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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A systematic literature review on residential demand response with a focus on opportunities for low-income communities

Demand Response improves efficiency and grid stability, reducing peak load and total system costs. It can potentially contribute to other societal aspects, including mitigating climate change and improving air quality, health, stress levels, and comfort. This paper reviewed the literature on residential demand response programs in the United States, through the lens of the five energy justice principles, identifying and categorizing a set of 165 papers from 1960 to 2024. As a result, we observe significant progress in distributive justice, however, mostly focused on increasing participation rather than attending specific needs of historically marginalized communities. Concordantly, recognition and procedural justice dimensions are lagging, while cosmopolitan justice studies are scarce, underscoring a need to improve the measurement of impacts resulting from the implementation of demand response programs. Future research opportunities include the consolidation of standardized definitions and metrics, to better understand burdens and needs of diverse residential users. In terms of policy, collection and transparent access to standardized data is required as a condition to the design of flexible, customizable, transparent, and easy to navigate demand response programs. As a result, a better understanding of and engagement with customers has the potential to increase adoption rates, persistence in the program, and overall impacts for the system, households, and society.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Renewable Energy Resources Portfolio Optimization in the Presence of Demand Response

In this paper we introduce a simple cost model of renewable integration and demand response that can be used to determine the optimal mix of generation and demand response resources. The model includes production cost, demand elasticity, uncertainty costs, capacity expansion costs, retirement and mothballing costs, and wind variability impacts to determine the hourly cost and revenue of electricity delivery. The model is tested on the 2024 planning case for British Columbia and we find that cost is minimized with about 31% renewable generation. We also find that demand responsive does not have a significant impact on cost at the hourly level. The results suggest that the optimal level of renewable resource is not sensitive to a carbon tax or demand elasticity, but it is highly sensitive to the renewable resource installation cost.

Behboodi, Sahand↗
DOE OSTI2021

Dynamic Linking of Upstream Energy and Freight Demands for Bio and Fossil Energy Pathways in the Global Change Analysis Model

Comprehensive study of the environmental impacts associated with demand for an energy resource or carrier in any one sector requires a full consideration of the direct and indirect impacts on the rest of the regional and global energy system. It is important to consider the energy that is consumed in producing primary energy resources and energy consumed in transporting these energy resources internationally and domestically in order to produce a complete picture of these impacts. Biofuels are especially complex since they have feedbacks not just to the energy system but also to regional and global crop markets. Different modeling and analysis strategies have been applied to consider these impacts, which occur “upstream” of final energy consumption, with some success. Traditional life cycle models allow for rich technological detail in linking upstream impacts of energy demand with downstream final consumption; however, they cannot by themselves represent dynamic economic feedbacks across multiple sectors and regions over time. Computable general equilibrium (CGE) modeling does account for economic feedback among all sectors and regions; however, CGE modeling does not lend itself to physical representation of technological detail. Partial equilibrium (PE) modeling is a heterogeneous category describing economic models that focus on a subset of the economy, and they differ in coverage and ease in incorporating more sectors and economic links. In this study, we present a strategy for dynamically including the direct and indirect impacts of energy demand with physical and technology detail by explicitly adding these upstream energy and transportation links to GCAM, a PE model of global energy, land use, and emissions. We incorporate the following inter-sectoral linkages: energy inputs to crop production, energy inputs to fossil resource production, and freight transport requirements of energy and agricultural commodities. We assess the implications of explicitly including these links by measuring the global impacts of increased corn ethanol demand in the United States with and without these links included. Although the net global impact of the upstream links on energy and emissions are relatively modest in the scenarios we studied, the inclusion of these links illustrates interesting trade-offs in energy and transportation demand among fossil fuel and agriculture sectors. These sectoral interactions suggest that this level of modeling detail could be important in evaluating future analytical questions.

Sampedro Martinez de Estivariz, Jon↗
DOE OSTI2021

Dynamic consensus and extended high gain observers as a tool to achieve practical frequency synchronization in power systems under unknown time-varying power demand

In this paper, we consider the frequency synchronization problem in a network of lossless, connected, and network-reduced power system. Frequency synchronization is an important problem in power systems as frequency deviation can lead to degraded power quality, tripping of generators, etc. We present a load-estimator-based consensus algorithm in a network of fourth-order generator models that achieves practical frequency synchronization in the presence of unknown time-varying power demand. We do not assume that the demand is generated by a known model. We follow a multiple time-scale approach for the stability analysis. It is shown that synchronization is achieved for the case of constant power demand, by showing that the system is exponentially stable. Furthermore, for the case of time-varying power demand, we show the error trajectories are ultimately bounded, and the synchronization error is proportional to an upper bound on the derivative of the time-varying power demand. We simulate the controller performance on a four area network which is equivalent to the IEEE New England 39-Bus system.

42 ENGINEERING↗
DOE OSTI2019

A multi-scale calibration approach for process-oriented aggregated building energy demand models

Long-term energy planning relies largely on projections of future energy demand and hourly load profiles. Aggregate building models are increasingly being utilized to characterize the sensitivity of current and future building stocks to changes in climate, population, and building technology on city-to-regional-to-national scales. Due to challenges in the availability of data, those analyses have been limited to projection of energy demand at a single scale, usually state or country, while long-term planning power system models and production cost models might operate at different spatial scales such as energy regions which are focused on ensuring adequate generation infrastructure. In this work, we propose and evaluate a novel method to calibrate an aggregate building energy demand model (PNNL’s BEND model) against the best available data at the spatial scale of balancing authorities. This approach extends previous work on aggregated building energy demand by facilitating analysis of building energy demand across scales, in particular policy and operational decision-making scales. We show that the bias-corrected model estimates building electric loads reasonably well compared with estimates from a statistical model, but has the additional feature of flexibility across spatial scales. While the calibration approach is presently U.S.-centric and associated with U.S. energy regions, it can be extrapolated to other worldwide regions with similar scale challenges between policy and operational implementation decision making. We discuss the significant challenges involved in formulating and calibrating a complex physical model based on simulations of roughly 100,000 individual buildings against available aggregate regional electric load data and highlight areas for potential future work and improved data collection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models

This study evaluates the effect of complete nationwide lockdown in 2020 on residential electricity demand across 13 Indian cities and the role of digitalisation using a public smart meter dataset. We undertake a data-driven approach to explore the energy impacts of work-from-home norms across five dwelling typologies. Our methodology includes climate correction, dimensionality reduction and machine learning-based clustering using Gaussian Mixture Models of daily load curves. Results show that during the lockdown, maximum daily peak demand increased by 150-200% as compared to 2018 and 2019 levels for one room-units (RM1), one bedroom-units (BR1) and two bedroom-units (BR2) which are typical for low- and middle-income families. While the upper-middle- and higher-income dwelling units (i.e., three (3BR) and more-than-three bedroom-units (M3BR)) saw night-time demand rise by almost 44% in 2020, as compared to 2018 and 2019 levels. Our results also showed that new peak demand emerged for the lockdown period for RM1, BR1 and BR2 dwelling typologies. We found that the lack of supporting socioeconomic and climatic data can restrict a comprehensive analysis of demand shocks using similar public datasets, which informed policy implications for India's digitalisation. We further emphasised improving the data quality and reliability for effective data-centric policymaking.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Perspectives on Cobalt Supply through 2030 in the Face of Changing Demand

Lithium-ion battery demand, particularly for electric vehicles, is projected to increase by over 300% throughout the next decade. With these expected increases in demand, cobalt (Co)-dependent technologies face the risk of significant impact from supply concentration and mining limitations in the short term. In this study, increased extraction and secondary recovery form the basis of modeling scenarios that examine implications on Co supply to 2030. Demand for Co is estimated to range from 235 to 430 ktonnes in 2030. This upper bound on Co demand in 2030 corresponds to 280% of world refinery capacity in 2016. Supply from scheduled and unscheduled production as well as secondary production is estimated to range from 320 to 460 ktonnes. Our analysis suggests the following: (1) Co price will remain relatively stable in the short term, given that this range suggests even a supply surplus, (2) future Co supply will become more diversified geographically and mined more as a byproduct of nickel (Ni) over this period, and (3) for this demand to be met, attention should be paid to sustained investments in refined supply of Co and secondary recovery.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Impacts of climate change on sub-regional electricity demand and distribution in the southern United States

New tools are employed to develop an electricity demand map for the southeastern United States at neighborhood resolution to serve as a baseline from which to project increases in electricity demand due to a rise in global and local temperature and to population shifts motivated by increases in extreme weather events due to climate change. We find that electricity demand increases due to temperature rise over the next 40 years have a much smaller impact than those due to large population influx. In addition, we find evidence that some, sections of the national electrical grid are more adaptable to these population shifts and changing demand than others are; and that detailed projections of changing local electricity demand patterns are viable and important for planning at the urban level.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Evidence of Residential Demand Flexibility in a 46 Townhome Neighborhood

Demand response is an important emerging part of smart grids and it has been well researched from the theoretical and modeling perspectives. The empirical evidence on demand response is scarce, resulting in a limited understanding of many important aspects of demand response, including latency, cycling, and overrides - including acceptable impacts to customer comfort, convenience, and productivity in the new remote work era. We attempt to provide additional information to address this knowledge gap by sharing early results from the 46-townhome testbed located in Atlanta, GA. We report our findings from the first three months of experimental work. These focus on the delays associated with device status updates, characteristics of user overrides of control signals, and the share of devices available for demand response. We also provide some information about the duration of demand response events and a discussion of some properties of the testbed neighborhood.

Tsybina, Eve↗
DOE OSTI2018

Supply and Demand Constraints on Future PV Power in the USA

An economic analysis with a defined uncertainty range is presented that considers whether the future deployment of utility-scale PV (UPV) systems in the USA will be limited by the supply of PV modules or by market demand for these systems. An analysis of global PV module manufacturing growth and the fraction of modules produced that will be available for installation in the USA gives a plausible range of cumulative capacity for the UPV segment of 50 - 170 GW in 2030 and 60 - 500 GW in 2040. A parallel analysis of the future demand for UPV systems in the USA indicates a plausible range for cumulative capacity of 80 - 230 GW in 2030 and 150 - 530 GW in 2040. The plausible ranges for supply and demand substantially overlap in both 2030 and 2040, suggesting that neither supply nor demand is more likely to limit PV deployment in the USA. Consequently, mechanisms for enabling growth in both supply and demand can benefit efforts intended to increase the deployment of PV in the USA.

14 SOLAR ENERGY↗
DOE OSTI2021

Collaborative Decision Approach for Electricity Pricing-demand Response Stackelberg Game

Demand response programs are considered as a valuable resource in smart grids that provide several advantages of load shifting, peak load reduction, mediating intermittency of renewable energy integration, etc. Flexible price-based incentives have been recognized as a critical strategy in motivating and compensating consumers' load adjustment actions for successful implementation of demand response. Game theoretical approaches, especially Stackelberg games are popularly adopted to model the relationship between electricity price and customers' demand response and solved by the classical centralized backward induction (BI) method. However, the BI method generally requires convexity of the follower's model for necessary optimality conditions, and the computational time of any centralized approach increases sharply with larger problem instances. In this paper, the Stackelberg game of electricity pricing-demand response between a distribution system operator (DSO) and load aggregators (LAs) is decomposed based on a collaborative optimization (CO) framework, where each LA is treated as a discipline with its own domain constraints (e.g. building temperature control), while the DSO at the system level tries to reduce the solution discrepancy and guide the searching towards optimality. Several groups of comparison experiments have demonstrated the effectiveness of the proposed collaborative decision approach in solving the demand response game.

Chen, Yang↗
DOE OSTI2018

Internet-of-Things Hardware-in-the-Loop Simulation Architecture for Providing Frequency Regulation With Demand Response

Following recent advances in network infrastructure, cloud computing, and embedded systems, fascinating work is underway exploring the utility of demand response in increasing grid stability while permitting high penetration of intermittent renewable distributed generation resources. Although works have demonstrated diverse theoretical advantages of demand response programs, little real-world data are available and utilities generally remain reticent in moving forward with large-scale implementation due to risks inherent to any modification of the power system. Deciding that the next pertinent step in bringing demand response from theory to technology is developing an Internet-of-things hardware-in-the-loop simulation power system integrated device capable of empirically testing the theoretical mechanisms, this work presents an architecture testbed for providing demand response (telemetric monitoring and actuation of loads), which is real node in a power system simulation where virtual node's parameters derive real node data. Finally, we test a demand response algorithm, which provides frequency regulation services.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Data-driven modeling to enhance municipal water demand estimates in response to dynamic climate conditions

Altered precipitation and temperature patterns from a changing climate will affect supply, demand, and overall municipal water system operations throughout the arid western U.S. While supply forecasts leverage hydrological models to connect climate influences with surface water availability, demand forecasts typically estimate water use independent of climate and other externalities. Stemming from an increased focus on seasonal water demand management, we use the Salt Lake City, Utah municipal water system as a test bed to assess model accuracy versus complexity trade-offs between simple climate-independent econometric-based models and complex climate-sensitive data-driven models to average to extreme wet and dry climate conditions—representative of a new climate normal. Here, the climate-independent model displayed low performance during extreme dry conditions with predictions exceeding 90% and 40% of the observed monthly and seasonal volumetric demands, respectively, which we attribute to insufficient model complexity. The climate-sensitive models displayed greater accuracy in all conditions, with an ordinary least squares model demonstrating a measurable reduction in prediction bias (3.4% vs. -27.3%) and RMSE (74.0 lpcd vs. 294 lpcd) compared to the climate-independent model. The climate-sensitive workflow increased model accuracy and characterized climate-demand interactions, demonstrating a novel tool to enhance water system management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Developing and Evaluating Metrics for Demand Flexibility in Buildings: Comparing Simulations and Field Data

Building demand flexibility (DF) has attracted significant attention in recent years among researchers, technology developers and control companies, aggregators, utilities, and many others. There are numerous challenges with today’s electricity systems such as managing peak demand capacity and integrating variable renewables into the grid. Flexible building loads can provide various grid services to help reduce electricity costs, smooth out renewables intermittency and balance supply and demand. Recognizing this, the US DOE is leading the Grid-interactive Efficient Buildings (GEB) initiative which includes research to evaluate the potential, availability and timing of flexible loads. In this paper we present load shed metrics for three building types – medium office, large office and retail store – and compare prototype simulation results with measured data from 12 actual buildings that participated in hot summertime utility demand response (DR) events. The DR strategies included varying zone temperature and reducing light levels. The magnitude of a key DF metric, “demand decrease intensity” (or “shed intensity”) (W/ft2), between the simulation results and field data are similar (14-32% differences) for both mean and median values, though the field data show much larger variation among DR events. The coefficient p-values from linear regression model tests showed that outside air temperature is a significant variable for the whole building shed intensity when the resetting zone temperature strategy is deployed. These findings support the concept of using prototype building simulation to estimate building DF and expanding future simulation research to additional building types and climate zones.

Liu, Jingjing↗
DOE OSTI2021

ambrosia: An R package for calculating and analyzing food demand that is responsive to changing incomes and prices

The ambrosia R package was developed to calculate food demand for staples and non-staple commodities that is responsive to changing levels of incomes and prices. ambrosia implements the framework to quantify food demand as established by Edmonds et al. (Edmonds et al. 2017) and allows the user to explore and estimate different variables related to the food demand system. Currently ambrosia provides 3 main functions: (1) calculation of food demand for any given set of parameters including income levels and prices (2) estimation of parameters within a given a dataset. Note: ambrosia is used to calculate parameters for the food demand model implemented in the Global Change Analysis Model(GCAM) (3) exploration and preparation of raw data before starting a parameter estimation

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

The Distributed Geothermal Market Demand Model (dGeo): Documentation

The National Renewable Energy Laboratory (NREL) developed the Distributed Geothermal Market Demand Model (dGeo) as a tool to explore the potential role of geothermal distributed energy resources (DERs) in meeting thermal energy demands in the United States. The dGeo model simulates the potential for deployment of geothermal DERs in the residential and commercial sectors of the continental United States for two specific technologies: ground-source heat pumps (GHP) and geothermal direct use (DU) for district heating. To quantify the opportunity space for these technologies, dGeo leverages a highly resolved geospatial database and robust bottom-up, agent-based modeling framework. This design is consistent with others in the family of Distributed Generation Market Demand models (dGen; Sigrin et al. 2016), including the Distributed Solar Market Demand (dSolar) and Distributed Wind Market Demand (dWind) models. dGeo is intended to serve as a long-term scenario-modeling tool. It has the capability to simulate the technical potential, economic potential, market potential, and technology deployment of GHP and DU through the year 2050 under a variety of user-defined input scenarios. Through these capabilities, dGeo can provide substantial analytical value to various stakeholders interested in exploring the effects of various techno-economic, macroeconomic, financial, and policy factors related to the opportunity for GHP and DU in the United States. This report documents the dGeo modeling design, methodology, assumptions, and capabilities.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Demand Driven Cycamore Archetypes

Future nuclear fuel cycle options may present advantages over today’s once-through fuel cycle. Nuclear fuel cycle simulation tools assess the performance of those fuel cycles as well as the dynamics of long-term technology transitions. In many nuclear fuel cycle simulation tools, it has historically been the responsibility of the user to manually define facility deployment schemes and all facility parameters. While this is straightforward in simple fuel cycles, transitions from one fuel cycle to another can be more complex. In particular, deployment schemes for supportive fuel cycle facilities beyond the reactor become complex if the analyst desires to avoid gaps in the nuclear fuel and power supply chain during those transition scenarios. As nuclear fuel cycle analysis approaches questions regarding the feasibility and performance of the deployment schemes and technology choices during technology transition, automation of this historically manual process is necessary. The main objective of this work was to develop and demonstrate Cyclus automation capabilities toward key nuclear fuel cycle transition scenarios. While deploying reactors to meet power demand is trivial, and existed in the earliest versions of CYCLUS, automated, predictive deployment and decommissioning of other facilities is more complex. These include mining, milling, enrichment, fuel fabrication, reprocessing, and others. For example, a balanced closed fuel cycle may require ensuring that there is enough fast reactor fuel for their operation and may drive deployment of a fleet of light water reactors. This concern comprises the main challenge that drove the project effort. The Demand-Driven Cycamore Archetype project (NEUP-FY16-10512) aimed to develop CYCAMORE demand-driven deployment capabilities and thereby automate transition scenario definition. The developed software package, d3ploy, in the form of a CYCLUS Institution agent, deploys Facilities to meet the front-end and back-end demands of the fuel cycle. The University of South Carolina and the University of Illinois applied multiple algorithmic approaches to this challenge. This project developed an in situ demand-driven development schedule calculation through non-optimizing, deterministic-optimizing, and stochastic-optimizing algorithms as CYCLUS archetypes and demonstrated these new archetypes in program-supporting fuel cycle transition scenarios. Both objectives were achieved. This report documents the results and deliverables obtained toward these achievements in detail.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Assessment of Potential Future Demands for Hydrogen in the United States

H2@Scale is a U.S. Department of Energy (DOE) initiative that brings together stakeholders to advance the affordable production, transport, storage, and utilization of hydrogen (H2) as an energy carrier to increase revenue opportunities in multiple energy sectors. The focus of the current work is to characterize the growth potential of diverse hydrogen industries in the United States, given research and development (R&D) advancements in hydrogen technologies. Current and emerging hydrogen production technologies utilize diverse energy sources, including natural gas (NG) reforming, as well as renewable and nuclear power for low-temperature and high-temperature water splitting. The produced hydrogen also enables emerging domestic industries that value conventional and renewable hydrogen as an energy carrier for intermediate and end use. The success of H2@Scale (Figure ES.1) depends not only on hydrogen demand from growing existing markets such as petroleum refining and ammonia (NH 3 ) production, but also on the development of new markets such as metals refining, synthetic fuel (synfuel) and chemical production, biofuels, light-duty (LD) and heavy-duty (HD) hydrogen fuel cell (FC) electric vehicles (FCEVs), and injection into NG pipelines, all of which can significantly increase hydrogen demand relative to current levels of approximately10 million metric tons [MMT] annually. This study focused primarily on five demand sectors — synfuels, upgrading of oil/biomass, NH 3 /fertilizer, metals refining, and hydrogen vehicles (transportation) — along with gas infrastructure. For each sector, a hydrogen demand potential was quantified, along with a “threshold price.” The hydrogen demand potential reflects a practical amount of hydrogen that could be used in that sector, barring economic considerations. The threshold price reflects the price at which the consumer would utilize hydrogen in lieu of an alternative that could supply the same performance.

08 HYDROGEN↗