Power Integration Research Articles

EU geographical islands as leaders of green energy transition

This paper reviews how European islands are taking the lead in the European Union (EU) Clean Energy Transition by reviewing the lessons learned in the EU Bridge initiative and in a number of EU co-funded projects such as NESOI, RE-EMPOWERED, REACT, IANOS, LOCALRES, MASTERPIECE, SINNOGENES, SMHYLES, STEPWISE, and ISLET. Islands encounter significant difficulties in the management of their energy systems, including strong seasonal variations in energy demand, high operational costs and GHG emissions for energy production, weak energy grids, lack of technical skills, and difficult access to finance. However, they also have positive features that make them ideal laboratories for energy transition, including high potential for renewables, small-scale and strong community structures, and high energy prices, which make most solutions cost-effective. Each of the projects contributing to the paper has been supporting the islands’ energy transition, leveraging different enabling technologies, such as renewable energy production systems, smart grids, advanced energy storage systems, and local energy community schemes. The results from these projects underline the need for tailored energy planning, considering geographical and socio-economic particularities, the need to engage the local population in the definition of the most suitable decarbonization pathways for the island, and a number of lessons learned on the technologies that have the highest potential for being tested on islands and then being replicated on the mainland. Therefore, this study concludes that renewable energy solutions coupled with different technologies (storage, mobility, district heating/cooling, etc.) and leveraging powerful community integration confirm that European islands can drive the decarbonization strategy of the EU.

Fixed-time tracking control for multiple nonholonomic mobile robots based on visual servoing

The problem of fixed-time tracking control for multiple nonholonomic mobile robots systems is studied, where a camera with uncalibrated parameters is used to measure the status of each robot. By employing graph theory, fixed-time stability theory, adding a power integrator technique and Lyapunov stability analysis, a novel type of fixed-time controller is proposed for each mobile robot, and the control schemes can make the tracking errors of each robot converge to zero within fixed time. The convergence time obtained in this paper is independent of initial conditions of the whole system, this control method is more practical in some situations. In the end, some simulation examples are presented to illustrate the effectiveness of the proposed method.

Integrated model and automatically designed solver for power system restoration

Power system restoration strategies schedule the power plants to re-energize the power network and loads after a blackout. These strategies are of great significance to ensure a resilient power system. Most power system restoration models consider either certain restoration stage(s) or all stages, with each or some of the stages managed independently. They destroy the essential characteristics of the interaction among the restoration stages and lead to sub-optimal solutions. To address this issue, in this paper, we first propose an improved integrated power system restoration (IPSR) model covering the entire restoration process without decoupling and sectionalization. We then develop both mathematical and metaheuristic solvers for the proposed model. In particular, we introduce the automated solver design paradigm to the highly non-linear constrained restoration problem-solving. By the paradigm, we automatically design the metaheuristic solver that gains enhanced performance beyond handcrafted solvers without algorithmic expertise. It is suggested that the automatically designed metaheuristic solver is a promising new option in variable and complicated power system operation scenarios without algorithmic expertise. Finally, a comprehensive case study demonstrates the proposed model’s significance and the proposed solver’s efficiency. Numerical tests show that the proposed IPSR model and solver obtained over 20% lower restoration time than current restoration methods, demonstrating IPSR’s and the proposed solver’s efficiency and effectiveness.

Power Quality Improvement of PV fed Grid Connected System using ANN Controlled Shunt Active Power Filter

Harmonics are common in integrated power systems, especially with the increasing use of nonlinear loads (NLL), such as those found in photo voltaic (PV) systems connected to the grid. Traditional LC filters Shunt active power filters (SAPF) have been developed to effectively correct harmonics and improve power quality performance. This study presents a three-phase voltage-fed SAPF implementation to mitigate harmonics using an artificial neural network (ANN) controller. The SAPF control system focuses on generating reference source currents to counterbalance the harmonic effects caused by NLL. The model's effectiveness is validated using experimental data gathered from a nonlinear load through MATLAB/Simulink simulations.

Cultural Assimilation and Identity Formation of Freedmen in the Roman Provinces: Case Studies from Gaul and Egypt

This essay discusses the cultural assimilation and identity formation of free people in Roman Gaul and the Egyptian provinces. Through comparative analyses, the article explores how freed people navigated new social roles and developed hybrid identities influenced by Roman, Celtic and Egyptian cultures. In Gaul, the freedmen underwent a mixed process of Romanisation and cultural exchange that resulted in the formation of a Roman-Celtic identity, especially among the elite. In contrast, Egyptian freedmen maintained a strong attachment to their native culture, despite widespread Roman and Hellenistic influences. The critical factors of urbanisation, socio-economic status and legal constraints shaped the distinctive identity trajectories of the freedmen in these provinces, providing insights into the complex interplay between power, identity and cultural integration in the Roman world. Overall, the cultural assimilation and identity formation of freed people in Roman Gaul and the Egyptian provinces highlight the complexity of cultural integration across different regions. By examining these processes, we gain deeper insights into the interplay between power, identity, and cultural adaptation within the Roman Empire.

Strategi Smart Power dalam Menghadapi Ancaman Gerakan Organisasi Papua Merdeka Guna Mendukung Keamanan Nasional

The security situation in Papua continues to be a sensitive issue affecting national stability, with the main threat coming from the Free Papua Organization (OPM). OPM seeks the separation of Papua from Indonesia through armed action, international campaigns, and propaganda, with a significant increase in violent incidents from 2015 to a peak in 2023. Although the Special Autonomy policy has been implemented, the measure has not been able to fully address the existing tensions. This study aims to analyze the Smart Power strategy in dealing with OPM threats and supporting national security, using qualitative descriptive methods and AHP SWOT analysis. The findings suggest that the success of this strategy is highly dependent on the integration of diplomacy, military power, political support, and infrastructure development. Operational collaboration between the TNI, the National Police, and BIN in implementing socio-economic programs plays a key role, with diplomacy supporting stability and conflict management. Understanding local culture is also a critical factor, where the integration of cultural approaches with infrastructure development efforts is aimed at easing tensions and increasing public support for the government. AHP's SWOT analysis identified key strengths such as inter-agency synergies, while weaknesses lie in reliance on consistent political support. The opportunity lies in improving well-being through sustainable development, while a major threat remains emerging from the OPM's activities and its international support. In conclusion, an adaptive Smart Power approach, with cross-sectoral collaboration and cultural sensitivity, is key in maintaining long-term stability and maintaining Indonesia's sovereignty in Papua.

Energy analysis of third-octave-mode chatter in tandem cold rolling

Mill chatter has become a complicated issue in the cold tandem rolling process due to the demand for higher rolling speeds and thinner specifications. The third-octave-mode chatter is a prominent vibration form that leads to serious product degradation and equipment damage. This study aims to investigate the energy generation and transmission of the mill-strip system during third-octave-mode chatter using the upper bound method and power flow approach. To obtain the strip forming power, a parabolic cross-section dynamic velocity field is used, which is considered more appropriate for the unsteady rolling process. Additionally, a new linear yield criterion, called the golden section (GS) yield criterion, is established to simplify the integration of internal plastic deformation power. The energy transmission and dissipation in the mill structure are analyzed using a finite element power flow model. The energy distribution variations affected by the rolling speed and time are discussed in detail, and the main cause of vibration intensification is explained. Since the intermediate roll shifting (IRS) has a significant influence on mill stiffness, the energy transfer concerning IRS is also investigated. The results are consistent with previous research. Furthermore, it is concluded that reducing IRS and improving mill stiffness are effective ways to promote mill stability and critical rolling speed.

A Q‐Learning and Fuzzy Logic Control of Hybrid Energy Storage System Using Two Stage Low‐Pass Filter to Smooth Power Fluctuations in Microgrid

ABSTRACTThe intermittent and fluctuating nature of wind turbine output power is increasingly being recognized as a significant issue adversely impacting the power quality and stability of electrical grids. With the increasing integration of wind power, this challenge cannot be underestimated. However, a potential solution for mitigating the adverse effects of such fluctuations lies in the Hybrid Energy Storage System (HESS), which encompasses battery energy storage systems (BESS) and supercapacitors (SC). The HESS is equipped with flexible operational modes for charging and discharging, thus enabling grid‐connected microgrids to possess the ability to counteract these oscillations. In this article, a control strategy based on the combination of Q‐learning and fuzzy logic control approaches is presented for tuning the parameters of a utilized two‐stage variable time constant low‐pass filter (LPF) in a grid‐connected microgrid. The proposed strategy adaptively tunes the time constants of LPFs to mitigate wind power fluctuations. Furthermore, practical constraints for the energy storage systems and their interfaced converters, such as preventing overcharge/discharge, ramp rate requirements, and certain maximum power conversion ranges, have been taken into account. Numerical simulation results verify the effectiveness of the proposed two‐stage variable time constant LPF for output wind power fluctuation reduction considering practical constraints of HESS.

The Need for Solar Power Energy Utilization in the Northern Part of Nigeria for Sustainability and Economic Growth (A case study of Nigeria)

Northern Nigeria is one of the most recognizable areas, having vast amounts of solar radiation and rapidly growing populations. Thus, there is an opportunistic aspect of the use of solar power in this region. Since this region enjoys all-year sunshine, it is one of the best places for the integration of solar energy. With huge problems of energy she presently faces such as irregular supply of electricity, people's growth and thus demand, solar power presents an immediate usable, reliable, and sustainable solution. The energy demand in northern Nigeria is expected to sharply increase from about 48 billion kWh in 2024 to 64.5 billion kWh by 2034. This escalated energy requirement directly puts a demand for an efficient and sustainable source of energy, and as such, the promise of steady and renewable energy supply through solar power makes it very appealing. The average regional solar radiation exceeds 6 kWh/m2 per day, which points towards a very vast potential for solar energy generation. But to meet the energy requirements that will arise in the future from solar, it would call for a tremendous investment in infrastructure. This is because it is estimated that approximately 246,000 km2 of solar panels might be needed by 2034 in order to be able to cover the energy needs of the country. However, this massive area is required, but the benefits of solar energy do not stop there at solving shortages of energy. Solar power would increase economic activities due to job creation, stimulation of local industries, and the end reliance on fossil fuels. It has implications on environmental sustainability as it reduces greenhouse gas emissions and promotes cleaner practices of energy consumption. Northern Nigeria will unlock a stable and reliable source of power through investment in this infrastructure. This is important for economic development, thereby eventually improving the quality of life. Indeed, the integration of solar power addresses the immediate needs of satisfying energy challenges but builds a foundation for sustainable growth and environmental stewardship over the long term. Solar power in northern Nigeria is important for ensuring future demands for energy and significant economic development while emphasizing sustainability within the environment. A lot still has to be tapped into by tapping into the solar resources that are available. Key words: Solar Energy,Sustainability,Economic Growth,Energy Demand,Renewable Resources

Static security assessment of renewable integrated power systems using ensemble of modified ELM with unsupervised feature learning technique

The integration of renewable energy sources into power systems poses various challenges for static security assessment, including intermittency and variability of renewable generation, uncertainty in forecasting and impact on grid stability. Overcoming these challenges involves utilizing advanced modelling methods, refining forecasting algorithms, enhancing monitoring and control systems for the grid, and developing robust static security assessment approaches specifically designed for power systems integrated with renewable energy generation. A modified Extreme learning machine (ELM) based ensemble approach is proposed in this study, where ELM is combined with Levenberg-Marquardt (LM) backpropagation technique to improve the accuracy and robustness of prediction. Further, computational efficiency is improved through an unsupervised feature learning technique in the form of autoencoder to reduce the curse of dimensionality. The ensemble technique provides a comprehensive solution for evaluating the static security of power systems in the presence of uncertainties introduced by renewable energy sources. The uncertainties are incorporated into the test systems by simulating random solar and wind scenarios using a well-established Monte Carlo (MC) simulation method. The effectiveness of this approach is demonstrated through numerical testing on modified IEEE 14-bus, 30-bus, 118-bus, and an Indian practical 75-bus systems. Results show that the proposed model outperforms base learners in terms of reliability and efficiency.

Predefined-time stabilisation of stochastic low-order nonlinear systems with asymmetric output constraint

This paper researches the predefined-time stabilisation problem for a class of stochastic low-order nonlinear systems with asymmetric output constraint whose nonlinearities satisfy high-order and low-order growth conditions. By introducing a novel nonlinear transformation and utilising the adding a power integrator technique, a continuous state feedback controller is designed. The proposed control scheme can guarantee that the output constraint will not be violated almost surely and the trivial solution of the closed-loop system is stochastic predefined-time stable. Finally, two simulation examples are provided to demonstrate the effectiveness of this control method.

An Optimization Strategy for Unit Commitment in High Wind Power Penetration Power Systems Considering Demand Response and Frequency Stability Constraints

To address the issue of accommodating large-scale wind power integration into the grid, a unit commitment model for power systems based on an improved binary particle swarm optimization algorithm is proposed, considering frequency constraints and demand response (DR). First, incentive-based DR and price-based DR are introduced to enhance the flexibility of the demand side. To ensure the system can provide frequency support, the unit commitment model incorporates constraints such as the rate of change of frequency, frequency nadir, steady-state frequency deviation, and fast frequency response. Next, for the unit commitment planning problem, the binary particle swarm optimization algorithm is employed to solve the mixed nonlinear programming model of unit commitment, thus obtaining the minimum operating cost. The results show that after considering DR, the load becomes smoother compared to the scenario without DR participation, the overall level of load power is lower, and the frequency meets the safety constraint requirements. The results indicate that a comparative analysis of unit commitment in power systems under different scenarios verifies that DR can promote rational allocation of electricity load by users, thereby improving the operational flexibility and economic efficiency of the power system. In addition, the frequency variation considering frequency safety constraints has also been significantly improved. The improved binary particle swarm optimization algorithm has promising application prospects in solving the accommodation problem brought by large-scale wind power integration.

Dynamic graph structure and spatio-temporal representations in wind power forecasting

Wind Power Forecasting (WPF) has gained considerable focus as a crucial aspect of the successful integration and operation of wind power. However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. Specifically, a graph structure learning (GSL) module is introduced to dynamically construct task-related correlation matrices via backpropagation to mitigate the inherent inconsistency and randomness of wind power data. Additionally, a dual-scale temporal graph learning (DTG) module is further proposed to explore the implicit spatio-temporal features at a fine-grained level using different skip connections from the constructed graph data. Finally, comprehensive experiments are performed on the collected Xuji Group Wind Power (XGWP) dataset, and the results show that DSTG outperforms the state-of-the-art spatio-temporal methods by 10.12% on the average of root mean square error and mean absolute error, demonstrating the effectiveness of DSTG. In conclusion, our model provides a promising approach.

State of Charge Balancing Control Strategy for Wind Power Hybrid Energy Storage Based on Successive Variational Mode Decomposition and Multi-Fuzzy Control

To address the instability of wind power caused by the randomness and intermittency of wind generation, as well as the challenges in power compensation by hybrid energy storage systems (HESSs), this paper proposes a state of charge (SOC) balancing control strategy based on Successive Variational Mode Decomposition and multi-fuzzy control. First, a consensus algorithm is used to enable communication between energy storage units to obtain the global average SOC. Then, the Secretary Bird Optimization Algorithm (SBOA) is applied to optimize the Successive Variational Mode Decomposition (SVMD) and Variational Mode Decomposition (VMD) for the initial allocation of wind power, resulting in the smoothing power for hybrid energy storage and the grid integration power. Finally, considering the deviation between the current SOC of the storage units and the global average SOC, dynamic partitioning is used for multi-fuzzy control to adjust the initial power allocation, achieving SOC balancing control. Simulations of the control strategy were conducted using Matlab/Simulink, and the results indicate that the proposed approach effectively smooths wind power fluctuations, achieving stable grid integration power. It enables the SOC of the HESS to quickly align with the global average SOC, preventing the HESS from entering unhealthy SOC regions.

Wind Turbine Operation Status Monitoring and Fault Prediction Methods Based on Sensing Data and Big Bang–Big Crunch Algorithm

As wind power generation technology rapidly advances, the threat of wind turbine failures to the secure and stable operation of the power grid is gaining increasing attention. Real-time monitoring of operation status and predicting potential failures in wind turbines are indispensable requirements for the safe integration of wind power. In this paper, a model based on the least squares support vector machine (LSSVM), whose parameters are optimized by the Big Bang–Big Crunch algorithm, is constructed to improve the monitoring of wind turbine operation status and fault prediction accuracy. The research methodology consists of several key stages. Firstly, the initial wind turbine sensing data are preprocessed, utilizing factor analysis to reduce dimensionality and obtain the main influencing factors of wind turbine operation. Then, an improved failure prediction model for wind turbines, based on the least squares support vector machine, is developed using the preprocessed data. The model parameters are optimized by utilizing the Big Bang–Big Crunch optimization algorithm to enhance the prediction accuracy of wind turbine failures. Finally, the feasibility and accuracy of the proposed method are validated through a case study conducted on a regional power grid with wind farm integration.

The Role of Predictive Analytics in Disease Prevention : A Technical Overview

This article explores the transformative potential of predictive analytics in healthcare, focusing on its applications in disease prevention and public health management. It examines the power of data integration from diverse sources, the use of predictive modeling for risk stratification, and the broader implications for public health surveillance and chronic disease management. The article also discusses the significant growth of AI in the healthcare market and highlights successful implementations of predictive analytics across various medical domains. Additionally, it addresses the key challenges in implementing these technologies, including data privacy concerns, integration issues, model accuracy, and ethical considerations. Through numerous case studies and statistical evidence, the article demonstrates how predictive analytics is revolutionizing healthcare by enabling more accurate, personalized, and proactive approaches to disease prevention and management.

Magnetically Integrated Multiport Converter for Energy Management in DC-Powered Buildings

Multiport converters have gained attention in the last few years as a key component for dc-powered buildings, more electric aircraft, and maritime applications. For buildings, the prevalence of dc appliances and electronic loads increases the need for efficient integration with both ac grid and local generation. Ac-dc multiport converters play an important role in this integration because dc distribution has been considered for power integration in modern buildings. In addition, the growing complexity of loads and sources within buildings has driven interest in developing flexible solutions to provide highly efficient integration. Furthermore, ongoing developments in standardization for in-building dc grids underscore the importance of considering specific requirements for future designs. This paper proposes a multiport power converter (ac/dc/dc) to integrate dc-powered buildings into an ac grid. By providing a multiport solution, it is possible to reduce the power processing stages and the component count. To evaluate the effectiveness of this concept, a 5-kW prototype was built and tested, including efficiency evaluation. In addition, emerging standards are discussed to tailor the power converter design for building applications.

Optimizing grid-dependent and islanded network operations through synergic active-reactive power integration

DG integration improves distribution network performance and also enables microgrid (MG) formation for islanded operation. However, existing studies assume an isolated operation approach for islanded mode, which is unrealistic and increases overall system costs while adding complexity to the grid. This paper presents a dual-stage methodology for the efficient operation of distributed generation (DG) and shunt capacitor banks (SCBs) in radial distribution networks (RDNs) for both grid-integrated and islanded modes. The first stage proposes an improved Jaya algorithm (IJaya) to optimize DG and SCB allocation during grid-connected operation, aiming to reduce power loss and improve voltage profiles. IJaya employs a dynamic weight-based grid search to address premature convergence. The second stage formulates a multiobjective function for the islanded operation to minimize power loss and underutilization of DG-SCB capacity. Case studies on IEEE 33- and 69-bus RDNs show IJaya reduces power loss by 38.84 % and improves voltage by 3.26 % in grid-connected networks compared to existing methods. The proposed analytical approach for islanded operation tunes the source power factor, reducing DG-SCB underutilization by up to 15.83 % for power factors between 0.8 and 0.93. The results demonstrate that operating distributed resources near optimal capacities meets a larger portion of the island network's energy needs.

Advancements in Nanowire-Based Devices for Neuromorphic Computing: A Review.

Neuromorphic computing, inspired by the highly interconnected and energy-efficient way the human brain processes information, has emerged as a promising technology for post-Moore's law era. This emerging technology can emulate the structures and the functions of the human brain and is expected to overcome the fundamental limitation of the current von Neumann computing architecture. Neuromorphic devices stand out as the key components of future electronic systems, exhibiting potential in shaping the landscape of neuromorphic computing. Especially, nanowire (NW)-based neuromorphic devices, with their advantages of high integration, high-speed computing, and low power consumption, have recently emerged as candidates for neuromorphic computing technology. Here, a critical overview of the current development and relevant research in the field of NW-based neuromorphic devices are provided. Neuromorphic devices based on different NW materials are comprehensively discussed, including Ag NW-based, organic NW-based, metal oxide NW-based, and semiconductor NW-based devices. Finally, as a foresight perspective, the potentials and the challenges of these NW-based neuromorphic devices for use as future brain-like electronics are discussed.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.