Grid System Research Articles

MXene-modified lemon peel-based composite phase change material with excellent photo-thermal conversion efficiency, thermal storage capacity and thermal conductivity for thermal management of electronic components

The preparation of multifunctional composite phase change materials using green technology to achieve an efficient energy storage and conversion remains an issue of concern. In this paper, a lemon peel-based porous carbon (LPC) composite phase change material (CPCM) was prepared by using polyethylene glycol (PEG) 6000 as a phase change medium and MXene nanosheet-modified carbonized lemon peel as a support carrier. MXene nanosheets were bonded to a lemon peel-based porous carbon skeleton to construct an excellent continuous three-dimensional thermally conductive grid system. What makes it noteworthy is that the presence of MXene nanosheets substantially improves the thermal conductivity (0.66 W/mK) and photo-thermal conversion efficiency (θ = 96.4 %) of MXene-modified lemon peel-based porous carbon/polyethylene glycol composite phase change materials (PEG/LPC@M). It is noteworthy that PEG/LPC@M has remarkable thermal stability and still has high enthalpy after 100 cycles, which further proves the recyclability of PEG/LPC@M and PEG/LPC@M embodies great potential in the field of thermal management of electronic components. In conclusion, a novel multifunctional CPCM was prepared by a simple and green process method in this study, which provides a new idea for waste recycling and reuse, and also has a wide range of applications in thermal management of electronic devices.

Advancements and Challenges in Integrating Renewable Energy Sources into Distribution Grid Systems: A Comprehensive Review

Abstract The issues in integrating renewable energy sources (RES) into distribution grid structures are thoroughly examined in this research. It highlights how important this integration is to updating the energy system and attaining environmental goals. The study explores the specific problems confronted by means of on-grid power structures, along with overall performance metrics and compatibility issues. Additionally, it presents a thorough assessment of the attributes for various RES hybrid systems, together with technology from the fields of solar, wind, batteries, and biomass. to be able to spotlight the significance of innovative solutions inside the dispersed technology environment, the integration of RES with CCHP structures is investigated. This study addresses the numerous problems with RES integration into the grid to better comprehend their intricacies. The viability of RES integration is supported by real-world case studies that provide operational examples of dispersed generation systems. The study concludes by discussing the technical, financial, and grid-related problems associated with distributed generating systems' limits and highlighting the contribution of cutting-edge technology and artificial intelligence to their removal. In conclusion, the report highlights the development toward smarter grids and improved distributed generating capacities as the essential component of a robust and sustainable energy future.

Axial behavior of concrete cylinders retrofitted with a hybrid system of CFRP textile grid and engineered geopolymer composite

Fiber-reinforced polymer (FRP) textile grid-reinforced engineered cementitious composites (ECCs) are effective in strengthening concrete structures. Considering environmental interest, engineered geopolymer composites (EGCs) have been employed as an alternative to ECC. In this study, we present a retrofit scheme for CFRP textile grid-reinforced EGC matrix composites for concrete cylinders and compare the axial behavior of strengthened concrete cylinders with a conventional FRP-reinforced ECC strengthening system. Axial compressive specimens were tested for various parameters, including the number of FRP textiles, original concrete strength, and damage level of the original concrete cylinder. The results indicated that the strengthened concrete cylinders failed in a ductile manner owing to the progressive rupture of the CFRP textile grids. The CFRP-EGC strengthening layers improved the axial bearing capacity, stiffness, and ductility of the concrete cylinders. The axial bearing capacity increased by 89.4%–170.4 %; therefore, the contribution of enhanced confinement effect was 36.7–113.6 %. The confinement effect provided by the grids ranged from 0.805 to 1.958 MPa and played a major role in the CFRP-EGC strengthening layers. Comparatively, the CFRP grid utilization is more efficient in the EGC matrix than in the traditional ECC matrix. Moreover, the influence of the pre-damage on the behavior of the concrete cylinders was compensated for by the strengthening layers.

A Hybrid Wild Horses Optimization (WHO) and Dwarf Mongoose Optimization (DMO) method for optimum energy management in SG system

AbstractA hybrid technique is proposed for the energy management (EM) of smart grid (SG) systems. The proposed method integrates the Wild Horse Optimization (WHO) and dwarf mongoose optimization (DMO) methods; hence, it is named the WHO–DMO approach. The Micro‐grid (MG)‐tied system is a combination of battery, micro turbine (MT), photovoltaic (PV), and Wind Turbine (WT). The key aim of the proposed approach is to manage the resources and power of the SG model and reduce the cost of electricity. The objective of the system is to improve load demand. The WHO method is enhanced by the DMO method, which minimizes the objective of the system. Access to power demand, state of charge (SOC), and renewable energy sources (RESs) for storing elements are considered constraints of the system. The unit of renewable power systems relies on batteries as energy sources to stabilize and sustain stable and consistent output power throughout the operation. The proposed technique is done in MATLAB platform and its implementation is calculated using the existing methods. From the simulation, the proposed method has less cost and higher power than the existing methods.

Energy Load Forecasting Techniques in Smart Grids: A Cross-Country Comparative Analysis

Energy management systems allow the Smart Grids industry to track, improve, and regulate energy use. Particularly, demand-side management is regarded as a crucial component of the entire Smart Grids system. Therefore, by aligning utility offers with customer demand, anticipating future energy demands is essential for regulating consumption. An updated examination of several forecasting techniques for projecting energy short-term load forecasts is provided in this article. Each class of algorithms, including statistical techniques, Machine Learning, Deep Learning, and hybrid combinations, are comparatively evaluated and critically analyzed, based on three real consumption datasets from Spain, Germany, and the United States of America. To increase the size of tiny training datasets, this paper also proposes a data augmentation technique based on Generative Adversarial Networks. The results show that the Deep Learning-hybrid model is more accurate than traditional statistical methods and basic Machine Learning procedures. In the same direction, it is demonstrated that more comprehensive datasets assisted by complementary data, such as energy generation and weather, may significantly boost the accuracy of the models. Additionally, it is also demonstrated that Generative Adversarial Networks-based data augmentation may greatly improve algorithm accuracy.

Optimized Dynamic Vehicle-to-Vehicle Charging for Increased Profit

Many challenges have arisen as a result of the rapid growth of the electric vehicles (EVs) market, due to the lack of charging infrastructure capable of handling such a large number of EVs. To alleviate power grid system overloads and reduce the cost of corresponding infrastructure deployments, a direct vehicle-to-vehicle (V2V) energy exchange strategy has become an emerging research topic. In this paper, we formulate the problem of V2V energy charging on a time–space network and develop a dynamic-programming solution methodology for efficiently finding the solution. The algorithm can pair and route the energy supplier (ES) and the requester (ER) in such a way that maximizes the supplier’s profit. Specifically, the ES is incentivized to rendezvous ERs at any encounter nodes in order to dispense the requested energy amount through platooning. Unlike existing V2V charging solutions, our approach involves charging while vehicles are in motion. We validate the effectiveness of our approach in maximizing the profit of the ES and reducing the incurred overhead on the ER in terms of increased trip time, distance, and energy consumption.

Impacts of wireless charging system for electric vehicles on power grid

Electric Vehicles (EVs) have become a viable choice for cutting carbon emissions in the field of road transportation. EVs will be incorporated into the electrical grid at an increasing rate in the near future. EVs themselves are becoming immense power consuming load on the power grid system as a result of the growing number of EVs, mostly light-duty EVs. In addition to posing major technical risks such power quality issues, voltage swings, harmonic injection, battery deterioration, and grid instability, the unplanned installation and rapid adoption of charging stations might impede the smooth functioning of the power distribution system. Therefore, this paper analyzes and summarizes the potential issues and solutions for Fast Wireless Charging (FWC) that allows for the wireless transmission of very high-power levels (>20 kW) from the power grid to explore the impact of EV charging on the power system network. This need can have far-reaching effects on the load profile, operation, and design of the distribution network. In order to avoid exceeding the grid's technical restrictions, it is crucial to find the maximum feasible deployment level before rolling out an FWC network. This study provides a method for assessing how FWC technologies influence distribution networks. Using a realistic model of an Indian MV distribution feeder, the proposed methodology is used, providing both quantitative and qualitative results that serve as potential directions for future research studies.

Multi-distributed resource coordination based on GAMS (general algebraic modeling system)

To achieve the economic operation of distributed energy resources in the grid, a GAMS-based multi-distributed resource optimization and control method is proposed. Firstly, considering the coexistence of electricity load and heat load in the grid system, a virtual power plant structure involving heat generation from distributed energy is established. Secondly, the General Algebraic Modeling System (GAMS) is utilized to create a detailed mathematical model for the distributed resources within the virtual grid. Subsequently, considering the influence of various uncertain factors, the CLPEX solver is employed for optimization and solution. Research results indicate that during the operation of the virtual power plant, the introduction of system heat load significantly enhances the expected profit and improves the economic efficiency of the operation.

Deep Fuzzy Nets Approach for Energy Efficiency Optimization in Smart Grids

Using smart grids has become crucial for achieving efficient and sustainable energy management. One of the main challenges in smart grids is optimizing energy efficiency by managing and controlling electricity generation, transmission, and distribution. The Deep Fuzzy Nets (DFN) approach has been proposed as a novel technique that combines the capabilities of deep learning and fuzzy login to optimize energy efficiency in smart grids. The proposed approach utilizes a deep learning architecture to learn the complex relationships between various parameters within the smart grid system. The fuzzy logic component handles uncertainties and imprecision’s in the data, making the DFN approach well-suited for real-world energy management applications. The proposed approach can provide accurate and reliable predictions and enhancing energy efficiency in a dynamic and evolving smart grid environment. The proposed deep fuzzy nets approach reached 91% sensitivity, 94.45% specificity, 92/37% prevalence threshold, and 96.54% critical success index. This approach has been tested in various energy systems and has demonstrated capabilities to improve system-level energy efficiency while still giving users control of their energy usage. As energy efficiency optimization in intelligent grids continues to be a primary focus of energy research, deep fuzzy nets could provide a powerful solution for energy optimization.

Analysis of the Monitoring and Identification Effect of Cognitive Service Technology on Dc System in Power Grid

This study investigates the impact of cognitive service technology on the monitoring and identification capabilities within a direct current (DC) system in a power grid. This paper introduces a novel Pattern Recognition Neural Network (PRNN) by integrating a Pattern Recognition Algorithm (PRA) and Convolutional Neural Networks (CNN) to analyse patterns in the DC system's data. With the increasing complexity and interconnectivity of modern power systems, the need for advanced monitoring and identification solutions becomes crucial. Cognitive service technology, known for its adaptability and learning capabilities, offers a promising avenue for enhancing the performance and reliability of DC systems. The research employs a comprehensive approach, incorporating data analysis, modelling, and simulation to assess the effectiveness of cognitive service technology in monitoring and identifying critical parameters within the DC system. The study aims to contribute valuable insights into the application of cognitive services for improving the overall efficiency and resilience of power grids operating on direct current, thereby fostering advancements in smart grid technologies

Research on intelligent auxiliary regulation technology of large power grid section based on artificial intelligence

In the modern era, large-scale renewable energy systems are integrated with advanced power systems and provide efficient operations. Also, optimized power systems require accurate energy generation and effective control systems to manage and ensure a stable power supply. Nevertheless, uncertainties are the intermittent balance of supply and high electricity demand. In addition, conventional power sources are not applicable for this challenging task and increase electricity costs. Therefore, an efficient Neuro Fuzzy Single phase Unified Power Quality Conditioner with Maximum Power Point Tracking (NF-SP UPQC -MPPT) strategy is developed for enhancing the grid-connected power systems. Here, Neuro-fuzzy logic is used as dynamic reactive power compensation in the grid. Also, this logic can efficiently handle the Energy storage system (ESS). Then SP UPQC was used to enhance power quality in electrical distribution systems. It combines both series and shunt compensators to mitigate various power quality issues such as voltage sags, harmonics, and unbalance. After that MPPT was utilized to extract the maximum power from the grid system. Model Predictive Control (MPC) controller to determine the overall stability and performance of the system. Moreover, the developed model was implemented on the MATLAB platform and performance is analyzed in terms of voltage deviation, grid current, reactive power fluctuations and Total Harmonic Distortion (THD).

An Overview of Opportunities and Challenges to Vehicle-to-Grid Integration and Bahrain Perspective

Transportation sector is among the largest consumers of fossil fuels, which also makes it a significant contributor to the greenhouse gas emissions. A great deal of effort has been made to reduce this reliance of transportation on fossil fuel for its operation. These efforts have resulted in innovations in vehicle technology which include vehicles operating on electric and hybrid energy. The advent of these technologies has showed the way towards new possibilities to further increase the efficiency of energy systems and their utilization. One such system utilizes the integration of electric vehicles with the national grid system, it is referred to as vehicle-to-grid (V2G) technology. This technology provides a cost-effective and efficient solution to address the problems associated with the high demand and variable supply of renewable energy systems. This study provides a brief overview of the research conducted in this area, consequently identifying the opportunities and challenges related to this technology. Research shows that V2G integration has environmental benefits in the form of reducing CO2 emissions and reducing reliance on traditional energy sources. In spite of this, the economic feasibility of this technology requires efforts for standardization, infrastructure expansion and smart charging strategies. This paper is expected to provide future direction of work for all stakeholders, including academia, industry, and governmental bodies. There are collaborative efforts required to standardize the vehicle and grid technology and infrastructure expansion, while the research needs to focus on optimization strategies. In the end, discussion on the implementation of this technology in Bahrain is also presented. In this context, it was found that the major challenges are due to the current reliance on fossil fuel and the trend of increase in car ownership which must be addressed before taking steps towards V2G systems.

Bidding strategies for multi-microgrid markets taking into account risk indicators

A large proportion of new energy generation is integrated into the power grid, making it difficult for the power grid system to maintain reliable, stable, and efficient operation. To address these challenges, this article proposes a multiple microgrid hierarchical optimization structure based on energy routers as the core equipment for energy regulation within microgrids. Considering the uncertainty of renewable energy generation within microgrids, a two-layer energy management bidding strategy based on risk indicators is further proposed. In the process of trading, with the goal of maximizing a comprehensive economy, the energy trading model of the distribution network center and energy routers is divided into two sub-objectives for solving. In the first stage, based on the interests and energy supply and demand relationships of each microgrid, a risk assessment model considering wind and solar uncertainty is established to determine the risk preferences and expected returns of each microgrid. In the second stage, the original problem is decomposed into two sub-problems: the minimum cost sub-problem and the maximum transaction volume sub-problem. An asymmetric bargaining mechanism is adopted to determine the production and sales payment of the microgrid containing energy routers based on the contribution values of energy routers in each microgrid. Finally, the rationality and effectiveness of energy routers as an intelligent decision-maker in energy optimization are verified in a three-node system.

Fault-tolerant shunt active power filter with synchronous reference frame control and self-tuning filter

This research proposes a novel method that combines Proportional-Integral (PI) control, Artificial Neural Networks (ANNs), and Synchronous Reference Frame (SRF) theory to improve the performance of a three-phase shunt Active Power Filter (APF) under fault situations. The main goal is to reduce power quality problems in electrical grids that are having problems, such harmonics and voltage sags. To precisely manage the APF, the reference frame in which the grid voltages and currents are synchronized is identified using the SRF theory. In order to provide quick and precise correction of voltage and current distortions, the PI controller is integrated to control the APF's compensatory action. The PI controller offers trustworthy control while running normally, but during errors or disruptions, its functionality may suffer. In order to overcome this difficulty, a self-tuning filter in the form of an Artificial Neural Network (ANN) is presented, which may adaptively modify the PI controller's settings under fault situations. To maintain ideal filter performance, the ANN constantly learns from the system's reaction and modifies in real-time. This self-adjusting feature makes sure that even in the event of grid failures, the APF maintains its ability to mitigate problems with power quality. The suggested method effectively reduces harmonics, voltage sags, and other power quality disturbances under both normal and fault circumstances, as shown by the simulation results. In complicated electrical grid systems, the combination of SRF theory, PI control, and ANN-based self-tuning provides a strong way to improve the dependability and effectiveness of three-phase shunt Active Power Filters.

A Survey on Multiple Workflow Scheduling Algorithms in Cloud Environment

The workflow approach is a standard for displaying processes and their implementation process. With the advent of electrical sciences, more cumbersome workflows were created with more processing requirements. New distributed systems, such as grid systems and computing clouds, allow users to access heterogeneous resources that are geographically located at different points and execute their workflow tasks. Therefore, the simultaneous receipt and execution of several workflows is obvious. As a result of discussing scheduling algorithms, it is necessary to consider arrangements for implementing multiple workflows on a shared resource set. Improving the execution of multiple workflows can accelerate the process of obtaining results when sending processes to the cloud. In this paper, we first discuss the classification of multiple workflow scheduling algorithms, and then briefly describe the scheduling algorithms for the cloud environment, andfinally, the algorithms of papers were compared with each other

Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems

Solar photovoltaic generation and electrical demand forecasting using multi-objective deep learning model for smart grid systems

Font Layout Design Experiment Based on the Context of Wooden Elements in Shanxi Ancient Architecture

As one of the representative section of Shanxi ancient architecture, the wooden elements of Shanxi ancient architecture are the figurative carriers of humanistic spirit and historical civilisation. It has typical regional characteristics, and its remains are complete and diverse, with a more integral chronological span. Using font layout design as a method of visual transformation to carry out research and analysis based on the wooden elements of ancient architecture in Shanxi, the presentation of the theme is more vivid and novel, with a clear venation.In order to explore the font layout design based on the context of the wooden elements of ancient architecture in Shanxi, the wooden elements has been taken as the research object, the composition and characteristics of these wooden elements are analysed through the inductive method, the literature review method, and the case study method. Then the intrinsic grid system is extracted and condensed. These grid systems are used as the basis, and font layout design is used as the entry point to explore the applicability of font layout design in the context of wooden elements of ancient architecture in Shanxi based on the characteristics of wooden elements.

Analyzing temporal and spatial forest carbon storage using Google Plus Code: a case study of Zijin Mountain National Forest Park, China.

It is always a challenging job to compare forest resources as there is not a standardized spatial unit with location information. Google Plus Code, the newest alphanumeric geocoding system, uses 20 specifically selected letters and numbers to assign a unique global ID to every cell at different levels of a hierarchical grid system which is established based on latitude and longitude. It can be used as a standardized, unique global geospatial unit to segment, locate, quantitate, evaluate, and compare natural resources with area, boundary, and location information embedded. For this proof-of-concept case study, forest inventory data from 1987, 2002, and 2019 for the Zijin Mountain National Forest Park in Jiangsu Province, China was analyzed based on Google Plus Code grid/cell. This enabled the quantification of carbon storage at each cell allowing for the comparison of estimated carbon storage at same or different locations over time. This methodology is used to quantify the impacts of changing forest conditions and forest management activities on carbon storage with high spatial accuracy through the 32-year study period. Furthermore, this technique could be used for providing technical support and validation of carbon credit quantification and management.

Current Trends, Advancements, and Challenges in Floating Solar Photovoltaic (FSPV) Systems for Off-Grid Applications: A Review

Increasing energy demands and the pursuit of sustainable and clean energy sources have intensified interest in Floating Solar Photovoltaic (FSPV) systems, particularly for off-grid applications. FSPV technology presents a strategic alternative for countries with limited land but ample water bodies, contributing to energy diversification and conservation of arable land. This paper provides a comprehensive technological trends, advancements, and challenges in the deployment of FSPV systems, drawing from an array of highly regarded publications and extensive patent searches via the Derwent Innovation database and various publications. While large-scale FSPV deployments have been successfully integrated with existing hydroelectric power plants and grid systems, the application of FSPV technology for local community use remains underexplored. The paper identifies the lack of comprehensive literature on stand-alone FSPV systems that include battery charging capabilities and integrated monitoring and control systems to mitigate environmental risks. Moreover, the paper discusses the economic, regulatory, technical, cultural, and environmental barriers to FSPV deployment. It suggests that continuous research and development, backed by supportive policy frameworks, are crucial for overcoming these challenges. The aim is to pave the way for resilient, community-centric FSPV installations that can withstand extreme weather and cater to localized energy needs.

Smarter, greener extractivism: digital infrastructures and the harnessing of new resources

ABSTRACT The past several years have seen increased scholarly attention to the concept of ‘extraction’ and ‘extractivism’ as critical frameworks in the humanities and social sciences. These are not only concepts and processes through which to understand material extraction but also expanded formations of extraction as requiring an assemblage of interlocking activities united under an umbrella of exploitative, material economic practices. This article demonstrates that digital data infrastructures, especially data centres, are acting as tools in which to manage the compounding contradictions of paired ‘green,’ digital growth in constrained electricity systems like Ireland. Building on theories of ‘green extractivism’ in the digital sphere and drawing upon fieldwork and policy analysis in data centre industry settings, this article argues that in the form of interconnected climate and digital infrastructures, tech capital is shoring up its role in green transformations, including in the grid systems that will need to adapt away from fossil fuel supply to intermittent energy sources and increasing demand from data centres. In this way, data centres are becoming technologies of green extractivism for overlapping projects of digital grid transformations and climate-friendly capitalism.