Optimizing Benefits of Testing Efforts
This presentation explores the need to prioritize testing efforts in order to achieve the highest confidence in PV reliability at the lowest cost.
14 SOLAR ENERGY↗
SEARCH · Search NASA
Results for “Policy optimization”
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.
Position Paper Evaluating and Prioritizing Nuclear Energy Market Attributes
This position paper provides the results of a short turn-around study focused on ensuring electrical grid resilience via adequate baseload (nuclear) representation. This includes: • Literature review • Report holistically evaluating grid reliability and resilience (INL/EXT-18-45010) • Report proposing a framework to quantify the value of inertia from nuclear power plants (INL/EXT-18-45018) • Multi-objective optimization tool that provides the ability to understand impacts to reliability and weather-based resilience as the fuel mix changes
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Cooperative Research and Development Agreement between National Energy Technology Laboratory and Korea Institute of Energy Research [Abstract]
The Korea Institute of Energy Research (KIER) and the National Energy Technology Laboratory (NETL) intend to collaborate under a CRADA to advance the development of oxy-combustion technology, with an emphasis on process modeling, optimization, and techno-economic analysis. This collaboration will result in the development of a full-scale process model of a commercial oxy-fueled circulating fluid bed combustion (oxy-CFBC) plant, informed from pilot-scale data and built using the Institute for the Design of Advanced Energy Systems (IDAES) process systems engineering framework. It will also generate cost comparisons of the optimized oxyCFBC process with an air-CFBC process with and without carbon capture and sequestration.
20 FOSSIL-FUELED POWER PLANTS↗
Acquire and Test IDAES Framework Components
The cooperation under the Grid Modernization Initiative and the Applied Energy Tri-Laboratory umbrellas requires mutual understanding of capabilities and a common effort to identify gaps existing in the available computational modeling and analysis tools. In this work, we report acquisition and testing of the National Energy Technology Laboratory’s Institute for the Design of Advanced Energy Systems (IDAES) framework, particularly including both electric power systems production cost model simulator Prescient as well as system optimization IDAES itself. Further, we consider the methods in which developments in Prescient and IDAES for grid-energy simulation and optimization may be included in the Framework for Optimization of ResourCes and Economics (FORCE) ecosystem that has been under collaborative development by Idaho National Laboratory, Argonne National Laboratory, and Oak Ridge National Laboratory. We propose two integration methods to introduce Prescient and IDAES into existing FORCE automated workflows. One integration involves using Prescient as a market reduced-order model and IDAES models similar to how existing Modelica models are used. The other integration involves wrapping the Prescient-IDAES dispatch optimization process with the stochastic capacity optimization workflow in FORCE.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Powering Data Centers with Clean Energy: A Techno-Economic Case Study of Nuclear and Renewable Energy Dependability
Rising data demands from artificial intelligence (AI) and large language models (LLMs) generating images, videos, and text have prompted increased need for larger and more robust data centers in the United States. Major companies interested in these larger data centers face the choice of linking them to existing regional grids, building stand-alone power supplies onsite, or a combination of both. The request, review, and approval process for new transmission lines to grids in the United States, however, has grown in recent years to times spans rivaling those of new construction for nuclear power plants. Building an islanded power supply for each data center is therefore becoming a prominent option. In this case study, several technologies are modeled in techno-economic simulations for long-term system costs subject to fixed electricity demand from a singular data center. A 250 MWe data center is assumed with additional 50 MWe for resiliency. Techno-economic simulations are conducted using the Holistic Energy Resource Optimization Network (HERON) software, which is a part of the Framework for Optimization of Resources and Economics (FORCE) tool suite. Technologies considered include solar, wind, lithium-ion batteries, and several types of nuclear reactors: large-scale reactors, small modular reactors, and microreactors. A low- and high-cost estimate for each technology is assumed to develop a range of expected economic performance. Low-cost estimates included several clean energy production tax credits. Different combinations of renewable energy generators with nuclear reactors are considered, ranging from a fully renewable-powered data center to a fully nuclear-powered data center. Historic time series of wind and solar availability from the Texas grid are used to train a reduced order model; this model then generates unique time series with similar characteristics of the training dataset. Multiple scenarios of weather and subsequent operations are simulated for each renewable-nuclear combination to determine total costs throughout the project lifetime. Fully renewable-powered configurations required large amounts of installed capacity (GW scale) in the simulations to meet the fixed demand of the data center. This is due to some scenarios in the historical dataset which captured low-wind and low-solar days, requiring over-building of these technologies as well as batteries to compensate for the low amounts of electricity generation. Fully nuclear-powered configurations outperformed the fully renewable and mixed renewable-nuclear configurations in terms of cost, with ranges between $1B and $10B in 2023 USDs compared to $40B+ for fully renewable configurations. Of the nuclear technologies, small modular reactors performed better economically than large-scale nuclear models due to lower projected capital costs, and both performed better than the microreactor models. These results demonstrate the applicability of firm, dispatchable electricity resources from baseload generators like nuclear power plants for operating facilities that run at constant power without daily variability.
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Optimality versus reality: Closing the gap between renewable energy decision models and government deployment in the United States
Energy decision models are widely used to evaluate the technical and economic feasibility of renewable energy, as well as to help inform the deployment of these technologies. However, a gap exists between the optimal model solutions and what is deployed. This paper explores why these gaps exist in the public sector using the results of interviews with 20 federal, state, and city government agencies that have used the Renewable Energy Integration and Optimization (REopt™) model to inform energy decisions. We then propose adaptations to technical modeling capabilities, and communication of results, which may help increase clean energy deployment. This research may be useful to both analytical modelers and the organizations using such decision tools to inform policy, regulation, planning, and deployment of clean energy systems.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Energy storage planning for enhanced resilience of power systems against wildfires and heatwaves
Extreme weather events pose significant risks to power grid stability due to their severe consequences and potential for widespread failures. Energy storage systems hold great potential for enhancing grid resilience against such events by providing reliable power during peak demand periods. However, accurately quantifying the size, location, and investment costs of new energy storage assets is a complex task, as energy storage planning decisions depend on the investment choices of other generation technologies and the integration of new transmission projects. Here, this paper presents a novel capacity expansion planning framework that simultaneously optimizes investments in energy storage, generation, and transmission, determining their optimal size, location, and type, while incorporating extreme weather events into long-term planning. More specifically, our stress-event-informed planning framework integrates the impact of heatwaves and wildfires into the planning process, identifying least-cost investment solutions that comply with policy goals and enhance grid resilience. The proposed framework employs machine-learning-based modeling to project heatwave-induced loads and performance-based risk assessment to evaluate wildfire-driven transmission line derates. Using industry-standard datasets to accurately represent the transmission topology of the Western Interconnection (WI) system, the proposed framework is applied to the WI 40-zone system, with investment decisions reported for the years 2030, 2035, and 2040. Simulation results reveal that with just a 10% increase in investment costs, resilience against extreme events can be significantly improved, with investment decisions heavily favoring energy storage, particularly 4-hour energy storage systems.
25 ENERGY STORAGE↗
Optimal experimental design: Formulations and computations
Questions of ‘how best to acquire data’ are essential to modelling and prediction in the natural and social sciences, engineering applications, and beyond. Optimal experimental design (OED) formalizes these questions and creates computational methods to answer them. This article presents a systematic survey of modern OED, from its foundations in classical design theory to current research involving OED for complex models. We begin by reviewing criteria used to formulate an OED problem and thus to encode the goal of performing an experiment. We emphasize the flexibility of the Bayesian and decision-theoretic approach, which encompasses information-based criteria that are well-suited to nonlinear and non-Gaussian statistical models. We then discuss methods for estimating or bounding the values of these design criteria; this endeavour can be quite challenging due to strong nonlinearities, high parameter dimension, large per-sample costs, or settings where the model is implicit. A complementary set of computational issues involves optimization methods used to find a design; we discuss such methods in the discrete (combinatorial) setting of observation selection and in settings where an exact design can be continuously parametrized. Finally we present emerging methods for sequential OED that build non-myopic design policies, rather than explicit designs; these methods naturally adapt to the outcomes of past experiments in proposing new experiments, while seeking coordination among all experiments to be performed. Throughout, we highlight important open questions and challenges.
97 MATHEMATICS AND COMPUTING↗
NAWI Seedling Project: Foundational Control Methods for Water Treatment Systems - Online Actuator and Sensor Data
This repository contains online actuator and sensor data and associated meta-data collected during the NAWI-funded seedling project with title: Foundational Control Methods for Water Treatment Systems. For detailed information on each dataset, please check the README included there.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput
Undoubtedly, quantum computing offers valuable acceleration for solving intricate problems. One of the primary hurdles lies in executing large-scale quantum applications on backend machines. Qubit noise, among other factors, dramatically influences the execution process. Implementing effective scheduling techniques for quantum circuits is crucial for practical quantum computing and preventing excessive waiting times. The quantum realm is distinct from classical computing in terms of optimization, performance, utilization, and waiting periods. Consequently, the parameters and components of quantum circuit scheduling diverge from those of classical computing. This paper presents Quantum Noise Mitigation: Introducing the Robust Quantum Circuit Scheduler for Enhanced Fidelity and Throughput, a straightforward yet effective scheduling framework and policy that enhances noise resilience, throughput, and the fidelity of quantum circuits. Drawing inspiration from classical methods, our scheduling approach incorporates additional constraints tailored for quantum logic. The outcome demonstrates a substantial improvement in fidelity and resource management, which is vital for real-world quantum applications.
Baheri, Betis↗
Building partnerships for development of sustainable energy systems with atmospheric measurements
Atmospheric dynamics often play a critical role in the sustainability and reliability of diverse forms of energy production. This is especially true for the growing number of renewable energy deployments that harness aspects of the environment for power production. While the University of Memphis has a strong research background in energy systems, we have little experience working with the Earth and Environmental Systems Science Division (EESSD) and their associated User Facilities. Of particular interest to us is the Atmospheric Science Research and the Atmospheric Radiation Measurement (ARM) user facility to address surface-boundary layer interactions and physical phenomena. One of the major challenges for understanding and developing energy systems and management platforms is accurate modeling/forecasting of atmospheric conditions across disparate spatial and temporal scales. These conditions are often required to understand the lowest levels of the atmospheric boundary layer, but are also important to understand higher atmospheric conditions where aerosols affect cloud development. The objective of this work was to develop partnerships with national laboratories for collaboration on environmental science and its intersection with sustainable energy systems, as well as to leverage the ARM user facility data repositories to enhance our research capabilities in energy systems and their inter-dependence on environmental systems for future engagement with EESSD. Specifically, we accomplished these objectives by (1) developing collaborations with Oakridge National Laboratory ARM Data Science and Integration Group which resulted in student internships, (2) employed ARM data to develope modeling of the atmospheric boundary layer optical turbulence, and (3) optimally-sized large-scale renewable energy systems and their associated energy storage systems with ARM repository data.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Advanced Information Systems Technology (AIST) Overview & Current and Recent Projects
"NASA's Advanced Information Systems Technology (AIST) Program identifies, develops, and supports adoption of software and information systems, as well as novel computer science technologies expected to be needed by the Earth Science Division in the 5-10-year timeframe. Overall, the AIST Program is focusing on advanced technologies and innovative concepts with three main objectives: O1. Enable new observation measurements and new observing systems design and operations through intelligent, timely, dynamic, and coordinated distributed sensing; O2. Enable agile science analyses that fully utilize the large amount of diverse observations using advanced analytic tools, visualizations, and computing environments, and that interact seamlessly with relevant observing systems; O3. Enable the development of integrated Earth Science frameworks that mirror the Earth with state-of-the-art models (Earth system models and others), timely and relevant observations, and analytic tools. This thrust will provide technology for enabling near- and long-term science and policy decisions (""""science decisions"""" including planning for the acquisition of new measurements; the development of new models or science analysis; the integration of Earth observations in novel ways; applications to inform choices, support decisions, and guide actions for societal benefit; etc.). AIST objectives aim at optimizing Earth Science mission return – NOS from an observation point of view and ACF/ESDT from an information analysis and utilization point of view. The assets and data accessed and utilized in these systems may come from NASA and non-NASA sources. "
Earth Science Remote Sensing; Information Systems↗
Dynamically Learning Incentives for Load Control
As electrical generation becomes more distributed and volatile, and loads become more uncertain, controllability of distributed energy resources (DERs), regardless of their ownership status, will be necessary for grid reliability. Grid operators lack direct control over end-users' grid interactions, such as energy usage, but incentives can influence behavior -- for example, an end-user that receives a grid-driven incentive may adjust their consumption or expose relevant control variables in response. A key challenge in studying such incentives is the lack of data about human behavior, which usually motivates strong assumptions, such as distributional assumptions on compliance or rational utility-maximization. In this paper, we propose a general incentive mechanism in the form of a constrained optimization problem -- our approach is distinguished from prior work by modeling human behavior (e.g., reactions to an incentive) as an arbitrary unknown function. We propose feedback-based optimization algorithms to solve this problem that each leverage different amounts of information and/or measurements. We show that each converges to an asymptotically stable incentive with (near)-optimality guarantees given mild assumptions on the problem. Finally, we evaluate our proposed techniques in voltage regulation simulations on standard test beds. We test a variety of settings, including those that break assumptions required for theoretical convergence (e.g., convexity, smoothness) to capture realistic settings. In this evaluation, our proposed algorithms are able to find near-optimal incentives even when the reaction to an incentive is modeled by a theoretically difficult (yet realistic) function.
demand response↗
Storage Enabled Flexibility of Conventional Generation Assets (StorFlex)
The power systems have faced progressively more demanding operational requirements over the last two decades. Several factors contribute to these challenging operating conditions, including load growth, aging infrastructure, increasing penetrations of distributed energy resources (DERs), electrification of the economy, and policy initiatives such as decarbonization. The power system and its components must provide high operational flexibility to mitigate these challenges. For example, the proliferation of intermittent DERs such as wind and solar has increased the need for conventional generation assets like hydropower plants to respond to sudden load-generation imbalances. The higher flexibility requirements for hydropower plants cause more wear and tear, potentially shortening the useful lifespan of hydropower turbines. To reduce the need for hydropower plants to follow sudden changes in the dispatch signal, we investigate their combined operation with the energy storage systems (ESSs; “ESS-based hybridization”). Our analyses focuses on improving the lifespan of hydropower plants through ESS-based hybridization. Wear and tear on hydropower turbines (particularly Francis turbines) is modeled using a loss-of-life concept that is based on damage experienced by the turbine due to various cycles of operation. Then, we show that using ESSs to offset some of the high variation increases the remaining life of the hydropower plants. To demonstrate this, a few modeling tools were developed for this work: (1) a dynamic model for various components of the turbine and its governor; (2) a control strategy that assigns a slow-varying dispatch signal to a hydropower unit versus a fastmoving signal to ESS, such that the overall power request remains the same; and (3) models for the financial analysis to quantify the economic merits of such a framework. We used the models we developed to analyze the dispatch pattern of an actual hydropower plant with a power output of 50 MW and a head height of 152 m. This work showed that ESS-based hybridization could extend the life of the hydropower plant by 5% on average. This extension in life was then used to estimate the economic benefit in terms of cost deferrals associated with hydropower plant maintenance and replacement: on average, $3.6 million. Sensitivity analysis with respect to the size of ESS and cost of turbines was performed to show the variation in benefits over the range of turbine costs and ESS sizes. Crucially, stacking damage reduction and lifetime extension with other ESS value streams such as providing ancillary services could substantially increase the financial benefits of ESS-based hybridization. The higher costs associated with ESS of appropriate size would make more financial sense when multiple value streams are stacked and co-optimized to extract the maximum benefit. This dimension will be explored in future work.
13 HYDRO ENERGY↗
A Review of Existing Policies Affecting the Jettison of Waste in Low Earth Orbit and Deep Space
The management of waste generated onboard spacecraft during future long-duration deep-space missions will require different solutions from those currently implemented on the International Space Station which consist exclusively of collecting, storing, and returning the waste to Earth. Alternative options for managing spacecraft waste are to process it for recycling and recovery of resources, and to jettison it overboard in a solid form (such as a compacted tile) or in a gaseous form after torrefaction or other trash-to-gas technologies. The waste generated during a deep-space mission is derived mainly from spacecraft logistics supplies, food and beverage residues, personal or scientific items used by the crew, human metabolic waste, and unused spare components. Uncontained and unprocessed trash is a potential health hazard and a habitat volume liability, which makes onboard long-term storage an inefficient and non-optimal option. However, the jettison of solid, processed waste appears to be an effective solution for crewed deep-space missions, leading not only to volume reduction and habitat safening, but also to considerable mass savings in the spacecraft’s propulsion system. However, the disposal of trash overboard also creates a navigation hazard for spacecraft and the potential risk of contamination of planetary bodies, interfering with the search for life. This paper investigates the requirements covered by existing policies that could affect the jettison concept of operations and system design.
Jettison↗
Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles
We report a real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.
42 ENGINEERING↗
An In-depth Review On the Scientific and Policy Issues Associated with Additive Manufacturing
Historically, manufacturing processes have been predominantly subtractive, i.e. three-dimensional objects were created by successively cutting material away from a solid block, by scraping, machining, turning or dissolving. Additive manufacturing (AM) or three-dimensional (3D) printing, in contrast, is controlled material addition, implemented by successively depositing layers of material until a predesigned shape is formed. AM represents an innovative manufacturing technology, and is set to transform production processes from design to manufacture, and to eventual distribution to end users, ultimately leading to an increase in energy efficiency and a reduction in gas emissions for future generation of industries. The unique capability of this technology to produce intricate geometries with customizable material properties has made it a widely interesting and welcome development among scientists, industry and the general public. Its wide acceptance has continued to make 3D printing technology more openly accessible, and low-cost desktop printing, with the capability to reproduce 3D objects from medical prostheses to weapons, is rapidly increasing in availability to the public. However, most research and media attention has been focused on the ingenuity of this ground-breaking technology and its wide range of possibilities. Very little consideration is being given to the adverse effects of the seemingly unstoppable advancement of AM technology and unrestricted access to 3D printing techniques. Also, proponents of AM technology rarely take into account the overall life-cycle cost and risks of failure of the manufactured part. Despite the promising prospects of this novel development, there are still concerns about how printed products will perform over time, the consistency of their quality, and the types and safety of materials used with this technology, especially with very large scale additive-manufactured products. This paper brings to light some scientific and policy risks and challenges concerning the material science and engineering aspects of these issues. Matters discussed include life-cycle cost analysis, end-product safety and quality assurance/control, regulation gaps, digital piracy, and resulting loopholes in safety and national security. The paper also presents potential options for curbing these risks, and otherwise adapting to the eventualities which, with certain inevitable factors in play, may lie beyond control.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Cross-Border Electricity Trade between India and Sri Lanka: Impact on Power System Operations
Recent developments in South Asia have the potential to increase the incentives for cross-border energy trade (CBET) between India and Sri Lanka. India's surplus power, including from its rapid expansion of wind and solar generation, provides an incentive for it to increase investment returns through exports. Sri Lanka is considering options to increase its power supply, and CBET could alleviate the need for investments in new thermal generation capacity, while giving it access to India's large and diverse power system. Finally, CBET can facilitate renewable energy (RE) integration by allowing the two countries to more efficiently harness balancing resources and smooth out wind and solar variability over larger geographic areas. This study builds on several prior analyses of a potential high-voltage direct current transmission link between India and Sri Lanka. Uniquely, the study makes use of a detailed one-year, 30-minute resolution unit commitment and economic dispatch (UC&ED) model of both countries to examine the technical and economic impacts of CBET between India and Sri Lanka in the year 2025. The study finds that CBET, enabled by a 500-MW high voltage direct current transmission link, could generate annual production cost savings of USD 180 million and improve power system operations. Through better utilization of the most efficient generators, generation from relatively expensive natural gas plants in Sri Lanka is displaced by lower cost imports from India. In addition, increased flexibility through exports reduces RE curtailment in India's Southern region by 400 GWh annually. While the study captures many previously unconsidered intertemporal operational constraints, it does not provide a complete characterization of system operations with a high-voltage direct current interconnection. Additional work should include a dynamic analysis of system stability following interruptions to the direct current interconnection, an AC power flow analysis and consideration of any non-technical barriers to trade which might prevent the optimization of CBET between India and Sri Lanka at 30-minute timescales.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗