High Electricity Use Research Articles

Do mineral imports increase in response to decarbonization indicators other than renewable energy?

While previous studies examine the impact of renewable energy generation capacity on mineral imports where minerals are broadly defined, this study examines the role of three decarbonization indicators (energy efficiency, energy's carbon footprint, and renewable generation) in driving import demand for four specific minerals used in multiple decarbonization technologies: copper, aluminum, nickel, and manganese. Our results, based on the cross-sectional autoregressive distributed lag modeling approach applied to panel data from 33 countries, suggest that imports for some minerals increase in response to (1) investments in energy efficiency (aluminum and manganese), (2) reducing energy's carbon footprint (copper), and (3) investments in renewable energy generation (copper and manganese). Nickel imports were found to increase with higher levels of energy use per GDP suggesting their role in cleaner vehicles, which heavily rely on electrification, higher electricity use, and urbanization. When imports are broadly defined to include all types of mineral groups, import values were found to be responsive to higher renewable generation but not the other decarbonization indicators. Our findings provide insights into developing mineral-specific strategies and policy frameworks to ensure supply chain resilience.

Wintertime patterns of residential electricity use and indoor temperature in the composite climate of India

Wintertime electricity use in India has received little attention due to the growth of residential air conditioning (AC) in the summer and monsoon seasons. This paper combines monitoring (time-series) and survey (contextual) data to examine the wintertime pattern of daily electricity current, indoor temperature, and relative humidity profiles across a sample of 64 dwellings in the city of Hyderabad representing the composite climate of India. Monitoring data were recorded for 31 days (December 2021 with a mean daily outdoor temperature of 25°C) using a CT clamp meter and Bluetooth enabled sensors. Contextual data covered dwelling and household characteristics, and use of appliances. The winter peak period of electricity use was found to occur from 6:30-10:30 in the morning, likely to be for hot water (geysers) and cooking. This is different to the late evening/night-time peak observed in the summer due to use of air conditioning (AC). Only one dwelling showed potential signs of using space heating. Higher electricity use aligned with lower peak temperatures in some homes particularly in the high-income group, indicating the potential use of AC during winter. The wintertime morning electricity peak in India could be managed using thermal stores in geysers through pre-heating which could also offer demand response opportunities.

Dynamic modelling of operational energy use in a building LCA: A case study of a Belgian office building

Strict energy performance requirements have reduced the building's operational energy use and related environmental impact in the past decades. Consequently, the embodied impacts of buildings gained importance, and hence focussing on the life cycle impacts has become essential to further reduce the environmental impact of buildings. Life cycle assessment (LCA) is an internationally accepted method used for this purpose. Current LCA studies of buildings typically look at their whole life cycle but ignore potential changes in operational energy use due to climate change. However, the latter is expected to influence the operational energy use of buildings to a significant extent. This paper aims to improve the assessment of the operational phase by including variations in the yearly operational energy use caused by climate change and expected changes in the electricity mix as a strategy for climate change mitigation. This adapted dynamic assessment method is used to assess a Belgian office building. The results indicate that a Representative Concentration Pathway (RCP) 8.5 climate change scenario causes a decrease of 18 % for the end use of gas for heating and an increase of 32.4 % for the electricity use for cooling and ventilation. For the case study, the reduction of the environmental impact due to the decrease in gas use compensates the impact increase due to the higher electricity use. An increase in environmental impact in future would however occur if both heating and cooling are provided by electricity. Furthermore, depending on the changes that will occur in the electricity mix, the global warming potential of the office building will change by −13 % for an electricity mix dominated by renewables and by + 10 % for one with mainly production by gas compared to the situation with the current mix. However, the inclusion of a more extended list of environmental indicators shows that the overall environmental impact will only reduce by 4 % or even increase by 7 % compared to the situation with the current mix.

Thermodynamic and thermal comfort performance evaluation of two geothermal high-temperature cooling systems in the mediterranean climate

The European Commission aims to reduce the greenhouse gas emissions of the European Union's building stock by 60% by 2030 compared with 1990. Meanwhile, the global demand for cooling is projected to grow 3% yearly between 2020 and 2050. High-temperature cooling systems provide cooling with lower exergy use than conventional cooling systems and enable the integration of renewable energy sources, and can play a crucial role in meeting the growing cooling demand with less energy use. The aim of this study is to analyse and critically evaluate two high-temperature cooling systems in terms of their energy and exergy use in a case study. We also consider thermal comfort performance, CO2 emissions, and sensitivity to changing operating conditions. The two systems considered are a mechanical ventilation system with heat recovery combined with geothermal cooling (GeoMVHR) and a radiant cooling system with ceiling panels connected to the same geothermal cooling (GeoRadiant) system. The study is conducted using building energy models of a typical office building belonging to a three-building school complex located in Sant Cugat near Barcelona, Spain. IDA ICE 4.8 simulation software was used for the simulations. The results show that the two different installations can produce near-identical thermal comfort conditions for the occupants. The GeoRadiant system achieves this result with 72% lower electricity use and 60% less exergy destruction than the GeoMVHR system. Due to the higher electricity use, the CO2 emissions caused by the GeoMVHR system are 3.5 times the emissions caused by the GeoRadiant system.

Linkage of Electricity with Agricultural Growth and Technology Factors: An Illustration of India’s Case

Understanding the relationship between electricity consumption and economic growth is crucial for formulating efficient energy policies for all sectors in general, and for agriculture in particular. To study this, an empirical examination of the long-run co-movement and the causal relationship between electricity consumption and real gross state domestic product (GSDP) from agriculture and its allied sectors is attempted. The agricultural sector involves the use of different input technologies that are further influenced by electricity consumption. To account for this technology-enabling effect of electricity, we further take up an analysis of the relationship between electricity consumption and agricultural technology factors: fertilizer consumption, the share of irrigation, area under cereal, and the extent of mechanization. We use both state (data from 17 states for the period 1993–2017 for the electricity–GSDP relationship) and country-level data (for the period 1980–2018 for the electricity–technology factors relationship) in the analysis. Since short-period time series datasets analysis may yield unreliable and inconsistent results, we employed modern heterogeneous panel co-integration and panel-based error correction model techniques for analyzing the energy–growth linkage. When the heterogeneous states effect is taken into account, the empirical results fully support a positive long-run co-integrated connection between GSDP and electricity consumption. We could detect both long-run and short-run unidirectional causality running from electricity consumption to agricultural growth. Further electricity consumption was also found to augment the use of technology factors in agriculture. This calls for implementing policies and strategies for achieving higher electricity use in agriculture and improved efficiency in its utilization simultaneously.

Potential of microbial electrosynthesis for contributing to food production using CO2 during global agriculture-inhibiting disasters

A sun-blocking global catastrophic risk (GCR) such as a nuclear winter could completely collapse the agricultural system. Producing alternative foods through methods requiring little to no sunlight has been identified as a cost-effective response to these types of GCRs. This preliminary techno-economic assessment evaluates the potential of acetic acid (AA) derived from carbon dioxide (CO 2 ) via microbial electrosynthesis (MES) as an alternative food source for GCRs. Production and retail costs are estimated using net present value analyses for catastrophe and non-catastrophe scenarios. Based on nonstop (24/7) facility construction, the speed of food production ramp-up is estimated from capital expenditures using a reference class forecasting correlation. Potential production bottlenecks are assessed via a global resource requirement analysis. In regular conditions, the production cost of AA produced via MES is estimated at $1.83–$5.20/kg (dry). MES production ramp-up is expected to fulfill less than 1% of global human caloric requirements by the end of the first year after the catastrophe. The retail cost of AA produced via MES in catastrophe conditions is estimated at $6–$15/kg (dry). Potential bottlenecks to ramp-up include high electricity use and platinum dependency, which could be palliated via alternative processes based on gasification or bioelectrodes. AA from MES is not currently recommended as an alternative food for GCRs, because it is significantly more expensive and resource intensive than alternatives. Future research may change this, and could perhaps even enable MES as a sustainable food production method outside of catastrophes, given its potential for CO 2 utilization. • The production cost of acetic acid from CO 2 via MES is estimated at $1.83–$5.20/kg (dry). • The retail cost in catastrophe conditions is estimated at $6–$15/kg (dry). • Acetic acid produced via MES is still far from being competitive as a food or commodity chemical. • Potential bottlenecks to ramp-up include high electricity use and platinum dependency. • Alternatives based on gasification or bioelectrodes could palliate bottlenecks.

Factors Affecting Renters’ Electricity Use: More Than Split Incentives

This paper uses data from the 2015 Residential Energy Consumption Survey to explore the extent to which renters’ electricity use in the United States exceeds that of otherwise similar non-renters. Renting households are found to use approximately 9% more electricity than non-renters when controlling for location, socioeconomic, and many appliance-quantity controls. There are multiple factors that explain this extra electricity use, including inferior energy efficiency of appliances, behavioral factors, differences in bill payment responsibilities, and additional reliance by renters on electric space and water heaters. The paper finds that none of these factors are dominant. The phenomenon of renters’ (conditionally) higher electricity use is thus best understood as one that emerges from multiple sources.

Factors Affecting Renters’ Electricity Use: More than Split Incentives

This paper uses data from the 2015 Residential Energy Consumption Survey to explore the extent to which renters’ electricity use in the United States exceeds that of otherwise similar non-renters. Renting households are found to use approximately 9% more electricity than non-renters when controlling for location, socioeconomic, and many appliance-quantity controls. There are multiple factors that explain this extra electricity use, including inferior energy efficiency of appliances, behavioural factors, differences in bill payment responsibilities, and additional reliance by renters on electric space and water heaters. The paper finds that none of these factors are dominant. The phenomenon of renters’ (conditionally) higher electricity use is thus best understood as one that emerges from multiple sources.

Coronavirus comes home? Energy use, home energy management, and the social-psychological factors of COVID-19

This study explores the dynamics of energy use patterns, climate change issues and the relationship between social-psychological factors, with residents’ acceptance of and willingness to pay (WTP) for home energy management systems (HEMS) during the COVID-19 pandemic in New York. The results of our survey suggest that there were no longer morning or evening usage peaks on weekdays, and a significant portion of respondents are experiencing higher or much higher electricity use than average. Most residents' perception of climate change issues during COVID-19 remained unchanged. Attitude, perceived behavioral control, and social norms are overall the strongest predictors of adoption intention and WTP for HEMS. Regarding WTP for specific well-being features, attitude was the strongest positive predictor of telemedical and home security features, and social norms are the strongest positive predictor of elderly assistance and job search. Technology anxiety, surprisingly, positively influences WTP for the well-being features. Trust in utilities is not related to adoption intention, but is positively associated with WTP for the well-being features. Although cybersecurity concerns are positively associated with HEMS adoption intention for energy and well-being features, this relationship is not significant in WTP. Residents who had moderate perceived risk of getting COVID-19 are willing to pay more than the high- and low-risk groups. This paper addresses the interactions among technology attributes, and users’ social-psychological and demographics factors. Additionally, this study provides insights for further research in examining technology adoption and energy dynamics during times of crises, such as the COVID-19.

Performance evaluation of a certified green-rated housing development in the warm humid climate of India

The Indian Green Building Council (IGBC) claims that India has the second largest registered green building footprint in the world. However, there is no evidence-based approach to ascertain the actual energy performance and occupant satisfaction vis-a-vis the building design and indoor environment. This paper seeks to apply a customised post-occupancy building performance evaluation approach for Indian green buildings (I-BPE), to evaluate the actual performance of a certified green housing development in the warm humid climate of India, from both technical and occupants’ perspectives. Electricity consumption, environmental data, and occupant opinion of 29 flats are gathered and assessed over a period of one month in the monsoon season of 2018. Results show that flats with air-conditioning (AC) units tend to have slightly higher electricity use than those without; however, mean temperature and relative humidity measurements tend to be about the same and the level of perceived comfort is no different across the two groups. Building and occupant surveys revealed that storage facilities were insufficient, resulting in corridor space becoming a dumping ground, making it unsightly and unhygienic. The methodological approach developed in the study can be used for gathering empirical data on the actual energy and environmental performance of housing in India.

A behavior-centered framework for real-time control and load-shedding using aggregated residential energy resources in distribution microgrids

A bottom-up method to generate synthetic residential loads realistically, but with minimal computational resources, is presented. Six energy services, associated with high electricity use, are considered. Each energy service is characterized by number of events on a given day, event start time, and event duration. Distributions for number of events, start time and duration are proposed for four demographic categories: singles, couples, families and retired people. The distributions are augmented by elasticity parameters that allow load control and shaping. The distributions are based on information from focus groups and online surveys. In principle, the method can produce data at arbitrary temporal and topological resolution, and is thus suitable for a range of applications from machine learning of energy consumption patterns to detailed transient power flow analysis. Data can be aggregated as needed, for example by meter, by distribution transformer, or by substation transformer. In the present framework, loads for individual appliances, associated with individual electric meters, are generated at 1 Hz resolution, to explore two important applications that are relevant to the development of control paradigms for distribution microgrids. In such microgrids, a distribution feeder may be islanded from the bulk grid. The applications considered are aggregated real-time power dispatch and load shedding, both of which are needed for effective management of distributed energy resources in a microgrid setting. It is shown that aggregated loads can be shaped to follow a desired signal, for example to balance intermittent solar generation. Significant load reduction achieved by residents’ behavioral response is also demonstrated. Such load reductions could be invoked in the case of low-probability, high-consequence events, and could contribute to increased energy resilience at the community level.

Towards an understanding of residential electricity consumption in India

ABSTRACT Residential electricity consumption in India has increased by a factor more than 50 since 1971. India’s electrified households on average still consume less than one-third of the world’s average for electricity. Rapid electrification, increasing incomes and technology development can result in increased appliance ownership and higher electricity use. A better understanding of this demand can help to develop effective energy-efficiency policies, optimize the addition of generation capacity, and tackle challenges of climate change and environmental pollution. However, research on residential electricity consumption has received limited attention in India until now. A systematic examination of determinants of residential electricity consumption will require an interdisciplinary approach involving the fields of engineering, economics (traditional and behavioural), anthropology, architecture and others. A two-step approach is presented to advance understanding. First, a critical literature review from different disciplines is presented that explores residential electricity consumption in India. Public data sets are also analyzed to gather insights and highlight inconsistencies. Second, an interdisciplinary approach is proposed and developed that can be adopted for research on residential electricity consumption in India. The data needs and systems to facilitate such an approach are provided.

Determinants of high electricity use and high energy consumption for space and water heating in European social housing: Socio-demographic and building characteristics

This paper examines the extent to which different household socio-demographic and building characteristics contribute to high energy use in Social Housing. Using data from the sample created in the framework of the EU funded ICE-WISH project (social housing pilots in ten European countries, of around 25 dwellings each), an odds ratio analysis was performed to examine the effect of socio-demographic and building's structural determinants on high residential energy consumption. The analysis is performed separately for electricity use for lighting and appliances and for energy consumption for space and water heating. Results indicate that high electricity consumption in social housing is related to both socio-demographic characteristics of the occupants and characteristics of the buildings in which they live (OR = 5.6 for households with three occupants compared to single households and OR = 3 for households living in houses greater than 80 m2), whereas high energy consumption for space and water heating is only related to building characteristics (OR = 1.15 for households with three occupants compared to single households and OR = 4.77 for households living in houses greater than 80 m2). The outcomes of this paper call for future energy policy to intelligently incorporate traditional and behavior-based energy efficiency interventions for reducing residential energy consumption and CO2 emissions.

Assessing fairness of dynamic grid tariffs

The increase in the supply of intermittent renewable energy and the higher electricity use lead to stronger variation in network usage, which either requires costly network extensions or the implementation of incentives to reduce peaks. This paper focuses on the latter, namely dynamic tariffs. However, a tension may exist between economic arguments for dynamic pricing and people perceiving such pricing as unfair. This paper seeks to assess the fairness of dynamic tariffs through a combination of theoretical and empirical research. Fairness is defined broader than inequality; it is understood more objectively than just people's perceptions and thus requires engagement with ethical theory; and the fairness analysis is not only based on abstract ethical reflection but also on analysing the underlying arguments for people's perceptions. Both the theoretical fairness assessment and the survey among Dutch households reveal that dynamic tariffs are less fair than transport and capacity tariffs and fairer than Ramsey pricing. The fairness of dynamic tariffs depends on implementation conditions such as: clear, non-economic arguments as justification, guarantying basic-needs fulfilment, decreasing perception that ‘peak use is only for the rich’, and increasing predictability.

How do urban households in China respond to increasing block pricing in electricity? Evidence from a fuzzy regression discontinuity approach

China is the largest electricity consumption country after it has passed the United States in 2011. Residential electricity consumption in China grew by 381.35% (12.85% per annum) between 2000 and 2013. In order to deal with rapid growth in residential electricity consumption, an increasing block pricing policy was introduced for residential electricity consumers in China on July 1st, 2012. Using difference-in-differences models with a fuzzy regression discontinuity design, we estimate a causal effect of price on electricity consumption for urban households during the introduction of increasing block pricing policy in Guangdong province of China. We find that consumers do not respond to a smaller (approximately 8%) increase in marginal price. However, consumers do respond to a larger increase in marginal price. An approximately 40% increase in marginal price induces an approximately 35% decrease in electricity use (284kWh per month). Our results suggest that although the increasing block pricing could affect the behavior of households with higher electricity use, there is only a limit potential to overall energy conservation.

Perceptions of electricity-use communications: effects of information, format, and individual differences

Electricity bills could be an effective strategy for improving communications about consumers’ electricity use and promoting electricity savings. However, quantitative communications about electricity use may be difficult to understand, especially for consumers with low energy literacy. Here, we build on the health communication and graph comprehension literature to inform electricity bill design, with the goal of improving understanding, preferences for the presented communication, and intentions to save electricity. In a survey-based experiment, each participant saw a hypothetical electricity bill for a family with relatively high electricity use, covering information about (a) historical use, (b) comparisons to neighbors, and (c) historical use with appliance breakdown. Participants saw all information types in one of three formats including (a) tables, (b) bar graphs, and (c) icon graphs. We report on three main findings. First, consumers understood each type of electricity-use information the most when it was presented in a table, perhaps because tables facilitate simple point reading. Second, preferences and intentions to save electricity were the strongest for the historical use information, independent of format. Third, individuals with lower energy literacy understood all information less. We discuss implications for designing utility bills that are understandable, perceived as useful, and motivate consumers to save energy.

The determinants of household electricity consumption in Taiwan: Evidence from quantile regression

This paper employs quantile regression to analyze the determinants of household electricity consumption in Taiwan over the period 1981–2011. Our results show that the effects of demographic, socioeconomic, and household dwelling characteristics on household electricity consumption may differ across quantiles and may change over time. We found that household income and household size were significant in all quantiles for each year. We identify the characteristics of high-electricity-consuming households. Households with higher income, larger household size, and more elderly members consumed more electricity. In terms of dwelling attributes, larger housing areas, homes with more appliances, and owner-occupied, business-used, and multi-floor houses contributed to higher household electricity consumption. Strategies for reducing electricity consumption should focus on specific groups that tend to exhibit higher electricity use. However, we also found that the low-income and small-size households may have higher electricity consumption on a per capita basis. Thus, as household size decreased, the increase of per capita electricity demand driven by the change of household size should be a matter of concern.

What Can We Learn from the Household Electricity Survey?

The reasons for high carbon emissions from domestic buildings are complex, and have both social and technical dimensions. At the same time, it is costly and very time-consuming to gather reliable data on energy use in the home. The authors had early access to data from the Household Electricity Survey—the most detailed survey of electricity consumption in UK homes ever undertaken—which monitored 250 homes. The data enabled the authors to investigate a series of socio-technical questions drawn up by the UK Government: Why do some households use far more energy than average, whereas others use much less? What potential is there for shifting “peak load” so that electricity demand is more even through the day? Why is base load electricity use so high? The answers were seldom definitive, but statistical tests found significant correlations between high electricity use and social grade, large household size, unemployment and middle age; and between low electricity use and single-person households, small dwellings, and retirement. This paper draws out key findings from the work, and examines how these insights affect our broader understanding of carbon emissions from the built environment.

Clustering analysis of residential electricity demand profiles

Little is known about variations in electricity use at finely-resolved timescales, or the drivers for those variations. Using measured electricity use data from 103 homes in Austin, TX, this analysis sought to (1) determine the shape of seasonally-resolved residential demand profiles, (2) determine the optimal number of normalized representative residential electricity use profiles within each season, and (3) draw correlations to the different profiles based on survey data from the occupants of the 103 homes. Within each season, homes with similar hourly electricity use patterns were clustered into groups using the k-means clustering algorithm. Then probit regression was performed to determine if homeowner survey responses could serve as predictors for the clustering results. This analysis found that Austin homes fall into one of two seasonal groups with some homes using more expensive electricity (from a wholesale electricity market perspective) than others. Regression results indicate that variables such as if someone works from home, hours of television watched per week, and education levels have significant correlations with average profile shape, but might vary across seasons. The results herein also indicate that policies such as time-of-use or real-time electricity structures might be more likely to affect lower income households during some high electricity use parts of the year.

Viability of off-grid electricity supply using rice husk: A case study from South Asia

Rice husk-based electricity generation and supply has been popularized in South Asia by the Husk Power Systems (HPS) and the Decentralised Energy Systems India (DESI), two enterprises that have successfully provided electricity access using this resource. The purpose of this paper is to analyze the conditions under which a small-scale rural power supply business becomes viable and to explore whether larger plants can be used to electrify a cluster of villages. Based on the financial analysis of alternative supply options considering residential and productive demands for electricity under different scenarios, the paper shows that serving low electricity consuming customers alone leads to part capacity utilization of the electricity generation plant and results in a high cost of supply. Higher electricity use improves the financial viability but such consumption behaviour benefits high consuming customers greatly. The integration of rice mill demand, particularly during the off-peak period, with a predominant residential peak demand system improves the viability and brings the levelised cost of supply down. Finally, larger plants bring down the cost significantly to offer a competitive supply. But the higher investment need and the risks related to monopoly supply of husk from the rice mill, organizational challenges of managing a larger distribution area and the risk of plant failure can adversely affect the investor interest. Moreover, the regulatory uncertainties and the potential for grid extension can hinder business activities in this area.