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Demand side response is becoming an increasingly significant issue for reliable power systems’ operation. Therefore, it is desirable to ensure high effectiveness of such programs, including electricity tariffs. The purpose of the study is developing a method for analysing electricity tariff’s effectiveness in terms of demand side response purposes based on statistical data concerning tariffs’ use by the consumers and price elasticity of their electricity demand. A case-study analysis is presented for residential electricity consumers, shifting the settlement and consequently the profile of electricity use from a flat to a time-of-use tariff, based on the comparison of the considered tariff groups. Additionally, a correlation analysis is suggested to verify tariffs’ influence of the power system’s peak load based on residential electricity tariffs in Poland. The presented analysis proves that large residential consumers aggregated by tariff incentives may have a significant impact on the power system’s load and this impact changes substantially for particular hours of a day or season. Such efficiency assessment may be used by both energy suppliers to optimize their market purchases and by distribution system operators in order to ensure adequate generation during peak load periods.

This study presents a approach to optimize the operation of the smart multi-carrier energy system (SMCES) in residential consumers taking into account the uncertain nature of gas and electrical prices. The optimal operation of the SMCES is implemented using a multi-stage interval optimization approach with a multifunctional hydrogen storage system and demand-side management. Modeling optimization approach as three-stage is done for minimizing operation costs of the SMCES under energy prices uncertainty. The demand-side management based on load-shifting and load-interruption approaches for electrical demand in the residential buildings for the first and second stages is considered, respectively. The load-shifting for electrical demand is modeled subject to optimal consumption at day-ahead. Also, load-interruption approach is implemented for peak clipping of electrical demand subject to bidding prices from energy operator to residential consumers. In the third stage optimization, uncertainty of the electricity and gas prices in the operation cost with multi-criteria problem such as deviation and average rates by interval optimization approach is modeled. The modified electrical demand in the first and second stages is linked in the third stage for managing uncertainties. Moreover, multifunctional hydrogen storage system based on gas and electrical generation alongside demand-side management in third stage optimization for covering uncertainties is taken into account. The improved sunflower optimization algorithm is used to solve all stages, and the TOPSIS method is proposed for choosing the best trade-off of the multiple-criteria problem in the third stage. Finally, the suggested optimization modeling is represented in the several case studies to validate the achieved results with participation of the demand-side management and hydrogen storage system in day-ahead optimal operation of the SMCES. The participation of the demand-side management and the hydrogen storage systems leads to minimizing the deviation and average rates by 2.14% and 2.64% in comparison with non-participation.

Purpose The COVID-19 pandemic has caused a dramatic impact on energy supply and demand. It is vital to understand households’ behaviour with regard to energy, particularly during the pandemic, to deploy future sustainable energy systems. This study aims to investigate the nexus of Malaysian households’ energy consumption behaviour in relation to various electrical appliances, their energy-saving appliance purchasing behaviour and their current possession of energy-saving appliances during the pandemic, especially during the lockdown period, from the perspective of the energy cultures framework. Design/methodology/approach The partial least squares structural equation modelling technique was used to test hypothesised relationships based on the 1,485 pieces of household data collected using an online and physical survey during the lockdown period in Malaysia. Findings The energy-saving behaviour cultivated due to the impact of the COVID-19 pandemic led to residential customers’ intentions to purchase energy-saving appliances which subsequently led to their current possession of energy-saving appliances. Indeed, energy-saving behaviours in the kitchen, entertainment, office, home lighting and cooling appliances have more than 77.4% influence on their purchasing behaviour. The consumer’s purchase behaviour for energy-saving appliances has a significant, partially mediating influence on the energy-saving behaviour of various electrical appliances and the consumers’ current possession of energy-saving appliances. Research limitations/implications This study could be enhanced by improving the sample using a higher-income group and involving other parts of Malaysia such as the southern region. The findings do extend the energy cultures framework by demonstrating the mediating role of households’ energy-saving appliance purchasing behaviour on the relationship between their energy consumption behaviour in relation to various electrical appliances and their current possession of energy-saving appliances. Practical implications The results of this study will help develop future action plans for transitioning to energy-saving appliance practices. Originality/value This paper examines the effects of the COVID-19 pandemic on future energy efficiency practices in developing countries from the perspective of the energy cultures framework.

Climate change and global warming are causing growing environmental concerns, prompting many countries to increase investments in renewable energies. The high growth rate of renewables in the energy systems brings significant intermittency challenges. Demand-side flexibility is presented as a viable solution to address this phenomenon. In this framework, this research study proposes a novel methodology to evaluate the flexibility potential that residential consumers can offer to the Distribution System Operator (DSO). Moreover, it pretends to provide guidelines and design of standardized parameters to disaggregate the aggregated energy consumption data of end-users. This step is essential to identify and characterize the primary energy consumption processes in the residential sector, laying the groundwork for future flexibility evaluation. Furthermore, the modeling of the energy consumption curves will enhance residential sector demand-side flexibility enabling end-users to modify their usual consumption patterns. The implemented methodology has been applied to real consumer data provided by the DSO of a Spanish municipality of about 29,000 habitants in the Alicante Province (Spain). Results achieved allowed the validation of the proposed methodology enabling the disaggregation of residential energy profiles and facilitating the subsequent dynamic assessment of residential end-user’s demand flexibility. Moreover, this work will provide valuable guidelines to carry out short-term energy resource planning and solve operational problems of the energy systems.

This paper proposes an architecture of locality electricity market (LEM) for peer-to-peer (P2P) energy trading among a group of residential prosumers (consumers and producers) with renewable energy resources, smart meters, information and communication technologies, and home energy management systems in a smart residential locality. Prosumers may sell(buy) their excess generation(demand) in LEM at a profitable prices compared to the utility prices in P2P fashion. In order to manage the trading in LEM, a common portal named as locality electricity trading system (LETS) is introduced. The purpose of LETS is to prepare a trading agreement between the participants by fixing a price for every deal based on the quoted price and day-ahead power trading schedule given by the participants. An enhanced intelligent residential energy management system (EIREMS) is proposed at the prosumers' premises to enable their participation in the day-ahead energy trading process and in real-time scheduling of schedulable loads and battery for reducing the electricity bill with due consideration to the operational constraints and LETS agreement. The performances of proposed LETS and EIREMS are validated through a few case studies on a locality with ten prosumers. The proposed methodology endorses marginal economic benefit for all the participants.

Providing a price tariff that matches the randomized behavior of residential consumers is one of the major barriers to demand response (DR) implementation. The current trend of DR products provided by aggregators or retailers are not consumer-specific, which poses additional barriers for the engagement of consumers in these programs. In order to address this issue, this paper describes a methodology based on causality inference between DR tariffs and observed residential electricity consumption to estimate consumers’ consumption elasticity. It determines the flexibility of each client under the considered DR program and identifies whether the tariffs offered by the DR program affect the consumers’ usual consumption or not. The aim of this approach is to aid aggregators and retailers to better tune DR offers to consumer needs and so to enlarge the response rate to their DR programs. We identify a set of critical clients who actively participate in DR events along with the most responsive and least responsive clients for the considered DR program. We find that the percentage of DR consumers who actively participate seem to be much less than expected by retailers, indicating that not all consumers’ elasticity is effectively utilized.

Electric vehicle (EV) loads are playing an increasingly important role in improving the flexibility of power grid operation. The prerequisite for EV loads to participate in demand response (DR) is that the DR regulation strategy and corresponding DR potential must be accurately analyzed and evaluated. However, due to the uncertainty and differences in travel and charging behavior, DR potentials of EVs exhibit randomness and differ in time and space. In addition, it is difficult to obtain refined travel data and charging load data of large-scale EVs. Accordingly, this paper focuses on how to consider the various influencing factors of potential, and realize the quantitative evaluation of DR time-varying potential of an EV group based on small sample data. First, a travel activity model of the EV is established. Based on the actual travel data, the probability distributions of the key parameters of the travel model are obtained by kernel density estimation and probability statistical fitting. Then, combined with the charging behavior model, and based on Monte Carlo simulation, the load curve of the EV in a residential area is predicted. Considering the travel need of the EV, the peak-shaving potential, vehicle-to-grid discharge potential, and valley-filling potential of the EV under different DR strategies are calculated and analyzed, and the time-varying characteristics of the potential are analyzed. Finally, a case study is carried out with the actual data. The results show that the DR time-varying potential under different time periods and control strategies can be effectively evaluated. The maximum peak-shaving potential of 1000 EV aggregates is 2.7 MW, and the minimum is 0.25 MW. The maximum valley-filling potential is 2.1 MW, and the minimum is 0.3 MW. The research results can provide effective guidance for EVs to participate in day-ahead scheduling, and for the screening of target EVs.

In the context of various attempts to regulate energy consumption and educate consumers in the spirit of sustainable behavior, this paper aims to identify the role of the main socio-demographic factors on the decision to adopt measures to reduce consumption and save energy. Many studies have approached similar topics, but correlating their conclusions, it can be deduced that psycho-socio-demographic factors interact differently from one country to another, depending on the economic and political context of the moment. From the fact that in the former communist countries, the severe political regime subjected the population to very restrictive living conditions, based on deprivations that led to the formation of a traditional saving behavior and, on the other hand, considering the new Sustainable Development Goals (SDGs) that shape the young generation in the spirit of sustainable society, the authors aimed to study the correlation between socio-demographic factors (age, gender, education, professional status, income) and consumption and energy saving behavior at residential level, in an ex-communist state, Romania. For this purpose, quantitative research was carried out based on the answers of 865 subjects to the questionnaire distributed at the Academy of Economic Studies in Bucharest and in the immediate environment to the members of the university community, using convenience sampling. Using descriptive statistical indicators and linear regression techniques, the intensity of correlation between selected variables was determined and the degree of differentiation of the purchasing and use behavior of green-label household appliances was analysed, as well as the population’s availability to adopt some energy-saving methods. Although the sample is not representative, the conclusions are that measures to reduce energy consumption must be voluntary and stratified, depending on the nature of social and demographic factors.

As the electricity consumption and controllability of residential consumers are gradually increasing, demand response (DR) potentials of residential consumers are increasing among the demand side resources. Since the electricity consumption level of individual households is low, residents’ flexible load resources can participate in demand side bidding through the integration of load aggregator (LA). However, there is uncertainty in residential consumers’ participation in DR. The LA has to face the risk that residents may refuse to participate in DR. In addition, demand side competition mechanism requires the LA to formulate reasonable bidding strategies to obtain the maximum profit. Accordingly, this paper focuses on how the LA formulate the optimal bidding strategy considering the uncertainty of residents’ participation in DR. Firstly, the physical models of flexible loads are established to evaluate the ideal DR potential. On this basis, to quantify the uncertainty of the residential consumers, this paper uses a fuzzy system to construct a model to evaluate the residents’ willingness to participate in DR. Then, based on the queuing method, a bidding decision-making model considering the uncertainty is constructed to maximize the LA’s income. Finally, based on a case simulation of a residential community, the results show that compared with the conventional bidding strategy, the optimal bidding model considering the residents’ willingness can reduce the response cost of the LA and increase the LA’s income.

There is an increasing demand for electricity on a global level. Thus, the utility companies are looking for the effective implementation of demand response management (DRM). For this, utility companies should know the energy demand and optimal household consumer classification (OHCC) of the end users. In this regard, data mining (DM) techniques can give better insights and support. This work proposes a DM-technique-based novel methodology for OHCC in the Indian context. This work uses the household electricity consumption (HEC) of 225 houses from three districts of Maharashtra, India. The data sets used are namely questionnaire survey (QS), monthly energy consumption (MEC), and tariff orders. This work addresses the challenges for OHCC in energy meter data sets of the conventional grid and smart grid (SG). This work uses expert classification and clustering-based classification methods for OHCC. The expert classification method provides four new classes for OHCC. The clustering method is employed to develop eight different classification models. The two-stage clustering model, using K-means (KM) and the self-organizing map (SOM), is the best fit among the eight models. The result shows that the two-stage clustering of the SOM with the KM model provides 88% of overlap-free samples and 0.532 of the silhouette score (SS) mean compared to the expert classification method. This study can be beneficial to the electricity distribution companies for OHCC and can offer better services to consumers.