Electricity Bill Research Articles

THE IMPACT OF SMART GRIDS ON ENERGY EFFICIENCY: A COMPREHENSIVE REVIEW

Smart grids have emerged as a key technology in the quest for energy efficiency and sustainability. This review provides a comprehensive analysis of the impact of smart grids on energy efficiency, highlighting key findings, challenges, and future directions. Smart grids leverage advanced sensing, communication, and control technologies to optimize the generation, distribution, and consumption of electricity. By enabling two-way communication between utilities and consumers, smart grids improve grid reliability, reduce energy losses, and facilitate the integration of renewable energy sources. One of the key findings of this review is the significant impact of smart grids on energy efficiency. Studies have shown that smart grids can reduce energy consumption by enabling real-time monitoring and control of energy usage. This not only helps consumers reduce their electricity bills but also reduces the overall carbon footprint of the electricity sector. However, the deployment of smart grids faces several challenges, including high upfront costs, interoperability issues, and data privacy concerns. Addressing these challenges will be crucial for the widespread adoption of smart grid technologies. Looking ahead, the future of smart grids holds great promise for further improving energy efficiency. Emerging trends such as the Internet of Things (IoT) integration, artificial intelligence (AI) algorithms, and distributed energy resources (DERs) management are expected to drive further innovation in smart grid technologies. In conclusion, smart grids have a transformative impact on energy efficiency, offering significant benefits for consumers, utilities, and the environment. However, addressing challenges such as cost and interoperability will be crucial for realizing the full potential of smart grids. By embracing emerging trends and technologies, stakeholders can further enhance the efficiency and sustainability of the electricity sector.
 Keywords: Impact, Smart Grid, Energy, Efficiency, Review.

A Comparative Study on Discrepancies in Residential Building Energy Performance Certification in a Mediterranean Context

Energy Performance Certification (EPC) systems are pivotal in addressing the global energy challenge, particularly in the building sector. This study evaluates the efficacy of the EPC offered by the Simplified Building Energy Model interface designed to indicate compliance with the Cypriot building regulations, widely known as iSBEM-Cy Version 3.4a, by examining a typical residential unit in Cyprus. Data on construction features and electromechanical systems were collected, and actual monthly electricity and oil bills were analyzed to determine the total primary energy consumption. Various factors were considered, including energy efficiency and operational parameters for heating, cooling, lighting, auxiliary systems, and domestic hot water. The building energy performance was simulated using iSBEM-Cy, allowing for comparison with real-world energy consumption. Notable discrepancies were observed, particularly in cooling, with deviations reaching 377.4%. Conversely, domestic hot water consumption exhibited minimal variance at 7%, while heating and lighting showed moderate discrepancies (24.3% and −113.9%, respectively). This study underscores the need for rigorous evaluations to shape effective EPC and provides insights into building energy performance in Mediterranean Cyprus. This research contributes to the broader discourse on sustainable construction practices by aligning simulation results with real-world energy consumption.

Implementation of a 3-phase grid-coupled solar electricity and back-up system at Mulanje Mission Hospital, Malawi.

In this report we describe the implementation of a new electricity supply system at Mulanje Mission Hospital, Malawi, which integrates the use of grid electricity, solar-generated electricity and battery back-up. To realize the system, suppliers from several countries had to be used and external expertise and funding were vital. The completed system provides reliable and good quality electricity to all departments in the hospital, prioritizing essential equipment when needed. Implementation of the system has reduced cost of electricity bills by 60%, ended black-outs and extended longevity of electrical equipment. We describe our approach, the materials used and results with challenges and recommendations to governments, donors interested in hospital infrastructure and other health facilities operating in similar circumstances. Others in similar settings can benefit from the experiences documented.

Environmental sustainability in hospitals: Dual analysis of electrical consumption and pollutant emissions

The emission of pollutants following electricity consumption is significant, and hospitals are major contributors to energy consumption. Hence, the aim of this study was a survey of electrical consumption rate and environmental pollutants emissions in selected hospitals. This descriptive-analytical and cross-sectional study was conducted over three years and based on electricity bills and bed indicators in selected hospitals in Tehran, Iran. Emissions Calculator software was used to calculate the emission of pollutants caused by electricity consumption. Kolmogorov Smirnov, Least Significant Difference (LSD), and one-way ANOVA tests were used to analyze the data. Results showed that the total average electricity consumption for each active day bed and occupied day bed was 9.6 and 13.6 times higher than the recommended standards. The average electricity consumption for each active day bed in hospitals A, B, and C was 13.7, 10.2, and 5 times higher than the standard value, respectively, while for occupied day beds, the consumption was 17.3, 15.1, and 8.5 times higher than the standard value, respectively. Additionally, the total electricity consumption in these hospitals resulted in the emission of 41837100 kg of CO2, 124815 kg of SO2, 103699 kg of NOx, 9769 kg of CO, 4885 kg of PM10, and 757 kg of VOCs into the environment. Based on the monthly active bed index and occupied day bed, there was a significant difference in the average electrical consumption among A, B, and C hospitals (p = 0.0001). Electricity consumption in the studied hospitals exceeds standards. Implementing optimal designs for lighting, heating, and cooling, along with energy optimization training, technical audits, and inspections are essential for both economic benefits and reducing environmental pollutants.

Optimal sizing of behind-the-meter battery energy storage systems under optimal battery operation: A case study in Ireland

The global shift toward a sustainable future has led to new policies to promote the use of renewable energy resources (RESs) in residential buildings. In Ireland, the number of installed roof-top photovoltaics (PVs) and home batteries, known as behind-the-meter (BTM) resources, has significantly increased in recent years, accelerated by grants administered by the Sustainable Energy Authority of Ireland (SEAI). In this work, an optimization tool that uses a combination of a Genetic Algorithm (GA) and a multi-objective mixed-integer linear programming (MOMILP) model is developed. This tool integrates an optimal battery control scheme from the MOMILP model aiming to minimize electricity bills and battery degradation to optimize the sizing of BTM batteries with minimum installation costs. Various scenarios have been simulated to assess the potential of the proposed model under different metering and billing systems, and optimization constraints. Compared to a case with a rule-based optimization model, results show that our optimization model can save 5.3 % more on annual bill costs while keeping battery degradation to a minimum of 2.1 % per year.

Sustainable and self-sufficient social home through a combined PV‑hydrogen pilot

Utilizing surplus renewable energy to produce and store green hydrogen can help meet energy demands during periods of low renewable energy production, and thus contributing greatly to decarbonize emission-intensive sectors, such as the residential and building sector. In this context, this manuscript reports the design, construction and operation of a hybrid PV‑hydrogen demonstrative pilot (PVHyP) for the 100% electrical self-sufficiency of a social housing in the town Novales (Spain). The work addresses i) the collection of real consumption data from the dwelling, ii) the design considering real characteristics and market availability of the devices, iii) final balance of plant implementation, and iv) the energy management strategy followed. The implemented control system allows the facility to be tracked and operated remotely. Besides, this flagship implementation in the SUDOE region (Spain, Portugal and southwest of France) has achieved the electrical independence from the grid under diverse climate conditions, eliminating 2260 kg of CO2 emissions associated to electricity consumption, saving almost 15,200 kWh of primary energy during two years of monitoring. Thus, around 1170 € where avoided in electricity bills (100% savings), resulting on multiple benefits for social dwellers in risk of energy poverty.

The cost of keeping warm: a relationship between a rise in the cost of domestic energy and burn injuries caused by personal heating equipment

IntroductionDuring 2022–2023, the UK found itself in the midst of a domestic energy crisis, with the average domestic gas and electricity bill rising by 75% between 2019 and 2022. As a result, the use of hot water bottles, radiant heaters, and electric blankets increased. An unintended consequence of this may be an increase in burn injuries caused by misfortune, misuse, or the use of items in a state of disrepair. PurposeThe aim of this study was to explore any increase in referrals to a single burns centre in England for injuries caused by hot water bottles, radiant heaters, or electric blankets. MethodsThis was a retrospective study of a prospectively maintained database of referrals. All referrals between January 2022 and January 2023 were selected and compared with the same period from 2020–2021 (before the rise in energy prices). Referrals were screened for the terms “hot water bottle,” “electric heater,” “electric blanket,” and “heater.” Total referrals in each period, demographic data (age, gender), anatomical location and the mechanism of injury were compared between cohorts. ResultsWe found a statistically significant increase in the number of burns relating to heating implements between 2020/21 and 2022/23, rising from 54 to 81 (p = 0.03) – a 50% increase in injuries. Injuries in working age adults increased significantly (52% to 69%, p < 0.05). The most frequently injured area was the leg (30%) followed by the hand (18%). The commonest type of injury described was scald (72%). We found a moderately-strong correlation between the number of referrals and the average cost of energy in 2022–23. ConclusionThe number of injuries sustained by people using personal heating equipment is significantly increasing, which correlated with the rise in domestic energy prices. The most affected demographic appears to be working age adults, with wider implications around lost work-time yet to be explored. Further prospective, population-based work is indicated to assess the strength of the correlation seen in this study.

Low carbon operation optimisation strategies for heating, ventilation and air conditioning systems in office buildings

Heating, ventilation, and air conditioning (HVAC) systems play a crucial role in production environments. Optimising the control strategy of an HVAC system is an effective approach to achieving energy savings and reducing emissions in a production environment. In existing optimal control models for HVAC systems, occupant behaviour has been incorporated as a key influencing factor. However, model predictive control algorithms have not introduced to examine its performance in optimising HVAC system operation. An optimisation strategy considering occupant (leaders and users of HVAC systems) behaviour (OSOB) is proposed based on model predictive control algorithm. The concept of environmentally specific transformational leadership is introduced to characterise the behaviours of leaders. Then the distinct decisions driven by double objectives (carbon emissions and electricity costs) can be explained. The OSOB was applied to a representative office building in Beijing. The findings indicate that incorporating occupant behaviour into algorithm can lead to a significant reduction in carbon emissions, up to 45%, as well as a decrease in electricity bills, up to 17%. This study not only incorporates occupant behaviour as a significant influencing factor, but also offers valuable insights for product manufacturers seeking to reduce the carbon emissions produced during building operations.

Innovative retrofitting approaches for energy saving in Saudi public schools

Saudi buildings use approximately 29% of the primary energy and about 80% of the electrical energy. Because of the hot and dry climate, air conditioners consume a lot of electrical energy to attain a comfortable temperature indoors. More than 70% of Saudi buildings need to be thermally insulated. The objective of this study is to assess the existing energy saving of public schools in Saudi Arabia. Indoor thermal environment monitoring of three school buildings has been undertaken by employing LSI LASTEM ‘R-LOG’ data loggers, and electric energy bills of the schools were also collected. The school building was modelled using DesignBuilder software, which was utilized to investigate the impact of various modifications made to the building envelopes, including changes to building orientation, the inclusion of thermal insulation layers, various types of glass and shading devices. According to the findings, implementing appropriate building envelope design techniques for existing schools can lead to significant reductions in cooling and overall energy consumption, with savings of 30% and 19%, respectively. After applying these strategies collectively, the school managed to reduce its total annual electricity consumption by nearly 19.2%, resulting in a decrease from 279.13 MWh to 225.5 MWh per year.

Detecting Cyber Attacks on Renewable Distributed Generation for Future Smart Electric Meters to Prevent Electricity Theft

Electricity theft represents a pressing problem that has brought enormous fiscal losses to electric mileage companies worldwide. In the United States alone, $ 6 billion of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption sphere via physical attacks that include line tapping or cadence tampering. With the advanced metering structure (AMI), smart measures are installed at the guests’ demesne and regularly report the guests’ consumption for monitoring and billing purposes. In this environment, vicious guests can launch cyber-attacks on smart measures to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables guests to install renewable-grounded distributed generation (DG) units at their demesne to induce energy and send it back to the grid driver and hence make a profit.

Green retailer: A stochastic bi-level approach to support investment decisions in sustainable energy systems

This paper presents a bi-level approach to support retailers in making investment decisions in renewable-based systems to provide clean electricity. The proposed model captures the strategic nature of the problem and combines capacity sizing decisions for installed technologies with pricing decisions regarding the electricity tariffs to offer to a reference end-user, representative of a class of residential prosumers. The interaction between retailer and end-user is modeled using the Stackelberg game framework, with the former acting as a leader and the latter as follower. The reaction of the follower to the electricity tariff affects the retailer’s profit, which is calculated as the difference between the revenue generated from selling electricity and the total investment, operation and management costs. To account for uncertainty in wholesale electricity prices, renewable resource availability and electricity request, the upper-level problem is formulated as a two-stage stochastic programming model. First-stage decisions refer to the sizing of installed technologies and electricity tariffs, whereas second-stage decisions refer to the operation and management of the designed system. The model also incorporates a safety measure to control the average profit that can be achieved in a given percentage of worst-case situations, thus providing a contingency against unforeseen changes. At the lower level, the follower reacts to the offered tariffs by defining the procurement plan in terms of energy to purchase from the retailer or potential competitors, with the final aim of minimizing the expected value of the electricity bill. A tailored approach that exploits the specific problem structure is designed to solve the proposed formulation and extensively tested on a realistic case study. The numerical results demonstrate the efficiency of the proposed approach and validate the significance of explicitly dealing with the uncertainty and the importance of incorporating a safety measure.

Next-Generation Billing Systems: The Smart eBill Revolution

Efficient monitoring of electrical consumption is essential for promoting energy conservation and optimizing electricity usage. This project introduces a "Smart EBill System" using current and voltage sensors, along with an LCD display for real-time energy consumption tracking. The system aims to provide users with instant information on their electricity usage, helping them make informed decisions and promoting energy efficiency. This integration of sensors and a display offers an intelligent solution for smart and user-friendly electricity bill monitoring. In today's digital age, electronic billing systems have become a cornerstone of modern business operations, offering convenience, efficiency, and sustainability compared to traditional paper-based billing methods. However, as electronic billing systems evolve, there is a growing need for smarter, more intuitive solutions that enhance user experience, streamline processes, and leverage emerging technologies. This paper presents Smart eBill, a next-generation electronic billing system designed to address these evolving needs. Smart eBill integrates advanced features such as artificial intelligence, machine learning, and data analytics to revolutionize the billing experience for businesses and consumers alike. The system employs AI algorithms to automate billing processes, including invoice generation, delivery, and payment reconciliation, reducing manual errors and administrative overhead. Furthermore, Smart eBill leverages machine learning techniques to analyze billing data and provide personalized insights and recommendations to users. By understanding user preferences, spending patterns, and historical data, the system offers tailored suggestions for optimizing billing arrangements, reducing costs, and improving financial management.

Microgrids with day-ahead energy forecasting for efficient energy management in smart grids: hybrid CS-RERNN

ABSTRACT By integrating smart grid technology with home energy management systems, households can monitor and optimise their energy consumption. This allows for more efficient use of energy resources, reducing waste and lowering energy bills. In this manuscript, a hybrid approach is proposed for smart grid home energy management with microgrids and day-ahead energy forecasts. The proposed control approach combines the Circle Search (CS) algorithm and Recalling-Enhanced Recurrent Neural Network (RERNN). Commonly it is named as CS-RERNN technique. The novelty of this paper is to optimise energy consumption and production within microgrids, thereby contributing to the overall efficiency of the smart grid system. The proposed method is used to reduce the electricity cost, peak-to-average ratio (PAR), and maximising consumer comfort. Energy management is performed based on the CS algorithm. A smart home connected to the external power grid (PG) is managed by the proposed method. Here, load demand is predicted by using RERNN. By then, the performance of the proposed method is implemented in MATLAB platform. The proposed method shows a high efficiency of 96%, and 22 $ of low electricity bill cost compared with other existing methods such as Particle swarm optimisation (PSO), Cuckoo search algorithm (CSA, and Border collie Optimisation (BCO).

Energy Savings Through Refrigeration Load Control With Assessment of Commercial Potential: Lowering peak power and electricity bills through optimal demand scheduling

Energy Savings Through Refrigeration Load Control With Assessment of Commercial Potential: Lowering peak power and electricity bills through optimal demand scheduling

Would manufacturing go for renewable energy? Manufacturers' preference towards sustainability

Malaysia needs to fully utilize its renewable energy resources to meet its goal of installed capacity of 31% of renewable energy in 2025 and 40% in 2035. In order to empower renewable energy sources, the government has established a fund known as the renewable energy fund (RE FUND). In Malaysia, most manufacturing sectors contribute to the RE FUND through their monthly electricity bills due to their electricity consumption exceeding 300kwh per month. As Malaysia's highest electricity consumer, the manufacturing sector needs government investment incentives to switch to renewable energy sources to generate electricity. Therefore, this study was conducted to identify attribute preferences of the manufacturing sector due to investing in renewable energy sources. The Choice Experiment method was employed where the Mixed Logit model was chosen to identify the willingness to pay for the manufacturing sectors based on their preferences among the four attributes: types of renewable energy, project location, annual reduction in GHG emissions, and RE FUND. The study results found that the manufacturing sector places the highest value on the project location, where they prefer to improve the project location from current condition to far location. This study can also help to achieve the Goal 7 in the Sustainable Development Goal (SDG), where investment in renewable energy sources can guarantee that all individuals obtain affordable, reliable, sustainable, and modern electricity in 2030.

Methodology for Energy Management in a Smart Microgrid Based on the Efficiency of Dispatchable Renewable Generation Sources and Distributed Storage Systems

This paper presents a methodology for energy management in a smart microgrid based on the efficiency of dispatchable generation sources and storage systems, with three different aims: elimination of power peaks; optimisation of the operation and performance of the microgrid; and reduction of energy consumption from the distribution network. The methodology is based on four steps: identification of elements of the microgrid, monitoring of the elements, characterization of the efficiency of the elements, and finally, microgrid energy management. A specific use case is shown at CEDER-CIEMAT (Centro para el Desarrollo de las Energías Renovables—Centro de Investi-gaciones Energéticas, Medioambientales y Tecnológicas), where consumption has been reduced during high tariff periods and power peaks have been eliminated, allowing an annual reduction of more than 25,000 kWh per year, which is equal to savings of more than 8500 €. It also allows the power contracted from the distribution company by CEDER (135 kW) not to be exceeded, which avoids penalties in the electricity bill.

Economic Feasibility of Reverse Osmosis (RO) Water Treatment Plants: A Case Study from Dimbulagala, Polonnaruwa, Sri Lanka

Social wellbeing is strongly linked with economic feasibility when it needs to attain with the technological advancements. Installation of Reverse Osmosis (RO) water treatment plants have been considered as a promising solution to provide clean water for human consumption, especially in the areas where the CKDu (chronic kidney disease of unknown etiology) prevails. At present, over 2,000 RO treatment plants were installed in the dry zone of Sri Lanka, yet economic feasibility for operation and maintenance of RO plants has not been assessed so far. The present study was intended to identify economic feasibility of operation and maintenance of RO plants. Investigations were carried out in six community RO plants which provided drinking water for over 17,000 people which accounts for 20% of total population in Dimbulagala Divisional Secretariat over a period of 12 months. Six in-depth interviews and questionnaire survey were caried out with RO plant operators. The operational cost per production of cubic meter of filtered water was computed by considering electricity consumption bills. The maintenance costs and service charges were also obtained from the records available with RO plant operators. The results found that the average electricity consumption to produce 1 cube of filtered water is approximately 9kWh and cost is LKR 734 (based on 2021 rates). The average water selling price ranged between LKR 1.00-2.50 per liter. The average monthly income generation from one RO plant is approximately between LKR 561- 875 per cube of filtered water and it largely depended on the type of water source, climatic conditions. It was found that the income generated from RO plants was sufficient enough to cover the operation cost (monthly electricity bill) and for the subsequent maintenance and service charges whenever required. It can be concluded that the use of RO treated water is an economically viable option to provide portable drinking water. &#x0D; Keywords: CKDu, Economic sustainability, Drinking water, Purification cost

Assessing vertical green walls for indoor corridors in educational buildings and its impact outdoor: A field study at the universities of Canada in Egypt

Developing Green Walls (GW) within the architectural realm involves integration of diverse functions to optimize the indoor performance of the GW, and outdoor in campus buildings. The methodology is fundamentally rooted in the selection and fulfillment of strategic incorporation of additional function-based GW design to various performance aspects through contextual design solutions. This approach is particularly crucial in the education sector, on utilizing GW to educate students as occupants in integrating gardening, ecology, botany, and beyond in their studies for decreasing the environmental pollution for new generations, to feel by environment indoor in accomplishing way for sustainability perceptions.The aim of the study is to assess the effective resources influencing vertical green walls needed for educational buildings to reduce the effect of indoor heat, to upgrade its air quality, simultaneously decreasing noise pollution to avoid the energy consumption. The Educational GW should be accessible easily, either by installing them naturally in accessible locations with appropriate heights or by designing the facilities to increase the student's integration with GW natural environment that will reflect on their academic progress and health. The study aims to assess the effect of green wall installation in the universities of Canada in Egypt campus in the heat gain reduction, energy consumption, CO2 emission reduction by using the Design-Builder simulation program to measure the effect of green wall installation in corridors and inner courtyard at the hottest period of the year (August 2:00 p.m.) and how it can decrease the cooling loads by 10.95 %:19.14 %, heating loads by 4.8 %:12.94 %, CO2 emission by 13 %:28.43 % and the monthly electricity bill by 35:41.5 % measuring the vertical greening cost in relative to short and long period as a needed sustainable technology to reduce building demand significantly.

The many channels of firm’s adjustment to energy shocks: evidence from France

Abstract Based on firm-level data in the French manufacturing sector, we find that firms adapt quickly, strongly and through multiple channels to energy shocks, even though electricity and gas bills represent a small share of their total costs. Over the period 1996–2019, faced with an idiosyncratic energy price increase, firms reduce their energy demand, improve their energy efficiency, increase intermediate inputs imports and optimize energy use across plants. Firms are also able to pass-through the cost shock fully into their export prices. Their production, exports and employment fall. A consequence of these multiple adjustment mechanisms is that the fall in profits is either non-significant, small or specific to only the most energy-intensive firms. We also find that the impact of electricity shocks has weakened over time, suggesting that only firms able to adapt their production process to energy cost shocks have survived. Importantly, when faced with large electricity and gas price increases, firms are less able to reduce their consumption. These results shed light on the mechanisms of resilience of the European manufacturing sector in the context of the present energy crisis.

Managed residential electric vehicle charging minimizes electricity bills while meeting driver and community preferences

Transitions to electric vehicles (EV) are expected to increase electricity use in residences, where most tend to recharge. We develop a mathematical programming framework for shifting residential EV charging during low electricity pricing hours to minimize the additional electricity costs that a household incurs from charging their vehicle. The model also aims to meet additional household and their community preferences by adopting four secondary objectives: charging as soon as possible when the user arrives at home, charging as late as possible before the user leaves home, charging for valley filling and peak shaving of the residential load, and charging in a shared community hub by using a fast charging station. We analyze granular residential energy data from a sample of Austin households in 2018. We conduct an empirical analysis to compare households’ electricity bills under four electricity pricing schemes, including flat rates and time-of-use rates, both with and without a separate meter for EV charging. Our findings indicate that if charging behaviors remain unchanged, installing a separate meter for EVs is more expensive than treating EVs as another residential plug load. However, under time-of-use EV charging tariffs, households should adjust their charging behaviors, as validated by our optimization model. Implementing all four secondary charging objectives successfully avoids the on-peak periods of the EV charging rate and reduces households’ overall daily electricity costs by 38.87 % during the summer and by 44.3 % during the winter. Charging as soon as possible, charging as late as possible, and charging at a shared station provide drivers with increased flexibility. Charging as soon as possible and charging as late as possible lead to the lowest charger utilization, with the former having the longest charging stop time before home departure and the latter allowing for the longest charging start time upon home arrival. Charging at a shared station gives rise to the lowest share of charging time over dwell time. Charging for valley filling and peak shaving of the residential load offers less flexibility but has the advantage of flattening the load curve and mitigating high peak loads. This proves crucial in safeguarding the community’s energy distribution infrastructure.