Residential Consumers Research Articles

Impact of Digital Technology on Traditional Banking: A Case From the Credit Market in the European Union

ABSTRACTThe aim of this study is to investigate whether digital technologies affect the credit market in the European Union (EU). The impact of digital technology on three types of bank loans (residential mortgage loans, consumer loans and corporate loans) is analysed separately for two groups of EU countries: Central and Eastern Europe and Western Europe. A dynamic panel regression model is employed based on yearly data on the individual bank level. The sample panel data cover the period 2010–2019. The results show that digital technology affects the growth of loans for households, mortgages and consumer loans. We also find heterogeneity between the Central and Eastern European and Western European banking sectors. Furthermore, the impact of digital technology on bank loan development is significantly stronger in foreign banks. Finally, the findings confirm the dominant role of digital technologies in loans for households during the analysed period. This is the first comprehensive study on the determinants of different loan types in the context of digital technology use in the EU.

Increasing Renewable Energy Penetration on Low-Voltage Networks: An Expert Knowledge Approach

While South Africa is deemed one of the countries with the highest irradiation levels, it still utilises coal as its primary energy source due to its abundance. Due to the world-wide drive towards carbon neutrality, residential, commercial, agricultural, and industrial consumers are considering small-scale embedded generation systems. The National Rationalised Specifications 097-2-3 document specifies the scale of the embedded generation capacity a consumer is allowed to install. However, specifications do not yet make the required provisions for the addition of energy storage. The effective collective management of the grouped small-scale embedded generation systems could provide a high level of energy security and increase the percentage of renewable energy generation in the total energy mix. Potential challenges come into play when considering the stochastic nature of photovoltaic generation and its effect on the storage capacity and the dispersion in load profiles of the residential units typically present on a low-voltage network. This paper contributes by investigating the utilisation of photovoltaic generation in conjunction with storage as the basis for virtual power plant control, with the aim to safely increase renewable energy penetration and improve energy security, all while remaining within the South African low-voltage regulatory limits. A two-level virtual power plant controller is proposed with the dispersed energy storage units as the primary controllable resources and the dispersed photovoltaic generation as the secondary controllable resources. The objective of the controller is to achieve nodal energy management, energy sharing, and ancillary service provision and finally to increase renewable energy penetration. A representative single-feeder low-voltage network is simulated, and test cases of 50% and 75% renewable energy penetration are investigated as the basis for evaluation. The proposed controller architecture proved to maintain network integrity for both test cases. The adaptability of the controller architecture was also confirmed for a changed feeder topology; in this case, it was a multi-feeder topology. Future work is warranted to inform policy on the allowed levels of renewable energy penetration to be based not only on demand but also on the level of energy storage present in a network.

MAXIMIZING SELF-CONSUMPTION FROM PHOTOVOLTAICS

The present study refers to the maximization of self-consumption through the automation of a residential consumer that is or not fed from the low-voltage electrical network and from its own photovoltaic system. The level of self-consumption from photovoltaic sources in the residential sector is strictly dependent on the power installed in the own sources, as well as the load profile of the respective consumer. Two major directions are relevant regarding the possibility of increasing and optimizing self-consumption: the management of electrical energy storage systems (especially through the use of battery systems), respectively the implementation of load management systems, through the partial or total redistribution of their operation over time, but also through prioritization of critical consumers.

Sustainability and energy transition in Swiss universities of applied sciences

The University of Applied Science (UAS) in Switzerland were reformed around 25 years ago and have since achieved considerable success in education and applied research. At the core of the UAS mission are the challenges of realizing sustainability in the industrial context and determining how to tackle the energy transition. This paper presents two examples (residential buildings and consumer products) to demonstrate how the aforementioned challenges are confronted in the UAS. These examples bring the necessity to confront sustainability in a comprehensive and interdisciplinary way. Engineering skills should be complemented with knowledge of integration among the human and technological aspects of a project.

Toward sustainable energy transition: understanding electricity-use behavior among low-income urban households in Kumasi

ABSTRACT The residential sector is a critical contributor to global energy consumption. In Ghana, it accounts for 47% of the total electricity consumed. However, inadequate knowledge of categories of residential consumers, their profiles, and the implications on their consumption patterns could impede the development of targeted and sustainable interventions. This study examined the behavioral patterns among low-income urban households in Kumasi, using their socio-demographic and housing characteristics, and self-reported values. This is coherent to the current discourse on Ghana’s transition to zero carbon by 2070 given that Kumasi is the fastest-growing city in the country. Using quantitative methods within a transdisciplinary research design, a survey of 285 households was conducted. The findings yielded varied outcomes. Households reported good electricity use behavior (e.g. covering liquids in the refrigerator) and poor behaviors (e.g. not adjusting the power setting of refrigerators to weather conditions). Logistic regression operations showed associations between energy use behavior, the type of housing units and the type of electricity meters. A significant proportion of respondents exhibited pro-environmental behaviors across all nine value domains. The study concludes that low-income households engage in energy-saving practices. We suggest that policy mediation could enhance the effectiveness of behavioral interventions toward electricity use efficiency.

Optimal strategy for transition into nearly zero energy residential buildings: A case study

This study suggests a customized design for turning an existing residential structure into a nearly zero energy building, along with a peak-power limiting strategy. The proposed work focuses on curtailing peak power demand of residential consumers through shifting the appliances connection time. The load scheduling is modeled as an optimization problem and solved using whale optimization algorithm and fuzzy logic. This is followed by reviewing the required size of the rooftop solar photovoltaic system for various types of residential consumers in a community using self-consumption and self-sufficiency-based indices. The proposed optimization algorithm effectively reduces the peak power drawn by 27 % and the peak-to-average ratio by 30.69 % on average in the residential community. Further, the rooftop PV size assessed substantiates that the energy drawn from the grid is nearly zero, attaining energy independence.

Solar surge and cost shifts: Heterogenous effects of redistribution in the electricity bills in Japan

This study investigates the impact of daily intermittency in solar energy on retail electricity bills, focusing on the “Duck Curve” and “Peak Shifting” phenomena and their varying effects on different consumer groups. Notably, commercial and industrial users exhibit higher demand around midday, while residential users peak during morning and evening hours. This prompts a crucial question: do peak shifting and redistribution effects differ among consumer groups due to their distinct electricity consumption patterns? Our analysis centers on Japan's power system, which has witnessed significant solar energy penetration, with solar capacity growing from 42 GW in 2016 to 83 GW in 2022. Utilizing monthly electricity consumption data and employing robustness checks through instrumental variable methods, we empirically demonstrate that increased solar penetration leads to higher electricity prices for residential consumers but reduced costs for commercial-industrial users. Additionally, we uncover the mediating role of pumped hydro storage (PHS) stations in stabilizing prices during peak and off-peak periods by managing energy demand variability, thereby mitigating economic impacts across consumer groups. This study highlights the issue of cross-class subsidization, where certain groups bear disproportionately higher electricity costs due to increased renewable energy penetration. It underscores the necessity for carefully crafted policy interventions to uphold market fairness and sustainability alongside large-scale renewable penetration, ensuring a balanced and equitable energy transition.

Residential consumer enrollment in demand response: An agent based approach

Residential consumers play an important role in the sustainable transition of the energy system by leveraging their household loads for demand response (DR). This paper aims to analyze the enrollment rates of residential consumers within DR through an agent-based model (ABM). Both economic and noneconomic (social/behavioral) parameters that influence the consumer enrollment in DR are considered. An energy management model, a home energy management system (HEMS), is used to identify the potential economic savings of consumers enrolling in DR. Consumers are randomly assigned to different neighborhoods and have different social relationships (e.g., friends, neighbors), which, in turn, influences their decision-making in the ABM. The results of this paper highlight the indirect relationship of expected annual savings and direct relationship of the share of consumers having electric vehicles (EV), photovoltaics (PV), and battery energy storage systems (BESSs) on the DR enrollment rates. Based on the enrollment rates, the maximum energy savings were obtained in April and the minimum during the last quarter of the year. Monte Carlo analysis is employed to handle the randomness associated with different variable selections, which provides a ±10% variation of consumer enrollment rate in DR. The results of this study have practical implications for energy flexibility in the residential sector.

System-wide benefits of temporal alignment of wholesale–retail electricity prices

Exposing residential consumers to real-time pricing (RTP) can yield significant efficiency gains, but may also pose challenges due to the inherent complexity. Using an extensive spectrum of temporal granularities for retail electricity pricing, we analyze to what extent these capture the benefits of RTP while concurrently considering the influence of three distribution tariff types in distorting the pricing signal. Via a mixed complementarity model, we consider both operational and investment decisions at retail as well as wholesale levels, accounting for their interdependencies. Our analysis reveals three key findings. Firstly, we find that decreasing temporal granularity of retail electricity prices increases total system cost due to an increasingly inefficient generation mix, however, three-hourly or six-hourly pricing can approximate RTP benefits. Secondly, decreasing temporal granularity of retail electricity prices raises average offtake and lowers average injection prices for consumers due to changes in wholesale market prices as well as consumption patterns of consumers. Thirdly, capacity-based tariffs reduce system offtake peaks and associated price increases by incentivizing battery discharge during peak consumption while volumetric tariffs, over-incentivizing solar PV investments, result in highest offtake prices and injection peaks.

Invisible threats: An investigation of electrical hazards and safety practices among residential electricity consumers

Understanding electrical hazards and implementing safety measures is paramount to protecting lives and property. Therefore, this research investigates electrical hazards in households and safety measures taken by residents in Sokode–Etoe, Ghana. The primary objective is to identify gaps in knowledge regarding electrical hazards among domestic electricity consumers and offer recommendations to enhance safety and mitigate the risks. The data were systematically collected from 200 participants, including both homeowners and tenants, using a structured questionnaire. The results were presented using Likert scale analysis, sample t-test, binary logistic regression analysis, involving statistical hypothesis testing of predictor variable coefficients, Importance-Performance Map Analysis (IPMA) and Necessary Condition Analysis (NCA). Participants showed a high awareness of electrical hazards, yet demonstrated a weaker grasp of safety practices, correct emergency procedures, and infrequent testing of wiring systems by homeowners. The predominant electrical accident that emerged was electrical shock. Most homeowners have not engaged certified electrical inspectors for a decade, reflecting uncertainty about the safety protocols in place. Furthermore, respondents expressed a degree of uncertainty regarding the safety measures implemented in their households concerning electricity usage. This study underscores the pressing need to raise awareness and promote safe electrical practices in residential environments. Such an educational initiative could utilize a variety of communication channels, social media influencers, renowned personalities, customised mobile applications and other platforms. This research stands out as the inaugural investigation offering a comprehensive examination of the hazards related to energy consumption and safety precautions in Ghana. It focuses on an often-overlooked demographic of electricity users in Ghana, shedding light on domestic electrical safety issues and the growing hazards.

Challenges, improvements, and opportunities market with the liberalization of the residential electricity market

Numerous countries have adopted electricity market liberalization for residential consumers. Although the benefits of the free market are notorious, when this opening refers to the residential consumer, a bigger debate about such benefits is necessary, in addition to the carefully planned implementation. Hence, this paper sought to shed more light on the main barriers a country faces in opening the retail market, the technological and market improvements required to successfully implement the retail market for the residential class, and the market opportunities the opening promotes to stakeholders. Extensive systematic literature and grey literature reviews revealed 16 barriers, 17 improvements, and 12 opportunities, which were analyzed, discussed, and grouped into four primary barriers, five primary market improvements, and five primary opportunities. These insights provide a solid foundation for developing practical and applicable recommendations for energy stakeholders and policymakers to promote effective improvements, appropriate strategies, and sound policy in the context of the retail market for residential electricity.

Hierarchical transactive home energy management system groups coordination through multi-level consensus sharing-based distributed ADMM

Coordinating residential building groups requires a hierarchical structure in which aggregate objectives and coupled constraints are incorporated into decision-making processes at different layers of the electric distribution system. Failure to handle these matters can raise issues, such as rebound peaks and contingencies. This paper proposes a Hierarchical Transactive Coordination Mechanism (HTCM) capable of dealing with residential consumers’ objectives/constraints and local and grid coordinators’ shared objectives/coupled constraints under a bottom-up strategy. Particularly, the proposed multi-level framework distributes local and grid coordinators’ shared objectives among consumers to flatten the aggregate consumption profile and minimize the aggregate energy cost at each level. The suggested scheme is enhanced by developing two additional operations. A gain-sharing technique is designed to fairly divide the total gain acquired by the grid coordinator across the hierarchy from higher to lower levels, successively. Besides, a coupled constraint-sharing method is devised to link these levels and fulfill the coupled constraints by revising consumers’ decisions. The proposed approach is applied to a society of buildings comprising Home Energy Management System (HEMS) groups with demand response-enabled electric Baseboard Heaters (BHs), and its effectiveness is investigated through different case studies. The results demonstrate that the recommended HTCM is able to improve the society’s aggregate power profile load factor by 89%, from 0.45 up to 0.85, and decreases its overall electricity cost by 6.2%.

Empowered or enchained? Exploring consumer perspectives on Direct Load Control

Demand Response has become a central focus in policy discussions, attracting heightened attention for its potential key role in fostering enhanced demand flexibility. Direct Load Control (DLC) holds particular promise, requiring reduced consumer involvement compared to strategies relying on manual responses to time-varying pricing. However, the participation rate of residential consumers in DLC programs remains low, emphasizing the need for a more profound understanding of consumers' perspectives on DLC. Drawing from 15 in-depth interviews with Swedish households participating in a program involving direct load control of heat pumps, this study aims to explore the experiences and perceptions of consumers, providing in-depth understanding on factors that may serve as motivators, barriers and enablers for participation. Key findings include: financial benefits and interest in technology were the main motivations for participation; pre-existing knowledge and awareness of energy-related matters shaped consumers’ attitudes to DLC; trust in the service provider was a key enabling factor for participation. The study further suggests that DLC does not inevitably lead to a perceived loss of control among participants, but, if implemented in alignment with their specific conditions, needs and preferences, may also foster a sense of empowerment.

Explainable finite mixture of mixtures of bounded asymmetric generalized Gaussian and Uniform distributions learning for energy demand management

We introduce a mixture of mixtures of bounded asymmetric generalized Gaussian and uniform distributions. Based on this framework, we propose model-based classification and model-based clustering algorithms. We develop an objective function for the minimum message length (MML) model selection criterion to discover the optimal number of clusters for the unsupervised approach of our proposed model. Given the crucial attention received by Explainable AI (XAI) in recent years, we introduce a method to interpret the predictions obtained from the proposed model in both learning settings by defining their boundaries in terms of the crucial features. Integrating Explainability within our proposed algorithm increases the credibility of the algorithm’s predictions since it would be explainable to the user’s perspective through simple If-Then statements using a small binary decision tree. In this paper, the proposed algorithm proves its reliability and superiority to several state-of-the-art machine learning algorithms within the following real-world applications: fault detection and diagnosis (FDD) in chillers, occupancy estimation and categorization of residential energy consumers.

Investigating Electricity Usage Profile for Residential Consumers in India

Electricity has become one of the essential needs in modern society. The demand and necessity for electricity have increased with population, trade, and industry, which has resulted in increased stress on the grid. As the development of an electricity storage system is costly, utilities are working on customer segmentation to classify the consumers according to their usage patterns to understand their demand and further develop resources accordingly. In this work, the K-Means clustering algorithm has been applied to a two-year monthly consumption data set (January 2019 to December 2020) of 100 residential consumers from Kutheda village Hamirpur (Himachal Pradesh, India) to visualize the diverse consumption behaviour. It resulted in four different clusters of consumers. It also showed that there is a group having a large number of consumers whose consumption is low and consumption pattern is very less flexible in different seasons. Another group of consumers with high consumption also shows high flexibility in consumption patterns, but their number is less. Also, the winter season shows the highest consumption of electricity and the monsoon season shows the lowest consumption of electricity in this particular region. This analysis will certainly help the electricity provider to make production planning for a different group of consumers in a specific season based on electricity consumption patterns.

End-to-End Top-Down Load Forecasting Model for Residential Consumers

This study presents an efficient end-to-end (E2E) learning approach for the short-term load forecasting of hierarchically structured residential consumers based on the principles of a top-down (TD) approach. This technique employs a neural network for predicting load at lower hierarchical levels based on the aggregated one at the top. A simulation is carried out with 9 (from 2013 to 2021) years of energy consumption data of 50 houses located in the United States of America. Simulation results demonstrate that the E2E model, which uses a single model for different nodes and is based on the principles of a top-down approach, shows huge potential for improving forecasting accuracy, making it a valuable tool for grid planners. Model inputs are derived from the aggregated residential category and the specific cluster targeted for forecasting. The proposed model can accurately forecast any residential consumption cluster without requiring any hyperparameter adjustments. According to the experimental analysis, the E2E model outperformed a two-stage methodology and a benchmarked Seasonal Autoregressive Integrated Moving Average (SARIMA) and Support Vector Regression (SVR) model by a mean absolute percentage error (MAPE) of 2.27%.

Understanding switching intentions towards renewable energy technologies using push-pull-mooring framework

Due to climate change threats, governments worldwide are taking measures to move towards cleaner technologies. One such alternative in the energy sector is Renewable Energy Technologies (RET). Due to its merit over conventional technology, RET is being promoted in the industry and households. However, residential consumers switching from incumbent technologies to RET is still low. To fill this gap, this study builds on the Push-Pull-Mooring (PPM) framework to uncover the key factors affecting the switching intention of residential consumers toward RET. We adopted a sequential explanatory mixed-methods approach that involved a quantitative investigation followed by a qualitative study. In the quantitative phase, we used structural equation modeling techniques to analyze survey data from 291 participants to test our conceptual model. Subsequently, in the qualitative phase, in-depth interviews with 25 participants were conducted to gain deeper insights and validate the relationships identified in the quantitative analysis. The results indicate that environmental concern (push factor), economic benefit, and independence benefit (pull factor) have a positive association with switching intention. However, switching cost (mooring factor) was found to have a negative association with switching intention. Further, switching cost was also found to moderate the association between economic benefit and switching intention. The qualitative study provided further insights into the results of the quantitative investigation. It was clarified from the qualitative study that neighbor experiences and societal preferences of the causes may mitigate the influence of subjective norms on switching intention. Lastly, implications were drawn for academicians, marketers, and public policymakers. Marketers can utilize these findings to develop appropriate marketing strategies for this untapped market, which has huge potential.

Optimal energy scheduling for residential consumers with demand management strategies

ABSTRACT The enhancement of energy networks and cost reduction can be achieved through the implementation of solutions like optimal energy scheduling for residential consumers. This paper proposes day-ahead electrical and thermal energy scheduling for residential sections in energy networks as a bi-criteria optimization approach. The bi-criteria optimization approach is modeled to minimize the operation cost and maximize the reliability of the energy system. Demand management strategies such as demand reduction and demand shifting are considered for residential consumers. The participation of residential consumers in demand management is presented considering electricity prices in the energy markets. The solving bi-criteria optimization approach is done via the weight sum method and LINMAP procedure-based-fuzzy approach. The proposed modeling is implemented in GAMS optimization software for verifying the demand management strategies in optimal energy scheduling in two case studies.

Intelligent Demand Side Management for Residential Consumer Comprise of Electric Vehicle, Energy Storage System and Renewable Energy Source

Intelligent Demand Side Management for Residential Consumer Comprise of Electric Vehicle, Energy Storage System and Renewable Energy Source

A Low-Cost Energy Monitoring System with Universal Compatibility and Real-Time Visualization for Enhanced Accessibility and Power Savings

Energy management is important for both consumers and utility providers. Utility providers are concerned with identifying and reducing energy wastage and thefts. Consumers are interested in reducing their energy consumption and bills. In Pakistan, residential and industrial estates account for nearly 31,000 MW of the maximum total demand, while the transmission and distribution capacity has stalled at about 22,000 MW. This 9000 MW gap in demand and supply, as reported in 2022, has led to frequent load shedding. Although the country now has an excess generation capacity of about 45,000 MW, the aging transmission and distribution network cannot deliver the requisite power at all times. Hence, electricity-related problems are likely to continue for the next few years in the country and the same is true for other low- and middle-income countries (LMICs). Several energy monitoring systems (EnMS) have been proposed, but they face limitations in terms of cost, ease of application, lack of universal installation capability, customization, and data security. The research below focused on the development of an economical, secure, and customizable real-time EnMS. The proposed EnMS comprises low-cost hardware for gathering energy data with universal compatibility, a secured communication module for real-time data transmission, and a dashboard application for visualization of real-time energy consumption in a user-preferred manner, making the information easily accessible and actionable. The experimental results and analysis revealed that approximately 40% cost savings in EnMS development could be achieved compared to other commercially available EnMSs. The performance of the EnMS hardware was evaluated and validated through rigorous on-site experiments. The front-end of the EnMS was assessed through surveys and was found to be interactive and user-friendly for the target clients. The developed EnMS architecture was found to be an economical end-product and an appropriate approach for small and medium clients such as residential, institutional, commercial, and industrial consumers, all on one platform.