Residential Electricity Demand Research Articles

Residential demand-side management using integrated solar-powered heat pump and thermal storage

Due to the mismatch between rooftop solar energy generation and residential electrical demand, considerable solar energy is exported into electricity networks causing a range of issues such as voltage instability and connection overload. To address these problems, we propose and analyse a residential hot water, heating and cooling system, which features a heat pump combined with thermal energy storage to align peak thermal loads with output from a rooftop solar system. This work quantifies the impacts of thermal storage on residential space-conditioning peak load reduction. Annual hourly thermal and domestic hot water loads were determined for a representative Australian house, located in Brisbane, using building energy simulation software and verified using measured data. Combined with the measured sub-metered electrical loads of other electrical appliances, this data was used to simulate the solar system export, heat pump demand, and thermal storage system performance. Results show that by combining a 5-kW solar system, the proposed system can reduce annual grid-electricity demand by approximately 76% compared with a conventional non-thermal storage system. Peak electrical load was also observed to undergo a temporal shift and reduce by approximately 45%. Furthermore, the solar fraction for air-conditioning and domestic hot water loads reached 84%, while solar self-consumption increased to about 56%. This study demonstrates that the proposed system is an effective means of managing electricity demand, shifting peak load and improving solar utilization, thus relieving stress on electricity networks from high penetration of solar photovoltaics.

Quantification of climate-induced interannual variability in residential U.S. electricity demand

We assess the sensitivity of residential electricity demand in 48 U S. states to seasonal climate variations and structural changes pertaining to state-level household electricity demand. The main objective is to quantify the effects of seasonal climate variability on residential electricity demand variability during the winter and summer seasons. We use state-level monthly demographic, energy, and climate data from 2005 to 2017 in a linear regression model and find that interannual climate variability explains a significant share of seasonal household electricity demand variation: in 42 states, more than 70% and 50% of demand variability in summer and winter, respectively, is driven by climate. Our work suggests the need for new datasets to quantify unexplained variance in the winter and summer electricity demand. Findings from this study are critical to developing seasonal electricity demand forecasts, which can aid power system operation and management, particularly in a future with greater electrification of end-use demands.

Synthetic Domestic Electricity Demand in Thailand using A Modified High Resolution Modelling Tool by CREST

A residential electricity demand profile is one of the key roles for investigating the impacts of high penetration of low carbon technologies, such as photovoltaic systems and electric vehicles, on distribution networks. However, it is difficult to identify the true daily electricity consumption of Thailand household, caused by the lack of routine real time demand monitoring and residential electricity meter is normally on monthly which is a low time resolution. In this paper, the CREST Demand Model is employed to simulate a high resolution domestic electricity demand in Thailand, without installing new monitoring devices and customer interruption, through a stochastic process which is a combination of patterns of active occupancy, the outdoor ambient light characteristic and daily activity profiles. Due to the model is based on time use survey data in UK, the outdoor irradiance and appliance configuration are adapted to fit for the Thailand case study. In order to verify the model, the synthetic load profiles by CREST Demand Model is compared against measured data from the actual monitoring in a real low voltage network in Thailand. The results show that it is promising to apply the high resolution demand model by CREST to simulate the domestic electricity demand profiles in Thailand.

Estimating income and price elasticities of residential electricity demand with Autometrics

This paper estimates the income and price elasticities of the residential electricity demand for twelve major European countries using annual time series from 1975 to 2018. In the modelling exercise we adopt a novel econometric approach that features automatic model selection, saturation methods for detecting outliers and structural breaks, and the automatic model selection algorithm Autometrics. The selected specification for each country is an error correction model, from which it emerges a cointegrating relationship between electricity consumption, income, electricity price and climate variables, once that outliers and breaks are accounted for. The empirical results show that the estimated long-run income elasticities are less than one for all countries, and that the long-run price elasticities are in all cases less than one in absolute value. These results suggest that for European countries electricity is a normal good and that demand is price inelastic.

Price and income elasticities of residential electricity demand in Brazil and policy implications

The research objective was to estimate Brazil's residential electricity consumption behavior, given alterations in price and income. A balanced panel with monthly data was used for the 27 states of the country between 2004 and 2019, distributed into the five regions of Brazil. This approach enabled the calculation of price elasticity and income parameters, in the short and long terms, for both national and regional scenarios. They were used, as control variables, the average temperature, which was not used in previous research due to the use of a database with a higher level of aggregation, and the rainfall, that can be considered an important parameter in the Brazilian case where most of the electricity is obtained from hydroelectric generation, however, it has never been used in previous studies. The econometric model was estimated using the Generalized Method of Moments (GMM), version System - GMM (SY - GMM), to avoid dynamic bias of the panel and a serial correlation problem, which has not been considered in previous studies in Brazil. The results show that, although the short and long-term elasticities for the national scenario are similar to previous studies when analyzed regionally, the parameters proved to be quite different from previous research. Based on the results, adjustments were proposed for the Brazilian electricity price policy.

Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computing

The short-term residential electricity demand forecast tasks are essential for designing the portfolio of the decision-making process of aggregators for the selection of control targets and derivation of specific control target values for demand response. The implementation of edge computing on the forecast functionality, which provides forecasting and minimal communication functions based on the data accumulated at the end-user-side using an inexpensive computational resource deployed locally, is an attractive framework for practical implementation. To achieve good accuracy while forecasting the demands of individual households during practical long-term operation, it is necessary to follow the changes in demand characteristics quickly by updating the prediction models frequently. This study focused on the concept of deep reservoir computing, which has a high affinity for implementation in edge computing frameworks from the viewpoint of hardware implementability, while utilizing the deep architecture and avoiding high computational cost. In particular, this study proposed an online learning scheme for the implementation of edge computing, which does not require large-scale computation for updating the prediction models. The effectiveness of the proposed approach was evaluated considering the calculation cost and accuracy through numerical experiments using real-world residential load data.

Uncovering urban residents’ electricity conservation and carbon reduction potentials in megacities of China–A systematic path of behavioural interventions

The ratio and quantity of residential electricity consumption in China are increasing steadily with the country's economic development; conservation of electricity consumption has special significance for reduction of carbon emissions. Based on data from 635 questionnaire surveys in four typical Chinese megacities (Beijing, Hangzhou, Guangzhou, and Guiyang), this study analysed the determinants of residential electricity consumption and electricity saving behaviours using the multivariate regression method, estimated the price elasticities of heterogeneous resident groups by combining the results of an elasticity formula and a multinomial logit model, and assessed residential electricity conservation potential. The results were as follows: economic indicators proved to be the factors most strongly influencing residential energy conservation behaviours; electricity elasticity varied significantly among heterogeneous demographic and income groups towards economic intervention instruments, within a scale of 0.16-0.63; and price interventions can effectively reduce residential electricity demand and reduce carbon emissions from electricity use by 3.2%-9.3%, thereby improving energy conservation. An appropriate policy mix should be adopted to reduce household carbon emissions effectively, including addressing regional disparities and expanding ladder price differences across different income groups to fulfil every resident's well-being, along with increased publicity and education efforts to improve people's energy-saving awareness in the long term.

The Role of Temperature Variability on Seasonal Electricity Demand in the Southern US

The reliable and affordable supply of energy through interconnected systems represent a critical infrastructure challenge. Seasonal and interannual variability in climate variables—primarily precipitation and temperature—can increase the vulnerability of such systems during climate extremes. The objective of this study is to understand and quantify the role of temperature variability on electricity consumption over representative areas of the Southern United States. We consider two states, Tennessee and Texas, which represent different climate regimes and have limited electricity trade with adjacent regions. Results from regression tests indicate that regional population growth explains most of the variability in electricity demand at decadal time scales, whereas temperature explains 44–67% of the electricity demand variability at seasonal time scales. Seasonal temperature forecasts from general circulation models are also used to develop season-ahead power demand forecasts. Results suggest that the use of climate forecasts can potentially help to project future residential electricity demand at the monthly time scale.Capsule Summary: Seasonal temperature forecasts from GCMs can potentially help in predicting season-ahead residential power demand forecasts for states in the Southern US.

How price-responsive is residential retail electricity demand in the US?

Using a panel data analysis of a newly developed sample of monthly data by state for January 2005 to December 2019, we estimate a series of error correction models for US residential electricity demand postulated to move with electricity price, natural gas price, income, and weather. Our key findings are as follows. First, the short-run own-price elasticity estimate is not statistically different from zero (p-value > 0.8). Second, the long-run own- and cross-price elasticity estimates are −0.054 (p-value = 0.000) and 0.019 (p-value = 0.000) under the double-log specification, smaller in size than the long-run own- and cross-price elasticity estimates of −0.120 (p-value = 0.000) and 0.069 (p-value = 0.000) under the linear demand specification. Third, price elasticity estimates have been shrinking in size over time. Fourth, erroneously ignoring the panel data's cross-sectional dependence tends to more than double the long-run price elasticity estimates. Fifth, mismatching the timing of price information's availability and consumption decision leads to anomalous price elasticity estimates. Finally, our new empirics' key takeaway of low price-responsiveness supports continuation of energy efficiency standards and demand-side management programs.

A life-cycle theory analysis of French household electricity demand

This paper develops a pseudo-panel approach to examine household electricity demand behavior through the household life cycle and its response to income variations to help strengthen the energy policy-making process. Our empirical methodology is based on three rich independent microdata surveys (the National Housing Surveys), which are representative of the French housing sector. The resulted sample covers the 2006–2016 period. Using within estimations, this paper finds striking evidence that the income elasticity of French residential electricity demand is 0.22, averaged over our four cohorts of generations. In light of other works, our estimate stands in the lower range. The empirical results also show that residential electricity consumption follows an inverted U-shaped distribution as a function of the age of the household’s head. Most notably, it appears that households at the mid-point of their life cycle are relatively the largest consumers of electricity. This outcome has important implications for policy-making. Any public policy aimed at reducing household energy consumption should consider this differentiation in consumption according to the position of households over the life cycle, and therefore target as priority households at the highest level of consumption.

Agent Based Modelling of a Local Energy Market: A Study of the Economic Interactions between Autonomous PV Owners within a Micro-Grid

Urban Photovoltaic (PV) systems can provide large fractions of the residential electric demand at socket parity (i.e., a cost below the household consumer price). This is obtained without necessarily installing electric storage or exploiting tax funded incentives. The benefits of aggregating the electric demand and renewable output of multiple households are known and established; in fact, regulations and pilot energy communities are being implemented worldwide. Financing and managing a shared urban PV system remains an unsolved issue, even when the profitability of the system as a whole is demonstrable. For this reason, an agent-based modelling environment has been developed and is presented in this study. It is assumed that an optimal system (optimized for self-sufficiency) is shared between 48 households in a local grid of a positive energy district. Different scenarios are explored and discussed, each varying in number of owners (agents who own a PV system) and their pricing behaviour. It has been found that a smaller number of investors (i.e., someone refuse to join) provokes an increase of the earnings for the remaining investors (from 8 to 74% of the baseline). Furthermore, the pricing strategy of an agent shows improvement potential without knowledge of the demand of others, and thus it has no privacy violations.

Residential high-resolution electricity demand optimization with a cooperative PSO algorithm

Electricity use in residential sector accounts for almost a quarter of the total. Occupant’s indoor activities will highly affect the electricity use pattern. This paper presents a cooperative heuristic optimization algorithm for residential schedules of controllable appliances considering user’s indoor activities. First of all, Markov Chains are employed to model occupant’s behaviors, in which transition matrices are generated by using American Time Use Survey data. Then, engineering models of electric appliances are established and electricity consumption is able to be simulated by corresponding people’s activities to common electricity usage. Finally, a cooperative PSO algorithm is employed to find optimal schedules for these appliances. Experiments are conducted. The results indicate that peak loads can be shaved, electricity bills can be decreased by 3.1%, and a reduction of 5.9% of total power can be reached by optimizing high-resolution residential demand.

Design and optimization of seagull airfoil wind energy conversion device

ABSTRACT Remote areas in Northwest China are blind regions without large-scale power grid coverage. To satisfy the residential electricity demand, it is essential to utilize small-scale distributed wind power systems and increase wind energy utilization. The seagull airfoil wind energy conversion device is adopted to improve the aerodynamic performance and increase the use of wind energy. Based on the wind tunnel experiment and numerical simulation, the bending shape of the seagull airfoil is optimized. Further, the cascade structure is constructed using the central bending seagull airfoil to explore the flow mechanisms. The front and rear cascades are the main and secondary regions, accounting for 84% and 16% of the force generated, respectively. The deformation of the blade is significantly larger at the trailing edge than at the leading edge, thereby exhibiting unique deformation characteristics of seagull airfoils. When the impeller is rotated, the average force of each seagull airfoil was 15.1% more than that of the flat airfoil, thereby enabling the absorption of more wind energy and device optimization. Owing to the stress and deformation distribution, the seagull airfoil blade can adapt to different aerodynamic loads by changing the cambers and angles of attack to ensure smooth operation of the device.

Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand

Smart meters with automatic meter reading functionalities are becoming popular across the world. As a result, load measurements at various sampling frequencies are now available. Several methods have been proposed to infer device usage characteristics from household load measurements. However, many techniques are based on highly intensive computations that incur heavy computational costs; moreover, they often rely on private household information. In this paper, we propose a technique for the detection of appliance utilization patterns using low-computational-cost algorithms that do not require any information about households. Appliance utilization patterns are identified only from the system status behavior, represented by large system status datasets, by using dimensionality reduction and clustering algorithms. Principal component analysis, k-means, and the elbow method are used to define the clusters, and the minimum spanning tree is used to visualize the results that show the appearance of utilization patterns. Self organizing maps are used to create a system status classifier. We applied our techniques to two public datasets from two different countries, the United Kingdom (UK-DALE) and the US (REDD), with different usage patterns. The proposed clustering techniques enable effective demand-side management, while the system status classifier can detect appliance malfunctions only through system status analyses.

Impact of climate change on sectoral electricity demand in Turkey

ABSTRACT This paper analyzes the impact of climate change on Turkey’s residential, industrial and commercial electricity demand by applying the Structural Time Series Model. The model consists of explanatory variables and uses annual time series data for the period from 1980 to 2017. The variables included are electricity price, income per capita, urbanization rate, manufacturing added value, population-weighted cooling degree days (as a climate variable) and, finally, stochastic Underlying Energy Demand Trend. The effect of cooling degree days on the residential, industrial and commercial electricity demand is found to be significantly elastic. Moreover, residential, industrial and commercial electricity demands in 2031 are expected to increase considerably, and at different ratios, depending on the chosen climate change scenario. The findings of this study are significant for efforts to establish regulations to shape energy security policies in the presence of climate change.

Factors dominating peak electricity demand in Bangladeshi urban households: an assessment through the energy cultures framework

ABSTRACT Residential electrical peak demand has attracted much attention as this demand is unpredictable. Several approaches have been used in the literature to analyze residential electrical demand. However, no previous studies have considered the energy cultures framework (ECF) to assess residential peak demand. This study investigates the applicability of ECF in accounting for residential demand variability. The ECF consists of three elements: material culture, cognitive norms, and energy practices. The ECF has been employed in a sample of 50 households in Bangladesh, of which 32% of households (HHs) were ‘peaky’ (peak time demand is much higher than average) and 68% HHs ‘non-peaky’ (average demand during peak hours). The analysis revealed that peaky HHs in Bangladesh is dominated by material culture. Non-peaky HHs’ demands are driven by cognitive norms. Spearman test results were found to be in line with ECF analysis.

Validating Clustering Frameworks for Electric Load Demand Profiles

Large-scale deployment of smart meters has made it possible to collect sufficient and high-resolution data of residential electric demand profiles. Clustering analysis of these profiles is important to further analyze and comment on electricity consumption patterns. Although many clustering techniques have been proposed in the literature over the years, it is often noticed that different techniques fit best for different datasets. To identify the most suitable technique, standard clustering validity indices are often used. These indices focus primarily on the intrinsic characteristics of the clustering results. Moreover, different indices often give conflicting recommendations, which can only be clarified with heuristics about the dataset and/or the expected cluster structures-information that is rarely available in practical situations. This article presents a novel scheme to validate and compare the clustering results objectively. Additionally, the proposed scheme considers all the steps prior to the clustering algorithm, including the preprocessing and dimensionality reduction steps, in order to provide recommendations over the complete framework. Accordingly, the proposed strategy is shown to provide better, unbiased, and uniform recommendations as compared to the standard clustering validity indices.

Household differentiation and residential electricity demand in Korea

Abstract This paper investigates the impact of rapid demographic changes in Korea on residential electricity use. Demographic changes in Korea are characterized as i) household differentiation (increasing number of nuclear (one- or two-person) households despite the stagnant population growth) and ii) unprecedentedly fast-paced population ageing. Per capita electricity demand is estimated using a dynamic panel model with the system GMM. Results show that household differentiation has increased per capita electricity use in both short- and long-run; the ageing population has the negative impact on per capita electricity consumption. The impact of household differentiation, which is projected to be accelerated with the persistent low fertility in Korea, is substantial. Nuclear households use more electricity per person, but pay less electricity bill since Korea has the progressive electric billing system. Stakeholders and industry analysts should consider household differentiation to prepare the long-run electricity provision policy as well as the proper billing system. Lessons from the study have wider policy implications in other developed countries with the similar demographic changes.

Forecasting residential electricity demand in the Philippines using an error correction model

This study uses an Error Correction Model (ECM) to forecast residential electricity demand in the Philippines using household final consumption expenditure, residential electricity price, and temperature as explanatory variables. Results show that there is a long-run relationship between household final consumption expenditure and residential electricity demand. Estimates from the ECM are consistent with economic theory, and the model passed standard diagnostic and parameter stability tests. Forecast performance based on within-sample and out-of-sample forecasts of the ECM is also shown to be superior, relative to a benchmark Autoregressive Distributed Lag (ARDL) model. Simulations show that by 2040, residential electricity consumption will range from 42,500 gigawatthours (GWh) based on a weak income growth scenario and 62,000 GWh based on a combined changes scenario.

The impact of extreme climate change on the fluctuation of electricity energy demand——Evidence from China's prefecture-level cities

Climate change directly make the demand for electricity diversified and uncertain, which increase the risk of power grid operation. This paper attempts to explore the impact of extreme climate change on the fluctuation of China's electricity energy demand from the perspective of climate change. Based on the panel data of 90 prefecture-level cities in China from 1989 to 2017, the author builds an econometric model to test the impact of extreme low temperature and extreme high temperature on electricity demand. The results show that the occurrence of extreme high temperature weather has a positive effect on residential electricity demand while the emergence of extreme low temperature weather has a negative effect.