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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

BIL GRID-READY WIND: Reliable and Economical Grid Services Design, Implementation, and Demonstration at the Great Pathfinder Wind Power Plant

The GRID-READY WIND project aimed to demonstrate the ability of utility-scale wind power plants (WPPs) to deliver traditional and emerging grid services (GSs) in both grid-following (GFL) and grid-forming (GFM) modes through a centralized control framework, supplemented by local control adjustments at the wind turbine generator as needed. Throughout the project, the team offered recommendations on how the grid services demonstrated with WPPs could be applied to other inverter-based resources (IBRs), such as photovoltaic (PV) systems and battery energy storage systems (BESS), while considering the unique dynamics of these resources arising from their energy generation characteristics. The project sought to enhance the confidence of system operators (SOs) and planners in utilizing WPPs, alongside other IBRs such as PV plants and BESS, to provide essential grid services over extended periods under diverse operating conditions, thereby supporting their integration into the bulk power system.

17 WIND ENERGY↗
DOE OSTI2024

Distributionally Robust Decentralized Volt-Var Control With Network Reconfiguration

Here, this paper presents a decentralized volt-var optimization (VVO) and network reconfiguration strategy to address the challenges arising from the growing integration of distributed energy resources, particularly photovoltaic (PV) generation units, in active distribution networks. To reconcile control measures with different time resolutions and empower local control centers to handle intermittency locally, the proposed approach leverages a two-stage distributionally robust optimization; decisions on slow-responding control measures and set points that link neighboring subnetworks are made in advance while considering all plausible distributions of uncertain PV outputs. We present a decomposition algorithm with an acceleration scheme for solving the proposed model. Numerical experiments on the IEEE 123 bus distribution system are given to demonstrate its outstanding out-of-sample performance and computational efficiency, which suggests that the proposed method can effectively localize uncertainty via risk-informed proactive timely decisions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Local Atomic Configuration Control of Superconductivity in the Undoped Pnictide Parent Compound BaFe 2 As 2

Emergent superconductivity is strongly correlated with the symmetry of local atomic configuration in the parent compounds of iron-based superconductors. While chemical doping or hydrostatic pressure can change the local geometry, conventional approaches do not provide a clear pathway in predictably tuning the detailed atomic arrangement due to the parent compound’s complicated structural deformation in the presence of the tetragonal-to-orthorhombic phase transition. Here, we demonstrate a systematic approach to manipulate local structural configurations in BaFe 2 As 2 epitaxial thin films by controlling two independent structural factors, orthorhombicity (in-plane anisotropy) and tetragonality (out-of-plane/in-plane balance), from lattice parameters. We tune superconductivity without doping utilizing both structural factors separately and controlling local tetrahedral coordination in the designed thin film heterostructures with substrate clamping and biaxial strain. We further show this allows quantitative control of the structural phase transition, the associated magnetism, and superconductivity in parent material BaFe 2 As 2 . Furthermore, this approach will advance the development of tunable thin film superconductors in a reduced dimension.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Battery energy storage control systems and methods for a grid tie inverter coupled to a photovoltaic system

A distributed control system uses a central controller in Internet communication with a local controller to manage grid tie attachment with a battery to form an integrated battery energy storage system (BESS). The BESS is capable of charging or discharging the battery, as well as correcting grid phase with volt amp reactive (VAR) leading or lagging operation modes. Examples shown include simple BESS charging and discharging, BESS integrated with renewable energy sources (here photovoltaic), and direct current fast charge (DCFC) connections with an electric vehicle.

Gadh, Rajit↗
DOE OSTI2023

Automatic voltage regulation application for PV inverters in low-voltage distribution grids – A digital twin approach

This paper proposes a hierarchical coordinated control strategy for PV inverters to keep voltages in low-voltage (LV) distribution grids within specified limits. The top layer of the proposed architecture consists of the designed automatic voltage regulation (AVR) application, which has access to voltage measurements and grid parameters from the LV distribution grid, both current and historical. The AVR application solves a constrained optimization problem, which provides a set of local control set-points that bring the voltage across the grid within bounds. The middle layer consists of a local Volt/VAR controller, which is adjusted by the AVR app, while the bottom layer is the inner-loop controller of the PV inverter. The proposed method not only improves the voltage quality in the grid but also manages the reactive power outputs of PV inverters efficiently. Further, a digital twin of the cyber-physical system has also been employed that interacts with the control system to ensure its appropriate operation. The effectiveness of the proposed methodology is demonstrated on a representative low-voltage feeder located in Denmark.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Secure Communications Concept and API Concept for Integrating XENDEE Positronix with TESLA PowerPack System at Site 300 (Final Deliverable)

The CleanStart DERMS project focuses on the management of Distributed Energy Resources (DER) for enhanced distribution grid resilience. The demonstration site has changed from Riverside Public Utility to the LLNS Site 300 DERS demonstration site. This project has so far focused only on device level controllers and local area controllers. These controllers potentially lack the ability to perform supervisory control and grid interactive control functions, essential for grid-level optimal DER management. This project seeks to close that gap in development of secure communication concept and appropriate Application Programming Interfaces (API) to enable integration with DERs, device level and local area controllers, such as Distributed Energy Resources Management System (DERMS).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Ripple-Type Control for Enhancing Resilience of Networked Physical Systems

Distributed control agents have been advocated as an effective means for improving the resiliency of our physical infrastructures under unexpected events. Purely local control has been shown to be insufficient, centralized optimal resource allocation approaches can be slow. In this context, we put forth a hybrid low-communication saturation-driven protocol for the coordination of control agents that are distributed over a physical system and are allowed to communicate with peers over a “hotline” communication network. According to this protocol, agents act on local readings unless their control resources have been depleted, in which case they send a beacon for assistance to peer agents. Our ripple-type scheme triggers communication locally only for the agents with saturated resources and it is proved to converge. Moreover, under a monotonicity assumption on the underlying physical law coupling control outputs to inputs, the devised control is proved to converge to a configuration satisfying safe operational constraints. The assumption is shown to hold for voltage control in electric power systems and pressure control in water distribution networks. Numerical tests corroborate the efficacy of the novel scheme.

distributed control↗
DOE OSTI2024

Direct Power Control of Back to Back Modular Multilevel Converter with Advanced Grid Support Functions for Grid Forming Application

Possibility of using back-to-back modular multilevel converter system with a direct power control architecture has been investigated in this paper. The rectifier side is controlled to be in grid following mode whereas the inverter side is controlled in grid forming mode. Both the sides’ local controllers use Lyapunov energy function based architecture to accomplish the objective of active and reactive power control as well as maintaining a fixed user defined voltage over the dc bus on the rectifier side. Therefore, the proposed direct power control architecture has embedded dc bus control architecture to maintain fixed user defined value of the capacitors. Similar architecture is implemented on the inverter side to accomplish either voltage control during grid forming or power control during grid following. Advanced grid support functionalities based on IEEE-1547-2018 is implemented and utilized to generate the reference values for either the rectifier or the inverter sides. The overall system is modeled based on MATLAB/Simulink and PLECS domain and various important case studies to verify the efficacy of the overall system has been presented.

direct power control (DPC)↗
DOE OSTI2022

Medium Voltage Energy Hub Based on Multilevel Cascaded H Bridge-Dual Active Bridge Back-to-Back Converter for Power Distribution Feeders Interconnection and Multiple Simultaneous Grid Services

This paper presents a medium voltage energy hub based on a modular design of a multilevel cascaded H bridge (CHB)-dual active bridge (DAB) converter. The energy hub composed of the two CHB-DAB modules with a back-to-back topology can be utilized as a grid interconnection component between distribution feeders. The energy hub can provide multiple simultaneous grid services such as voltage regulation, power factor correction, as well as active power flow control between the connected feeders, contributing to grid flexibility, efficiency, reliability, and resilience. Circuit structure and control schemes for the energy hub including both local controllers for each converter and outer loop controllers are proposed to enable simultaneous grid services with coordination to prevent the overloading of the hub. To validate the performance of the hub, the hub system is interconnected between two IEEE 4-bus feeders and provides voltage regulation for both feeders, while controlling active power flow between them. The system and the feeders are implemented in Real-Time Digital Simulator (RTDS) for real-time simulation verification.

Choi, Jongchan↗
DOE OSTI2022

PowerNet: Multi-agent Deep Reinforcement Learning for Scalable Powergrid Control

This paper develops an efficient multi-agent deep reinforcement learning algorithm for cooperative controls in powergrids. Specifically, we consider the decentralized inverter-based secondary voltage control problem in distributed generators (DGs), which is first formulated as a cooperative multi-agent reinforcement learning (MARL) problem. We then propose a novel on-policy MARL algorithm, PowerNet, in which each agent (DG) learns a control policy based on (sub-)global reward but local states and encoded communication messages from its neighbors. Motivated by the fact that a local control from one agent has limited impact on agents distant from it, we exploit a novel spatial discount factor to reduce the effect from remote agents, to expedite the training process and improve scalability. Furthermore, a differentiable, learning-based communication protocol is employed to foster the collaborations among neighboring agents. In addition, to mitigate the effects of system uncertainty and random noise introduced during on-policy learning, we utilize an action smoothing factor to stabilize the policy execution. To facilitate training and evaluation, we develop PGSim, an efficient, high-fidelity powergrid simulation platform. Here, experimental results in two microgrid setups show that the developed PowerNet outperforms the conventional model-based control method, as well as several state-of-the-art MARL algorithms. The decentralized learning scheme and high sample efficiency also make it viable to large-scale power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Pt Atomic Single-Layer Catalyst Embedded in Defect-Enriched Ceria for Efficient CO Oxidation

The local coordination structure of metal sites essentially determines the performance of supported metal catalysts. Here, using a surface defect enrichment strategy, we successfully fabricated Pt atomic single-layer (Pt ASL ) structures with 100% metal dispersion and precisely controlled local coordination environment (embedded vs adsorbed) derived from Pt single-atoms (Pt 1 ) on ceria-alumina supports. The local coordination environment of Pt 1 not only governs its catalytic activity but also determines the Pt 1 structure evolution upon reduction activation. For CO oxidation, the highest turnover frequency can be achieved on the embedded Pt ASL in the CeO 2 lattice, which is 3.5 times of that on the adsorbed Pt ASL on the CeO 2 surface and 10–70 times of that on Pt1. The favorable CO adsorption on embedded Pt ASL and improved activation/reactivity of lattice oxygen within CeO 2 effectively facilitate the CO oxidation. This work provides new insights for the precise control of the local coordination structure of active metal sites for achieving 100% atomic utilization efficiency and optimal intrinsic catalytic activity for targeted reactions simultaneously.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Modular Wide-bandgap String Inverters for Low-cost Medium-voltage Transformerless PV Systems

The proposed technology combines advances in wide-bandgap power electronics with breakthroughs in distributed and decentralized control to produce ultra-low-cost medium-voltage transformerless PV inverters that are composed of stackable lightweight blocks. Taken together, the proposed circuit designs and accompanying control strategies will yield integrated control+circuit (C2) blocks, each comprising a converter and local controller, that can be assembled in a modular fashion to obtain distributed conversion interfaces for next-generation commercial and utility-scale PV systems. We will utilize SiC devices to obtain C2 blocks that can individually operate at a voltage and power in excess of 1 kV and 100 kW, respectively, such that ensembles of series-connected blocks perform direct dc to three-phase ac conversion at medium voltages (e.g., 12 kV–35 kV) and at multi-MW power levels.

42 ENGINEERING↗
DOE OSTI2022

Extremum-Seeking-Based Ultra-local Model Predictive Control and Its Application to Electric Motor Speed Regulation

Electric vehicle (EV) market is rapidly expanding. As a critical component of EV, an electric motor needs to accurately follow a reference speed signal while respecting the electrical current constraint for safety. Those requirements are usually formulated as a model predictive control (MPC) problem. However, the performance of traditional model-based MPC depends on the accuracy of the system model, which may not always be guaranteed in reality. Therefore, we utilize a data-driven, model-free predictive control strategy, called ultra-local MPC (ULMPC), to control the speed of an electric motor. To further enhance the control performance of ULMPC, we employ the extremum-seeking control (ESC) to tune the control gain of the ULMPC online. Simulation and hardware experiments demonstrate the enhancement of the extremum-seeking-based ULMPC over a constant-gain ULMPC.

Zhou, Yujing↗
DOE OSTI2026

How Improved Forecasting Can Increase the Bulk Power System Value of Price-Responsive Electric Vehicle Managed Charging

Personal light-duty vehicle (LDV) electric vehicle managed charging (EVMC) can reduce power system costs by better aligning electric vehicle (EV) charging with locations and times of low energy cost or infrastructure use. The need to coordinate charging demand across thousands to millions of vehicles while preserving mobility service is a barrier to realizing the value of EVMC. Price-responsive dispatch mechanisms like time-of-use rates (TOU) and hourly real-time prices (RTP) are attractive compared to direct load control (DLC) because they only require one-way communications and local controls. However, increasing participation in price responsive mechanisms can induce costly-to-serve spikes in load and otherwise increase, rather than decrease, production costs. We quantify the ability of improved EVMC forecasting to sustain savings from price responsive mechanisms beyond the limit of 14% of LDVs actively participating observed in previous work. Perfect forecasting of price-responsive EV load makes TOU and RTP value-competitive with a low-error DLC formulation with up to 27% (within-week flexibility) to 45% or more (within-session flexibility) of LDVs participating in EVMC in an envisioned New England power system with 84% clean energy. Additional costs of implementing DLC should be no more than tens of dollars per vehicle-year if DLC is to be value-competitive with accurately forecast price-responsive EVMC for double-digit percentage shares of LDVs participating.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2023

A Privacy Preserving Distributed Model Identification Algorithm for Power Distribution Systems: Preprint

Distributed control/optimization is a promising approach for network systems due to its advantages over centralized schemes, such as robustness, cost-effectiveness, and improved privacy. However, distributed methods can have drawbacks, such as slower convergence rates due to limited knowledge of the overall network model. Additionally, ensuring privacy in the communication of sensitive information can pose implementation challenges. To address this issue, we propose a distributed model identification algorithm that enables each agent to identify the sub-model that characterizes the relationship between its local control and the overall system outputs. The proposed algorithm maintains the privacy of local agents by only communicating through dummy variables. We demonstrate the efficacy of our algorithm in the context of power distribution systems by applying it to the voltage regulation of a modified IEEE distribution system. The proposed algorithm is well-suited to the needs of power distribution controls and offers an effective solution to the challenges of distributed model identification in network systems.

data-driven modeling↗
DOE OSTI2024

Unsupervised Learning for Equitable DER Control: Preprint

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.

asymptotic stability↗