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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Electricity demand in South Asia – data gaps and pathways for research and modeling

South Asia is one of the fastest growing regions - economically and energy-wise in the world. However, there are significant uncertainties in how the energy demand would grow given the rapidly rising incomes, urbanization, industrialization, access to energy, warming climate and technological change. The transition to clean energy in the region is a priority and is critical to limiting emissions in the region and increasing regional energy security. The power sector will play an important role in decarbonization of energy systems, lowering emissions in other sectors through electrification. However, as existing end uses are scaled up more and new end uses are electrified, the temporal and spatial patterns of electricity consumption could change. These changes have implications for the magnitude, shape, and timing of peak demand, which in turn affects the power sector’s investments and operations, and ability to provide reliable power. In this report, we outline key uncertainties in demand growth that South Asian countries would face in the near to medium term, along with summarizing insights from stakeholder consultations we held over the past 6 months.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decom- pose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high- level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below $5\%$ MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2023

Regional Medium-Term Hourly Electricity Demand Forecasting Based on LSTM: Preprint

This paper aims to forecast high-resolution (hourly) aggregated load for a certain region in the medium term (a few days to over a year). One region is defined as some places with similar climate characteristics because the climate influences people's daily lifestyles and hence the electric usage. We decompose the electric usage records into two parts: base load and seasonal load. Considering both temperature and time factors, different deep-learning methods are adopted to characterize them. The first goal of our approach is to predict the peak load which is critical for power system planning. Furthermore, our proposed forecast method can provide the depiction of the hourly load profile to provide customized load curves for high-level real-time applications. The proposed method is tested on real-world historical data collected by CAISO, BPA, and PACW. The experimental results show that trained by three years of data, our method could reduce the prediction error for a one-year lead hourly load below 5% MAPE, and predict the occurrence of the peak load for next year in CAISO with an error within three days. Furthermore, as a byproduct, an interesting observation on the impact of COVID-19 on human life was made and discussed based on these case studies.

deep learning↗
DOE OSTI2022

Integrated hydrological, power system and economic modelling of climate impacts on electricity demand and cost

Impacts of climate-related water stress and temperature changes can cascade through energy systems, although models have yet to capture this compounding of effects. Here, we employ a coupled water–power–economy model to capture these important interactions in a study of the exceedance of water temperature thresholds for power generation in the western United States. We find that not all reductions in reserve electricity-generation capacity result in impacts, and that when they occur, intermittent interruptions in electricity supply at critical times of the day, week and year account for much of the economic impacts. Finally, we find that impacts may be in different locations from the original water stress. Herein, we estimate that the consumption loss can be up to 0.3% annually and the drivers identified in coupled modelling can increase the average cost of electricity by up to 3%. Integrated models will be needed to capture the cascading effects of climate change through climatic, water, energy and economic systems. Webster et al. now develop a coupled hydrologic–power-production–economic model to estimate water-stress impacts on electricity cost.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Latent demand for electricity in sub-Saharan Africa: A review

Universal access to electricity is an essential part of sub-Saharan Africa's path to development. With the United Nations setting Goal 7 of its sustainable development goals to be universal access to clean, reliable and affordable electricity, substantial research efforts have been made to optimize electricity supply based on projected demand in sub-Saharan African (SSA) countries. Our study reviews the literature on electricity demand, with a specific focus on latent demand (i.e., electricity demand that would exist if the necessary techno-economic conditions were met) in SSA. We found that out of 57 electricity demand papers reviewed, only 3 (5%) incorporated latent demand in their electricity demand projections. Furthermore, majority of the literature on electricity consumption and demand estimation in SSA use econometric models to identify determinants of electricity consumption and project future demand. We find that population density, urbanization, household income, electricity price, market value of crops and availability of natural resources to be significant determinants of electricity consumption in SSA. We conclude the review by proposing a methodology, and providing an initial proof of concept, for more accurately projecting latent demand in sub-Saharan Africa. Incorporating latent demand in electrification models would help inform energy sector stakeholders (e.g., investors and policymakers) about which sectors and geographic locations hold potential for wealth creation via electricity access.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2026

Preliminary Assessment for the Electric Load Shifting Potential of Integrating Thermal Energy Storage with Heat Pumps in Residential Buildings in Texas of United States

The widespread adoption of electric-driven heat pumps for heating and cooling is expected to significantly increase electric demand. Cooling electric demand will result in an increase in peak hours electric loads, placing additional strain on the grid during peak hours. This challenge, combined with the current rapidly increasing demand from data centers, exacerbates the electric demand duck curve problem. Integrating thermal energy storage (TES) with heat pump can shift electric use for heating and cooling from peak to off-peak hours of the electric grid, which can help flatten the daily electric demand profile of a building. This paper presents a novel design that integrates heat pump with TES (HP-TES), which uses phase change materials (PCM). Heat pump charges TES by melting or freezing PCM during off-peak hours when there is no thermal demand from the building. TES is then discharged (i.e., by freezing or melting PCM) during peak hours to provide a more favorable heat source or heat sink for the heat pump to meet the thermal demand of the building with lower electricity use than conventional air-source heat pumps. Computer simulations were developed to predict the performance of HP-TES applied to a typical single-family house in the US. The building-level simulation results were scaled up to preliminarily assess the aggregated impacts of deploying HP-TES across all single-family houses in Texas of the United States, including reduction of peak demand of the electric grid.

Anees, Fady [ORNL]↗
DOE OSTI2026

Revisiting the relationship between demand growth and electricity prices

Here, in this Commentary, we aim to clarify the relationship between electricity demand and prices in the short- and longer-term. We outline three dimensions that shape this relationship: system capacity utilization, system expansion costs, and cost allocation in the rate design process. We explain why demand growth has historically been largely associated with falling electricity prices and explore arguments that future C&I demand growth may increase prices.

O'Shaughnessy, Eric [Lawrence Berkeley National La↗
DOE OSTI2025

Hourly Load Profile Dataset for Electric Airport Ground Support Equipment in the United States

Currently, there are limited data on the magnitude and timing of electricity demand from electric ground support equipment (eGSE) across U.S. airports. To address this gap, this study presents a modeling approach for estimating hourly annual electricity demand from eGSE at the 50 largest U.S. commercial airports. These datasets, accessible at data.nrel.gov/submissions/279, provide critical insights into the potential grid impacts and electricity demand associated with eGSE adoption.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2021

Heterogeneity in own-price residential customer demand elasticities for electricity under time-of-use rates: Evidence from a randomized-control trial in the United States

Regulators, policymakers, and stakeholders in several states have raised concerns about the price elasticity of residential customers’ electricity demand, especially as related to different customer subpopulations, management of critical end-uses, and enrollment approaches. This analysis sought to quantify the diversity of residential customer price elasticity in a time-of-use rate across these different dimensions using data generated from a utility pricing experiment. Finally, customers were found to be more elastic during the peak period of critical peak days, who were predicted to own and use air conditioning, and who volunteered for time-of-use (TOU) rates (but those defaulted on were 25% as elastic).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗