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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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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Self-Security for Grid-Interactive Smart Inverters Using Steady-State Reference Model

Smart inverters exchange information with other devices through a shared communication link, making the inverters more prone to receive harmful commands from external parties. This erroneous data can be received due to an anomaly in the system, such as a device fault, unintentional utility operator action, or a cyber-attack. In this paper, a device-level self-security strategy is implemented using reference models for a grid-interactive inverter to examine the incoming power setpoints, detect the anomalies, and protect the system accordingly. The PQ setpoints received from the utility supervisory controller are autonomously examined using the inverter’s normal and stable operating regions before engaging the setpoints to the inverter’s local controller. Grid parameters are estimated in real-time during the examination process. The efficacy of the self-security algorithm is tested using a three-phase 3-kVA SiC-MOSFET inverter and a 12-kW NHR 9410 regenerative grid emulator. The results verify that the proposed method can detect harmful PQ setpoints that can cause abnormal or unstable inverter operation.

Gursoy, Mehmetcan↗
DOE OSTI2022

Numerical and experimental investigation of the flame kernel growth in a methane/air mixture near the lean flammability limit

Lean combustion has the potential to improve the thermal efficiency of spark-ignition engines, but it faces the significant challenge of increased cycle-to-cycle variation due to low mixture reactivity and unstable flame dynamics. Computational fluid dynamics (CFD) employing predictive models can guide engine design and optimize operating strategies for lean combustion. However, ignition and combustion models have rarely been validated at fuel-lean conditions, and a fundamental understanding of the early flame kernel growth process is also lacking for a successful sub-model development. Here, the present study develops a numerical simulation framework used to investigate early flame kernel growth in methane/air mixtures. A nanosecond-pulsed discharge (NPD) approach is employed to effectively decouple the flame kernel growth from the electrical discharge due to their difference in timescales, and equivalence ratios near the experimentally measured lean flammability limit (LFL) are selected to focus on challenging mixture conditions. Three numerical investigations, such as the choice of turbulence modeling, grid size, and grid control strategies, are examined to match both LFL and flame kernel structure measured from experiments. It is demonstrated that a quasi-direct numerical simulation (QDNS) with a fixed grid embedding of 10 μm can predict the LFL as φ CFD =0.61 and match the displacement speed of the kernel’s boundary marked in schlieren images. To predict the LFL and flame kernel shape, a fine grid (Δ≤12.5 μm) is needed to capture the consumption of formaldehyde (CH 2 O) in kernel’s reaction branches attached to the anode, and adaptive mesh refinement is replaced with the fixed embedding due to loss of simulation accuracy. Also, it is found that a large-eddy simulation (LES) using the Dynamic Structure model is not suitable for the NPD-induced flame kernel simulation because artificial sub-grid turbulent kinetic energy induced by shock dynamics alters the flow velocity calculation, resulting in divergence of LES from QDNS. Lastly, the simulation well matches the experimental data for the flame kernel evolution in three mixture conditions (φ = 0.7, 0.61, 0.55), showing toroidal flame kernel expansion and flame kernel growth/extinction.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

K-anonymity applied to the energy grid of things distributed energy resource management system

Smart grid infrastructure relies on information exchange between multiple actors in order to ensure system reliability. These actors include but are not limited to smart loads, grid control, and energy management technologies. As information exchange between these actors is susceptible to cyber-attacks, security and privacy issues are indispensable to ensure a reliable and stable grid. This position paper proposes a privacypreserving, trust-augmented secure scheme for a smart grid implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Distributed Secondary Control of Grid-Forming Inverters and AC Microgrids: Impacts of Voltage Feedback Choices

In this paper, the operational choices in distributed secondary control are examined, and their impacts on grid-forming (GFM) inverters and grid operation are investigated. In particular, the effects of feedback voltage choices, either average voltage feedback (AVF) or terminal voltage feedback (TVF), are studied with respect to reactive power sharing, voltage regulation, and grid-forming operation during black start. The secondary control used is a subgradient-based distributed cooperative control, and it provides the voltage, angle, and frequency references to the GFM inverters and their primary tracking controls. The overall control architecture employs multi-rate sampling such that the secondary control is slower than the primary control. The operational choices and their control performance are illustrated using a four-GFM-inverter microgrid in MATLAB/SIMULINK. Furthermore, the results show the effectiveness of the secondary controller, and several conclusions are drawn on the voltage feedback signals as the design choices.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Privacy-preserving Information Security for the Energy Grid of Things

Smart grid infrastructure relies on information exchange between multiple actors in order to ensure system reliability. These actors include but are not limited to smart loads, grid control, and energy management technologies. Further, as information exchange between these actors is susceptible to cyber-attacks, security and privacy issues are indispensable to ensure a reliable and stable grid. This position paper proposes a privacy-preserving, trust-augmented secure scheme for a smart grid implementation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Grid Edge Visibility: Gaps and a Road Map

Behind-The-Meter (BTM) resources at the grid edge are rapidly becoming an important component of the electric grid, requiring a substantial reconfiguration of traditional grid practices, such as policy changes, market redesign, and infrastructure upgrades. This adjustment is challenged by the fact that, by definition, grid edge elements are not easily observable by grid control entities. Increasing the visibility of these resources is therefore an important goal, one that is experiencing much research and discussion by various power system stakeholders. For example, policy makers are analyzing the tradeoffs of using grid edge meters to impose charges on grid edge electricity generation. System operators, such as the Midcontinent Independent System Operator (MISO) in the United States, can identify visibility information on the electrical location and the size of the grid edge resources as a critical consideration across the transmission-and-distribution (T&D) spectrum. This article summarizes the challenges and needs of grid entities resulting from the introduction of grid edge resources as well the gaps in the extant grid edge visibility frameworks.

behind-the-meter↗
DOE OSTI2023

Utilizing grid–supportive load response to shape resilient frequency control of the power grid

The increasing penetration of renewable energy sources and the retirement of conventional generation units have decreased system inertia, making power systems more vulnerable to resilience and stability issues. To address this problem, this paper proposes a novel approach using grid-supportive loads (GSLs) to provide a fast and concise primary frequency response and a deep deterministic policy gradient agent-based secondary controller to restore the system frequency to the nominal value. The proposed method is evaluated on the single-area and multi-area test systems. The simulation results demonstrate that using GSLs enhances the power system's stability and resilience. Compared to conventional controllers, the frequency nadir is improved with GSLs. Additionally, the proposed method effectively enhances resilience even with high penetration. These findings indicate that the proposed approach can improve the resilience and stability of power systems and provide a promising solution for future power systems. The results of this study underscore the importance of utilizing innovative approaches to enhance the stability and resilience of power systems in the context of high penetration of renewable energy sources and the retirement of conventional generation.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Curriculum-based Reinforcement Learning for Distribution System Critical Load Restoration

This paper focuses on the critical load restoration problem in distribution systems following major outages. To provide fast online response and optimal sequential decision-making support, a reinforcement learning (RL) based approach is proposed to optimize the restoration. Due to the complexities stemming from the large policy search space, renewable uncertainty, and nonlinearity in a complex grid control problem, directly applying RL algorithms to train a satisfactory policy requires extensive tuning to be successful. To address this challenge, this paper leverages the curriculum learning (CL) technique to design a training curriculum involving a simpler steppingstone problem that guides the RL agent to learn to solve the original hard problem in a progressive and more effective manner. We demonstrate that compared with direct learning, CL facilitates controller training to achieve better performance. To study realistic scenarios where renewable forecasts used for decision-making are in general imperfect, the experiments compare the trained RL controllers against two model predictive controllers (MPCs) using renewable forecasts with different error levels and observe how these controllers can hedge against the uncertainty. Results show that RL controllers are less susceptible to forecast errors than the baseline MPCs and can provide a more reliable restoration process.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS-Solar) (Final Technical Report)

Solar photovoltaic (PV) installations have experienced unprecedented growth in the United States. PV will become not only an energy producer but also a necessary provider of ancillary services at multiple timescales. Conventional methods to simulate power systems operations - such as long-term production simulation (which typically considers schedules from hours to minutes by using an optimization framework) and short-term transient studies (which simulate dynamics from seconds to sub-seconds using state variables and differential equations) - are not sufficient for studying the multiple-timescale variation of solar generation and its impact on system reliability. Long-term system economics and short-term system dynamics are highly coupled, particularly when the penetration level of renewable generation is extremely high, because the uncertainty and variability of solar generation will impact both power system steady-state and dynamic performance. This project helps meet and exceed the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office goal of systems integration by directly addressing this stability and reliability challenge for power grid planning and operation. We have developed a temporally comprehensive, closed-loop simulation model, named Multi-timescale Integrated Dynamics and Scheduling (MIDAS), that seamlessly simulates power system operations from economic scheduling (day-ahead to hours) to dynamic response analysis (seconds to sub-seconds). For schedules with very high levels of inverter-based resources (IBRs), up to and including 100%, the stability of grid controls has been evaluated through electromagnetic transient (EMT) simulations and power-hardware-in-the-loop (PHIL) simulations of key transient events at key schedule points. Specifically, MIDAS provides: 1) a closed-loop simulation framework for simulating timescales from economic scheduling to dynamic stability analysis; 2) machine learning-based stability assessment; 3) EMT modeling and analysis for large-scale power systems; 4) MIDAS PHIL test bed. We worked with Hawaii Electric Companies to apply the MIDAS study framework to a Maui grid study. The entire island's transmission system was modeled in detail - from a yearly scheduling model, to a second-level frequency dynamic model, down to a sub-second-scale EMT model to address critical stability issues. The project demonstrated how MIDAS can help system planners and operators assess system reliability and stability while the power grid is marching toward a high-renewable, high-IBR future. In this Maui grid study, we found that 100% instantaneous IBR operation is achievable in EMT simulation and PHIL testing, and grid planners and operators might need new analysis/simulation tools to assess grid reliability and stability in the scheduling stage. MIDAS will bring Maui and other systems closer to 100% clean and stable energy futures. (In this study, we examined transient stability. Other topics necessary for 100% IBR operation, such as protection and resource adequacy, were not examined.)

100% Renewables↗
DOE OSTI2026

Addressing Rising Energy Demand Through Innovation

The U.S. is facing a significant increase in energy demand, driven by AI advancements, the rapid expansion of data centers, manufacturing and industrial growth, and the electrification of transportation and buildings. Buildings alone account for approximately 75% of U.S. electricity consumption and 40% of total energy use. To address these challenges, NLR leverages its state-of-the-art research facilities, advanced energy modeling, hardware-in-the-loop emulation, and real-world demonstrations to provide data-driven insights that de-risk emerging energy solutions, increase efficiency and demand flexibility, optimize grid controls, and identify vulnerabilities to enhance energy security. This presentation will highlight our research ecosystem and its role in supporting a more reliable, affordable, and adaptive energy infrastructure in the face of accelerating demand.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC)

Review of the FY20 and FY21 progress for the Grid-Enhanced, Mobility-Integrated Network Infrastructures for Extreme Fast Charging (GEMINI-XFC) project. GEMINI-XFC will use first-of-a-kind integrated high-fidelity grid and transport modeling to identify effective pathways for widespread electrification, to design and evaluate integrated vehicle-grid control schemes, and to optimize electric vehicle integration at a full regional scale with individual customer resolution.

ADVANCED PROPULSION SYSTEMS,POWER TRANSMISSION AND↗
DOE OSTI2020

Decentralized Control of Cascaded H-Bridge Inverters for Medium-Voltage Grid Integration: Preprint

This paper introduces a fully decentralized control strategy that allows for individualized active and reactive power control of H-bridge inverters connected in series. After formulating a model of the system power flows, we outline a communications-free approach where each inverter can act on independent commands. Inverter-level feedback is accomplished locally via a measurement of current flowing through the stack. A key feature is the lack of any centralized control routines or phase-locked loop. In this framework, the reactive power control loops achieve voltage synchronization among the converters and the grid, and the active power control loops modulate the terminal voltage amplitudes that influence power delivery. Furthermore, pole placement via state feedback is used to ensure stability of voltage synchronization while also enabling tracking of bidirectional power commands. Simulations are presented to validate the proposed approach.

communication-free modular converter control↗
DOE OSTI2022

Model-based interface design for smart field-device integration

Operational complexity is ever-increasing for electric utilities that face challenges including integration of DERs, customer expectation of energy choices, the proliferation of non-utility-owned resources, new business models with energy service providers, and new technology with IT/OT convergence. To maintain and improve the quality of operations, planning, and decision-making in general, utilities need to manage and navigate the complexity. Managing complexity requires a modular, scalable, and flexible solution. Connecting large amounts of DERs and introducing new services requires increased grid control and evolving applications. In this paper, we show an approach utilizing model-based standardized interfaces that simplifies integration and deployment of new algorithms and smart field devices for interoperability across legacy or new systems. A modular design is presented for a reference implementation of widely used DNP3 and IEEE 2030.5 interfaces within an open-source, standards-based data integration platform for integration of smart field devices with independently developed, best-of-breed applications.

Model-driven development, system integration, smar↗
DOE OSTI2024

Enhancing Distribution System Resilience: A First-Order Meta-RL Algorithm for Critical Load Restoration

The increasing frequency of extreme events and the integration of distributed energy resources (DERs) into modern grids have elevated the need for resilient and efficient critical load restoration strategies in distribution systems. However, the stochastic nature of renewable DERs, limited energy resource availability and the intricate nonlinearities inherent in complex grid control problem make the problem challenging. Although reinforcement learning (RL) and warm-start RL methods have shown promising results, their performance often falls short in rapidly adapting to new, unseen situations and typically requires exhaustive problem-specific tuning. To address these gaps, we propose a First-Order Meta-based RL (FOM-RL) algorithm within an online framework for adaptive and robust critical load restoration. By harnessing local DERs as the enabling technology, FOM-RL allows the RL agent to swiftly adapt to new unseen scenarios by leveraging previously acquired knowledge of different tasks. Experimental results provide evidence that proposed algorithm learns more efficiently and showcases generalization capabilities across diverse set of operational scenarios. Moreover, a rigorous theoretical analysis yields a tight sublinear regret bound, sensitive to temporal variability, with a task-averaged optimality gap bounded by O(VM+D*/(Tsquare root(M))). These results suggest that optimality improves with task similarity and an increased number of tasks M, reaffirming the efficacy and scalability of the proposed approach in addressing the complexities of critical load restoration in distribution systems.

complexity theory↗
DOE OSTI2021

Improved Control Strategy of Grid Interface for EV High-Power Dynamic Wireless Charging

This paper proposes a control strategy for the grid interface converter in high-power dynamic wireless charging system (DWCS) to address two issues on distribution network integration. Due to the unique pulsating load profile of DWCS, load transient response capability is critical for the grid interface to maintain the dc-bus voltage stable. Besides, the inherent unbalanced situation of distribution network would lead to 2nd-order oscillations on the dc-bus voltage, which would further affect the stable operation of the entire system. In this paper, the DWCS model is developed, and the relationship between the dc-bus voltage and the input/output power is analyzed. Based on the developed model, a control strategy based on direct power control is presented. Both simulation results and hardware-in-the-loop (HIL) results demonstrate that the proposed control strategy not only improves load transient response capability, but also eliminates the 2nd-order oscillations on the dc-bus voltage under imbalanced distribution network conditions.

Zeng, Rong↗
DOE OSTI2024

Federated Deep Reinforcement Learning for Decentralized VVO of BTM DERs

The future of grid control requires a hybrid approach combining centralized and decentralized methods to fully utilize the potential of smart edge devices with artificial intelligence (AI) capabilities. This paper aims to develop and evaluate a federated deep reinforcement learning (FDRL) framework for decentralized adaptive volt-var optimization (VVO) of behind-the-meter (BTM) distributed energy resources (DERs). First, this paper models a single deep reinforcement learning (DRL) agent using the Markov Decision Process (MDP) framework for decentralized adaptive VVO of BTM DERs. Two DRL algorithms, soft actor-critic (SAC) and twin-delayed deep deterministic policy gradient (TD3), are compared for their effectiveness in optimizing VVO. Results show that TD3 outperforms SAC, achieving a 71.3% improvement in mean reward. Finally, the DRL agent is deployed within the FDRL framework, using the Flower platform, to enhance learning, provide adaptive control, and ensure data privacy for BTM DERs.

Ravi, Abhijith↗
DOE OSTI2022

Open-Source Architecture for Multi-Party Update Verification for Data Acquisition Devices

Power grids are integral parts of modern daily life and are increasingly under cyberattack. Common software update processes for grid control devices rely on only one organization to provide verification. This paper roposes a multi-signature software update process to help better secure data acquisition devices from malicious actions by using standard cryptosystems such as TLS. A prototype system build on Linux and off-the-shelf hardware has shown successful update and attack prevention with our customized multi-party update software.

Newberg, Benjamin↗