Power System Research Articles

Ramifications of integrating wind farms on stability: a thorough investigation incorporating TCSC applications

ABSTRACT The stochastic nature of wind speed and nonlinear wind power behavior complicates stability, particularly under severe disturbances, where traditional controller designs are insufficient and lead to transient stability. This paper proposes a Power System Stabilizers (PSS), Power Oscillation Dampers (POD), and Thyristor Controlled Series Capacitors (TCSC) control strategy to enhance stability. Further, this study analyzed the intermittent nature of wind speed and the replacement of Synchronous Generators (SG) with Wind Farm (WF). The results show that the system is unstable and stable weekly without and with the control of PSS, POD, and TCSC. A coordinated controller of TCSC with POD is proposed to enhance the stability effectively and mitigate undamped fluctuations even under extreme conditions. The proposed coordinated controller demonstrates superior performance, robustness and reliability compared to standalone controllers. It has been validated and shown to be effective using the IEEE-11 bus model on the MATLAB/SIMULINK platform across diverse scenarios. This research presents a cohesive framework for addressing stability issues in WF-integrated power systems by streamlining methodologies and results.

A comparative study of pi and eems on emu for hybrid fuelcell power systems

A comparative study of pi and eems on emu for hybrid fuelcell power systems

Secure Optimization Dispatch Framework with False Data Injection Attack in Hybrid-Energy Ship Power System Under the Constraints of Safety and Economic Efficiency

Hybrid-energy vessels offer significant advantages in reducing carbon emissions and air pollutants by integrating traditional internal combustion engines, electric motors, and new energy technologies. However, during operation, the high reliance of hybrid-energy ships on networks and communication systems poses serious data security risks. Meanwhile, the complexity of energy scheduling presents challenges in obtaining feasible solutions. To address these issues, this paper proposes an innovative two-stage security optimization scheduling framework aimed at simultaneously improving the security and economy of the system. Firstly, the framework employs a CNN-LSTM hybrid model (WOA-CNN-LSTM) optimized using the whale optimization algorithm to achieve real-time detection of false data injection attacks (FDIAs) and post-attack data recovery. By deeply mining the spatiotemporal characteristics of the measured data, the framework effectively identifies anomalies and repairs tampered data. Subsequently, based on the improved multi-objective whale optimization algorithm (IMOWOA), rapid optimization scheduling is conducted to ensure that the system can maintain an optimal operational state following an attack. Simulation results demonstrate that the proposed framework achieves a detection accuracy of 0.9864 and a recovery efficiency of 0.969 for anomaly data. Additionally, it reduces the ship’s operating cost, power loss, and carbon emissions by at least 1.96%, 5.67%, and 1.65%, respectively.

Nodal Carbon Emission Factor Prediction for Power Systems Based on MDBO-CNN-LSTM

Carbon emission estimation for power systems is essential for identifying emission responsibilities and formulating effective mitigation measures. Current carbon emission prediction methods for power systems exhibit limited computational efficiency and inadequate noise immunity under complex operating conditions. In this study, we address these limitations by improving population initialization, search mechanisms, and iteration strategies and developing a hybrid strategy Modified Dung Beetle Optimization (MDBO) algorithm. This led to the development of an MDBO-enhanced Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) network hybrid prediction model for carbon emission prediction. Firstly, the theoretical calculation mechanism of carbon emission flow in power systems is analyzed. Subsequently, an MDBO-CNN-LSTM deep network architecture is constructed, with detailed explanations of its fundamental structure and operational principles. Then, the proposed MDBO-CNN-LSTM model is utilized to predict the nodal carbon emission factor of power systems with the integration of renewable energy sources. Comparative experiments with conventional CNN-LSTM models are conducted on modified IEEE 30-, 118-, and 300-bus test systems. The results show that the maximum mean squared error of the proposed method does not exceed 0.5734% in the strong-noise scenario for the 300-bus system, which is reduced by half compared with the traditional method. The proposed method exhibits enhanced robustness under strong noise interference, providing a novel technical approach for precise carbon accounting in power systems.

Optimisation Study of Investment Decision-Making in Distribution Networks of New Power Systems—Based on a Three-Level Decision-Making Model

This paper addresses the scientific needs for investment decision-making in distribution networks against the backdrop of new power systems, constructing a three-tier decision-making system that includes investment scale decision-making, investment structure allocation, and investment project prioritization. Initially, it systematically analyzes the new requirements imposed by the new power systems on distribution networks and establishes an investment index system encompassing four dimensions: “capacity, self-healing, interaction, and efficiency”. Next, the Pearson correlation coefficient is employed to screen key influencing factors, and in conjunction with the grey MG(1,1) model and the support vector machine algorithm, precise forecasting of the investment scale is achieved. Furthermore, distribution network projects are categorized into ten classes, and an investment direction decision-making model is constructed to determine the investment scale for each attribute. Then, for the shortcomings of the traditional project comparison method, the investment project decision-making model is established with the attribute investment amount as the constraint and the maximisation of project benefits under the attribute as the goal. Finally, the effectiveness of the decision-making system is verified by taking the Lishui distribution network as a case study. The results show that the system keeps the investment scale prediction error within 5.9%, the city’s total investment deviation within 0.3%, and the projects are synergistically optimized to provide quantitative support for distribution network investment decision-making in the context of a new type of electric power system, and to achieve precise regulation.

THE ROLE OF POWER SECTOR CYBERSECURITY IN SAFEGUARDING FOOD SYSTEMS AND NATIONAL STABILITY

The increasing reliance on digital infrastructure within the power sector has significantly improved operational efficiency but also introduced critical vulnerabilities that threaten national stability. This paper examines the interconnectedness of power sector cybersecurity and food security, highlighting how disruptions in energy infrastructure—whether due to cyberattacks or systemic failures—can severely impact agricultural production, cold storage, water supply, transportation, and healthcare services. Drawing on recent global incidents, we explore how energy disruptions cascade into broader crises affecting food availability, public health, and socio-economic resilience. Particular attention is given to the vulnerabilities faced by rural communities and smallholder farmers, who often lack backup systems. We argue that ensuring cybersecurity in power systems is not only a technical imperative but also a strategic necessity for national food security and societal well-being. The paper concludes with a call for integrated policies, increased investment, public education, and cross-sector collaboration to build cyber-resilient infrastructure capable of withstanding evolving digital threats.

Neutron Resilience of Flexible Perovskite Solar Cells Using PTAA‐Derived Hole Transport Layers

Flexible perovskite solar cells hold promise of being an enabling technology for space missions: by reducing the encumbrance and weight of the payload's power system, launch costs can be minimized. The increased interest, however, must be accompanied by thorough testing under a broad range of space‐related sources of degradation and investigation of their effects on the layer stack. In this work, the resilience against atmospheric neutron radiation (<800 MeV) of flexible devices is studied, using two different hole transporting materials: a commercial PTAA and a PTAA‐based in‐house synthesized polymer. After 5 109 particles/cm2 irradiation, 380 times higher than the yearly fast neutron fluence in low Earth orbit, the devices show good stability, with efficiency losses below 20%. Further investigation by light intensity‐dependent JV scans, hyperspectral photoluminescence microscopy, X‐ray diffraction, X‐ray reflectivity, and atomic force microscopy reveals that the neutron radiation mainly affects the perovskite/hole transport layer interface, to a different extent depending on the material. This work confirms that accelerated stress testing is an important tool to determine the feasibility of this technology for space applications and provides insights on the damages caused by atmospheric neutrons which will help inform future decisions for the fabrication of space‐resilient flexible perovskite solar cells.

Power Quality Improvement in Micro Grid System Using Fuzzy-UPQC Controller

This research proposes a micro grid based on a single stage converter to reduce the number of converters in a single ac or dc grid. Microgrids, characterized the ability to operate in both grid-connected and islanded modes has made devices an essential element for integrating renewable energy sources and enhancing local energy resilience. However, Issues with the intermittent nature of renewable energy sources like solar and wind creates power quality issues such voltage sags, swells, harmonics, and flickers. To deal with these challenges, this research proposes a new book approach for using power quality in microgrid systems using a fuzzy logic-based Unified Power Quality Conditioner (Fuzzy-UPQC) controller. The power system experienced distortions as a result of either non-linear load utilisation or variations in load. The Fuzzy-UPQC integrates to simultaneously reduce voltage and current disturbances, utilise a shunt and series converters. By reducing the output power aberrations, fuzzy logic controllers and the conventional proportional integral (PI) are used to improve power quality. known for their ability to handle nonlinearities and uncertainties, enable the UPQC to adjust dynamically its operation based on data received in real time from the microgrid, ensuring optimal performance even under fluctuating load and generation conditions. Simulation results demonstrate that the Fuzzy-UPQC controller effectively improves voltage stability and reduces harmonic distortion in a microgrid environment. The proposed system also shows robust performance in compensating for transient disturbances, making it a viable solution for improve the modern microgrid's electricity quality applications. Its study highlights the potential of fuzzy logic-based control systems in improving the reliability and efficiency of microgrids, paving the path for more sustainable and resilient energy systems..

Investigation on Corrosion-Induced Wall-Thinning Mechanisms in High-Pressure Steam Pipelines Based on Gas–Liquid Two-Phase Flow Characteristics

In high-pressure thermal power systems, corrosion-induced wall thinning in steam pipelines poses a significant threat to operational safety and efficiency. This study investigates the effects of gas–liquid two-phase flow on corrosion-induced wall thinning in pipe bends of high-pressure heaters in power plants, with particular emphasis on the mechanisms of void fraction and inner wall surface roughness. Research reveals that an increased void fraction significantly enhances flow turbulence and centrifugal effects, resulting in elevated pressure and Discrete Phase Model (DPM) concentration at the bend, thereby intensifying erosion phenomena. Simultaneously, the turbulence generated by bubble collapse at the bend promotes the accumulation and detachment of corrosion products, maintaining a cyclic process of erosion and corrosion that accelerates wall thinning. Furthermore, the increased surface roughness of the inner bend wall exacerbates the corrosion process. The rough surface alters local flow characteristics, leading to changes in pressure distribution and DPM concentration accumulation points, subsequently accelerating corrosion progression. Energy-Dispersive Spectroscopy (EDS) and Scanning Electron Microscopy (SEM) analyses reveal changes in the chemical composition and microstructural characteristics of corrosion products. The results indicate that the porous structure of oxide films fails to effectively protect against corrosive media, while bubble impact forces damage the oxide films, exposing fresh metal surfaces and further accelerating the corrosion process. Comprehensive analysis demonstrates that the interaction between void fraction and surface roughness significantly intensifies wall thinning, particularly under conditions of high void fraction and high roughness, where pressure and DPM concentration at the bend may reach extreme values, further increasing corrosion risk. Therefore, optimization of void fraction and surface roughness, along with the application of corrosion-resistant materials and surface treatment technologies, should be considered in pipeline design and operation to mitigate corrosion risks.

Research on Secure Communication of Electric Power IoT in Shore Power Scenarios

In the complex electromagnetic environment of port shore power systems, the operation of various power electronic devices generates both persistent interference within fixed frequency bands and transient narrowband pulse interference. To address this challenge, this paper proposes a novel frequency-hopping sequence generation method that integrates a lightweight encryption algorithm with the dual-band method. The generated sequences not only satisfy stringent wide-interval requirements but also exhibit excellent randomness characteristics

Improving the operation of an asymmetric inverter with magnetically coupled inductors for energy storage systems

Introduction. Bidirectional DC-DC converters are widely used in energy storage systems for efficient energy transfer. One of the effective converters for such systems is the asymmetric inverter with a magnetically coupled inductors. To enhance the efficiency of this converter for energy storage applications, it is necessary to optimize its parameters. Objective. The objective is to develop a mathematical model of an asymmetric inverter with magnetically coupled inductors and based on this model, to establish the conditions for improving the energy efficiency of the inverter in energy storage systems. Methods. The study uses the state-space averaging method and simulation modelling to analyse operational processes. Results. Analytical expressions were derived for calculating current parameters of the magnetically coupled inductor within switching intervals. A correlation was identified between the inductor’s inductance and power source parameters under conditions that eliminate circulating currents, thus reducing static energy losses in the inverter. Novelty. Based on these expressions, new analytical and graphical dependencies were established, illustrating relationships between the inductor parameters and the magnetic coupling coefficient of its windings. These dependencies determine the boundaries of the discontinuous conduction mode for the asymmetric inverter with a magnetically coupled inductors within its switching range. Practical value. The application of these dependencies during the design phase allows for a reduction in both static and dynamic energy losses in the inverter using discontinuous conduction mode. This will also improve the dynamics of transient processes during changes in the direction of energy flow, which is a significant advantage in the development of hybrid power systems for electric vehicles. References 19, figures 9.

Research on CPU waste heat utilization system of CAES coupled oxyfuel coal-fired unit

In order to reduce carbon emission and increase flexibility of thermal power units, a new power system of compressed air energy storage coupled with oxyfuel coal-fired units using CPU waste heat(CFPP+CAES+CPU) was proposed. Using Ebsilon Professial and Aspen Plus software to build a model, the thermodynamic characteristics and economic analysis of the system were carried out. The results show that: In fixed coal consumption mode, the exergic power of CFPP+CAES+CPU unit increased by 0.745% at the lowest and 1.216% at the highest. Exergic damage of exergic power of CFPP+CAES+CPU unit decreased compared with control unit, while exergic efficiency of heat exchanger in CPU increased. The maximum economic mode of CFPP+CAES+CPU unit is that when CO2 and carbon emission rights are sold, the minimum LCOE is 0.374¥/kWh when carbon tax is not considered

Prediction of bundle-conductor ampacity based on transformer-LSTM

The traditional method cannot meet the demand of new power system for dynamic regulation of transmission lines. In order to solve this defect, based on finite element simulation and neural network, an overhead bundle-conductor dynamic bundle-conductor ampacity prediction method is proposed in this paper. Considering the four bundle- JL/G1A-400/35 steel-core aluminum stranded wire, the three-dimensional electric-thermal-fluid coupling model of the conductor is established by using the synergistic optimization of transformer and long-short-term memory neural network (LSTM). The results show that the mean square error and average absolute error of the proposed model are 31.14 and 6.93, respectively. Compared with the bidirectional long and short-term memory network (BiLSTM), the mean square error and average absolute error are reduced by 74.55% and 7.35%, respectively. The maximum improvement of load capacity prediction margin is 10.04%. It can effectively tap the dynamic potential of transmission lines, and provide technical support for real-time scheduling of smart grid.

Automated Data Pipeline Optimization for Large-Scale Energy Analytics: MLOps for Energy Sector

Electric power systems now generate data at scales that overwhelm traditional processing methods, with smart meters, renewable generators, weather sensors, and trading platforms creating continuous information streams. Machine Learning Operations emerged from the technology sector as a discipline for managing artificial intelligence in production, but power grids demand specialized adaptations that standard frameworks cannot provide. This article presents an MLOps framework built specifically for energy applications, where automated feature engineering incorporates physics-based knowledge about how electricity actually behaves. The framework tackles problems unique to utilities - measurement devices fail in harsh outdoor conditions, regulators demand explanations for every automated decision, and predictions must achieve accuracy levels that prevent blackouts and equipment damage. Real-world deployments in load forecasting, renewable generation prediction, and electricity market trading show how the framework improves forecast accuracy while meeting operational deadlines measured in milliseconds. The implementation guidance helps energy companies deploy machine learning without sacrificing the reliability standards that keep lights on across entire regions. Adaptive learning mechanisms detect when consumption patterns shift due to new technologies like electric vehicles or behavioral changes like remote work, automatically updating models to maintain accuracy. The framework proves that utilities can adopt advanced analytics while respecting the engineering principles and regulatory constraints that govern critical infrastructure.

Power system impacts of potential environmental constraints for hydropower in Norway

Abstract New environmental constraints for hydropower improve ecosystems but reduce energy production and flexibility, creating a dilemma between protecting nature and ensuring a sufficient and reliable energy supply. Approximately 88% of Norway’s power comes from hydropower, produced by over 1770 plants. We assess how 285 potential new or revised environmental constraints affect hydropower production and flexibility in the Northern European power system. To do so, we combine a method for estimating environmental flow releases in bypass reaches and stochastic power system optimization. Our results show that reductions in hydropower output due to these constraints are consistent across power system scenarios and two power system models. The reductions amount to 3 TWh yr-1 (2% of Norway’s production), by our estimates. These reductions are primarily driven by flow diversions to bypass river sections. Further, we find that high power prices increase and low prices decrease, reflecting reduced system flexibility. Price increases typically occur in dry spring periods and are linked to activation of season-dependent reservoir restrictions in that period, while price decreases result from higher reservoir volumes entering periods of rain-heavy summer or autumn periods, indirectly linked to restrictions active earlier in the year. In our simulations, we observe that flexibility losses due to environmental constraints to some degree are offset by increased flexible operation of unaffected reservoirs and transmission interconnectors. Finally, our findings suggest that reservoir restrictions may cause spilling, as higher water levels in regulated reservoirs increase the risk of spilling during wet summer or autumn periods. This system-level understanding is crucial for regulatory authorities designing new environmental requirements and revising the terms of hydropower licenses. Our study contributes to informed discussions for balancing hydropower production with local environmental benefits and offers a framework for studying similar constraints in other regions, such as Sweden, North America and the Alpine region.

Application of TOPSIS for Multi-Criteria Decision Analysis (MCDA) in Power Systems: A Systematic Literature Review

In this study, the authors present the results of a systematic literature review on applications of the technique for order of preference by similarity to ideal solution (TOPSIS) in power systems. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) approach was used in the identification of publications used in this research. The SCOPUS database was utilized to locate the publication, and a total of 78 articles published between 2014 and 2024 were included in the review. A bibliometric analysis was performed, and reports were given on the annual number of publications and the top 10 cited journals. The main themes emerging from the content review of the publications were types of TOPSIS approaches, calculation of weights in multi-criteria decision-making (MCDM) problems, energy markets applications, renewable energy technologies assessment, heating and cooling systems combined with power systems, power system operation strategies, power system stability assessment, power system operations planning, and other power systems applications. Research trends and developments in the area were analyzed to identify the existing gaps. Proposed future research areas were identified based on trends and gaps presented.

Prediction of Uncertainty Ramping Demand in New Power Systems Based on a CNN-LSTM Hybrid Neural Network

Under the background of “dual-carbon”, expanding renewable energy grid integration exacerbates grid net load volatility, and system climbing requirements escalate. In this paper, the problem of uncertain ramping demand prediction caused by net load prediction error in new power systems is investigated. First, the total system ramping demand calculation model is constructed, and the effects of deterministic and uncertain ramping demand on the total system ramping demand are analyzed. Secondly, a prediction model based on a CNN-LSTM hybrid neural network is proposed for the uncertain ramp-up demand, which extracts the spatial correlation features of the multi-source influencing factors through the convolutional layer, captures the dynamic evolution law in the time series by using the LSTM layer, and realizes the high-precision point prediction and reliable interval prediction by combining the quantile regression method. Finally, the actual operation data and forecast data of a provincial power grid are used for example verification, and the results show that the proposed model outperformed traditional models (SVM, RF, BPNN) and single deep learning models (CNN, LSTM) in point prediction performance, achieving higher prediction accuracy and validating the effectiveness of the spatio-temporal feature extraction module. In terms of interval prediction quality, compared with the histogram and QRF benchmark models, the proposed model achieves a significant reduction in the average width of the prediction interval, average upward ramp-up demand, and average downward ramp-down demand while maintaining 100% interval coverage. This demand realizes a better balance between prediction economic efficiency and safety, providing more reliable technical support for the precise assessment of uncertain ramp-up demand in new power systems.

Electromechanical Model and Transient Behaviors of Power Systems

A large-scale power system is often represented by a set of differential-algebraic equations, so-called electromechanical models. In this paper, it is shown that these models can have several analytical properties, which can be exploited to help the study of power system dynamics. These analytical properties, associated with the complete power system model and subsystem models, are described in detail. The implications of such analytical properties are also examined.

Coordinated Optimization of Control Parameters for the Stability of Wind-Solar Hybrid Power System via Improved Snake Optimizer Algorithm

Background: Wind-solar hybrid power systems, playing a pivotal role in renewable energy integration and diversification of energy sources, frequently face low-frequency oscillation issues due to inadequate damping under disturbances. These oscillations pose challenges for realizing system stability through coordinated control strategies. Objective: This study aims to utilize intelligent algorithms for optimizing controller parameters, effectively suppress the occurrence of low-frequency oscillations, and thereby significantly improve the overall stability and reliability of wind-solar hybrid power systems. Methods: The power system stabilizer and flexible AC transmission system devices are utilized to enhance the stability of the wind-solar hybrid power system, and an improved snake optimizer algorithm is proposed to optimize the parameters of power system stabilizer and flexible AC transmission system devices, as well as the installation location of flexible AC transmission system devices. Results: Simulations demonstrate that the proposed algorithm shows a notable enhancement in system stability and reliability, with better performance in optimization precision and computation speed when compared to conventional methods. Conclusion: The proposed method effectively mitigates low-frequency oscillations, significantly improving stability and reliability in wind-solar hybrid power systems.

Phasor measurement unit optimization in smart grids using artificial neural network

The wide area measurements systems (WAMS) play a vital role in the operation of smart grids. The phasor measurement units (PMU) or synchrophasors are one of the principle components under WAMS. PMU in a smart grid converts power system signals into phasor from voltage and current which enhances the observability of the power system. A variety of operations is performed by the PMUs such as adaptive relaying, instability prediction, state estimation, improved control, fault and disturbance recording, transmission and generation modeling verification, wide area protection and detection of fault location. The PMUs can improve the performance of grid operations and monitoring. Thus, PMU optimization is very necessary to achieve the desired power system observability. The performance of the PMUs can be optimized using artificial intelligence (AI) technologies. The novice method of monitoring maximum power transfer using PMUs equipped with artificial neural networks has been discussed in this paper. In this paper, a two-bus system model is developed that can be generalized to multiple bus systems. The proposed method is novel, simple, feasible, and cost effective for smart grids.