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At least 1,387 records · Page 77
All sourcesNASA NTRSDOE OSTI
NASA NTRS2024

FUN3D Manual: 14.1

This manual describes the installation and execution of FUN3D version 14.1, including optional dependent packages. FUN3D is a suite of computational fluid dynamics simulation and design tools that uses mixed-element unstructured grids in a large number of formats, including structured multiblock and overset grid systems. A discretely-exact adjoint solver may be used for formal design optimization, error estimation, and mesh adaptation. FUN3D also offers a reacting, real-gas capability and provides GPU acceleration of many common simulation options.

William K Anderson↗
DOE OSTI2020

Identification of Worst Impact Zones for Power Grids During Extreme Weather Events Using Q-Learning: Preprint

Both the frequency and intensity of extreme weather events have been trending higher in recent years, leading to significant infrastructure loss in the electric grid. The impact of these extreme weather events is desired to be analyzed and quantified in order to help transmission and distribution system operators to prepare and prevent significant losses. In this paper, we developed an approach that models the impact of extreme weather on the power grid and identifies the worst impact zone using Q-learning (a reinforcement learning approach). The identification results reveal grid vulnerability to weather events and provide insights for system operators to help achieve optimal resource allocation and crew dispatch in order to minimize the adverse impact of extreme weather. Simulation studies are conducted on the IEEE 123-node system to demonstrate the performance of the proposed approach.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Scenario Creation and Power-Conditioning Strategies for Operating Power Grids with Two-Stage Stochastic Economic Dispatch

A significant difficulty associated with the use of stochastic programming to solve optimal power flow problems on a 5-minute timescale is the quality of renewable energy scenarios input by the user. This is especially true when considering power systems with high penetrations of renewable energy, e.g. wind power. This paper introduces the use of stochastic programming to solve the DC optimal power flow problem with scenarios drawn directly from high-fidelity data sets. Hence, the proposed method avoids the problem of lost physics by finding high-fidelity analogs that can describe future states of the system. Furthermore, this method can be simply extended to output multi-period scenarios to the stochastic program. We demonstrate the effectiveness of this technique by simulating dispatch operations on a synthetic test system over the course of a week.

analog forecast↗
DOE OSTI2022

Designing for Zero Energy and Zero Carbon on a Multi-Building Scale Using URBANopt: Preprint

Groundbreaking efforts are necessary to mitigate contributors increasing impacts of climate change. In parallel to inventing pioneering clean energy technologies it is even more fundamental to rethink designing energy systems within a singular facility and collectively to function as a district. Facilities should not be continuously passive by just consuming; there is a need to shift to perform more dynamically. Designing for zero energy and zero carbon on a multi-building scale can uncover opportunities for building energy efficiency, decarbonization, demand flexibility, and resiliency that are not accessible at an individual building scale. This approach can be challenging without innovative tools to evaluate the multitude of possibilities. As an investigated result, we highlight the use of a campus-scale energy modeling platform - URBANopt™ - for the expansion of the National Renewable Energy Laboratory's (NREL's) South Table Mountain campus in Golden, Colorado. Programmatic growth included the design of three new all-electric, zero-energy, and zero-carbon, mixed used buildings (a combination of research laboratories and office space). This investigation is critical to NREL reaching net-zero emissions for its operational footprint, which will occur in phases over the next decade. Leveraging URBANopt's capabilities, we evaluate 1) high-performance building energy efficiency and decarbonization measures, 2) 4th generation district heating and cooling (4th GDHC) systems, 3) optimized onsite generation and energy storage assets that meet zero-energy and zero-carbon targets at minimum life-cycle costs, and 4) cost-optimal distributed energy technology mixes, dispatch strategies, and associated capacities that increase resiliency to grid outages. This work demonstrates the use and capabilities of URBANopt through a real-world case study on a multi-building scale.

community energy model↗
DOE OSTI2024

Chapter 7: Learning Stable Local Volt/Var Controllers in Distribution Grids

This chapter describes a framework to synthesize provably stable local Volt/Var controllers for distributed energy resources (DERs) in power distribution grids (DGs). The goal is to control the reactive power injections of DERs to improve the system performance as quantified by a generic optimal reactive power flow (ORPF) problem. To achieve this, we jointly design for each DER the control function, which prescribes the reactive power update rule, and the equilibrium function, which approximates the ORPF solutions from local measurements of voltages and powers. We provide conditions on the equilibrium functions and the control parameters ensuring the stability of the closed-loop system. In particular, we discuss the trade-offs between each set of conditions accounting for practical considerations, like fully exploiting the DERs' generation capabilities and reducing the optimality gap. These conditions are then translated into learning constraints on the neural networks' parameters that are enforced in the training phase. We validate our framework with numerical simulations on the IEEE 37-bus network and through a comparison with an optimized version of standard piece wise linear control rules.

closed-loop asymptotic stability↗
DOE OSTI2025

A Demand Bidding Model for Multi-Product Industrial Plants

The growing contribution of renewable energy sources has increased volatility and uncertainty in electricity markets, challenging traditional grid operation paradigms. Demand bidding (DB), a market participation model where (large) electricity users communicate their willingness to pay for electricity to the grid operator, was shown in previous work to enhance grid stability and lower generation cost. We present a DB model for multi-product industrial plants, based on an extended optimal power flow problem where the plant dynamics are represented using autoregressive with extra inputs (ARX) models. We compare DB to price-based demand-side management, showing that, under certain assumptions, the two approaches are equivalent, while DB provides more transparency and predictability to the grid operator. A case study based on an industrial air separation unit is discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Online Stochastic Optimization of Networked Distributed Energy Resources

Our work investigates distributed control and incentive mechanisms to coordinate distributed energy resources (DERs) with both continuous and discrete decision variables as well as device dynamics in distribution grids. We formulate a multi-period social welfare maximization problem, and based on its convex relaxation propose a distributed stochastic dual gradient algorithm for managing DERs. Moreover, we extend it to an online realtime setting with time-varying operating conditions, asynchronous updates by devices, and feedback being leveraged to account for nonlinear power flows as well as reduce communication overhead. The resulting algorithm provides a general online stochastic optimization algorithm for coordinating networked DERs with discrete power setpoints and dynamics to meet operational and economic objectives and constraints. We characterize the convergence of the algorithm analytically and evaluate its performance numerically.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Photovoltaic and Behind-the-Meter Battery Storage: Advanced Smart Inverter Controls and Field Demonstration

Electric utilities have little visibility of the electrical distribution system, and consequently, limited diagnostic capabilities. The distribution grid was designed for a unidirectional power flow, where energy is supplied by few large centralized power plants; however, this is set to change to meet California’s aggressive decarbonization goals. The large-scale deployment of distributed renewable generation, such as photovoltaics (PV), can have many negative effects on an unprepared grid. As illustrated in the California “duck curve,” PV generation modifies load profiles during the day, which causes steep ramping in the evening. This effect indicates the need to revise the electrical grid’s design and operation. This project sought to (a) create a centralized resource in California to test and validate distribution technology controls with industry standard PV, storage, and high-fidelity sensors, (b) support strategic and operational decisions for new grid architectures by providing simulation models, (c) promote new ways to control clusters of PV smart inverters, in accordance with California Rule 21, and (d) innovate, develop, and field test a predictive controller to maximize economic profit for the customer while supporting the grid. The controller was built using the state-of-the-art model predictive control methodology to optimally control behind-the-meter PV and battery storage. In consideration of the duck curve, the controller optimally controls the battery dispatch by charging during excess generation periods and discharging during the critical afternoon demand hours. The controller was evaluated in annual simulations and revealed the potential cost-effectiveness of behind-the-meter battery storage. The simulations showed that the annual electricity bill could be reduced by as much as 35 percent, with a payback period of the investment in battery storage in about 6 years – significantly shorter than the manufacturer’s 10-year warranty. All developed simulation models, the grid event library, and the MPC controller are open-source and available online.

Model Predictive control↗
DOE OSTI2023

Power System Recovery Coordinated with (Non-)Black-Start Generators

Power restoration is an urgent task after a black-out, and recovery efficiency is critical when quantifying system resilience. Multiple elements should be considered to restore the power system quickly and safely. This paper proposes a recovery model to solve a direct-current optimal power flow (DCOPF) based on mixed-integer linear programming (MILP). Since most of the generators cannot start independently, the interaction between black-start (BS) and non-black-start (NBS) generators must be modeled appropriately. The energization status of the NBS is coordinated with the recovery status of transmission lines, and both of them are modeled as binary variables. Also, only after an NBS unit receives the cranking power through connected transmission lines, will it be allowed to participate in the following system dispatch. The amount of cranking power is estimated as a fixed proportion to the maximum generation capacity. The proposed model is validated on several test systems, as well as a 1393-bus representation system of the Puerto Rican electric power grid. Test results demonstrate how the recovery of NBS units and damaged transmission lines can be optimized, resulting in an efficient and well-coordinated recovery procedure.

Zhao, Meng↗
DOE OSTI2022

A Blockchain Based Co-Simulation Framework for Integrating DERs to Wholesale Electricity Markets

As the number of distributed energy resources (DERs) continue to increase across energy-delivery systems, there remains a need for integrating their capabilities into traditional grid operations. In this paper, a blockchain-based solution is proposed to facilitate FERC's Order No. 2222 implementations. The presented use-case enables small-scale DERs to participate in wholesale market operations through DER aggregators, while also enabling local distribution system operators to enforce distribution system constraints in a secure and traceable manner. The presented use case is built around the Unified Testing Platform (UTP) being developed as a part of the Blockchain for Optimized Security and Energy Management (BLOSEM) project. This is a multi-lab effort intended to simplify the deployment of blockchain-powered grid solutions by enabling the integration of simulation tools, and blockchain technologies through the use of system-agnostic interfaces that provide a modular, interoperable, and reusable connectivity layer.

blockchain interoperability↗
DOE OSTI2020

Multi-Agent Safe Policy Learning for Power Management of Networked Microgrids

This paper presents a supervised multi-agent safe policy learning (SMAS-PL) method for optimal power management of networked microgrids (MGs) in distribution systems. While unconstrained reinforcement learning (RL) algorithms are black-box decision models that could fail to satisfy grid operational constraints, our proposed method considers AC power flow equations and other operational limits. Accordingly, the training process employs the gradient information of operational constraints to ensure that the optimal control policy functions generate safe and feasible decisions. Furthermore, we have developed a distributed consensus-based optimization approach to train the agents’ policy functions while maintaining MGs’ privacy and data ownership boundaries. After training, the learned optimal policy functions can be safely used by the MGs to dispatch their local resources, without the need to solve a complex optimization problem from scratch. Lastly, numerical experiments have been devised to verify the performance of the proposed method.

, policy gradient↗
DOE OSTI2014

Sci—Thur PM: Planning and Delivery — 02: Treatment planning workflow for very high-energy electron beam radiotherapy

Purpose: To develop treatment planning workflow for rapid radiotherapy delivered with very-high energy electron (VHEE) scanning beam. Methods: VHEE radiotherapy treatment planning was performed by linking Monte Carlo (MC) dose calculations with inverse optimization in a research version of RayStation. In order to study a number of treatment parameters, a Matlab graphical user interface (GUI) for calculation of VHEE beamlet dose was developed. Through the GUI, EGSnrc MC simulations were run for a number of beam energies, number of beams, beamlet spot and grid sizes, and machine bore sizes. VHEE plans for a pediatric patient with a 4.3 cm{sup 3} brain target optimized with spot-scanning algorithm in RayStation were compared to the clinically delivered 6 MV VMAT plan. Results and Discussion: VHEE beam energy had the largest effect on the quality of dose distributions. For the same target dose, the mean doses to critical organs decreased by 10–15% when planned with 100 MeV compared to 60 MeV. VHEE plans calculated with 36 beams outperformed plans calculated with 13 and 17 beams. While beamlet spacing and bore size had a small effect on VHEE dose distributions, 0.1-3mm beamlet sizes resulted in identical dose distributions. Critical organ doses were by up to 70% lower in the best VHEE plan compared to the clinical 6 MV VMAT plan. Conclusions: We have developed a GUI for MC beamlet generation for treatment planning of VHEE radiotherapy. We have demonstrated that pediatric VHEE plans resulted in significant critical organ dose sparing compared to the clinical VMAT plan.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1995

Computational analysis of forebody tangential slot blowing on the high alpha research vehicle

A numerical analysis of forebody tangential slot blowing as a means of generating side force and yawing moment is conducted using an aircraft geometry. The Reynolds-averaged, thin-layer, Navier-Stokes equations are solved using a partially flux-split, approximately-factored algorithm. An algebraic turbulence model is used to determine the turbulent eddy viscosity values. Solutions are obtained using both patched and overset grid systems. In the patched grid model, and actuator plane is used to introduce jet variables into the flow field. The overset grid model is used to model the physical slot geometry and facilitate modeling of the full aircraft configuration. A slot optimization study indicates that a short slot located close to the nose of the aircraft provided the most side force and yawing moment per unit blowing coefficient. Comparison of computed surface pressure with that obtained in full-scale wind tunnel tests produce good agreement, indicating the numerical method and grid system used in the study are valid. Full aircraft computations resolve the changes in vortex burst point due to blowing. A time-accurate full-aircraft solution shows the effect of blowing on the changes in the frequency of the aerodynamic loads over the vertical tails. A study of the effects of freestream Mach number and various jet parameters indicates blowing remains effective through the transonic Mach range. An investigation of the force onset time lag associated with forebody blowing shows the lag to be minimal. The knowledge obtained in this study may be applied to the design of a forebody tangential slot blowing system for use on flight aircraft.

Gee, Ken↗
DOE OSTI2023

The Empirical Effect of Fleet Optimization on Synchronization and Rebound Effects in Heat Pump Water Heaters

Demand response is a growing concept in light of the internet of things and an increasing need for grid flexibility. Water heaters are one of the preferred devices for providing demand response for grid services and peak management due to their capability to store energy. The efficient use of water heaters for demand response requires consideration of the associated load effects such as synchronization of device schedules and rebound effect. These effects present a significant challenge. Despite the importance of the mentioned effects for water heater queuing and scheduling, there has been no effort to quantify and empirically validate their impact. This study attempts to address this gap by offering two methods - Ward clustering and Euclidean K-means - to evaluate the extent of synchronization in a fleet of 42 water heaters in Atlanta, GA. Using the aforementioned methods on the measured data, we find evidence of convergence of water heater loads as a result of optimization compared to an idle period and analyzed their impact.

demand response↗
DOE OSTI2024

Microgrid design and multi-year dispatch optimization under climate-informed load and renewable resource uncertainty

Microgrids are an increasingly popular solution to provide energy resilience in response to increasing grid dependency and the growing impacts of climate change on grid operations. However, existing microgrid models do not currently consider the uncertain and long-term impacts of climate change when determining a set of design and operational decisions to minimize long-term costs or meet a resilience threshold. In this paper, we develop a novel scenario generation method that accounts for the uncertain effects of (i) climate change on variable renewable energy availability, (ii) extreme heat events on site load, and (iii) population and electrification trends on load growth. Additionally, we develop a two-stage stochastic programming extension of an existing microgrid design and dispatch optimization model to obtain uncertainty-informed and climate-resilient energy system decisions that minimizes long-term costs. Use of sample average approximation to validate our two case studies illustrates that the proposed methodology produces high-quality solutions that add resilience to systems with existing backup generation while reducing expected long-term costs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Interleaved Cuk Converter Wave Energy System With Advanced Control and Grid Support Functions for Seamless Integration

This paper presents an innovative wave energy conversion system that integrates an interleaved Cuk converter with advanced nonlinear control for seamless grid integration. The system efficiently extracts power on the DC side using the interleaved Cuk converter, while an inverter manages power transfer to the grid or load on the AC side. A nonlinear control architecture, based on the Lyapunov energy function, ensures stable and optimal operation under varying wave conditions, effectively addressing the challenges of variability and unpredictability inherent in wave energy. The proposed system also incorporates features to enhance power quality and minimize losses, making it a robust solution for renewable energy integration. The system's effectiveness in harnessing wave energy and achieving smooth grid integration is validated through comprehensive computer simulations in MATLAB/Simulink, with case study results demonstrating its capabilities.

HYDRO ENERGY,TIDAL AND WAVE POWER↗
DOE OSTI2023

Multiphysics simulations of Self-Regulating performance of an optimized molten metal fuel microreactor design

Advanced microreactors are expected to play an indispensable role in reliable energy solutions for civilian/ military applications in off-grid regions and space applications such as power supplies for Lunar and Mars bases. An innovative fast neutron spectrum heat pipe microreactor (HP-MR) concept was recently proposed with two unique features: molten U-Mn fuel and heat pipes as the heat removal mechanism. The microreactor was designed to be self-regulated, solely relying on reactivity feedbacks from fuel temperature. The reactor design has been further optimized to provide sufficient engineering safety margin and effective heat removal. In this study, the self-regulating performance of the optimized molten metal fuel microreactor was investigated by multi-physics simulations (neutronics, heat-transfer and heat pipe modeling) based on the MOOSE MultiApps system. In conclusion, the responses of the reactor to a series of transient scenarios were simulated to demonstrate the exceptional and inherent safety features of the novel microreactor concept.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗