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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Development of Real-Time System Identification to Detect Abnormal Operations in a Gas Turbine Cycle

Here, we present a novel online system identification methodology for monitoring the performance of power systems. This methodology was demonstrated in a gas turbine recuperated power plant designed for a hybrid configuration. A 120-kW Garrett microturbine modified to test dynamic control strategies for hybrid power systems designed at the National Energy Technology Laboratory (NETL) was used to implement and validate this online system identification methodology. The main component of this methodology consists of an empirical transfer function model implemented in parallel to the turbine speed operation and the fuel control valve, which can monitor the process response of the gas turbine system while it is operating. During fully closed-loop operations or automated control, the output of the controller, fuel valve position, and the turbine speed measurements were fed for a given period of time to a recursive algorithm that determined the transfer function parameters during the nominal condition. After the new parameters were calculated, they were fed into the transfer function model for online prediction. The turbine speed measurement was compared against the transfer function prediction, and a control logic was implemented to capture when the system operated at nominal or abnormal conditions. To validate the ability to detect abnormal conditions during dynamic operations, drifting in the performance of the gas turbine system was evaluated. A leak in the turbomachinery working fluid was emulated by bleeding 10% of the airflow from the compressor discharge to the atmosphere, and electrical load steps were performed before and after the leak. This tool could detect the leak 7 s after it had occurred, which accounted for a fuel flow increase of approximately 15.8% to maintain the same load and constant turbine speed operations.

algorithms↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources

Integrating a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problems requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (control-lable DERs), including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperforms other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2021

DC input current ripple reduction in SRM drive for high volumetric power density applications

A control system for a switched reluctance machine includes a capacitor and an inverter connected to the capacitor, wherein the inverter generates current signals and a ripple current returned to the capacitor. A switched reluctance machine (SRM) receives the current signals and generates a position signal. A controller receives the ripple current, current signal values of the current signals, and the position signal and generates desired reference current waveforms received by the inverter to adjust the current signals received by the SRM.

Sozer, Yilmaz↗
DOE OSTI2023

Model Predictive Control-Based Dual-Mode Operation of an Energy-Stored Quasi-Z-Source Photovoltaic Power System

The energy-stored quasi-Z-source inverter (ES-qZSI) has attracted much attention for photovoltaic (PV) power generations, due to its capability to stabilize the PV power fluctuations with simple structure and other advantages of Z-source inverter. The improved dual-mode (DM) ES-qZSI is able to support all-weather operation even at night or cloudy days when PV power is extremely low. However, the traditional proportional–integral (PI) based control suffers from complicated controller design and poor dynamic response during mode transition, due to two sets of PI control required for the daytime and night operation modes. In order to overcome that, here in this article, we propose a model predictive control strategy for the DM-ES-qZSI PV power system. The system predictive models in both day and night operating modes are derived. The control strategy is disclosed to ensure high performance of the system, through calling the predictive models and defined cost functions of the two modes within a single control loop. Simulation and experiment are carried out to verify the effectiveness of the proposed control strategy.

14 SOLAR ENERGY↗
DOE OSTI2022

Scalable Predictive Control and Optimization for Grid Integration of Large-Scale Distributed Energy Resources: Preprint

Integration of a large number of distributed energy resources (DERs) into the power grid needs a scalable power balancing method. We formulate the power balancing problem as a look-ahead optimization problem to be solved sequentially by a power distribution system aggregator based on a model predictive control (MPC) framework. Solving large-scale look-ahead control problem requires proper configuration of the control steps. In this paper, to solve large-scale control problems, we propose a variable time granularity where control time steps nearby the current control step have finer resolutions. The aggregator objective includes maximization of power production revenue and minimization of power purchasing expense, renewable power curtailment, and mileage costs for energy storage and electric vehicle (EV) charging stations while satisfying system capacity and operational constraints. The control problem is formulated as a mixed-integer linear program (MILP) and solved using the XpressMP solver. We perform simulations considering a copper plate representation of a large distribution network consisting of 2507 devices (controllable DERs) including curtailable photovoltaics (PVs), energy storage batteries, EV charging stations, and buildings with heating, ventilation, and air conditioning units (HVACs). We show the effectiveness of the proposed approach in managing DERs interactively for maximum energy trading profit and local supply-demand power balancing. Finally, we demonstrate that the proposed method outperformed other benchmark controllers regarding computation time without compromising operational performance.

DER↗
DOE OSTI2024

H ∞ Control for Energy Dispatch in Autonomous Nanogrid With Communication Delays

This paper proposes an optimal controller and estimator for energy dispatch to balance the power supply and demand considering communication delays. The proposed algorithm involves modeling an autonomous nanogrid (ANG) consisting of distributed energy resources, energy storage systems, loads, an $H$ ∞ controller with a reference power modulation technique, and a state estimator. The ANG was developed to express the dynamic supply-demand energy balance of a nanogird system. Reference power modulation was designed to generate the desired ESS power based on the imbalanced energy. Random communication delays were modeled using a stochastic variable satisfying the Bernoulli random binary distribution. The optimal $H$ ∞ controller and estimator were developed using a linear matrix inequality approach to exponentially stabilize the closed-loop system. Simulations were performed using real daily demand forecasts obtained from the Korea Meteorological Administration to demonstrate the effectiveness of the proposed real-time optimization algorithm.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Adaptive Load Shedding for Grid Emergency Control via Deep Reinforcement Learning

Emergency control, typically such as under-voltage load shedding (UVLS), is broadly used to grapple with low voltage and voltage instability issues in real-world power systems under contingencies. However, existing emergency control schemes are rule-based and cannot be adaptively applied to uncertain and floating operating conditions. Here, we propose an adaptive UVLS algorithm for emergency control via deep reinforcement learning (DRL) and expert systems. We first construct dynamic components for picturing the power system operation as the environment. The transient voltage recovery criteria, which poses time-varying requirements to UVLS, is integrated into the states and reward function to advise the learning of deep neural networks. The proposed method has no tuning issue of coefficients in reward functions, and this issue was regarded as a deficiency in the existing DRL-based algorithms. Case studies illustrate that the proposed method outperforms the traditional UVLS relay in both the timeliness and efficacy for emergency control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

sCO2 Primary Power Large-Scale Pilot Plant FEED Summary

Echogen Power Systems and its project partners the Electric Power Research Institute, CDM Smith, Riley Power Inc. (a Babcock Power Inc. company), and the University of Missouri completed the Front-End Engineering Design (FEED) study of a nominal 10 MWe supercritical carbon dioxide (sCO2) large-scale pilot plant based on Recompression Brayton Cycle (RCBC) architecture. The FEED study was completed under Phase II of the U.S. Department of Energy Fossil Fuel Large-Scale Pilots program. The FEED study scope included the preliminary design of the combustion and environmental control systems, the primary heater, the sCO2 power cycle and its associated turbomachinery, and the balance of plant equipment to determine accurate cost and schedule requirements for fabrication and installation of the system at the University of Missouri’s Combined Cooling, Heat, and Power Plant, located in Columbia, Missouri. The RCBC sCO2 cycle presents superior thermodynamic efficiency compared to steam-Rankine cycles and is adaptable to a broad range of heat sources. The risk aversion of the power generation industry requires a successful large-scale pilot plant demonstration to enable financing and deployment of commercial-scale plants. Therefore, successful construction and demonstration of a 10 MWe RCBC sCO2 power plant would empower a transformational step forward for the efficiency of the next generation of utility power plants. This paper provides details on the primary heater, environmental control systems, and sCO2 power cycle design and performance.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Positive-Sequence Modeling of Droop-Controlled Grid-Forming Inverters for Transient Stability Simulation of Transmission Systems

Here, this paper describes a positive-sequence model to represent two widely reported droop-controlled grid-forming inverters for bulk power systems simulation study. Methods of how to develop the equivalent voltage source behind impedance to represent inverters with and without inner control loops, modeling of P-f and Q-V droop controls, active and reactive power limiting modeling, and modeling of fault current limiting controls are described in detail. The model is implemented in commercially available positive-sequence simulation tools and compared with detailed electromagnetic transient models in both a single-grid-forming-inverter infinite-bus system and a modified IEEE 39-bus system. Finally, the model is tested on the full US Western Electricity Coordinating Council (WECC) system. Study results show that the model has a good level of accuracy compared to the detailed EMT models, and at the same time also achieves a high computational efficiency, which is suitable for the bulk power system simulation study.

42 ENGINEERING↗
DOE OSTI2021

MPC-based double-layer real-time conditional cSelf-restoration for interconnected microgrids

In this report, we propose a novel model predictive control(MPC)-based real-time conditional self-restoration energy management system (CSR-EMS) for interconnected microgrids (IMGs) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). Superior to the existing IMG self-restoration methods, the “conditionality” of the proposed CSR-EMS can economically realize self-restoration and grid-assisted restoration during energy deficiency or faults, in both islanded and grid-connected modes. Cost minimization is implemented as the objective function to judge in real-time which restoration mode is economically preferred. The proposed CSR-EMS comprises two layers–the lower layer operates locally to eliminate electricity fluctuations created by RESs and ensure economic effectiveness within an MG, whereas the upper layer oversees the real-time operational status of the IMG system and determines power exchange among microgrids (MGs) during abnormalities. In detail, when a microgrid inside the IMG system experiences an energy deficiency, the CSR-EMS, on an MPC basis, intelligently optimizes power production from each dispatchable distributed generator (DG), ESS, power imported from the main grid, and power exchange among the IMGs to maintain the demand–supply balance, while considering system recovery cost, state of charge (SoC) of ESSs and operation modes of the IMGs (i.e., grid-connected or islanded mode). Simulation results and comparisons with existing IMG self-healing EMSs demonstrate the economic efficacy of the proposed CSR-EMS strategy during normal and abnormal operations, which can be used as an energy control framework for modern power systems with multiple interconnected microgrids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Blockchain Enabled Intelligence of Federated Systems (BELIEFS): An attack-tolerant trustable distributed intelligence paradigm

In this article, a Blockchain Enabled Intelligence of Federated Systems (BELIEFS) is proposed to conduct cooperative control for the multi-regional large-scale power system with a multi-agents system (MAS). By establishing a two levels blockchain, each regional AI agent can simultaneously manage intra-regional controllers and cooperate with other AI agents. Under the consensus mechanism, the agents, which respectively conducted distributed deep reinforcement learning (DDRL) algorithm in multi-regions, can have the tolerant capability of malicious attacks in their training process. The demonstration of the proposed approach is within a multi-regional large-scale interconnected power system. Under the mode of "centralized dispatching and hierarchical management", this article aims to definite a mathematical model to deal with the control problem of the power systems. With the comparison experiments, the effectiveness and efficiency of our proposed method in the training process are verified. In addition, malicious attacks are set on the main chain and shard chains to verify the attack-tolerant capability. We expect that such approach and results can suggest a new paradigm of attack-tolerant trustable distributed AI deployment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Development of Supervisory Control System for Thermal Energy Distribution System

The integrated energy system (IES) refers to the combination of nuclear energy generation with other energy sources to enable the efficiency and reliability of power generations. To create technologically viable and economically competitive systems, supervisory control strategies are critical for optimizing performance and ensuring stability across different energy generation, transportation, and utilization. This work focuses on the control strategies for the thermal energy distribution system (TEDS), which is a cornerstone of the Dynamic Energy Transport and Integration Laboratory at Idaho National Laboratory. TEDS currently relies on operators to coordinate across different components to manage energy storage and ensure efficiency. This work demonstrates the use of model predictive control (MPC) with surrogate models in determining optimal setpoints for major TEDS components. The capability of MPC-based supervisory control system is evaluated by autonomously matching the power outputs from a Dymola-based TEDS with target heat demands.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Reverse and Forward Engineering of Local Voltage Control in Distribution Networks

The increasing penetration of renewable and distributed energy resources in distribution networks calls for real-time and distributed voltage control. As such, in this paper we investigate local Volt/VAR control with a general class of control functions, and show that the power system dynamics with non-incremental local voltage control can be seen as a distributed algorithm for solving a well-defined optimization problem (reverse engineering). The reverse engineering further reveals a fundamental limitation of the non-incremental voltage control: the convergence condition is restrictive and prevents better voltage regulation at equilibrium. This motivates us to design two incremental local voltage control schemes based on the subgradient and pseudo-gradient algorithms respectively for solving the same optimization problem (forward engineering). The new control schemes decouple the dynamical property from the equilibrium property, and have much less restrictive convergence conditions. This work presents another step towards developing a new foundation—network dynamics as optimization algorithms—for distributed real-time control and optimization of future power networks.

42 ENGINEERING↗
DOE OSTI2023

Inter-Area Oscillation Damping with Type-5 Wind Power Plant

This paper investigates the potential of using brushless excitation (BLE) for not only riding through the fault but also to damp inter-area power oscillation with the help of a synchronous generator (SG) used in Type 5 Wind Power Plant (WPP). In BLE, an auxiliary synchronous generator (ASG) behaving like an exciter is coupled and driven by the rotor of the main SG. The BLE's field current's ASG is fed by two separate loops of the automatic voltage regulator (AVR) and power system stabilizer (PSS). The AVR system implements a control loop to regulate the generator terminal voltage, VT. For the PSS, the kinetic energy of the wind turbine is utilized according to the estimated rotational speed of the synchronous generator shaft of the SG. It may mitigate the necessity of any curtailment of active power for damping. The effectiveness of the proposed control scheme is verified with a three-phase short circuit fault in a two-area power system.

brushless↗