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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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At least 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Sensor impacts on building and HVAC controls: A critical review for building energy performance

Building operations rely heavily on control systems and sensors. This paper provides a sophisticated literature review on sensor systems in building/HVAC systems, particularly in the context of controls, and their impacts on energy efficiency and thermal comfort. This study aims to understand the previous and current research and identify future research opportunities on this subject. The reviewed sensor systems were used for heuristic rule-based controls, local controls, and advanced optimal controls for existing and emerging technologies. Five major aspects of sensors are reviewed here: control loops for sensors, sensor types, sensor locations, sensor data, and a sensor impact evaluation framework. To augment the literature review, comprehensive standardized interviews were also conducted with relevant industry experts and practitioners. These interviews were designed and performed to (1) identify significant factors for selecting sensor sets and current undergoing issues, (2) identify potential improvements in sensor configuration/deployment, and (3) integrate expert (e.g., researcher, building operation practitioner) knowledge and experiences to develop structured use-case scenarios. Researchers collected and analyzed 31 interview responses for this paper.

Building control↗
DOE OSTI2023

Decarbonizing all-electric communities via carbon-responsive control of behind-the-meter resources

The progression of electrification in the building and transportation sectors brings new opportunities for energy decarbonization. With higher dependence on the grid power supply, the variation of the grid carbon emission intensity can be utilized to reduce the carbon emissions from the two sectors. Existing coordinated control methods for buildings with distributed energy resources (DERs) either consider electricity price or renewable energy generation as the input signal, or adopt optimization in the decision-making, which is difficult to implement in the real-world environment. This paper aims to propose and validate an easy-to-deploy rule-based carbon responsive control framework that facilitates coordination between all-electric buildings and electric vehicles (EVs). The signals of the grid carbon emission intensity and the local photovoltaics (PV) generation are used for shifting the controllable loads. Extensive simulations were conducted using a model of an all-electric mixed-use community in a cold climate to validate the control performance with metrics such as emissions, energy consumption, peak demand, and EV end-of-day state-of-charge (SOC). Our study identifies that 4.5% to 27.1% of annual emission reduction can be achieved with limited impact on energy costs, peak demand, and thermal comfort. Additionally, up to 32.7% of EV emission reduction can be obtained if the EV owners reduce the target SOC by less than 21.2%.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Nuclear β − -decay with statistical de-excitation

he accurate description of nuclear β − -decay has far-reaching consequences for applications spanning nuclear reactors to the creation of heavy elements in astrophysical environments. We present the nuclear particle spectra associated with the β -decay of neutron-rich nuclei calculated with the well benchmarked coupled Quasi-particle Random Phase Approximation and Hauser–Feshbach (QRPA+HF) model. This approach begins with the population of the daughter nucleus via semi-microscopic Gamow-Teller or First-Forbidden strength distributions (QRPA) and follows the statistical de-excitation (HF) until the initial available excitation energy is exhausted. At each stage of de-excitation the emission by neutrons and $γ$-rays is considered obeying quantum mechanical selection rules. For completeness we also provide parsed Auger and Internal Conversion (IC) electron spectra from Evaluated Nuclear Data Files (ENDF). Our results are tabulated and provided in parsable ASCII formatted tables that are suitable for inclusion in various applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Aggregation in bottom-up vulnerability assessments and equity implications: The case of Jordanian households’ water supply

Bottom-up methods for water resources modeling rely on acceptability thresholds to find, through a response surface, which deeply uncertain futures lead to system failure. They commonly treat water users as aggregate actors, which may preclude analysis of the equity impacts of interventions. This paper explores how aggregation choices for large groups of water users lead to different policy recommendations in response surface assessments. Herein, two aggregation methods with varying parameters are considered: percentile satisfaction targets and generalized mean. A 2-dimensional stress-test assessment across groundwater availability and population is applied to household water supply in Jordan. The study compares six different policies covering supply enhancement and rebalancing, using a country-wide multi-agent model that characterizes households across socioeconomic strata. For different aggregation levels, policies are ordered by their associated robustness index. Results show that aggregation choices may modify response surfaces as much as policy changes and strongly determine policy preference. Modifying allocation rules can substantially reduce the disparity in household vulnerability. Preferences defined by aggregation intervals provide a finer understanding of trade-offs among water users and may improve deliberation over equity under deep uncertainty.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Extrapair offspring of the blue-footed booby show no sign of higher fitness in the first 10 years of life

According to the good genes and genetic compatibility hypotheses, females of socially monogamous species obtain genetic benefits for their offspring by performing extrapair copulations with males of higher quality than their social mates or males with whom they are more genetically compatible. If extrapair offspring do receive genetic benefits in the form of advantageous alleles or more compatible allele combinations, they should outperform their within-pair half-siblings' survival and/or reproductive output. Here, in this study, we followed 52 extrapair and 737 within-pair blue-footed booby, Sula nebouxii, offspring during their first 10 years of life (excluding the embryonic period and the first 10 days after hatching) to assess whether they differed in fledging probability, fledgling body condition, recruitment probability, age at first reproduction, number of breeding events or accumulated breeding success. Extrapair and within-pair offspring did not differ in any of these proxies of fitness. Furthermore, we found that extrapair offspring were equally likely to occur in any hatching position. However, differences in fledgling production over the lifetime could not be ruled out, and because only within-pair production of eggs and fledglings was tallied, the possibility remains that extrapair offspring could produce more extrapair offspring later in life than do within-pair offspring. Furthermore, the possibility of context-dependent genetic benefits occurring only under stressful conditions cannot be discounted because our sample of offspring was obtained in a single exceptionally favourable reproductive season.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

AI-Guided Reasoning-Based Operator Support System for the Nuclear Power Plant Management

The Decision-making process in the Nuclear Power Plant (NPP) control room faces some challenges: operator incomplete knowledge, insufficient time for responding to the highly dynamic events, and a large number of indicators to monitor. Because of the complexity of the NPP system, it is hard to pre-plan all the failures/mitigative actions. An intelligent operator support system is vital to mitigate these shortcomings. In this paper, an AI declarative approach (Answer Set Programming (ASP)) is employed to represent our knowledge of the nuclear power plant in the form of logic rules. This represented knowledge is structured to form a reasoning-based operator support system. When an incident occurs, this ASP-based reasoning support system is demonstrated to be capable of fault identification (diagnosis), informing the operator of different scenarios and consequences, and generating the control options (decision making).

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Quantum field theory with the generalized uncertainty principle II: Quantum Electrodynamics

Highlights: • Relativistic Generalized Uncertainty Principle gives Frame independent minimum length. • Quantum Gravity modified Dirac equation from Modified Klein–Gordon Equations. • There exists a Quantum Gravity modified Lagrangian for a spinor field theory. • Feynman vertices contain 2 fermions & up to 5 gauge bosons are allowed. • Minimum length modifies the amplitude of Electrodynamic electron–muon scattering. Continuing our earlier work on the application of the Relativistic Generalized Uncertainty Principle (RGUP) to quantum field theories, in this paper we study Quantum Electrodynamics (QED) with minimum length. We obtain expressions for the Lagrangian, Feynman rules and scattering amplitudes of the theory, and discuss their consequences for current and future high energy physics experiments. We hope this will provide an improved window for testing Quantum Gravity effects in the laboratory.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Parametrizations of the spin density matrix

Highlights: • Proposed two methods for parametrizing the SDM of spinning, unstable particles. • All non-negativity conditions fulfilled; adaptability to various constraints. • First method: based on a theorem; applicable in a simple and flexible way. • Second method: particularly suitable for especial conditions. • Applications: two of them interesting in the search for new physics. We propose for the spin density matrix two parametrizations which automatically fulfill the non-negativity conditions, without setting any bound on the parameters. The first one relies on a theorem, that we prove, and it is rather simple and easily adaptable to some specific reactions, where, for example, parity is conserved or angular momentum conservation entails selection rules. Moreover, in the case when the rank is less than the order of the density matrix, we show how to improve the fits to the data, either by implementing previous suggestions, or by elaborating an alternative method, for which we prove a second theorem. Our second parametrization is a variant of previous treatments, it appears suitable for some particular processes. Moreover, we discuss about the possibility of inferring the elements of the density matrix from the differential decay width. Last, we illustrate various examples of current interest, both in strong and weak decays; some of them may be helpful in the investigation of physics beyond the standard model.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Theory of multi-orbital topological superconductivity in transition metal dichalcogenides

We study possible superconducting states in transition metal dichalcogenide (TMD) monolayers, assuming an on-site pairing potential that includes both intra- and inter-orbital terms. We find that if the mirror symmetry with respect to the system’s plane is broken (e.g., by a substrate), this type of pairing can give rise to unconventional superconductivity, including time-reversal-invariant nodal and fully gapped topological phases. Using a multi-orbital renormalization group procedure, we show how these phases may result from the interplay between the local Coulomb repulsion, Hund’s rule coupling, and phonon-mediated attraction. In particular, for a range of interaction parameters, the system transitions from a trivial phase to a nodal phase and finally to a gapped topological phase upon increasing the strength of the mirror symmetry breaking term.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Intelligent multi-zone residential HVAC control strategy based on deep reinforcement learning

Residential heating, ventilation, and air conditioning (HVAC) has been considered as an important demand response resource. However, the optimization of residential HVAC control is no trivial task due to the complexity of the thermal dynamic models of buildings and uncertainty associated with both occupant-driven heat loads and weather forecasts. In this paper, we apply a novel model-free deep reinforcement learning (RL) method, known as the deep deterministic policy gradient (DDPG), to generate an optimal control strategy for a multi-zone residential HVAC system with the goal of minimizing energy consumption cost while maintaining the users’ comfort. Here, the applied deep RL-based method learns through continuous interaction with a simulated building environment and without referring to any prior model knowledge. Simulation results show that compared with the state-of-art deep Q network (DQN), the DDPG-based HVAC control strategy can reduce the energy consumption cost by 15% and reduce the comfort violation by 79%; and when compared with a rule-based HVAC control strategy, the comfort violation can be reduced by 98%. In addition, experiments with different building models and retail price models demonstrate that the well-trained DDPG-based HVAC control strategy has high generalization and adaptability to unseen environments, which indicates its practicability for real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Optimizing design and dispatch of a renewable energy system

Renewable energy technologies are becoming increasingly important due to their cost-competitiveness, and because of enhanced climate concerns. We demonstrate the capabilities of an integer-programming optimization model that minimizes capital (investment) and operational costs, and utility charges, while adhering to system sizing constraints, demand requirements, and interoperability characteristics of the systems chosen. Furthermore, the model recommends an optimally sized mix of renewable energy, conventional generation, and energy storage technologies, while simultaneously optimizing the corresponding dispatch strategy. Our case studies explore several venues, i.e., a small campus and a local hospital, with complex utility rate tariffs, multi-technology integration opportunities, and incentives for renewable power production. Using an optimization model, versus applying rules of thumb, can produce millions of dollars in savings over a 25-year time horizon and result in thousands of kilowatts of installed renewable energy.

25 ENERGY STORAGE↗
DOE OSTI2021

A forecast-driven decision-making model for long-term operation of a hydro-wind-photovoltaic hybrid system

Hydro-wind-photovoltaic (PV) hybrid system has the potential to increase the integration of renewable energy sources into an existing grid. For the long-term operation of the system, due to the non-storable nature of wind and PV power, it is essentially to decide the optimal long-term carryover storage of cascade reservoirs. However, it remains a challenge due to high uncertainties of long-term forecasts and complicated hydraulic/electrical relationships between cascade reservoirs. Here in this study, a forecast-driven decision-making model is proposed for the hybrid system, which converts the multi-stage long-term operation process into a two-stage operation problem (including current stage and carryover stage) to avoid using longer-horizon forecast information with lower accuracy. First, the carryover stage energy surfaces (CESs) considering the forecast uncertainties of wind, solar and hydro resources are proposed to characterize carryover stage benefit quantitatively. Then a CESs-based forward decision-making optimization model is developed to guide the long-term operation of a hydro-wind-photovoltaic hybrid system. The applications in a hydro-wind-PV hybrid system of Yalong River basin results show that: compared with conventional operation, 1) power generation increases 9.03%; 2) in terms of the carryover storages control, the reservoir impounding and drawdown timing are delayed, and the drawdown depth is increased, which can be used to formulate better reservoir operation rules.

13 HYDRO ENERGY↗
DOE OSTI2021

Managing the techno-economic impacts of partial string failure in multistring energy storage systems

The role of energy storage systems (ESSs) is becoming increasingly important for today’s electric power systems. Unavailability of an ESS assigned to critical grid services may cause unwanted disruption of those services and hence, may have a significant techno-economic impact. Like any physical equipment, an ESS is vulnerable to various types of faults. Failure of one or more strings in a multistring ESS does not have to be the cause of shutting down the entire ESS. It can still operate with a partial number of strings and continue providing critical services to the grid, if there are no reliability or safety issues and is acceptable under applicable standards. However, it is important to make sure that the control strategies are adaptable to the changes in ESS capacity caused by failed strings. Also, depending on the previous operation and type of failure, the reallocation of duty cycle burden among available strings could be non-uniform. These complexities suggest that mitigation of the impact of partial failure in multistring ESSs is not trivial and needs careful consideration. This is the topic of this paper. The proposed work investigates the impact of partial failure of a large multistring ESS on the assigned service and develops strategies to adjust the ESS control duty cycles for reducing such impacts. In doing so, the paper proposes a novel two-stage framework that plans for the multistring failure using robust optimization theory and then adjusts in real-time using a rule-based algorithm, based on the real-time information on the power availability of the string. Illustrations provided in this work are based on frequency regulation use-case, which is a common application for many utility-scale ESSs. A 750 kilowatt (kW)/1500 kilowatt-hour (kWh) 3-string ESS is used for the demonstration in this work and the efficacy of the proposed method is demonstrated and compared against methods that do not incorporate string failure in their strategy. In particular, here we show that revenue loss of 93% can be incurred when partial string failure is not included in operation and planning. These losses in revenue are reduced by 60% with the proposed method.

25 ENERGY STORAGE↗
DOE OSTI2022

Site demonstration and performance evaluation of MPC for a large chiller plant with TES for renewable energy integration and grid decarbonization

Thermal energy storage (TES) for a cooling plant is a crucial resource for load flexibility. Traditionally, simple, heuristic control approaches, such as the storage priority control which charges TES during the nighttime and discharges during the daytime, have been widely used in practice, and shown reasonable performance in the past benefiting both the grid and the end-users such as buildings and district energy systems. However, the increasing penetration of renewables changes the situation, exposing the grid to a growing duck curve, which encourages the consumption of more energy in the daytime, and volatile renewable generation which requires dynamic planning. The growing pressure of diminishing greenhouse gas emissions also increases the complexity of cooling TES plant operations as different control strategies may apply to optimize operations for energy cost or carbon emissions. This paper presents a model predictive control (MPC), site demonstration and evaluation results of optimal operation of a chiller plant, TES and behind-meter photovoltaics for a campus-level district cooling system. The MPC was formulated as a mixed-integer linear program for better numerical and control properties. Compared with baseline rule-based controls, the MPC results show reductions of the excess PV power by around 25%, of the greenhouse gas emission by 10%, and of peak electricity demand by 10%.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Analysis of precooling optimization for residential buildings

To reduce peak demand and/or energy cost for residential buildings, optimal precooling strategies are becoming important as an alternative to rule-based precooling strategies that are intuitive but may not be optimal. Since precooling optimization is heavily dependent on a variety of factors such as the home thermal properties, HVAC system, weather, thermal comfort criteria, and utility rate structure, the individual and collective impact of these factors on precooling performance needs to be analyzed. In addition, since the indoor air temperature is affected by heat transfer to and from the interior wall surface, performance analysis in view of the interior wall surface temperature is also essential. Therefore, in this paper, an optimal precooling strategy that accounts for the aforementioned factors and utilizes a second-order thermal network model, is proposed. With this strategy, the HVAC on/off control signal that minimizes 24-hour energy cost while maintaining thermal comfort, is determined. Through extensive simulations, it is found that the proposed optimal precooling strategy is able to adapt to changing conditions and that having a sufficiently low interior wall surface temperature during precooling is critical for avoiding expensive on-peak operation. Here, the reason for the latter is that such a temperature indicates that enough “cooling energy” has been stored. It is also found that weather has the most dominant impact on the precooling performance, followed by home thermal condition, with the rated cooling capacity and utility rate structure having the least impact.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings

In recent years, advanced control strategies based on Deep Reinforcement Learning (DRL) proved to be effective in optimizing the management of integrated energy systems in buildings, reducing energy costs and improving indoor comfort conditions when compared to traditional reactive controllers. However, the scalability and implementation of DRL controllers are still limited since they require a considerable amount of time before converging to a near-optimal solution. This issue is currently addressed in literature through the offline pre-training of the DRL agent. However this solution results in two main critical issues: (1) the need to develop a building surrogate model to perform the training task, and (2) the need to perform a fine-tuning process over several training episodes to obtain a near-optimal control policy. In this context, this paper introduces an Online Transfer Learning (OTL) strategy that exploits two knowledge-sharing techniques, weight-initialization and imitation learning, to transfer a DRL control policy from a source office building to various target buildings in a simulation environment coupling EnergyPlus and Python. A DRL controller based on discrete Soft Actor–Critic (SAC) is trained on the source building to manage the operation of a cooling system consisting of a chiller and a thermal storage. Several target buildings are defined to benchmark the performance of the OTL strategy with that of a Rule-Based Controller (RBC) and two DRL-based control strategies, deployed in offline and online fashion. The strategy adopted for OTL emulates the real world implementation with a simulation process by implementing the transferred DRL agent for a single episode in the target buildings. Target buildings have the same geometrical features and are served by the same energy system as the source building, but differ in terms of weather conditions, electricity price schedules, occupancy patterns, and building envelope efficiency levels. The results show that the OTL strategy can reduce the cumulated sum of temperature violations on average by 50% and 80% respectively when compared to RBC and online DRL while enhancing the energy system operation with electricity cost savings ranging between 20% and 40%. Furthermore, the OTL agent performs slightly worse than the offline DRL controller but it does not require any modeling effort and can be implemented directly on target buildings emulating a real-world implementation.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Model-based predictive control of multi-stage air-source heat pumps integrated with phase change material-embedded ceilings

This paper presents a model-based predictive control strategy to optimize the operations of phase change material (PCM) ceiling panels coupled with a multi-stage air-source heat pump. A three-stage prototype heat pump unit has been built and tested in the laboratory, with the low and medium stages designed for space heating/cooling and the high compression stage dedicated to charging of the PCM energy storage. To facilitate optimal control of the integrated heat pump system, a mixed-integer linear programming formulation is derived through linearization of the heat pump model and a mixed-integer reformulation of the PCM dynamic governing equations. A predictive control strategy is synthesized based on the resultant control formulation and implemented in a receding horizon scheme that optimizes the PCM charging and the zone temperature schedules simultaneously to leverage both the passive (associated with building construction materials) and active (PCM) storage capacities of a building. The control strategy has been tested along with three benchmarking control scenarios using a co-simulation platform for a prototypical detached house in Atlanta, GA. In this study, test results showed that application of the proposed control strategy to the PCM-integrated heat pump could provide 27.1% electricity cost savings while a fine tuned rule-based control strategy could achieve cost savings of 20.4%, compared to a baseline case without PCM storage, under a time-of-use rate tariff.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

The interaction of wholesale electricity market structures under futures with decarbonization policy goals: A complexity conundrum

Competitive wholesale electricity markets can help facilitate energy system decarbonization by incentivizing investments in clean energy technologies that meet evolving system needs. We explore market structure impacts on generator operations and deployment by risk-averse, heterogeneous investor firms using the Electricity Markets and Investment Suite - Agent-based Simulation (EMIS-AS) model. Here we apply clean energy targets of 45%-100% by 2035 considering energy, ancillary services, capacity, and clean energy credit products and pricing and eligibility rules. Results highlight a complexity conundrum, whereby finding the "right" market design to achieve decarbonization goals and avoid unintended consequences can be a highly-nuanced, non-incremental challenge. Carefully designed energy-only markets can achieve the same clean energy targets as capacity market structures but with different revenue and profitability outcomes. Operating reserve demand curve-based scarcity pricing can substitute capacity markets for similar deployment outcomes. Carbon pricing alone is most effective at achieving decarbonization levels at low clean energy targets, and clean energy credit markets and carbon pricing are substitutionary at high clean energy targets. Restricting technology participation in capacity and operating reserve markets can impact deployment and operations, even for nonrestricted technologies. Adding an inertia product with fast frequency response yields insufficient provision at high clean energy targets, but work is needed to understand frequency requirements and capabilities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗