Modern Power Research Articles

Enhancing hosting capacity for electric vehicles in modern power networks using improved hybrid optimization approaches with environmental sustainability considerations

The increasing adoption of electric vehicles (EVs) presents both opportunities and challenges for power networks. While EVs have the potential to reduce carbon emissions, accommodating their growing power demand requires careful planning to prevent overloading and mitigate environmental impacts. This paper introduces an integrated hosting capacity model to facilitate higher EV penetration while maintaining environmental standards. In addition to EV charging stations, the model incorporates transmission lines, reactive power compensators, energy storage systems, and thyristor-controlled series compensators to ensure a reliable power supply. The model aims to maximize EV charging station deployment, minimize greenhouse gas emissions, and optimize net present value through hosting capacity strategies. Three hosting capacity plans are proposed to analyze the impact of prioritizing one of these objectives over the others in network configurations. Accurate EV demand forecasting is critical for this model, and a swarm intelligence forecasting algorithm is proposed to explore various forecasting approaches. The model is complex and involves nonlinear multi-objective optimization. To solve it, a new hybrid optimization algorithm is introduced, combining the features of the Marine Predators Algorithm and the Honey Badger Algorithm. Three hybridization schemes—Series Hybrid Scheme, Population Division Scheme, and Switching Strategy Scheme—are developed to address the optimization challenges effectively. The results show that the first and second hybridization schemes are the most effective for solving the EV load forecasting models, with a robustness of at least 90%. In contrast, the robustness of the third scheme reaches only 30% in some models. Simulation studies on the IEEE 9-bus network and the IEEE 30-bus system validate the model’s effectiveness in integrating EVs while achieving environmental sustainability objectives. The findings show the superiority of the proposed hybrid schemes in solving the hosting capacity model in terms of finding optimal solutions. However, the third scheme required less computing time than the others, with its convergence time being at least 33.3% shorter.

Data-driven stochastic dynamic economic dispatch for combined heat and power systems using particle swarm optimization

In modern power systems, the uncertain behavior of renewable energy sources (RESs) may result in deviations from the optimal operating dispatches for the various power sources. The electric power generation from Combined Heat and Power (CHP) units is characterized by high efficiency with less pollution. To use CHP units more efficiently, the dynamic economic dispatch problem is applied to obtain the optimal power and heat sources’ outputs to satisfy heat and power demands while meeting the different operational constraints. In this paper, data-driven stochastic optimization is used to model these uncertainties utilizing the Generative Adversarial Networks in scenario generation that are accurate and do not require fitting models or modeling probability distributions. Then, the Fast Forward Selection technique is used to decrease the number of scenarios to improve system tractability. The results obtained indicate that the variability in electrical load, photovoltaic (PV), and wind turbine (WT) output significantly impacts the overall operational costs of the power system. Therefore, it is essential to incorporate the uncertainties associated with electrical load, PV, and WT when aiming for accurate results in economic dispatch. the study found that the overall operation cost of the dynamic economic dispatch of the electrical system for 24 hours after integrating RES achieved a daily savings of 2.5 % in the first scenario. Furthermore, the findings demonstrate that RESs positively contribute to reducing the total operational costs of the power system. The economic dispatch of CHP systems is influenced by the integration of RESs and the uncertainties associated with electrical parameters.

Dynamic control of grid-following inverters using DC bus controller and power-sharing participating factors for improved stability

Integrating Grid-Following Inverters (GFLs) into power systems presents significant stability challenges, particularly in systems with reduced strength and high renewable energy penetration. This study delves into the dynamic interactions between parallel synchronous generators (SGs) and GFLs, highlighting the impact of reduced system inertia and transient stability margins. A novel DC bus controller is proposed to enhance the inertia and stability of GFLs during grid disturbances by dynamically adjusting power references based on load demand. Through mathematical modelling, small signal analysis, and MATLAB simulations, the study evaluates the effects of inverter-based resources (IBRs) on power system stability. The proposed GFL configuration demonstrates improved stability control, achieving an 80 % increase in stability under weak grid conditions. This paper provides insights into optimized control strategies, emphasizing the importance of power-sharing participation factors (PSPF) for effective GFL integration in modern power systems with high renewable energy penetration.

Oscillation Suppression of Grid-Following Converters by Grid-Forming Converters with Adaptive Droop Control

The high penetration of renewable energy sources (RESs) and power electronics devices has led to a continuous decline in power system stability. Due to the instability of grid-following converters (GFLCs) in weak grids, the grid-forming converters (GFMCs) have gained widespread attention featuring their flexible frequency and voltage regulation capabilities, as well as the satisfactory grid-supporting services, such as inertia and damping, et al. Notably, the risk of wideband oscillations in modern power grids is increasingly exacerbated by the reduced number of synchronous generators (SGs). Thus, the wideband oscillation suppression method based on adaptive active power droop control of GFMCs is presented in this paper. First, the stability of the hybrid grid-forming and grid-following system is obtained according to the improved short circuit ratio (ISCR), where the GFMC is in parallel at the point of common coupling (PCC) of the GFLC. Then, an adaptive adjustment strategy of the active power droop control is proposed to enhance the oscillation suppression capability across the full frequency range, thereby mitigating the wideband oscillation caused by phase-locked loop (PLL) synchronization in the GFLCs. Additionally, a first-order inertia control unit is added to the active and reactive power droop controllers to mitigate frequency and voltage variations as well as suppress potential mid-to-high frequency resonance. Finally, the wideband oscillation suppression strategy is validated by the simulation and experimental results.

Optimal configuration of dynamic VAR compensators considering uncertainty and correlation

AbstractThe transient voltage stability of modern power systems is challenged by the increasing uncertainty on source‐load dual sides. To cope with it, a robust optimal configuration method for dynamic VAR compensators (DVCs) is proposed. First, a series of power‐flow scenarios are generated with uncertainty and correlation considered, where nonparametric kernel density estimation is used to predict the marginal distribution of wind speeds and combined Copula function is employed to characterize correlations between nearby wind farms. Then, the variation‐of‐information indicator is introduced to assess the similarity among power‐flow (PF) scenarios, and representative PF (RPF) scenarios are screened with the help of Spectral clustering algorithm. Finally, locating and sizing of DVCs for RPF scenarios are achieved. By synthesizing the compensation schemes for RPF scenarios, the robust configuration scheme is suggested. Simulation studies in the modified New England 10‐machine 39‐bus system show the proposed method can ensure transient voltage security of the studied power system under source‐load uncertainty.

Sinusoidal PWM Generation for 3 Phase Inverter and RPM Measurement using Hercules TMS570LC43xx Launchpad

This project focuses on implementing a 3 phase Sinusoidal PWM generation using the Hercules TMS570LC43xx Launchpad Development Kit (Launchpad). The primary objective is to generate synchronized Sinusoidal nature PWM signals using the onboard High-End Timer (HET) and Enhanced Pulse Width Modulation (ePWM) module, which can be given to the Inverter for conversion of Direct current (DC) power into Alternating current (AC) power. This conversion is essential in various applications where AC power is required but the power source provides DC power. It is used in Solar photovoltaic (PV) systems and other renewable energy installations. These systems generate power suitable for powering household appliances or feeding into the electrical grid. Itis alsoused in electric vehicles (EVs) to drive the electric motor with variable speed. For verification of the wave nature, we have used an external lowpass filter (LPF) to transform the dynamic PWM signals into sinusoidal waveforms, ensuring compatibility with various applications like Inverters which can be further used in equipment and machinery such as Brushless DC motors, pumps and compressors. With the addon functionality to control the signal’s frequency which will be given to the Inverter to control the speed of the motor. Additionally, the project incorporates RPM measurement of the motor using an optical encoder setup interfaced with the Enhanced Quadrature Encoder Pulse (eQEP) module on the Launchpad. This feature enables accurate measurement of rotational speeds, position and Revolution per minute (RPM), enhancing the functionality of the system in real world applications like the speed of conveyor belts and other automated transport systems. Through successful implementation, this project demonstrates wide control for the Inverter, achieving reliable synchronized 3 phase signals with its variable speed having 120-degree phase shift signals alongside precise RPM measurement. The project highlights the Launchpad’s capabilities in handling complex signal processing tasks essential for modern power electronics applications. Looking forward, this project establishes a foundation for future enhancements and innovations in power electronics.

Synergistic Control of Active Filter and Grid Forming Inverter for Power Quality Improvement

This paper addresses the challenges and opportunities associated with integrating grid-forming inverters (GFMs) into modern power systems, particularly in the presence of nonlinear loads. Nonlinear loads introduce significant harmonic distortions in the source voltage and current, leading to reduced power factor, increased losses, and an overall reduction in system performance. To mitigate these adverse effects, active filters are employed. The objective of this study is to investigate a synergistic approach to modeling and control in integrated power systems with GFMs, focusing on enhancing power quality and grid stability by reducing harmonic distortions through the use of voltage-source active filters. This research contributes to sustainability by supporting the reliable and efficient integration of renewable energy sources, thereby reducing dependency on fossil fuels and minimizing greenhouse gas emissions. Additionally, improving power quality and system efficiency helps reduce energy waste, which is crucial for achieving sustainable energy goals. Simulations are conducted on a 1000 kW GFM connected to a grid with a nonlinear variable load, demonstrating the system’s effectiveness in adapting to dynamic conditions, reducing harmonics, and promoting a stable, resilient, and sustainable power grid.

A machine learning based fault location method for power distribution systems using wavelet scattering networks

This paper proposes a novel machine learning based method for localizing single-line-to-ground faults in modern power distribution systems using single-end measurements. The challenge of identifying the faulty lateral is formulated as a support vector machine model-based classification problem, where a class represents a different part of the distribution network. The challenge of finding the exact fault distance is formulated as an ensemble model-based regression problem. Both models are trained with scattering coefficients extracted from the application of a wavelet scattering network on the captured faulty phase voltage signal. The performance of the proposed fault location method is evaluated with a comprehensive simulation study, conducted for the IEEE 34-bus test distribution system. The results demonstrate the efficacy of the proposed method in terms of fault location accuracy, as well as its sufficient insensitivity against several influencing factors, such as load, DG, external system strength, and network topology variations. Comparison of the proposed method with other well-established machine learning based fault location methods for power distribution systems reveals its great performance.

Six Pulse Type Segmented Thyristor Controlled Reactor with Fixed Capacitor for Reactive Power Compensation

This article presents a Six Pulse Type Segmented Thyristor Controlled Reactor (TCR) integrated with a Fixed Capacitor (FC) for reactive power compensation. The primary objective is to improve voltage stability and power factor in electrical networks, addressing issues related to reactive power imbalance and harmonic distortion. The proposed configuration combines the advantages of segmented TCR and FC, providing a flexible and efficient approach to reactive power management. The novelty of this work lies in the segmentation of the TCR, which enhances the dynamic control of reactive power by allowing more precise regulation and reduced harmonics compared to conventional TCR systems. This segmented approach also minimizes switching losses and thermal stress on thyristors, leading to enhanced reliability and longevity of the system. Additionally, the integration of a fixed capacitor optimizes the overall power factor correction, contributing to improved system efficiency. Key findings from simulation and experimental results demonstrate that the Six Pulse Type Segmented TCR with FC significantly reduces reactive power, stabilizes voltage levels, and effectively suppresses harmonics within permissible limits, adhering to IEEE standards. The system shows a marked improvement in power quality, making it a viable solution for industrial applications where reactive power control is critical. This innovative approach not only provides superior compensation characteristics but also offers a scalable and adaptable framework for modern power systems, highlighting its potential to enhance operational performance and energy efficiency in various electrical grids.

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Research on power metering method for nonlinear loads in power system based on improved S-transform

Abstract Traditional power metering algorithms are no longer suitable for new power systems to address the issue of distorted voltage and current waveforms caused by a growing number of nonlinear loads in modern power systems. The paper proposes a power-metering algorithm based on an Improved Stockwell Transform (IST). Based on the Stockwell transform, several parameters that can control the Gaussian window are introduced to improve the flexibility of the Gaussian window. Additionally, the inequality constraint based on the energy concentration measure is used to optimally select the best window parameters, enhancing the time–frequency domain analysis capability of the IST. The study uses IST to decompose and reconstruct the voltage and current fundamental characteristic signals and distortion characteristic signals in the power system. This provides good time resolution at low frequencies and good frequency-domain resolution at high frequencies. Power metering based on unsteady voltage and current is achieved by calculating the resulting fundamental and harmonic powers, respectively. The simulation results demonstrate that the IST-based power metering method has a higher level of accuracy than the existing metering methods.

Optimization of distributed energy resources planning and battery energy storage management via large-scale multi-objective evolutionary algorithm

This paper investigates the synergistic integration of renewable energy sources and battery energy storage systems to enhance the sustainability, reliability, and flexibility of modern power systems. Addressing a critical gap in distribution networks, particularly regarding the variability of renewable energy, the study aims to minimize energy costs, emission rates, and reliability indices by optimizing the placement and sizing of wind and solar photovoltaic generators alongside battery energy storage systems. An improved large-scale multi-objective evolutionary algorithm with a bi-directional sampling strategy is employed.Two scenarios are considered. In the first scenario, six study cases are analyzed to determine the optimal number, location, and size of distributed generators at peak load demand. The proposed algorithm outperforms existing state-of-the-art methods for small-scale distributed resource allocation. In the second scenario, a multi-period load demand across various seasons is evaluated, introducing new opportunities for battery energy storage systems. The problem is modeled with intertemporal constraints, creating a large-scale optimization challenge. Results demonstrate significant improvements: cost savings of up to 51.67 %, power reductions of up to 76.78 %, and a 99.9 % improvement in voltage deviation. Emission rates decreased by 99.92 % in case 1, 95.19 % in case 2, and 97.38 % in case 3, compared to the base case. The proposed algorithm shows superior convergence and performance in solving both small- and large-scale optimization problems, outperforming recent multi-objective evolutionary algorithms.This study provides a robust framework for optimizing renewable energy integration and battery energy storage, offering a scalable solution to modern power system challenges.

Selected Papers from the VIth Modern Power Systems and Units Conference

Selected Papers from the VIth Modern Power Systems and Units Conference

Coordination of smart inverter-enabled distributed energy resources for optimal PV-BESS integration and voltage stability in modern power distribution networks: A systematic review and bibliometric analysis

Integrating photovoltaic (PV) and battery energy storage systems (BESS) in modern power distribution networks presents opportunities and challenges, particularly in maintaining voltage stability and optimizing energy resources. This systematic review and bibliometric analysis investigates the coordination of smart inverter-enabled distributed energy resources (DERs) for enhancing PV-BESS integration and ensuring voltage stability. The study synthesizes recent advancements in smart inverter technologies, which provide grid support functions such as Volt/VAr control, and their applications in DER coordination. A comprehensive review of the literature is conducted to identify prevailing trends, research gaps, and emerging techniques in the field. Bibliometric analysis is employed to quantify the research landscape, highlighting key publications, citations, publications per country, and collaborative networks. The findings reveal that smart inverters play a crucial role in mitigating voltage violations and improving the hosting capacity of PV systems in distribution networks. Furthermore, optimal inverter settings, strategic placement of PV-BESS, and advanced control algorithms are identified as critical factors for effective DER integration. The study concludes by proposing future research directions, including the exploration of smart inverter interactions with legacy grid management systems and the development of robust algorithms for dynamic and adaptive DER coordination. This review serves as a valuable resource for researchers and practitioners aiming to enhance the stability and efficiency of power distribution networks through advanced DER management strategies.

Enhanced DC surface discharge characteristics of epoxy composites treated by dielectric barrier discharge plasma fluorination and its mechanisms

Surface flashover is a destructive high-voltage discharge phenomenon greatly limiting the update of modern power electronic and electrical equipment. Surface plasma treatment is a high-energy, eco-friendly, and convenience method, it changes the surface properties and owns potential to improve flashover voltage. In the present work, plasma fluorination (PF) technique are used to treat EP/Al2O3 composites, surface morphology, element and bonds content, surface trap, conductivity, charging, and DC flashover voltage are tested and simulated, the effects of plasma on surface discharge are explained through the investigation from microscopic molecular reactions to macroscopic flashover characteristics. It indicates the 20 min PF modified sample exhibits the superior DC flashover performance, increasing by 17 % compare to untreated sample. During PF modification, the epoxy chains are degraded and fluorinated, the degradation rather than fluorination of molecular chains introduce large amounts of shallow traps into the composites, accelerating carriers’ migration and improving surface conductivity, causing surface charges to dissipate through surface conductance. Consequently, the surface electric field distortion degree reduces, and the subsequent gas ionization and plasma formation in the gas phase is suppressed, leading to the improvement of DC flashover voltage after PF modifications.

Operational electricity cost reduction using real-time simulators

Ensuring the safe and cost-effective electricity transmission to consumers, along with the efficient and sustainable operation of power systems, has long been a primary objective for power system managers and operators. However, achieving optimal performance in a modern power system requires timely planning and operational optimal routines to be run within the realm of real-time simulation programs, and sometimes, the divergence becomes a problem with these developed routines. Despite extensive research aimed at advancing these objectives, the growing complexity of networks and their constraints has often led to the oversight of simultaneously considering both technical and economic aspects during operation. In this paper, an innovative real-time framework is introduced to reduce the operational costs of a power system swiftly. This framework addresses various nonlinear constraints, such as transmission losses, generation-consumption balance, power device limitations, and transient stability constraints, across various operational scenarios within a real-time simulator. This novel approach leverages scattered search and intentional contingencies within the network to pinpoint the optimal operating point, taking into account various network dynamics, including Automatic Voltage Regulators (AVR), governors, and tap of the transformers. The proposed method offers a fast and robust approach to identifying the most effective operating conditions for a power system. It incorporates considerations for sudden contingencies and extends capabilities through parallel simulations. It not only facilitates pre-determined preventive actions but also enables the adjustment of control parameters during post-contingency periods, such as fine-tuning of the active and reactive power generation of generators and adjusting the tap settings of transformers within power networks. Since the network simulation can be executed on distributed computers, referred to as ’Global,’ it is possible to achieve global network optimization. This approach allows for the consideration of grid networks, including renewable energy sources with models distributed through PCs. Also, it accounts for the induction motor losses within all factories involved in the global simulation. The results obtained from simulating the proposed method on a commercial real-time simulator demonstrate the superior effectiveness of the proposed framework compared to the existing methodologies, highlighting its potential to enhance the operational efficiency and economic viability of power systems.

Synergistically Constructing High-Dielectric Poly(m-phenylene isophthalamide)/Silica/Cellulose Nanofiber Composite Insulation Paper through Dynamic Covalent Chemistry and Hydrogen Bonding.

The rapid advancement of modern power equipment and high-power devices has imposed increasingly stringent demands upon the mechanical and dielectric properties of electrical insulation materials. Herein, we report a poly(m-phenylene isophthalamide) (PMIA) composite insulating paper with excellent dielectric breakdown strength, reliable mechanical properties, and high thermal stability. Enhanced surface activity of PMIA is achieved through surface modification, facilitating the synergistic integration of modified PMIA (MPMIA), bovine serum albumin (BSA)/silica (SiO2), and cellulose nanofiber (CNF) into composite paper using dynamic covalent bonds and hydrogen bonding. The prepared MPMIA-BSA/SiO2-CNF composite paper exhibits a laminated stacked structure with high tensile strength (32.68 MPa) and strain at break (9.57%). Meanwhile, MPMIA-BSA/SiO2-CNF composites have excellent dielectric breakdown strength (24.75 kV/mm) and good temperature resistance. Therefore, the MPMIA-BSA/SiO2-CNF composite paper has a broad application prospect in the field of high-voltage and high-power electrical equipment insulation.

Optimizing Microgrid Load Fluctuations through Dynamic Pricing and Electric Vehicle Flexibility: A Comparative Analysis

In the context of modern power systems, the reliance on a single-time-of-use electricity pricing model presents challenges in managing electric vehicle (EV) charging in a way that can effectively accommodate the variable supply and demand patterns, particularly in the presence of wind power generation. This often results in undesirable peak–valley differences in microgrid load profiles. To address this challenge, this paper introduces an innovative approach that combines time-of-use electricity pricing with the flexible energy storage capabilities of electric vehicles. By dynamically adjusting the time-of-use electricity prices and implementing a tiered carbon pricing system, this paper presents a comprehensive strategy for formulating optimized charging and discharging plans that leverage the inherent flexibility of electric vehicles. This approach aims to mitigate the fluctuations in the microgrid load and enhance the overall grid stability. The proposed strategy was simulated and compared with the no-incentive and single-incentive strategies. The results indicate that the load peak-to-trough difference was reduced by 30.1% and 18.6%, respectively, verifying its effectiveness and superiority. Additionally, the increase in user income and the reduction in carbon emissions verify the need for the development of EVs in tandem with clean energy for environmental benefits.

Mathematical Analysis of Novel Method to Solve Protection Issues Pertaining To Solar PV Integration

The integration of Solar Photovoltaic (PV) systems into modern power grids presents protection challenges, such as voltage fluctuations, fault detection complexities, and bidirectional power flow issues. These challenges compromise grid reliability, requiring advanced methods to address them. This study introduces a novel mathematical analysis approach to solve protection issues in solar PV integration, focusing on developing an adaptive protection scheme using advanced mathematical models. The proposed approach utilizes differential equations, optimization techniques, and machine learning algorithms to create dynamic protection settings responsive to varying grid conditions. The method integrates fault detection and classification algorithms based on wavelet transform and support vector machines (SVM), allowing for rapid and accurate identification of fault types and locations. The findings demonstrate that the proposed adaptive protection scheme significantly enhances fault detection accuracy, reducing false trip rates by over 30% compared to conventional protection systems. Moreover, the method efficiently distinguishes between transient and permanent faults, ensuring swift isolation and minimizing disruption to solar PV operations. The developed model also exhibits robustness in handling variations in solar irradiance and load fluctuations, making it suitable for real-world grid applications. This research provides a substantial contribution to the field of solar PV integration by offering a mathematically grounded, adaptive protection solution that ensures improved reliability and resilience in power systems. The proposed method paves the way for the development of more advanced protection schemes, ensuring the seamless integration of renewable energy sources into modern grids while maintaining system stability and security.

Power Quality Disturbance Monitoring in PV Integrated Power System with Mode Decomposition and Ensemble Extreme Learning Machine

Power quality disturbance (PQD) monitoring has become an important issue in modern power system due to integration of several renewable energy sources such as photovoltaic (PV), wind energy system (WES), Fuel cells etc. This research presents a mode decomposed based ensemble extreme learning machine (EELM) to recognise and classify the PQD events with higher accuracy in terms of rapid learning speed and smaller computational burden in a complete PV based power system, The PQD signals are decomposed using a variational mode decomposition (VMD) to obtain effective band limited intrinsic mode functions (BIIMF) which leads to compute robust features and improves classification accuracy. For Power Quality Disturbances detection and classification, an ensemble extreme learning machine is suggested since an ensemble outperforms any single contributing model in terms of performance and prediction. The proposed VMD-EELM approach is validated in a modified IEEE 13 Bus system integrating PV with eleven types of PQDs. The proposed research having 100% classification accuracy for no noise and 99.88%, 99.94% 99.94% for 20dB, 30dB and 40dB noises respectively. It is being demonstrated that the suggested method can reliably identify and track PQD occurrences both with and without noise.