Electric Power Utilities Research Articles

Cloud based Power Failure Sensing and Management Model for the Electricity Grid in Developing Countries: A Case of Zambia

In most developing countries, huge parts of the electric power grid are not monitored making it difficult for the service provider to determine when there is a power failure in the electric grid, especially if the power failure occurs in the Low Voltage level. Clients usually have to call and inform the utility’s customer service centre to report a power failure. However, this system of addressing power outages is not very effective and usually results in long durations of system interruptions. This paper proposes a cloud based power failure sensing system to enable automatic power failure sensing and reporting as well as monitoring of the low voltage power network in Zambia, a developing country in Southern Africa. A baseline study was conducted to determine the challenges faced by both the electric power utility company called Zambia Electricity Supply Corporation (ZESCO) and the electricity consumers in the current power failure reporting management model. The results from the baseline study indicate that challenges are being faced by electricity consumers when it comes to reporting power failures. These include failure to get through to the customer call centre due to constantly engaged lines, unanswered calls, failed calls and network failure. The challenge faced by the electricity service provider is the inability to attend to all the customers through the call centre as customer calls are rejected due to limited Call Centre system resources. To address these challenges the proposed cloud based power failure sensor model made use of a Voltage sensor circuit, Arduino Microcontroller board, SIM808 GSM/GPRS/GPS module, cloud architecture, Web Application and Google Map API. Results from the proposed model show improved reporting time, location information and quick response to power failures.

Electricity Cost Prediction using Autoregressive Integrated Moving Average (ARIMA) in Korea

Electricity cost plays a vital role due to the immense increase in power utilization, rise in energy rates and alarms about the variations and impact on the environment which ultimately affects electricity cost. We claim that electrical power utilization data became more beneficial if it is presented to the customers along with the prediction of power consumption, prediction of energy prices and prediction of its expected electricity cost. It will assist the residents to alter their power utilization behavior, and thus will have an optimistic influence on the electricity production companies, dissemination network and electricity grid. In this study, we present a residential area power cost prediction by applying the Autoregressive Integrated Moving Average (ARIMA) technique in Korean apartments. We have investigated the energy utilization data on the foundation of daily, weekly and monthly power utilization. The accumulated data constructed on daily, weekly and monthly utilization are selected. Then we predict the maximum and average power consumption cost for each of the predicted daily, weekly and monthly power consumption. The power consumption and general price (General Electricity Price in Korea) data of Korea are used to analyze the efficiency of the prediction algorithm. The accuracy of the power cost prediction using the ARIMA model is verified using the absolute error.

Influence characteristics of electrical parameters and vibration isolation properties of the stretcher system based on parallel mechanism and self-powered magneto-rheological damper

For reducing multidimensional vibrations endured by supine patients on ambulances, a new ambulance stretcher composed of parallel mechanism and self-powered magneto-rheological damper is introduced. The main thrust of this work is to investigate the vibration isolation capability and energy conversion efficiency of the proposed stretcher with changes in electrical parameters. First, the kinematic and dynamic equations are deduced by geometry and Lagrange approach, respectively. Subsequently, the circuit model of self-powered magneto-rheological damper is created. The frequency response function is derived by Kirchhoff’s law, which indicates the electrical power utilization rate. Then, the vibration isolation capability of the self-powered stretcher system is analyzed with changes in electrical parameters. Compared with passive control, the vibration isolation capability of the self-powered stretcher is improved significantly in horizontal, longitudinal, vertical, and pitch directions. The first two resonance peaks of displacement response are insensitive to changes in electrical parameters in all directions. Last, the energy conversion efficiency of the self-powered stretcher is discussed. Decreasing resistance and inductance could improve energy conversion efficiency significantly.

A P2P-Dominant Distribution System Architecture

Peer-to-peer interactions between small-scale energy resources exploit distribution network infrastructure as an electricity carrier, but remain financially unaccountable to electric power utilities. This status-quo raises multiple challenges. First, peer-to-peer energy trading reduces the portion of electricity supplied to end-customers by utilities and their revenue streams. Second, utilities must ensure that peer-to-peer transactions comply with distribution network limits. This paper proposes a peer-to-peer energy trading architecture, in two configurations, that couples peer-to-peer interactions and distribution network operations. The first configuration assumes that these interactions are settled by the utility in a centralized manner, while the second one is peer-centric and does not involve the utility. Both configurations use distribution locational marginal prices to compute network usage charges that peers must pay to the utility for using the distribution network.

Alterações de Taxas e Gestão de Resultados nas Distribuidoras de Energia Elétrica Doméstica

Objective: To verify whether the concessionaires of electric energy adopt earnings management (EM) practices, before and after the Periodic Tariff Review (PTR), in order to obtain better rates and attract investors and/or pay bonuses to their managers. Method: The sample of this study contains all electric power utility companies of B3, covering the period of 2010-2016. We analyzed the impact of PTR using the panel data method. Originality/Relevance: It was analyzed whether the EM is related to PTR, since the interest of the consumer, the government and the investors is different of the concessionary´s interest with respect to the value of the tariff. Notice that investors can make better decisions on how and when to allocate their resources to a specific PTR event. Theoretical/Methodological Contributions: The findings indicate that in the years of tariff review, there is a greater generation of accruals and dimishments 2 years after the PTR. On the other hand, in EM (operational) the cyclical behavior is similar, but the PTR is negative in the year of the tariff review and positive two periods ahead. Social contributions to management: These results are understood as long as the concessionaires know that the result of the year of PTR will not influence the Regulatory Agency in the determination of the present revision and reverse them 2 periods ahead, in order to anticipate for the next PTR.

Design of direct load control device and its effect on load reduction

Demand for air conditioning is rising at an unprecedented level. Sudden increase in air conditioning usage has a potential of destabilizing the power grid and at extreme condition can cause power outages. Ability to control air conditioning is therefore a very significant subject for both the electric power utility and electric power consumer. In some developed countries, this is realized by a program called demand-side management. The progress of information technology and the increasing affordability of the cost of fabricating electronic devices have given rise to new concepts in the implementation of demand-side management programs, namely the direct load control method. Direct load control is a mechanism that allows demand-side management programs to run automatically without interference from consumers. This is done by directly controlling consumer electricity equipment through the internet or other wireless communication networks. This study analyzes a method to design a controlling device to be used in direct load control implementation. Load demand of the system is compared between before and after the installation of direct load control devices. Significant load reduction is noticed after the installation of direct load control devices.

Impacts of photovoltaic distributed generation and energy efficiency measures on the electricity market of three representative Brazilian distribution utilities

This paper evaluates the economic (electric power rates and utilities revenues), environmental (GHG emissions), social (job creation) and political (tax collection) impacts of a wider diffusion of photovoltaic distributed generation (PVDG) and energy efficiency (EE) measures on the markets of electric power distribution utilities in Brazil. The impacts are calculated for three substantially different power distribution utilities, which are representative of the types of utilities found in the country. Key indicators were selected to reflect the main interests of the following stakeholders: power distribution utilities, the government (Federal, State and Municipal) and low-voltage consumers. The results show that the most important issues that should be addressed concerning a large diffusion of EE measures are tax collection and tariff rises. The simulations show that tariff rises can be hampered by EE actions targeted to specific locations and specific actions. The most important impact of PVDG diffusion is on tariff rises, considering the current regulatory framework in the country. On the other hand, the net jobs creations of EE measures and, particularly, PVDG are significant and the cost of job creation in relation to fiscal waiver by investment on PVDG is cost-effective. The GHG emissions avoided by the higher levels of PVDG and EE in 2040, simulated in this study, showed to be significant for both EE measures and PVDG.

Effect of Two Common Head-Mounted Augmented Reality Systems on Muscle Force and Blink Rate of Electric Utility Power Plant Operators

Augmented reality (AR) is a technology that combines real and virtual information presented to the user in an interactive way in real time. The Microsoft HoloLens and RealWear HMT-1 are two common types of head-mounted AR available to industrial field workers. These two AR systems were tested on how they affected blink rate, an indicator of eye strain, and electromyographic (sEMG) activity of the neck and shoulder muscles of electric utility power plant operators while they performed five routine inspection tasks of coal equipment. The inspection tasks were conducted under three conditions: HoloLens, HMT-1, and No AR (regular method). Workers communicated with the HoloLens with a right-hand gesture and by voice input to the HMT-1. The duration of the inspection tasks ranged from an average of 10 to 28 sec. Twelve experienced power plant operators participated in the study. sEMG) of the right and left sternocleidomastoid, splenius, semispinalis capitis, and upper trapezius muscles were measured, and a small camera recorded blink rate of the right eye. Results reveal generally no significant differences in 50th and 90th percentile sEMG between the three conditions for all eight muscles. Although the means of blink rate appeared consistently lower with the HoloLens than the No AR and HMT-1 (approximately 4.4 blinks/min), these differences were not significant at the 0.05 level (p=0.06 to 0.17). Future studies should investigate a larger sample size of workers wearing AR devices for longer time periods (> 30 min) to determine long-term effects of AR devices on muscle activity and eye strain. A system of hardware, software, and experimental protocol was developed that follow-up studies may employ to test physiological effects of AR devices.

Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China

The remarkable prediction performance of electricity consumption has always assumed particular importance for electric power utility planning and economic development. On account of the complexity and uncertainty of the electricity system, this paper establishes a self-adaptive grey fractional weighted model to predict Jiangsu’s electricity consumption, which efficiently enhances the prediction quality of electricity consumption. This newly constructed grey model introduces the fractional weighted coefficients to design a novel initial condition. Compared with the old one in the conventional grey models, the newly optimized initial condition has a flexible structure, which has advantages in capturing the dynamic characteristics of the electricity consumption observations. In addition, to further promote the forecasting precision, the adjustable fractional weighted coefficients and corresponding time parameter of the initial condition are estimated by utilizing the Particle Swarm Algorithm (PSO). Furthermore, five competing models are employed to forecast Jiangsu’s electricity consumption in China, which certifies the validity of the established model. Experimental results illustrate that the newly designed model has significant advantages over other five competing models. According to the forecasted results, electricity consumption in Jiangsu Province is expected to reach 6778 billion kilowatt-hours in 2020, while the growth rate will fall down by 1.11%. Therefore, several proposals are made for decision-makers.

Performance Evaluation of Battery and Super-Capacitor for Electric Vehicle with Hybrid Techniques

In recent trends, Plug-in Hybrid Electric Vehicles (PHEVs) have an excessive opportunity due to its charging facilities and charge storage system. Through appropriate design and development, Electric Vehicles (EVs) not only eliminate the pollution but also make the system more efficient over traditional vehicles. However, still it is in the progress of the investigation and has several unanswered issues. The performance of PHEV depends upon proper utilization of electric power which is solely affected by the battery State-Of-Charge (SOC). SOC determination becomes a vital problem in whole area where it comprises a battery storage; because it has several drawbacks such as weak power density, longevity, etc. one probable and favourable resolution for this issue is multi-source EV. Both the batteries and Ultra-Capacitors (UC) can reduce the aforementioned disadvantages in existing systems. This proposed work has more efficiency because of the combined design model of both battery and super-capacitor. Hence, in this work, the Particle Swarm Optimization (PSO) technique with Fuzzy Logic Controller (FLC) is implemented for optimal sharing of power between the battery and the super-capacitor.A

Transformer Oil Regeneration as a Panacea for Electric Power Utility Company’s Equipment Optimization

Power transformers constitute the most costly equipment which often posed constraints to electric power utility companies’ management. These transformers develop faults often due to oil insulation problems resulting from poor level of insulation oil, lack of routine maintenance, contamination, age, carbonization arising from system tripping as well as degradation of paper insulation due to ageing. However, the most economical way of maintaining stability in power supply to customers is creating a routine program of transformer oil regeneration for power transformer in the network. This paper therefore presents the optimization process of transformer oil regeneration for electric power utility company equipment. In this study, combined techniques of hot oil circulation, oil purification and oil reclamation of transformer oil regeneration was used for analysis of two 15MVA, 33/11kV power transformer. The process is aimed at drying the solid insulation of the transformer through the circulation of hot oil. The results of the transformer oil test before and after carrying out oil regeneration processes for the two 15MVA transformers are obtained and presented. For each transformer, the results are in five categories of properties namely; Physical, Electrical, Chemical, Dissolved metals and Dissolved gas analysis properties. The results indicated that the viscosity of transformer 1 is better than that of transformer 2. In addition, the dielectric breakdown voltage of oil transformer 1 is of more quality than the oil in transformer 2. The results are in agreement with standard ASTMD, IEC and ISO because the transformer properties has individual standard with each having its own mark. The comparison shows that transformer oil regenerated was very close to reality because the oil in the two power transformers is close to 90 %.

Strategic control and cost optimization of thermal energy storage in buildings using EnergyPlus

An operational strategy to optimize building operating energy costs for suppliers and consumers is an important challenge for electrical power utilities. There are various supply-side measures that utilities have to take to ensure continuous energy supply for building heating and air-conditioning. During peak energy demand, utilities are often forced to use more expensive and less efficient generation, thereby increasing the cost of energy. However, some demand-side management practices behind the consumer meter can help in meeting this challenge. One such measure is the use of thermal storage for heating, ventilation, and air-conditioning applications in commercial buildings. There is a gap of adequate knowledge of an optimal control strategy of cold storage operation in buildings adapting to applicable time of day tariffs to minimize annual energy use and annual energy cost of operation. There is also a need to use commercially available tools to avoid the use of complex mathematical models. This study demonstrates strategic controls with six operating modes for using thermal energy storage to shift peak electricity demand, using the time of day tariffs as a decision variable, and reducing operating costs, while also minimizing the size of the system. EnergyPlus was used to model a standard reference large office building for three thermal energy storage system cases: mixed chilled water storage, stratified chilled water storage, and ice storage. An annual average shifting of 25–78% of peak electricity was achieved from the simulation results. The strategy was able to achieve an annual 10–17% cost reduction for consumers using the time of use rates available from a local utility.

Power Big Data: New Assets of Electric Power Utilities

AbstractBig data has emerged as an attractive concept in various industry domains. For power engineering, enormous amounts of data from different devices, different geographical locations of the po...

Cybersecurity Strategies of Japanese Electric Power Utilities

Cybersecurity Strategies of Japanese Electric Power Utilities

Comparative Analysis of Electrical Power Utilization in Nigeria: From Conventional Grid to Renewable Energy-based Mini-grid Systems

The electrical power grid in Nigeria is limited in terms of reach and utilization, and this leaves a large section of the population without access to power supply. This paper therefore reviews literature on the current state of the conventional electrical power grid in Nigeria. The generation, transmission and distribution sectors of the grid are briefly reviewed before examining the extent, capacity and power generation technologies used by some currently deployed mini-grid systems. It is observed that a majority of deployed mini-grid systems depend on solar photovoltaic renewable energy sources and such systems are mostly isolated from the conventional grid. Therefore, to make a case for improved access to electrical power supply in the country, statistical and demographic analysis has been carried out to reveal the size of the electrical energy market available to mini-grids and the need to integrate current and future mini-grids to the conventional grid. It is noted that with substantial but targeted investments, a number of existing smart grid technologies can be employed to integrate mini-grids to the conventional grid thereby providing affordable access to electricity for communities that are hitherto unserved or underserved by the conventional grid. Some of the available smart grid technologies that have been identified to be suitable for integration purposes include advanced metering infrastructure (AMI), flexible alternating current transmission system (FACTS) and high voltage direct current (HVDC) transmission lines. A top-level schematic of how and where such technologies can be deployed is provided.

Blackout Avoidance through Intelligent Load Shedding in Modern Electrical Power Utility Network

During the last few decades many blackouts have occurred throughout the world. It seems that modern power systems are more vulnerable to major blackouts. Power system in Pakistan is no exception where blackouts affect the economy and hinder the development of the country. Pakistan Electric Power Company (PEPCO) is responsible for generation, transmission, and distribution of electric power in Pakistan. Major blackouts occur due to failure of the protection system of the power transmission network. The remedy for such collapses is disconnection of non-preferred load through Intelligent Load Shedding (ILS) technique. In this paper, the Intelligent Load Shedding is simulated in Electrical Transient Analysis Program (ETAP) software. The technique has been applied on 220kV transmission network of Quetta Electric Supply Company (QESCO), a power distribution company in Balochistan province of Pakistan. This paper focuses on preventing entire QESCO network from cascaded tripping and blackout during N-2 contingency situations. It has been established that implementation of Intelligent Load Shedding is not only helpful for protecting the transmission network of the distribution company, like QESCO, from blackout, but also protects smart grid from blackouts and other power collapses.

Blackout Avoidance through Intelligent Load Shedding in Modern Electrical Power Utility Network

During the last few decades many blackouts have occurred throughout the world. It seems that modern power systems are more vulnerable to major blackouts. Power system in Pakistan is no exception where blackouts affect the economy and hinder the development of the country. Pakistan Electric Power Company (PEPCO) is responsible for generation, transmission, and distribution of electric power in Pakistan. Major blackouts occur due to failure of the protection system of the power transmission network. The remedy for such collapses is disconnection of non-preferred load through Intelligent Load Shedding (ILS) technique. In this paper, the Intelligent Load Shedding is simulated in Electrical Transient Analysis Program (ETAP) software. The technique has been applied on 220kV transmission network of Quetta Electric Supply Company (QESCO), a power distribution company in Balochistan province of Pakistan. This paper focuses on preventing entire QESCO network from cascaded tripping and blackout during N-2 contingency situations. It has been established that implementation of Intelligent Load Shedding is not only helpful for protecting the transmission network of the distribution company, like QESCO, from blackout, but also protects smart grid from blackouts and other power collapses.

Developing the Indonesian Qualifications Framework’s descriptors for Electrical Engineering as the basis of the Recognition of Prior Learning (RPL)

This study aims to: 1) describe the mechanism of the level 2 IQF (the Indonesian Qualifications Framework) descriptor development for electrical engineering, and 2) formulate a set of detailed-level-2 descriptors for electrical engineering. The method used to develop the descriptors is brainstorming between the researcher and several experts from both academicians and practitioners. Meanwhile, the method to formulate the IQF descriptors for electrical engineering is literature review referring to The Indonesian National Work Competency Standard (SKKNI) and focus group discussion involving experts from academicians, Professional Certification Institute Gema PDKB, the Association of Indonesian Electrical Works (APEI), Consortium for Electrical Installation Safety (KONSUIL), and industry practitioners such as PT. LEN Bandung, PT. Bukaka Teknik Utama, PT. Schneider Electric Indonesia, and PT. Smart Energi Semesta. The descriptors are in the form of the guttman scale questionnaire. The questionnaire was then tested on limited-scale respondents. The KR 20 method was employed to examine the reliability and resulted in r11 = 0.9; thus, the guttman scale questionnaire was considered “very reliable”. The respondents consisted of 8 academicians, 8 electrical engineering teachers at vocational high schools, and 8 industrial practitioners. The collected data were then analyzed descriptively. This study resulted in a set of level 2 IQF descriptors which were designed according to the taxonomy of electricity system, namely electric power transmission system descriptors, electric power distribution system descriptors, and electric power utilization descriptors. In regard to electric power transmission system, 103 out of 154 (67%) descriptors were approved by more than 80% of respondents; for electric power distribution system, 83 descriptors (75%) were approved by more than 80% of respondents; and for electric power utilization system, 61 descriptors (94%) were approved by more than 80% of respondents

Risk-Sensitive Learning and Pricing for Demand Response

We consider the setting in which an electric power utility seeks to curtail its peak electricity demand by offering a fixed group of customers a uniform price for reductions in consumption relative to their predetermined baselines. The underlying demand curve, which describes the aggregate reduction in consumption in response to the offered price, is assumed to be affine and subject to unobservable random shocks. Assuming that both the parameters of the demand curve and the distribution of the random shocks are initially unknown to the utility, we investigate the extent to which the utility might dynamically adjust its offered prices to maximize its cumulative risk-sensitive payoff over a finite number of ${T}$ days. In order to do so effectively, the utility must design its pricing policy to balance the tradeoff between the need to learn the unknown demand model (exploration) and maximize its payoff (exploitation) over time. In this paper, we propose such a pricing policy, which is shown to exhibit an expected payoff loss over ${T}$ days that is at most ${O(\sqrt {T}\log (T))}$ , relative to an oracle pricing policy that knows the underlying demand model. Moreover, the proposed pricing policy is shown to yield a sequence of prices that converge to the oracle optimal prices in the mean square sense.

Electrothermal regeneration by Joule heat effect on carbon cloth based MnO2 catalyst for long-term formaldehyde removal

Carbon-based materials (e.g., activated carbon) have been widely used as contaminant removal media due to their unique adsorption properties. However, some of their other characteristics, such as electrothermal properties, have been greatly ignored in the field of environmental remediation. Herein, a kind of carbon cloth (CC)-supported MnO2 composite (MnO2-CC) was prepared and investigated for catalytic oxidation of gaseous formaldehyde (HCHO) under the assistance of electric power. The MnO2-CC composite exhibited unique Joule heat property and could efficiently convert electrical energy into heat energy. The surface temperature of MnO2-CC composite could be increased to as high as 138 °C within 20 s when 8 V input voltage was imposed. In addition, porous MnO2 nanosheets dispersed uniformly on the surface of CC surface, forming a kind of hierarchical core-shell structure. The close contacted interface could reduce energy loss during heat transfer. As a result, the used MnO2-CC composite could exhibit long-term HCHO removal efficiency by highly effective electrothermal regeneration. This work represents a convenient and fast approach that allows direct utilization of electric power to accelerate the complete mineralization of HCHO. The in-situ fast electric heating process along with the uniform dispersion of porous MnO2 on CC make the MnO2-CC fabric an excellent material for indoor HCHO abatement.