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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 OSTI2022

Partner with a Third-Party Delivery Service or Not? A Prediction-and-Decision Tool for Restaurants Facing Takeout Demand Surges During a Pandemic

Amidst the COVID-19 pandemic, restaurants become more reliant on no-contact pick-up or delivery ways for serving customers. As a result, they need to make tactical planning decisions such as whether to partner with online platforms, to form their own delivery team, or both. In this paper, we develop an integrated prediction-decision model to analyze the profit of combining the two approaches and to decide the needed number of drivers under stochastic demand. We first use the susceptible-infected-recovered (SIR) model to forecast future infected cases in a given region and then construct an autoregressive-moving-average (ARMA) regression model to predict food-ordering demand. Using predicted demand samples, we formulate a stochastic integer program to optimize food delivery plans. We conduct numerical studies using COVID-19 data and food-ordering demand data collected from local restaurants in Nuevo Leon, Mexico, from April to October 2020, to show results for helping restaurants build contingency plans under rapid market changes. Our method can be used under unexpected demand surges, various infection/vaccination status, and demand patterns. Here, our results show that a restaurant can benefit from partnering with third-party delivery platforms when (i) the subscription fee is low, (ii) customers can flexibly decide whether to order from platforms or from restaurants directly, (iii) customers require more efficient delivery, (iv) average delivery distance is long, or (v) demand variance is high.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Crosslinked Anionic Exchange Membranes (Spring Quarter Report)

With ever growing climate concerns, studying and optimizing hydrogen gas electrolyzers and fuel cells are of high importance. Polymeric anionic exchange membranes (AEM) are attractive replacements to the industry standard proton exchange membranes (PEM) due to their lack of precious metal catalysts and acidic conditions. However, AEMs on the market have yet to preform comparatively to PEMs. To bring AEMs to a competitive level, crosslinking chemistry within the polymer membranes is being investigated to enhance mechanical properties and performance. For this quarter, electrochemical systems, important AEM properties and considerations, and some characterization methods will be reviewed.

08 HYDROGEN↗
DOE OSTI2023

Reinventing CEMENT: Carbonation-Enabled Mineralization to Engender Novel Technology

Cement is the most important building material used in civil infrastructure but it is extremely energy intensive to produce. We developed novel ways to make cements at low temperatures by mimicking the chemistry of the calcium silicate mineral pseudowollastonite. Pseudowollastonite is interesting because when it is exposed to hot (90-150oC), alkaline conditions with CO2 and water it reacts to form mineral phases that are much stronger than commercial cements. The resulting phases are also much more resistant to acid attack and have lower permeability, which will make them last longer. Many of the mineral phases in our cements are similar to those that exist in ancient Roman cements that have lasted thousands of years. Our objective was to understand how waste materials from industrial processes (such as power production, municipal solid waste treatment, or power plants) can be used to replicate the conditions that give pseudowollastonite cement its remarkable properties. We worked to optimize our formulations and incorporate the cements into concrete blends that the industry can test and consider using in different applications. Our cements could be used in pre-cast cement structural elements, which makes up nearly 20% of the total cement market and is growing rapidly. This work supports ARPA-E program objectives to significantly improve the industrial efficiency of materials production.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Pricing and Energy Trading in Peer-to-Peer Zero Marginal-Cost Microgrids

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to addresses this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

batteries↗
DOE OSTI2024

Microgrid Controller Survey Report: 2024 Update

In the past decade, the number of microgrids deployed by electric utilities, end-use customers, and third parties has been increasing significantly both in the US and worldwide. The increase is driven by various reasons, such as carbon emission reduction, energy efficiency improvement, reliability and resilience enhancement, energy infrastructure modernization, and more. Microgrid controllers, as the most important components in these deployed microgrids, optimally coordinate the operation and control of the microgrid assets (e.g., distributed energy resources [DERs], energy storage systems, demand response, and even electric vehicles) and ensure that the microgrid is being operated in an efficient, clean, and resilient manner. To that end, the market for microgrid controllers has been growing rapidly in recent years. Various microgrid controllers have been released and continuously updated by vendors to satisfy the different needs of microgrid customers. However, the functionalities demonstrated by microgrid controllers released by different vendors show a huge difference because of a lack of widely known and accepted industrial standards, and various microgrid customers have specific requirements for different applications and scenarios. To obtain a clear view of the current state of the commercial microgrid controllers’ functionalities and identify potential research gaps, a survey of the functionalities of commercial microgrid controllers and the Advanced Distribution Management System (ADMS) developed by vendors and national laboratories was conducted in 2023. The results of the survey are presented in this report with the current status of commercial microgrid controllers analyzed, potential research gaps identified, and future research trends revealed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Low Cost High-Performance HRE-Free 3-in-1 Electric Drive Unit

The project team completed work to expand upon American Axle & Manufacturing’s (AAM) baseline technology and develop a high-speed, AC induction electric drive unit (EDU) with total direct-oil-cooling and integrated power electronics. The project accelerated advancements in EDU technology by allowing AAM to build upon the progress made in developing the underlying baseline technologies. The following seven improvements to AAM’s baseline technology were implemented in this research and development program: 1) Increased speed (30k RPM) AC induction motor, 2) Silver-sintering of discrete silicon carbide (SiC) metal-oxide-semiconductor field-effect transistors (MOSFET) to heat sinks, 3) Electrically insulated rotor bars, 4) Optimized lamination steel, 5) EDU-integrated 650VDC inverter package, 6) Over-molded stator with molded liners, and 7) 650VDC power-dense stator design. The successful development of the technology resulted in a meaningful cost reduction for EDU systems in the market. Coupled with falling battery system costs, lower EDU costs will likely accelerate the market acceptance and related production scaling of battery electric vehicles. The objective of this project was to research, develop, and test a Heavy Rare Earth (HRE)-free 3-In-1 electric drive unit (EDU) that has class leading power density and cost. The key technologies to be developed were anticipated to meet or exceed the Department of Energy (DOE) targets of a cost ≤ $\$$7/kilowatt (kW), power density ≥ 12 kW/liter, and operating voltage ≥ 600 VDC.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Tightest Mixed-Integer Programming Formulations for Quadratic SCUC Optimization

In this project, we developed new, tighter Mixed-Integer Programming (MIP) formulations for the combined Alternating Current (AC) Security-Constrained Unit Commitment (SCUC) and Security-Constrained Optimal Power Flow (SCOPF). The work addresses a critical challenge in power system operations: efficiently determining which generation units to commit and how to optimally dispatch them while maintaining network reliability constraints for both normal and contingency scenarios. Our efforts: 1. Advance the Understanding of SCUC/SCOPF Modeling: By introducing tighter MIP formulations and leveraging cutting-edge optimization tools (Julia/JuMP, PowerModels.jl), this project has pushed forward the state of the art in efficient power systems scheduling. 2. Enhance Technical and Economic Feasibility: The methods developed provide more accurate and potentially faster solutions to large-scale, realistic scheduling and dispatch problems in electric power systems, which can translate into improved reliability and potentially lower costs for grid operations. 3. Benefit to the Public: Greater efficiency in power system operations leads to cost savings for utilities and end-users. Improved reliability and integration of advanced modeling approaches can facilitate the adoption of clean energy resources and better accommodate uncertainties in renewable generation. Because this technology could impact bulk power markets and reliability, these innovations have far-reaching public benefits in terms of cost savings, reliability, and sustainability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Sensitivity analysis of an automated fault detection algorithm for residential air-conditioning systems

The state of the art of fault detection and diagnosis (FDD) for residential air-conditioning systems is expensive and not yet amenable to widespread implementation. FDD for homes can significantly reduce utility costs, and increase the lifespan of the equipment. The cost barriers currently, however, make FDD for homes economically unviable for large scale implementation. In prior work, we offered a solution to reduce FDD costs by proposing an automated fault detection algorithm to serve as a screening step before more expensive FDD tests can be conducted. The algorithm uses only the home thermostat and local weather information to identify thermodynamic parameters and detect high-impact air-conditioning faults, including those that occur during equipment installation. We had tested the algorithm on a single EnergyPlus™ model of a home in Orlando, Florida. The thermodynamic parameter identification process is highly nonconvex involving several local optimal solutions. In this paper we propose a novel method to select the best model for fault detection from among the list of local optimal solutions to make the algorithm more robust to homes of different construction, without which the fault detection process would be infeasible. Another unique contribution of the paper is implementing the solution on real-world data. We also bring the algorithm closer to market by testing it on real-world data. We implement the algorithm on data obtained from experiments conducted by the Florida Solar Energy Center (FSEC) on a laboratory home equipped with a heat pump where faults were intentionally added for a period of seven months. The algorithm successfully detected an undercharge fault with 70.6% accuracy, concurrent duct leakage and undercharge faults with 85.2% accuracy, and duct leakage faults with 69.1% accuracy. A sensitivity analysis is also performed on EnergyPlus models of nine types of homes that vary in construction to demonstrate the robustness of the algorithm. Finally, the algorithm achieves an average accuracy of 71% for no-fault condition, 77% for 40% undercharge fault, and 76% for duct-leak fault.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

A hybrid surrogate modeling framework for the Digital Twin of a Fluoride-salt-cooled High-temperature Reactor (FHR)

While nuclear energy is a non-greenhouse-gas emitting energy source, expensive operational costs due to the high-level of safety requirements decreases their competitiveness in the sustainable energy market. Advanced reactor concepts paired with Digital Twins aim to increase the commercialization gains of nuclear energy by reducing operational costs, increasing reactor reliability and enhancing power generation. To support Digital Twin tasks such as real-time autonomous control, proactive maintenance monitoring or optimizing power demand operations, a fast and accurate virtual representation of the Nuclear Power Plant (NPP) is required. The computational cost of high-fidelity, physics-based models are unsuitable for real-time analysis or scalability. Here, in this work, a hybrid surrogate modeling framework is developed fora Fluoride-salt-cooled High-temperature Reactor (FHR) that leverages physics-inspired models for key reactor components and uses data-driven methods for rapid system state space prediction. The Xenon reactivity feedback model is integrated to inform the surrogate model about the reactor core and the homologous pump theory model is the basis for representing pump degradation. Using a detailed, two dimensional thermal hydraulics model to generate data on the FHR, we train a network of Vectorized Autoregressive Moving-Average with eXogenous input (VARMAX) models to predict the remaining state values. The result is a surrogate model that provides a detailed reactor state representation of 41 system states and a pump degradation analysis. The framework is applied to Load Follows profiles, yielding high accuracy and a speedup that is more than 4000x faster compared to the higher- fidelity thermal hydraulics model, enabling real-time operational intelligence and applications in long horizon predictions. While the surrogate model framework is demonstrated for the particular case of FHR, the hybrid physical/data-driven modeling approach including the network of surrogates and the underlying modularity has the potential to be applied to other physical asset systems.

Digital Twins↗
DOE OSTI2021

Mineral-Ground Micro-Fibrillated Cellulose Reinforcement for Polymer Compounds

Additive Manufacturing (AM) is quickly becoming a mainstream, energy efficient manufacturing technique for complex and custom components. ORNL has taken polymer AM into the next level with Big Area Additive Manufacturing (BAAM). However, we need to develop the strength of AM materials (specifically polymers) to satisfy the mechanical requirements of structural parts. Also, it is crucial that the developed process can easily be scaled up to have a significant impact in the manufacturing industry of US. Furthermore, renewable, bio-derived AM materials are desirable from a sustainability perspective. In the first phase of this project the mechanical properties of polymer AM components were significantly improved developing a micro fibrillated cellulose (MFC)-reinforced composite material system that is derived from biomass and compatible with BAAM. Unfortunately, the improvements were observed only with freeze-dried MFCs, and freeze-drying is a very slow process and is not industrially scalable. Furthermore, MFCs were not available in large quantities at low cost. However, FiberLean has reached 12,000 tons/year capacity (60,000 tons/year including the minerals). FiberLean offers MFCs at a scale and order of magnitude lower price than current market participants. With their recently developed novel (patent pending) drying process FiberLean can achieve over 50% solid content commercially. In the 2nd phase of the project, the original objective was to investigate this new novel drying process along with compounding with different formulations to optimize and adapt to large scale additive manufacturing. However, the compounded materials prepared via this approach were very brittle and demonstrated poor mechanical performance. Therefore, the project was focused more on a scalable chemical surface modification of MFCs to enable their drying via conventional oven process with minimal agglomeration and better dispersion in PLA polymer matrix. Fiberlean and ORNL have worked together in this project to develop mineral-ground microfibrillated cellulose (MFC) - reinforced additive manufacturing feedstock materials with significantly improved mechanical properties. Properly surface modified and dried Fiberlean cellulose microfibrils led to tensile strength increases up to ~40% and elastic modulus increases up to 70% with the addition of 30% MFC into PLA matrix. The work has led to a high impact journal article and a provisional patent. Opportunities for a follow up work to demonstrate the technology in large scale and application of it to AM applications are currently being searched.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfill their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.

behind-the-meter↗
DOE OSTI2022

A Peer-to-Peer Market-Based Control Strategy for a Smart Residential Community with Behind-the-Meter Distributed Energy Resources: Preprint

This paper presents a distributed peer-to-peer market control strategy to manage and to enable resource sharing of behind-the-meter distributed energy resources in a residential community. In the proposed strategy, each consumer or prosumer determines the flexibility of their point of connection to the power network such that the obtained flexibility is network-feasible. Based on the feasible flexibility, the consumers and the prosumers trade power among each other at each time instance to fulfill their preferred load requirements while maximizing their payoffs and helping to regulate node voltages inside the community. Because the problem to be solved is non-convex, a distributed particle swarm optimization algorithm is used to coordinate the consumers/prosumers in a fully autonomous manner without any centralized or hierarchical coordination. Numerical simulations performed on a community of 48 homes demonstrate the efficacy of the proposed approach.

behind-the-meter↗
DOE OSTI2022

Cloud-Control of Legacy Building Automation System: A case study

As Internet of Things devices and cloud-based platforms become more mature, Energy Management and Information Systems (EMIS) are increasingly gaining momentum in the building industry. In large commercial buildings, Fault-Detection and Diagnostic (FDD) and energy information systems (EIS) are now established technologies with tens of providers and thousands of deployment sites across North America. The new frontier for the EMIS technology is now represented by control systems that use advanced system optimization (ASO) methods to improve the operations of the HVAC system. Given the complexity of the integration of such systems with the existing building automation systems (BAS) and the higher risk involved with direct control of the HVAC, these systems are still emerging in the market. This paper presents the results of a project in which a start-up company partnered with a research institution to develop a cloud-based software EMIS solution and deployed it in a university campus in California. The software system included advanced sensing, data acquisition, storage and advanced control and analytics applications developed on top of the native BAS. The new platform controls ten buildings on the campus and the FDD and the ASO applications deployed on this platform were able to generate energy savings of up to 35% and 25% in certain buildings for each functionality respectively. Where the platform did not save energy, it improved building service (air quality). Lessons learned include the importance of collaborating with and training the building operators and evaluating whether the legacy system can work reliably with the new technology.

Prakash, Anand Krishnan↗
DOE OSTI2025

Reinventing wastewater treatment plants: energy neutral treatment and enhanced fertilizer production through a novel resource recovery center

Wastewater treatment plants (WWTPs) are typically energy intensive, mainly due to the secondary treatment processes such as activated sludge (AS) for treatment of organics as well as nutrients like nitrogen. Nitrogen removal presents a big problem for WWTPs. The main form of nitrogen in wastewater is ammonium, and an AS process uses oxygen to convert ammonium into nitrite and nitrate which is then converted to nitrogen through denitrification process. During anaerobic digestion (AD), organic nitrogen gets degraded, resulting in an effluent stream (centrate) with a high nitrogen content, mostly in the form of ammonium. This contributes 15-30% of total nitrogen to the wastewater influent which further increases energy consumption for aeration. The project aims to transform this conventional municipal WWTPs into energy-neutral, resource-recovering facilities by integrating three core technologies: • Cloth Media Filtration (CMF) to replace conventional primary sedimentation (CPS) and increase the diversion of organics from the energy intensive secondary treatment to AD. This results in reduced energy demand for aeration in the secondary process while simultaneously increasing the biogas production in the anaerobic digesters. • Anerobic Digester to increase biogas and ammonia production. • Membrane Evaporation (ME) to recover ammonia from AD centrate and produce marketable fertilizer. The benefits of proposed WWTP process modifications were evaluated using techno economic analysis (TEA) and life cycle assessment (LCA). For CMF portion of the research a statistical analysis was employed to develop data-driven tools that could be used to enhance and optimize its performance in terms of energy savings and effluent quality. The main objective of this project is to reduce the energy demand for secondary treatment at municipal WWTPs by at least 50%, increase anaerobic digester (AD) biogas and ammonia production by 100% and 120%, respectively, and recover 90% of ammonia from the AD. Integrated CMF, AD, and ME was shown to work synergistically toward achieving these decarbonization targets through energy-positive treatment and fertilizer recovery techniques.

42 ENGINEERING↗
DOE OSTI2022

Parametric Modeling and Economic Analysis of a 2MW th 3-Stream sCO 2 Heat Exchanger

Here, this paper presents the design and cost optimization of a novel 2MW th 3-stream sCO2 plate-fin heat exchanger. This heat exchanger design is unique in that it uses reduced metal oxide particle-to-sCO2 heat exchanger for cost-effective energy storage applications. The design uses low velocity, laminar air as the re-oxidizing reactant to transfer the heat of the re-oxidizing reaction to a sCO2 power loop. The design of the heat exchanger is based on a 2-D, 3-fluid plate/fin heat transfer model. The model parameterizes the size, shape, and number of passages of the heat exchanger to calculate the temperature profile, pressure drop, and fluid velocities of all three fluids. Global heat exchanger parameters such as the effectiveness and total heat transferred to the sCO2 are then calculated for overall performance. Due to the value and increased use of sCO2 heat exchangers in power cycles, a cost model of the system based on the unique high temperature/high pressure operating conditions was created using quotes from reference projects and market analysis. These quoted air-to-sCO2 heat exchangers are then processed using multiple weighting factors pertinent to heat exchanger design, including heat exchanger type, maximum temperature, differential pressures, fluids, duty, and more. These factors are then used in an exponential function in order to generate a parameterized cost curve. The design and cost of the heat exchanger are then optimized using the SMPSO genetic algorithm in Python. The optimization objectives for the system are to maximize the overall system effectiveness, including an air recuperator for preheating, and to minimize unit costs. Additional constraints are added to the system for the sCO2 and air pressure drops, air velocity to reduce particle entrainment, and the length and volume of the heat exchanger.

Cost Model↗
DOE OSTI2025

A Demand Bidding Model for Multi-Product Industrial Plants

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

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Iteration-based Linearized Distribution-level Locational Marginal Price for Three-phase Unbalanced Distribution Systems

Distributed energy resources (DERs) are rocking the utilities’ business landscape. It calls for competitive market environments that incentivize DERs to form maximum operating efficiency. Among proposed pricing schemes, distribution-level locational marginal price (DLMP) is effective in signaling the marginal generation cost differences driven by energy losses and network constraints. It can be derived from a distribution-level optimal power flow (OPF) framework, as it essentially presents the sensitivity of optimized generation cost towards incremental loads. However, due to the high resistance-to-inductance ratio and unbalanced characteristics of distribution networks, computational affordable DLMPs are highly challenged. This article provides a linear-approximated DLMP that can be solved efficiently and generalized to account for reactive power flow, three-phase unbalanced loads and meshed network structure. The successive linear programming technique is introduced to enhance the model accuracy. Case studies on an IEEE 123-Bus system validate its accuracy against a nonlinear benchmark and capability in offering proper incentives.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Federated Architecture for Secure and Transactive Distributed Energy Resource Management Solutions (FAST-DERMS)

This document provides system-level specifications for a federated architecture for secure and transactive distributed energy resource management solutions (FAST-DERMS), presents a solution, and describes operational concepts for the proposed solution. FAST-DERMS enables the provision of reliable, resilient, and secure transmission and distribution (T&D) grid services through the scalable aggregation and near-real-time management of utility-scale and small-scale distributed energy resources (DERs). We first present the principles and objectives of FAST-DERMS. Then, after discussing important system concepts, we present the specifications for FAST-DERMS and a solution that employs a distributed and federated control methodology in which the DERs connected to a single point of common coupling with the rest of the system, such as individual substations, are optimized coordinately to provide system-level grid services. FAST-DERMS aims to aggregate and coordinate the operations of DERs to support T&D grid operations. The key optimization and control component of this FAST-DERMS reference implementation is a flexible resource scheduler (FRS) that aggregates the DERs within a substation service area. These FRSs operate at the substation level and perform constrained economic dispatch of DERs, either directly or through a transactive market or aggregator, as shown in Figure ES-1. An FRS Coordinator at the distribution system operator (DSO) level aggregates distribution substations operated by FRSs and interfaces with the transmission system operator (TSO) to provide transmission services. FAST-DERMS also allows for the integration of the FRS Coordinator with an existing distribution utility management system that could be employed by the DSO to enhance distribution grid operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗