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The Electric Power Grid: Today and Tomorrow
Abstract
In the coming decades, electricity's share of total global energy is expected to continue to grow, I and more intelligent processes will be introduced into the electric power delivery (transmission and distribution) networks. It is envisioned that the electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades. A key challenge is how to redesign, retrofit, and upgrade the existing electromechanically controlled system into a smart self-healing grid that is driven by a well-designed market approach. Revolutionary developments in both information technology and materials science and engineering promise significant improvements in the security, reliability, efficiency, and cost effectiveness of electric power delivery systems. Focus areas in materials and devices include sensors, smart materials and structures, microfabrication, nanotechnology, advanced materials, and smart devices.
... The electrical grid has evolved to cater to new demands in the past for both residential and non-residential energy consumption; however, with ambitious new climate policy and evolving aforementioned paradigm shifts in energy generation and distribution, new challenges have arisen that require novel approaches to achieving climate objectives [2,7]. With the advancement of electrical-dependent technologies such as electric vehicles (EV), heating systems, and smart appliances, among a plethora of other devices in use today, coupled with the proliferation of distributed energy resources (DERs), such as photovoltaic (PV) and storage technologies, the grid must cope with larger demands and disturbances as well as bidirectional energy flows for which it was not originally designed [7,8]. ...
... The electrical grid has evolved to cater to new demands in the past for both residential and non-residential energy consumption; however, with ambitious new climate policy and evolving aforementioned paradigm shifts in energy generation and distribution, new challenges have arisen that require novel approaches to achieving climate objectives [2,7]. With the advancement of electrical-dependent technologies such as electric vehicles (EV), heating systems, and smart appliances, among a plethora of other devices in use today, coupled with the proliferation of distributed energy resources (DERs), such as photovoltaic (PV) and storage technologies, the grid must cope with larger demands and disturbances as well as bidirectional energy flows for which it was not originally designed [7,8]. Consequently, new DERs on the grid reduce the dependency on centralized generators such as gas, nuclear, and hydro turbine generation, which reduces the amount of available inertia that supports grid stability during disruption; however, counterpoise to loss of inertia, DER can also provide stability by leveraging storage technologies such as batteries to maintain grid stability. ...
... Consequently, new DERs on the grid reduce the dependency on centralized generators such as gas, nuclear, and hydro turbine generation, which reduces the amount of available inertia that supports grid stability during disruption; however, counterpoise to loss of inertia, DER can also provide stability by leveraging storage technologies such as batteries to maintain grid stability. Given these factors, distribution system operators (DSOs) and transmission system operators (TSOs) now need to adapt and leverage these technologies towards stable grid operations and constant and reliable energy for end-users practically during natural and anthropogenic disruptive events (storms, earthquakes, or malicious attacks) and demand side generation (wind and PV curtailment, heat waves, local/national events) [7][8][9][10]. To this end, the propagation of information communication technology (ICT), Internet of Things (IoT) hardware/software technologies, and 5G communication has enabled sophisticated solutions to address the above issues with the grid, allowing stakeholders to better manage complex grid loads and disruptions [11][12][13]. ...
Decarbonization of the built environment by electrifying energy systems and decarbonizing the electrical grid coupled with the digitization of these systems is a central strategy implemented by the European Commission (EC) to meet carbon reduction policies. The proliferation of technologies such as renewable energy sources (RES) and demand-side management (DSM) systems can be improved by using digital twins to predict and optimize their integration with existing systems. Digital twins in the built environment have been used for multiple purposes, such as predicting the performance of a system before its inception or optimizing its operation during use. To this end, a novel application of a combination of these technologies towards optimized DSM is peer-to-peer (P2P) energy trading, which can improve the local use of RES in the built environment. This paper investigates the potential of P2P energy trading in optimizing local RES of a remote island, Inishmore, Republic of Ireland, using a combination of data-driven and predictive digital twins towards the island’s journey to net zero. Data-driven digital twins are used to evaluate the current energy use at the pilot site. Predictive digital twins are applied to estimate the impact of applying P2P in the future and its influence on RES consumption at the pilot site. The findings show that in scenarios with limited RES coverage, P2P can significantly increase the local consumption of excess RES energy, reducing the risk of transmission or curtailment losses. However, P2P is limited in scenarios with widespread RES installation without storage or behavioral change to shift energy loads.
... Over the past century, electrical power systems have undergone significant changes, expanding from small-scale setups to large-scale networks comprising extensive transmission lines and numerous generating units to facilitate and maintain a secure operation [1][2][3]. The ever-growing electricity demands and depletion of fossil fuel resources are the primary factors driving electric grid modernization [4]. ...
... The NPHC algorithmic utilizes numerous homotopy continuation traces to find all isolated complex solutions of the original targeted system (i.e., the PF equations given as a set of polynomials) from the solutions of a simple start system. 2 In previous studies, such as [34,35], the NPHC method was applied to calculate the distance to the loadability boundary. However, it is important to note that tracing all the isolated solutions can impose a significant computational burden. ...
... The term ( gen, ) min corresponds to the lower limit. The quadratic form of (10) can be described as, gen, − ( gen, ) max + ( ) 2 ...
... Power systems architecture has evolved from a traditional electric power grid with fewer elements to a scale that comprises thousands of miles of transmission lines, a diversity of generation mix, and a substantial amount of multitudinous equipment to address the growing electricity demands [1][2][3]. Rising electricity markets and depletion of fossil fuel resources are pushing for a trans-regional to transnational to an intercontinental electric power grid [4,5]. Furthermore, the Distributed Energy Resources (DERs) integration like wind farms, photovoltaics, and battery storage systems have shifted the grid design immensely, providing a more reliable, resilient, and greener electric power grid [6,7]. ...
... These equations can have multiple isolated solutions representing either stable or unstable equilibria of the power systems' dynamical model [14,15]. Past contributions primarily focus on identifying just a single real-valued solution 2 of the PF equations instead of targeting all the isolated solutions or equilibria [16,17]. ...
... These boundaries were identified by calculating the loadability points onto the 3 − | 3 | solution curves. 2 −| 3 |. Next, we look at the PF solution space and loadability points in 2 − | 3 | space. ...
Calculating loadability margins is indispensable for maintaining secure and stable operations of electric power grids. Loadability margins closely resemble boundedness placed by the Power-Flow (PF) equations solution space. Estimating these limits is a computationally demanding task linked to the non-convex structure of the PF solution space, technological constraints, and intricate grid topology. Furthermore, multiple loadability points exist within the PF solutions space due to the non-convex topology, representing either a structural change in the number of equilibria or the disappearance of all system equilibria. To address the problem of accurately estimating loadability margins, we present a computationally robust two-stage algorithmic framework that guarantees to identify all isolated loadability points within the PF solution space for a given set of parameters. Numerical results from different test cases validate the proposed algorithmic framework computational performance and also illustrate the convoluted structure of the PF solution space with disjoint sections.
... Power systems architecture has evolved from a traditional electric power grid with fewer elements to a scale that comprises thousands of miles of transmission lines, a diversity of generation mix, and a substantial amount of multitudinous equipment to address the growing electricity demands [1][2][3]. Rising electricity markets and depletion of fossil fuel resources are pushing for a trans-regional to transnational to an intercontinental electric power grid [4,5]. Furthermore, the Distributed Energy Resources (DERs) integration like wind farms, photovoltaics, and battery storage systems have shifted the grid design immensely, providing a more reliable, resilient, and greener electric power grid [6,7]. ...
... These equations can have multiple isolated solutions representing either stable or unstable equilibria of the power systems' dynamical model [14,15]. Past contributions primarily focus on identifying just a single real-valued solution 2 of the PF equations instead of targeting all the isolated solutions or equilibria [16,17]. ...
... These boundaries were identified by calculating the loadability points onto the 3 − | 3 | solution curves. 2 −| 3 |. Next, we look at the PF solution space and loadability points in 2 − | 3 | space. ...
... As described in [Amin 2008], the continental-scale grid is configured as a multiscale, multilevel hybrid system consisting of vertically integrated hierarchical networks including the generation layer and three basic levels: transmission level, sub transmission level, and distribution level. The power system adaptation to disturbances can be characterized as having multiple states, or modes, during which specific operational and control actions and reactions are taking place. ...
... The vision of electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades as described in [Amin 2008] is one goal of Smart Grid too. While challenges still need to be addressed, the unprecedented "developments in both information technology and materials science and engineering promise significant improvements in the security, reliability, efficiency, and cost effectiveness of electric power delivery systems" [Amin 2008]. ...
... The vision of electric power grid will move from an electromechanically controlled system to an electronically controlled network in the next two decades as described in [Amin 2008] is one goal of Smart Grid too. While challenges still need to be addressed, the unprecedented "developments in both information technology and materials science and engineering promise significant improvements in the security, reliability, efficiency, and cost effectiveness of electric power delivery systems" [Amin 2008]. ...
... For example, high winds cause fallen debris that induce failures to overhead power distribution lines in minutes [14]. Outages caused by failures occur in seconds or sub-seconds within a distribution infrastructure [16], [22]. Recovery occurs in seconds for restoring outages and in days for difficult manual repairs [9], [14]. ...
... Failures can further cause outages in a distribution system, where certain components lose power but are not damaged [16]. Outages occur in seconds or less [16], [22]. Therefore, the temporal scale of failure data should ideally be less than a second. ...
Large scale power failures induced by severe weather have become frequent and damaging in recent years, causing millions of people to be without electricity service for days. Although the power industry has been battling weather-induced failures for years, it is largely unknown how resilient the energy infrastructure and services really are to severe weather disruptions. What fundamental issues govern the resilience? Can advanced approaches such as modeling and data analytics help industry to go beyond empirical methods? This paper discusses the research to date and open issues related to these questions. The focus is on identifying fundamental challenges and advanced approaches for quantifying resilience. In particular, a first aspect of this problem is how to model large-scale failures, recoveries and impacts, involving the infrastructure, service providers, customers, and weather. A second aspect is how to identify generic vulnerability (i.e., non-resilience) in the infrastructure and services through large-scale data analytics. And, a third is to understand what resilience metrics are needed and how to develop them.
... This global consumption increase has led to an urgent need to improve the existing (traditional) grid to meet the growing demand. This is because the traditional grid [1] still faces many issues as it operates the way it did many years ago. There are several problems that are related to the traditional, outdated grid: (1) it is a centralized grid, where power is carried from a central generator to the users. ...
... This is because the traditional grid [1] still faces many issues as it operates the way it did many years ago. There are several problems that are related to the traditional, outdated grid: (1) it is a centralized grid, where power is carried from a central generator to the users. Usually, traditional grids are powered by non-renewable energy resources such as diesel and natural gas; (2) it has a one-way communication infrastructure, where the user is receptive and cannot include his power needs and preferences into the grid; and (3) it is not well equipped with advanced sensors and monitors which weakens its capabilities in detecting anomalies and problems. ...
Continuous advancements in Information and Communication Technology and the emergence of the Big Data era have altered how traditional power systems function. Such developments have led to increased reliability and efficiency, in turn contributing to operational, economic, and environmental improvements and leading to the development of a new technique known as Demand Side Management or DSM. In essence, DSM is a management activity that encourages users to optimize their electricity consumption by controlling the operation of their electrical appliances to reduce utility bills and their use during peak times. While users may save money on electricity costs by rescheduling their power consumption, they may also experience inconvenience due to the inflexibility of getting power on demand. Hence, several challenges must be considered to achieve a successful DSM. In this work, we analyze the power scheduling techniques in Smart Houses as proposed in most cited papers. We then examine the advantages and drawbacks of such methods and compare their contributions based on operational, economic, and environmental aspects.
... Research has yielded technologies for monitoring infrastructure condition and performance, new materials for constructing and repairing infrastructure components, and new knowledge about the interrelated nature of water and wastewater, power, transportation, and telecommunications systems. Self-diagnosing, self-healing, and self-repairing systems can be designed to provide for greater resiliency, fewer long-term service disruptions, and lower life-cycle costs (Amin and Stringer, 2008). An array of financing mechanisms, strategies, plans, and approaches to infrastructure renewal that offer new ways to provide for essential services has been developed through local, state, and regional initiatives. ...
... Today, self-diagnosing, self-healing, and self-repairing systems can be designed to provide for greater resiliency, fewer long-term service disruptions, and lower life-cycle costs (Amin and Stringer, 2008). Buildings can be designed to supply their own electricity using solar collectors and, in some cases, to generate excess electricity that can be sold back to power companies. ...
Much has been written in the past 20 years about the
deteriorating
condition and quality of the nation’s roads and
bridges and its power and water systems and about the trillions
of dollars that it will take to fix them. The issues, however, are
much more complex: How we as a nation choose to renew our
infrastructure systems in the coming years will help determine
the quality of life for future generations. It will also help determine
our success in meeting other national challenges, including
those of remaining economically competitive, reducing our
dependence on imported oil, and dealing with issues related to
global climate change, national security, and disaster resilience.
Many of the ideas and much of the information in this report
were generated at a workshop held May 7 and 8, 2008, at the
National Academies in Washington, D.C. In the time between
the workshop and the completion of this report, global and
domestic financial markets and systems have faltered in ways
reminiscent of the 1930s. An economic stimulus package has been
approved that includes funding for some infrastructure projects.
Nonetheless,
the message of this report—that a paradigm shift
is needed in how the nation thinks about, builds, operates, and
invests in critical infrastructure systems in the long term—is more
relevant than ever.
... In this study, the term 'grid' refers to the whole system rather than only the transmission part of the power system. Electrical energy storage (EES) technologies are one of the most promising technologies that can alleviate the grid reliability issues and the mismatch between supply and demand [6]. They can be used to store energy when there is excess supply in the grid, and then give it back to the grid when demand increases, while at the same time, there is a shortage of generation power [7]. ...
... Oversizing and underutilization of the existing capacity result in low overall efficiency of the electrical grid, leading to a high operation cost [58,6]. CAES (A-CAES) system is shown in Fig. 2. ...
Compressed Air Energy Storage (CAES) systems, if designed right, can provide a range of high-value grid services that are required for stable operation of the electrical grid. In this paper, a new design approach for customized CAES based on user-centered design (UCD) methodology is developed. To this end, a study of the electrical grid infrastructure and challenges, with a focus on Ontario (Canada) is presented. Afterward, a system approach methodology called CAES-by-Design is developed that incorporates the impact of grid profiles. Hourly load profiles of the Ontario grid are analyzed. Using a thermodynamic model, it is shown how the performance requirements of an adiabatic CAES (A-CAES) system, in terms of capacity, charge and discharge rates and duration are affected by the grid demand and supply profile. For example, it is observed that a compressor capacity of 13% of maximum excess energy and a turbine capacity of 10% of maximum required energy would be sufficient to capture more than 50% of the charging and discharging opportunities. Furthermore, the impact of charging and discharging cycle dynamics on the sizing and operating characteristics of thermal energy storage (TES) system is discussed. Results of this analysis suggest that a comprehensive assessment tool can be developed based on the presented methodology.
... The electrical power system is a complex grid consisting of various interconnected components that work together to achieve the primary goal of generating and delivering electricity to end-users. This system includes assets such as conventional and distributed generators, transformers, transmission and distribution lines, switchgear, instrument transformers (ITs), protective devices, and other miscellaneous equipment [1,2]. Condition monitoring and diagnostic processes of these assets are critical for grid operators. ...
This paper serves as a comprehensive “starter pack” for electrical diagnostic methods for power transformers. It offers a thorough review of electrical diagnostic techniques, detailing the required instrumentation and highlighting key research directions. The methods discussed include frequency response analysis, partial discharge testing, dielectric dissipation factor (tan delta), direct current (DC) insulation resistance, polarization index, transformer turns ratio test, recovery voltage measurement, polarization–depolarization currents, frequency domain spectroscopy, breakdown voltage testing, and power factor and capacitance testing. Additionally, the paper brings attention to less-explored electrical diagnostic techniques from the past decade. For each method, the underlying principles, applications, necessary instrumentation, advantages, and limitations are carefully examined, alongside emerging trends in the field. A notable shift observed over the past decade is the growing emphasis on hybrid diagnostic approaches and artificial intelligence (AI)-driven data analytics for fault detection. This study serves as a structured reference for researchers—particularly those in the early stages of their careers—as well as industry professionals seeking to explore electrical diagnostic techniques for power transformer condition assessment. It also outlines promising research avenues, contributing to the ongoing evolution of transformer diagnostics.
... The smart grid is a revolution in the conventional power grid by adding bidirectional electricity and information, supported by the development of artificial intelligence (AI) in both equipment and control systems [5]. The smart grid concept is the integration of smart sensors, intelligent control methods, and advanced digital communications into the classic electricity network, both at the transmission and distribution levels [6], [7]. The main goal of a smart grid is to optimize production, transmission, and consumption from distributed and varied power sources [6]. ...
span>Based on the emergence of increasingly advanced technology, the conventional power grid can be upgraded to a smart grid by adding bidirectional communication, computer algorithms, and equipment that uses artificial intelligence (AI). A smart grid is a revolution in the current electricity network that can control the two-way generation and transmission process by utilizing an intelligent system so that the distribution of electric power can be handled optimally and in real time. The challenge of the smart grid is that there are distributed generators and microgrids that must be controlled in real time with rapidly changing loads. To meet these criteria, several points are proposed, i.e., finding an effective procedure to construct self-healing capability; developing a protection system based on AI; and proposing a systematic procedure to realize self-healing and protection systems with the help of a multi-agent system (MAS). Multi-agent systems are one of the AI approaches. Each agent can work independently and can also communicate with one another and with other devices on the network. Agents used as models can be classified into several categories, such as grid component agents, distributed resource agents, end-user agents, failure control agents, data analysis agents, and graphical visualization agents.</span
... The electrical energy power grids are often structured into multiple voltage levels, interconnected through three-phase transformers [20], i.e. transmission and distribution grids. The grid is a composite of primary and secondary technologies, with the primary technology encompassing components directly involved in the generation, transformation, and transport of electrical energy [21]. The secondary technology complements this by monitoring, controlling, and protecting these components. ...
The power grid is a critical infrastructure essential for public safety and welfare. As its reliance on digital technologies grows, so do its vulnerabilities to sophisticated cyber threats, which could severely disrupt operations. Effective protective measures, such as intrusion detection and decision support systems, are essential to mitigate these risks. Machine learning offers significant potential in this field, yet its effectiveness is constrained by the limited availability of high-quality data due to confidentiality and access restrictions. To address this, we introduce a simulation environment that replicates the power grid's infrastructure and communication dynamics. This environment enables the modeling of complex, multi-stage cyber attacks and defensive responses, using attack trees to outline attacker strategies and game-theoretic approaches to model defender actions. The framework generates diverse, realistic attack data to train machine learning algorithms for detecting and mitigating cyber threats. It also provides a controlled, flexible platform to evaluate emerging security technologies, including advanced decision support systems. The environment is modular and scalable, facilitating the integration of new scenarios without dependence on external components. It supports scenario generation, data modeling, mapping, power flow simulation, and communication traffic analysis in a cohesive chain, capturing all relevant data for cyber security investigations under consistent conditions. Detailed modeling of communication protocols and grid operations offers insights into attack propagation, while datasets undergo validation in laboratory settings to ensure real-world applicability. These datasets are leveraged to train machine learning models for intrusion detection, focusing on their ability to identify complex attack patterns within power grid operations.
... Furthermore, it offers useful services that enable consumers to act autonomously regarding the usage of energy with actual cost being obtained from distribution networks. The term Smart Grid is used to describe an electrical system that integrates advanced sensing, control and communication technologies into of the transmission and distribution system [9][10] [11]. ...
Energy management, financial savings, efficient planning, and safe and dependable power grid operation all depend heavily on energy optimization. This study takes into account the use of efficient ML methods for smart grid energy management, which may improve the grids' dependability and stability via energy prediction algorithms. A research applies a complex data set that captures variables that include energy production, use, elasticity, and response time. Data preparation involves standardization and outliers removal so that the data is ready for analysis. Various ML models, like XGBoost, GB, RF, and SVM, are trained to classify grid stability into stable and unstable states. Voting Classifier belongs to the group of methods for increasing the accuracy of models, which is accomplished by fusing predictions of multiple classifiers. AUC-ROC metrics, F1-score, recall, accuracy, and precision are used to evaluate the models' performance. As a result, it is shown that ensemble models, like the Voting Classifier, have a higher accuracy of 99. 8% in forecasting grid stability. The study shows how ML approaches have the potential to enhance energy supply and the operation effectiveness of smart grids.
... Several factors have pushed the energy conservation problem to the forefront of current technological inspection and study, including the projected growth of computing devices, environmental/resource concerns, and last but not least, economics. In terms of growth, the persistent increase in the demand for computing technology, from embedded systems to smartphones, notebooks, tablet computers, servers, and data centers, is expected to place ever increasing pressure on the electrical grid in the coming years [Amin and Stringer 2008]. In turn, these devices are typically supported by large data centers which themselves consume significant amounts of power. ...
... Over the last century, the size of the power system has grown from fewer elements to a scale consisting of thousands of miles transmission lines, hundreds of generating units, with a substantial amount of several types of equipment to facilitate and maintain a secure operation [1,2,3]. ...
... In response to these challenges, the realm of computational digital technology and artificial intelligence has ushered in a plethora of advanced tools and techniques. Among these, neural networks stand out as a progressive and highly precise method for addressing the complexities of energy distribution and optimization [2]. ...
This paper addresses the critical objective of optimizing power flow within a region, particularly focusing on the Mangystau region, amidst evolving energy demands and the integration of renewable resources. The escalating challenges associated with maintaining both system stability and economic viability underscore the significance of this research, as suboptimal power flow conditions can exacerbate climate change. To expedite the solution to the optimal power flow problem, machine learning algorithms are explored. Initially, load data from the region is analyzed, and various supervised learning algorithms are tested using simulation data to predict power flow patterns. The primary concern in the Mangystau region lies in the aging infrastructure of oil companies, which operates under suboptimal conditions. This study employs neural networks in Matlab to simulate the electrical system’s parameters, unveiling the intricate relationship between optimal system parameters and those of the examined system. Comparing these results with analytical grid modeling, the study reveals that system optimization aligns with target values, particularly concerning optimal receiver replacement schemes.
... The Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) the latter transports lower voltage electricity from the substations directly to the customers (Amin and Stringer 2008). The transmission networks are controlled by transmission system operators (TSOs) which often are state-owned, like the Norwegian Statnett SF, the Finnish Fingrid Oyj, and the Swedish Svenska Kraftnät (ENTSO-E 2022). ...
Accurate day-ahead nominations of grid losses in electrical distribution networks are important to reduce the societal cost of these losses. We present a modification of the CatBoost ensemble-based system for day-ahead grid loss prediction detailed in Dalal et al. (2020), making four main changes. Base models predict on the log-space of the target, to ensure non-negative predictions. The model ensemble is changed to include different model types, for increased ensemble variance. Feature engineering is applied to consumption and weather forecasts, to improve base model performance. Finally, a non-negative least squares-based stacking method that uses as many available models as possible for each prediction is introduced, to achieve an improved model selection that is robust to missing data. When deployed for over three months in 2022, the resulting system reduced mean absolute error by 10.7% compared to the system from Dalal et al. (2020), a reduction from 5.05 to 4.51 MW. With no tuning of machine learning parameters, the system was also extended to three new grids, where it achieved similar relative error as on the old grids. Our system is robust and easily scalable, and our proposed stacking method could provide improved performance in applications outside grid loss.
... In other terms, it is used for connecting producers of electricity to consumers of electricity. However, the existing electric grid faces few challenges [1]. These challenges vary from country to country based on the energy demands. ...
The residential sector is a major contributor to the global energy demand. The energy demand for the residential sector is expected to increase substantially in the next few decades. As the residential sector is responsible for almost 40% of overall electricity consumption, the demand response solution is considered the most effective and reliable solution to meet the growing energy demands. Home energy management systems (HEMSs) help manage the electricity demand to optimize energy consumption without compromising consumer comfort. HEMSs operate according to multiple criteria, including electricity cost, peak load reduction, consumer comfort, social welfare, environmental factors, etc. The residential appliance scheduling problem (RASP) is defined as the problem of scheduling household appliances in an efficient manner at appropriate periods with respect to dynamic pricing schemes and incentives provided by utilities. The objectives of RASP are to minimize electricity cost and peak load, maximize local energy generation and improve consumer comfort. To increase the effectiveness of demand response programs for smart homes, various demand-side management strategies are used to enable consumers to optimally manage their loads. This study lists out DSM techniques used in the literature for appliance scheduling. Most of these techniques aim at energy management in residential sectors to encourage users to schedule their power consumption in an effective manner. However, the performance of these techniques is rarely analyzed. Additionally, various factors, such as consumer comfort and dynamic pricing constraints, need to be incorporated. This work surveys most recent literature on residential household energy management, especially holistic solutions, and proposes new viewpoints on residential appliance scheduling in smart homes. The paper concludes with key observations and future research directions.
... To achieve these goals, advanced technologies have been developed and implemented in the energy system. These are mainly various technologies for sensing energy parameters, control methods, optimization of smart grids, intelligent communications, which have been integrated into the current energy system [28,29]. The smart grid should have the following key features: ...
The optimal design of a building and city, including the balance of their energy performance, must include requirements from a wide range of areas, especially electrical engineering, informatics, technical equipment of buildings, construction and architecture, psychology and many other fields. It is the optimal design, simulation and modelling that are most reflected in the energy requirements of buildings while meeting the requirements of energy sustainability. The impact of buildings and cities on the environment is crucial and unmistakable. It should be emphasized that an inappropriately (architecturally or technologically) designed building with state-of-the-art control technology will still have worse properties than an optimally designed building without a control system. This inspired us to design a building energy model (BEM) with the implementation of a Smart Grid in a decentralized sustainable energy system, which is a microgrid from renewable energy sources (RES). This inspired us to conduct an analysis of simulation models (simultaneous simulations) to show the possibility of their application in the process of fully satisfying energy needs in a given urban region. The main goal is to design an original methodology for the design of smart “Nearly Zero Energy Buildings” (NZEB) and subsequent energy sustainability solutions. This led us to use Hybrid Optimization of Multiple Energy Resources (HOMER), PV*SOL (2D solar software design tool for the photovoltaic system performance), Monte Carlo and DesignBuilder. The EMB was designed based on the Six Sigma design quality management methodology. In the process of integrating Smart Grids with energy efficiency solutions for buildings, an original optimization basis was designed for smart buildings and smart urban areas. The proposed EMB was verified in an experiment.
... Smart devices and processes and distribution) networks. In the coming two decades, i electronically controlled network rather than challenge is how to update, redesign, re (Amin M. and Stringer J., 2008). ...
In these last decades, electrical power grids become more intelligent. Hence, sophisticated software and hardware were introduced to the power grid, which makes it a smart grid. This chapter is an introduction on smart-grid technology; thus, microgrids are explained, and the use of multiagent system in centralized/decentralized energy management systems are discussed and compared. Smart agents are an emerging technology for decentralized computation and data storage. Hence, in this chapter, decentralized energy management system is created basing on multi-agent system technique where sources and loads are considered as separated agents each of them. After that, these sources and load create a microgrid and each microgrid can be considered as an agent. The work proposes an approach for load supplying optimization to decrease the microgrid cost and enhance its efficiency.
... Smart devices and processes and distribution) networks. In the coming two decades, i electronically controlled network rather than challenge is how to update, redesign, re (Amin M. and Stringer J., 2008). ...
In these last decades, electrical power grids become more and more intelligent. Hence, sophisticated software and hardware are introduced to the power grid which makes it smart grid. This chapter is an introduction on smart-grid technology; thus, microgrids is explained and the use of multiagent system in centralized/decentralized energy management systems are discussed and compared. Smart-agents are an emerging technology for decentralized computation and data storage. Hence, in this chapter decentralized energy management system is created basing on multi-agent system technique; where, sources and loads are considered as separated agents each of them. After that, these sources and load create a microgrid and each microgrid can be considered as an agent. The work proposes mainly an approach for load supplying optimization to decrease the microgrid cost and enhance its efficiency.
... Absence of electric energy grossly affects the quality of life; hence the provision of adequate, reliable, and sustainable energy has always been both the focus as well as the challenge for electricity providers. This challenge is commonly evident in the experiences of supply-demand imbalance and their undesirable effects [1][2][3]. Improper use of appliances and wastages contribute significant portions of consumed energy in residential and commercial buildings with that of the residential being 40% higher than the commercial buildings [4]. This realization underscores the need for energy management in buildings. ...
Objectives: It is a common responsibility for individuals and organizations to effectively manage energy in buildings for economic and environmental concerns. In this study, the development of an energy management system integrated with a presence detecting sensor and an android mobile application for remote control of electrical appliances is undertaken. Methods/ findings: The integrated system is basically composed of a human presence detecting Passive Infrared (PIR) sensor mounted on a WeMos D1 microcontroller and remotely monitored and controlled from a web server using an android mobile application via a wireless network. The developed system provides multiple benefits. It eliminates human involvement and manpower often required to manually toggle power outlets and appliances on/off. Application: Furthermore, it conserves electrical energy and prevents wastages by ensuring that energy is only consumed when needed. The developed system is recommended for residential and small office applications.
... The electric grid is a large interconnection of both generation and transmission electrical subsystems usually managed by utilities to facilitate efficient distribution of electricity to consumers [1]. Providing adequate, reliable and sustainable energy has always been the focus as well as a perennial challenge for utilities. ...
Energy measurement and management are amongst the major grid challenges and research efforts have been geared towards addressing them. The emergence of smart meters in consonance with the smart grid concept is as a result of these efforts. This paper explores the integration of Energy Management and Control System (EMCS) technologies into smart meters to mitigate energy wastages and ensure energy accountability. The integrated system presented in this paper leverages on the Global System for Mobile Communication (GSM) to provide a tripod communication between the meter, utility and the consumers. Results from the system prototype show that the system offers the consumer the opportunity to make personal energy budgets in line with the energy conservation, provides more consumption details than the traditional meters and provides for the utility a better management platform of the consumer component of the grid.
... This paper builds upon the mechanism proposed in (Čaušević et al. 2017) that enables decentralized supply and demand balancing in energy systems. Consumers, prosumers and producers of electricity rely on local knowledge of their production and/or demand to organize themselves (self-organize) into small-scale local energy communities (clusters) that locally match supply and demand (Amin 2001;Farhangi 2010;Amin and Stringer 2008;Brazier et al. 2015). The main objective of clusters is to minimize the supply and demand mismatch within the cluster. ...
Abstract Ensuring access to reliable and sustainable power supply is becoming more and more challenging due to a combination of factors such as more frequent power grid outages caused by extreme weather events, the large-scale introduction of renewable energy resources that increases the complexity of the power system, but also aging infrastructure, supply and demand imbalance and power theft in some areas. Combined, all these factors can cause outages and together they can make electricity supply unreliable. The implications of this are many, ranging from minor inconveniences to major failures of critical infrastructures. A potential solution to ensure power supply during outages is to use local generation in the form of renewable resources to supply energy. This paper proposes a community-based mechanism that demonstrates that when community members can determine for themselves how excess energy generation is distributed, the power supply of specific members can be ensured. Self-determination is achieved by prioritizing and differentiating between community members as well as automatically and continuously redistributing energy, thereby adapting to sudden changes in supply and demand. Simulation results show that the proposed mechanism can be used to empower local communities to decide for themselves how local resources are distributed during events such as outages, ensuring prolonged power supply for differentiated members of affected communities. Harnessing the potential of renewable resources and smart technologies for intelligent coordination through empowerment of consumers to become pro-active participants is a promising solution for the future power systems.
... However, there are few papers to give a comprehensive review of this important application area even though many excellent review papers of fiber-optic sensors have been given. [17][18][19][20][21][22] This paper is given to fill the gap. It would be benefit to the researchers in power grid to know fiberoptic sensing technologies used already and also gives a shortcut for the researchers in fiber-optic sensing to know the requirements and huge potential market in power grid. ...
Fiber-optic sensing technology is best adapted to health monitoring and evaluation of power grids because of its immunity of electromagnetic interference, capabilities of multiplexing and distributed sensing, and tolerance to harsh environments. We review key fiber-optic sensing technologies, including fiber Bragg gratings, fiber-optic interferometers, optical time domain reflectometries, and their applications in three main parts of power grids, transformers, power towers, and overhead transmission lines, during the past 20 years. In particular, optical fiber composite overhead ground wire and optical phase conductor applied in power grids are the areas of great potential to go further. The perspectives of an intelligent fault diagnosis subsystem for power grids based on a fiber-optic sensing network are discussed, and related on-going work is described. The review shall be of benefit to both engineers and researchers in power grids and fiber-optic sensing. © 2019 Society of Photo-Optical Instrumentation Engineers (SPIE).
... In the United States alone, the electric network includes about 15,000 generators which send power through 339,000 km of high voltage transmission lines. The network has about 5,600 distribution facilities [1]. Fig. 2.1 shows the conceptual structure of the electric power system. ...
The electricity delivery infrastructure -consisting of power plants, transmission lines and distribution systems- is known as the power grid. The power grid in its present form is one of the most remarkable engineering developments. The grid infrastructure has played a critical role in making electric power reach the people in a reliable and economic way. The National Academy of Science, USA has ranked the power grid as the most beneficial engineering innovation of the 20th century. Power grid is a complicated and highly meshed network. The complexity of the grid has been ever increasing with the increase in electricity demand. The high reliability and power quality requirement for the digital society is challenging. The smart grid is a power grid that uses real time measurements, two-way com-munication, and computational intelligence. The smart grid is expected to be safe, secure, reliable, resilient, efficient, and sustainable. Measuring devices like phasor measurement units (PMUs) can radically change the monitoring way of the grids. However, there are several challenges like deployment of sufficient number of PMUs and managing the huge amount of data. Two-way communication is an essential requirement of the smart grid. A communication system that is secure, dedicated and capable of handling the data traffic is required. The integration of renewable sources will alter the dynamics of the grid. This situation calls for better monitoring and control at the distribution level.
... Smart grid is an intelligent power network which recently attracted a lot of attention because it enables many things to positively affect the environment on the Earth (Amin and Schewe, 2007;Amin, 2008;Amin and Stringer, 2008;Gellings et al., 2009;Kupzog et al., 2009). Figure 1 shows the evolution from a conventional power network [ Figure 1(a)] to a smart grid with microgrids [ Figure 1(b)] (Aida et al., 2004). ...
An increasing demand of real-time monitoring and data analysis for the power grid, is facing challenges from the processing rate of massive real-time measurement data such as voltage, current and power from billions of sensors. To guarantee process rate, many measurement data scheduling method had been studied, while the problem for the balance of realtime requirement and load balance has not been solved well, which leads to uneven load distribution and data congestion. Thus, this paper proposed a measurement data scheduling method based on genetic algorithm. The proposed method considers the characteristics of historical measurement data, task allocation matrix, and creates an adaptive parameter iterative solution based on using genetic algorithm. The results show that the proposed method can achieve appreciate load balance with time latency guarantee.
In this note, we consider the problem of choosing which nodes of a linear dynamical system should be actuated so that the state transfer from the system's initial condition to a given final state is possible. Assuming a standard complexity hypothesis, we show that this problem cannot be efficiently solved or approximated in polynomial, or even quasi-polynomial, time.
Understanding how interdependence among systems affects cascading behaviors is increasingly important across many fields of science and engineering.Inspired by cascades of load shedding in coupled electric grids and other infrastructure, we study the Bak-Tang-Wiesenfeld sandpile model on modular random graphs and on graphs based on actual, interdependent power grids. Starting from two isolated networks, adding some connectivity between them is beneficial, for it suppresses the largest cascades in each system. Too much interconnectivity, however, becomes detrimental for two reasons. First, interconnections open pathways for neighboring networks to inflict large cascades. Second, as in real infrastructure, new interconnections increase capacity and total possible load, which fuels even larger cascades. Using a multitype branching process and simulations we show these effects and estimate the optimal level of interconnectivity that balances their tradeoffs. Such equilibria could allow, for example, power grid owners to minimize the largest cascades in their grid. We also show that asymmetric capacity among interdependent networks affects the optimal connectivity that each prefers and may lead to an arms race for greater capacity. Our multitype branching process framework provides building blocks for better prediction of cascading processes on modular random graphs and on multi-type networks in general.
The electric grid entails a diverse range of components with pervasive heterogeneity. Conventional electricity models in the U.S.A. encounter challenges in terms of affirming the stability and security of the power system, particularly, when dealing with unexpected incidents. This study explored various electric grid models adopted in various nations and their shortcomings. To resolve these challenges, the research concentrated on consolidating machine learning algorithms as an optimization strategy for the electricity power grid. As such, this study proposed Ensemble Learning with a Feature Engineering Model which exemplified promising outputs, with the voting classifier performing well as compared to the rainforest classifier model. Particularly, the accuracy of the voting classifier was ascertained to be 94.57%, illustrating that approximately 94.17% of its predictions were correct as contrasted to the Random Forest. Besides, the precision of the voting classifier was ascertained to be 93.78%, implying that it correctly pinpointed positive data points 93.78% of the time. Remarkably, the Voting Classifier for the Ensemble Learning with Feature Engineering Model technique surpassed the performance of most other techniques, demonstrating an accuracy rate of 94.57%. These techniques provide protective and preventive measures to resolve the vulnerabilities and challenges faced by geographically distributed power systems. | KEYWORDS Electricity Power Grid, Voting classifier, Gradient booster, Random forest, Machine learning algorithms | ARTICLE INFORMATION
The power grid has undergone significant transformations over the past century, expanding its role beyond providing reliable electricity to consumers. Today, it has evolved into a sophisticated and intelligent network, encompassing various applications that rely on advanced technologies. This article explores the concept of Smart Grid 3.0, the next phase of evolution in power grid systems, which has been made possible by recent advancements in computational power, storage capabilities, and high-speed communication. One key aspect of Smart Grid 3.0 is proactive intelligence, which enhances the grid's efficiency and reliability. This chapter highlights the importance of proactive intelligence and discusses how emerging technologies such as artificial intelligence (AI), the Internet of Things (IoT), Blockchain, Big Data, 5G, edge computing, cloud computing, etc., can equip the power grid with proactive intelligence. These technologies enable the grid to gather and analyze real-time data, make informed decisions, and take preemptive actions to optimize performance. It will delve into the various technologies, their applications in a smart grid context, and the relevant protocols employed. By presenting a thorough analysis, this chapter aims to serve as valuable reference material for future research on smart grid systems and their integration with cutting-edge technologies, leading to a more efficient, resilient, and sustainable energy infrastructure.
Different stakeholders of the electric power grid sector need data for their research and decision-making tasks. However, the power grid field lacks an accessible and easy-to-use system that contains openly available data sources that is open to use for everyone. In this study we propose a prototype of an accessible and usable web interface for power grid databases, following the user-centered design (UCD) methodology and using different qualitative research methods and research methods from human-computer interaction (HCI). The prototype is gradually developed through an iterative approach, where we identify user requirements and test it for its accessibility and usability from the perspective of the stakeholders. Finally, we discuss possible future work to further extend this study and to make the software prototype more effective.
Based on the perspective of smart grid patent, this study crawls the smart grid patent data from 2009 to 2020, and extracts the hotspots of innovation space in different stages. By mining the patent information, this paper identifies the hot innovation fields of smart grid in China and the world, and selects IPC classifications of H02J, G01R and H04L as the main hot smart grid technologies for innovation space analysis. In further study, methods such as global autocorrelation analysis are used to comprehensively analyze the spatial distribution of smart grid innovation in China. It is found that innovation activities of China's smart grid industry are spatially agglomerated. However, during 2017–2020, such spatial distribution represents China's smart grid industry has approached the distribution pattern of normal innovation industries which tends to be discrete or competitive. According to autocorrelation analysis, China's smart grid innovation space has obvious agglomeration characteristics, showing a gradual decline trend from the center to the periphery, and the diffusion effect has initially appeared. Finally, the smart grid patent statistics and the spatial ellipse parameters intuitively present the innovation characteristics of China's smart grid industry in terms of the evolution of the spatial differentiation during 2009–2012, 2013–2016, and 2017–2020.
This paper introduces a dynamic panel threshold model to empirically investigate whether the effects of technological progress on metal consumption in the electricity industry differs by the change of industrial structure upgrading level (INSTRU). We consider a panel data set including 41 countries over the period 2000-2016. The estimated results suggests 2.72, 2.39 and 2.84 as the average threshold parameters of INSTRU for basic ferrous metals (Iron and Chromium), basic non-ferrous metals (Aluminium, Copper and Nickel) and critical metals (Indium, Neodymium and Dysprosium) respectively. When INSTRU is lower than the corresponding threshold, domestic technological progress (LnSRD) and foreign technological spillover progress (LnSIM) significantly promote the consumption of the above-mentioned metals. However, when INSTRU is higher than the corresponding threshold, LnSRD and LnSRD will reduce the consumption of basic ferrous metals such as Iron and Chromium, but will continue to increase the consumption of critical metals such as Indium, Neodymium and Dysprosium. Last, we confirm that LnSRD play a more important role in changing metal consumption structure in the electricity industry than that of LnSIM. These findings are of great significance to government policies that ensure the transition to low-carbon electricity systems.
Our modern digitized socio-technological systems are vulnerable to destructive events such as disease, floods, and terrorist attacks. Data science might help these systems to become more resilient if a variety of challenges described in this paper can be addressed.
It is known that the main consumers of reactive power are electrical machines and electric drives, which are widely used in various mechanisms of industrial enterprises. Reactive power is necessary to create magnetic fields in electric machines, but not accounting or excessive consumption of reactive power leads to a decrease in the power factor of the power supply system, which will negatively affect the power system as a whole. Therefore, the study of methods and methods of reactive power compensation is an urgent issue, for the solution of which an integrated approach is required. The article presents the results of the experience of reactive power compensation at industrial enterprises, and also proposes a methodology for carrying out measures to compensate for reactive power. The article analyzes in detail the main problems in industrial enterprises due to consumers of reactive power. A methodology for calculating and choosing reactive power compensators for industrial enterprises is proposed, which differs from other methods with simplicity and availability in practical conditions for use.
One of the important elements of the high-voltage transmission line is 110 kV, the damage of which leads to under-supply of electricity, i.e. reducing the reliability of the lines. Damage to the reinforcement from the applied load occurs due to material fatigue. The main causes of damage to the reinforcement are ambient temperatures and wind forces on this element. Periodic kinks of the wire are observed at the places of installation of the connecting and supporting clamps, vibration dampers, and cyclic lateral forces arise. The simultaneous impact of the above efforts leads to fatigue damage to the suspension systems. From the cyclic load, the nodes of rigid structures are destroyed, which bear the maximum load.
The U.S. lacks an overall infrastructure renewal strategy. As a result, many states are forced to develop specific initiatives involving infrastructure asset management practices. For critical water infrastructure systems, states are mitigating the risk and burden of aging water infrastructure and declining water quality concerns by requiring water utilities to develop asset management plans and programs. Michigan was the first state to pass a law requiring that a Water Asset Management Program be implemented by January 1, 2018, and an asset management plan (AMP) is completed and submitted by water systems serving a population greater than 1,000. So what happened when over 500 water systems all needed to submit AMPs following various guidelines? This paper reviews the process and findings and identifies improvements for water utility engineering firms to better support utilities as well as recommendations to improve data and water pipe condition assessment results to better support capital planning needs and affordability concerns.
This report is one of six reports developed under the U.S. Department of Energy (DOE) program in Power System Integration and Reliability (PSIR). The objective of this report is to review, analyze, and evaluate critical reliability issues demonstrated by recent disturbance events in the North America power system. Eleven major disturbances are examined, most occurring in this decade. The strategic challenge is that the pattern of technical need has persisted for a long period of time. For more than a decade, anticipation of market deregulation has been a major disincentive to new investments in system capacity. It has also inspired reduced maintenance of existing assets. A massive infusion of better technology is emerging as the final option to continue reliable electrical services. If an investment in better technology will not be made in a timely manner, then North America should plan its adjustments to a very different level of electrical service. It is apparent that technical operations staff among the utilities can be very effective at marshaling their forces in the immediate aftermath of a system emergency, and that serious disturbances often lead to improved mechanisms for coordinated operation. It is not at all apparent that such efforts can be sustained through voluntary reliability organizations in which utility personnel external to those organizations do most of the technical work. The eastern interconnection shows several situations in which much of the technical support has migrated from the utilities to the Independent System Operator (ISO), and the ISO staffs or shares staff with the regional reliability council. This process may be a natural and very positive consequence of utility restructuring. If so, the process should be expedited in regions where it is less advanced.
The electrostatic forces among three charged, perfectly conducting spheres cause them to start spinning is mathematically proven. For an isolated spherical shell held at constant potential, excess charges repel each other and move apart to maximize the distance between all pairs of charge. It is reported that the experiment could test whether a spin-up of the spheres really occurred.
Scitation is the online home of leading journals and conference proceedings from AIP Publishing and AIP Member Societies
Enabling technologies for wireless sensor networks have gained considerable attention in research communities over the past few years. It is highly desirable, even necessary in certain situations, for wireless sensor nodes to be self-powered. With this goal in mind, a vibration based piezoelectric generator has been developed as an enabling technology for wireless sensor networks. The focus of this paper is to discuss the modeling, design, and optimization of a piezoelectric generator based on a two-layer bending element. An analytical model of the generator has been developed and validated. In addition to providing intuitive design insight, the model has been used as the basis for design optimization. Designs of 1 cm3 in size generated using the model have demonstrated a power output of 375 µW from a vibration source of 2.5 m s−2 at 120 Hz. Furthermore, a 1 cm3 generator has been used to power a custom designed 1.9 GHz radio transmitter from the same vibration source.
Vibration energy scavenging, harvesting ambient vibrations in structures for conversion into usable electricity, provides a potential power source for emerging technologies including wireless sensor networks. Most vibration energy scavenging devices developed to date operate effectively at a single specific frequency dictated by the device's design. However, for this technology to be commercially viable, vibration energy scavengers that generate usable power across a range of driving frequencies must be developed. This paper details the design and testing of a tunable-resonance vibration energy scavenger which uses the novel approach of axially compressing a piezoelectric bimorph to lower its resonance frequency. It was determined that an axial preload can adjust the resonance frequency of a simply supported bimorph to 24% below its unloaded resonance frequency. The power output to a resistive load was found to be 65–90% of the nominal value at frequencies 19–24% below the unloaded resonance frequency. Prototypes were developed that produced 300–400 µW of power at driving frequencies between 200 and 250 Hz. Additionally, piezoelectric coupling coefficient values were increased using this method, with keff values rising as much as 25% from 0.37 to 0.46. Device damping increased 67% under preload, from 0.0265 to 0.0445, adversely affecting the power output at lower frequencies. A theoretical model modified to include the effects of preload on damping predicted power output to within 0–30% of values obtained experimentally. Optimal load resistance deviated significantly from theory, and merits further investigation.
The North American electric power system was developed over the last 100 years without a conscious awareness and analysis of the system-wide implications of its current evolution under the forces of deregulation, system complexity, power-market impacts, terrorism, and human error. The possibility of power delivery beyond neighboring areas was a distant secondary consideration. Today, the North American power network may realistically be considered to be the largest machine in the world. With the advent of deregulation and competition in the electric power industry, new ways are being sought to improve the efficiency of that network without seriously diminishing its reliability and security. Controlling a heterogeneous, widely dispersed, yet globally interconnected system is a serious technological problem in any case. It is even more complex and difficult to control it for optimal efficiency and maximum benefit to the ultimate consumers while still allowing all its business components to compete fairly and freely. In this paper we present an overview of key issues and the context in which the electricity infrastructure is being operated under the above forces along with a strategic vision extending to a decade, or longer, that would enable more secure and robust systems operation, security monitoring, and efficient energy markets.
Research over the last decade at Oak Ridge National Laboratory has led to the development of solid-state thin-film lithium and lithium-ion batteries. The batteries, which are less than 15 μm thick, have important applications in a variety of consumer and medical products, and they are useful research tools in characterizing the properties of lithium intercalation compounds in thin-film form. The batteries consist of cathodes that are crystalline or nanocrystalline oxide-based lithium intercalation compounds such as LiCoO2 and LiMn2O4, and anodes of lithium metal, inorganic compounds such as silicon–tin oxynitrides, Sn3N4 and Zn3N2, or metal films such as Cu in which the anode is formed by lithium plating on the initial charge. The electrolyte is a glassy lithium phosphorus oxynitride (‘Lipon’). Cells with crystalline LiCoO2 cathodes can deliver up to 30% of their maximum capacity between 4.2 and 3 V at discharge currents of 10 mA/cm2, and at more moderate discharge–charge rates, the capacity decreases by negligible amounts over thousands of cycles. Thin films of crystalline lithium manganese oxide with the general composition Li1+xMn2−yO4 exhibit on the initial charge significant capacity at 5 V and, depending on the deposition process, at 4.6 V as well, as a consequence of the manganese deficiency–lithium excess. The 5-V plateau is believed to be due to oxidation Mn of ions to valence states higher than +4 accompanied by a rearrangement of the lattice. The gap between the discharge–charge curves of cells with as-deposited nanocrystalline Li1+xMn2−yO4 cathodes is due to a true hysteresis as opposed to a kinetically hindered relaxation observed with the highly crystalline films. This behavior was confirmed by observing classic scanning curves on charge and discharge at intermediate stages of insertion and extraction of Li+ ions. Extended cycling of lithium cells with these cathodes at 25 and 100°C leads to grain growth and evolution of the charge–discharge profiles toward those characteristic of well crystallized films.
Considerations necessary for the design of a total control system for the improvement of the reliability of the generation-transmission system are discussed. The control system is made of automatic functions, human participation, and an information system. In the first part of the paper the framework of the design is established and the basic overall strategy for maintaining reliability is described. The second part describes the thinking and the work being done at Cleveland Electric Illuminating (CEI) Company for the practical implementation of the design concepts on the CEI system. The primary purpose is to contribute an organized approach to the solution of the problem of control for improved reliability. The aim is to be as comprehensive as possible so that all of the requirements within the framework of the approach are covered even though not all of the solutions have as yet been formulated. In the process of describing how the requirements may be met, major difficulties are pointed out as an aid in identifying areas where research in depth would be of value. Mathematical formulations are not used, but a discussion of the mathematical treatment of some aspects of the control system will be written in a separate paper. Copyright © 1967 by The Institute of Electrical and Electronics Engineers, Inc.
In this article, we present the security, agility, and robustness/survivability of a large-scale power delivery infrastructure that faces new threats and unanticipated conditions. By way of background, we present a brief overview of the past work on the challenges faced in online parameter estimation and real-time adaptive control of a damaged F-15 aircraft. This work, in part, provided the inspiration and laid the foundation in the 1990s for the flight testing of a fast parameter estimation/modeling and reconfigurable aircraft control system that allowed the F-15 to become self-healing in the face of damaged equipment.
The effect of power outage on the electricity infrastructure of the North America including 15,000 generators in 10,000 power plants and a large number of transmission lines and distribution networks is discussed. The blackouts resulted in huge price spikes and the issue of grid reliability at the national level was raised. The power outages also resulted in shortage of power engineers, energy-research organizations and university power-engineering programs. To cope up with the situation, it is suggested that a coordinating council should be formed to ensure that the necessary research on electric power systems is carried out and implementation is done expeditiously.
Virtually every crucial economic and social function depends on
the secure, reliable operation of energy, telecommunications,
transportation, financial, and other infrastructures. However, with
increased benefit has come increased risk. As they have grown more
complex to handle a variety of demands, these infrastructures have
become more interdependent. This strong interdependence means that an
action in one part of one infrastructure network can rapidly create
global effects by cascading throughout the same network and even into
other networks. Moreover, interdependence is only one of several
characteristics that challenge the control and reliable operation of
these networks. These characteristics, in turn, present unique
challenges in modeling, prediction, simulation, cause-and-effect
relationships, analysis, optimization, and control. Deregulation and
economic factors and policies and human performance also affect these
networks. The Complex Interactive Networks/Systems Initiative (GIN/SI)
is a joint program by the Electric Power Research Institute (EPRI) and
the US Department of Defense (DOD) that is addressing many of these
issues. The goal of the 5-year, $30 million effort, which is part of the
Government-Industry Collaborative University Research (GICUR) program,
is to develop new tools and techniques that will enable large national
infrastructures to self-heal in response to threats, material failures,
and other destabilizers. Of particular interest is how to model
enterprises at the appropriate level of complexity in critical
infrastructure systems
The Internet, computer networks, and the digital economy have increased the demand for reliable and disturbance-free electricity. Banking and finance depend on the robustness of electric power, cable, and wireless telecommunications. Links between the power grid and telecommunications and between electrical power and oil, water, and gas pipelines continue to be a linchpin of energy supply networks. This strong interdependence means that an action in one part of one infrastructure network can rapidly create global effects by cascading throughout the same network or into other networks. The author explains how the Complex Interactive Network/Systems Initiative (CIN/SI), a joint industry-government initiative, is developing a mathematical basis and practical tools for improving the security, performance, reliability, and robustness of energy, financial, telecommunications, and transportation networks. One challenge is to develop appropriate models for this degree of complexity and create tools that let components adaptively reconfigure the network as needed.
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