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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 577 records · Page 32
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A tri-level distribution locational marginal price-based demand response framework

Here, in this paper, we propose a tri-level, nested, two-stage price-based demand response (PBDR) framework that considers distribution locational marginal price (DLMP) as DR enabler between load-serving entities (LSE), demand response providers (DRPs), and customers in the day-ahead distribution market. It enables LSE and customer interactions by using multiple DRPs, positioned in-between, and independently optimizes their objectives. The problem is formulated using linear power flow with approximated power losses and its application in DLMP as DR pricing. The tri-level problem is solved using a nested reformulation & decomposition (R&D) method and tested on the real Indian-108 bus distribution system under various dynamic pricings. Further, the temporal–spatial variations in DLMPs are assessed using fairness criteria. Numerical analyses demonstrate that DLMP applications can effectively improve economic efficiency, and transparency in DR programs valuation with a favorable fairness margin. The results show that DLMP as DR pricing signal induces (0-2) % variation in DLMP for DR participation up to 10 %. Further, it gives over 90 % fairness over temporal–spatial variation for all the customers.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A tool for assessing demand side management and operating strategies for isolated microgrids

Globally, power system operators are exploring ways to leverage the capabilities of smart meters to implement fine-grained demand side management (DSM). Here this paper presents a new simulation tool to evaluate operating strategies for smart-meter-enabled (SME) islanded microgrids being advanced for sustainable rural electrification in emerging economies. Based on MATLAB/Simulink, the tool's component-level models of small microgrids can be easily configured. Alternative rule-based operating strategies are implemented with controllable supply and storage components as well as other components driven by external factors (e.g., weather and time of day). To account for the wide range of possible operating conditions in real-world applications, statistical metrics can be evaluated using Monte Carlo (MC) methods based on user-defined, time-dependent probability distributions for demands, supplies, and environmental variables. Features of the tool are demonstrated with case studies for a representative microgrid in rural Rwanda.

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DOE OSTI2017

The value of demand response in Florida

Many electrical loads may be operated flexibly to provide grid services, including peaking capacity, reserves, and load shifting. The authors model 14 demand end uses in Florida and analyze their operational impacts and overall value for a wide range of solar penetrations and grid flexibility options. They find demand response is able to reduce production costs, reduce the number of low-load hours for traditional generators, reduce starting of gas generators, and reduce curtailment.

14 SOLAR ENERGY↗
DOE OSTI2013

A Cumulative Energy Demand indicator (CED), life cycle based, for industrial waste management decision making

Highlights: • We developed a methodology useful to environmentally compare industrial waste management options. • The methodology uses a Net Energy Demand indicator which is life cycle based. • The method was simplified to be widely used, thus avoiding cost driven decisions. • This methodology is useful for governments to promote the best environmental options. • This methodology can be widely used by other countries or regions around the world. - Abstract: Life cycle thinking is a good approach to be used for environmental decision-support, although the complexity of the Life Cycle Assessment (LCA) studies sometimes prevents their wide use. The purpose of this paper is to show how LCA methodology can be simplified to be more useful for certain applications. In order to improve waste management in Catalonia (Spain), a Cumulative Energy Demand indicator (LCA-based) has been used to obtain four mathematical models to help the government in the decision of preventing or allowing a specific waste from going out of the borders. The conceptual equations and all the subsequent developments and assumptions made to obtain the simplified models are presented. One of the four models is discussed in detail, presenting the final simplified equation to be subsequently used by the government in decision making. The resulting model has been found to be scientifically robust, simple to implement and, above all, fulfilling its purpose: the limitation of waste transport out of Catalonia unless the waste recovery operations are significantly better and justify this transport.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Octane-On-Demand: Onboard Separation of Oxygenates from Gasoline

Increasing standards for emissions and fuel economy have driven the need for downsizing of the internal combustion engines. To accommodate this change and prevent the possibility of knocking, fuels with higher octane numbers are needed. However, octane requirements are not uniform across the whole vehicle drive cycle, which leads to inefficient use of the high-octane fuel components. In approach that has been shown to substantially increase fuel economy, a dual-fuel solution, also called octane-on-demand, can be used and supply fuel with the required octane rating by metering in the appropriate ratio of high and low octane fuels, as dictated by the driving conditions. Barriers associated with introducing two fuels in the marketplace for an octane-on-demand approach can be mitigated by the use of an onboard separation system, which separates a single fuel, such as market E10 gasoline, into the high-octane oxygenate, like ethanol, and the a lower-octane base fuel. Currently investigated onboard separation systems rely on pervaporation membranes, which lose efficiency as the oxygenate is removed, and do not separate the entirety of it, leading to loses of this valuable component. Here, we present chemical separation strategies, such as liquid-solid and liquid-liquid extraction that provide advantages over membrane separation. This paper introduces applications of tailored chemical reactions, solid sorbent materials, and ionic liquids that are shown to have a high oxygenate recovery, and utility extended beyond ethanol to potential future oxygenate additives, such as isomers of butanol.

ethanol separation, octane-on-demand, SAMMS, Ionic↗
DOE OSTI2018

Demand Response Operation of Electricity-Intensive Chemical Processes for Reduced Greenhouse Gas Emissions: Application to an Air Separation Unit

A recent push for reduced greenhouse gas (GHG) emissions has led (in part) to the addition of renewable electricity generation sources to the power generation mix. Renewables such as wind and solar are desynchronized from grid demand, requiring the use of fossil fuels to bridge the gap. We propose a novel production scheduling formulation, emissions-minimizing production (EMP), which utilizes time-based information on the nature of the power generation mix to lower GHG emissions related to the transmission and generation of electricity for industrial users. We demonstrate the application of EMP on a single-column air separation unit. The scheduling problem is cast as a mixed integer linear program that can be solved in a practical amount of time. Extensive numerical studies are used to place EMP in the context of other production scheduling methods (such as demand response), and demonstrate its potential for significant reductions in GHG emissions.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Energy, power, and infrastructure demands from electrifying airport ground support equipment at United States airports

As the airline industry seeks to reduce costs and transition to clean energy, electric ground support equipment is emerging as a favorable option. The integration of electric ground support equipment into airport operations requires careful planning for vehicle deployment, charging infrastructure, and grid impacts. We develop a flexible, bottom-up modeling framework to assess energy and infrastructure needs across more than 300 U.S. airports. Our analysis estimates site-specific power and energy demand, equipment counts by type, charger requirements, and costs. Here we quantify the magnitude of new electrical loads created by the electrification of airport ground support equipment, finding that peak power demand at the largest airports can reach up to 20 megawatts, with annual electricity consumption approaching 51,000 megawatt-hours. We further show that behind-the-meter battery energy storage systems and solar photovoltaic systems can reduce peak load and lower total system costs by as much as 10 million dollars.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2011

First drop dissimilarity in drop-on-demand inkjet devices

As inkjet printing technology is increasingly applied in a broader array of applications, careful characterization of its method of use is critical due to its inherent sensitivity. A common operational mode in inkjet technology known as drop-on-demand ejection is used as a way to deliver a controlled quantity of material to a precise location on a target. This method of operation allows ejection of individual or a sequence (burst) of drops based on a timed trigger event. This work presents an examination of sequences of drops as they are ejected, indicating a number of phenomena that must be considered when designing a drop-on-demand inkjet system. These phenomena appear to be driven by differences between the first ejected drop in a burst and those that follow it and result in a break-down of the linear relationship expected between driving amplitude and drop mass. This first drop, as quantified by high-speed videography and subsequent image analysis, can be different in morphology, trajectory, velocity, and volume from subsequent drops within a burst. These findings were confirmed orthogonally by both volume and mass measurement techniques which allowed quantitation down to single drops.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Energy efficiency, human behavior, and economic growth: Challenges to cutting energy demand to sustainable levels

Increasing energy efficiency in households, transportation, industries, and services is an important strategy to reduce energy service demand to levels that allow the steep reduction of greenhouse gases, and a full fledged switch of energy systems to a renewable basis. Yet, technological efficiency improvements may generate so-called rebound effects, which may ‘eat up’ parts of the technical savings potential. This article provides a comprehensive review of existing research on these effects, raises critiques, and points out open questions. It introduces micro-economic rebound effect and suggests extending consumer-side analysis to incorporate potential ‘psychological rebound effects.’ It then discusses meso-economic rebound effects, i.e. producer-side and market-level rebounds, which so far have achieved little attention in the literature. Finally, the article critically reviews evidence for macro-economic rebound effects as energy efficiency-induced economic growth impacts. For all three categories, the article summarizes assessments of their potential quantitative scope, while pointing out remaining methodological weaknesses and open questions. As a rough “rule of thumb”, in the long term and on gross average, only half the technical savings potential of across-the-board efficiency improvements may actually be achieved in the real world. Policies that aim at cutting energy service demand to sustainable levels are well advised to take due note of detrimental behavioral and economic growth impacts, and should foster policies and measures that can contain them.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

A low-complexity non-intrusive approach to predict the energy demand of buildings over short-term horizons

Reliable, non-intrusive, short-term (of up to 12 hours ahead) prediction of a building's energy demand is a critical component of intelligent energy management applications. A number of such approaches have been proposed over time, utilizing various statistical and, more recently, machine learning techniques, such as decision trees, neural networks and support vector machines. Importantly, all of these works barely outperform simple seasonal auto-regressive integrated moving average models, while their complexity is significantly higher. Here, we propose a novel low-complexity non-intrusive approach that improves the predictive accuracy of the state-of-the-art by up to ~10%. The backbone of our approach is a K-nearest neighbours search method, that exploits the demand pattern of the most similar historical days, and incorporates appropriate time-series pre-processing and easing. In the context of this work, we evaluate our approach against state-of-the-art methods and provide insights on their performance.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Optimization-based trip chain emulation for electrified ride-sourcing charging demand analyses

Range anxiety remains one of the key concerns for ride-sourcing drivers to adopt battery electric vehicles (BEVs). To investigate the feasibility of using BEVs for ride-sourcing services, we propose an optimization-based methodology to estimate the daily driving trip patterns of ride-sourcing vehicles based on widely available non-identifiable trip data. Furthermore, we investigate the charging needs of electrified ride-sourcing vehicles using agent-based simulation. The methodologies are illustrated through a case study in the city of Chicago. Through sensitivity analysis on driver working hours and initial charging status, we quantify the range of daily average vehicle miles traveled (VMT) per car and identify the hot spots of current public charging demand and potential unsatisfied charging demand. This study can be used to determine the priorities of future charging infrastructure investment to further mitigate range anxiety and promote the adoption of electrified ride-sourcing services

42 ENGINEERING↗
DOE OSTI2020

A Comprehensive Scheduling Framework using SP-ADMM for Residential Demand Response with Weather and Consumer Uncertainties

This paper presents a comprehensive scheduling framework for residential demand response (DR) programs considering both the day-ahead and real-time electricity markets. In the first stage, residential customers determine the operating status of their responsive devices such as heating, ventilation, and air conditioning (HVAC) systems and electric water heaters (EWHs), while the distribution system operator (DSO) computes the amount of electricity to be purchased in the day-ahead electricity market. In the second stage, the DSO purchases insufficient (or sells surplus) electricity in the real-time electricity market to maintain the supply-demand balance. Due to its computational complexity and data privacy issues, the proposed model cannot be directly solved in a centralized manner, especially with a large number of uncertain scenarios. Therefore, this paper proposes a combination of stochastic programming (SP) and the alternating direction method of multipliers (ADMM) algorithm, called SP-ADMM, to decompose the original model and then solve each sub-problem in a distributed manner while considering multiple uncertain scenarios. The simulation study is performed on the IEEE 33-bus system including 121 residential houses. Here, the results demonstrate the effectiveness of the proposed approach for large-scale residential DR applications under weather and consumer uncertainties.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Distribution System State Estimation With High Penetration of Demand Response Enabled Loads

Demand-side operations incentivize utility customers to take part in various grid services. A demand response enabled load (DREL) is a flexible grid asset that schedules electricity consumption in response to a time-of-use (TOU) energy price. Consequently, its energy profile differs from that of a conventional load that is insensitive to price. This difference may cause new challenges for distribution system state estimation (DSSE). It is well known that DSSE often needs to use pseudo-measurements based on historic load profiles to increase system observability. However, historic profiles of conventional loads are not representative of DREL behaviors. Additionally, the inaccuracy impacts DSSE results and other DSSE-dependent operations. In this paper, we propose an online pseudo-measurement generation approach for DSSE with DRELs. We formulate an optimization model to represent DRELs self-adjusting actions. Sampling-based stochastic optimization techniques are proposed to account for uncertainties in DRELs. A set of representative DREL behavior data corresponding to the samples are used to characterize DREL pseudo-measurements. Case studies with modified IEEE 123-bus test system verify the validity of the proposed work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Optimal Demand Response Incorporating Distribution LMP with PV Generation Uncertainty

The utilization of aggregated demand-side flexibility via demand response (DR) has become a promising pathway for the integration of renewable energy resources in power systems. Nowadays, there are several management strategies for DR such as the price-based transactive control strategies. However, many of such existing price-based control strategies neglect the physics and operational constraints of the underlying distribution networks when computing the price, raising concerns regarding their theoretical and practical values. This paper studies this issue and investigates optimal DR (ODR) by incorporating the distribution locational marginal price (DLMP). In particular, we discuss DR in connection with DLMPs and propose a multiperiod bilevel optimization problem to find the ODR strategy. Here, the objective is to minimize the peak load, load fluctuation, and payments of load aggregators. In addition, a robust bilevel ODR model is formulated to provide a robust ODR strategy while minimizing operating costs under the worst-case realization of uncertainties; this mitigates the impact of forecasting errors on renewable energy resources. Then, we propose an efficient solution approach by employing the Karush-Kuhn-Tucker conditions and strong duality. Simulation results are presented to illustrate the mutual impacts of the interaction between DR and DLMP and the benefits of the robust ODR strategy.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Modeling of Electric Water Heaters for Demand Response: A Baseline PDE Model

Demand response (DR)control can effectively relieve balancing and frequency regulation burdens on conventional generators, facilitate integrating more renewable energy, and reduce generation and transmission investments needed to meet peak demands. Electric water heaters (EWHs) have a great potential in implementing DR control strategies because: (a) the EWH power consumption has a high correlation with daily load patterns; (b) they constitute a significant percentage of domestic electrical load; (c) the heating element is a resistor, without reactive power consumption; and (d) they can be used as energy storage devices when needed. Accurately modeling the dynamic behavior of EWHs is essential for designing DR controls. Various water heater models, simplified to different extents, were published in the literature; however, few of them were validated against field measurements, which may result in inaccuracy when implementing DR controls. In this paper, a partial differential equation physics-based model, developed to capture detailed temperature profiles at different tank locations, is validated against field test data for more than 10 days. The developed model shows very good performance in capturing water thermal dynamics for benchmark testing purposes

Balancing service, demand response, electric water↗
DOE OSTI2018

Data-Driven Pricing Strategy for Demand-Side Resource Aggregators

We consider a utility who seeks to coordinate the energy consumption of multiple demand-side flexible resource aggregators. For the purpose of privacy protection, the utility has no access to the detailed information of loads of resource aggregators. Instead, we assume that the utility can directly observe each aggregator's aggregate energy consumption outcomes. Furthermore, the utility can leverage resource aggregator energy consumption via time-varying electricity price profiles. Based on inverse optimization technique, we propose an estimation method for the utility to infer the energy requirement information of aggregators. Subsequently, we design a data-driven pricing scheme to help the utility achieve system-level control objectives (e.g., minimizing peak demand) by combining hybrid particle swarm optimizer with mutation algorithm and an iterative algorithm. Case studies have demonstrated the effectiveness of the proposed approach against two benchmark pricing strategies-a flat-rate scheme and a time-of-use scheme.

data-driven optimization↗
DOE OSTI2020

An Edge-Cloud Integrated Solution for Buildings Demand Response Using Reinforcement Learning

Buildings, as major energy consumers, can provide great untapped demand response (DR) resources for grid services. However, their participation remains low in real-life. One major impediment for popularizing DR in buildings is the lack of cost-effective automation systems that can be widely adopted. Existing optimization-based smart building control algorithms suffer from high costs on both building-specific modeling and on-demand computing resources. To tackle these issues, this paper proposes a cost-effective edge-cloud integrated solution using reinforcement learning (RL). Beside RL’s ability to solve sequential optimal decision-making problems, its adaptability to easy-to-obtain building models and the off-line learning feature are likely to reduce the controller’s implementation cost. Using a surrogate building model learned automatically from building operation data, an RL agent learns an optimal control policy on cloud infrastructure, and the policy is then distributed to edge devices for execution. Simulation results demonstrate the control efficacy and the learning efficiency in buildings of different sizes. A preliminary cost analysis on a 4-zone commercial building shows the annual cost for optimal policy training is only 2.25% of the DR incentive received. Results of this study show a possible approach with higher return on investment for buildings to participate in DR programs.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Practical Challenges in Real-Time Demand Response

We report on a real-time demand response experiment with 100 controllable devices. The experiment reveals several key challenges in the deployment of a real-time demand response program, including time delays, uncertainties, characterization errors, multiple timescales, and nonlinearity, which have been largely ignored in previous studies. To resolve these practical issues, we develop and implement a two-level multi-loop control structure integrating feed-forward proportional-integral controllers and optimization solvers in closed loops, which eliminates steady-state errors and improves the dynamical performance of the overall building response. Furthermore, the proposed methods are validated by Hardware-in-the-Loop (HiL) tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗