Residential Electricity Demand Research Articles

Residential electricity demand in Taiwan: the effects of urbanization and energy poverty

The residential electricity demand were studied using the panel dataset of 21 cities and counties in Taiwan from 1998 to 2018. Observations were grouped and estimated separately for the urban and rural regions. Using the equality tests in the corresponding effects between the urban and rural models, the urban-rural gap of the electricity consumption was examined. The estimated results indicate that the effect of urbanization annually increased 0.52% of residential electricity demand in the urban regions, and decreased 0.02% of that in the rural regions. Considering the high replacement costs of appliances, low-income households tend to use the old and energy-inefficient ones that consume more electricity given the same electrical services. From 2011 to 2018, the decrease of the percentage of low-income households had reduced the electricity consumption by 0.68% in the rural regions.

How Prices, Income, and Weather Shape Household Electricity Demand in High-Income and Middle-Income Countries

This analysis provides an international perspective geared towards understanding the future demands being placed on the world’s electricity system. It focuses upon the household or residential demand for electricity in a number of high-income and middle-income countries that may raise power demands for cooling in a warming world. Panel estimates on 26 high-income and 29 middle-income countries over the 1978-2013 period provide critical information on how household electricity demand responds to income, weather, and prices. Our dynamic panel estimates address nonstationarity, heterogeneity, and cross-sectional dependence. We believe these are the first panel estimates for middle-income/non-OECD countries and the first panel estimates for high-income/OECD countries to address all three of the previously identified statistical issues. Relative to high-income country responses, long-run elasticities for middle-income nations are larger for income (0.8 compared to 0.6), larger for cooling (0.3 versus insignificant), and smaller for prices (-0.08 relative to -0.2). As middle-income economies are likely to grow more rapidly than high-income/OECD economies, the trends related to income and cooling responses are likely to place greater pressure on a warming world unless the power sector can be decarbonized globally.

Design for a Standalone Wind - Hydrogen Plant A Novel in Tripoli - Libya

This work provides an evaluation of hydrogen conversion technologies and proposes the establishment of an integrated wind-hydrogen power plant to provide daily electrical residential requirements in Tripoli. While planning the wind-hydrogen plant there are some factors that should be considered while establishing the project, including the residential electricity demand, the availability of water and enough wind, and the hydrogen production rate. The data calculation of the project is undertaken using (Renewable. Ninja) website from (Imperial College London and ETH Zurich) to model the wind speed and the amount electricity production, and Excel for modelling the hydrogen demand and production. This paper presents a stand-alone wind hydrogen plant to cover the daily electricity demand of a residential unit consisting of six apartments. Wind power was obtained through International Global wind speed and wind turbine, while hydrogen was extracted through water electrolysis process. The system fulfilled the specified residence’s electricity demand with enough electricity, with zero greenhouse gas emissions. This renewable wind-hydrogen community has demonstrated and clarified that it is possible to be fully reliant on renewable electricity daily. In addition, it is a real example of the efficiency and the effectiveness of hydrogen usage in energy storage, along with its high reliability as a substitute for oil and gas.

Forecast for the Cameroon’s Residential Electricity Demand Based on the Multilinear Regression Model

The electricity needs of populations in Cameroon are increasing and are still very inadequate. Companies, public buildings and households are facing frequent blackout which constrain development and social well-being. Therefore, the present work tried to forecast the electricity demand in the residential sector in Cameroon, in order to contribute significantly to the mastery of electricity consumption and highlight decision-makers in this sector. Six macroeconomics parameters covering the period 1994-2014 are used for these issues. Stationarity tests within gross domestic product, gross domestic product per capita, electricity consumption, population and numbers of subscribers and households respectively; reveal that all the series are I(1). Thus, the VAR (Vector Autoregressive) model has been retained to forecast the electricity demand until 2020. The cusum test and the cusum of squared test attest the stability of that model with a margin of error of 0.02%. Previsions are then more reliable and show that the electric request will skip from 1721 GWh in 2014 to more than 2481 GWh in 2020 approximatively, following a growing yearly rate of 5.36%. In order to reach its emergence, Cameroon ought to speed up its production in the domain of hydroelectric and thermal grid in order to meet the requirements in electric power in short and long term.

Rooftop photovoltaics potential assessment：A framework to integrate technological, economic, environmental and social benefit

Buildings have a significant impact on global and local energy demand and climate change. Consequently, the issue of photovoltaic (PV) potential assessment of building roofs have been extensively studied. Although studies have adopted different methods to predict the various potentials of rooftop PVs, as yet there is no understanding of a comprehensive framework for evaluating the potential of rooftop PVs. This study aims to develop a new framework to evaluate the technical, economic, environmental, and social benefits of rooftop PVs in old communities in Nanjing. The study found that the total annual electricity generation of rooftop PVs in the two districts of Nanjing was 431,136,314 KWh, which can meet about 20% of the residential electricity demand in the study area; the total electricity generation in the life cycle of 25 years was 5327909814 KWh; The economic benefit was 4.06 yuan/wp; the CO2 emission efficiency was 20.02%; the social benefit was 11.224 yuan/KWh. The results of this study will potential provide valuable reference suggestions for urban planners from the perspective of the benefits brought by rooftop PV in the two districts.

Эластичность спроса на электроэнергию и региональные эффекты: пространственно-эконометрический подход

This paper presents an empirical analysis of residential electricity demand in the regions of Russia. The purpose of the study is to estimate regional electricity demand considering the existence of spatial effects. Spatial econometrics models are estimated for 78 regions of Russia for the period 2005–2017. The results show that demand depends not only on factors that stressed out in the theory of consumer choice, but also there are spatial spillovers. Furthermore, the results show absence of income elasticity, but rather high price elasticity compared to other countries.

Optimal Scheduling of Residential Electricity Demand Based on the Power Management of Hybrid Energy Resources

Abstract The present study sought to address the scheduling of the grid-connected hybrid energy resources under uncertainty of renewable sources, and load in the residential sector. After introducing hybrid resources, scheduling model was implemented through a power management algorithm in an attempt to optimize resource cost, emissions, and energy not supplied (ENS). The stated problem consists of two decision-making layers with different weight coefficients based on the prioritization of each objective function. The proposed algorithm is selected for energy optimal management based on technical constraints of the dispatchable and non-dispatchable resources, uncertainty parameters and day ahead real time pricing (RTP). Furthermore, the impact of demand response programs (DRP) on the given algorithm was investigated using load shedding and load shifting techniques. Finally, the results obtained led to the optimization of the functions in all decision-making layers with different modes of operation.

Techno-enviro-economic assessment of household and community energy storage in the UK

Residential electricity demand is expected to rise in the next few decades due to the electrification of heating and transport. Both European and UK national policies suggest that efforts should be made to reduce carbon emissions and increase the share of renewable energy, an important element of which is encouraging generation, typically PV, in partnership with energy storage systems in the residential sector. The scale of the energy storage system is important, i.e. in individual properties or as a community resource. Many advantages of community energy storage (CES) over household energy storage (HES) have been identified, but the design and operation of CES has received significantly less attention. Most existing research has analysed CES at community level only, but the performance and impact on individual households has yet to be fully explored. In this study an agent-based model is proposed to investigate and analyse CES based on a range of criteria. Results indicate that both HES and CES can significantly reduce the grid peak power import and export, improve the community self-consumption rate (SCR) and self-sufficiency rate (SSR), and contribute to much higher energy saving. Furthermore, optimising the CES capacity leads to more effective use of PV power and better demand localisation during high PV-generation periods. It is found that an important challenge for CES systems is to realise the value of the shared electricity equitably amongst the participants and potentially to seek other revenue streams.

Country residential building stock electricity demand in future climate – Portuguese case study

The future residential energy demand is expected to be significantly affected by increasing electrification rates and climate change. This study uses a Monte Carlo-based approach and an ensemble of climate models to address potential changes in electricity demand in this sector. The whole Portuguese residential building stock in 2050 is used as a case study. It is performed a sensitivity analysis for the retrofitting and new construction, floor area of new buildings, electrification of domestic hot water and cooking and, finally, adoption of heat pumps for space heating and cooling. Results show a potential increase of 5 to 60% of the total electricity consumption in the sector. Space heating is expected to decrease by 33% while space cooling shows a possible 20-fold increase. The electrification of domestic hot water and the development of housing stock characteristics are the factors with the largest impact on the overall electricity consumption changes.

Estimating and forecasting residential electricity demand in Odisha

AbstractThe growing demand for electricity has put pressure on generation of electricity based on fossil fuel, resulting in emission of carbon dioxide. In order to design policy for demand side management, proper knowledge on determinants of electricity demand as well as prediction of future demand is required. However, study on estimation and forecasting of residential demand in developing countries like India has received less attention. This study is the first attempt to estimate and forecast residential electricity demand in the state of Odisha, which is the pioneer of electricity reform in India. It employs ARDL model to estimate residential electricity demand; while ARIMA, VAR and VEC models are employed to forecast future demand. The results show that income and price of electricity are significant determinants of residential electricity demand. The higher price elasticity compared to income elasticity reveals that price could be used as an effective instrument for demand side management. The forecast results show that VAR has the lowest error, which predicts per capita residential electricity demand to be double by 2030–31. This would help the policy makers to plan for demand side management and electricity generation so as to avoid shortage of electricity supply.

Price and income elasticities of residential and industrial electricity demand in the European Union

This study examines the short- and long-run price and income elasticities of residential and industrial electricity demand in the European Union between 1996 and 2016. Instrumental variable models using the between estimator, as well as dynamic panel models are employed to present robust estimates, and to assess the impact of different methodologies on the reported elasticities. The long-run price elasticity of residential electricity consumption is estimated between −0.53 and −0.56. These elasticities are more inelastic than that of industrial electricity use, which is reported between −0.75 and −1.01. The choice of different econometric methodologies has only moderate impact on the estimates. While long-run residential income elasticity estimates are moderately inelastic and estimated around 0.61, industrial electricity use tends to be closely tied to income, with elasticities between 0.76 and 1.08. Electricity demand in all sectors is highly price and income inelastic in the short run. Additionally, population density, temperatures, and policy measures also influence sectoral electricity use. The above estimates can aid the design of European energy and environmental policy.

Assessing the macroeconomic impacts of individual behavioral changes on carbon emissions

In the last decade, instigated by the Paris agreement and United Nations Climate Change Conferences (COP22 and COP23), the efforts to limit temperature increase to 1.5 °C above pre-industrial levels are expanding. The required reductions in greenhouse gas emissions imply a massive decarbonization worldwide with much involvement of regions, cities, businesses, and individuals in addition to the commitments at the national levels. Improving end-use efficiency is emphasized in previous IPCC reports (IPCC 2014). Serving as the primary ‘agents of change’ in the transformative process towards green economies, households have a key role in global emission reduction. Individual actions, especially when amplified through social dynamics, shape green energy demand and affect investments in new energy technologies that collectively can curb regional and national emissions. However, most energy-economics models—usually based on equilibrium and optimization assumptions—have a very limited representation of household heterogeneity and treat households as purely rational economic actors. This paper illustrates how computational social science models can complement traditional models by addressing this limitation. We demonstrate the usefulness of behaviorally rich agent-based computational models by simulating various behavioral and climate scenarios for residential electricity demand and compare them with the business as usual (SSP2) scenario. Our results show that residential energy demand is strongly linked to personal and social norms. Empirical evidence from surveys reveals that social norms have an essential role in shaping personal norms. When assessing the cumulative impacts of these behavioral processes, we quantify individual and combined effects of social dynamics and of carbon pricing on individual energy efficiency and on the aggregated regional energy demand and emissions. The intensity of social interactions and learning plays an equally important role for the uptake of green technologies as economic considerations, and therefore in addition to carbon-price policies (top-down approach), implementing policies on education, social and cultural practices can significantly reduce residential carbon emissions.

Impacts of hybrid time-varying tariffs on residential electricity demand: The case of Zhejiang Province

With the further liberalization of China's electricity market, electricity price becomes more important in the power supply-demand balance. We investigate the impacts of a hybrid time-varying tariff policy on residential electricity demand. In our estimation, we maximize the residential users' utility, taking average price responsiveness as their perceived price. Four indices related to power usage and expenses are designed to measure the response effects of time-varying tariff policy using a case study of Zhejiang province.

Short-Term Load Forecasting for a Single Household Based on Convolution Neural Networks Using Data Augmentation

Advanced metering infrastructure (AMI) is spreading to households in some countries, and could be a source for forecasting the residential electric demand. However, load forecasting of a single household is still a fairly challenging topic because of the high volatility and uncertainty of the electric demand of households. Moreover, there is a limitation in the use of historical load data because of a change in house ownership, change in lifestyle, integration of new electric devices, and so on. The paper proposes a novel method to forecast the electricity loads of single residential households. The proposed forecasting method is based on convolution neural networks (CNNs) combined with a data-augmentation technique, which can artificially enlarge the training data. This method can address issues caused by a lack of historical data and improve the accuracy of residential load forecasting. Simulation results illustrate the validation and efficacy of the proposed method.

Price Elasticity of Residential Electricity Demand by Region in Japan: Have They Changed Since the Great East Japan Earthquake?

Price Elasticity of Residential Electricity Demand by Region in Japan: Have They Changed Since the Great East Japan Earthquake?

Household demand for electricity: The role of market distortions and prices in competition policy

This paper examines residential demand for electricity in Sri Lanka using survey data collected over a five-year period between 2011 and 2015. The study finds that the major determinants of demand for residential electricity are the result of price or market distortions (i.e. subsidies), socioeconomic variables and energy saving technology. The effects of these variables are particularly relevant to competition policy. Estimated elasticities with respect to average price, subsidies under marginal cost pricing, subsidies under average cost pricing and income are found to be −0.015, 0.021, 0.036 and 0.046 respectively. We find that demand for electricity is inelastic and is categorised as a normal good. However, elasticities with respect to subsidy variables are found to be higher than the price variable. This implies, that under an increasing block rate system any price change used as a policy measure to control electricity consumption will not be effective. This is because price changes could alter the subsidy received by the consumer and therefore reverse the objective of the price change. Further, results of this study find that price and elasticities with respect to subsidy variables are relatively higher for low income groups while income elasticity is relatively larger for high income groups.

Natural disasters and electricity consumption behavior: a case study of the 2011 Great East Japan Earthquake

A better understanding of the price mechanisms behind electricity demand fluctuations could help develop effective tools to forecast energy demand after natural disasters. To this end, this study empirically analyzed the price elasticity of residential electricity demand in Japan over the 1990–2015 period using regional data. In particular, it attempted to shed light on the price mechanisms behind demand fluctuations by analyzing the effects of both price increases and decreases on power demand. The results showed that households tend to consume electricity according to past patterns. Given this electricity consumption behavior, once the habit of saving electricity is established, it persists. The results also show that changes in the power supply configuration associated with natural disasters may alter people’s electricity consumption behaviors. Consumer responses seem to be more sensitive to price rises than to price falls. This suggests the possibility that residential electricity demand will further decrease if the trend of rising electricity rates continues in the future.

Examining the Role of Population Age Structure upon Residential Electricity Demand: A Case from Korea

In this article, we empirically investigate the impact of the population age structure on electricity demand. Our study is motivated by suggestions from existing literature that demographic factors can play an important role in energy demand. Using Korean regional level panel data for 2000 to 2016, we estimate the long-run elasticities through employing cointegration regression and the short-run marginal effects by developing a panel error correction model. It is worth investigating the Korean case, since Korea is aging faster than any other advanced economy, and at the same time is one of the heaviest energy users in the world. To our knowledge, this is the first study analyzing how the population age structure affects residential electricity demand, based on regional data in Korea. Our analysis presents the following results. First, an increase in the youth population raises the residential electricity demand in the short- and long-run. Second, an increase in the population of people aged 65 and over also increases this electricity demand in the short- and long-run. Third, among the group of people aged 65 and over, we further investigate the impact of an older population group, aged 80 and over, but separately, on their residential electricity demand. However, in general there is no strong relationship in the short- and long-run.

How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers

This study explores the effect of slum rehabilitation on appliance ownership and its implications on residential electricity demand. The low-income scenario makes it unique because the entire proposition is based on the importance of non-income drivers of appliance ownership that includes effects of changing the built environment (BE), household practices (HP) and appliances characteristics (AC). This study demonstrates quantitatively that non-income factors around energy practices influence appliance ownership, and therefore electricity consumption. The methodology consists of questionnaire design across the dimension of BE, HP and AC based on social practice theory, surveying of 1224 households and empirical analysis using covariance-based structural equation modelling. Results show that higher appliance ownership in the slum rehabilitation housing is due to change in household practice, built environment and affordability criteria of the appliances. Change in HP shifts necessary activities like cooking, washing and cleaning from outdoor to indoor spaces that positively and significantly influences higher appliance ownership. Poor BE conditions about indoor air quality, thermal comfort and hygiene; and product cost, discounts and ease of use of the appliances also triggers higher appliance ownership. The findings of this study can aid in designing better regulatory and energy efficiency policies for low-income settlements.

Determinants of Residential Electricity Consumption: A Comparison between Urban and Rural Households in Kandy District in Sri Lanka

This study attempts to identify the key determinants of residential demand for electricity in rural and urban households in Sri Lanka. Yearly data for 108 randomly selected households (54 from each - urban and rural) for a five years period were used for the estimation. Fixed effect model is used to determine the responsiveness of electricity consumption to its own price, differences in price, price of alternative fuel, income and variables relating to the social-economic characteristics of the household. Price elasticity and income elasticity for electricity in the urban households are estimated to be -0.38 and 0.15 while they are -0.04 and 0.14 for rural households, respectively, which confirm the previous findings, as the electricity is inelastic and a normal good. Furthermore, the estimated model demonstrates the importance of including the household level information for identifying the determinants of electricity demand, which is not possible using aggregate level data in a country.