Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “DISPATCHES”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A cross-platform execution engine for the quantum intermediate representation

Hybrid languages like the quantum intermediate representation (QIR) are essential for programming systems that mix quantum and conventional computing models, while execution of these programs is often deferred to a system-specific implementation. Here, we develop the QIR Execution Engine (QIR-EE) for parsing, interpreting, and executing QIR across multiple hardware platforms. QIR-EE uses LLVM to execute hybrid instructions specifying quantum programs and, by design, presents extension points that support customized runtime and hardware environments. We demonstrate an implementation that uses the XACC quantum hardware-accelerator library to dispatch prototypical quantum programs on different commercial quantum platforms and numerical simulators, and we validate execution of QIR-EE on IonQ, Quantinuum, and IBM hardware. Our results highlight the efficiency of hybrid executable architectures for handling mixed instructions, managing mixed data, and integrating with quantum computing frameworks to realize cross-platform execution.

LLVM↗
DOE OSTI2021

Modeling and Optimization Methods for Controlling and Sizing Grid-connected Energy Storage: A Review

Purpose of Review Energy storage is capable of providing a variety of services and solving a multitude of issues in today's rapidly evolving electric power grid. This paper reviews recent research on modeling and optimization methods for optimally controlling and sizing grid-connected battery energy storage systems (BESSs). Open issues and promising research directions are discussed. Recent Findings Recent studies on BESS dispatch, evaluation, and sizing focus on advanced modeling and optimization methods to maximize stacked value streams from multiple services. BESS models have been improved to better represent operational characteristics or capture degradation effects. Different solution methods and optimization techniques have been proposed to improve the benefits and cost-effectiveness of BESSs, using deterministic approaches prevalently but with impressive progress in capturing and addressing uncertainties. Summary Recent progress in BESS scheduling and sizing better supports planning and operational decision-making in different use cases, which is highly important to advance the deployment of BESSs. Additional research is required to properly model the trade-off between short-term benefits and service life with multiple degradation effects explicitly considered in the decision-making process. Advanced methods are to be developed for effectively determining optimal BESS sizes that maximize overall benefits within a varying lifetime considering diversified system, conditions as well as uncertainties at planning and operational stages.

Wu, Di↗
DOE OSTI2021

Chapter 9: Impact of Variable Renewable Energy Sources on Bulk Power System Planning and Operations

Wind and solar photovoltaics (PV) have experienced remarkable growth in recent years, with many consequent benefits within and outside of power systems. At the same time, wind and solar PV have unique characteristics relative to the historically dominant dispatchable technologies like coal, gas, and nuclear power plants that have required and will continue to require changes in power system planning and operations. This chapter discusses planning and operational challenges of integrating wind and solar PV into bulk power systems. We first present the key characteristics of wind and solar PV that differentiate it from conventional technologies, such as variable and uncertain electricity generation, asynchronous interconnection to the power system, and near-zero marginal costs. We then link these characteristics to power system planning and operational challenges at low through high wind and solar penetrations. Finally, we discuss near- and long-term solutions to those challenges, such as diversifying the generation mix and wind and solar fleets, improving system flexibility, diversifying ancillary service products, and integrating generation and transmission planning.

bulk power system↗
DOE OSTI2021

Chapter 10 - Economic Considerations for Hydrogen Production with a Focus on Polymer Electrolyte Membrane Electrolysis

This Chapter provides an overview of key economic considerations for hydrogen produced from water electrolysis, with the analyses presented here ranging from high-level summaries to detailed considerations of key market and technological drivers. As the electric power sector evolves to account for increasing shares of renewable energy, opportunities are emerging for technologies capable of storing renewable energy in various forms, including as hydrogen. Opportunities for reducing the cost of hydrogen produced from electrolysis include ensuring access to low-cost wholesale electricity, reducing the capital cost of electrolyzers, and development of systems capable of being dispatchable loads without experiencing significant efficiency penalties throughout lifetime. Our analysis indicates that numerous pathways exist for making hydrogen from electrolysis cost-competitive with hydrogen produced from conventional technologies, and significant opportunities exist for research and development to address performance targets for the future electrolysis systems envisioned here.

cost reduction↗
DOE OSTI2022

Integration of Heat Pumps With Solar Thermal Systems for Energy Storage

This chapter considers the combination of solar thermal systems with an energy storage device known as a Carnot Battery which charges thermal storage with a heat pump or electric heater. Integrating these systems can provide a variety of advantages, such as dispatchable renewable power generation and electricity storage services. In this chapter a variety of methods for hybridizing these systems are described, and ideal cycle analysis is used to assess their relative merits.

Carnot Battery↗
DOE OSTI2021

Urban cells: Extending the energy hub concept to facilitate sector and spatial coupling

The rapid growth of urban areas and concerns over climate change make it vital to improve the energy sustainability of cities. Understanding the complex interactions within different sectors (sectoral) and localities (spatial) of cities plays a crucial role in improving efficiency and sustainability, which is extremely challenging due to the complex urban morphology. State-of-the-art energy concepts do not facilitate a detailed consideration of both sectoral and spatial coupling that energy infrastructure maintains at the urban scale. This has become a significant challenge when designing interconnected urban energy infrastructure. The Urban Cell concept is introduced to address this bottleneck. A novel computational model using a modular approach is introduced to create an interconnected urban infrastructure, including the energy, building, and transportation sectors. Optimal sizing of the distributed energy system (including renewables, energy storage, and dispatchable sources) and optimal urban morphology is determined within a modular unit. A game-theoretic approach is used to model the interactions between urban cells (modular units). The study revealed that the urban cell concept can reduce the net present value of the interconnected energy infrastructure by 37% while increasing the installed renewable energy capacity by 25%. This demonstrates the benefit potential of urban cells and the importance of considering interactions between different sectors and different parts within a city. The Urban Cell concept can be used to present the complex interactions maintained within a city.

Perera, ATD↗
DOE OSTI2020

A comprehensive review of solar only and hybrid solar driven multigeneration systems: Classifications, benefits, design and prospective

Depending on the application and demands, different products can be generated utilizing multigeneration systems. To drive such systems, solar energy can be used as a primary energy source or in hybridization with other renewable or nonrenewable energy sources. Solar driven multigeneration systems are appealing due to the broad availability of solar energy and related technologies. Here, the objective of this study is to review, categorize and discuss different configurations, benefits, potentials, challenges, future research directions and market perspectives of solar energy-driven multigeneration systems, comprehensively. This study also reviews how solar energy systems can be used to hybridize systems for multigeneration purposes. Regardless of using fossil fuel backup sources, these systems are classified as (1) solar only-driven and (2) hybrid solar-driven, in which solar energy is used together with other renewable sources. Reviewing the literature demonstrates numerous ways to build solar energy-driven multigeneration systems by coupling a variety of different cycles and devices. Such systems offer multiple benefits such as enhancing the efficiency, reducing capital and operating costs and carbon dioxide emission. Hybridizing solar with other renewable energies enables continuous operation and dispatchability management. This review shows that for configuration selection and design, different thermodynamics, economic and environmental aspects should be considered. To advance the solar driven multigeneration systems development, further multidisciplinary research on different aspects of such systems is necessary. Particularly, investigations should focus on building different prototypes and on conducting experimental assessments. Also, new incentives and long-term purchase agreements for the products must be established.

14 SOLAR ENERGY↗
DOE OSTI2020

Assessment of wind power scenario creation methods for stochastic power systems operations

Probabilistic scenarios of renewable energy production, such as wind, have been gaining popularity for use in stochastic variants of power systems operations scheduling problems, allowing for optimal decision-making under uncertainty. The quality of the scenarios has a direct impact on the value of the resulting decisions, but until now, methods for creating scenarios have not been compared under realistic operational conditions. Here, we compare the quality of scenario sets created using three different methods, based on a simulated re-enactment of stochastic day-ahead unit commitment and subsequent dispatch for a realistic test system. We create scenarios using a dataset of forecasted and actual wind power values, scaled to evaluate the effects of increasing wind penetration levels. We show that the choice of scenario set can significantly impact system operating cost, renewable energy use, and the ability of the system to meet demand. This result has implications for the ability of system operators to efficiently integrate renewable production into their day-ahead planning, highlighting the need for the use of performance-based assessments for scenario evaluation.

17 WIND ENERGY↗
DOE OSTI2021

Evaluating the Feasibility of Transactive Approach for Voltage Management Using Inverters of a PV Plant

This article evaluates the feasibility of a double-auction-based Transactive Energy System (TES) for engaging the reactive power (Var) capability of inverters in a utility-scale pho¬tovoltaic (PV) plant for voltage management in distribution feed¬ers. In this approach, a PV plant owner (seller) provides Var supply curves in terms of price-quantity pairs consider¬ing the inverters’ P-Q capability, efficiency, opportunity cost for real power curtail¬ment, losses, etc. At the same time, the utility (buyer) proposes Var demand curves in price-quantity pairs exhibiting the marginal price of Var. Then, market clearing points are obtained from the intersection points of supply and demand curves which are then used to create Var dispatch sig-nals from the PV plant. To conduct a fea¬sibility study of this approach, an Australian distribution network with a utility-scale PV plant (capacity 3.275 MWP) is modeled in RSCAD and simu¬lated in real-time on MATLAB, RSCAD-RTDS co-simulation platform using real historical data. Consid¬ering a use case, namely, conservation voltage reduction (CVR), the operational feasibility along with cost-benefit analysis is demonstrated in software-in-the-loop (SIL) and hardware-in-the-loop (HIL) platform. Another use case, exploring the benefit from tap-change reduction of step-voltage-regulator, is investigated. These studies explore further use cases, e.g., PV hosting capacity, network reconfiguration, etc., to be incorporated in TES for experiencing substantial economic benefits.

Alam, Mollah Rezaul↗
DOE OSTI2021

Replacing liquid fossil fuels and hydrocarbon chemical feedstocks with liquid biofuels from large-scale nuclear biorefineries

Liquid fossil fuels (1) enable transportation and (2) provide energy for mobile work platforms and (3) supply dispatchable energy to highly variable demand (seasonal heating and peak electricity). We describe a system to replace liquid fossil fuels with drop-in biofuels including gasoline, diesel and jet fuel. Because growing biomass removes carbon dioxide from the air, there is no net addition of carbon dioxide to the atmosphere from burning biofuels. In addition, with proper management, biofuel systems can sequester large quantities of carbon as soil organic matter, improving soil fertility and providing other environmental services. In the United States liquid biofuels can potentially replace all liquid fossil fuels. The required system has two key features. First, the heat and hydrogen for conversion of biomass into high-quality liquid fuels is provided by external low-carbon energy sources--nuclear energy or fossil fuels with carbon capture and sequestration. Using external energy inputs can almost double the energy content of the liquid fuel per unit of biomass feedstock by fully converting the carbon in biomass into a hydrocarbon fuel. Second, competing effectively with fossil fuels requires very large biorefineries—the equivalent of a 250,000 barrel per day oil refinery. This requires commercializing methods for converting local biomass into high-density storable feedstocks that can be economically shipped to large-scale biorefineries.

09 - BIOMASS FUELS↗
DOE OSTI2021

Technoeconomic assessment of hydrogen cogeneration via high temperature steam electrolysis with a light-water reactor

Increased electricity production from renewable energy resources, coupled with low natural gas (NG) prices, has caused existing light-water reactors (LWRs) to experience diminishing returns from the electricity market. This reduction in revenue is forcing LWRs to consider alternative revenue streams, such as introduction hydrogen production or desalination, to remain profitable. This paper performs a technoeconomic assessment (TEA) regarding the viability of retrofitting existing pressurized-water reactors (PWRs) to produce green hydrogen (H 2 ) via high-temperature steam electrolysis (HTSE). Such an integration would allow nuclear facilities to expand into additional markets that may be more profitable in the long term and eliminate CO 2 emissions from the hydrogen production process. Here, to accommodate such an integration, a detailed single market levelized cost of hydrogen (LCOH) and multimarket analyses were conducted of HTSE process operation, requirements, costing, and flexibility. Alongside this costing analysis, market analyses were conducted on the electric and hydrogen markets in the PJM interconnect. Utilizing a novel stochastic, dispatch optimization approach results suggest that a positive gain is achievable, and by operating in multiple markets, the nuclear facility can avoid the sale of electricity during times of low electricity market pricing, while maintaining the ability to capitalize on the high electricity market pricing. It should be noted that the analysis conducted is a differential cash flow analysis and, as such, does not present profit levels. LCOH analysis results demonstrate the potential exists to produce hydrogen at a cost as low as $1.20/kg. This price is lower than traditional steam methane reforming (SMR) allowing nuclear based hydrogen production to disrupt the existing hydrogen production market.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Thermal energy grid storage: Liquid containment and pumping above 2000 °C

As the cost of renewable energy falls below fossil fuels, the key barrier to widespread sustainable electricity has become availability on demand. Energy storage can enable dispatchable renewables, but only with drastic cost reductions compared to current battery technologies. One electricity storage concept that could enable these cost reductions stores electricity as sensible heat in an extremely hot liquid (>2000°C) and uses multi-junction photovoltaics (MPV) as a heat engine to convert it back to electricity on demand hours, or days, later. Furthermore, this paper reports the first containment of silicon in a multipart graphite tank above 2000°C, using material grades that are affordable for energy storage at GWh scales. Low cost molded graphite with particle sizes as large as 10 μm successfully contained metallurgical grade silicon, even with as much as twothirds iron by mass for up to 10 hours and temperatures as high as 2300°C, in tanks as large as two gallons.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Exploring the design space of PV-plus-battery system configurations under evolving grid conditions

In this study, we explore how the energy and capacity values of coupled systems comprising solar photovoltaic arrays and battery storage (PV-plus-battery systems) could evolve over time based on the evolution of the bulk power system. Using a price-taker model with simulated hourly energy and capacity prices projected from the present to 2050, we simulate the revenue-maximizing dispatch of a range of DC-coupled PV-plus-battery configurations in three locations in the United States. These configurations are defined by the inverter loading ratio (ILR, the ratio of the PV array capacity to the inverter capacity, which we vary from 1.4 to 2.6) and the battery-inverter ratio (BIR, the ratio of the battery power capacity to the inverter capacity, which we vary from 0.25 to 1.0). Based on each configuration's total value, we estimate the breakeven costs needed to justify each incremental increase in ILR (holding BIR constant) or BIR (holding ILR constant). We find that, in a future with low-cost renewable energy technologies, PV-plus-battery system ILRs can be economically increased to around 2.0-2.4 at a BIR of 1.0, depending on solar resource. Our results indicate that a likely evolution of PV-plus-battery system design will be increasingly greater battery power capacity to mitigate the declining PV capacity value, which will, in turn, enable increasingly higher ILRs to further increase energy value. The extent to which PV-plus-systems will be deployed with increasingly higher ILRs depends primarily on whether PV cost declines outpace declining value and increasing curtailment over time.

14 SOLAR ENERGY↗
DOE OSTI2022

Atikokan Digital Twin: Machine learning in a biomass energy system

The Atikokan Generating Station, operated by Ontario Power Generation, has a 200 MW, biomass-fired tower boiler that operates on a dispatch schedule with a five-minute cycle. The boiler is generally operated in the range of 40–100 MW using two of five burner levels. In order to optimize boiler performance, we propose the implementation of a unique digital twin. Our digital twin abstraction couples Bayesian inference from science-based models and from observations (machine learning) with decision theory to predict operating-variable set points that optimize the physical asset (the boiler) in the presence of uncertainty (artificial intelligence). We focus this paper on the continuous Bayesian machine learning part of the Atikokan Digital Twin; we discuss decision theory in a companion paper. We identify and learn about 12 operational, model, and measured-output parameters and their uncertainties from high-fidelity, science-based simulations of the Atikokan boiler and from the observed measurements at the power plant. Since the goal of the Atikokan Digital Twin is to implement it online in real time, we require fast function evaluations for the quantities of interest extracted from the simulations in the Bayesian analysis. We use Gaussian process regression/interpolation to create accurate, robust surrogate models. We define the Bayesian priors and likelihood function and solve for the posterior distributions of the 12 parameters. Here we then propagate these distributions (i.e., parameters with uncertainty) into the predicted distributions of 790 quantities of interest to learn about the relative importance of various sources of error including experimental, model, and operating-parameter errors.

09 BIOMASS FUELS↗
DOE OSTI2022

Optimizing the physical design and layout of a resilient wind, solar, and storage hybrid power plant

We report as wind and solar technologies improve and their costs decrease, the share of power produced by these sources will increase. As the market penetration increases, these power sources will need to provide grid services, such as dispatchability, in addition to providing energy. One way to reduce variability, provide higher quality power to the grid, and address local grid stability issues is through colocating wind and solar power plants. In addition to operating reliably during normal operating conditions, in scenarios with high penetrations of renewable generation, it is important that these hybrid plants can withstand production disruptions and continue to supply power despite prolonged resource reduction, extreme weather events, or other disruptions. In this paper, we present a methodology to optimize a wind-solar-battery hybrid power plant down to the component level that is resilient against production disruptions and that can continually produce some minimum required power. We introduce the models and assumptions we used to simulate a hybrid power plant as well as the design variable parameterization and specific methods we used to optimize the plant. We demonstrate the performance of our method by comparing a plant optimized for different objectives, generation outage durations, minimum power requirements, and power purchase agreements. Although the plant design is sensitive to model parameters and various other assumptions, our results demonstrate some of the optimal designs that occur in different scenarios and what one should expect when designing a hybrid wind-solar-storage power plant.

14 SOLAR ENERGY↗
DOE OSTI2022

A Multi-Model Framework for Assessing Long- and Short-Term Climate Influences on the Electric Grid

Climate change influences many aspects of the electric grid, but prior work and industry practices often ignore the potential effects of changing climate, or they only consider a single effect or individual effects in isolation. Challenges vary with each grid and include adapting to long-term trends such as changing temperature and precipitation or shorter-term events such as drought or storms that could increase in frequency or intensity. Here we present a multi-model framework designed to analyze the effects of long and short-term climate impacts in combination. This framework couples capacity expansion and production cost models with hydrologic models and future climate scenario data to analyze alternative climate and energy futures at high spatial, temporal, and process resolutions. Furthermore, we constructed and evaluated the results of a suite of simulated scenarios exploring climate impacts on capacity investment and stress-tested the resulting future infrastructures using hourly dispatch modeling under alternative drought and load conditions. We demonstrate the approach through a case study of the U.S. Western Interconnection, where climate impacts depend on interactions between temperature-induced load, water availability for hydropower, technology competitiveness, and demand flexibility. Changes in 2038 generating capacity range from -8.5-16.6 GW, and changes in 2038 transmission capacity range from -1-2 GW. Capacity increases are driven by higher load from higher temperatures, while capacity reductions can be achieved in scenarios with higher future hydropower availability and increased demand flexibility. Scenarios requiring additional capacity cost an additional $\$5$-$\$17$ billion (discounted) from 2018 to 2038; however, scenarios with capacity reductions cost $\$1$-$\$18$ billion less. Stress tests on four 2038 infrastructures demonstrated that the identified systems were able to serve at least 99.999% of load and 99.96% of reserves. However, drought and unexpected high-load conditions can result in reduced capacity to respond to contingency events we did not model. Although these results are system and scenario specific, they highlight the importance of considering multiple climate change impacts simultaneously in long-term planning efforts and demonstrate a multi-model, multiscale approach that can be flexibly applied to any system and set of climate change concerns.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Investigation of Stochastic Unit Commitment to Enable Advanced Flexibility Measures for High Shares of Solar PV

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV's variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%-20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%-100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days.

ancillary services↗
DOE OSTI2022

Deep reinforcement learning based optimization for a tightly coupled nuclear renewable integrated energy system

New ways to integrate energy systems to maximize efficiency are being sought to meet carbon emissions goals. Nuclear-renewable integrated energy system (NR-IES) concepts are a leading solution that couples a nuclear power plant with renewable energy, hydrogen generation plants, and energy storage systems, such that thermal and electrical power are dispatchable to fulfill grid-flexibility requirements while also producing hydrogen and maximizing revenue. Here, this paper introduces a deep reinforcement learning (DRL)-based framework to address the complex decision-making tasks for NR-IES. The objective is to maximize revenue by generating and selling hydrogen and electricity simultaneously according to their time-varying prices while keeping the energy flow in the subsystems in balance. A Python-based simulator for a NR-IES concept has been developed to integrate with OpenAI Gym and Ray/RLlib to enable an efficient and flexible computational framework for DRL research and development. Three state-of-the-art DRL algorithms have been investigated, including two-delayed deep deterministic policy gradient (TD3), soft-actor critic (SAC), proximal policy optimization (PPO), to illustrate DRL’s superiority for controlling NR-IES by comparing it with a conventional control approach, particle swarm optimization (PSO). In this effort, PPO has shown more-stable performance and also better generalization capability than SAC and TD3. Comparisons with PSO have demonstrated that, on average, PPO can achieve 13.9% more mean episode returns from the training process and 29.4% more mean episode returns from the testing process when different hydrogen-production targets are applied.

08 HYDROGEN↗