Integrated Power System Research Articles

Analysis the Principle and Applications for Locomotive Engine

Abstract. The evolution of locomotives is the cornerstone of modern transport and has had a profound impact on economic growth and social mobility. From the pioneering steam locomotives of the 19th century, which marked the beginning of a transformative journey for rail transport, to today's complex diesel and electric locomotives, each technological advance has shaped the efficiency, speed and environmental impact of rail systems. With this in mind, this study will offer a systematically analysis for the principle as well as applications for such engine. Steam locomotives revolutionized the transportation of goods and passengers, laying the foundation for the future of rail travel. Diesel locomotives further enhance operational flexibility and reliability, while electric locomotives are becoming mainstream due to their energy efficiency and low emissions. This study explores the historical development of locomotives, highlighting key technological milestones and their impact on rail transport. Meanwhile, the current advances in locomotive technology, including the integration of hybrid power systems and alternative energy sources, are studied. As the rail industry faces increasing pressure for sustainability, it is important to understand the evolution and future prospects of locomotives. By analyzing these developments, this paper aims to provide insights into different locomotive principles and how locomotive technology is evolving in an era of increased environmental awareness and technological innovation, shaping the future of rail transportation.

A day-ahead wind speed correction method: Enhancing wind speed forecasting accuracy using a strategy combining dynamic feature weighting with multi-source information and dynamic matching with improved similarity function

Forecasting error of day-ahead wind speed (WS) seriously affects wind power integration and power system security and stability. In this regard, this paper fully considers the spatiotemporal correlation of wind farms (WFs) in different geographical locations, and proposes a day-ahead WS combined correction method that integrates multi-source station dynamic information weighting. Different from the previous WS correction methods, this paper fully considers the dynamic correlation of WS between the WFs, introduces an improved weighted similarity function to screen and dynamically weight the information of WFs with dynamic correlation, and introduces the dynamic weighting feature into the WS correction process. A combined decomposition mechanism is proposed, which combines sequential variational mode decomposition (SVMD) and feature mode decomposition (FMD) models to extract the most relevant trend components and non-stationary components of forecasted and measured WS. A combined correction model is introduced, and a combined architecture of Non-stationary Transformer combined with bidirectional long short-term memory network (Ns-Transformer-BILSTM) is used to correct the stationary WS component. A dynamic matching mechanism of fluctuation components considering improved similarity is proposed for the correction of non-stationary components. The proposed method is applied to several regional WFs in China. The experimental results show that the average correction of NRMSE, NMAE and R can reach 2.4 % ∼ 3.7 %, 2.0 % ∼ 3.0 % and 3.3 % ∼ 9.7 %, respectively. The NRMSE and NMAE corresponding to the corrected WS of certain individual WFs can be reduced by 10 % and 9 %, respectively, and R can be increased by 33 %.

A molten salt energy storage integrated with combined heat and power system: scheme design and performance analysis

To investigate the flexibility and economic characteristics of a molten salt-combined heat and power (CHP) integrated system under different heat sources, this paper proposes a design scheme for a molten salt-CHP system based on flue gas heat storage, comparing it with main steam heat storage and reheated steam heat source schemes. First, a molten salt heat release sub-loop is designed, where the steam heated by the molten salt can either compensate for heating demands or enter the low-pressure turbine for work, meeting the flexible operation requirements of the unit. Secondly, in response to fluctuations in temperature and pressure parameters of the main steam and reheated steam in the flue gas heat storage scheme, a water-spray desuperheating device is arranged. Finally, a simulation experiment based on a 350 MW CHP was conducted, and the results show that the flue gas molten salt heat storage technology significantly reduces energy loss on the boiler side. Underrated load, compared to the main steam and reheated steam heat storage schemes, the flue gas heat storage scheme has the highest boiler exergy efficiency and total energy utilization efficiency, at 55.8% and 59.1% respectively. During the thermal storage process, the coal consumption index of the flue gas heat storage scheme decreases with increasing load, while conversely, during the heat release process, it increases with the load. The peak-shaving capacity increases with the load, reaching 78.4 MWh under the 75% THA condition. In the steam extraction heating scenario, coupling with the molten salt subsystem can expand the safe heating operation range of the original unit, increasing the maximum peak shaving range by 13.9%, and increasing the maximum heating capacity by 65.4%.

AN ADAPTIVE LOAD SHEDDING METHODOLOGY FOR RENEWABLE INTEGRATED POWER SYSTEMS

AN ADAPTIVE LOAD SHEDDING METHODOLOGY FOR RENEWABLE INTEGRATED POWER SYSTEMS

Ship Power System Network Reconfiguration Based on Swarm Exchange Particle Swarm Optimization Algorithm

As one of the important components of a ship, the ship’s integrated power system is an important safeguard for ships. In order to improve the service life of the ship’s power grid, the power system should be able to realize rapid reconstruction to ensure continuous power supply of important loads when the ship is attacked or fails suddenly. Therefore, it is of vital importance to study the reconfiguration technology of the ship’s integrated power system to ensure that it can quickly and stably cope with all kinds of emergencies in order to guarantee the safe and reliable navigation of the ship. This paper takes the ship’s ring power system as the research object and sets up the maximum recovery load and the minimum number of switching operations. The load is divided uniformly and the generator efficiency is balanced for the reconstruction of comprehensive function. It also sets up the system capacity, topology, and branch current limitations of the constraints to establish a mathematical model. The load branch correlation matrix method is used for branch capacity calculation and generator efficiency equalization calculation, and the load backup power supply path matrix is added on the basis of the matrix to judge the connectivity of some loads before reconfiguration. In this paper, for the network reconfiguration of the ship circular power system, which is a discrete nonlinear problem with multiple objectives, multiple time periods, and multiple constraints, we choose to use the particle swarm algorithm, which is suitable for global optimization, with a simple structure and fewer parameters; improve the particle swarm algorithm using the swarm exchange strategy by setting up two main and auxiliary swarms for global and local search; and exchange some of the particles with the golden ratio in order to keep the diversity of the populations. The simulation results of the network reconfiguration of the ship power system show that the improved algorithm can solve the power system network reconfiguration problem more effectively and provide a feasible reconfiguration scheme in a shorter time compared with the chaotic genetic algorithm under the same fault case test, and it also proves that the use of the swarm exchange particle swarm algorithm greatly improves the performance of reconfiguring the power grid of the ship.

Management of Hybrid Hydropower and Floating Solar Systems at Num Ngum 1 in Lao PDR

The integration of a hybrid hydro-floating solar power (HPP-FPV) system is covered in this study with the goal of improving energy management and producing more electricity. The production capacity of the hybrid system is almost twice as high as that of the original HPP system, demonstrating its potential for higher energy generation. Because it forecasts weather and modifies electricity generation accordingly. The analysis highlights how crucial it is to make sure that the hybrid system's electricity generation doesn’t go beyond the current power grid's maximum transmission capacity. This is essential to preserving the consistency and caliber of the power that is provided. The research's overall goal of increasing the output of renewable energy through creative system integration and practical management techniques is captured in the abstract.

An efficient control scheme for operational performance enhancement of vehicular fuel cell integrated power system

Recent advancements in fuel cell (FC) technology have positioned it as a promising alternative energy source across various stationary, mobile, and transportation applications. As fuel cell vehicles (FCVs) become increasingly prominent in the transportation sector, they also offer the potential to function as supplementary stationary energy providers when parked, thereby contributing to the grid. In this study, a control method to enhance the energy transfer capability of an FCV-integrated grid system is proposed. To manage energy transfer between grid and FCVs, classical strategies using fixed parameter controllers suffer from performance degradation due to the nonlinear and external parameter-dependent nature of the FC stacks. The proposed adaptive fractional-order proportional-integral strategy bears the advantage of self-tuning parameters feature for designing the control parameters of power conditioning unit. Using fractional-order control endows the system with memory and heredity, enhancing its ability to handle nonlinearity and uncertainty. Through case studies, it is demonstrated that the proposed strategy reduces vehicular FC output power variations by over 60 % while enhancing transient response by 50 % compared to classical control methods. Thus, the limitations such as low control flexibility, slow transient response, and high undershoot/overshoot rates addressed by the classical controllers are countered thanks to the developed strategy.

Moisture dynamics during high‐load fluctuations in transformers: Localised accumulation and interfacial transfer within oil/pressboard insulation

AbstractPower systems grapple with the challenges of high load rates and intermittent new energy sources integration. Transformers, as vital equipment, employ oil/pressboard (oil/PB) insulation. Uneven moisture distribution in this insulation can jeopardise safety thresholds, necessitating precise moisture assessment for grid stability. A novel mathematical model, adsorption–desorption and porous media moisture transfer (ADP‐MoT), is presented. This model incorporates adsorption and desorption processes within the porous pressboard, enabling a description of the dynamic moisture transfer between the oil and pressboard. Using this mathematical model, simulations for moisture dynamics were performed on a 750‐kV transformer across four typical days. The results indicate that temperature fluctuations are the primary driving factor for moisture migration at the oil/PB interface. Convection and diffusion contribute to moisture movement towards cooler regions. Fluid properties and structural characteristics induce a distinctive streamline‐shaped moisture flow within horizontal oil channels, with localised moisture accumulation in specific areas. Moreover, the analysis of 96 transient results uncovers potential free‐state moisture formation during severe conditions, underscoring the importance of monitoring the pressboard at winding bases during high load fluctuations. In conclusion, this study significantly contributes to scientifically identifying and addressing risks tied to new energy sources integration in power systems.

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.

Modeling Nuclear-Centric Scenarios for Ukraine’s Low-Carbon Energy Transition Using Diffusion and Regression Techniques

This study presents a mathematical model for forecasting the development of Ukraine’s Integrated Power System (IPS) until 2040, with a specific focus on the expansion of nuclear energy as a cornerstone of the nation’s low-carbon transition. The model is an extension of Frank Bass’s mixed influence diffusion model, incorporating both economic and technological factors. These factors are treated as stochastic variables essential for accurately predicting the evolution of an integrated energy system, particularly in the context of rapid renewable energy sources (RES) growth. The model employs regression techniques using generalized logistic curves, improving forecasting efficiency by aligning modeling parameters with experimental data. The study’s results indicate the potential for optimizing IPS components, including nuclear and thermal power generation, through the model’s application. The model is distinguished by its inclusion of economic and technological impacts, such as state matrices, control actions, and external influence matrices, which enhance the accuracy of simulations and predictions. The validation of the model, based on scenarios of electricity consumption and generation, shows significant alignment with observed trends, confirming the model’s reliability. The findings suggest that this model is an effective tool for developing and refining energy system scenarios, with nuclear energy playing a pivotal role in Ukraine’s sustainable energy future.

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.

Probabilistic Tide Calculation for Multi-Wind Farm Distribution Network Based on CATTSTS Model

Traditional probabilistic tide calculation methods often fall short in capturing the dynamic nature of these systems, leading to discrepancies between calculated and actual data. To address this, a dynamic probabilistic tide calculation model is proposed in this study, incorporating a wind farm probabilistic model enhanced by the CATTSTS model. This integration aims to optimize prediction accuracy by improving feature focusing and data fitting, thereby reducing prediction errors and providing more reliable data for power system adjustments and optimizations. Experimental results demonstrate that the model's predictions for wind speed series in multi-wind farm distribution networks closely match actual conditions, underscoring its practical applicability and reliability in real-world settings. Furthermore, the study highlights the impact of spatial and temporal correlations on model performance. This research contributes to advancing the management of renewable energy integration in power systems, offering insights for more informed decision-making and operational efficiency.

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.

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Aperiodic Coordination Scheduling of Multiple PPLs in Shipboard Integrated Power Systems

Application of cascaded neural network for prediction of voltage stability margin in a solar and wind integrated power system

Voltage stability is a paramount concern in the management of renewable-rich power systems. As the diffusion of renewable energy sources continues to increase, accurately estimating voltage stability margins becomes essential. This paper addresses the critical need for effective voltage stability margin estimation and presents a novel approach to predict the same on the solar and wind integrated power system. A new machine learning methodology is developed based on the cascaded neural network (CNN). The proposed CNN methodology effectively estimates the loadability margin in a renewable-rich power system under normal operating conditions, varied loading directions, and various contingency situations. The results of CNN are compared with different machine learning techniques and tested in the IEEE 30 bus system and IEEE 118 bus system under various operational circumstances. The results illustrate the efficiency of the suggested method for online assessment of a power system's Voltage Stability Margin. The proposed approach gives real-time insights into voltage stability margins by leveraging the power of data and powerful algorithms, allowing grid operators to proactively manage and optimize renewable energy integration while ensuring grid dependability and stability.

Modeling of heat pumps load profiles for power systems integration

Heat pumps (HPs) are one of the most efficient heating technologies; their mass adoption will be required to decarbonize energy systems. However, to do so will require a better understanding of how they will impact electric grid load. Methods are needed to estimate not just their peak demand but also their impact on hourly load profiles. In this paper, we propose two methods, using easily accessible data, for estimating future hourly load profiles following the adoption of large populations of residential HPs. The first method uses feeder load data disaggregation while the second method uses annual space heating end-use energy consumption, both taking into account the temperature dependencies on coefficient of performance and output heat capacity. A case study based on data from Summerside, PE, Canada, is used to demonstrate and evaluate the two methods.

First-Principles Investigation on Ru-Doped Janus WSSe Monolayer for Adsorption of Dissolved Gases in Transformer Oil: A Novel Sensing Candidate Exploration.

Using first-principles theory, this work purposes Ru-doped Janus WSSe (Ru-WSSe) monolayer as a potential gas sensor for detection of three typical gas species (CO, C2H2, and C2H4), in order to evaluate the operation status of the oil-immersed transformers. The Ru-doping behavior on the WSSe surface is analyzed, giving rise to the preferred doping site by the replacement of a Se atom with the formation energy of 0.01 eV. The gas adsorption of three gas species onto the Ru-WSSe monolayer is conducted, and chemisorption is identified for all three gas systems with the adsorption energy following the order: CO (-2.22 eV) > C2H2 (-2.01 eV) > C2H4 (-1.70 eV). Also, the modulated electronic properties and the frontier molecular orbital are investigated to uncover the sensing mechanism of Ru-WSSe monolayer upon three typical gases. Results reveal that the sensing responses of the Ru-WSSe monolayer, based on the variation of energy gap, to CO, C2H2, and C2H4 molecules are calculated to be 1.67 × 106, 2.10 × 105, and 9.61 × 103, respectively. Finally, the impact of the existence of O2 molecule for gas adsorption and sensing is also analyzed to uncover the potential of Ru-WSSe monolayer for practical application in the air atmosphere. The obtained high electrical responses manifest strong potential as a resistive sensor for detection of three gases. The findings hold practical implications for the development of novel gas sensing materials based on Janus WSSe monolayer. We anticipate that our results will inspire further research in this domain, particularly for applications in electrical engineering where the reliable detection of fault gases is paramount for maintaining the integrity and safety of power systems.

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Optimizing Microgrid Planning for Renewable Integration in Power Systems: A Comprehensive Review

Leveraging the Performance of Integrated Power Systems with Wind Uncertainty Using Fractional Computing-Based Hybrid Method

Leveraging the Performance of Integrated Power Systems with Wind Uncertainty Using Fractional Computing-Based Hybrid Method

Performance and optimization evaluation for integration of sCO2 power system into the aircraft propulsion system

The aviation industry accounts for part of the CO2 emissions contributing to climate change. The industry has established a target to reduce 2050 net aviation carbon emissions by 50 % relative to 2005 levels. With this in mind, waste heat recovery is a key pathway to achieve reduced emissions and improve system efficiency. The waste heat may potentially be converted to electric power using a supercritical CO2 Brayton power cycle. The sCO2 power system offers the advantage of compactness owing to the high working fluid density, which is an important consideration for aircraft performance. The present work focuses on the integration of the sCO2 power system into the aircraft propulsion system and evaluation of its performance. Detailed optimization of the sCO2 waste heat system will be evaluated with a focus on cycle efficiency and net power under different operating conditions, including ground, takeoff, climb, cruise, and landing operations. The study is divided into two parts with two different turbofan engines, one with a nominal thrust of 30 kN and the other with a nominal thrust of 9 kN. The first part shows the effect and operation of the waste heat recovery unit under the different operating conditions. The second part is focused on cycle optimization and performance evaluation. The results demonstrate the potential of waste heat recovery during a range of operational conditions. The sCO2 cycle efficiency can reach between 25 and 39 % (depending on aircraft engine) with net power output in the range of 100 to 260 kW.