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As countries pursue decarbonization goals, the rapid expansion of transmission capacity for renewable energy (RE) integration poses a significant challenge due to hurdles such as permitting and cost allocation. However, we find that large-scale reconductoring with advanced composite-core conductors can cost-effectively double transmission capacity within existing right-of-way, with limited additional permitting. This strategy unlocks a high availability of increasingly economically viable RE resources in close proximity to the existing network. We implement reconductoring in a model of the US power system, showing that reconductoring can help meet over 80% of the new interzonal transmission needed to reach over 90% clean electricity by 2035 given restrictions on greenfield transmission build-out. With $180 billion in system cost savings by 2050, reconductoring presents a cost-effective and time-efficient, yet underutilized, opportunity to accelerate global transmission expansion.

The intermittency and uncertainty of renewable energy generations, such as wind power, present great challenges to the secure and stable operation of power grids. To accommodate a high penetration of renewable energy, it is vital to coordinate multiple flexible resources to deal with the intermittency and uncertainty of renewable energy and ensure the network security. In this paper, we propose a two-stage stochastic flexible dispatching method for power systems with large-scale wind power, which considers the coordination of unit commitment, optimal transmission switching, and optimal control of phase-shifting transformers within a unified framework. On the grid side, flexibility is improved through phase-shifting transformer regulation and optimal transmission switching. On the source side, flexibility is fully exploited through two-stage stochastic unit commitment. In the day-ahead scheduling stage, transmission topology optimization and unit commitment schemes are determined based on the predicted load demand and renewable energy output. In the real-time dispatching stage, phase-shifting transformers and unit outputs are adjusted and dispatched based on the possible scenarios of load demand and renewable energy output. The effectiveness of the proposed method is verified through case studies on the IEEE RTS-24 system and IEEE 118-bus system. [ABSTRACT FROM AUTHOR]

The increased share of new energy sources in Northeast China's power mix has strained grid stability. Energy storage technologies are essential for maintaining grid stability by addressing peak shaving and frequency regulation challenges. However, a clear quantitative assessment of the region's energy storage needs is lacking, leading to weak grid stability and limited growth potential. This paper analyzes power supply data from Northeast China and models the stochastic characteristics of new energy generation. A joint optimization model for energy storage and thermal power is developed to optimize power allocation for peak shaving and frequency regulation at minimal cost. The empirical distribution method quantifies the relationship between storage power, capacity, and confidence levels, providing insights into the region's future energy storage demands. The study finds that under 10 typical scenarios, the demand for peaking power at a 15 min scale is ≤500 MW, and the demand for frequency regulation at a 1 min scale is ≤1000 MW. At the 90% confidence level, the required capacity for new energy storage for peak shaving and frequency regulation is 424.13 MWh and 197.65 MWh, respectively. The required power for peak shaving and frequency regulation is 247.88 MW and 527.33 MW, respectively. The durations of peak shaving and frequency regulation are 1.71 h and 0.38 h. It also forecasts the energy storage capacity in the northeast region from 2025 to 2030 under the 5% annual incremental new energy penetration scenario. These findings provide theoretical support for energy storage policies in Northeast China during the 14th Five-Year Plan and practical guidance for accelerating energy storage industrialization. [ABSTRACT FROM AUTHOR]

This review paper examines the current landscape of electricity market modelling, specifically focusing on stochastic approaches, transitioning from Mean Field Games (MFGs) to Neural Network (NN) modelling. The central objective is to scrutinize and synthesize evolving modelling strategies within power systems, facilitating technological advancements in the contemporary electricity market. This paper emphasizes the assessment of model efficacy, particularly in the context of MFG and NN applications. Our findings shed light on the diversity of models, offering practical insights into their strengths and limitations, thereby providing a valuable resource for researchers, policy makers, and industry practitioners. The review guides navigating and leveraging the latest stochastic modelling techniques for enhanced decision making and improved market operations. [ABSTRACT FROM AUTHOR]

To address global warming challenges, industry, transportation, residential, and other sectors must adapt to reduce the greenhouse effect. One promising solution is the use of renewable energy and energy-saving mechanisms. This paper analyzes several renewable energy sources and storage systems, taking into consideration the possibility of integrating them with smart homes. The integration process requires the development of smart home energy management systems coupled with renewable energy and energy storage elements. Furthermore, a real-life solar energy power plant composed of programmable components was designed and mounted on the roof of a single-family residential building. Based on a long-term analysis of its operation, the main advantages and disadvantages of the proposed implementation solution are highlighted, exemplifying the concepts presented in the paper. Being composed of programmable components, which allow the implementation of custom algorithms and monitoring applications to optimize its operation, the system will be used as a prototyping platform in future research. The evaluation of the developed system over a period of one year showed that, even when using a basic implementation such as the one in this paper, significant savings regarding a household's energy consumption can be achieved (36% of the energy bought from the supplier, meaning EUR 545 from a total of EUR 1497). Finally, based on the analysis of the developed prototype system, the main technical challenges that must be addressed in the future to efficiently manage renewable energy storage and use in today's smart homes were identified. [ABSTRACT FROM AUTHOR]

This paper presents the application of quantitative and qualitative methods to assess reliability and security in urban electrical substations. The method is a visual technique based on a conceptual analysis of the different substation configurations. We also performed a sensitivity analysis considering the effects of connecting and disconnecting various elements of a power system. The procedure considers evaluating the loadability levels of transformers, buses, and lines, as well as the current state of the individual elements and the number of connected elements. A new index was proposed for urban electrical substations, evaluating the non-attended demand risk. The technique was tested in a power system case study with a meshed subtransmission network and distribution circuits to supply power to the loads. The results showed that the proposed method is a useful qualitative method to obtain a quantitative description of the system during operation in critical cases and the non-attended demand risk. In addition, 30% of the electrical substations showed low reliability indicators for critical cases such as failures in transformers that connect different internal configurations. These findings could be of interest for utilities and operators, as this document provides a simplified and graphic method that can integrate components such as configurations, non-attended demand risk, and loadability indicators as key parameters to identify critical points that affect the reliability and security of power systems. The case study showed that the electrical substations with the highest non-attention demand risk, around 50%, were those with single- and double-bar configurations in their respective switchyards. On the other hand, the substations with the lowest risk of unmet demand, equal to or less than 20%, were electrical substations with a double-bar + bypass switch configuration, a double-bar and ring configuration in the 110 kV switchyard, and a single-bar configuration in the 13.8 kV switchyard. This study showed that those substations that had couplings had a higher probability of withstanding contingencies. [ABSTRACT FROM AUTHOR]

The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction. [ABSTRACT FROM AUTHOR]

In this paper, a generalized decentralized controller design is presented for robust stabilization and achieving certain L $ _2 $ 2 gain of a class of nonlinear interconnected systems via dynamic output feedback control within the LMI framework. This large-scale system is composed of linear subsystems coupled by nonlinear time-varying interconnections satisfying quadratic constraints. The proposed controller design uses the available neighbouring information to design a decentralized controller that operates at each subsystem. An LMI approach is sufficiently proposed as a feasibility problem of a BMI with respect to controller gains via dynamic output feedback structure. The application of power systems shows that the proposed controller can effectively stabilize the nonlinear interconnected systems and achieve desired performance, even in the presence of disturbances and uncertainties. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and machine learning (ML) can assist in the effective development of the power system by improving reliability and resilience. The rapid advancement of AI and ML is fundamentally transforming energy management systems (EMSs) across diverse industries, including areas such as prediction, fault detection, electricity markets, buildings, and electric vehicles (EVs). Consequently, to form a complete resource for cognitive energy management techniques, this review paper integrates findings from more than 200 scientific papers (45 reviews and more than 155 research studies) addressing the utilization of AI and ML in EMSs and its influence on the energy sector. The paper additionally investigates the essential features of smart grids, big data, and their integration with EMS, emphasizing their capacity to improve efficiency and reliability. Despite these advances, there are still additional challenges that remain, such as concerns regarding the privacy of data, challenges with integrating different systems, and issues related to scalability. The paper finishes by analyzing the problems and providing future perspectives on the ongoing development and use of AI in EMS. [ABSTRACT FROM AUTHOR]

Historical building reuse is aimed at preservation, where buildings are recovered for new uses connected to cultural activities. This paper presents the analysis of the impact of thermo-fluid dynamics due to a 500 kW electrical power transformer installed inside a historical building. The analysis is performed using computational fluid dynamics simulations validated through measurement campaigns carried out during the summer period. High temperatures and wide humidity variations can damage building plasters and cause malfunctions in power equipment. To avoid these situations, two different installation layouts were studied. One consists of the power transformer directly installed in the environment and cooled by an inlet fan, and the other consists of the power transformer being insulated from the external environment by an enclosure connected to a forced ventilation system. The second layout showed better results both inside and outside the transformer enclosure. The maximum indoor condition was about 4.3 °C, with a −7.2% RH and an airflow rate of 1100 m3/h, and the maximum outdoor air condition was 3.3 °C, with a −1.39% RH and a flow rate of 2200 m3/h. However, the temperatures and humidity inside the building and outside the transformer enclosure were almost the same. [ABSTRACT FROM AUTHOR]

The progressive shift towards renewable energy sources in electric power production requires a revolution in transmission system control. In the latter, progressive reduction of high-power conventional power plants in favor of small distributed ones based on renewable energies (which present different dynamic performance), as well as the integration of new elements (regulating HVDC links, STATCOMs, etc.), makes the present transmission system structure and behavior significantly different compared to those taken as bases for the design of voltage control systems. Moreover, limitations due to technology obsolescence of the existing architecture and the greatly increased capabilities of modern Phase Measurement Units (PMU) contribute to requiring a revision of the voltage control architecture, which should also provide robustness to parameter variation and be adaptive in nature. To this end, in this work the applicability of different multiple input multiple output control methods to the transmission system secondary voltage regulation is investigated. Specifically, the Decoupling control method and the Linear Quadratic Regulator with Integral action control method are applied to the secondary voltage regulation of the Italian transmission system. Three different control system architectures, resulting from the application of these methods to the peculiarities of Italian transmission system voltage control architecture, are proposed in the paper. Their dynamic performance is tested on the model of a portion of the Italian transmission network in different scenarios, and compared with current Italian voltage control. The results suggest one of the proposed architectures as a promising solution to address the power system’s evolution.

This paper presents the application of quantitative and qualitative methods to assess reliability and security in urban electrical substations. The method is a visual technique based on a conceptual analysis of the different substation configurations. We also performed a sensitivity analysis considering the effects of connecting and disconnecting various elements of a power system. The procedure considers evaluating the loadability levels of transformers, buses, and lines, as well as the current state of the individual elements and the number of connected elements. A new index was proposed for urban electrical substations, evaluating the non-attended demand risk. The technique was tested in a power system case study with a meshed subtransmission network and distribution circuits to supply power to the loads. The results showed that the proposed method is a useful qualitative method to obtain a quantitative description of the system during operation in critical cases and the non-attended demand risk. In addition, 30% of the electrical substations showed low reliability indicators for critical cases such as failures in transformers that connect different internal configurations. These findings could be of interest for utilities and operators, as this document provides a simplified and graphic method that can integrate components such as configurations, non-attended demand risk, and loadability indicators as key parameters to identify critical points that affect the reliability and security of power systems. The case study showed that the electrical substations with the highest non-attention demand risk, around 50%, were those with single- and double-bar configurations in their respective switchyards. On the other hand, the substations with the lowest risk of unmet demand, equal to or less than 20%, were electrical substations with a double-bar + bypass switch configuration, a double-bar and ring configuration in the 110 kV switchyard, and a single-bar configuration in the 13.8 kV switchyard. This study showed that those substations that had couplings had a higher probability of withstanding contingencies.

Synchrophasor data contain a trove of information on the power system and its dynamics. These measurements have a high potential to unlock our ability to cope with changing system conditions and challenges posed by distributed and intermittent energy sources. While Phasor Measurement Units (PMUs) have seen a large deployment in the grid, their applications are limited by the software platforms that are deployed in control centers to monitor the grid. In this paper, we present an open source toolkit that enables fast prototyping of PMU applications. The toolkit is akin to a software development kit (SDK) for synchrophasor applications, providing a number of functionalities that enable high-level PMU application development within the LabVIEW environment. This Smart-grid Synchrophasor SDK (S3DK) proposes a paradigm based on the concept of distributed applications, which allows development and deployment to be independent of the existing software stack deployed in control centers and to leverage PMU data at any level of a synchrophasor system hierarchy. This paper serves to introduce the S3DK, which is released as open source software to facilitate broader and fast prototyping of synchrophasor applications.

Abstract The integration of super large‐scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two‐stage collaborative planning of points of common coupling siting and transmission network expansion for super large‐scale offshore wind clusters, based on a multi‐voltage‐level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two‐stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30‐bus system and a 65‐bus system.

The load margin is an important index applied in power systems to inform how much the system load can be increased without causing system instability. The increasing operational uncertainties and evolution of power systems require more accurate tools at the operation center to inform an adequate system load margin. This paper proposes an optimization model to determine the parameters of a Physics-Informed Neural Network (PINN) that will be responsible for predicting the load margin of power systems. The proposed optimization model will also determine an optimal location of Phasor Measurement Units (PMUs) at system buses whose measurements will be inputs to the PINN. Physical knowledge of the power system is inserted in the PINN training stage to improve its generalization capacity. The IEEE 68-bus system and the Brazilian interconnected power system were chosen as the test systems to perform the case studies and evaluations. Three different metaheuristics called the Hiking Optimization Algorithm, Artificial Protozoa Optimizer, and Particle Swarm Optimization were applied and evaluated in the test system. The results achieved demonstrate the benefits of inserting physical knowledge in the PINN training and the optimal selection of PMUs at system buses for load margin prediction.

Abstract Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave’s Advantage™ quantum annealer, Fujitsu’s digital annealer V3, D-Wave’s quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms.

This paper presents RMSPowerSims.jl, an open-source Julia package for the time-domain simulation of power systems. The package is designed to be used in conjunction with PowerModels.jl, a widely used Julia package for power system optimization. RMSPowerSims.jl provides a framework for the simulation of power systems in the time domain, allowing for the study of transient stability, frequency stability, and other dynamic phenomena. The package is designed to be intuitive and flexible, allowing users to easily define custom models for network components and disturbances, while also providing a range of pre-constructed models for common power system components. RMSPowerSims.jl simplifies the process of performing RMS simulations on power system models developed using the PowerModels.jl ecosystem, and provides an easy-to-use modeling that reduces the barrier to entry for new users wishing to perform RMS simulations. The accuracy of the package is verified against DIgSILENT PowerFactory for short-circuit and load-increase disturbances, using the New England 39-bus system. The active power generation delivered by several generators in the network, and the voltage magnitudes of selected busbars are analyzed and noted to be in close agreement with those obtained using PowerFactory. The computational performance of the package is compared to that of PowerFactory and is found to be comparable for load-step simulations; however, PowerFactory is found to be considerably faster for short-circuit simulations. As computational performance is not a priority at this stage of development, this is expected, and speed optimization is planned for future work. RMSPowerSims.jl is available under an open-source license and can be downloaded from GitHub. [ABSTRACT FROM AUTHOR]

The integration of super large‐scale offshore wind clusters into the onshore power grid brings challenges for the flexibility of the transmission network. Therefore, this paper proposes a two‐stage collaborative planning of points of common coupling siting and transmission network expansion for super large‐scale offshore wind clusters, based on a multi‐voltage‐level stratified integration mode which enables offshore wind power to transmit to larger regions for accommodation. At first, the transmission network flexibility indexes are established. Then, a two‐stage collaborative planning model for points of common couplings siting and transmission network expansion is proposed, which comprises a collaborative planning model and an optimal power flow operation model. The proposed model reduces wind curtailment and load shedding by optimizing the annual total cost and flexibility transmission capability of the network. Finally, the effectiveness of the proposed method is verified using a modified IEEE 30‐bus system and a 65‐bus system. [ABSTRACT FROM AUTHOR]

The optimal power flow (OPF) problem, characterized by its inherent complexity and strict constraints, has traditionally been approached using analytical techniques. OPF enhances power system sustainability by minimizing operational costs, reducing emissions, and facilitating the integration of renewable energy sources through optimized resource allocation and environmentally aligned constraints. However, the evolving nature of power grids, including the integration of distributed generation (DG), increasing uncertainties, changes in topology, and load variability, demands more frequent OPF solutions from grid operators. While conventional methods remain effective, their efficiency and accuracy degrade as computational demands increase. To address these limitations, there is growing interest in the use of data-driven surrogate models. This paper presents a critical review of such models, discussing their limitations and the solutions proposed in the literature. It introduces both Analytical Surrogate Models (ASMs) and learned surrogate models (LSMs) for OPF, providing a thorough analysis of how they can be applied to solve both DC and AC OPF problems. The review also evaluates the development of LSMs for OPF, from initial implementations addressing specific aspects of the problem to more advanced approaches capable of handling topology changes and contingencies. End-to-end and hybrid LSMs are compared based on their computational efficiency, generalization capabilities, and accuracy, and detailed insights are provided. This study includes an empirical comparison of two ASMs and LSMs applied to the IEEE standard six-bus system, demonstrating the key distinctions between these models for small-scale grids and discussing the scalability of LSMs for more complex systems. This comprehensive review aims to serve as a critical resource for OPF researchers and academics, facilitating progress in energy efficiency and providing guidance on the future direction of OPF solution methodologies. [ABSTRACT FROM AUTHOR]

Renewable energy sources–based power systems are increasing rapidly every year with higher chances of destabilization and low power quality due to harmonics, subharmonics, and interharmonics. The elimination of the root causes of these harmonics is difficult because harmonics are generated at different levels due to various electronic components. However, the accurate estimation of power signal harmonic can be done and eliminated with a counter signal. Therefore, in this paper, the evolutionary mating heuristic is implied to accurately estimate the parameters of power signal harmonics. The proposed heuristic was tested under multiple scenarios to accurately estimate the phase and amplitude parameters of power signal harmonics at various noise levels. The tuning of the evolutionary mating heuristic is carried out for encountering predator probability and crossover probability Epp <0.8, Cp <0.9. The simulation analysis depicts that the best fitness achieved by the proposed heuristic for noise levels (no) 104, 78, 52, 26, and 13 dB are 2.8169e−11, 9.6969e−09, 3.7703e−06, 1.3501e−03, and 2.8624e−02, respectively, for iteration I = 1249. The comparative analysis shows that the proposed heuristic performance is significantly better than other heuristics. [ABSTRACT FROM AUTHOR]

Due to its vulnerability to a variety of cyber attacks, research on cyber security for power systems has become especially crucial. In order to maintain the safe and stable operation of power systems, it is worthwhile to gain insight into the complex characteristics and behaviors of cyber attacks from the attacker's perspective. The consensus-based distributed state estimation problem is investigated for power systems subject to collaborative attacks. In order to describe such attack behaviors, the denial of service (DoS) attack model for hybrid remote terminal unit (RTU) and phasor measurement unit (PMU) measurements, and the false data injection (FDI) attack model for neighboring estimation information, are constructed. By integrating these two types of attack models, a different consensus-based distributed estimator is designed to accurately estimate the state of the power system under collaborative attacks. Then, through Lyapunov stability analysis theory, a sufficient condition is provided to ensure that the proposed distributed estimator is stable, and a suitable consensus gain matrix is devised. Finally, to confirm the viability and efficacy of the suggested algorithm, a simulation experiment on an IEEE benchmark 14-bus power system is carried out. [ABSTRACT FROM AUTHOR]

Electrical power systems are the lifeblood of modern civilization, providing the essential energy infrastructure that powers our homes, industries, and technologies. As our world increasingly relies on electricity, and modern power systems incorporate renewable energy sources, the challenges have become more complex, necessitating advanced forecasting and optimization to ensure effective operation and sustainability. This review paper provides a comprehensive overview of electrical power systems and delves into the crucial roles that forecasting and optimization play in ensuring future sustainability. The paper examines various forecasting methodologies from traditional statistical approaches to advanced machine learning techniques, and it explores the challenges and importance of renewable energy forecasting. Additionally, the paper offers an in-depth look at various optimization problems in power systems including economic dispatch, unit commitment, optimal power flow, and network reconfiguration. Classical optimization methods and newer approaches such as meta-heuristic algorithms and artificial intelligence-based techniques are discussed. Furthermore, the review paper examines the integration of forecasting and optimization, demonstrating how accurate forecasts can enhance the effectiveness of optimization algorithms. This review serves as a reference for electrical engineers developing sophisticated forecasting and optimization techniques, leading to changing consumer behaviors, addressing environmental concerns, and ensuring a reliable, efficient, and sustainable energy future. [ABSTRACT FROM AUTHOR]

When Hurricane Maria struck the island of Puerto Rico in September, 2017, it devastated the island's critical infrastructures, including the well-documented total loss of electric power systems. The strong interdependencies or associations among critical infrastructures in modern society meant that the failure of power systems propagated to and exacerbated the failure of other infrastructure systems. Moreover, these associations impact systems recovery just as they impact system failure. This study is a follow-up of previous research by the first author on Hurricane Maria. In this research authors extracted and quantified the recovery associations of Hurricane Fiona (September 2022) made landfall in Puerto Rico and inflicted considerable damage to its critical infrastructures. The recovery efforts following the disaster provided an opportunity to follow up on the previous research and examine the recovery associations. Significant money and efforts have gone into upgrading the infrastructures of Puerto Rico to make them more resilient to natural disasters such as hurricanes or tropical storms following Hurricane Maria. This paper explores the new recovery resiliency characteristics of Puerto Rico's critical infrastructure systems (CISs) that the recovery efforts following Hurricane Fiona illustrate. This research shows that the power systems and other CISs of Puerto Rico are much more resilient when compared to their state of resiliency in 2017. Moreover, examining the recovery interdependencies reveals that some of the CISs are strongly dependent on power systems recovery. Outcomes of this study suggest that CIS relationships based on recovery data from Puerto Rico, are transferable to similar disaster-prone areas such as the Caribbean islands or other island nations, as they have similar characteristics and challenges. [ABSTRACT FROM AUTHOR]

Power generators are one of the critical assets of power grids. The early detection of faults in power generators is essential to prevent cutoffs of the electrical supply in the power grid. This work presents a comparative analysis of machine learning (ML) models for the generator fault diagnosis. The objective is to show the ability of simple and ensemble ML models to diagnose faults using as attributes partial discharges and dissipation factor data. For this purpose, a generator fault database was built, gathering information from operational data curated by power generator experts. The hyper-parameters of the ML models were selected using a grid search (GS) and cross-validation (CV) optimization. ML models were evaluated with class imbalance and multi-classification metrics, a correspondence analysis, and model performance by class (fault type). Furthermore, the selected ML model was validated by experts through a diagnosis system prototype. The results show that the gradient boosting model presented the best performance according to the performance metrics among single and ensemble ML models. Likewise, the model showed a good capacity to detect type 3 and 4 faults, which are the most catastrophic failures for the generator and must be detected in a timely manner for prompt correction. This work gives an insight into the need and effort required to implement an online diagnostic system that provides information about the power generator health index to help engineers reduce the time taken to find and repair incipient faults and avoid loss of power generation and catastrophic failures of power generators. [ABSTRACT FROM AUTHOR]

This examination presents an original Broadband Oscillation Concealment Procedure in Power Systems utilizing a Genetic Algorithm (GA). The philosophy's suitability is deliberately assessed through comprehensive examinations, including affiliation investigation, strength appraisal, and near investigations with elective optimization algorithms. Results show that the GA-based approach displays predominant affiliation, appearing at a health worth of 0.05 after 100 ages, beating Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Simulated Annealing (SA). Strength examination features the versatility of the proposed procedure, with a standard wellbeing worth of 0.08 ± 0.02 under changing power framework conditions. Similar investigation against related work reveals the procedure's advantage, showing its genuine breaking point with regards to helpful broadband oscillation concealment. The GA-based philosophy changes speedy mixing and computational capacity, with an ordinary execution season of 120 seconds. The examination contributes important pieces of information into power framework strength, offering a good answer for mitigating broadband oscillations in various working situations. [ABSTRACT FROM AUTHOR]

This study delves into the exploration of broadband measurement techniques for power systems, utilizing wavelet transform as a foundational tool for signal analysis. The research rigorously evaluates the efficacy of several machine learning algorithms, namely Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest, in interpreting and analyzing broadband signals within power systems. Through a detailed analytical process, the performance of each algorithm is meticulously assessed based on several critical metrics: accuracy, precision, recall, and F1-score. The research investigates broadband measurement methods for power systems using wavelet transform and evaluates the performance of Support Vector Machines (SVM), Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Random Forest. Results show SVM achieving an accuracy of 85%, precision of 86%, recall of 82%, and F1-score of 84%. ANN yields 82% accuracy, 84% precision, 78% recall, and 81% F1 score. KNN demonstrates 87% accuracy, 88% precision, 84% recall, and 86% F1 score. DT achieves 79% accuracy, 80% precision, 75% recall, and 77% F1 score. Overall, the study provides insights into machine learning algorithms' effectiveness in broadband power system measurement. [ABSTRACT FROM AUTHOR]

Selecting the most preferred software for teaching power systems engineering at the undergraduate level is a complex problem in developing countries, and it requires making an informed decision by compromising on various criteria. This study proposes a multi-criteria framework to determine the most preferred software solution for instructing undergraduate power system modules using the Fuzzy-ARAS (additive ratio assessment) method and expert opinions. Twelve evaluation criteria were used to evaluate eight widely used software packages. A questionnaire was designed to capture views from professionals in academia and industry on the criteria weights and ranking of software options. Linguistic terms were used to represent the experts' judgment, and weights were assigned to each criterion. The Fuzzy-ARAS multi-criteria decision approach was applied to obtain ratings for each software alternative. Based on the result, MATLAB emerged as the most preferred software for instructing power systems analysis, whereas MATPOWER (V 8.0) was rated as the least preferred choice. In addition, the Fuzzy-TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) approach was used, producing a separate ranking; the most preferred software was MATPOWER, while the least preferred software was NEPLAN (V 360 10.5.1). A new coefficient that combines the findings of the two approaches was suggested to reconcile the ranks. The combined ranking aligns with the result of the Fuzzy-TOPSIS method by returning MATLAB as the most preferred, while the least preferred software was NEPLAN. This study significantly contributes to the choice of software for undergraduate power systems analysis instruction by providing direction to educators and institutions looking for software solutions to improve undergraduate power systems analysis education. [ABSTRACT FROM AUTHOR]

Power flow (PF) analysis is a foundational computational method to study the flow of power in an electrical network. This analysis involves solving a set of non-linear and non-convex differential-algebraic equations. State-of-the-art solvers for PF analysis, therefore, face challenges with scalability and convergence, specifically for large-scale and/or ill-conditioned cases characterized by high penetration of renewable energy sources, among others. The adiabatic quantum computing paradigm has been proven to efficiently find solutions for combinatorial problems in the noisy intermediate-scale quantum (NISQ) era, and it can potentially address the limitations posed by state-of-the-art PF solvers. For the first time, we propose a novel adiabatic quantum computing approach for efficient PF analysis. Our key contributions are (i) a combinatorial PF algorithm and a modified version that aligns with the principles of PF analysis, termed the adiabatic quantum PF algorithm (AQPF), both of which use Quadratic Unconstrained Binary Optimization (QUBO) and Ising model formulations; (ii) a scalability study of the AQPF algorithm; and (iii) an extension of the AQPF algorithm to handle larger problem sizes using a partitioned approach. Numerical experiments are conducted using different test system sizes on D-Wave's Advantage™ quantum annealer, Fujitsu's digital annealer V3, D-Wave's quantum-classical hybrid annealer, and two simulated annealers running on classical computer hardware. The reported results demonstrate the effectiveness and high accuracy of the proposed AQPF algorithm and its potential to speed up the PF analysis process while handling ill-conditioned cases using quantum and quantum-inspired algorithms. [ABSTRACT FROM AUTHOR]

This paper designs a multi-agent-based intermediate observer for malfunction and state estimation of a multi-area interconnected power system. By building a simplified linear model of the power systems, a distributed malfunction observer based on intermediate variables is designed to achieve the estimation of actuator and sensor malfunction signals while considering the system's own and neighboring output estimation errors. The method overcomes the dependence of the conventional observer on the matching condition of the observer and calculates the gain of the observer by solving the linear matrix inequality. Eventually, through an example of simulation, the effectiveness of the design is verified. [ABSTRACT FROM AUTHOR]

The increasing use of renewable energy sources (RESs) in distribution networks, combined with an ever-increasing load, makes balancing between generation and load difficult. Demand response programs (DRPs) and energy storage come together in a significant way for microgrid operators to avoid purchasing power at a higher cost from the upstream network and to move toward a self-reliant system. This work considers Electric vehicles (EVs), being the most common type of flexible load, can be a demand response (DR) resource for demand-side management (DSM). The work proposes a technique that decides SoC over the number of EV plugged-in chargers in a charging station with time. To cope with the large number of chargers, the work conceptualizes cluster control in dealing with a large population of EV chargers. To achieve the goal, the control concept is implemented by categorizing EV chargers into different bins or groups based on the SoC of plugged-in EVs. The proposed model forecasts EV state transitions using the Markov state transition approach. The probabilistic approach of the state transition matrix is determined to understand the status of the battery SoC of EVs. Hence the significant contribution of the proposed technique does not necessitate sending the SoC values of plugged-in EVs to the control room at sampled intervals. The work then proposed two control techniques based on identifying and prioritizing the chargers of various charging stations to redistribute the EV loads within the short span of the load curve. Two control concepts, on/off control and charging power control, have been developed and applied to the final state transition matrix as a part of the DRP. The results related to the load reduction show the justification of the concept of controlling the power consumption of chargers. [ABSTRACT FROM AUTHOR]

Due to the increasing demand for electricity, competitive electricity markets, and economic concerns, power systems are operating near their stability margins. As a result, power systems become more vulnerable following disturbances, particularly from a dynamic point of view. To maintain the stability of power systems, operators need to continuously monitor and analyze the grid's state. Since modern power systems are large-scale, non-linear, complex, and interconnected, it is quite challenging and computationally demanding to monitor, control, and analyze them in real time. State Estimation (SE) is one of the most effective tools available to assist operators in monitoring power systems. To enhance measurement redundancy in power systems, employing multiple measurement sources is essential for optimal monitoring. In this regard, this paper, following a brief explanation of the SE concept and its different categories, highlights the significance of Hybrid State Estimation (HSE) techniques, which combine the most used data resources in power systems, traditional Supervisory Control and Data Acquisition (SCADA) system measurements and Phasor Measurement Units (PMUs) measurements. Additionally, recommendations for future research are provided. [ABSTRACT FROM AUTHOR]

This paper presents an extra-high voltage synthetic test system that consists of 500 kV and 765 kV voltage levels, specifically designed for transmission expansion planning (TEP) studies. The test network includes long transmission lines whose series impedance and shunt admittance are calculated using the equivalent π circuit model, accurately reflecting the distributed nature of the line parameters. The proposed test system offers technically feasible steady-state operation under normal and all single contingency conditions. By incorporating accurate modeling for long transmission lines and EHV voltage levels, the test system provides a realistic platform for validating models and theories prior to their application in actual power systems. It supports testing new algorithms, control strategies, and grid management techniques, aids in transmission expansion planning and investment decisions, and facilitates comprehensive grid evaluations. Moreover, a TEP study is conducted on this test system and various scenarios are evaluated and compared economically. [ABSTRACT FROM AUTHOR]

Accurate load forecasting is essential for the efficient and reliable operation of power systems. Traditional models primarily utilize unidirectional data reading, capturing dependencies from past to future. This paper proposes a novel approach that enhances load forecasting accuracy by fine tuning an attention-based model with a bidirectional reading of time-series data. By incorporating both forward and backward temporal dependencies, the model gains a more comprehensive understanding of consumption patterns, leading to improved performance. We present a mathematical framework supporting this approach, demonstrating its potential to reduce forecasting errors and improve robustness. Experimental results on real-world load datasets indicate that our bidirectional model outperforms state-of-the-art conventional unidirectional models, providing a more reliable tool for short and medium-term load forecasting. This research highlights the importance of bidirectional context in time-series forecasting and its practical implications for grid stability, economic efficiency, and resource planning. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable and sustainable electricity has driven the development of microgrids (MGs) as a solution for decentralized energy distribution. This study reviews advancements in MG planning and optimization for renewable energy integration, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses methodology to analyze peer-reviewed articles from 2013 to 2024. The key findings highlight the integration of emerging technologies, like artificial intelligence, the Internet of Things, and advanced energy storage systems, which enhance MG efficiency, reliability, and resilience. Advanced modeling and simulation techniques, such as stochastic optimization and genetic algorithms, are crucial for managing renewable energy variability. Lithium-ion and redox flow battery innovations improve energy density, safety, and recyclability. Real-time simulations, hardware-in-the-loop testing, and dynamic power electronic converters boost operational efficiency and stability. AI and machine learning optimize real-time MG operations, enhancing predictive analysis and fault tolerance. Despite these advancements, challenges remain, including integrating new technologies, improving simulation accuracy, enhancing energy storage sustainability, ensuring system resilience, and conducting comprehensive economic assessments. Further research and innovation are needed to realize MGs' potential in global energy sustainability fully. [ABSTRACT FROM AUTHOR]

In today's era of rapid technological advancement, the emergence of drone technology and intelligent power systems has brought tremendous convenience to society. However, the challenges associated with drone image recognition and intelligent grid device fault detection are becoming increasingly significant. In practical applications, the rapid and accurate identification of drone images and the timely detection of faults in intelligent grid devices are crucial for ensuring aviation safety and the stable operation of power systems. This article aims to integrate Transformer models, transfer learning, and generative adversarial networks to enhance the accuracy and efficiency of drone image recognition and intelligent grid device fault detection.In the methodology section, we first employ the Transformer model, a deep learning model based on self-attention mechanisms that has demonstrated excellent performance in handling image sequences, capturing complex spatial relationships in images. To address limited data issues, we introduce transfer learning, accelerating the learning process in the target domain by training the model on a source domain. To further enhance the model's robustness and generalization capability, we incorporate generative adversarial networks to generate more representative training samples.In the experimental section, we validate our model using a large dataset of real drone images and intelligent grid device fault data. Our model shows significant improvements in metrics such as specificity, accuracy, recall, and F1-score. Specifically, in the experimental data, we observe a notable advantage of our model over traditional methods in both drone image recognition and intelligent grid device fault detection. Particularly in the detection of intelligent grid device faults, our model successfully captures subtle fault features, achieving an accuracy of over 90%, an improvement of more than 17% compared to traditional methods, and an outstanding F1-score of around 91%.In summary, this article achieves a significant improvement in the fields of drone image recognition and intelligent grid device fault detection by cleverly integrating Transformer models, transfer learning, and generative adversarial networks. Our approach not only holds broad theoretical application prospects but also receives robust support from experimental data, providing strong support for research and applications in related fields. [ABSTRACT FROM AUTHOR]

Maintenance planning plays a key role in power system operations under uncertainty as it helps providers and operators ensure a reliable and secure power grid. This paper studies a short-term condition-based integrated maintenance planning with operations scheduling problem while considering the possible unexpected failure of generators as well as transmission lines. We formulate this problem as a two-stage stochastic mixed-integer program with failure scenarios sampled from the sensor-driven remaining lifetime distributions of the individual system elements whereas a joint chance constraint consisting of Poisson Binomial random variables is introduced to account for failure risks. Because of its intractability, we develop a cutting-plane method to obtain an exact reformulation of the joint chance constraint by proposing a separation subroutine and deriving stronger cuts as part of this procedure. We also derive a second-order cone programming-based safe approximation of this constraint to solve large-scale instances. Furthermore, we propose a decomposition algorithm implemented in parallel fashion for solving the resulting stochastic program, which exploits the features of the integer L-shaped method and the special structure of the maintenance and operations scheduling problem to derive valid and stronger sets of optimality cuts. We further present preprocessing steps over transmission line flow constraints to identify redundancies. To illustrate the computational performance and efficiency of our algorithm compared with more conventional maintenance approaches, we design a computational study focusing on a weekly plan with daily maintenance and hourly operational decisions involving detailed unit commitment subproblems. Our computational results on various IEEE instances demonstrate the computational efficiency of the proposed approach with reliable and cost-effective maintenance and operational schedules. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms—Discrete. Supplemental Material: The online appendix is available at https://doi.org/10.1287/ijoc.2022.0154. [ABSTRACT FROM AUTHOR]

Transient stability analysis is critical for maintaining the reliability and security of power systems. This paper provides a comprehensive review of research methods for transient stability analysis under large disturbances, detailing the modeling concepts and implementation approaches. The research methods for large disturbance transient stability analysis are categorized into five main types: simulation methods, direct methods, data-driven methods, analytical methods, and other methods. Within the analytical method category, several common analytical strategies are introduced, including the asymptotic expansion method, intrusive approximation method, and other analytical methods. The fundamental principles, characteristics, and recent research advancements of these methods are detailed, with particular attention to their performance in various aspects such as computational efficiency, accuracy, applicability to different system models, and stability region estimation. The advantages and disadvantages of each method are compared, offering insights to support further research into transient stability analysis for hybrid power grids under large disturbances. [ABSTRACT FROM AUTHOR]

The growth of renewables in public energy networks requires suitable strategies to assess the stable operation of the respective power electronic devices, e.g., inverters. Different assessment methods can be performed with regard to the available knowledge and the assessment objective, e.g., a specific frequency range or the input signal characteristics that are typically classified into small-signal and large-signal disturbances. This paper addresses the limits of the measurement-based small-signal stability analysis in the harmonic frequency range of commercially available single-phase inverters for photovoltaic applications. The harmonic stability is analyzed, and the results for a sinusoidal background voltage and distorted background voltages are assessed based on measurements. The measurements prove that even in the harmonic frequency range, the harmonic stability analysis can only provide a sufficient but not a necessary condition in terms of the statement towards an instable operation. [ABSTRACT FROM AUTHOR]

Economic dispatch is an important issue in the management of power systems and is the current focus of specialists. In this paper, a new metaheuristic optimization algorithm is proposed, named Social Small Group Optimization (SSGO), inspired by the psychosocial processes that occur between members of small groups to solve real-life problems. The starting point of the SSGO algorithm is a philosophical conception similar to that of the social group optimization (SGO) algorithm. The novelty lies in the introduction of the small group concept and the modeling of individuals' evolution based on the social influence between two or more members of the small group. This conceptual framework has been mathematically mapped through a set of heuristics that are used to update the solutions, and the best solutions are retained by employing a greedy selection strategy. SSGO has been applied to solve the economic dispatch problem by considering some practical aspects, such as valve-point loading effects, sources with multiple fuel options, prohibited operating zones, and transmission line losses. The efficiency of the SSGO algorithm was tested on several mathematical functions (unimodal, multimodal, expanded, and composition functions) and on power systems of varying sizes (ranging from 10-units to 1280-units). The SSGO algorithm was compared with SGO and other algorithms belonging to various categories (such as: evolution-based, swarm-based, human behavior-based, hybrid algorithms, etc.), and the results indicated that SSGO outperforms other algorithms applied to solve the economic dispatch problem in terms of quality and stability of the solutions, as well as computation time. [ABSTRACT FROM AUTHOR]

Problem definition: Hydrogen fuel-cell vehicles (HFVs) have been proposed as a promising green transportation alternative. For regions experiencing renewable energy curtailment, promoting HFVs can achieve the dual benefit of reducing curtailment and developing sustainable transportation. However, promoting HFVs faces several major hurdles, including uncertain vehicle adoption, the lack of refueling infrastructure, the spatial mismatch between hydrogen demand and renewable sources for hydrogen production, and the strained power transmission infrastructure. In this paper, we address these challenges and study how to promote HFV adoption by deploying HFV infrastructure and utilizing renewable resources. Methodology/results: We formulate a planning model that jointly determines the location and capacities of hydrogen refueling stations (HRSs) and hydrogen plants as well as electricity transmission and grid upgrade. Despite the complexity of explicitly considering drivers' HFV adoption behavior, the bilevel optimization model can be reformulated as a tractable mixed-integer second-order cone program. We apply our model calibrated with real data to the case of Sichuan, a province in China with abundant hydro resources and a vast amount of hydropower curtailment. Managerial implications: We obtain the following findings. (i) The optimal deployment of HRSs displays vastly different spatial patterns depending on the HFV adoption target. The capital city, a transportation hub, is excluded from the plan under a low target and only emerges as the center of HFV adoption under a high target. (ii) Promoting the HFV adoption can overall help reduce hydropower curtailment, but the effectiveness depends on factors such as the adoption target and the grid upgrade cost. (iii) Being a versatile energy carrier, hydrogen can be transported to various locations, which allows for strategic placement of HRSs in locations distinct from hydrogen plant sites. This flexibility offers HFVs greater potential cost savings and curtailment reduction compared with other alternative fuel vehicles (e.g., electric vehicles) under current cost estimates. Funding: W. Qi acknowledges the support from the National Natural Science Foundation of China [Grants 72242106, 72188101, and 72272014] and the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2019-04769]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0381. [ABSTRACT FROM AUTHOR]