Power System Infrastructure Research Articles

Power Wheeling Pricing Considering Congestion in Power Network: A Review

Limited capacity of the power system infrastructure has a limiting effect on the quantity of electrical power that can be transmitted thereby resulting in the system been congested which also affects the quality of electrical energy supplied. Congestion is an undesired effect in power system with serious economic, reliability and security effect on power system infrastructure, which are yet to be adequately quantify in term of cost. Congestion occurrence and management on the power system network has been studied extensively, causes of congestion and various management techniques have been proposed which are at great financial consequences. While setting transmission wheeling prices, congestions are not adequately considered, nor are the management methods accurately financially compensated. Some wheeling pricing techniques however recognises the short coming and takes into consideration the financial cost of congestion and it’s management methods when setting their transmission wheeling costs. This paper is a review of wheeling methods that takes into consideration the cost of congestion and the management techniques when setting wheeling prices and makes a recommendation using financial and economic implication of the occurrence of congestion and the management techniques adopted in reducing the impact when setting wheeling prices, in order to encourage investment into the transmissions system infrastructures.

Assessing the Vulnerabilities: Cybersecurity Challenges in Power System Infrastructure in Nigeria

This study examined the Cyber security Challenges in Power System Infrastructure in Nigeria. Four research questions guided the study which employed a survey research design. The population of the study was 46 respondents comprising 23 Technical Units Heads and 23 Heads of Cyber security Units from the (23) power-generating plants in Nigeria. The entire population was used hence, there was no sampling. The instrument used for data collection was a structured questionnaire titled: Cyber security Challenges in Power System Infrastructure Questionnaire (CCPSIQ) developed by the researchers. A reliability index of 0.89 was obtained using the Cronbach Alpha reliability method. The mean statistic was used to answer the research questions. The findings of the study revealed that the primary cyber threats targeting power system infrastructure in Nigeria including network-based attacks, insider threats, supply chain vulnerabilities, advanced persistent threats (APTs), physical attacks, zero-day exploits, data breaches, and ransom ware/extortion; it further reveal that cyber-attacks on power grid infrastructure in Nigeria lead to significant service disruptions, financial losses, equipment damage, safety compromises, erosion of customer confidence, regulatory non-compliance, increased vulnerability to future incidents, reputation damage, and prolonged recovery periods, underscoring the multifaceted impact of cyber threats on operational efficiency. The study recommended that: Collaboration among stakeholders, including government agencies, industry partners, and cyber security experts, is paramount to effectively address emerging threats and enhance cyber security resilience. By implementing proactive measures and fostering a culture of cyber security awareness, Nigeria can strengthen its power system infrastructure against cyber threats, safeguarding the reliability and security of critical services for its citizens.

Electrifying the Road to Net-Zero: Implications of Electric Vehicles and Carbon Emission Coefficient Factors in European Power Systems

The global trend is shifting towards adopting low-carbon transportation solutions, with electrification emerging as a prominent approach. The effectiveness of this transition in mitigating climate change hinges significantly on the source of electricity used for charging electric vehicles. This study focuses on four European Union countries: Switzerland, Italy, Germany, and Poland, each characterized by varying levels of carbon emissions from their power systems. Assumptions are made for the short-term (10%), medium-term (30%), and long-term (60%) penetration of electric vehicles, aligning with the 2050 net zero emissions targets. The study investigates the impact of these penetration levels on energy demand, exploring scenarios ranging from 100% renewable source-generated electricity to 100% coal-generated electricity for EV charging. Finally, utilizing PSS®E 35.5 simulation software, the study assesses the implications of the electric vehicles’ load on medium-voltage transmission lines. The findings highlight the substantial influence of electrifying the transport sector on both environmental sustainability and the power system infrastructure, underscoring the critical role of regional energy mixes and the power system carbon emissions coefficient factor. Regions with lower carbon emission coefficient factors witness significant benefits even with a modest transition to electric vehicles, whereas regions with high carbon emission coefficient factors experience minimal impact despite large-scale EV adoption. Additionally, densely populated urban areas may encounter challenges related to transmission line congestion to meet the growing demand for electric vehicle charging.

Unraveling fundamental properties of power system resilience curves using unsupervised machine learning

Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

Improving the reliability of power distribution substations equipment for life extension by considering Condition Based Maintenance

Maintenance of aged power equipment is a proven technique for establishing systems reliability and longevity. It is extensively relevant when combined with Condition Based Maintenance (CBM) approaches for predicting power system infrastructure life, especially with imminent obsolescence. The research focuses on distribution companies with mixed aged power infrastructure, which continuously degradate. A significant setback in older systems is the scarcity of replacement parts leading to unplanned investment in substantial infrastructure due to unexpected failed components that are no longer in production. To guarantee excellent infrastructure health in older equipment, utility companies should consider applying Condition Based Maintenance (CBM) using Artificial Neural Network (ANN) to stabilize older equipment and restore malfunctioning assets. Studies have shown that the life span of older equipment can be extended when adequate maintenance strategies are put in place through the prediction of possible failures using equipment inspections and risk matrix. CBM's strategy improved the Key Performance Indicator (KPIs) System Average Interruption Duration Index (SAIDI) has improved by lowering the average monthly customers power interruptions by 19,15 % lesser time spent without electricity. The System Average Interruption Frequency Index (SAIFI) was improved by lowering the average monthly power interruptions frequency by 20,63% lesser outage frequency. The Distribution Supply Loss Index (DSLI) was improved by dropping the average monthly power losses by 7,5% power losses saved. The Customers Average Interruption Duration Index (CAIDI) was improved by lowering the average customer interruption by 36.1% improvement from the average period before the CBM optimization. The Average System Availability Index (ASAI) was improved by shortening the average annual interruption durations by 1,36%. The ASUI was improved by increasing the average duration of electrical power availability by 1.28%. The optimization of CBM was measured over 24 moths period.

Integrated, Multi-Stakeholder Analysis of Electricity System Structures: Methodology and a Case Study

Historically, investment in power system infrastructure has been modeled from the perspective of individual utilities or a central planner, with limited consideration of the diversity of individual consumers', individual suppliers', and societal objectives, incentives, and actions. However, the actual power system involves a variety of stakeholders with different, and sometimes conflicting, objectives. In this paper, we introduce an integrated methodology for capturing the characteristics and behavior of these multi-stakeholder systems with diverse objectives as well as its implementation in the flexible and extensible Holistic Electricity Model (HEM). HEM was developed to simultaneously analyze the interacting decisions of different stakeholders, whose objectives, decision-making processes, and constraints are all configurable. This approach enables analysts, decision makers, and other stakeholders to directly compare electricity systems in different regulatory and policy environments on a consistent basis. We illustrate HEM's capabilities in a case study of distributed photovoltaics adoption in a stylized bulk power system comprised of natural gas and utility-scale photovoltaic generators. The simultaneous and cross-comparable illustration of multiple key dynamics between stakeholders and how they depend on regulatory and policy environment demonstrates the advantages of the integrated analysis approach embodied in HEM.

Energy and battery management systems for electrical vehicles: A comprehensive review & recommendations

Electric vehicle technology has recently drawn a lot of interest on a global scale due to improved performance in its efficiency and the capability to solve the problems of carbon emission. As such, electric vehicles are the key to achieving sustainable development goals. This review article analyzes deeply the previous technical developments of electric vehicles, focusing on important topics like battery management systems, technologies of power electronics, techniques of charging, and the relevant algorithms and improvements. In addition, several critical problems, and difficulties are presented in order to pinpoint the gaps in the literature. To address the analysis of battery behavior, battery condition monitoring, real-time control design, temperature control, fault diagnostics, and efficiency of battery model are considered. This study highlighted the estimation techniques that predict the internal battery conditions such as internal temperature, state of health, and state of charge, which are difficult to be directly monitored and determined. A lithium-ion battery, a super-capacitor, and related bidirectional DC/DC converters constitutes the infrastructure of a hybrid power system. This review offers useful and practical recommendations for the future development of electric vehicle technology which in turn help electric vehicle engineers to be acquainted with effective techniques of battery storage, battery charging strategies, converters, controllers, and optimization methods to satisfy the requirements of sustainable development goals. Accordingly, this review article will be a platform and future guide for those who are interesting in the field of energy management and its development.

Critical zone identification framework for bulk electric system security assessment

Climate change introduces a new set of vulnerabilities. It might cause unexpected voltage and frequency violation problems that can damage the existing power system configurations. Identifying the zones containing more critical violations in a bulk system under different climatic conditions and contingencies is crucial to securing the power system operation and infrastructure safety. This paper presents an advanced critical zone identification framework that can retrieve information from extensive data yielded by dynamic contingency analysis results from a large interconnected power grid. A machine learning (ML) based clustering approach is applied to identify the critical zone under hundreds of scenarios. The developed framework is tested using the 2028 western electricity coordinating council (WECC) system, and the detailed results are discussed in this paper.

Resilience assessment of mobile emergency generator-assisted distribution networks: A stochastic geometry approach

Escalation of extreme weather events represents substantial threat to power system infrastructure. Mobile emergency generators (MEGs) can form part of a flexible restoration strategy against such destructive events. However, with continued expansion of distribution networks, quantification of the impact of MEGs has become increasingly challenging owing to extreme-weather-event-induced uncertainties. In this paper, we propose a stochastic geometry-based method for assessing the impact of MEG deployment on distribution networks affected by extreme weather events through investigation of structural features. First, we propose a distance measure to represent the electrical connection between power grid components. Subsequently, we adopt the point process and Voronoi tessellation to describe the spatial distribution of power grid components and the service coverage provided by MEGs under different scenarios. Then, we propose a set of assessment metrics to evaluate the survivability of power grid components and the resilience of the entire distribution network under extreme weather events. Finally, we derive accurate analytical expressions for the distance distribution and resilience metrics, such as coverage probability and load shedding, enabling us to explore the relationship between MEG deployment decisions, structural features, and power grid resilience. The proposed method enables analytic assessment of the impact of MEG deployment on the resilience of distribution networks, and provides beneficial insights to help formulate efficient measures for enhancing resilience. Case studies demonstrated that the proposed method is accurate and efficient in dealing with network analysis and assessment problems for distribution networks under massive potential failure scenarios.

Resilience Enhancement with Transportable Storage Systems and Repair Crews in Coupled Transportation and Distribution Networks

The significance of resilience within power system infrastructures has been progressively acknowledged. To address this challenge, this paper introduces an innovative framework to enhance distribution network (DN) resilience by incorporating transportable storage systems (TSSs). To do so, we take into account the interconnection between the DN and the transportation network (TN) to optimize the trajectory planning of TSSs. To account for real-world features, the traffic flow of TN is modeled according to traffic counter cameras of Sioux Falls city. In addition, the effects of blocked roads on TSSs' movements through microgrids (MGs), and clearing operation of obstructions by repair crews are considered as these factors have a great impact on system operational cost. Further, an optimal reconfiguration program is proposed to add more flexibility to the system. The developed model is formulated as mixed-integer linear programming (MILP) and various case studies are presented including analyzing TSSs and repair crews’ operations, both individually and concurrently. Different case studies reveal the positive impact of TSSs, reducing the total cost of the system by approximately 13.11%. In post-event, blocked roads directly increase the operation costs by 17.02%; however, repair crews' cooperation with TSSs, achieved a remarkable 12.75% reduction in operation costs.

Planning and Operation Objectives of Public Electric Vehicle Charging Infrastructures: A Review

Planning public electric vehicle (EV) charging infrastructure has gradually become a key factor in the electrification of mobility and decarbonization of the transport sector. In order to achieve a high level of electrification in mobility, in recent years, different studies have been presented, proposing novel practices and methodologies for the planning and operation of electric vehicles charging infrastructure. In this paper, the authors present an up-to-date analysis of the existing literature in this research field, organized by considering the perspectives and objectives of the principal actors/operators of the EV public charging infrastructure value chain. Among these actors, the electric vehicle, the charging operators and service providers, and the power system infrastructure (transmission and distribution system) are analyzed in depth. By classifying the reviewed literature based on this manifold viewpoints approach, this paper aims to facilitate researchers and technology developers in exploring the state-of-the-art methodologies for each actor’s perspective, and identify conflicting interests and synergies in charging infrastructure operation and planning.

Age-dependent resilience assessment and quantification of distribution systems under extreme weather events

The impact of extreme weather events on power system resilience can be seen in historical electrical disruptions. Due to the increasing intensity and frequency of extreme weather events following global warming and climate change, a greater focus is required to design power systems with high resilience and low cost through assessing and quantifying power system resilience. In this way, we propose a transparent methodology and a set of metrics to quantify the resilience of distribution systems subjected to hurricanes. This methodology includes a probabilistic and detailed approach to modelling the failure probability of overhead lines and the restoration time of damaged lines. Moreover, the fragility analysis of the power system infrastructure is considered age-dependent, and the system’s resilience by considering different lifetimes is assessed. The time-dependent resilience assessment constitutes an essential component in risk-informed decision-making for resilience enhancement strategies in the future. Specifically, this paper provides two resilience curve-based metrics: vulnerability rate and restoration rate. The first metric indicates the power system’s ability to resist extreme events, and the second indicates its ability to bounce back to normal performance. Simulations are performed on the IEEE 69-bus distribution test system to validate the suggested methodologies. The results indicate the capability of the proposed methodology and metrics to precisely assess and quantify the power system resilience. A significant correlation between the age of power distribution systems and the system’s resilience can be seen. These findings can support decision-making concerning system management and plans such as expansion planning and resource allocation.

False data injection attack in smart grid cyber physical system: Issues, challenges, and future direction

Smart grid integrates the physical power system infrastructure with internet-of-things-based digital communication networks that work together for grid stability, sustainability, and reliability. A significant number of smart devices converge in cyber-physical systems to make the smart grid more competitive and efficient in addressing the energy challenges and vulnerabilities in power system confidentiality, integrity, and availability in smart grid cyber-physical security systems. False data injection attacks are the most malicious threats in the smart grid paradigm and have been widely applied recently. Last few years, several detection algorithms for identifying the false data injection attack have been developed. Addressing these issues, this paper reports a false data injection attack and threat mathematical model, impacting the on-grid system, economy, and society. The classification of false data injection attack detection algorithms and mathematical models are mainly presented. Finally, issues and challenges are identified from existing research and recommended for future research direction.

XTM: A Novel Transformer and LSTM-Based Model for Detection and Localization of Formally Verified FDI Attack in Smart Grid

The modern smart grid (SG) is mainly a cyber-physical system (CPS), combining the traditional power system infrastructure with information technologies. SG is frequently threatened by cyber attacks such as False Data Injection (FDI), which manipulates the states of power systems by adding malicious data. To maintain a reliable and secure operation of the smart grid, it is crucial to detect FDI attacks in the system along with their exact location. The conventional Bad Data Detection (BDD) algorithm cannot detect such stealthy attacks. So, motivated by the most recent deep learning (DL) developments and data-driven solutions, a new transformer-based model named XTM is proposed to detect and identify the exact locations of data intrusions in real-time scenarios. XTM, which combines the transformer and long short-term memory (LSTM), is the first hybrid DL model that explores the performance of transformers in this particular research field. First, a new threshold selection scheme is introduced to detect the presence of FDI, replacing the need for conventional BDD. Then, the exact intrusion point of the attack is located using a multilabel classification approach. A formally verified constraints satisfaction-based attack vector model was used to manipulate the data set. In this work, considering the temporal nature of power system, both hourly and minutely sensor data are used to train and evaluate the proposed model in the IEEE-14 bus system, achieving a detection accuracy of almost 100%. The row accuracy (RACC) metric was also evaluated for the location detection module, with values of 92.99% and 99.99% for the hourly and minutely datasets, respectively. Moreover, the proposed technique was compared with other deep learning models as well, showing that the proposed model outperforms the state-of-the-art methods mentioned in the literature.

A novel framework for resilient overhead power distribution networks

The natural disasters that are classified as high impact low probability (HILP) events have a major effect on the power system infrastructure, in particular, the overhead power distribution networks (OPDNs) causing significant challenges in maintaining continuity of service to critical loads (CLs). A resilient OPDN should maintain operational normalcy by quickly absorbing, adapting and recovering from the HILP events resulting in an improved customer satisfaction through continuity of electricity supply and minimal safety hazards. Modern OPDS predominantly consists of overhead electric lines and poles with diverse characteristics by virtue of their age, maintenance, location etc. Unlike the normal operating conditions, quick and accurate decision making from the network operator becomes very critical in maintaining the resiliency of a OPDN during HILP events. This paper proposes a novel framework for resiliency assessment, quantification, enabling and enhancement that facilitates resilient reconfiguration of an OPDN during HILP events. Two novel physical system metrics with multiple sub- factors specific to the overhead networks are also proposed for resiliency assessment followed by resiliency quantification using diamond pairwise comparison, kth-order additive fuzzy measure and Choquet integral. The resiliency enabling and enhancement algorithms for contingency scenarios as well as cost-benefit analysis for network planning strategies are presented in this work. The novel resiliency framework presented in this work is used to enable the resiliency of a OPDN and provide the relevant information about the feasible network options with their resiliency value. The performance of the proposed framework is illustrated on the 415V OPDN in Nayabad area of the CESC Limited, Kolkata, India through simulation case studies.

Scheduling Post-Disaster Power System Repair With Incomplete Failure Information: A Learning-to-Rank Approach

This paper proposes a novel repair rule set (RRS) for scheduling the power system infrastructure repair after the occurrence of extreme events. RRS is made up of multiple repair rules, each of them can be applied in arbitrary post-disaster failure scenarios to rank the repair actions by priority. A learning-to-rank technique called AdaRank is used to train the repair rules by combining the weak learners derived from the dynamic repair scheduling model. Then, RRS is constructed by iteratively clustering the training cases and retraining the repair rule for each cluster. Increasing the number of repair rules within RRS allows it to differentiate various types of failure scenarios, thereby improving its performance. Further combined with multi-label K nearest neighbor (ML-KNN) technique, RRS is able to schedule the repair without the full knowledge of real-time failure information, such as the estimated repair time. The results of case studies on IEEE-118 test systems show that the proposed method has a desirable performance compared to the exact mathematical optimization model. Moreover, it reduces the requirement for failure information while significantly improving the computational efficiency.

A Brief Review of Power Quality Issues Emerged due to Modernization of Power System Infrastructure

The architecture of power systems is evolving daily due to technological advancements aimed at reducing energy consumption and improving the environment. However, this improvement in the architecture of the network has affected the quality of the electricity and is producing several problems associated with the quality of power supplied. In a broad sense, the recent standard procedures for power generation, transmission and distribution are the root cause of the recently discovered problems with power quality. Investigating these problems is essential to accurately forecast the stochastic impacts on today network's functioning and locate the best remedy for these problems. This study includes a discussion on quality challenges that evolved due to modernization in the infrastructure of power network and an evaluation of the latest solutions presented in the literature to overcome those difficulties. This paper also makes recommendations for the future scope of research that should be done to address these new power quality problems.

A Configurable Monitoring, Testing, and Diagnosis System for Electric Power Plants.

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price–performance ratio makes it an original and handy solution for electric power infrastructures.

A comprehensive review on electric vehicles: charging and control techniques, electric vehicle-grid integration

Abstract Energy consumption in the field of transportation comes next to industrial consumption worldwide. If transportation is completely powered by renewable energy, the utilization of fossil fuels can be drastically reduced, which will result in a lesser amount of greenhouse gas emissions. Electric vehicles (EVs) can act as an alternative to make transportation pollution-free. Large-scale usage of EVs causes high electricity demand on the supply system. This problem can be overcome by utilizing renewable energy sources (RESs) for Electric Vehicle charging. Due to the unpredictability of RESs, coordinating EV charging with other loads and renewable generation is problematic. By using EVs as energy units, power fluctuations in the electric grid can be compensated. This paper presents a summary of recent research in the domain of integration of electric vehicles (EVs) to the smart grid. Electric vehicles-smart grid integrated systems face several issues related to communication, grid infrastructure and control in the future power system. Smart grid technologies are summarized in Section 2. The existing research articles in this area are classified into two based on the purpose: EVs integration into the electric grid and Vehicle to grid services. Finally, the research gaps and future scope of incorporating electric vehicles with renewable energy sources and the Smart grid are highlighted.

Validation of power plant models using field data with application to the Mostar hydroelectric plant

With new technologies being integrated into the existing power system infrastructure and current plants aging, it is increasingly important to maintain accurate dynamic models. A method to individually validate plant component models is proposed, and then is applied to the Mostar hydroelectric power plant using field data obtained during maintenance tests. This validation methodology is also tested in simulation. When the plant’s model is proven invalid, we propose to re-identify a new model.