Electricity Expenses Research Articles

Fe/P dual-doping NiMoO4 with hollow structure for efficient hydrazine oxidation-assisted hydrogen generation in alkaline seawater

Electrosynthesis of hydrogen straightforwardly from seawater represents a potential solution towards carbon-neutral economy. However, with the sluggish oxygen evolution reaction (OER) at anode, the sustainable and cost-effective application is greatly hindered by extra energy consumption and serious chlorine chemistry in seawater. Herein, based on the advanced hydrazine-assisted electrolysis strategy, we reported a trifunctional Fe, P dual-doping NiMoO4 nanorods with a hollow structure for highly active hydrogen evolution reaction (HER) (23 mV @ 10 mA cm−2) and OER (213 mV @ 10 mA cm−2) activity, which also significantly decreased the cell voltage (activity variation for ∼1.40 V) in two-electrode system by replacing OER with thermodynamically beneficial hydrazine oxidation reaction (HzOR). Notably, a record low electricity expense of 2.3 kW h m−3 was obtained among commercial reactor in alkaline seawater/0.5 M N2H4. Meanwhile, the corrosion resistance of catalyst allows stable catalytic performance in seawater without any ClO- generation. Density functional theory calculations showed that Fe/P co-doping effectively endowed optimized electronic structure and modulated d-band centre for intermediates adsorption/desorption.

A Machine Learning-Based Electricity Consumption Forecast and Management System for Renewable Energy Communities

The energy sector is currently undergoing a significant shift, driven by the growing integration of renewable energy sources and the decentralization of electricity markets, which are now extending into local communities. This transformation highlights the pivotal role of prosumers within these markets, and as a result, the concept of Renewable Energy Communities is gaining traction, empowering their members to curtail reliance on non-renewable energy sources by facilitating local energy generation, storage, and exchange. Also in a community, management efficiency depends on being able to predict future consumption to make decisions regarding the purchase, sale and storage of electricity, which is why forecasting the consumption of community members is extremely important. This study presents an innovative approach to manage community energy balance, relying on Machine Learning (ML) techniques, namely eXtreme Gradient Boosting (XGBoost), to forecast electricity consumption. Subsequently, a decision algorithm is employed for energy trading with the public grid, based on solar production and energy consumption forecasts, storage levels and market electricity prices. The outcomes of the simulated model demonstrate the efficacy of incorporating these techniques, since the system showcases the potential to reduce both the community electricity expenses and its dependence on energy from the centralized distribution grid. ML-based techniques allowed better results specially for bi-hourly tariffs and high storage capacity scenarios with community bill reductions of 9.8%, 2.8% and 5.4% for high, low, and average photovoltaic (PV) generation levels, respectively.

The optimization and energy efficiency analysis of a multi-tank solar-assisted air source heat pump water heating system

This article undertakes an optimization study of a solar-assisted air source heat pump water heating system by harnessing the advantages inherent in a multiple water tank arrangement. The Hooke-Jeeves algorithm was adopted to optimize the system parameters. The optimization variables encompass pivotal parameters such as water tank volume, collector area, dip angle, azimuth angle and heat pump power, while the objective function is the annual value of life cycle cost. By research and analysis of the monthly power consumption, electricity cost, solar fraction, average coefficient of performance (COP) and annual value of carbon emissions of the single-tank, dual-tank, and triple-tank system, it is discovered that the triple-tank system exhibits superior performance in overall aspects. Compared to the pre-optimized version, it achieves an annual energy consumption reduction of approximately 3.9%, leading to a 22.5% decrease in electricity expenses. Furthermore, it brings a 5.2% reduction in the annual lifecycle cost and a 6.3% decrease in carbon emissions, while simultaneously improving exergy efficiency by 4.8%.

Gravity grain cleaning machine and its importance in grain logistics and sustainable agriculture

The design schematic of a gravity-based seed cleaning machine was developed, and experimental tests were conducted. The gravity grain cleaning machine (GUTM) demonstrated a cleaning efficiency of 95%, ensuring that seeds met standard requirements in a single cleaning cycle. The compact and lightweight design of the machine, capable of performing the functions of four processing machines, led to a 50-60% reduction in energy consumption and an economic efficiency increase of SUM 1600 thousand/ton. Despite utilizing only one sieve as the primary component, the gravity seed cleaning machine achieved a seed purity level of up to 98.5%, particularly with seed wheat. The device’s implementation resulted in a significant 50-60% reduction in production costs, streamlined inter-district transportation, prevented the mixing of elite seed varieties, and lowered loading and electricity expenses.

Development of an Integrated Solar-Biomass Energy System for Near-Zero Energy Buildings: A Technical and Environmental Study

The present study aims to satisfy the energy demands of a set of Norwegian residential structures with the least carbon dioxide and most renewable energy. Real-time data on building domestic hot water (DHW), heating, and electricity usage is used to plan and expand the renewable energy supply side. PVT panels provide DHW and electricity in the hybrid solar and biomass energy system. Heat is produced by the digester and heat pump. Also used is a twofold effect absorption refrigeration system for cooling. Rule-based regulation manages heat streams and redirects flows on the supply side. We provide the plant's size and execute the dynamic energy simulation. Electricity and biomass expenses determine building heating. The system is then tuned for operational conditions and compared to the design point. PVT may generate over 80% of annual DHW. Summertime radiation is more intense and can be turned into cooling energy, therefore 64.8% of cooling output comes from it. Digester/CC heats 66.55% of the structure, suggesting designers use biomass in winter due to increased energy costs. A parametric analysis shows that increasing PVT duration and tank size affects efficiency and emissions differently. Cost, efficiency, and emission index at TOPSIS are 9.73 $/hr, 36.8%, and 7.75 kg/MWh, according to optimization findings.

Techno-economic assessment of small-size residential solar PV + battery systems under different tariff structures in Brazil

This paper proposes a methodology to assess the energy and economic impact of adopting small-scale residential photovoltaic (PV) systems paired with lithium-ion battery energy storage (BESS) systems in single-family homes, under the current energy feed-in legislation. The suggested methodology was applied to a case study of a prosumer unit (PU) in Brazil. Data from energy bills, measured PV generation, BESS behaviour, and energy consumption were analysed to compare different viewpoints and estimate key economic indicators. As a result, the PV + BESS combination increases residential users' energy self-consumption and self-sufficiency, resulting in respective increases of 15 % and 2 %. Under the conventional tariff, adopting the PV + BESS system led to an average 29 % reduction in electricity costs compared to scenarios without PV + BESS. Furthermore, by transitioning to the white tariff mode in conjunction with PV + BESS, the PU achieved a 49 % reduction. This result emphasizes the advantages of time-of-use adoption for PV + BESS consumers. A greater discrepancy between tariff periods enhances the feasibility of storing off-peak energy for use during peak periods. Currently, BESS implementation costs impede competitiveness, with attractive LCOS metrics achieved only at costs below $600.00/kWh and MRAs below 9 %. BESS + PV lowers generation costs compared to off-peak rates, increasing the viability of shared usage. Integrating PV + BESS systems with rate switching significantly diminishes electricity expenses. The substantial BESS implementation cost necessitates a noteworthy rate differential for viability, which could potentially impact non-PV and non-BESS consumers.

Real-Time Prototype Electricity Monitoring and Forecasting System based on Wemos D1 R1 ESP8266 and IoT

Background - Technology helps people do things. IoT is a buzzword, but some need explanation. It means internet-connected devices. IoT seeks continuous internet benefits. Lighting, fans, automated doors. These things have internet-connected sensors. IoT technology allows internet-controlled household appliances. Internet-connected computers or smartphones monitor this tool.
 
 Purpose - The purpose of this research is to create a system that can monitor the electric power in the room of the house in real-time through the Blynk application using a smartphone and make it easier for consumers to estimate the cost of electricity for a month.
 
 Design/methodology/approach - Systematics simplify and summarize this study. This study will use the Blynk smartphone app to monitor room electric power in real-time and anticipate monthly electricity expenses. Literature review expands research theories. Analysis and design, explaining the analysis based on the background and literature review, then constructing tools and designing the source code and workflow of the Wemos D1 R1 ESP8266 Microcontroller system. Implementation and testing discuss experimental results and how to test new technology. Study report analysis and development ideas conclude this section.
 
 Findings – This study produced a guide for designing IoT-based power monitoring device applications. Allow users to estimate their household electricity costs quickly and keep this option available.
 
 Research limitations - This research is limited by the Wemos ESP8266 D1 R1 microcontroller, INA219 current sensor, and led for room lights. Wemos D1 R1 ESP8266 and IOT will be used to create a monitoring system tool and estimate electric power in real time using room fans. Future accomplishment. This method requires many components.
 
 Originality/value - This research may reveal how to lower electricity bills by monitoring and cutting usage online.

Electricity access and unreliability in the creation of sustainable livelihoods in Mozambique

Although Mozambique has abundant energy resources and strives to increase access to electricity as a strategy to fight poverty and to pursue the Sustainable Development Goals, the country faces a double challenge, the lack of access or affordability of electric services and their unreliability. This work assesses the level of electricity reliability in the country, surveys the perception of electricity unreliability by micro, small and medium enterprises in the provincial capitals of the central and northern regions of the country, and estimates the impact of electricity reliability and electricity expenses in the generation of sustainable livelihoods in the country. Using data from 2014 to 2018, it is shown that the studied areas experienced frequent and prolonged electricity outages, especially during working hours. Modelling of empirical results through the nonparametric Wild Bootrap estimation method shows that electricity unreliability and electricity expenses contribute negatively to the generation of sustainable livelihoods. Electricity projects that expand productive capacity in sectors employing most of the country´s active population can contribute to the generation of sustainable livelihoods. Long-run policies might build resilient infrastructures and activate structural changes in the country's production system towards increased production, job creation, and generation of sustainable livelihoods.

Long-term performance of roof-top GCPV systems in central Viet Nam

In pursuit of the objective of achieving "net zero emissions," many countries worldwide, including Viet Nam, have prioritized the utilization of photovoltaic technology for energy conversion. Specifically, the implementation of roof-top grid-connected photovoltaic systems (GCPV) has emerged as a highly efficient solution in urban areas. These systems offer several advantages, such as minimizing land usage, lowering monthly electricity expenses, preventing building heat, generating income for households, and reducing transmission and distribution costs. This article focuses on a comprehensive long-term analysis conducted on 51 roof-top GCPV systems in the tropical monsoon climate of Hue City, Viet Nam, during the period from 2019 to 2023. The analysis findings reveal that roof-top GCPV systems with a capacity of 3-6 kW are well-suited for households in the central region of Viet Nam, characterized by a tropical monsoon climate. These systems exhibit an average sizing ratio of 1.03. The annual average daily final yield peaked at 3.28 kWh/kWp/day in 2021 and reached its lowest point at 2.97 kWh/kWp/day in 2022. Notably, the typical slope of the yield gradually increases with the installed capacity and the studied year. Furthermore, the monthly average daily final yield demonstrates a seasonal pattern, with higher yields observed from March to August and lower yields from September to January, aligning with the climate of the study area. As the years progress, the capacity factor and performance ratio of roof-top GCPV systems display a declining trend. Throughout the entire study period, these systems successfully mitigated 664 metric tons of CO2 emissions. The evaluation of long-term yield data offers valuable insights for photovoltaic installers, operators, and system owners, aiding in system maintenance and optimizing load utilization across different time periods. Long-term performance can be used by energy managers and owners of roof-top GCPV systems to identify supply shortfalls and initiate countermeasures.

Enhanced Operation of Ice Storage System for Peak Load Management in Shopping Malls across Diverse Climate Zones

There exists a notable research gap concerning the application of ice storage systems in shopping mall settings at the urban scale. The characteristics of large pedestrian flow, high energy consumption, and high peak loads in shopping malls make their advantages in energy conservation. This study researches sustainable cooling solutions by undertaking an economic analysis of the ice storage systems within shopping malls across 11 distinct cities, each system operating under varied electricity pricing frameworks. The methodology begins with creating baseline mall models using AutoBPS and refining them with OpenStudio. Before starting to adjust the model, measured data were used to verify the accuracy of the baseline model, the coefficient of variation of the root mean square error (CVRMSE) and normalized mean bias error (NMBE) metrics were calculated for the model energy consumption, with CVRMSE values of 8.6% and NMBE values of 1.57% for the electricity consumption, while the metrics for the gas consumption were 12.9% and 1.24%, respectively. The study extends its inquiry to encompass comprehensive economic evaluations based on the unique electricity pricing of each city. This rigorous assessment discerns the relationship between capacity, operational strategies, and economic performance. Particularly striking are the so-called peak-shaving and valley-filling effects verified in regions characterized by lower latitudes and substantial cooling loads. The interaction between ice storage capacity and operational schedules significantly influences both economic viability and cooling efficiency. Based on the temporal dynamics of time-of-use (TOU) power pricing, a finely calibrated operational schedule for the ice storage system is proposed. This operational strategy entails charging during periods of reduced electricity pricing to undertake cooling loads during peak electricity pricing intervals, culminating in substantial reductions in electricity charges of buildings. Moreover, the strategic reallocation of energy, characterized by a reduced chiller capacity and a corresponding elevation in ice storage system capacity, augments cooling efficiency and diminishes cooling-related electricity expenses. This study offers valuable insights for optimizing and deploying ice storage systems in diverse climatic regions, particularly for shopping malls. As a guiding reference, this paper provides stakeholders with a framework to reasonably apply and adjust ice storage systems, ushering in an era of energy-efficient and environmentally conscious cooling solutions tailored to shopping mall environments.

Performance analysis of MW-scale grid connected rooftop and ground-mounted solar power plants installed in Assam, India

Solar energy-based electricity generation has become an essential component of sustainable development and meeting the growing demand for energy. A study is conducted to compare the performance of rooftop and ground-mounted solar power plants installed in Assam, India. The study found that ground-mounted plants generated an average of 3.41 kWh per kWp of energy per day, while rooftop plants generated 2.89 kWh per kWp. The study also observed that rooftop solar power plants had non-operating days due to inverter faults, grid failures, and poor weather conditions, which were 1.1 and 0.95 days, respectively. The capacity factors were 14.2 % and 11.4 %, while the performance ratios were 77.8 % and 70.1 % for ground-mounted and rooftop plants, respectively. The average system efficiency of ground-mounted plants and rooftop plants is 12.59 ± 0.26 % and 11.88 ± 0.11 %, respectively. The study used the Weibull distribution function to describe the daily normalized energy generation frequency curve, which revealed a peak probability of 0.38 at a daily normalized energy generation of 2.9 kWh per kWp for rooftop plant and 0.34 at a daily normalized energy generation of 3.4 kWh per kWp for ground-mounted plant. The installation of a solar power plant has resulted in an average reduction of Rs. 43.7 lakhs per year in the University's electricity bill. This study demonstrates the successful operation of rooftop solar power plants, which not only reduces the electricity expenses of organizations but also has a positive impact on sustainability.

Design of Intelligent Street Lamp Control System

Design of Intelligent Street Lamp Control System

Optimization and thermo-economic performance of a solar-powered vapor absorption cooling system integrated with sensible thermal energy storage

Higher demand for refrigeration systems adversely affects power grids, causing blackouts, increased electricity expenses, and carbon emissions. However, using renewable energy thermally driven refrigeration absorption systems is a promising alternative. The higher initial cost and dependency on local weather, large space area for installation, and complex design are hurdles for the widespread of these systems compared to the conventional vapor compression refrigeration systems. This study uses response surface methodology to model a real vapor absorption machine (VAM) incorporated with the measured data. This VAM is the refrigeration machine of a solar-powered absorption cooling system (SPACS) integrated with thermal energy storage for milk chilling installed and operated in Jaipur (India). The weather data for 2022 is used in performance and productivity analysis in this study. The results show that most of the summer months, the system can produce desired cooling to take down the 1000 l of milk within 3 h as per standards ISO 5708 – 2 II. Moreover, the solar loop still has sufficient driving heat to charge the 1000 l cold storage tank/another milk tank. During winter days, it takes a significant system response time (SRT) to reach the thermal energy storage (TES) up to 95 °C; after that, VAM operates significantly underperforming due to weak solar insolation; even the system cannot provide cooling to the first milking. The system produces monthly cooling energy of up to 2356 kWh in summer and is reduced to 620 kWh during monsoon and winter. The maximum value of the coefficient of performance (COPVAM) is achieved as 0.55 with an average of 0.41–0.46 during summer months whereas 0.34–0.39 during monsoon and winter days. The system's energy efficiency ratio (EER) ranges from 2.5 to 6.5, with an overall average of 4.55, which is far better than the conventional vapor compression refrigeration systems. The LCOE for the SPACS has been estimated to be 0.177 $/kWh, whereas the simple payback period is 12.4 years, and the discounted payback period is 19.7 years.

Techno-economic evaluation and environmental benefit of hybrid evaporative cooling system in hot-humid regions

Reducing the energy consumption of buildings is of great importance to achieve carbon neutrality targets. The air conditioning (AC) system in buildings, as a large energy consumer, should be taken into account as one of the main building service systems for energy saving integrated with energy-efficient and environmental-friendly technologies. Indirect evaporative cooler (IEC), constrained by its cooling principle, originally exerts energy saving potential in hot-arid regions. Recently, progress has been made in expanding its application to hot-humid regions. However, due to the large cooling load in hot-humid regions, an IEC-integrated hybrid system should be developed as an improved solution to effectively save energy and maintain the indoor thermal comfort. In view of the lack of a complete hybrid IEC system being reported thus far, in this study, the IEC is combined with a cooling coil unit as a primary air handling unit (IEC-PAU) for fresh air handling and supply. Accordingly, the model of the IEC-PAU with indoor fan coil units (FCUs) is constructed and incorporated into a typical office building, and the results are compared with a baseline case under hot-humid climate conditions. Comprehensive energy, economic, and environmental benefits are analyzed considering different setpoint temperatures in typical cities of the Great Bay area (GBA) of China. Results show that the proposed system could reduce the energy consumption of 4.6 kWh/m2 on average, with the acceptable thermal comfort level as they are in the reference cases. The greatest energy saving ratio is 8.3%, while the performance of the proposed system declines under the higher setpoint temperature. The saved electricity expenses can lead to the average discounted payback period of 8.2 years in the GBA cities. Furthermore, an annual greenhouse gas emission of 4.8 t on average can be diminished. In summary, this work demonstrates the feasibility of the hybrid IEC system as an effective approach for energy saving in hot-humid regions, which is promising to prompt the carbon neutrality in China.

Thermal-Comfort Aware Online Co-Scheduling Framework for HVAC, Battery Systems, and Appliances in Smart Buildings

Energy management in buildings is vital for reducing electricity costs and maximizing the comfort of occupants. Excess solar generation can be used by combining a battery storage system and a heating, ventilation, and air-conditioning (HVAC) system so that occupants feel comfortable. Despite several studies on the scheduling of appliances, batteries, and HVAC, comprehensive and time scalable approaches are required that integrate such predictive information as renewable generation and thermal comfort. In this paper, we propose an thermal-comfort aware online coscheduling framework that incorporates optimal energy scheduling and a prediction model of PV generation and thermal comfort with the model predictive control (MPC) approach. We introduce a photovoltaic (PV) energy nowcasting and thermal-comfort-estimation model that provides useful information for optimization. The energy management problem is formulated as three coordinated optimization problems that cover fast and slow time-scales by considering predicted information. This approach reduces the time complexity without a significant negative impact on the result's global nature and its quality. Experimental results show that our proposed framework achieves optimal energy management that takes into account the trade-off between electricity expenses and thermal comfort. Our sensitivity analysis indicates that introducing a battery significantly improves the trade-off relationship.

Biphasic Transition Metal Nitride Electrode Promotes Nucleophile Oxidation Reaction for Practicable Hybrid Water Electrocatalysis

AbstractGlycerol electrooxidation (GOR), as a typical nucleophile oxidation reaction, is deemed as a promising alternative anodic route to assist cathodic hydrogen evolution reaction. However, the investigations of high‐performance catalysts and industrial‐scale application of GOR remain a grand challenge. Herein, biphasic Ni3N/Co3N heterostructure nanowires (denoted as Ni3N/Co3N‐NWs) are proposed as an efficient bifunctional catalyst, which realizes a high Faradaic efficiency of 94.6% toward formate production. Importantly, the flow electrolyzer achieves an industry‐level current density of 1 A cm−2 at 2.01 V with impressive stability for steady running over 200 h, realizing lower electricity expense of 4.82 kWh m−3H2 and energy saving efficiency of 9.7%, as well as outstanding co‐production rates of 11 and 21.4 mmol cm−2 h−1 toward formate and H2, respectively. Theoretical calculations reveal that the efficient electron transfer on Ni3N/Co3N heterointerfaces simultaneously optimizes nucleophile reaction tendency and glycerol dehydrogenation kinetics, thus contributing to excellent GOR performance.

Real-Time Multi-Home Energy Management with EV Charging Scheduling Using Multi-Agent Deep Reinforcement Learning Optimization

Energy management for multi-home installation of solar PhotoVoltaics (solar PVs) combined with Electric Vehicles’ (EVs) charging scheduling has a rich complexity due to the uncertainties of solar PV generation and EV usage. Changing clients from multi-consumers to multi-prosumers with real-time energy trading supervised by the aggregator is an efficient way to solve undesired demand problems due to disorderly EV scheduling. Therefore, this paper proposes real-time multi-home energy management with EV charging scheduling using multi-agent deep reinforcement learning optimization. The aggregator and prosumers are developed as smart agents to interact with each other to find the best decision. This paper aims to reduce the electricity expense of prosumers through EV battery scheduling. The aggregator calculates the revenue from energy trading with multi-prosumers by using a real-time pricing concept which can facilitate the proper behavior of prosumers. Simulation results show that the proposed method can reduce mean power consumption by 9.04% and 39.57% compared with consumption using the system without EV usage and the system that applies the conventional energy price, respectively. Also, it can decrease the costs of the prosumer by between 1.67% and 24.57%, and the aggregator can generate revenue by 0.065 USD per day, which is higher than that generated when employing conventional energy prices.

Smart Home Appliances’ Scheduling by Two-Stage Optimization with Real-Time Price Model

Residential load control focuses on reducing both the monthly electricity expense and the peak demand for electricity. The efficient scheduling of smart home appliances’ operational procedures can accomplish both goals. Because rescheduling appliances to reduce one goal can lead to an increase in the other, these two goals are inherently in conflict with one another. This work proposes an algorithm utilizing artificial intelligence methods to accomplish both goals simultaneously. The proposed method has been successfully tested on real data of energy dynamic pricing options applicable in two utilities, namely Alectra Utilities Corp., Canada, and ComEd Northern Illinois Power Company, serving residential consumers with varying monthly power use. It is also proposed that both utilities use a cost function that is based on real-time prices to mitigate the risks associated with real-time pricing. In addition, this research proposes a novel method for determining the absolute maximum hourly power usage. The suggested algorithm accomplishes its dual goals at once as proof of its efficacy in solving optimization problems.

Water Electrolysis toward Elevated Temperature: Advances, Challenges and Frontiers.

Since severe global warming and related climate issues have been caused by the extensive utilization of fossil fuels, the vigorous development of renewable resources is needed, and transformation into stable chemical energy is required to overcome the detriment of their fluctuations as energy sources. As an environmentally friendly and efficient energy carrier, hydrogen can be employed in various industries and produced directly by renewable energy (called green hydrogen). Nevertheless, large-scale green hydrogen production by water electrolysis is prohibited by its uncompetitive cost caused by a high specific energy demand and electricity expenses, which can be overcome by enhancing the corresponding thermodynamics and kinetics at elevated working temperatures. In the present review, the effects of temperature variation are primarily introduced from the perspective of electrolysis cells. Following an increasing order of working temperature, multidimensional evaluations considering materials and structures, performance, degradation mechanisms and mitigation strategies as well as electrolysis in stacks and systems are presented based on elevated temperature alkaline electrolysis cells and polymer electrolyte membrane electrolysis cells (ET-AECs and ET-PEMECs), elevated temperature ionic conductors (ET-ICs), protonic ceramic electrolysis cells (PCECs) and solid oxide electrolysis cells (SOECs).

Machine learning-based demand response in PV-based smart home considering energy management in digital twin

Energy management (EM) systems need to have the flexibility to take the optimum real-time decision in the face of constantly varying market factors. Demand response (DR) has become the newest method of improving the performance and reliability of the electrical system. Here, an hour-ahead DR algorithm is proposed for EM at home. This paper presents an artificial neural network approach that uses stable cost predictions as a method for dealing with upcoming price uncertainties. For making optimum and decentralized decisions for various household devices, multi-agent reinforcement learning has been used along with predicted upcoming costs. This paper conducts the simulation with shiftable, non-shiftable, and controllable loads to determine the effectiveness of this suggested EM strategy. Based on the outcomes of the experiments, this suggested DR algorithm has capable of handling EM for a number of devices, minimizing consumer electricity expenses and discomfort prices, and helping the consumer considerably reduce its energy expenses in comparison to a benchmark using no DR.