Electricity Bill Research Articles

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.

Techno-Economic Assessment of Solar–Grid–Battery Hybrid Energy Systems for Grid-Connected University Campuses in Kenya

This paper presents the techno-economic feasibility of using grid-connected PV hybrid systems to supply power in large grid-dependent academic institutions. The study was conducted using the administration building of Moi University in Kenya. The power consumption profile of the building was collected using a PCE-360 power analyzer. The peak load demand was found to be 60 kW. Using random variability constants of 4% for day-to-day and 4% time-step load variability, a peak demand of 70.58 kW was obtained, which was used in our simulation. The solar radiation and temperature data for this site were collected from the weather station of the university. The hybrid system was simulated using HOMER Pro software. It was found from the simulation results that the optimal system was the solar PV/grid without battery storage, which had a levelized cost of energy (LCOE) of KSH 8.78/kWh (USD 0.072), net present cost (NPC) of KSH 27,974,492 (USD 230,813), capital expenditure (CAPEX) of KSH 26,300,000 (USD 216,997), and a simple payback period (SPBP) of 5.08 years for a 25-year life span. This system, when compared to the existing grid, showed an 83.94% reduction in the annual electricity bill of the administration building. These results demonstrate a reduction in energy cost by a renewable energy fraction of 67.1%.

IoT Based Smart Energy Meter

In Modern day smart grid technology relies heavily on communication networks for two way communication between load, generation, transmission, and control centre. As a part of the smart grid, smart meters use advanced metering infrastructures (AMI) that are widely distributed and interconnected to the communication network. In modern Technology the smart grid plays very important role for two way communication between Supplier and consumer. Smart meter is main component of smart grid. Currently we are paying electricity bill like one person came to home to read the electricity board and handover the bills to the owner of that house. The drawback of this it requires more man power, time, and many times it has error in bill amount. To eliminate this error smart energy meter is introduced. This system also helps in detecting and control over power theft. this smart energy meter helps us in home automation using IoT and enabling wireless communication which is a great step towards Digital India .Efficient energy utilization plays a very vital role for the development of smart grid in power system By providing real-time monitoring and control of energy consumption, it can help users to reduce their energy usage, save money, and promote sustainable energy consumption. As a first step towards such cyber-attack related researches and searching for possible mitigation techniques, it is necessary that a smart meter be developed that has the feature of network integration. The present contribution reports the design, fabrication, and operation of an Internet of Things (IoT) based smart meter using Arduino to serve as an integral part of a smart grid system.

Dynamic investment planning of CVR implementation considering PEVs’ reactive power compensation capability

AbstractConservation voltage reduction (CVR) is a strategy that tries to save energy consumption by managing consumers’ voltage. On the other hand, enhancing reactive power compensation in the power system gives rise to CVR implementation more effectively. This paper addresses how the power system planner could encourage investment to establish the above‐mentioned topics. Here, a model for encouraging industrial loads to dynamically invest in implementing the CVR strategy over the planning horizon is presented. To make CVR implementation more efficient, investment in upgrading plug‐in electric vehicles (PEVs) chargers to be utilized as reactive power compensators is also considered. Industrial loads benefit from saving in their electricity bills and incentive payments that might be paid by utilities (if necessary). The objective function of the proposed planning model is to maximize the energy‐saving of industrial loads and to minimize incentive payment by electric utilities over the planning horizon. This objective is achieved subject to that the investment plan is economically feasible. Moreover, power system operation constraints in the planning horizon based on AC power flow equations and voltage‐dependent load models are considered. The proposed planning problem is modelled as a non‐linear optimization problem that can be solved by off‐the‐shelf software, for example, GAMS. The proposed model is applied to the Ontario transmission system. The numerical results show the proposed model's effectiveness in designing an investment plan for industrial loads to implement the CVR strategy and upgrade PEVs’ chargers.

Nontechnical Loss Detection for Metered Customers in Alexandria Electricity Distribution Company Using Support Vector Machine

Non-technical losses (NTL) during transmission and distribution (T&amp;D) of electrical energy is a major problem faced by utility companies which is very difficult to fight and detect. For that, more of power utilities spend thousands dollar for research centres to find efficient methods for detecting and controlling abnormalities. Electricity theft and billing irregularities forms the main portion of NTL. With the introduction of smart meter, the frequency of reporting energy consumption data to the utility company has been increased. Incoming and outgoing energy could be monitored and analyzed. Electricity theft is a complex problem with many parameters to be evaluated before implementing any measures to detect and control that. These parameters include some issues like social, economic, regional, managerial, infrastructural, and corruption. In recent years, several data mining and research studies on fraud detection and prediction techniques have been carried out in the electricity distribution sector. Support vector machine (SVM) technique has dominated the research for classifying data and detecting fraudulent electricity customers. SVM technique has good ability in data mining and data classification. The paper objective is to analyze the metered energy consumption data recorded and predict the pattern or the form of daily user’s energy consumption, then using SVM to classify the data whether normal or theft. The suggested technique is then tested using real data from Alexandria Electricity Distribution Company (AEDC). The proposed technique was able to distinguish between healthy and theft cases.

Simulation-Based Education Tool for Understanding Thermostatically Controlled Loads

Thermostatically controlled loads have great potential to make a significant contribution to improving energy efficiency in the building sector, which is responsible for 40% of greenhouse gas emissions in the EU. This, in addition to the environmental damage, represents a huge expense in terms of the electricity bill. Therefore, it is very important to train engineers on how to design energy management systems for TCLs. With this goal in mind, it would be very useful to have a simulation-based educational tool (SBET) to understand thermostatically controlled loads, their characteristics, and the possibilities in terms of energy efficiency. In addition, it would be very useful if this tool could be introduced in engineering curricula to help students become better trained and enter the labor market with more opportunities. Based on the shortcomings detected, this work develops an SBET specifically designed to teach on the subject of TCLs (SBET-TCLs), both about their intrinsic characteristics and their better management. To verify the developed SBET-TCLs, it was tested in a real scenario: a survey was carried out among the students of the subject ‘Alternative Energy Sources’ in the degrees of Industrial Engineering. The results show that the use of an SBET-TCLs has very positive effects on the learning process.

Community-centric distributed energy resources for energy justice and decarbonization in dense urban regions

Low-to-moderate income (LMI) households suffer from higher energy cost burden as they spend a larger portion of their income on electricity bills than higher-income households due to factors including limited access to renewable energy. Although distributed energy resources (DERs) can be owned by individuals, supposedly enabling energy justice, reality shows otherwise since it requires capital investment and property ownership. To mitigate these barriers, the paper proposes “Community Energy Cells” (CECs) which consists of a group of DERs and controllable loads in proximity, represented by a Cell Aggregator (CA) as a single controllable entity. CECs can operate independently during disruptions, deploy renewable energy to LMI households, and provide multiple value streams for their DERs, including the option to aggregate and sell in the wholesale energy market. A financial case study using National Renewable Energy Laboratory’s (NREL) System Advisory Model (SAM) was performed focusing on select buildings in West Harlem, New York City, with approximate 2.5 MW solar and 15 MW storage potentials. Considering that their CA benefits from investment tax credits, and the costs including installation and electricity purchase, it has a net present value of over $2 M, an internal rate of return of 35 % after 25 years, and sufficient cash flow to cover debt service. These returns are sensitive to key values: battery price and committed capacity for incentive programs for DERs and loads. The realization of CEC has interdisciplinary challenges that the paper elaborates on. Nevertheless, CEC is indispensable for tackling climate change and resiliency while attaining energy justice.