Utility Power System Research Articles

Analysing the impact of diverse factors on electricity generation cost: Insights from Sri Lanka

The primary responsibility of a power system utility is to provide continuous, high-quality electricity at a fair and competitive price. To maintain low generation costs while utilising diverse energy sources, optimisation methodologies are crucial. Water value and generation costs exhibit significant variability on seasonal, daily and timely basis and reflect dynamic interactions between electricity demand and power plant availability. Currently, power system planners often overlook these seasonal and time-of day dependent variations in water value and generation costs. This research paper addresses this gap by analysing how fluctuations in electricity demand and power plant availability affect generation costs in the Sri Lankan power system. It introduces a novel economic dispatch model and presents three methods for estimating water value to calculate seasonal and time-of-day electricity costs. The discussion highlights how these findings can utilise policymaking, particularly in the context of electricity pricing debates and water management for agriculture versus electricity generation, as well as in calculating the water value for each reservoir.

Failure evaluation of outdoor polymeric insulators based on statistical condition-assessment methods

Field aging of outdoor polymeric insulators results in unscheduled outages in power systems particularly in harsh climate conditions. In this paper, a strategic maintenance framework is proposed in order to prevent overhead line outages. The presented scheme is based on condition assessment of the real aged polymeric insulators. Data of tensile test, hardness test, thermogravimetric analysis (TGA), and Fourier transform infrared spectroscopy (FTIR) are used to extract critical features of the aged samples based on classification of fuzzy c-means clustering. Then, a lifetime prediction analysis is done based on Cox hazards analysis to develop further maintenance strategy. The proposed model is validated through numerical analyses on polymeric insulators for various validation evaluations including pollution severity analysis and hydrophobicity class (HC) measurement. Obtained results verify accuracy of the intelligent model to predict lifetime of the aged polymeric insulators. The presented strategic framework can be effectively applied to online monitoring, condition assessment and preventive maintenance programs of the power system utilities.

Motor Fault Diagnosis and Detection with Convolutional Autoencoder (CAE) Based on Analysis of Electrical Energy Data

This study develops a Convolutional Autoencoder (CAE) and deep neural network (DNN)-based model optimized for real-time signal processing and high accuracy in motor fault diagnosis. This model learns complex patterns from voltage and current data and precisely analyzes them in combination with DNN through latent space representation. Traditional diagnostic methods relied on vibration and current sensors, empirical knowledge, or harmonic and threshold-based monitoring, but they had limitations in recognizing complex patterns and providing accurate diagnoses. Our model significantly enhances the accuracy of power data analysis and fault diagnosis by mapping each phase (R, S, and T) of the electrical system to the red, green, and blue (RGB) channels of image processing and applying various signal processing techniques. Optimized for real-time data streaming, this model demonstrated high practicality and effectiveness in an actual industrial environment, achieving 99.9% accuracy, 99.8% recall, and 99.9% precision. Specifically, it was able to more accurately diagnose motor efficiency and fault risks by utilizing power system analysis indicators such as phase voltage, total harmonic distortion (THD), and voltage unbalance. This integrated approach significantly enhances the real-time applicability of electric motor fault diagnosis and is expected to provide a crucial foundation for various industrial applications in the future.

Fulfilling the potentials of residential solar energy in Egypt

Energy plays a very important role in Egypt’s economic development, but the country has a gap between its produced energy and the demand of its growing population. Utilization of solar power systems in Egypt could help the country to close this gap and fulfil its national and international obligations. However, since 1980, the focus in Egypt has been on large-scale industrial solar projects. Limited attention is given to smaller systems for typical residential buildings. The aim of this research, therefore, is to highlight the potential of small residential solar systems (SRSS) in Egypt. With the huge number of residential buildings accommodating more than 115 million Egyptians, SRSS could be the unearthed gem of a sustainable source of energy in Egypt. The geographical location of Egypt and climate were used to generate solar data using the Global Solar Atlas application. The amounts of monthly and annual solar irradiations were calculated and analysed to decide the best orientation of the system (facing east, west, north, and south), identify the optimum tilt angle of the system, and determine the size of the solar panels. A case study was used to illustrate the procedures of designing SRSS for a typical residential building in Egypt. The results showed that a 26 kWp SRSS oriented facing the east with an optimum tilt angle between 15° and 30° could produce an annual total output of electricity more than the annual demand of the occupants of the studied residential building. Such a system would fit easily on the roof of the building. It was concluded that the installation of SRSS in Egypt could help the country meet the demand of its ever-increasing population if properly regulated, financed, and managed. It is recommended that Egypt develop and implement policies to make installations of SRSS an attractive choice among homeowners and investors by introducing encouraging incentives and creating a competitive market with affordable SRSS.

Multiobjective optimal TCSC placement using multiobjective grey wolf optimizer for power losses reduction

This study investigates the application of the multiobjective grey wolf optimizer (MOGWO) for optimal placement of thyristor-controlled series compensator (TCSC) to minimize power loss in power systems. Two conflicting objectives are considered: (1) minimizing real and reactive power loss, and (2) minimizing real power loss and TCSC capital cost. The Pareto-optimal method is employed to generate the Pareto front for these objectives. The fuzzy set technique is used to identify the optimal trade-off solution, while the technique for order preference by similarity to the ideal solution suggests multiple optimal solutions catering to diverse utility preferences. Simulations on an IEEE 30 bus test system demonstrate the effectiveness of TCSC placement for power loss minimization using MOGWO. The superiority of MOGWO is confirmed by comparing its results with those obtained from a multiobjective particle swarm optimization algorithm. These findings can assist power system utilities in identifying optimal TCSC locations to maximize their performance.

The Concept of Urban Railway Surplus Power System Utilization for Supplying Power to Hydrogen Charging Stations

The Concept of Urban Railway Surplus Power System Utilization for Supplying Power to Hydrogen Charging Stations

Studying the Intelligent Power Distribution and Consumption of the Power Grid and its Cost Structure Using Big Data Analysis

Based on the analysis of big data, this paper studies the impact of user behavior response on the cost structure of the microgrid system in the power grid system. The article first conducts in-depth research on distributed power generation and energy storage systems, focusing on the principles and output characteristics of smart power distribution and utilization in power grids, researches smart power distribution and utilization systems in power grids, and makes a more comprehensive discussion of the current situation. Secondly, a big data analysis platform was built, and distributed storage and computing were studied. The platform was used to perform distributed storage and regulation of electricity consumption data, and the electricity consumption information data was divided into the important load, controllable load and transferable load, constructed a microgrid system model based on electricity consumption behavior response, and analyzed a calculation example. After that, a micro-grid system was simulated based on HOMER software, and the optimal capacity configuration of the system was performed. Under this configuration, the micro-grid system has the highest economic efficiency. At the same time, a demand-side load control system was built. Introduced distributed power as a controllable load, integrated new energy access and load control technology, coordinated the contradiction between the power grid and distributed power, and completed a cost-benefit analysis. Finally, for demand-side management electricity price response, peak-valley time-of-use price, the most important implementation method, according to the cost structure theory, the peak-valley period is divided according to the membership function in fuzzy mathematics, and the user's response model to peak-valley time-of-use price is established. The experiment uses the original data to simulate, find the user response model parameters based on the load transfer rate, and complete the comparative analysis of the effect under the peak-valley time-of-use electricity price, which is of great significance to the implementation and improvement of the peak-valley time-of-use electricity price project. The analysis results of the calculation examples show that the method constructed in this paper can effectively realize the power quality analysis of the distribution network in the big data environment. The research results provide technical support for the management of the rural grid voltage deviation of the power company, and lay the foundation for improving the safe operation and management of the power grid.

Digitization of Academic Library Resources: A Case Study of Gautam Budh Central Library of Babasaheb Bhimrao Ambedkar University, Lucknow

This study examine the digitization of academic library resources in Babasaheb Bhimrao Ambedkar University. For this study, three research objectives have been formulated and research design adopted for this study was a descriptive survey. Questionnaire is used a tools for getting primary data. Total 20 library staff was involved in this study. The findings of the study, majority of the library staff is have practical approaches for digitization and most of the library professional are facing various problems during digitization process likewise as standby power generating set, solar power system, utility power systems.

Coordinated Frequency Regulation between DFIG-VSWTs and BESS Hybrid Systems

As the utilization of wind power systems continues to increase, reducing overall system inertia, there is a consequential negative impact on the power system’s ability to regulate frequency. Consequently, this study focuses on examining the fast-frequency regulation in high penetration of wind power, especially doubly fed induction generators—the most commonly installed wind turbine type, and an energy storage system installed in the wind farm. This study proposes a coordinated control of wind turbine generators and battery energy storage systems that provides fast-frequency regulation to the system while simultaneously ensuring the safety of the battery. Firstly, the fast-frequency regulation capability of the wind turbine will be studied. Secondly, primary frequency control of the battery energy storage system considering adaptive droop control based on state of charge is proposed to prevent both over-charging and over-discharging of the battery. Finally, this study proposed a coordinated fast-frequency regulation approach for the hybrid wind-storage system, which is evaluated under various wind speed scenarios. This approach involves a detailed analysis of the operational characteristics of the wind turbine generators to ensure optimal performance. The proposed method is validated through simulation using a MATLAB model of the wind-storage system, and comparative results with alternative control methods confirm the effectiveness of the proposed approach in raising the frequency nadir and avoiding the secondary frequency dip.

Optimal Load Shedding Based On Hybrid Energy Generation

The demand for electricity is growing at an exponential rate with no corresponding increase in the electricity production, which has led to voltage collapse in many practical power systems all over the world. This has, therefore, been a serious concern to power system engineers, investors and utilities as its effect is highly detrimental to power system operation and its reliability. Consequently, in the quest for providing solutions to this problem, integration of various renewable energy sources are deployed in recent years. This paper, therefore, presents an effective approach to solving voltage collapse problems through load shedding optimization. HOMER software was deployed due to its great features of quality design and evaluation, coupled with its technicality and economical options for both standalone and grid-connected power system. The results show that the utilization of the hybrid Hydro/PV produces 91kWh annually, whereas 88.5% of the electricity is produced by hydroelectric system while the remaining 11.5% of the electricity comes from the integrated photovoltaic system with excess electricity of 84kwh per year. The results obtained, therefore, show that there is an assurance of stable electricity with the introduction of backup battery banks even with the intermittent nature of the renewable energy systems.

Fault Prediction and Awareness for Power Distribution in Grid Connected RES Using Hybrid Machine Learning

This study addresses defects in electrical power systems, focusing on short circuits that can disrupt normal operation. The method emphasis is on hybrid microgrid systems connected to the grid, crucial for efficient power management. The novel method proposes an adaptive electricity management technique for fault scenarios in grid-tied conversions, minimizing sensor requirements. Power flow involves the hybrid energy storage system (HESS), renewable energy sources (RES), the electrical grid, and loads through a parallel hybrid RES arrangement. Semiconductor switch failures are common vulnerabilities, impacting grid functionality. The novel electricity management technique considers converter failures, renewable energy availability, and HESS state of charge restrictions. Power quality goals such as harmonic reduction, reactive power support, and a power factor of one are addressed. The proposed method was evaluated using MATLAB–based Simulink, showcasing its performance. The approach employs an Adaptive Power Management Strategy (APMS) in a hybrid renewable energy system, integrating wind and solar sources with battery and supercapacitor storage. A Hybrid Machine Learning-based Active Power Management System (HML-APMS) optimizes energy production, storage, and distribution for stability, reliability, and efficiency in the power utility system. The study underscores the significance of technology integration in achieving sustainable, reliable, and cost-effective power solutions.

Harmonics Cancelation in 3-Phase 4-Wire System using Modified Reference Frame

In this paper, A modified synchronous reference frame (SRF) or (d-q theory) with Self-Tuning Filter (STF) for the three-phase four wires system is presented to control of 4-legs shunt active power filter (SAPF) under balanced supply voltages. The main goal of the paper is to apply this control method using unit vector generation with STF instead of PLL circuit. In utility power system, it has been observed that harmonics build a major role in reducing the power quality. These harmonics caused by nonlinear loads. Many consumers appliances demand quality of power continuously for their operation. The performance of the end user equipment is heavily dependent on the quality of power supplied to it. This power is affected by various external and internal factors. They are like voltage and frequency variations, faults, outages etc. The solution to overcome these problems is to filter out these harmonics. For this purpose, there are many filters' topologies and control methods are proposed and studied in the literature to eliminate harmonics currents. In our study, 4-legs shunt active power filter is proposed and controlled by d-q method with modified unit vector generation. The SAPF is proposed to solve problems of line harmonic currents and neutral line currents of three phase 4-wire power distribution system. The use of STF in the modified control method can optimize the performance of this topology regarding to classical one (high pass and low pass filters). The proposed control strategy is simulated in MATLAB Simulink and the results are presented.

Design and Optimization of Low Impact Shift Control Strategy for Aviation Transmission Power System Based on Response Surface Methodology

The utilization of a variable-speed power system significantly improves the forward flight speed and cruising range of the helicopter. Nevertheless, the shock of speed and torque during the shift process brings stability and safety problems that cannot be ignored. Thus, swift and stable shift control is a key issue in the research on aviation power systems. This study focuses on the design and optimization of low-impact shift control strategies for a variable-speed power system, which involves multiple control variables, long adjustment times, and uncontrollable risks due to the nonsteady state. A comprehensive power system model that integrates the engine, a two-speed dual-clutch transmission system, and the main rotor was proposed. By selecting the engine fuel flow, friction clutch hydraulic pressure, and rotor pitch angle as input signals, regression fitting models between the input signals’ starting time points and speed or torque shock were obtained using Response Surface Methodology (RMS). The interaction effect of multiple time series was analyzed, and four kinds of low-impact nonlinear programming multi-objective optimized models for speed or torque are proposed. The results indicate that the P values of the RMS fitting models at upshift and downshift are less than 0.0001 and 0.05, respectively, which are highly significant and can effectively predict the shift dynamic response; under the optimized upshift and downshift control strategy, the speed and torque shock are reduced by 5–10%.

Impact of liquefaction ratio and cold energy recovery on liquefied natural gas production

The partial liquefaction of natural gas (NG) is commonly conducted in the liquefied natural gas (LNG) industry toward facilitating the use of non-liquefied gas for power generation and utility systems, while simultaneously achieving the desired heating value of LNG. This study investigated the impact of the partial liquefaction of NG and the recovery of cold energy from non-liquefied gas on the efficiency of the liquefaction process, in terms of specific work. The optimal liquefaction ratio (LR) with no recovery was 64.2% (778.7 kJ/kg LNG), which is 21.7% reduction of specific work in relation to that achieved through full liquefaction (995.0 kJ/kg LNG). The result showed a trade-off relationship between a higher LR to compensate energy consumption for cooling NG to its dew-point temperature with no liquid production and produce flash gas at a cryogenic temperature and lower LR to avoid the liquefaction of light components, which are difficult to condense. However, this trade-off resolved upon cold energy recovery, because there was no energy consumption until an LR of 8.1% could be achieved through an expansion of high-pressure NG feed by the recovery. For practical reason, a case study was conducted with lower bounds of the LR at 85%, 90%, and 95%. The findings of this study can guide the decision-making of future LNG projects for an optimal LR.

Power outage prediction using data streams: An adaptive ensemble learning approach with a feature- and performance-based weighting mechanism.

A wide variety of weather conditions, from windstorms to prolonged heat events, can substantially impact power systems, posing many risks and inconveniences due to power outages. Accurately estimating the probability distribution of the number of customers without power using data about the power utility system and environmental and weather conditions can help utilities restore power more quickly and efficiently. However, the critical shortcoming of current models lies in the difficulties of handling (i) data streams and (ii) model uncertainty due to combining data from various weather events. Accordingly, this article proposes an adaptive ensemble learning algorithm for data streams, which deploys a feature- and performance-based weighting mechanism to adaptively combine outputs from multiple competitive base learners. As a proof of concept, we use a large, real data set of daily customer interruptions to develop the first adaptive all-weather outage prediction model using data streams. We benchmark several approaches to demonstrate the advantage of our approach in offering more accurate probabilistic predictions. The results show that the proposed algorithm reduces the probabilistic predictions' error of the base learners between 4% and 22% with an average of 8%, which also result in substantially more accurate point predictions. The improvement made by our algorithm is enhanced as we exchange base learners with simpler models.

Economic dispatch optimization considering operation cost and environmental constraints using the HBMO method

Increasing electrical energy use has led to the development of power systems so nowadays some power systems have been expanded into geographical regions whose total size is equal to a continent. In line with this development, which has several benefits, like other fields, it has been discussed new issues in the power system field such as economic distribution. Over several decades and with the growing consumption of electrical energy, its supply systems have increased so that today the load distribution among energy production units with the lowest cost has become one of the most widely and complicated issues of power system utilization. In this paper, a mutant version of the honey bee mating optimization (HBMO) algorithm based on collective intelligence is proposed for power system economic-emission dispatch (E-ED). The proposed algorithm has been used to solve the E-ED problem with nonlinear cost functions including plant-induced limitations such as steam inlet valves, balancing the production and consumption in the system, restricted zones, production limits, and increasing and decreasing rates. Moreover, considering production cost functions, environmental pollution, and losses, the load distribution issue, which is one of the most important issues in today’s system, has been investigated. In this paper, we attempt to increase the efficiency and balance of the standard algorithm for local and final searches using the adaptive nonlinear system. In the proposed algorithm, the final optimized answer is considered a criterion to be stopped the program optimization. In addition, to improve the search for the final answer, improvements have been made in the local and final search structure. The simulation results show that the efficiency of this algorithm in solving the problem of economic load distribution is better than other algorithms.

Load frequency control of power system based on improved AFSA-PSO event-triggering scheme

Aiming at the impact of redundant information transmission on network resource utilization in current power systems, an improved event-triggered scheme based on particle swarm optimization and artificial fish swarm algorithm for power system load frequency control (LFC) with renewable energy is proposed. First of all, to keep the stability and security of power systems with renewable energy, the load frequency control scheme is investigated in this paper. Then, to relieve the communication burden and increase network utilization, an improved event-triggered scheme based on the particle swarm algorithm and artificial fish swarm algorithm is explored for the power system load frequency control. Then, by utilizing improved Lyapunov functional and the linear matrix inequality method, sufficient condition for the H∞ stability of the load frequency control system is established. Finally, a two-area load frequency control system and IEEE-39 node simulation models are constructed to verify the effectiveness and applicability of the proposed method.

The role of electricity market design for energy storage in cost-efficient decarbonization

The role of electricity market design for energy storage in cost-efficient decarbonization

Multi-Objective Based Generation Expansion Planning for Utility Power System of Tamil Nadu State Considering Recuperation of Older Power Plants

Generation Expansion Planning (GEP) is a problem that comprises multiple contradictory features while planning to construct new generating units. It must be solved by considering cost, reliability and environmental emission. Hence the mathematical representations have been developed to be accurate, and to improve the understanding of the multifaceted and contradictory aspects of the GEP problem. In this study, the Multi-Objective Comprehensive Learning Particle Swarm Optimization (MOCLPSO) algorithm is implemented for solving Multi-Objective GEP (MOGEP) problem. The objectives such as minimization of overall cost, decrement of the pollutant emission and enhancement of reliability have been considered by considering the constraints. The real-world MOGEP problem has been solved for seven-year (from the year 2020 to 2027) and fourteen-year (from the year 2020 to 2034) planning span for the utility power system of Tamil Nadu state, India. The problem is solved for four different cases with the consideration of retirement and recuperation of the older generating units. coal, gas, oil, nuclear, hydel, wind, Solar-photovoltaic (SPV) and biomass power plants were considered in this planning study. The results establish the competence of MOCLPSO to produce well-spread Pareto optimal non-dominated solutions of the MOGEP problem.

Optimal Placement of PMUs in Smart Grid for Voltage Stability Monitoring using AMPSO and PSAT

Efficient energy use is critical for a growing nation like India. The smart grid (SG) idea enables the creation of a highly dependable electricity system that optimizes existing resources. The Indian electricity grid as it now exists needs fundamental modifications to satisfy increasing demand and to make the system more intelligent and dependable. Since the past several decades, power system stability has been seen as a significant challenge to power system researchers and utilities. With a not many strategically placed Phasor Measurement Units (PMUs), it may be feasible to observe the power system stability of the network. This article suggests an optimum location for PMUs, considering the effect of power system stability-related serious situations. The disturbances have been prioritized according to their voltage stability boundary (the gap among the stand case working and nose points). Changes in the voltage stability tolerance due to shifting load conditions were also considered in the crucial contingency determination. PMUs were inserted in the system based on Adaptive Mutated Particle Swarm Optimization (AMPSO) findings for the intact system and crucial contingency scenarios on the basis of voltage stability. The effectiveness of the suggested PMUs placement strategy was determined by examining nose curves produced with PMUs data and pseudo-observations under increasing demands to nose curves calculated offline using continuation power flow data. Using the software tool Power-System Analysis Toolbox (PSAT), case studies were conducted on a conventional IEEE14 bus system and a realistic 246 bus Indian Power Grid system.