Implementation Of Demand Response Programs Research Articles

Оптимизация потребления сети MicroGrid с учетом потребностей на электроэнергию

Electricity grid is a product of urbanization expansion and rapid development of various infrastructures worldwide and over the past centuries. Although power companies are located in diverse regions, they typically use the same technologies to generate and distribute electricity. Proper implementation of the demand response (DR) program should be provided with some equipment to make subscribers aware of electricity price at any time and accordingly provide a proper response to the grid to reduce costs. This, in turn, reduces demand during peak hours. The intelligent grid, using the two-way communication network and the transmission of information to subscribers, and an advanced metering network provide a good structure for fully implementing DR programs. The present study applies a demand response economic model to implement DRs. The model uses price elasticity of demand, which provides subscribers a more precise consumption behavior regarding the factors influencing demand (e.g., electricity prices, bonuses, and fines). Applying the model to the microgrid, the operating cost significantly decreases in both operating modes.

Integrated Home Energy Management with Hybrid Backup Storage and Vehicle-to-Home Systems for Enhanced Resilience, Efficiency, and Energy Independence in Green Buildings

This study presents an innovative home energy management system (HEMS) that incorporates PV, WTs, and hybrid backup storage systems, including a hydrogen storage system (HSS), a battery energy storage system (BESS), and electric vehicles (EVs) with vehicle-to-home (V2H) technology. The research, conducted in Liaoning Province, China, evaluates the performance of the HEMS under various demand response (DR) scenarios, aiming to enhance resilience, efficiency, and energy independence in green buildings. Four DR scenarios were analyzed: No DR, 20% DR, 30% DR, and 40% DR. The findings indicate that implementing DR programs significantly reduces peak load and operating costs. The 40% DR scenario achieved the lowest cumulative operating cost of $749.09, reflecting a 2.34% reduction compared with the $767.07 cost in the No DR scenario. The integration of backup systems, particularly batteries and fuel cells (FCs), effectively managed energy supply, ensuring continuous power availability. The system maintained a low loss of power supply probability (LPSP), indicating high reliability. Advanced optimization techniques, particularly the reptile search algorithm (RSA), are crucial in enhancing system performance and efficiency. These results underscore the potential of hybrid backup storage systems with V2H technology to enhance energy independence and sustainability in residential energy management.

Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning

Promoting flexible energy demand through response programs in residential neighborhoods would play a vital role in addressing the issues associated with increasing the share of distributed solar systems and balancing supply and demand in energy networks. However, accurately identifying baseline-related energy measurements when activating energy demand response events remains challenging. In response, this study presents a deep learning-based, data-driven framework to improve short-term estimates of demand response baselines during the activation of response events. This framework includes bidirectional long-term memory (BiLSTM), long-term memory (LSTM), gated recurrent unit (GRU), convolutional neural networks (CNN), deep neural networks (DNN), and recurrent neural networks (RNN). Their performance is evaluated by considering different aggregation levels of the demand response baseline profile for 337 dwellings in the city of La Rochelle, France, over different time horizons, not exceeding 24 h. It is also compared with fifteen traditional statistical and machine learning methods in terms of forecasting accuracy. The results demonstrated that deep learning-based models, compared to others, significantly succeeded in minimizing the gap between the actual and forecasted values of demand response baselines at all different aggregation levels of dwelling units over the considered time-horizons. BiLSTM models, followed by GRU and LSTM, consistently demonstrated the lowest mean absolute percentage error (MAPE) in most comparison experiments, with values up to 9.08%, 8.71%, and 9.42%, respectively. Compared to traditional statistical and machine learning models, extreme gradient boosting (XGBoost) was among the best, with a value up to 11.56% of MAPE, but could not achieve the same level of forecasting accuracy in all comparison experiments. Such high performance reveals the potential of the proposed deep learning approach and highlights its importance for improving short-term estimates of future baselines when implementing demand response programs in residential neighborhood contexts.

Optimal scheduling and demand response implementation for home energy management

The optimal scheduling of the loads based on dynamic tariffs and implementation of a direct load control (DLC) based demand response program for the domestic consumer is proposed in this work. The load scheduling is carried out using binary particle swarm optimization and a newly prefaced nature-inspired discrete elephant herd optimization technique, and their effectiveness in minimization of cost and the peak-to-average ratio is analyzed. The discrete elephant herd optimization algorithm has acceptable characteristics compared to the conventional algorithms and has determined better exploring properties for multi-objective problems. A prototype hardware model for a home energy management system is developed to demonstrate and analyze the optimal load scheduling and DLC-based demand response program. The controller effectively schedules and implements DLC on consumer devices. The load scheduling optimization helps to improve PAR by a value of 2.504 and results in energy cost savings of ₹ 12.05 on the scheduled day. Implementation of DLC by 15% results in monthly savings of ₹ 204.18. The novelty of the work is the implementation of discrete elephant herd optimization for load scheduling and the development of the prototype hardware model to show effects of both optimal load scheduling and the DLC-based demand response program implementation.

Bin modelling approach to cluster control the EVs for implementing demand response program

Bin modelling approach to cluster control the EVs for implementing demand response program

Navigating urban day-ahead energy management considering climate change toward using IoT enabled machine learning technique: Toward future sustainable urban

The rapid evolution of technology and urban population growth has led to a significant increase in energy consumption, particularly in electricity production. However, the ability to meet energy demands, especially during peak periods, is constrained by various factors including environmental concerns, technological limitations, and economic constraints. Effective energy management strategies are crucial to mitigate these challenges and ensure a sustainable energy future. One promising approach is the implementation of demand response programs. In this context, we explore the potential of Home Energy Management Systems (HEMS) leveraging modern Internet of Things (IoT) technology. HEMS systems are at the forefront of research and development in the field of smart grids (SGs), offering capabilities in demand-side management and power availability control. These systems achieve efficient energy usage by optimizing the operation of home appliances (HAs). To further enhance the performance of HEMS, this study introduces a novel metaheuristic known as the Grey Wolf and Crow Search Optimization Algorithm (GWCSOA). This algorithm is employed to determine the optimal scheduling of HAs within the HEMS framework. Additionally, we utilize MATLAB and ThingSpeak modules to implement and test the HEMS design. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs, minimizing the peak-to-average ratio (PAR), and enhancing consumer satisfaction. The findings of this study indicate that the devised system holds the promise of substantially diminishing both power expenses and microgrid emissions. Through the optimization of Home Energy Management Systems (HEMS) operations, this methodology plays a vital role in realizing a sustainable urban energy ecosystem. This aligns seamlessly with the overarching objectives of combating climate change and securing the enduring sustainability of our cities. The proposed HEMS, integrated with GWCSOA, demonstrates remarkable capabilities in reducing daily power costs by an average of 25.98 %, minimizing the peak-to-average ratio (PAR) by 30 %, and enhancing consumer satisfaction. The outcomes of this research reveal that the developed system has the potential to significantly reduce both power costs and microgrid emissions. By optimizing HEMS operations, this approach contributes to achieving a sustainable urban energy ecosystem, aligning with the goals of addressing climate change and ensuring the future sustainability of our cities.

Social welfare evaluation during demand response programs execution considering machine learning-based load profile clustering

In the last decade, with the introduction of smart meters to smart grids, demand response programs (DRPs) have been widely adopted to establish a generation and consumption balance. DRPs provide many benefits for efficient grid management. However, these programs are conducive to higher levels of dissatisfaction by changing grid customers' consumption patterns. This paper aims to investigate the effects of DRPs on social welfare (SW). To this end, the paper presents a mathematical model for SW during the implementation of DRPs. In the proposed model, the level of customer satisfaction is assumed the main factor contributing to SW. This mathematical model considers different types of DRPs in terms of their impacts on SW. The paper also seeks to obtain linear and nonlinear models of DRPs and the coefficient of participation (CoP). CoP as an indicator shows the percentage of customers who actively participate in each DRP and plays a significant role in the assessment of the SW level. Moreover, owing to the sparsity and variety of distribution network customers, load patterns are classified into different clusters to take the load types into account. As a matter of fact, this process aims to identify similar patterns and thus, the same level of satisfaction for each separate cluster. The classification process is performed by using a machine learning-based clustering method known as the Affinity Propagation (AP) algorithm. Then, the model calculates the level of SW for the clusters based on the usage of electrical equipment and the time of day when they are turned on. The obtained levels of SW help operators select the best programs for every cluster in terms of customer satisfaction, and achieve the highest performance of DRPs. Lastly, the model is evaluated using real data of a distribution network to ensure the effectiveness and accuracy of the model.

Data-driven approaches to achieve energy transition: Statistical analysis of demand-side management and smart decision making in urban energy hub networks

Energy hubs (EHs) have emerged as a crucial solution to address resource scarcity and environmental concerns by integrating various technologies for energy production, conversion, and storage. These hubs encompass diverse elements such as integrated heat, electricity, and cooling systems, renewable energy sources, batteries, and thermal energy storage units (TES). This study focuses on a residential EH model that receives inputs in the form of electricity, gas, and solar energy to satisfy the household's electricity, heat, and cooling requirements. To enhance the operational flexibility of this energy hub, the study employs a Demand Response Program (DRP), which facilitates load shifting, load curtailment, and the modeling of flexible heating loads. The primary objective is to develop an efficient heat and power management system that optimally schedules household devices, generation equipment, and storage units. In pursuit of this goal, Long Short-Term Memory (LSTM) techniques have been selected for renewable energy, market price, and load forecasting, enhancing the system's realism and predictive accuracy. The Wild Goats algorithm is employed to solve an optimization problem for three distinct scenarios, aiming to minimize overall power costs while maintaining user comfort levels with regard to temperature and hot water availability. Furthermore, this study incorporates an environmentally conscious approach by considering the reduction of CO2, SO2, and NO pollution originating from consumers in a multi-objective optimization framework. The findings reveal that the implementation of DRP, intelligent Plug-in Hybrid Electric Vehicle (PHEV) management, TES technologies, and LSTM-based forecasting significantly contribute to reducing power costs within the proposed EH scheme. This research underscores the potential of data-driven strategies in advancing energy transition initiatives and fostering sustainable urban energy hubs while acknowledging the role of advanced forecasting techniques in enhancing system realism and efficiency.

Multi-criteria dispatch optimization of a community energy network with desalination: Insights for trading off cost and security of supply

The idea of community energy network is being advocated to enhance the elasticity of diverse energy systems required for efficiently integrating a substantial volume of distributed energy resources. On the other hand, the interest in renewables-based desalination systems has received significant interest recently to consider freshwater as an additional end-use product in the community energy network system. Within this context, this paper introduces a multifaceted method for community energy networks with a focus on desalination-capable systems. The central goals involve diminishing the cumulative long-term expenses of the configuration, all while concurrently augmenting the system's capacity to store electrothermal energy on a daily basis that varies – all aimed at enhancing the reliability and security of resource provisioning. Importantly, the model co-optimizes the community energy network expenditure and reserve capacities, whilst integrating electrical, thermal, and natural gas vectors, as well as providing a platform for supplying freshwater needs. The overall freshwater provisioning infrastructure incorporates a water storage system, a desalination unit, a water well component, and a water pumping system. Furthermore, for the purpose of enhancing the adaptability, the community energy network concept put forth here utilizes coordinated electrothermal responsive load initiatives. These are coupled with meticulously planned electrothermal reservoir setups to curtail the wastage of surplus renewable production amidst diverse origins of unpredictability. The normalized weighted sum method is employed to convert the proposed formulation to a single-objective problem that is amenable to commercially available solvers in GAMS software. Then, the modelling framework is adapted to a system populated for a hypothetical site. The results verify the validity of the model in yielding globally optimum results for complex community energy networks with intertwined vectors of energy and end-use products. They also indicate that relatively small raises in the size of the electric and thermal reservoirs – and insubstantial raises in the expenditure of the system – can have potentially significant impacts on the ability of the system in serving loads during contingency conditions. In particular, by implementing demand response programs a cost reduction of 2.07% is shown, which is significant in the day-ahead operational planning phase.

RETRACTED: An optimal multi-objective demand side management of a smart microgrid consists of various building loads, considering relativity index and uncertainty

This paper addresses microgrid energy management in the presence of distributed generation and active loads while accounting for operational, economic, environmental, and reliability factors. The objective is to minimize the cost of shutdown during N-1 contingencies, projected emission-related expenses, and the overall operational cost of microgrids integrated with distributed generations. A stochastic scheduling technique that optimizes short-term microgrid operation, reducing costs and pollution through renewable resources is introduced. Utilizing demand response programs among residential, commercial, and industrial participants is proposed to counter the uncertainty of renewable resource-generated power. The demand response program implementation suggests considering financial incentives as bid prices, along with energy packages managed by demand response providers. Simulation results demonstrate three strategies to decrease operating costs and pollutants, contrasting scenarios with and without responsive load engagement. Equations governing AC power flow, microgrid operations, reliability, distributed generation, and, demand response programs and batteries, form the analytical core. Stochastic programming addresses uncertainties tied to demand, energy prices, renewable distributed generation production, and microgrid equipment availability. To attain dependable, optimal solutions with dynamic responsiveness, the combined ant lion optimization and crow search algorithm are employed. The proposed method is tested on a sample smart microgrid for validation. Numerical findings unequivocally underscore demand-side management potency in reducing power generation uncertainties from wind turbines and photovoltaics. This paper offers insights into microgrid energy management complexities, paving the way for resilient, cost-effective, and environmentally conscious energy distribution paradigms.

A tri-layer stochastic framework to manage electricity market within a smart community in the presence of energy storage systems

In recent years, with the restructuring of the electricity market, the main goal of all market participants is to maximize their profits. Among these, the stability of the system as well as the consumers' comfort index (CI) are two important points that should be considered in the scheduling. Hence, this paper presents a tri-layer model for managing a competitive electricity market in the presence of microgrids and demand response (DR) aggregators, in which microgrids determine their strategy for market participation by considering the CI of consumers. In the first layer of the proposed model, the DR aggregator buys part of the consumers load through shiftable and curtailable demand response programs (DRPs), and offers it for sale in the market. The second layer, microgrids do their scheduling with the aim of minimizing operating costs and maximizing the CI, and submit their offers/bids into the market. Finally, in the third layer, the market-clearing price (MCP) is determined by the market manager with the aim of maximizing social welfare. This model is formulated in mixed-integer linear programming (MILP) format and is solved through CPLEX solver in GAMS environment. The simulation results show that the two-objective modelling of the operation problem, despite a 12.18 % increase in total operating costs, increases the average CI by 5.57 %. The results also demonstrate that DR aggregator through the implementation of DRPs leads to a reduction in MCP during the peak period and thus a 5.01 % reduction in total operating costs.

Clustering Appliance Operation Modes With Unsupervised Deep Learning Techniques

In smart grids, consumers can be involved in demand response programs to reduce the total power consumption of their households during the peak hours of the day. Unfortunately, nowadays, utility companies are facing important challenges in the implementation of demand response programs because of their negative impact on the comfort of end-users. In this paper, we cluster the different operation modes of household appliances based on the analysis of their power signatures. For this purpose, we implement an autoencoder neural network to create a better data representation of the power signatures. Then, we cluster the different operational programs by using a K-means algorithm fitted to the new data representation. To test our methodology, we study the operation modes of some washing machines and dishwashers whose power signatures were derived from both submeters and non-intrusive load monitoring techniques. Our clustering analysis reveals the existence of multiple working programs showing well-defined features in terms of both average energy consumption and duration. Our results can then be used to improve demand response programs by reducing their impact on the comfort of end users. Furthermore, end users can rely on our framework to favor lighter operation modes and reduce their overall energy consumption.

Analysis of Price Based Demand Response Program Using Load Clustering Approach

The K-Medoids algorithm is adopted along with the proposed elephant herd optimization (EHO) algorithm in this peculiar work to cluster the consumers according to their required load consumption patterns and for price-based demand response (DR) program implementation. Consumer baseline load (CBL) estimation is done using the clustered load data. The novel approach of clustering the data analysis in this proposed work is done by collecting the load data from the transformer centers under the control of the 33/11 kV substation in the state of Goa, India, as well as the price data from the state load dispatch center and Indian energy exchange. Further the aggregated load data obtained for the 11 kV feeder is used to estimate the reference consumer load consumption by using regression and an artificial neural network model. Consumer response towards load change with price is modeled based on price elasticity and the consumer benefit function. An EHO algorithm is proposed for implementing the time of use (TOU) DR program for domestic consumers. The algorithm’s effectiveness and robustness is demonstrated by comparing the results with those of the genetic algorithm and particle swarm optimization technique. The proposed work of CBL estimation, consumer segregation, and optimum evaluation of the TOU DR program using the EHO algorithm assists in the implementation of DR programs. The proposed research can be used for analyzing the demand reduction potential, deployment of alternative energy sources, reduction in the use of conventional energy sources, promotion of energy efficiency, and the reduction of CO2 emissions.

Residential PV capacity estimation and power disaggregation using net metering measurements

As the intermittency and uncertainty of photovoltaic (PV) power generation poses considerable challenges to the power system operation, accurate PV generation estimates are critical for the distribution operation, maintenance, and demand response program implementation because of the increasing usage of distributed PVs. Currently, most residential PVs are installed behind the meter, with only the net load available to the utilities. Therefore, a method for disaggregating the residential PV generation from the net load data is needed to enhance the grid-edge observability. In this study, an unsupervised PV capacity estimation method based on net metering data is proposed, for estimating the PV capacity in the customer’s premise based on the distribution characteristics of nocturnal and diurnal net load extremes. Then, the PV generation disaggregation method is presented. Based on the analysis of the correlation between the nocturnal and diurnal actual loads and the correlation between the PV capacity and their actual PV generation, the PV generation of customers is estimated by applying linear fitting of multiple typical solar exemplars and then disaggregating them into hourly-resolution power profiles. Finally, the anomalies of disaggregated PV power are calibrated and corrected using the estimated capacity. Experiment results on a real-world hourly dataset involving 260 customers show that the proposed PV capacity estimation method achieves good accuracy because of the advantages of robustness and low complexity. Compared with the state- of-the-art PV disaggregation algorithm, the proposed method exhibits a reduction of over 15% for the mean absolute percentage error and over 20% for the root mean square error.

Evaluating the effects of demand response programs on distribution cables life expectancy

• Evaluating the effects of DRPs on the LOL rate reduction of distribution cables. • Evaluating the effects of DRPs’ tariffs on the LOL rate of distribution cables. • Proposing a new economic benefit index. • Prioritizing various models of DRPs based on AHP method. • Maximizing utility's and customer's total benefits by determining the optimal model. Recently due to economic issues, asset management has taken more attention all over the world. Distribution cables are one of the most expensive assets of the networks. The life of a cable extremely depends on the power flowing through the cable. On the other hand, demand response programs are known as the one of the effective solutions for managing networks’ power consumption. They can affect the power flowing through the distribution cables. However, the impacts that the demand response programs might have on the life expectancy of the distribution cables are generally neglected in previous research studies. So, this paper proposes a new approach to evaluating the effects of demand response programs on the life expectancy of distribution cables. Simulations have been carried out on a realistic distribution network, and the results are investigated considering various models of the demand response program and different levels of their tariffs. The results show a significant economic benefit due to distribution cables’ life extension resulted from demand response programs implementation, despite differences among various models. However, the benefit's scale dramatically depends on the model of the demand response program and the level of tariffs. Also, in order to select the optimal demand response model, economic analysis is conducted from both utility's and customer's perspectives. For this purpose, a new economic benefit index is proposed, and the best solution is determined by the distribution system's regulator based on an Analytical Hierarchy Process so as to it can satisfy all the stakeholders of demand response programs.

A novel energy management model among interdependent sections in the smart grids

AbstractTechnically, residential energy management systems are fundamental sectors in the smart grids for implementing demand response programs in the layer of households for managing energy consumption and reducing energy bills. The paper proposes a novel energy management scheme that takes production and usage into account based on a heuristic searching operation. In addition to modelling the grid, renewable energy sources, batteries, and electric vehicles, various kinds of electrical and thermal devices have been examined, including air conditioners, water heaters, vacuum cleaners etc. A method is developed for solving the objective constraint issue in a smart home in order to reduce energy consumption and determine feasible operation states among the various loads. Moreover, this paper proposes a grey wolf optimization method for solving the issue over a longer simulation period. Various cases were examined to evaluate the effectiveness of this suggested robust optimization algorithm. The outcomes show that the suggested model could not only reduce energy costs significantly but has also shown good performance for energy management purposes.

Developing a novel zonal congestion management based on demand response programs considering dynamic transmission ratings

Congestion is one of the main obstacles to fair competition in today’s electricity markets. In this regard, a set of rules are legislated to create a fair competitive environment and ensure adequate control over consumers to maintain an acceptable level of reliability with maximum economic efficiency. In this paper, using power tracing techniques and sensitivity analysis, a new dynamic zonal congestion management (ZCM) model based on the demand response program (DRP) is developed. The congestion control zones are determined by an introduced congestion index (CI) including some candidates with both high effectiveness in congestion alleviation (the sensitivity analysis) and high responsibility in the congestion creation (the power tracing technique). Then, through the intelligent implementation of DRP in the zone, a conceptual framework with a comprehensive solution method for congestion alleviation is proposed. Throughout the proposed partitioning based DRP, the optimal amount of load decrease and incentives are determined considering economic issues. In power system operation, contingency analysis such as generator and line outages and uncertainty of lines capacities are inevitable, so they will be taken into account in our proposed methodology. The capacity fluctuates a lot due to the environmental conditions and it will be shown through a comparative study that dynamic line rating (DLR) has a significant impact on results. Using the proposed method, the system is simplified and power system operators can put their focus on the candidate zone than the whole network and alleviate the congestion effectively in the least possible time. The proposed methodology is applied on IEEE 118-bus test system and the results show the effectiveness of the proposed model.

Optimal load dispatch of energy hub considering uncertainties of renewable energy and demand response

As an important modeling method for multi-energy systems, the energy hub (EH) plays an important role in the planning and operation research of multi-energy systems. In this regard, this paper proposes an optimal load dispatch model for the EH system by considering the coupling relationship of electric and thermal energy. The target of the proposed model is to reduce the total cost of the EH system, including resource cost, carbon emission cost, demand response (DR) cost, and system maintenance cost. An adjustable robust optimization method is presented to model the uncertainty of renewable energies (REs) and DR programs. Through duality theory, the proposed robust optimization problem is transformed into a mixed-integer linear programming problem and solved efficiently by a commercial solver. This paper carries out simulation experiments on three different cases with or without energy storage systems and DR programs. The simulation results show that the total cost of the EH system can be effectively reduced by installing energy storage systems and implementing DR programs. At the same time, the results also show that some economic benefits of the EH system need to be sacrificed to deal with the uncertainty of REs and DR programs.

Enhancement of loadability and voltage stability in grid-connected microgrid network

– The implementation of demand response programs in microgrid network applications enables end-consumers to participate in active load shaping, peak shaving, and supply-demand balancing activities. However, the impact of demand response programs on microgrid performance subjected to renewable energy intermittency, network flow constraints, loadability, and voltage stability is not acknowledged to the full extent. To cover this research gap, a tri-level stochastic framework is proposed to enhance the grid-connected microgrid performance in terms of loadability, voltage stability, and optimal scheduling. At the first level, a stochastic scenario-based approach deals with the intermittency of renewable sources. The interdependency of loading level on voltage profile is investigated in level two, implementing utility-oriented and customer-oriented demand-side management schemes. Unlike most previous research, flexible price elasticity-based incentive-driven and price-driven demand response programs are implemented to shape the load demand. Moreover, four distinct seasonal load profiles were considered: summer, winter, spring, and fall. Finally, the recently reported nature-inspired Harris Hawk Optimizer is incorporated in level three to optimize the costs and enhancement of microgrid loadability. The enhanced version of the IEEE 69 bus radial distribution network has been implemented and analyzed to demonstrate the effectiveness of the proposed tri-level framework. The obtained simulation results show that the voltage profile is enhanced by 6.58%, and microgrid loadability is improved by 34.64%. The significance of the proposed work to attain sustainable development goals and various practical insights for the benefit of multi-stakeholder is also discussed.

A measurement model for electricity Consumers’ awareness with covariance structure Analyses. A solid pillar for boosting demand response programs

Recent energy policies aim to raise the awareness on climate change, fossil fuels, collective actions and social cohesion. Additionally, they seek to sensitize people to the usage of Renewable Energy Sources (RES) and rational electricity consumption. Demand Response (DR) programs are therefore significant and more present in the grid operators, aggregators and retailers’ strategies to engage consumers to change their consumption behavior. Furthermore, the electricity consumers awareness and DR programs are important in the actual context of conventional resource scarcity to motivate consumers to reduce or adjust consumption and take pro-environmental measures. One of the barriers that restricts the implementation of DR programs is the consumers awareness and their perceptions about energy usage, as well as, the DR programs potential, in terms of cost savings and contribution to the reduction of environmental pollution. A DR program shows higher adoption levels if it creates positive perceptions and expectations on outcomes, leading to the conclusion that it is essential to carefully design and create DR programs. In a large-scale adoption of DR programs, we have considered consumers perceptions and expectations using first-order and hierarchical Confirmatory Factor Analysis (CFA), such as second-order and bi-factor CFA. Thus, we statistically demonstrate the influence of the electricity consumers attitude, Internet access, perceived expectations, consumer-supplier relationship, and pro-environmental measures (the latent factors) on their awareness and DR programs implementation. The simulations involve pre-trial questionnaire data offered by the Irish Commission for Energy Regulation. Although many studies have been performed with this data, a measurement model for electricity consumers awareness with covariance structure analyses is an original approach that provides interesting insights. Several models are assessed here, and the best bi-factor CFA model is built providing outstanding performance results: Comparative Fit Index, CFI = 0.95 on average, Standardize Root Mean Square Residual, SRMR = 0.03, Root Mean Square Error of Approximation RMEAS = 0.02. The bi-factor CFA model provides the most interesting results and confirms the hypothesis that the five latent factors significantly impact on the consumers’ awareness.