Search NASA⌕ Search

SEARCH · Search NASA

Results for “DEMAND”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Stochastic Model Predictive Control for Demand Response in a Home Energy Management System

This paper presents a chance constrained, model predictive control (MPC) algorithm for demand response (DR) in a home energy management system (HEMS). The HEMS optimally schedules controllable appliances given user preferences such as thermal comfort and energy cost sensitivity, and available residentially-owned power sources such as photovoltaic (PV) generation and home battery systems. The proposed control architecture ensures both the DR event and indoor thermal comfort are satisfied with a high probability given the uncertainty in available PV generation and the outdoor temperature forecast. The uncertainties are incorporated into the MPC formulation using probabilistic constraints instead of computationally limiting sampling-based approaches. Simulation results for various user preferences and probabilistic model parameters show the effectiveness of the HEMS algorithm response to DR requests.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Hierarchical Power Flow Control in Smart Grids: Enhancing Rotor Angle and Frequency Stability with Demand-Side Flexibility

Large-scale integration of renewables in power systems gives rise to new challenges for keeping synchronization and frequency stability in volatile and uncertain power flow states. To ensure the safety of operation, the system must maintain adequate disturbance rejection capability at the time scales of both rotor angle and system frequency dynamics. This calls for flexibility to be exploited on both the generation and demand sides, compensating volatility and ensuring stability at the two separate time scales. This article proposes a hierarchical power flow control architecture that involves both transmission and distribution networks as well as individual buildings to enhance both small-signal rotor angle stability and frequency stability of the transmission network. The proposed architecture consists of a transmission-level optimizer enhancing system damping ratios, a distribution-level controller following transmission commands and providing frequency support, and a building-level scheduler accounting for quality of service and following the distribution-level targets. Furthermore, we validate the feasibility and performance of the whole control architecture through real-time hardware-in-loop tests involving real-world transmission and distribution network models along with real devices at the Stone Edge Farm Microgrid.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimal Control of SOEC-Based Hydrogen Production Systems for Demand Response Using Deep Reinforcement Learning in Smart Grids

Solid oxide electrolysis cell (SOEC) hydrogen production technology can range in size from small, appliance-size equipment to large-scale, central production facilities that can be tied directly to renewable or non-greenhouse-gas-emitting forms of electricity production, making it an ideal resource for demand response (DR). The SOEC hydrogen production system is a complex integrated system that encompasses fluid dynamics, electrical dynamics, and electrochemical and thermal dynamics, all of which involve non-linearity and non-convexity. Proper control of the SOEC hydrogen production system is crucial to enable its participation in the DR program. Here, to overcome the difficulty of designing an explicit control law for such nonlinear systems with nonconvex optimization features in DR applications, deep reinforcement learning (DRL) is explored to achieve the optimal control of the SOEC system for DR participation. Specifically, a twin delayed deterministic policy gradient (TD3) control framework is applied to achieve optimal response performance during DR events by considering power tracking error and hydrogen production efficiency with a suitable reward function. Two case studies with grid connections for tracking different DR commands were investigated. The first case study involved operating conditions reaching the boundaries, while the second involved operating conditions within the boundaries. The results showed that the proposed DRL-based control for SOEC can track the DR signal in a timely manner while maintaining high energy efficiency.

08 HYDROGEN↗
DOE OSTI2024

Distributionally Robust Bilevel Optimization Model for Distribution Network With Demand Response Under Uncertain Renewables Using Wasserstein Metrics

Here, we consider a distribution network integrating demand response (DR) participants in the presence of uncertain renewable suppliers and outdoor temperatures. A bilevel optimization model is proposed to capture the intricate dynamics between price-incentivized DR participants and distribution system operations, including energy procurement and active/reactive power flows. The model is formulated as a distributional robust bilevel optimization using Wasserstein metrics. We show favorable data-driven properties including out-of-sample guarantee and asymptotic consistency. Furthermore, we present a tractable mixed-integer linear programming reformulation and characterize the worst-case distribution. Computational experiments are conducted on a modified 33-bus system. Our findings underscore the efficacy of the pricing strategies derived from the proposed bilevel optimization model. These strategies not only effectively manage DR participants' behavior but also bring equity considerations among households with various characteristics to light. The results contribute to a deeper understanding of the interplay between distribution system operators and DR participants.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Community Public Mobility Using On-Demand, Low-Speed Electric Vehicles: A Case Study in Downtown St. Louis, Missouri

Legacy fixed route transit systems designed to serve commuters struggle to provide efficient and effective service for short neighborhood trips and for population groups unable to access and egress transit stops using active modes (e.g., elderly, disabled). Neighborhood on-demand transit (ODT) services using low-speed electric vehicles (LSEV) are an innovative technological solution that can help fill this gap in service (e.g., short, high-frequency trips) for diverse populations and trip types. This study evaluated user characteristics and travel behavior for a neighborhood ODT service (using LSEVs) in downtown St. Louis, Missouri using responses from a community survey (n=244), ridership data, and vehicle trajectory information. A comparative analysis between neighborhood ODT, fixed route transit, and transportation network companies (TNC) was also conducted from the perspectives of total travel time, cost, and greenhouse gas emissions. Ultimately, the goal of the analysis was to motivate and inform holistic public mobility systems where different services are optimized to meet specific community needs. Findings indicate that the neighborhood ODT was effective at reaching diverse populations (elderly (20%), lower income (27%), and households with limited access to private vehicles (34%)). ODT reduced total travel time by 32% compared to fixed route transit, produced 2.4 - 4.3 times less greenhouse gas emissions per passenger mile (compared to transit and TNCs), and was more affordable (free to users) than alternative options ($1 for transit, $10-12 for TNCs). Overall satisfaction rates were high, with 80% of respondents rating the service a 4 or 5 out of 5.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2014

Calculating Impacts of Energy Standards on Energy Demand in U.S. Buildings under Uncertainty with an Integrated Assessment Model: Technical Background Data

This report presents data and assumptions employed in an application of PNNL’s Global Change Assessment Model with a newly-developed Monte Carlo analysis capability. The model is used to analyze the impacts of more aggressive U.S. residential and commercial building-energy codes and equipment standards on energy consumption and energy service costs at the state level, explicitly recognizing uncertainty in technology effectiveness and cost, socioeconomics, presence or absence of carbon prices, and climate impacts on energy demand. The report provides a summary of how residential and commercial buildings are modeled, together with assumptions made for the distributions of state–level population, Gross Domestic Product (GDP) per worker, efficiency and cost of residential and commercial energy equipment by end use, and efficiency and cost of residential and commercial building shells. The cost and performance of equipment and of building shells are reported separately for current building and equipment efficiency standards and for more aggressive standards. The report also details assumptions concerning future improvements brought about by projected trends in technology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

On the Inclusion of Energy-Shifting Demand Response in Production Cost Models: Methodology and a Case Study

In the context of future power system requirements for additional flexibility, demand response (DR) is an attractive potential resource. Its proponents widely laud its prospective benefits, which include enabling higher penetrations of variable renewable generation at lower cost than alternative storage technologies, and improving economic efficiency. In practice, DR from the commercial and residential sectors is largely an emerging, not a mature, resource, and its actual costs and benefits need to be studied to determine promising combinations of physical DR resource, enabling controls and communications, power system characteristics, regulatory environments, market structures, and business models. The work described in this report focuses on the enablement of such analysis from the production cost modeling perspective. In particular, we contribute a bottom-up methodology for modeling load-shifting DR in production cost models. The resulting model is sufficiently detailed to reflect the physical characteristics and constraints of the underlying flexible load, and includes the possibility of capturing diurnal and seasonal variations in the resource. Nonetheless, the model is of low complexity and thus suitable for inclusion in conventional unit commitment and market clearing algorithms. The ability to simulate DR as an operational resource on a power system over a year facilitates an assessment of its time-varying value to the power system.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2011

Demand Response in the ERCOT Markets [Slides]

ERCOT grid serves 85% of Texas load over 40K+ miles transmission line. Demand response: voluntary load response, load resources, controllable load resources, and emergency interruptible load service.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2014

Reducing Residential Peak Electricity Demand with Mechanical Pre-Cooling of Building Thermal Mass

This study uses an advanced airflow, energy and humidity modelling tool to evaluate the potential for residential mechanical pre-cooling of building thermal mass to shift electricity loads away from the peak electricity demand period. The focus of this study is residential buildings with low thermal mass, such as timber-frame houses typical to the US. Simulations were performed for homes in 12 US DOE climate zones. The results show that the effectiveness of mechanical pre-cooling is highly dependent on climate zone and the selected pre-cooling strategy. The expected energy trade-off between cooling peak energy savings and increased off-peak energy use is also shown.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2013

Market and Policy Barriers for Demand Response Providing Ancillary Services in U.S. Markets

This study provides an examination of various market and policy barriers to demand response providing ancillary services in both ISO/RTO and non-ISO/RTO regions, especially at the program provider level. It is useful to classify barriers in order to create a holistic understanding and identify parties that could be responsible for their removal. This study develops a typology of barriers focusing on smaller customers that must rely on a program provider (i.e., electric investor owned utility or IOU, ARC) to create an aggregated DR resource in order to bring ancillary services to the balancing authority. The barriers were identified through examinations of regulatory structures, market environments, and product offerings; and discussions with industry stakeholders and regulators. In order to help illustrate the differences in barriers among various wholesale market designs and their constituent retail environments, four regions were chosen to use as case studies: Colorado, Texas, Wisconsin, and New Jersey.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2015

Pilot Testing of Commercial Refrigeration-Based Demand Response

Supermarkets potentially offer a substantial demand response (DR) resource because of their high energy intensity and use patterns. This report describes a pilot project conducted to better estimate supermarket DR potential. Previous work has analyzed supermarket DR using heating, ventilating, and air conditioning (HVAC), lighting, and anti-condensate heaters. This project was concerned with evaluating DR using the refrigeration system and quantifying the DR potential inherent in supermarket refrigeration systems. Ancillary aims of the project were to identify practical barriers to the implementation of DR programs in supermarkets and to determine which high-level control strategies were most appropriate for achieving certain DR objectives. The scope of this project does not include detailed control strategy development for DR or development of a strategy for regional implementation of DR in supermarkets.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Turnkey Heating, Ventilating, and Air Conditioning and Lighting Retrofit Solution Combining Energy Efficiency and Demand Response Benefits

NREL worked with the Bonneville Power Administration's Technology Innovation Office to demonstrate a turnkey, retrofit technology that combines demand response (DR) and energy efficiency (EE) benefits for HVAC and lighting in retail buildings. As a secondary benefit, we also controlled various plug loads and electric hot water heaters (EHWH). The technology demonstrated was Transformative Wave's eIQ Building Management System (BMS) automatically responding to DR signals. The BMS controlled the HVAC rooftop units (RTU) using the CATALYST retrofit solution also developed by Transformative Wave. The non-HVAC loads were controlled using both hardwired and ZigBee wireless communication. The wireless controllers, manufactured by Autani, were used when the building's electrical layout was too disorganized to leverage less expensive hardwired control. The six demonstration locations are within the Seattle metro area. Based on the assets curtailed by the BMS at each location, we projected the DR resource. We were targeting a 1.7 W/ft2 shed for the summer Day-Ahead events and a 0.7 W/ft2 shed for the winter events. While summarized in Table ES-1, only one summer DR event was conducted at Casino #2.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Aligning PEV Charging Times with Electricity Supply and Demand

Plug-in electric vehicles (PEVs) are a growing source of electricity consumption that could either exacerbate supply shortages or smooth electricity demand curves. Extensive research has explored how vehicle-grid integration (VGI) can be optimized by controlling PEV charging timing or providing vehicle-to-grid (V2G) services, such as storing energy in vehicle batteries and returning it to the grid at peak times. While much of this research has modeled charging, implementation in the real world requires a cost-effective solution that accounts for consumer behavior. To function across different contexts, several types of charging administrators and methods of control are necessary to minimize costs in the VGI context.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2019

Tank 241-AN-107 Study for Semi-Volatile Organics in 2017 Grab Samples and Carbonation of Composite for Solids Precipitation and Caustic Demand

Tank 241-AN-107 waste is known to contain high concentrations of complexing organics, and caustic sodium hydroxide has been added due to depleted free hydroxide in the waste. Eight grab samples were taken in August 2017 for routine chemical analyses. Investigations of the tank’s organic contents and solids precipitation at lower pH levels by CO 2 gas, as well as the performance of caustic demand testing at lower pH ranges were examined. Sub-micron particles were visible, suspended in the supernate, using scanning electron microscopy. Trace amounts of precipitation floating on the surface of the supernate were observed and were collected with the surface liquid samples for investigation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Hawaiian Electric Companies Demand Response Tool

This document introduces the Hawaiian Electric Companies Demand Response Tool, which was developed at the Pacific Northwest National Laboratory. The battery storage evaluation tool by PNNL was modified to run one-year simulations that evaluate the benefits of behind-the-meter behind-the-meter photovoltaics and storage from the customer perspective, while optimizing use of these assets to minimize customer energy bills and improve reliability.

14 SOLAR ENERGY↗
DOE OSTI2015

Guidance on Demand-Controlled Kitchen Ventilation

This report provides guidance on Demand Control Kitchen Ventilation (DCKV) systems in commercial food service facilities. The report explains how DCKV works and describes the components of a typical system.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗