abstract Large‐scale power systems exhibit more complex dynamics due to the increasing integration of inverter‐based resources (IBRs). Therefore, there is an urgent need to enhance the situational awareness capability for better monitoring and control of power grids dominated by IBRs. As a pioneering Wide‐Area Measurement System, FNET/GridEye has developed and implemented various advanced applications based on the collected synchrophasor measurements to enhance the situational awareness capability of large‐scale power grids. This study provides an overview of the latest progress of FNET/GridEye. The sensors, communication, and data servers are upgraded to handle ultra‐high density synchrophasor and point‐on‐wave data to monitor system dynamics with more details. More importantly, several artificial intelligence (AI)‐based advanced applications are introduced, including AI‐based inertia estimation, AI‐based disturbance size and location estimation, AI‐based system stability assessment, and AI‐based data authentication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Keyless Infrastructure Security Solution (KISS) (Technology Landscape Analysis Report)

The following landscape analysis explores blockchain or distributed ledger technologies application to securing electricity infrastructure. The exploration of how distributed ledger technologies can be used to increase the trustworthiness and integrity of the grid’s edge is imperative to the economic, security and well-being of all modern societies that rely on electricity. Blockchain technology’s ability to secure, track and optimize complex data transactions provides an exciting value proposition to securing and optimizing the U.S. power grid. An energy delivery system (EDS) operating at the grid’s edge requires unprecedented levels of security and trustworthiness to verify integrity of data and manage complex transactive and distributed Energy Resources (DERs) exchanges. Moreover, grid-edge devices lack visibility, control and security to conduct real-time energy transactions with the required security, speed and scale. The U.S. power grid is a complex system of systems that requires secure, reliable and trustworthy EDSs. Grid modernization has increased the speed and size of data sets exchanged on these systems (Gordes and Mylrea 2014). Exasperating the challenge is these systems are increasingly distributed creating new data fidelity and interoperability challenges for grid operators struggling to balance and incorporate DERs. Blockchain technology provides an atomically verifiable cryptographic signature to help increase the trustworthiness of EDSs at the grid’s edge. This feature is especially important at distribution level since EDSs and field devices have increasing operational and security requirements that are often diametrically opposed. For example, as data, speed and analytic requirements increase, security and functionality requirements increase. Further, as the grid’s edge incorporate DERs and transacts in real time, availability is prioritized over the integrity and confidentially of that data. Blockchain presents the prospect of solving some of these complex challenges related to grid security and modernization. However, there are more questions about blockchain applicability than answers. There are number of theoretical blockchain applications to the energy sector, but few energy utilities have actually applied, implemented or even experimented with the underlying distributed ledger technology and consensus algorithms that enable blockchain to exchange data more efficiently and securely.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

5G Energy FRAME Report on 5G for Grid Use Case (Year 3 Final Report)

This report provides an extensive overview of the interrelationships among energy, communication, and computing—especially in the context of decarbonization goals, challenges, and opportunities. Technical examples enabled by 5G technologies and their performance are presented, discussed, based on the experiment performed at Pacific Northwest National Laboratory. This is the first use case focused on using 5G for the U.S. power grid and will be referred to as the 5G for Grid Use Case from here on. Specifically, this use case looks at the workflow, which integrates the performance data of a real-world 5G communication testbed and a grid transmission and distribution co-simulation platform. The cross-domain information flow and logic design are illustrated with a combination of power grid contingencies and events. Lastly, a summary of the project achievement and outcome is provided, along with a technology roadmap envisioned by the project team.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Intelligent System Partitioning for Agent-Based Security Constrained Optimal Power Flow

This project developed scalable, computationally efficient algorithms to solve realistic large-scale power system optimization problems as part of a larger series of competitions run by ARPA-E. These problems are important because the secure and reliable operation of the power grid, especially under increased uncertainty and variability, is growing increasingly challenging. The economic feasibility of the proposed methods developed by our team is quite low, considering it’s a purely software-based solution to operate power grids more efficiently. The technical effectiveness, as evidenced by our performance in the competition, balances heuristics and approximations to provide a tradeoff between speed and accuracy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Physics-Guided Deep Learning for Time-Series State Estimation Against False Data Injection Attacks

The modern power grid is a cyber-physical system. While the grid is becoming more intelligent with emerging sensing and communication techniques, new vulnerabilities are introduced and cyber security becomes a major concern. One type of cyber-attacks - False Data Injection Attacks (FDIAs) - exploits the limitations in traditional power system state estimation, and modifies system states without being detected. In this paper, we propose a physics-guided deep learning (PGDL) approach to defend against FDIAs. The PGDL takes real-time measurements as inputs to neural networks, outputs the estimated states, and reconstructs measurements considering power system physics. A deep recurrent neural network - Long Short-Term Memory (LSTM) - is employed to learn the temporal correlations among states. This hybrid learning model leads to a time-series state estimation method to defend against FDIAs. The simulation results using IEEE 14-bus test system demonstrate the accuracy and robustness of the proposed time-series state estimation under FDIAs.

Cyber security, state estimation, physics-guided d↗
DOE OSTI2023

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

Grid-forming (GFM) control of Type-3 and Type-4 wind turbine generators have attracted substantial attention in power system research. However, the limited over-current capability of power electronics converters continues to deteriorate the grid strength of the evolving power systems. This paper developed the generic model of synchronous wind power generators, also known as Type-5 wind turbines, for the power system integration studies. The Type-5 wind turbine interface to the 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 IEEE 14-bus testbed 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↗
DOE OSTI2012

Magnet Technology for Power Converters: Nanocomposite Magnet Technology for High Frequency MW-Scale Power Converters

Solar ADEPT Project: CMU is developing a new nanoscale magnetic material that will reduce the size, weight, and cost of utility-scale PV solar power conversion systems that connect directly to the grid. Power converters are required to turn the energy that solar power systems create into useable energy for the grid. The power conversion systems made with CMU’s nanoscale magnetic material have the potential to be 150 times lighter and significantly smaller than conventional power conversion systems that produce similar amounts of power.

ARPA-E↗
DOE OSTI2025

Efficient Simulation of Cascading Outages Using an Energy Function-Embedded Quasi-Steady-State Model

Here, this paper proposed an energy function-embedded quasi-steady-state model for efficient simulation of cascading outages on a power grid while addressing transient stability concerns. Compared to quasi-steady-state models, the proposed model incorporates short-term dynamic simulation and an energy function method to efficiently evaluate the transient stability of a power grid together with outage propagation without transient stability simulation. Cascading outage simulation using the proposed model conducts three steps for each disturbance such as a line outage. First, it performs time-domain simulation for a short term to obtain a post-disturbance trajectory. Second, along the trajectory, the system state with the local maximum potential energy is found and used as the initial point to search for a relevant unstable equilibrium by Newton's method. Third, the transient energy margin is estimated based on this unstable equilibrium to predict an out-of-step condition with generators. The proposed energy function-embedded quasi-steady-state model is tested in terms of its accuracy and time performance on an NPCC 140-bus power system and compared to a quasi-steady-state model embedding transient stability simulation.

Guo, Zhenping [Univ. of Tennessee, Knoxville, TN (↗