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At least 451 records · Page 25

All sources NASA NTRS DOE OSTI

DOE OSTI2019

Cascaded systems and methods for controlling energy use during a demand limiting period

A cascaded control system is configured to control power consumption of a building during a demand limiting period. The cascaded control system includes an energy use setpoint generator and a feedback controller. The energy use setpoint generator is configured to use energy pricing data and measurements of a variable condition within the building to generate an energy use setpoint during the demand limiting period. The feedback controller is configured to use a difference between the energy use setpoint and a measured energy use to generate a control signal for building equipment that operate to affect the variable condition within the building during the demand limiting period.

Wenzel, Michael J.↗

DOE OSTI2019

Real-time forecasting of electricity demand in a streams-based architecture with applications

A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Ghosh, Soumyadip↗

DOE OSTI2018

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy’s Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration’s National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Miscellaneous Electric Loads, MELs, Models, Nation↗

DOE OSTI2019

Improving Characterization of Miscellaneous Energy Loads in Energy Demand Models

Miscellaneous energy loads (MELs) comprise a significant and growing portion of total building energy consumption. However relatively little is known about the products that make up MELs end uses, and MELs are modeled with less granularity than major end uses in energy demand models such as the U.S. Department of Energy's Scout energy efficiency impact analysis tool or the U.S. Energy Information Administration's National Energy Modeling System (NEMS). This paper identifies differences in the way MELs versus major building end uses are modeled, and then reviews potential sources of MELs baseline technology data that could be used to improve their characterization in energy demand models. Case studies of dry distribution transformers, hot water circulation pumps, and Japanese-style automatic bidets are presented to compare and contrast the level of baseline data available for different end uses. We show that there is potential to use data from energy conservation standards and other sources to significantly improve representation of some MELs in energy demand models, while other emerging MELs require further data collection before they can be modeled precisely.

Fares, Robert↗

DOE OSTI2018

Modeling Smart Microgrids for the Developing World with Probabilistic Supply and Demand Inputs

Developing world microgrids often balance insufficient supply with growing, unpredictable demand. Deterministic and probabilistic simulators exist to model these microgrids, and each focuses on different technical aspects. With the addition of smart meters into microgrids, monitoring and control is now available at high granularity, which enriches microgrid planning and operation. This research is designing a new simulator to model smart microgrids with discrete probability distributions as supply and demand inputs. In our model, smart meters allow real-time power clipping for demand side management, effectively smoothing the system load curve as needed. To compare clipping schemes for grid operation and generation mixes for planning, we aggregate inputs by convolution then compute expected energy sold and probability of avoiding power cuts. The simulator plots these values for different combinations of power clipping threshold and number of customers clipped.

13 HYDRO ENERGY↗

DOE OSTI2021

Cascaded systems and methods for controlling energy use during a demand limiting period

Wenzel, Michael J.↗

DOE OSTI2022

PCM Selection for Heat Pump Integrated with Thermal Energy Storage for Demand Response in Residential Buildings

Phase Change Materials (PCM) based Thermal energy storage (TES) is a widespread solution to shift buildings’ peak energy demand and add stability to the grid. PCMs can be used for space heating and cooling applications in residential buildings by integrating into the heat pump equipment or building envelope via several possible configurations. The heat pump integrated active PCM storage can provide significant energy savings and reduce peak demand, but the benefits vary substantially depending on the system configuration, storage capacity, and the temperature range of the PCM used. It is critical to provide a review of the materials and their suitability in heat pump integrated PCM systems. This paper will present an analysis of the PCM material selection for heat pump integrated TES in various configurations. The comprehensive review will compare the phase change materials with different melting temperatures, and configurations, corresponding to the energy savings and demand impact reported in the literature. This work is significant to guide the design of high-performance heat pump integrated PCM systems.

Sultan, Sara↗

DOE OSTI2025

Data-driven modeling of dynamic occupant thermostat override behavior for demand response applications

Buildings consume nearly 40% of global energy and produce similar emissions. Whiletechnological advances address efficiency, occupant behavior causes energy use variations up to 300% between identical buildings. This gap between predicted and actual building performance impacts building design, operations, and grid demand management programs. Through analyses of smart thermostat data from 1,400 single-occupant homes, the researchdemonstrates that occupants respond to 8°F thermostat setpoint changes within a median of 15 minutes, while 2°F changes trigger responses within a median of 30 minutes. This highlights an understudied temporal relationship between thermostat setbacks and response time of occupant behaviors. Models of such behavior dynamics are required to incorporate occupant impacts into building performance simulation. A key contribution of this dissertation is the Thermal Frustration Theory (TFT), which positsthat thermal discomfort driven behaviors are caused by the time-accumulation of discomfort, not simply a temperature deviation threshold or a delay from an initiating event. Using a dataset of 634 thermostats, each with 25+ manual setpoint changes, a comparative analysis of TFT and comfort zone and a delayed response theories demonstrated that personalized TFT models better predict when manual setpoint change occur. This was measured by the area under the curve statistical measure (AUC); all three models perform similarly by a Matthews Correlation Coefficient measure. Higher AUC performance is especially important for modeling occupant behavior in demand response programs where false negatives of rare occupant interactions could adversely affect grid stability. EnergyPlus based simulations were conducted with TFT-derived occupant models, demonstrating the ability to identify parameters of known TFT models from only data observable with smart thermostats, even under the presence of noise from routine overrides. Overall, the dissertation highlights that thermostat interactions are neither static,instantaneous, nor driven solely by the environment. Instead, temporal accumulation of discomfort and routine-based behavior play important roles. The methodology and results offer a pathway towards more accurate modeling of human-building interactions for policy assessment, building design, and demand response programs.

Sharma, Kunind [Northeastern University] (ORCID:00↗

DOE OSTI2019

Holistic modeling framework of demand response considering multiti-mescale uncertainties for capacity value estimation

Demand response (DR) is regarded as an effective tool to mitigate the operational uncertainties and enhance the reliability of power supply in smart grids. However, with the time-varying attribute, to what extent DR could be committed is a major concern for utilities. This paper proposes a new approach to assess the capacity value (CV) of DR with a methodological framework developed for the uncertainty modeling of DR. The novelty of this framework is its inclusion of both physical and human-related analyses in DR programs, which allows the characterization of DR variability to be accurate for the CV estimation. To achieve this, the demand-side activities in DR are disaggregated into several modules as load usage, contract selection and actual performance. Based on their intrinsic properties, different parametric models are proposed to represent the impact of each technical/social factor on the availability of DR. The parameters of these models are determined using learning based algorithms to adapt to various behavior patterns of consumers. The outputs of the framework will serve as the quantifiers of DR capability and are integrated into reliability-based CV evaluation. The results of case studies verify the effectiveness of the proposed methodology.

Capacity value↗

DOE OSTI2021

Identification and Online Updating of Dynamic Models for Demand Response of an Industrial Air Separation Unit

Demand-response operation of air separation units requires frequent changes in production rate(s), and scheduling calculations must explicitly consider process dynamics to ensure feasibility of the solutions. To this end, scale-bridging models (SBMs) approximate the scheduling-relevant dynamics of a process and its controller in a low-order representation. In contrast to previous works that have employed nonlinear SBMs, this paper proposes linear SBMs, developed using time-series analysis, to facilitate online scheduling computations. Using a year-long industrial dataset, we nd that compact linear SBMs are suitable approximations over typical scheduling horizons, but that their accuracies are unpredictable over time. We introduce a strategy for online updating of the SBMs, based on Kalman ltering schemes for online parameter estimation. The approach greatly improves the accuracy of SBM predictions and will enable the use of linear SBM-based demand-response scheduling in the future.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Building and grid system benefits of demand flexibility and energy efficiency

The global electricity system is undergoing a dramatic transformation. Technology advancements in clean energy generation (e.g., solar photovoltaics and wind) and electrified demand (e.g., transportation and building heating) are providing a cost-competitive pathway to mitigate the urgent and severe consequences associated with a changing climate. In addition, as extreme events continue to increase in frequency and magnitude, solutions are needed for adapting to the current climate. Through energy efficiency and demand flexibility, buildings play a prominent role in meeting these challenges; recently in Joule, a paper by Langevin et al. provides clear insight into the magnitude of buildings’ technical potential.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Improving future travel demand projections: a pathway with an open science interdisciplinary approach

Transport accounts for 24% of global CO 2 emissions from fossil fuels. Governments face challenges in developing feasible and equitable mitigation strategies to reduce energy consumption and manage the transition to low-carbon transport systems. To meet the local and global transport emission reduction targets, policymakers need more realistic/sophisticated future projections of transport demand to better understand the speed and depth of the actions required to mitigate greenhouse gas emissions. In this paper, we argue that the lack of access to high-quality data on the current and historical travel demand and interdisciplinary research hinders transport planning and sustainable transitions toward low-carbon transport futures. We call for a greater interdisciplinary collaboration agenda across open data, data science, behaviour modelling, and policy analysis. These advancemets can reduce some of the major uncertainties and contribute to evidence-based solutions toward improving the sustainability performance of future transport systems. The paper also points to some needed efforts and directions to provide robust insights to policymakers. We provide examples of how these efforts could benefit from the International Transport Energy Modeling Open Data project and open science interdisciplinary collaborations.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

Efficiency and Demand Flexibility in Large Office Buildings: The Potential for Cost Savings and CO 2 Reductions from Lighting and Cooling Measures

This report presents the estimated impact of lighting and cooling efficiency and demand flexibility measures in large office buildings in each state in the contiguous United States. It provides modeled results for three different metrics: bill savings, regional grid operational costs savings, and carbon dioxide (CO 2 ) emissions reductions. Lighting efficiency and demand flexibility are estimated to reduce load by up to 80 MWh/yr in a single large office building. These load reductions result in customer bill savings of up to $8,800/yr per building, with the highest savings in southern and midwestern states. Grid operating cost savings are estimated at up to $3,240/yr/building, with greatest benefit in southern and northeastern states. CO 2 emissions reduction potential is highest in the Dakotas, Nebraska, across the Midwest, in West Virginia, and in Mississippi (<48,200 kg/yr/building). Comparatively, cooling measures are found to have less load reduction potential (<28.5 MWh/yr/building), with the greatest potential in southern states including Texas, which ranks top of the list across several of the metrics studied. In numerous states, shifting cooling load to off-peak hours is found to increase costs and CO 2 emissions because precooling results in increased load during high-cost or high-CO 2 emissions periods. In general, focusing on cooling efficiency and load shedding has the potential for more savings. In all cases, the specific rate structure is a significant determinant in actual bill savings, which are up to $4,000/yr/building. To realize the full potential for bill savings through an energy measure, building operators must identify how the measure will change the building load pattern and the interaction of this load change with the applicable rate tariff. To realize CO 2 emissions reductions, industry and state coordination is needed to verify which fuel source is on the margin and then to create incentives for end users to reduce load during high-CO 2 emissions hours. Regular updates to data sets and analyses are critical. Regulators and policymakers are well positioned to facilitate the necessary coordination between the electric industry and building energy managers to develop appropriate price signals and incentives.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2007

A Demand Access Protocol for Space Applications

This paper describes a demand access protocol for space communications, which is a messaging procedure that facilitates the exchange of resource requests and grants between users and service providers. A minimal set of operational and environmental needs and constraints are assumed since the intent is to keep the protocol flexible and efficient for a wide-range of envisioned NASA robotic and human exploration missions. The protocol described in this document defines the message format and procedures used to ensure proper and correct functioning of a demand access communications system, which must operate under customized resource management policies applied by the users and service providers. This protocol also assumes a minimal set of capabilities from the underlying communications system so that no unique requirements are imposed on the communications sub-systems.

demand access↗

DOE OSTI2026

On-Demand Transit: Features, Benefits, Challenges, Costs, and Evaluation

The U.S. Department of Energy's National Laboratory of the Rockies works in partnership with communities and transit agencies across North America to support and evaluate customized on-demand transit (ODT) systems. ODT, also known as on-demand mobility or microtransit, is a flexible form of public mobility. Unlike traditional fixed-route transit, ODT adjusts where and when service is provided rather than following predetermined schedules and routes. Having evolved from commercial ride-hailing business models, ODT leverages smartphone connectivity and real-time route optimization to provide highly responsive and dynamic service. ODT trips, which are usually shared, can function as a standalone service or as a first-/last-mile service connecting riders to other types of transit.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Process-structure-property relationships for droplet-on-demand liquid-metal-jetted parts

Liquid metal jetting droplet-on-demand additive manufacturing is an attractive alternative to industry-standard, laser-based metal additive manufacturing techniques that use powder feedstocks and produce large heat affected zones. Liquid metal jetting provides a low feedstock footprint, minimal materials waste, less contamination and inclusions from feedstock, and is compatible with a large range of materials. However, little has been reported on the liquid metal jetting process-structure-property relationships that are crucial to understanding how to consistently print high quality metal parts. We investigate the effects of build plate temperature, infill pattern, and track raster rate on the macro- and microstructure, density, hardness, and tensile strength of metal parts created with our custom droplet-on-demand setup using pure tin feedstock as a surrogate for more relevant structural materials. We found that although the build plate temperature had the largest influence on the structure and properties, the infill pattern is an important factor for optimal structural integrity. It was observed that the track raster rate influenced track-to-track porosity and should be considered in toolpath development. Despite printing in ambient air, we could not identify oxide on the surface or in the bulk of the parts and saw no adverse effects on microstructure or tensile performance. The results and methods of this study will help guide future work in optimizing the liquid metal jetting process to reliably print parts using any liquid metal jetting system for a wide variety of metal feedstocks.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Microgrid to enable optimal distributed energy retail and end-user demand response

In the face of unprecedented challenges in environmental sustainability and grid resilience, there is an increasingly held consensus regarding the adoption of distributed and renewable energy resources such as microgrids (MGs), and the utilization of flexible electric loads by demand response (DR) to potentially drive a necessary paradigm shift in energy production and consumption patterns. However, the potential value of distributed generation and demand flexibility has not yet been fully realized in the operation of MGs. This study investigates the pricing and operation strategy with DR for a MG retailer in an integrated energy system (IES). Based on co-optimizing retail rates and MG dispatch formulated as a mixed integer quadratic programming (MIQP) problem, our model devises a dynamic pricing scheme that reflects the cost of generation and promotes DR, in tandem with an optimal dispatch plan that exploits spark spread and facilitates the integration of renewables, resulting in improved retailer profits and system stability. Main issues like integrated energy coupling and customer bill reduction are addressed during pricing to ensure rates competitiveness and customer protection. By evaluating on real datasets, the system is demonstrated to optimally coordinate storage, renewables, and combined heat and power (CHP), reduce carbon dioxide emission while maintaining profits, and effectively alleviate the PV curtailment problem. Finally, the model can be used by retailers and MG operators to optimize their operations, as well as regulators to design new utility rates in support of the ongoing transformation of energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2017

Foresee: A user-centric home energy management system for energy efficiency and demand response

This paper presents foresee, a user-centric home energy management system that can help optimize how a home operates to concurrently meet users' needs, achieve energy efficiency and commensurate utility cost savings, and reliably deliver grid services based on utility signals. Foresee is built on a multiobjective model predictive control framework, wherein the objectives consist of energy cost, thermal comfort, user convenience, and carbon emission. Foresee learns user preferences on different objectives and acts on their behalf to operate building equipment, such as home appliances, photovoltaic systems, and battery storage. In this work, machine-learning algorithms were used to derive data-driven appliance models and usage patterns to predict the home's future energy consumption. This approach enables highly accurate predictions of comfort needs, energy costs, environmental impacts, and grid service availability. Simulation studies were performed on field data from a residential building stock data set collected in the Pacific Northwest. Results indicated that foresee generated up to 7.6% whole-home energy savings without requiring substantial behavioral changes. When responding to demand response events, foresee was able to provide load forecasts upon receipt of event notifications and delivered the committed demand response services with 10% or fewer errors. Foresee fully utilized the potential of the battery storage and controllable building loads and delivered up to 7.0-kW load reduction and 13.5-kW load increase. As a result, these benefits are provided while maintaining the occupants' thermal comfort or convenience in using their appliances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗