Electric Utility Companies Research Articles

Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters

The adoption of smart meters and dynamic pricing programs is rapidly increasing among electric utility companies. In “Data-Driven Clustering and Feature-Based Retail Electricity Pricing with Smart Meters,” Keskin, Li, and Sunar analyze how utility companies should use smart meter data for better pricing decisions. Utility companies typically have access to consumption patterns and high-dimensional features on customer characteristics and exogenous factors. The authors identify that such feature data can exhibit different forms of heterogeneity—over time and over customers. They show that the different forms of feature heterogeneity significantly worsen the best profit performance that can be achieved by a data-driven dynamic pricing policy. The authors also develop a policy based on joint spectral clustering and contextual dynamic pricing and prove that this policy achieves near-optimal profit performance.

Machine Learning Model Development to Predict Power Outage Duration (POD): A Case Study for Electric Utilities.

In the face of increasing climate variability and the complexities of modern power grids, managing power outages in electric utilities has emerged as a critical challenge. This paper introduces a novel predictive model employing machine learning algorithms, including decision tree (DT), random forest (RF), k-nearest neighbors (KNN), and extreme gradient boosting (XGBoost). Leveraging historical sensors-based and non-sensors-based outage data from a Turkish electric utility company, the model demonstrates adaptability to diverse grid structures, considers meteorological and non-meteorological outage causes, and provides real-time feedback to customers to effectively address the problem of power outage duration. Using the XGBoost algorithm with the minimum redundancy maximum relevance (MRMR) feature selection attained 98.433% accuracy in predicting outage durations, better than the state-of-the-art methods showing 85.511% accuracy on average over various datasets, a 12.922% improvement. This paper contributes a practical solution to enhance outage management and customer communication, showcasing the potential of machine learning to transform electric utility responses and improve grid resilience and reliability.

Efficiency and Mergers and Acquisitions of Electric Utility Companies

Since the 1990s, market liberalization of the electricity industry has advanced all around the world. To survive in the drastically changing business environment, incumbent electric utility companies have conducted operational reforms, including Mergers and Acquisitions (M&As), to enhance and/or complement existing business capabilities. The purpose of this study was to measure the operational efficiencies of 31 of the world’s largest electric utility companies using data from 2010 to 2020 and examine regional differences in and the impacts of M&As on the efficiencies. For this purpose, we applied a new type of Data Envelopment Analysis (DEA) and Tobit model regression. We provide findings from the empirical analyses and discuss the business implications of M&As for electric utility companies. The operational efficiency measures were different among regions, but did not show statistically significant changes over the study period from 2010 to 2020. Furthermore, the results of regression analyses indicate that the increasing number of M&A buying transactions and M&A total transactions has a negative marginal impact on the operational efficiency or leads to a lower operational efficiency for utility companies. Since electricity utility companies have not received gains in operational efficiency from increasing the number of M&A transactions, they need to be more cautious about whether M&A transactions can provide value to the operation and technology management.

Coupling fire and energy in the Anthropocene: Deploying scale to analyze social vulnerability to forced electricity outages in California

Extreme events such as wildfires and winter storms result in disruptions to grid-based electricity delivery. Electricity supply disruptions are both reactive, whereby specific events cause damage to physical infrastructure, or anticipatory where electricity suppliers—namely electric utility companies—preemptively de-energize sections of an electrical grid or distribution network based on elevated potential of extreme conditions that may cause wildfire ignition. De-energization has been promoted as a strategy to mitigate risk of wildfire ignition and spread when active fires may encounter distribution/transmission lines. Provision of basic energy services such as electricity are necessary for maintaining a range of essential functions such as communication, which become critical during extreme events. In recent years, Public Safety Power Shutoffs (PSPS) have increasingly been deployed by utility companies in Western U.S states as wildfire risk increases due to combined impacts of anthropogenic climate change, fuel accumulation, and expansion of development in fire prone lands. While the PSPS policy was designed to reduce liability of utilities in igniting fires, there is a dearth of research critically analyzing how the policy affects social vulnerability for populations subjected to periods of de-energization during high-risk fire conditions. This article aims to deepen current understandings of the way scale can be deployed to illustrate the highly spatial nature of relationships coupling electricity supply outages with demographic data to advance limited knowledge on social vulnerability characteristics for specific communities subjected to PSPS. The research engages scale to compare social vulnerability to outages experienced both in Butte County, located in northern California and the state as a whole.

SOLAR TECHNOLOGY: EMPOWERING SERBIA’S RENEWABLE ENERGY FUTURE

This study investigated the solar power potential and performance in Serbia, a country with favorable solar conditions but limited resource utilization. The principal objectives of the investigation were to analyze the performance ratio (PR) and assess the electricity production of simulated solar power plants in different distribution areas. The Photovoltaic System software (PVsyst 7.3) was employed to simulate five grid-connected solar power plants, each with a capacity of 10 MW, in five distribution areas of the Serbian Public Electric Utility Company. The study found that the PR values varied from 81.7 % to 84 %, indicating favorable conditions for harnessing the solar potential. The simulations projected an annual electricity delivery to the grid of 64.41 GWh. According to the study’s estimates, installed solar power plants would be capable of meeting 0.22 % of the electricity needs in Serbia.

MODERATING EFFECTS OF WEATHER CONDITIONS ON THE RELATIONSHIP BETWEEN ELECTRICITY SUPPLY RELIABILITY AND SME SUSTAINABILITY

This paper aims to investigate the moderating effects of weather conditions on the relationship between electricity supply reliability and SME sustainability within the context of a developing country. The method used for analysis is a hierarchical regression with the use of SPSS AMOS 24 and slope analysis. SPSS 23 was used to test the assumptions for parametric analysis. Survey data was collected from a sample of 54 owners and managers of SMEs in the Fako Division of Cameroon. Findings revealed significant positive relationships between electricity supply reliability and SME sustainability and an insignificant positive relationship between weather conditions and SME sustainability. Furthermore, the results indicated that weather conditions moderate the relationship between electricity supply reliability and SME Sustainability in the Fako Division of Cameroon. The study found that the electricity utility company of Cameroon has the daunting task of ensuring a robust electricity supply network capable of withstanding harsh weather conditions to ensure regular electricity. The implications of the findings of the study are that prevailing weather has an influence on the reliability of electricity supply, which affects the operations of businesses. Furthermore, when the business operations of SMEs are affected, the economy of the country suffers significantly. The study recommends the involvement of the government and policymakers to institute measures to ensure the obligation of the electricity utility company to maintain minimal interruptions of electricity supply. This study’s contribution to the body of knowledge is embedded in developing a theoretical model that explains electricity supply reliability in Cameroon from a weather perspective.

Electric utility construction: Causes and types of field change orders

The electric power industry provides most of the energy for global commerce, and controlling its construction costs is important to provide affordable energy. Controlling original contract costs and any added costs due to change orders requires effective planning and design of projects. While cost overruns due to change orders occur in other industries, the electric power utility industry faces technical complexity, poor understanding of field conditions, lack of jobsite visits, and poor collaboration among stakeholders during design and planning. Although published data exist for costs of change orders in other industries, almost none are available for the electric power sector. To mitigate these problems, project managers must understand these risks, provide better cost forecasts, and take other measures to avoid change orders. Using a descriptive statistical analysis of the project database of an electric utility company, this study addresses the data gap by analyzing the magnitude, frequency, and causes of field change orders of both overhead and underground electrical construction. The results identified nine causes of field change orders (FCOs) during construction. The percentage increases in the electrical construction contract costs appear to be higher than in other industries, which is an alarming finding considering the tendency to bury transmission lines due to fire hazards. The result showed that FCOs with added scopes occurred more frequently in underground projects than in overhead projects. The analysis indicated that most causes of field change orders could be managed during planning and design and through the construction phase. The findings of this study can be used by electric utilities when they convert from overhead to underground systems due to security and climate change factors.

ERGONOMIC ANALYSIS OF PRUNING TREES IN CONFLICT WITH POWER LINES

Ergonomic analysis of pruning trees in conflict with power lines. Pruning trees in conflict with power lines is associated with several ergonomic risks due to postural issues and exposure to environmental conditions, such as noise and vibration in work activities. Additionally, there are life-threatening risks from falls or contact with power lines. The objective of this work was to apply the Ergonomic Workplace Analysis (EWA) method and assess the exposure of workers involved in pruning of trees conflicting with power lines to noise and vibration. The survey was conducted considering outsourced employees that provide services to an electric utility company in the state of Bahia, Brazil. The results obtained through EWA showed a regular risk rate, with concerning aspects, such as verbal communication (dialogue among workers regarding technical/operational issues), irregular posture of back, arms, and shoulders, high attention demand, and activity limitations. Quantitative analysis showed 84.9 dB (A-weighted) for noise and 2.440 m s-2 for vibration, both close to the action level. The pole saw vibration reached 3.703 m s-2, exceeding the tolerance limit. The importance of breaks, at-work exercises, and training is emphasized as ergonomic measures. A rotation system should be implemented in tree pruning activities to reduce exposure to vibration.

Enhancing the Security of Smart Grid using Neural Networks

The primary goal of this initiative is to contend with the global issue theft of electricity concern, which has adverse effects on both electricity consumers and utility companies. This illegal activity interrupts the economic growth of utility companies, poses electric hazards, and leads to higher energy costs for users. To combat this problem, we aim to develop an effective approach to predict and detect theft of electricity from smart grids, using Artificial Neural Networks (ANN) as the core technology.

Portfolio benefits of taxonomy orientated and renewable European electric utilities

This paper investigates carbon and energy mix risk in the equity prices of EU-Taxonomy orientated and renewable European electric utility companies. We calculate carbon intensity and energy mix factors to measure possible carbon and energy mix premia while investigating the performance of portfolios of EU-Taxonomy orientated and renewable European electric utilities. We use a unique dataset to extend the three-factor model presented by Fama and French (1993) and find evidence of a positive renewable energy mix premium for portfolios of EU-Taxonomy orientated firms and firms with a high level of renewable energy in the energy mix. A positive low-carbon premium is also found for these same portfolios. Lastly, based on the three-factor model, an EU-Taxonomy orientated portfolio outperforms both a non-orientated portfolio and a non-reporting portfolio while a renewable energy portfolio outperforms a conventional energy portfolio. Our results are important for regulators, investors and European electric utilities in assessing the impact environmental regulations have on a firm’s cost of capital.

Forecasting weather‐related power outages using weighted logistic regression

AbstractWeather is a key driving factor of power outages. In this article, a methodology to forecast weather‐related power distribution outages one day‐ahead on an hourly basis is presented. A solution to address the data imbalance issue is proposed, where only a small portion of the data represents the hours impacted by outages, in the form of a weighted logistic regression model. Data imbalance is a key modelling challenge for small and rural electric utilities. The weights for outage and non‐outage hours are determined by the reciprocals of their corresponding number of hours. To demonstrate the effectiveness of the proposed model, two case studies using data from a small electric utility company in the United States are presented. One case study analyses the weather‐related outages aggregated up to the city level. The other case study is based on the distribution substation level, which has rarely been tackled in the outage prediction literature. Compared with two variants of ordinary logistic regression with equal weights, the proposed model shows superior performance in terms of geometric mean.

DETECTION OF ELECTRICITY THEFT CYBER-ATTACKS IN RENEWABLE DISTRIBUTED GENERATION FOR FUTURE IOT-BASED SMART ELECTRIC METERS

Electricity theft represents a pressing problem that has brought enormous financial losses to electric utility companies worldwide. In the United States alone, $6 billion worth of electricity is stolen annually. Traditionally, electricity theft is committed in the consumption domain via physical attacks that includes line tapping or meter tampering. The smart grid paradigm opens the door to new forms of electricity theft attacks. First, electricity theft can be committed in a cyber manner. With the advanced metering infrastructure (AMI), smart meters are installed at the customers’ premises and regularly report the customers’ consumption for monitoring and billing purposes. In this context, malicious customers can launch cyber-attacks on the smart meters to manipulate the readings in a way that reduces their electricity bill. Second, the smart grid paradigm enables customers to install renewable-based distributed generation (DG) units at their premises to generate energy and sell it back to the grid operator and hence make a profit.
 Therefore, this project evaluating performance of various deep learning algorithms such as deep feed forward neural network (DNN), recurrent neural network with gated recurrent unit (RNN-GRU) and convolutional neural network (CNN) for electricity cyber-attack detection. Now-a-days in advance countries solar plates are used to generate electricity and these users can sale excess energy to other needy users and they will be maintained two different meters which will record consumption and production details. While producing some malicious users may tamper smart meter to get more bill which can be collected from electricity renewable distributed energy. This attack may cause huge losses to agencies. To detect such attack, this project is employing deep learning models which can detect all possible alterations to predict theft.

Peak Shaving Mechanism Employing a Battery Storage System (BSS) and Solar Forecasting

Maximum demand (kW) has contributed significantly to expensive electricity bills. A modern-day solution for overcoming the penalty demand charges is to utilize the peak shaving method. To perform peak shaving, a battery storage system (BSS) is used. This methodinvolves the charging and discharging of the battery during high and low demand respectively, thus reducing the penalty incurred from the electricity utility company. To charge the battery, a photovoltaic (PV) system is coupled with the BSS. There is currently no BSS algorithm in existence under the microgrid to shave maximum demand with the aid of solar forecasting. In this paper, an algorithm for the BSS to achieve peak shave will be developed with the use of solar PV forecasting. The load profile of a building is used in this study as a reference for future consumption. The developed algorithm releases the energy stored in the BSS to shave the critical demand based on solar forecasting and the BSS state of charge (SOC). In short, this algorithm provides a green solution for reducing the demand charges from the electricity company.

An Optimization Strategy of Price and Conversion Factor Considering the Coupling of Electricity and Gas Based on Three-Stage Game

In order to improve the profits of electricity utility company (EUC) and gas utility company (GUC) and reduce the electricity and heat cost of users, an energy trading and pricing scheme based on three-stage game is proposed. Firstly, a three-stage optimization problem is established, and the conversion between electricity and gas is considered. Meanwhile, the conversion factor is introduced and coordinated with the energy price to adjust the balance of supply and demand. Then, the equilibrium solution of the game is obtained by using Lagrange function and backward induction method. In addition, an iterative algorithm is developed to obtain the optimal conversion factor between electricity and natural gas. Numerical results show that the profits of EUC and GUC are increased by 31.6% and 14.4%, the electricity and heat profits of energy hubs (EHs) are increased by 3% and 6.4%, and the electricity and heat cost of users are reduced by 9.25% and 14.05%. Note to Practitioners—In the multi-energy market, trading and pricing strategies have attracted more and more attention. Based on this, many scholars only studied pricing strategy to balance the supply and demand. However, in the context of multi-energy coupling, the previous pricing strategy is not effective. In this paper, we propose a new pricing strategy, with which the conversion factor can cooperate with the energy price to achieve the balance between supply and demand. The new pricing strategy can increase the profits of electricity utility companies and gas utility companies and reduce the costs of users.

Review of the Data-Driven Methods for Electricity Fraud Detection in Smart Metering Systems

In smart grids, homes are equipped with smart meters (SMs) to monitor electricity consumption and report fine-grained readings to electric utility companies for billing and energy management. However, malicious consumers tamper with their SMs to report low readings to reduce their bills. This problem, known as electricity fraud, causes tremendous financial losses to electric utility companies worldwide and threatens the power grid’s stability. To detect electricity fraud, several methods have been proposed in the literature. Among the existing methods, the data-driven methods achieve state-of-art performance. Therefore, in this paper, we study the main existing data-driven electricity fraud detection methods, with emphasis on their pros and cons. We study supervised methods, including wide and deep neural networks and multi-data-source deep learning models, and unsupervised methods, including clustering. Then, we investigate how to preserve the consumers’ privacy, using encryption and federated learning, while enabling electricity fraud detection because it has been shown that fine-grained readings can reveal sensitive information about the consumers’ activities. After that, we investigate how to design robust electricity fraud detectors against adversarial attacks using ensemble learning and model distillation because they enable malicious consumers to evade detection while stealing electricity. Finally, we provide a comprehensive comparison of the existing works, followed by our recommendations for future research directions to enhance electricity fraud detection.

Techno-Economic Assessment of PV Power Systems to Power a Drinking Water Treatment Plant for an On-Grid Small Rural Community

This paper shows the technical–economic assessment of two power systems based on renewable electricity to cover the energy consumption requirements of a drinking water treatment plant in the town of Pile, Ecuador, with a planning horizon of 15 years. A stand-alone and a grid-connected solar PV system were proposed to power this plant, which was designed considering the maximum daily potable water supply condition. This plant operated under two scenarios: (1) 12 h during daylight hours and (2) 24 h. Both schedules were proposed to assess the impact of PV power systems on plant operation. We modeled and optimized a total of four scenarios, where each scenario consisted of one of the proposed PV power systems and the plant with one of its operating schedules. Homer Pro software was used to size and find an optimal solution in each scenario, considering the net present cost (NPC) as the main criterion for optimization. The results showed that the change in the plant operation schedule significantly influenced the parameters of each scenario, such as component sizing, electricity production, initial capital, NPC, and electricity purchase/sale capacity from the plant as a grid power service user to the electric utility company.

Performance Analysis Method for Robotic Process Automation

Recent studies show that decision making in Business Process Management (BPM) and incorporating sustainability in business is vital for service innovation within a company. Likewise, it is also possible to save time and money in an automated, intelligent and sustainable way. Robotic Process Automation (RPA) is one solution that can help businesses improve their BPM and sustainability practices through digital transformation. However, deciding which processes to automate with RPA technology can be complex. Consequently, this paper presents a model for selecting indicators to determine the profitability of shifting to RPA in selected business processes. The method used in this work is the Performance Analysis Method, which allows for predicting which processes could be replaced by RPA to save time and money in a service workflow. The Performance Analysis Method consists of collecting data on the speed and efficiency of a business process and then using that data to develop discrete event simulations to estimate the cost of automating parts of that process. A case study using this model is presented, using business process data from an international utility company as input to the discrete event simulation. The model used in this study predicts that this Electric Utility Company (EUC) will save a substantial amount of money if it implements RPA in its call center.

Implementation of Work-Based Learning Among Electrical Engineering Diploma Students in an Electric Utility Company

Engineering students graduating from a higher education institution (HEI) are expected to have hands-on experience to be relevance in employment. Work-Based Learning (WBL) is defined as a learning method that engages or includes students in real-world employment so that they may learn the needs of job competencies, experience the job culture first-hand, and uncover career options. Industrial training (IT) is a work-based learning approach required of engineering students as part of their diploma courses. Implementation of IT with duration of 18 weeks was employed for 27 students Diploma in Electrical Engineering students. Students undergo fields rotation with different appointed supervisors throughout the IT period. The IT that was implemented enabled students to obtain important industrial exposure. Industrial visitation at a coal power plant was arranged to give additional industrial exposure to the students. Oral presentation at the end of the course, logbook and final report submission are assessed by assigned supervisors and lecturers for evaluation of each student. The study has offered insights into current IT implementation as well as some suggestions for the future implementation. To track the institution's IT implementation, the study should be applied to the following cohorts.

Intelligent Load Management System Development with Renewable Energy for Demand Side Management

Electric smart grid reliability and stability could be increased by the application of demand response initiatives and renewable energy resources. This study provides a brand new demand side management paradigm for smart grids with renewable energy integration that is based on intelligent optimisation. The suggested system combines real-time demand response programmes from electric utility companies and makes use of fuzzy logic to forecast consumer energy consumption patterns. Using demand response programmes, a smart energy management controller adjusts consumer energy usage forecasts to produce an operation schedule. Using simulations employing real-world data, we assess the efficacy of the suggested intelligent demand side management framework. According to the findings, compared to the load management-free method, total electricity costs and carbon emissions have significantly decreased. A potential strategy for demand side management with the integration of renewable energy, the proposed intelligent hybrid optimisation method of load management achieves superior performance in regulating energy consumption, peak loads, and carbon emissions. By presenting a useful and effective paradigm for demand-side management with renewable energy integration, this research makes a contribution to the field of energy management.

Assessing Service Quality and Customer Satisfaction of Electric Utility Provider’s Online Payment System during the COVID-19 Pandemic: A Structural Modeling Approach

The COVID-19 pandemic has rapidly changed people’s lives, especially technological advancements. Most service industries have opted to adopt online payments since the pandemic happened. Electric utility companies are one of the major industries that utilize online payments as an alternative mode of payment to traditional cash payments to minimize person-to-person contact. The current study aimed to assess electric utility companies’ service quality and customer satisfaction to improve the payment system. The study was conducted in Occidental Mindoro Electric Cooperative, Inc. (OMECO), which utilized an online payment system due to COVID-19-protocol. The research model comprises 60 questionnaires with ten sections developed from the literature and distributed to 400 participants. The data were analyzed using the structural equation modeling and path analysis technique. The results showed that Online Payment Security had the most positive effect on service quality, thus affecting customer satisfaction. Moreover, tangibility, reliability, online payment method, and COVID-19-protocol positively affected service quality. While reliability, assurance, and empathy had a negative effect on the service quality of OMECO. Aligning with the result, OMECO was poorly evaluated by the participants in terms of empathy, assurance, responsiveness, and reliability. Moreover, the result of the study can be used by major electric companies to improve their service in terms of online payment. Finally, the current research is one of the first studies that integrate service quality and customer satisfaction while utilizing the structural modeling approach.