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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Mitigating Impact Through Community-Engaged Flood Modeling

Urban pluvial flooding poses a growing threat to the city of Baltimore, driven by heavy rainfall, increased impervious area, and aging infrastructure. Adapting to the risks posed by pluvial flooding is critical for building greater climate resiliency in Baltimore's Inner Harbor Watershed. This study addresses these challenges through community-informed decision analysis, which uses hydrologic modeling and optimization tools to identify robust flooding adaptation pathways. We will collaborate with community partners to identify key concerns and objectives regarding flooding. These concerns have been purposefully built in to a combined surface-subsurface dynamic flow simulation model. Model outputs are used to identify flooding locations within the Inner Harbor, and to test adaptation methods. Machine learning will be used search for solutions which meet diverse environmental, financial, and social goals, and solution performance will be examined under a wide range of potential future climatic conditions and integrated with an adaptive planning approach. This novel set of adaptation pathways will enhance the City's capacity to respond to evolving pluvial flood risk.

climate resilience↗
DOE OSTI2022

Techno-Economic Analysis Using REopt for Community Solar on Multifamily Affordable Housing Properties [Slides]

Multifamily affordable housing (MFAH) providers can identify and prioritize properties in their portfolios for which community solar development is feasible by following portfolio screening steps in a process outlined by NREL. Once MFAH providers have identified the most feasible sites, they can conduct more detailed analyses for a select number of sites to assess how distributed energy resources such as solar plus storage can help them meet their goals. The step is completed using NREL's REopt (https://reopt.nrel.gov/tool), a free techno-economic optimization model that determines DER sizes and dispatch strategies that minimize the life cycle cost of energy at a site. This presentation describes how to perform this step using REopt to help MFAH providers answer questions such as: What size solar PV system will result in the most energy bill savings at this site? What size solar-plus-storage system would be needed to power critical loads through a utility grid outage? What is the financial impact of rate switching, net metering, and/or meter aggregation? What percentage of the site's load can be offset with renewable energy? What are the emissions benefits of this renewable generation?

14 SOLAR ENERGY↗
DOE OSTI2019

Economic analysis of a nuclear hybrid energy system in a stochastic environment including wind turbines in an electricity grid

The electricity market in the U.S. is moving toward an energy mix including more variable renewable energy (VRE) sources like wind and solar. This increasing VRE penetration is altering the profile of the net electricity demand. Normally, flexible generators (e.g. gas turbines) are used to absorb fluctuations in the electricity demand. Therefore, an increase in VRE also leads to an increase in installed capacity for such flexible generators which, in turn, may increase the price of electricity. Nuclear-Renewable Hybrid Energy Systems (N-R HES) are seen as a solution to this problem. The N-R HES contains a nuclear power plant electrically and/or thermally linked to an industrial process (IP). This paper demonstrates a new methodology and presents the corresponding RAVEN/Modelica-based software framework to evaluate the financial performance of N-R HES. The novelty of the method is that it explicitly incorporates the stochastic nature of N-R HES inputs such as electricity demand and wind speeds. The method is applied to a generic (rather than region-specific) N-R HES. The analysis shows that under the right market conditions, economic benefits can be achieved by adding a suitable IP to the base-load generator. In particular, there exists an optimal relationship between the IP capacity and the demand. Furthermore, global trends indicate VRE penetration raises the cost of electricity due to the added volatility, an issue only mitigated when operating with high profit margins for the IP.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

High yield production of 3-hydroxypropionic acid using Issatchenkia orientalis

Biomanufacturing provides a more sustainable alternative to fossil-based chemical manufacturing. 3-Hydroxypropionic acid (3HP) is a top Department of Energy value-added chemical and precursor to bioplastics, yet cost-effective microbial production remains elusive. Here, we establish the acid-tolerant yeast Issatchenkia orientalis as a robust host for low-pH 3HP biosynthesis. Genome-scale modeling identifies the β-alanine pathway as optimal, offering the highest theoretical yield and lowest oxygen requirement. Thermodynamic analysis confirms its favorability under acidic conditions. Using sequence similarity network analysis, we discover highly active aspartate 1-decarboxylase (PAND), β-alanine-pyruvate aminotransferase (BAPAT), and 3HP dehydrogenase (YDFG), which significantly improve the pathway efficiency. Next, to further elevate the production, pathway optimization through multi-copy PAND integration, byproduct elimination (knockouts of pyruvate decarboxylase and glycerol-3-phosphate dehydrogenase), and reinforcement of aspartate flux by overexpression of pyruvate carboxylase and aspartate amino transferase improves the titer to 29 g/L in shake flasks. Fed-batch fermentation at pH 4 with low-cost corn steep liquor medium further increases the production to 92 g/L with 0.7 g/g yield and 0.55 g/L/h productivity. Techno-economic analysis indicates that such performance could potentially enable a financially viable process for sustainable acrylic acid production. This work establishes I. orientalis as a next-generation platform for cost-effective 3HP production and paves the way toward industrial commercialization.

Biotechnology↗
DOE OSTI2022

Stand-Alone and Hybrid Electric Thermal Energy Storage in the System Advisor Model

This project developed stand-alone electric-thermal energy storage (ETES), stand-alone pumped thermal energy storage (PTES), and hybrid molten-salt power tower (MSPT)-ETES performance and dispatch optimization techno-economic models. The models are available to the public through the System Advisor Model (SAM) software, scripting, and as open-source code. We compared results of the dispatch model to PLEXOS dispatch of a similar generator using the same initial grid pricing signal and found our dispatch model performed well, but closer agreement between the models was limited by the inherent differences between price-taker and unit commitment models. Nevertheless, the price-taker models developed in this project are useful to analyze proposed ETES and PTES technologies because they provide more detailed system and component models, solve several orders of magnitude faster, and are available as free open-source software. The model results represent the most optimistic returns considering grid arbitrage from the input electricity pricing, so the financial results can be applied as a feasibility stage-gate. We also submitted a journal draft paper that describes the ETES dispatch model methodology and demonstrates model functionality. An accepted journal article will serve as peer-reviewed documentation for the models, along with the open-source code, SAM help-menu content, and eventually this final project report.

14 SOLAR ENERGY↗
DOE OSTI2024

Application of Banking Scoring and Rating for Coherent Risk Measures in Electricity Systems ABSCORES

This project developed a framework for asset and system risk management that can be incorporated into current electricity system operations to improve economic efficiency and establish an Electric Assets Risk Bureau. We leveraged scoring and ratings from banking and financial institutions alongside current optimization methods in dispatching power systems to help system operators and electricity markets schedule resources. This approach is based on the observation that there are major discrepancies between the power scheduled by a system operator and the actual power generated/consumed. These discrepancies—exacerbated by unplanned contingencies (e.g., natural disasters)—are caused by multiple factors, including the different financial, environmental and risk preferences of power producers, consumers, and aggregators. We developed a framework that counteracts two failures in electricity system operations: imperfect information and missing markets for products. The technical approach included five tasks. Tasks 1 and 2 supported the development of risk scores at the asset level with historical data collected for this project. Tasks 3, 4, and 5 incorporated scoring into decision-making at the system level. The proposed effort achieved PERFORM's Program Objectives because the proposed outputs and algorithms do not exist in the electricity industry and are an innovative approach to managing risk. Since the acknowledged need to better assess and act upon risk profiles for grid assets has not been met by the industry, this project will also impact ARPA-E's Mission Areas, including improving energy efficiency and giving the U.S. a technological lead in advanced energy technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Prioritization of Early-Stage Research and Development of a Hydrogel-Encapsulated Anaerobic Technology for Distributed Treatment of High Strength Organic Wastewater

This study aims to support the prioritization of research and development (R&D) pathways of an anaerobic technology leveraging hydrogel-encapsulated biomass to treat high-strength organic industrial wastewaters, enabling decentralized energy recovery and treatment to reduce organic loading on centralized treatment facilities. To characterize the sustainability implications of early-stage design decisions and to delineate R&D targets, an encapsulated anaerobic process model was developed and coupled with design algorithms for integrated process simulation, techno-economic analysis, and life cycle assessment under uncertainty. Across the design space, a single-stage configuration with passive biogas collection was found to have the greatest potential for financial viability and the lowest life cycle carbon emission. Through robust uncertainty and sensitivity analyses, we found technology performance was driven by a handful of design and technological factors despite uncertainty surrounding many others. Hydraulic retention time and encapsulant volume were identified as the most impactful design decisions for the levelized cost and carbon intensity of chemical oxygen demand (COD) removal. Encapsulant longevity, a technological parameter, was the dominant driver of system sustainability and thus a clear R&D priority. Ultimately, we found encapsulated anaerobic systems with optimized fluidized bed design have significant potential to provide affordable, carbon-negative, and distributed COD removal from high strength organic wastewaters if encapsulant longevity can be maintained at 5 years or above.

Anaerobic Treatment↗
DOE OSTI2018

Modeling Alzheimer’s Disease Progression with Fused Laplacian Sparse Group Lasso

Alzheimer’s disease (AD), the most common type of dementia, not only imposes a huge financial burden on the health care system, but also a psychological and emotional burden on patients and their families. There is furthermore an urgent need to infer trajectories of cognitive performance over time and identify biomarkers predictive of the progression. Here, we introduce the multi-task learning with fused Laplacian sparse group lasso model, which can identify biomarkers closely related to cognitive measures due to its sparsity-inducing property, and model the disease progression with a general weighted (undirected) dependency graphs among the tasks. An efficient alternative directions method of multipliers based optimization algorithm is derived to solve the proposed non-smooth objective formulation. The effectiveness of the presented model is demonstrated by its superior prediction performance over multiple state-of-the-art methods and accurate identification of compact sets of cognition-relevant imaging biomarkers that are consistent with prior medical studies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2010

Real time intelligent process control system for thin film solar cell manufacturing

This project addresses the problem of lower solar conversion efficiency and waste in the typical solar cell manufacturing process. The work from the proposed development will lead toward developing a system which should be able to increase solar panel conversion efficiency by an additional 12-15% resulting in lower cost panels, increased solar technology adoption, reduced carbon emissions and reduced dependency on foreign oil. All solar cell manufacturing processes today suffer from manufacturing inefficiencies that currently lead to lower product quality and lower conversion efficiency, increased product cost and greater material and energy consumption. This results in slower solar energy adoption and extends the time solar cells will reach grid parity with traditional energy sources. The thin film solar panel manufacturers struggle on a daily basis with the problem of thin film thickness non-uniformity and other parameters variances over the deposited substrates, which significantly degrade their manufacturing yield and quality. Optical monitoring of the thin films during the process of the film deposition is widely perceived as a necessary step towards resolving the non-uniformity and non-homogeneity problem. In order to enable the development of an optical control system for solar cell manufacturing, a new type of low cost optical sensor is needed, able to acquire local information about the panel under deposition and measure its local characteristics, including the light scattering in very close proximity to the surface of the film. This information cannot be obtained by monitoring from outside the deposition chamber (as traditional monitoring systems do) due to the significant signal attenuation and loss of its scattering component before the reflected beam reaches the detector. In addition, it would be too costly to install traditional external in-situ monitoring systems to perform any real-time monitoring over large solar panels, since it would require significant equipment refurbishing needed for installation of multiple separate ellipsometric systems, and development of customized software to control all of them simultaneously. The proposed optical monitoring system comprises AccuStrata’s fiber optics sensors installed inside the thin film deposition equipment, a hardware module of different components (beyond the scope of this project) and our software program with iterative predicting capability able to control material bandgap and surface roughness as films are deposited. Our miniature fiber optics monitoring sensors are installed inside the vacuum chamber compartments in very close proximity where the independent layers are deposited (an option patented by us in 2003). The optical monitoring system measures two of the most important parameters of the photovoltaic thin films during deposition on a moving solar panel - material bandgap and surface roughness. In this program each sensor array consists of two fiber optics sensors monitoring two independent areas of the panel under deposition. Based on the monitored parameters and their change in time and from position to position on the panel, the system is able to provide to the equipment operator immediate information about the thin films as they are deposited. This DoE Supply Chain program is considered the first step towards the development of intelligent optical control system capable of dynamically adjusting the manufacturing process “on-the-fly” in order to achieve better performance. The proposed system will improve the thin film solar cell manufacturing by improving the quality of the individual solar cells and will allow for the manufacturing of more consistent and uniform products resulting in higher solar conversion efficiency and manufacturing yield. It will have a significant impact on the multibillion-dollar thin film solar market. We estimate that the financial impact of these improvements if adopted by only 10% of the industry ($7.7 Billion) would result in about $1.5 Billion in savings by 2015 (at the assumed 20% improvement). This can be achieved by optimizing the manufactured product and process in real time without changing the manufacturing technology.

14 SOLAR ENERGY↗
DOE OSTI2025

Techno-economic implications and cost of forecasting errors in solar PV power production using optimized deep learning models

Accurate solar Photovoltaic (PV) power forecasting is important for enhancing both the performance and economic feasibility of PV systems. This study evaluates several deep learning models, including Dense Neural Networks (DNN), Long Short-Term Memory (LSTM), Convolutional Neural Networks (CNN), and a hybrid LSTMCNN model, for predicting PV power production one day in advance. Prior to optimization, the models exhibited relatively high errors, with the best model (DNN) achieving a Root Mean Square Error (RMSE) of 31.13 kW and a coefficient of determination (R 2 ) of 62.15 %. After employing Bayesian optimization, the LSTM-CNN model demonstrated the best performance, with the RMSE reduced to 9.79 kW and R 2 improved to 97.62 %, showcasing significant enhancement in predictive accuracy. Here, the economic evaluation considered three cases: rewards for underestimation (0.08 USD/kWh), no rewards, and penalties for both over-and underestimation (120 % of the utility tariff). In the rewards scenario, the LSTM-CNN model reduced the Levelized Cost of Electricity (LCOE) by 4 %, while in the penalty scenario, a backup diesel generator would have increased the LCOE by 49 %. Additionally, the LSTM-CNN model minimized financial losses, achieving the lowest penalties and maximizing net cash flow compared to other models, demonstrating its overall technical and economic superiority.

Deep learning↗
DOE OSTI2016

Revenue Opportunities through Ancillary Services for Nuclear Power Plants with Integrated Firebrick Resistance- Heated Energy Storage (FIRES)

Deregulation of electricity markets has brought about major changes in the ways investments in and operation of power plants are planned and optimized. Although many NPPs have seen reduced revenue in recent years as power purchase agreement end and wholesale market electricity prices have remained low, there are opportunities to expand the technical capabilities of NPPs, participate in additional market services and increase revenue. This transaction summarizes the opportunities provided by participation in ancillary services markets potentially made possible by integration of thermal energy storage capacity, specifically the Firebrick Resistance-heated Energy Storage System (FIRES), into future nuclear power plants with high-temperature reactors and Nuclear Air-Brayton combined cycle (NACC) power conversion. The restructuring, or deregulation, of electric power systems in most of Europe and North America has had a substantial impact on the economic performance of currently operating nuclear power plants (NPPs) and the prospects for new builds. Owners of NPPs in some markets have expressed concern that low wholesale electricity market prices and insufficient compensation for capacity, reliability, and other services are threatening the financial viability of their plants. However, both current and future NPPs have the potential to increase their competitiveness by increasing the flexibility of their electricity output. Flexible output has previously only been possible in water-cooled reactors through load-following operation, which requires core power maneuvers and operation of the steam turbine at reduced efficiency. Integration of thermal energy storage systems (TESS) provides an alternative, providing the plant the option to either output electricity or store heat for greater later electric output. The plant owner may thus increase sales of electricity during hours when it is most valuable. Even more, some TESS technologies have the potential to greatly increase the net ramp rate capability of the plant. The ability to rapidly change net power output to the grid may make it possible for NPPs to secure additional revenue by providing additional minute-by-minute balancing services to the grid. FIRES is only one technology out of many that might provide additional technical capabilities at NPPs, and this discussion has only highlighted the opportunities in a single market. Analysis of other options, include steam accumulator systems, nitrate salt TESS, phase change material TESS, and opportunities in other markets are subjects of ongoing work. Deregulation of electricity markets has brought about major changes in the ways investments in and operation of power plants are planned and optimized. Although many NPPs have seen reduced revenue in recent years as power purchase agreements end and wholesale market electricity prices have remained low, there are opportunities to expand the technical capabilities of NPPs, participate in additional market services, and increase revenue. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Proof-of-concept chemometric approach for environmental forensic sourcing of crude oil samples using SPME-GC-MS

Environmental exposure to crude oil through seepage and spillage poses risks to the immediate environment and the broader ecosystem as areas along the oil distribution path are affected by the influx of crude petroleum as well as the environmental, economic, and civil unrest that accompanies it. There is a large financial burden associated with the lost resources, including the cost of rehabilitation, and the affected sources of revenue for communities affected by oil spills. As such, it is crucial to determine the responsible parties. This work outlines an environmental forensics approach to determining the source of an un-weathered crude oil sample. The researchers employed solid phase microextraction coupled with gas chromatography mass spectrometry (SPME-GC-MS) to capture and analyze the gaseous components emitted by crude oil samples sourced from five locations. Samples were analyzed using Spearman's rank correlation and 3D covariance analysis. Both chemometric approaches yielded optimal performance results with no misclassifications, true positive rate (TPR) = 100 % and false positive rate (FPR) = 0 %. The similarity metrics calculated by each test noted clear delineations between the values of same-source and differently sourced samples. The Spearman's rank correlation test and 3D covariance calculations both demonstrated the ability to correctly identify sample source origin in this dataset. Finally, the authors outline an approach to the future application of these tests and suggest their joint use in future crude oil sourcing endeavors.

3D covariance mapping↗
DOE OSTI2015

Project Management Plan/Progress Report: UT/GTKS Training Program Development for Commercial Building Operators (Final Report)

Universidad del Turabo (UT), in a collaborative effort with Global Turn Key Services, Inc. (GTKS), proposed to develop a training program and a commercialization plan for the development of Commercial Building Operators (CBOs). The CBOs will operate energy efficient buildings to help maintain existing buildings up to their optimal energy performance level, and ensure that net-zero-energy buildings continuously operate at design specifications, thus helping achieve progress towards meeting BTP Strategic Goals of creating technologies and design approaches that enable net-zero-energy buildings at low incremental costs by 2025. The proposed objectives were then: (1) Develop a Commercial Building Operator (CBO) training program and accreditation that will in turn provide a certification to participants recognized by Accreditation Boards such as the North American Board of Certified Energy Practitioners (NABCEP) and Leadership in Energy & Environmental Designs (LEED). (2) Develop and implement a commercialization and sustainability plan that details marketing, deployment, financial characterization, job placement, and other goals required for long-term sustainability of the project after the funding period. (3) After program development and deployment, provide potential candidates with the knowledge and skill sets to obtain employment in the commercial building green energy (net-zero-energy building) job market. The developed CBO training program will focus on providing skills for participants, such as displaced and unemployed workers, to enter the commercial building green energy (net-zeroenergy building) job market. This course was designed to allow a participant with minimal to no experience in commercial building green technology to obtain the required skill sets to enter the job market in as little as 12 weeks of intensive multi-faceted learning. After completion of the course, the CBO staff concluded the participant will meet minimum established accreditation standards established by UT and will complete the contact hours required of training to apply to the Certification on Energy Management (CEM) offered by the Association of Energy Engineers (AEE). The CBO training program consists of a combination of theory (classroom), online & computer simulation, laboratory & hands on (onsite) training lessons. The training is addressed four basic learning elements: (1) Learn the Technology; (2) Practice Skills with hands-on the Energy Simulation Builder program; (3) Final Project and Presentation; and, (4) Accreditation and Certifications.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

Molecular Dynamics Simulation and Analysis of Interfacial Water at Selected Sulfide Mineral Surfaces under Anaerobic Conditions

In this paper, we report on a molecular dynamics simulation (MDS) study of the behavior of interfacial water at selected sulfide mineral surfaces under anaerobic conditions. The study revealed the interfacial water structure and wetting characteristics of the pyrite (100) surface, galena (100) surface, chalcopyrite (012) surface, sphalerite (110) surface, and molybdenite surfaces (i.e., the face, armchair-edge, and zigzag-edge surfaces), including simulated contact angles, relative number density profiles, water dipole orientations, hydrogen-bonding, and residence times. For force fields of the metal and sulfur atoms in selected sulfide minerals used in the MDS, we used the universal force field (UFF) and another set of force fields optimized by quantum chemical calculations for interactions with interfacial water molecules at selected sulfide mineral surfaces. Simulation results for the structural and dynamic properties of interfacial water molecules indicate the natural hydrophobic character for the selected sulfide mineral surfaces under anaerobic conditions as well as the relatively weak hydrophobicity for the sphalerite (110) surface and two molybdenite edge surfaces. Part of the financial support for this study was provided by the U.S. Department of Energy (DOE) under Basic Science Grant No. DE-FG-03-93ER14315. The Division of Chemical Sciences, Geosciences, and Biosciences, Office of Basic Energy Sciences (BES), of the DOE, funded work performed by Liem X. Dang. Battelle operates Pacific Northwest National Laboratory for DOE. The calculations were carried out using computer resources provided by BES. The authors are grateful to Professor Tsun-Mei Chang for valuable discussions.

Jin, Jiaqi↗
DOE OSTI2015

Rate Theory Modeling and Simulations of Silicide Fuel at LWR Conditions

Uranium silicide (U 3 Si 2 ) fuel has higher thermal conductivity and higher uranium density, making it a promising candidate for the accident-tolerant fuel (ATF) used in light water reactors (LWRs). However, previous studies on the fuel performance of U 3 Si 2 , including both experimental and computational approaches, have been focusing on the irradiation conditions in research reactors, which usually involve low operation temperatures and high fuel burnups. Thus, it is important to examine the fuel performance of U 3 Si 2 at typical LWR conditions so as to evaluate the feasibility of replacing conventional uranium dioxide fuel with this silicide fuel material. As in-reactor irradiation experiments involve significant time and financial cost, it is appropriate to utilize modeling tools to estimate the behavior of U 3 Si 2 in LWRs based on all those available research reactor experimental references and state-of-the-art density functional theory (DFT) calculation capabilities at the early development stage. Hence, in this report, a comprehensive investigation of the fission gas swelling behavior of U 3 Si 2 at LWR conditions is introduced. The modeling efforts mentioned in this report was based on the rate theory (RT) model of fission gas bubble evolution that has been successfully applied for a variety of fuel materials at devious reactor conditions. Both existing experimental data and DFT-calculated results were used for the optimization of the parameters adopted by the RT model. Meanwhile, the fuel-cladding interaction was captured by the coupling of the RT model with simplified mechanical correlations. Therefore, the swelling behavior of U 3 Si 2 fuel and its consequent interaction with cladding in LWRs was predicted by the rate theory modeling, providing valuable information for the development of U 3 Si 2 fuel as an accident-tolerant alternative for uranium dioxide.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

A Guide to Energy Master Planning of High-Performance Districts and Communities

This guide was developed with partners throughout the United States to demonstrate how implementing district-scale high-performance strategies can be successful and scalable approaches to achieving deep energy savings that increase affordability, improve resilience, reduce emissions, and foster economic development. This document serves as a framework for districts, campuses, and communities, illustrating an iterative process of building support for, planning, and implementing high-performance districts by engaging stakeholders, setting aggressive energy goals, completing technical and financial planning, and implementing a high-performance energy master plan. The information in this guide is based on a 3-year U.S. Department of Energy Zero Energy District Accelerator and a range of real-world examples of emerging high-performance districts. It is particularly useful for architects, planners, engineers, local government agencies, and real estate developers in the early phases of planning a district with high-performance or other deep energy goals. For the purposes of this guide, a high-performance district is a multibuilding development that achieves aggressive energy and related goals such as zero energy, carbon neutrality, sustainability, ultra-efficiency, etc. High-performance districts optimize energy efficiency to reduce energy loads and use renewable energy resources to meet the remaining loads whenever possible. The Energy Independence and Security Act of 2007 defines a high-performance building as “a building that integrates and optimizes on a life cycle basis all major high-performance attributes, including energy conservation, environment, safety, security, durability, accessibility, cost-benefit, productivity, sustainability, functionality, and operational considerations.” High-performance districts are collections of such buildings that take advantage of the synergies available when energy consumption and production are considered at a district level rather than one building at a time.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2012

Advanced Computational Methods for Security Constrained Financial Transmission Rights: Structure and Parallelism

Financial Transmission Rights (FTRs) help power market participants reduce price risks associated with transmission congestion. FTRs are issued based on a process of solving a constrained optimization problem with the objective to maximize the FTR social welfare under power flow security constraints. Security constraints for different FTR categories (monthly, seasonal or annual) are usually coupled and the number of constraints increases exponentially with the number of categories. Commercial software for FTR calculation can only provide limited categories of FTRs due to the inherent computational challenges mentioned above. In this paper, a novel non-linear dynamical system (NDS) approach is proposed to solve the optimization problem. The new formulation and performance of the NDS solver is benchmarked against widely used linear programming (LP) solvers like CPLEX™ and tested on large-scale systems using data from the Western Electricity Coordinating Council (WECC). The NDS is demonstrated to outperform the widely used CPLEX algorithms while exhibiting superior scalability. Furthermore, the NDS based solver can be easily parallelized which results in significant computational improvement.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2023

Encoding trade-offs and design toolkits in quantum algorithms for discrete optimization: coloring, routing, scheduling, and other problems

Challenging combinatorial optimization problems are ubiquitous in science and engineering. Several quantum methods for optimization have recently been developed, in different settings including both exact and approximate solvers. Addressing this field of research, this manuscript has three distinct purposes. First, we present an intuitive method for synthesizing and analyzing discrete (i.e., integer-based) optimization problems, wherein the problem and corresponding algorithmic primitives are expressed using a discrete quantum intermediate representation (DQIR) that is encoding-independent. This compact representation often allows for more efficient problem compilation, automated analyses of different encoding choices, easier interpretability, more complex runtime procedures, and richer programmability, as compared to previous approaches, which we demonstrate with a number of examples. Second, we perform numerical studies comparing several qubit encodings; the results exhibit a number of preliminary trends that help guide the choice of encoding for a particular set of hardware and a particular problem and algorithm. Our study includes problems related to graph coloring, the traveling salesperson problem, factory/machine scheduling, financial portfolio rebalancing, and integer linear programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up to 16-level quantum variables, demonstrating that compact (binary) encodings are more amenable to QAOA than previously understood. We expect this toolkit of programming abstractions and low-level building blocks to aid in designing quantum algorithms for discrete combinatorial problems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗