Residential Electricity Demand Research Articles

Price responsiveness of solar and wind capacity demands

Accurate estimates of the price responsiveness of residential, commercial, and industrial electricity demands are essential for energy policy modelling, integrated resource planning, and determining a competitive wholesale electricity market's generation levels, prices, and capacity investments. Hence, we estimate the own-price elasticities of solar and wind capacity demands of a load serving entity (LSE) that provides retail electricity service, thereby answering two interrelated research questions: (1) does solar capacity demand far exceed wind capacity demand? and (2) are solar and wind capacity demands price-elastic? Inspired by the theory of input demand under input price uncertainty, our innovative methodology integrates (a) wholesale spot energy price forecasts by time of day; (b) pseudo data found by minimizing a LSE's annual risk-adjusted budget for procuring solar and wind capacities; and (c) econometric analysis of (b) to estimate the extent of substitutability between solar and wind capacities and the own-price elasticities of solar and wind capacity demands. Using Texas as an illustrative example, we find that when solar and wind power purchase agreements have similar energy prices, solar capacity demand is approximately four times wind capacity demand. Further, the own-price elasticity estimates are −5.34 for solar capacity demand and −5.65 for wind capacity demand. As a result, solar and wind capacity demands tend to substantially grow (shrink) in response to declining (rising) solar and wind energy prices. This lends support to proposals to raise solar and wind energy prices for mitigating the adverse effects of large-scale variable renewable energy development on an electric grid's efficient operation and system reliability. However, adopting such proposals also slows the grid's pace of decarbonization, thus underscoring the policy and regulatory challenges in the quest for a clean and sustainable electricity future. Hence, our policy recommendation of price managing solar and wind capacity demands is a topic of policy debate that deserves the attention of an electric grid's stakeholders.

Estimating the direct rebound effect for residential electricity use in seventeen European countries: Short and long-run perspectives

The energy saved by improving the building efficiency of European households may not be as much as expected due to a rebound effect. Based on Eurostat and World Bank annual panel data covering the period 1996–2020, this article develops dynamic panel approaches, a generalized method of moments, and first-difference specifications, to investigate the direct rebound effect of residential electricity consumption in selected European countries. Instrumental variable techniques are used to tackle the reverse causality of the electricity price in a demand model. Estimates of the direct rebound effect in residential electricity use are 18% in the short-run and 43% in the long-run. Our findings reject the hypothesis of a backfire effect in residential electricity demand. They have important implications for policy makers, suggesting the need for smart policies that consider energy consumption behaviors and decision-making processes among various households.

Matchmaking in Off-Grid Energy System Planning: A Novel Approach for Integrating Residential Electricity Demands and Productive Use of Electricity

Off-grid electrification planning increasingly recognizes the importance of productive use of electricity (PUE) to promote community value creation and (financial) project sustainability. To ensure a sustainable and efficient integration in the community and energy system, PUE assets must be carefully evaluated to match both the community needs and the residential electricity demand patterns. We propose a novel methodology interlinking qualitative interviews, statistical analysis and energy system modeling to optimize decision making for PUE integration in off-grid energy systems in rural Madagascar by aligning relevant PUE effectively with anticipated residential electricity demand patterns based on socio-economic determinants of the community. We find that a possible contribution of the PUE to reducing the electricity costs depends significantly on three factors: (1) The residential electricity consumption patterns, which are influenced by the socio-economic composition of the community; (2) The degree of flexibility of (i) PUE assets and (ii) operational preferences of the PUE user; and (3) The capacity of community members to finance and operate PUE assets. Our study demonstrates that significant cost reductions for PUE-integrated off-grid energy systems can be achieved by applying our proposed methodology. When matching PUE and residential consumption patterns, the integration of PUE assets in residential community energy systems can reduce the financial risk for operators, provided the PUE enterprise operates reliably and sustainably. We highlight that the consideration of local value chains and co-creation approaches are essential to ensure the energy system is addressing the community’s needs, creates value for the community, enhances the project’s financial sustainability and is achieving the overall objectives of decentralized energy system planning.

Impact of bi-directional electric vehicle and demand response on residential distributed PV capacity planning based on TOU pricing

The widespread deployment of residential distributed photovoltaic (RDPV) remains complex and challenging due to photovoltaic output intermittency, fluctuating electricity demand, and rising electric vehicle (EV) adoption. Simultaneously, the energy storage capabilities of EVs and residential demand response (DR) offer solutions for optimizing RDPV applications. This study proposes an integrated RDPV capacity planning model by encompassing EV charging, vehicle-to-home, and flexible load DR. Five scenarios are established to reveal the impact of various factors on the optimal photovoltaic installation capacity, electricity cost, self-consumption and self-sufficiency rate. A case study of three typical residential electricity demand patterns indicates that DR and vehicle-to-home significantly reduce the optimal photovoltaic installation capacity and total electricity cost. When the feed-in tariff during photovoltaic generation periods is higher than the off-peak pricing, DR results in a reduction in photovoltaic self-sufficiency rate and an increase in photovoltaic self-consumption rate. EV charging and vehicle-to-home have minimal impact on photovoltaic self-consumption rate, while EV charging significantly decreases self-sufficiency rate and vehicle-to-home exacerbates this effect.

Income elasticity of residential electricity consumption in rural South Africa

Analyses of residential electricity demand primarily rely on expenditure or aggregate data. However, newer sources of data, such as that from meter readings, are becoming available. In most circumstances, these newer sources cannot be matched to detailed information about the household. In this research, we make use of South Africa’s Domestic Electrical Load Study, one of the only sources available in a developing country that includes both meter and household level data. Due to some gaps in the meter readings, we focus our attention on average peak electricity consumption, estimating the income elasticity with respect to morning, evening, and the average across both morning and evening peaks. Although we find differences in income responsiveness in the morning, relative to the afternoon, and across quantiles of electricity consumption, these differences tend not to be statistically significant. We do, however, find heterogeneities in those elasticities that can be correlated with, in particular, appliance ownership, suggesting that the ownership of appliances makes electricity more of a necessity, or at least makes the services derived from electricity more necessary for the household.

A multi-step electricity prediction model for residential buildings based on ensemble Empirical Mode Decomposition technique

Residential electricity demand is increasing rapidly, constituting about a quarter of total energy consumption. Electricity demand prediction is one of the sustainable solutions to improve energy efficiency in real-world scenarios. The non-linear and non-stationary consumption patterns in residential buildings make electricity prediction more challenging. This paper proposes a multi-step prediction approach that first conducts cluster analysis to identify seasonal consumption patterns. Secondly, an improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method and autoencoder model has been deployed to remove irregular patterns, noise, and redundancy from electricity load time series. Finally, the Long Short-Term Memory (LSTM) model has been trained to predict electricity consumption by considering historical, seasonal, and temporal data dependencies. Further, experimental analysis has been conducted on real-time electricity consumption datasets of residential buildings. The comparative results reveal that the proposed multi-step model outperformed the existing state-of-the-art RF-LSTM-based prediction model and attained higher accuracy.

Residential electricity demand on CAISO Flex Alert days: a case study of voluntary emergency demand response programs

Abstract The California Independent System Operator (CAISO) utilizes a system-wide, voluntary demand response (DR) tool, called the Flex Alert program, designed to reduce energy usage during peak hours, particularly on hot summer afternoons when surges in electricity demand threaten to exceed available generation resources. However, the few analyses on the efficacy of CAISO Flex Alerts have produced inconsistent results and do not investigate how participation varies across sectors, regions, population demographics, or time. Evaluating the efficacy of DR tools is difficult as there is no ground truth in terms of what demand would have been in the absence of the DR event. Thus, we first define two metrics that to evaluate how responsive customers were to Flex Alerts, including the Flex Period Response, which estimates how much demand was shifted away from the Flex Alert period, and the Ramping Response, which estimates changes in demand during the first hour of the Flex Alert period. We then analyze the hourly load response of the residential sector, based on ∼200 000 unique homes, on 17 Flex Alert days during the period spanning 2015–2020 across the Southern California Edison (SCE) utility’s territory and compare it to total SCE load. We find that the Flex Period Response varied across Flex Alert days for both the residential (−18% to +3%) and total SCE load (−7% to +4%) and is more dependent on but less correlated with temperature for the residential load than total SCE load. We also find that responsiveness varied across subpopulations (e.g. high-income, high-demand customers are more responsive) and census tracts, implying that some households have more load flexibility during Flex Alerts than others. The variability in customer engagement suggests that customer participation in this type of program is not reliable, particularly on extreme heat days, highlighting a shortcoming in unincentivized, voluntary DR programs.

Techno-economic impact of electricity price mechanism and demand response on residential rooftop photovoltaic integration

Rooftop distributed photovoltaics show great potential for deep decarbonisation in the residential sector. However, the intermittency and undispatchability of photovoltaic output are urgent problems in the large-scale deployment of rooftop distributed photovoltaics. This study develops a techno-economic evaluation framework for rooftop distributed photovoltaics by comprehensively considering and exploring the uncertain effects of electricity price mechanisms, battery energy storage, demand response for residential flexible loads, and residential electricity demand difference to increase self-consumption and the economic benefits of rooftop distributed photovoltaics. The results show that electricity price mechanisms are the main factors affecting the economic benefits of rooftop distributed photovoltaics, which are more economical under time-of-use prices than under flat prices. Installing battery energy storage and participating in demand response for residential flexible loads can significantly improve self-consumption and self-sufficiency rates, and bring more electricity bill savings and less feed-in revenue. Specifically, the relative economic benefits of installing battery energy storage and demand response were greater at lower feed-in tariffs. Moreover, the greater the ratio of annual residential electricity demand to photovoltaic power generation, the greater the self-consumption rate. Further, only when battery energy storage is installed, the smaller the ratio, the greater the self-sufficiency rate. Consequently, the energy sector can design more efficient time-of-use pricing mechanisms and adopt lower feed-in tariffs to encourage residential consumers to deploy both rooftop distributed photovoltaics and battery energy storage and to participate in demand response.

Achievable load shifting potentials for the European residential sector from 2022–2050

This paper develops quantitative predictions for residential electricity demand and the potential for load shifting for Europe at a fine spatial scale for years 2022–2050. To date only theoretical and spatially coarse measurements of this potential exist for a subset of technologies used in the residential sector. As the EU moves towards an ‘Energy Union’, it will be increasingly necessary to consider the whole integrated energy system across the EU and thereby databases with large spatial coverage are needed. To fill this need, we generate hourly load profiles for the residential sector for key electricity consuming devices that take into account the most recent empirical studies of usage patterns, and of the cost and willingness of households to participate in load shifting programs. Additionally, we extend the list of investigated technologies by the important classes of electric mobility and heat pumps for electric space heating. Predictions and insights provided in our article serve multiple use cases, i. they can be used in energy system models to assess the value of load shifting for the future energy system including system costs, ii. they enable infrastructure planners and grid operators to calibrate their expectations of the future role of load shifting in the electricity systems and iii. enable policy makers and to include load shifting in their considerations of future market designs.

Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand

Consensus-based time-series clustering approach to short-term load forecasting for residential electricity demand

A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment

The increasing population migration to urban and peri-urban areas increases basic service needs for cities worldwide. Residential electricity demand increases with more customers and varies with novel uses, such as charging electric vehicles, which may add additional dynamics to the residential electricity demand profile. Widespread installation of smart electricity meters provides fine temporal resolution data reflecting current demands and supports predicting future demands. As part of a demand-side management program, understanding the main drivers of current electricity usage based on demand-driven and exogenous predictors represents a valuable tool for utilities facing new demand scenarios. This work presents the application of multiple models to forecast electricity demand based on the input data and the forecasting horizon. Models with exogenous variables as predictors are part of the input–output category, including a Feed Forward Neural Network, Random Forest, and a Linear Gaussian State Space model. The second category is demand-driven models, where predictors include only previous demand values. The demand-driven models in our analysis include a univariate Nonlinear Autoregressive Neural Network and a Linear Gaussian State Space. Using smart electricity meter data from the greater Chicago area, we compare the performance of the models on two different types of accounts: single- and multi-family residential users when forecasting one and multiple steps. Results show that the Linear Gaussian model reports an R2 of 0.99 compared to an average R2 of 0.92 from the Feed Forward Neural Networks and Random Forest when forecasting single- and multi-family electricity demand one step ahead. However, Nonlinear Autoregressive Neural Networks report an average R2 of 0.85 compared to the Linear Gaussian R2 of 0.58 when forecasting 48 steps. We also found that the most important predictors for single-family demand are temporal variables like weekdays, working and non-working days, and day hours. For multi-family demand, electricity demand at the same hour as the previous week replaces weekdays as a significant predictor. Different forecasting models can assist utilities and city planners to manage demand under different and novel residential electricity usage conditions.

전력 수요 시계열 분석과 장기예측

This study considers the time series characteristics in residential electricity demand function and forecasts long-run electricity demand in Korea. It is widely known that electricity demand and its determinants are unit root processes, so that estimating and forecasting the electricity demand function based on cointegration relationship are common in empirical analyses. We apply unit root tests on monthly time series data for the period 2006:01-2022:12. Korea has experienced the rapid development and social changes over this period, which provides convincing evidence of the possibility of structural changes. The unit root test of Dickey, Fuller (1979) tends to be biased in favor of non-rejection of the unit root null hypothesis if the process considered is trend-stationary with a slope change. What we found is that electricity demand and its determinants are trend-stationary processes allowing for a slope change, whereby there is no need to invoke either cointegration regression model or error-correction model under the unit root assumption. We apply a regression model with a structural change to estimate the residential electricity demand function and forecast the long-run electricity demand for the period 2023-2050.

Decentralized Biogas Production in Urban Areas: Studying the Feasibility of Using High-Efficiency Engines

The study examines decentralized waste treatment in an urban setting with a high-density population of 2500 inhab./km2. The co-digestion of food and garden waste was assumed by using several mid-size digesters, while centralized biogas and digestate valorization was considered. The studied configuration generates electricity and thermal energy, covering 1.3% of the residential electricity demand and 3.2% of thermal demand. The use of double-turbocharged engines under the most favorable scenario aids cities in reaching sustainability goals. However, the location of treatment plants is a factor that may raise social discomfort and cause a nuisance to citizens. Locating waste plants near residential areas causes discomfort due to possible odors, gaseous emissions, and housing market distortions. Such problematic aspects must be addressed for the decentralized alternative to work. These factors are of great relevance and must be given a practical solution if the circular economic model is to be implemented by considering the insertion of waste streams into the production system and generating local energy sources and raw materials.

Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System

With the rapid development of smart grids and distributed energy sources, the home energy management system (HEMS) is becoming a hot topic of research as a hub for connecting customers and utilities for energy visualization. Accurate forecasting of future short-term residential electricity demand for each major appliance is a key part of the energy management system. This paper aims to explore the current research status of household-level short-term load forecasting, summarize the advantages and disadvantages of various forecasting methods, and provide research ideas for short-term household load forecasting and household energy management. Firstly, the paper analyzes the latest research results and research trends in deep learning load forecasting methods in terms of network models, feature extraction, and adaptive learning; secondly, it points out the importance of combining probabilistic forecasting methods that take into account load uncertainty with deep learning techniques; and further explores the implications and methods for device-level as well as ultra-short-term load forecasting. In addition, the paper also analyzes the importance of short-term household load forecasting for the scheduling of electricity consumption in household energy management systems. Finally, the paper points out the problems in the current research and proposes suggestions for future development of short-term household load forecasting.

A hazai lakossági villamosenergia szektor paradigmaváltása és a napelemes HMKE-k

The European Union and Hungary are committed to increasing the share of green energy in the energy mix. There are two main challenges for the Hungarian energy sector today. The first is energy safety that is to provide enough energy to fulfil the industrial and residential electricity demands and meeting environmental requirements at the same time. The other is to prepare for potential shifts in industrial and residential consumer habits. We deal with the political and economic impacts on the energy balance, the booming of electricity consumption, the development of the Hungarian electrical grid and challenges induced by weather dependent energy sources. We present some best practices, in particular low or no cost examples that can contribute to the balance of electricity production and consumption. We examine the conditions of economical operation of microgeneration solar photo voltaic systems after the potential lapse of net metering.

Urban residential water and electricity consumption behavior: A systematic literature review

Consumer behavior is an understudied determinant of residential water and electricity demand and, when studied, capturing, measuring, and quantifying attitudes and behaviors is often problematic. This research explores behavioral determinants of the demand for water and electricity, contributing a set of categories and items that may be useful for understanding knowledge, attitudes, and practices related to consumption and constructing valid scales. These results may help researchers capture the complexity of consumer behavior regarding water and electricity and decision-makers in designing tailored demand management strategies.

An efficient user demand response framework based on load sensing in smart grid

The current residential electricity demand is increasing. The demand side response of smart grid power users aims to enable users to reasonably plan their own power consumption through price incentives, so as to solve the problems of unreasonable power energy structure and low utilization rate. It is prominent to mine the rules of user response behaviors and design a reasonable incentive mechanism to maximize the enthusiasm of all participants. The traditional demand response is to ensure the stability of the power system from the macro-control load of the grid, which cannot meet the personalized requirements of power users. The existing incentive mechanism also does not comprehensively consider the profits of grid companies, low-voltage users, aggregators and other parties. In this paper, we propose a user demand response framework based on load awareness. Firstly, we devise a user demand response behaviour model based on short-term memory network. Secondly, we propose a demand response incentive scheme based on electric power scores. We also construct a deviation optimization integration adjustment model based on game theory to achieve the balance of profits among grid, aggregators and low-voltage users. The extensive experimental results show the effectiveness of our proposed framework.

Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review

In this paper, a systematic literature review is presented, through a survey of the main digital databases, regarding modelling methods for Short-Term Load Forecasting (STLF) for hourly electricity demand for residential electricity and to realize the performance evolution and impact of Artificial Intelligence (AI) in STLF. With these specific objectives, a conceptual framework on the subject was developed, along with a systematic review of the literature based on scientific publications with high impact and a bibliometric study directed towards the scientific production of AI and STLF. The review of research articles over a 10-year period, which took place between 2012 and 2022, used the Preferred Reporting Items for Systematic and Meta-Analyses (PRISMA) method. This research resulted in more than 300 articles, available in four databases: Web of Science, IEEE Xplore, Scopus, and Science Direct. The research was organized around three central themes, which were defined through the following keywords: STLF, Electricity, and Residential, along with their corresponding synonyms. In total, 334 research articles were analyzed, and the year of publication, journal, author, geography by continent and country, and the area of application were identified. Of the 335 documents found in the initial research and after applying the inclusion/exclusion criteria, which allowed delimiting the subject addressed in the topics of interest for analysis, 38 (thirty-eight) documents were in English (26 journal articles and 12 conference papers). The results point to a diversity of modelling techniques and associated algorithms. The corresponding performance was measured with different metrics and, therefore, cannot be compared directly. Hence, it is desirable to have a unified dataset, together with a set of benchmarks with well-defined metrics for a clear comparison of all the modelling techniques and the corresponding algorithms.

Exploring the direct rebound effects for residential electricity demand in urban environments: evidence from Nice

Exploring the direct rebound effects for residential electricity demand in urban environments: evidence from Nice

HOUSEHOLD-SPECIFIC SOCIAL NORMS, THE ELASTICITY OF ELECTRICITY DEMAND, AND CARBON EMISSION REDUCTIONS IN THE RESIDENTIAL SECTOR: EVIDENCE FROM A NATURAL EXPERIMENT IN RUSSIA

In this paper, I estimate the price elasticity of residential electricity demand using household-level panel data for Russia. The study takes advantage of the variation in tariffs across regions and over time, as well as the introduction of increasing block rate (IBR) schemes in a number of regions. I show that in those regions consumers appear to be aware of the block cut-offs, even though the latter are based on the prescribed social norms and are household and dwelling specific, to the point that there are up to a total of 31 different tier cut-offs. Based on these results, I estimate the price elasticity of electricity demand to be around −0.1. I also predict the associated changes in electricity consumption, CO2 emissions, and revenues if similar IBR policies are implemented countrywide.