Grid System Research Articles

Energy Enhancement for Grid Connection by Boost Inverter in Tripolar Operation with Novelty Quadcoupling Control

In this paper a single-phase differential boost inverter is presented that has two functions, first is boost up and second is active power quadcoupling without adding active power electronics components for smart grid system. There are three operating principals: First is pulse energy modulation which operates converter in continuous conduction mode (CCM), Second is quadcoupler which is designed by adding four boost converters and third is Tripolar technique which used to add two inductors and one capacitor for each boost converter. The proposed model consist of four boost converters in parallel with interleaving switching technique to get lower duty cycle. The energy is delivered to the quadcoupler through tripolar technique. When two grids are connected through DC to AC conversion the boost inverter can increase voltages on required demand. A Simulink model is designed that shows and proves properties of proposed system with high energy, high efficiency and power control with lower voltage stress.

Fertility Status of Agricultural Soils in İstanbul Province

As a region of geopolitical significance and industrialization, İstanbul province has a traditional production system in which intensive agricultural production is carried out, especially in Silivri and Çatalca districts, where sunflower and wheat agriculture is carried out in alternation under irrigated conditions due to sufficient rainfall. These agricultural areas, which are also under intense urbanization pressure, need to be protected in order to be used sustainably. For this purpose, it is a priority to determine the current productivity status of agricultural areas. There is no study in the literature that reveals the current agricultural productivity status of İstanbul province. In this study, some physical and chemical soil properties of the agricultural areas where the same agricultural production system has been practiced for many years were determined in terms of sustainable agriculture. In order to determine these soil properties, surface soil sampling (0-20 cm) was carried out according to the grid system at 2.5 × 2.5 km intervals covering all agricultural areas and a total of 196 soil samples were taken and the field study was completed. All soil samples were analyzed for texture, pH, EC, organic matter, available phosphorus, available potassium and the current fertility status of the agricultural areas in the province of İstanbul was determined. According to the results of the research, the agricultural soils of İstanbul province are generally medium-heavy and heavy textured, medium alkaline and neutral pH, without salinity problems, 50% of them have very low CaCO3 content and the rest are calcareous soils with varying levels of CaCO3. It was determined that the high CaCO3 content in 11% of the soils was due to the rendzina great soil group formed on marl parent material. In terms of organic matter, 59% were classified as low, 26% as moderate, 50% as high and very high in terms of available phosphorus, and 79% as high and very high in terms of extractable potassium. After the classification of all analyzed parameters, the distribution maps of a parameter were created by using Geographical Information Systems (GIS). IDW, which is an inverse distance weighting method widely used in soil science, was used to create the distribution maps.

A Biomimetic Pose Estimation and Target Perception Strategy for Transmission Line Maintenance UAVs

High-voltage overhead power lines serve as the carrier of power transmission and are crucial to the stable operation of the power system. Therefore, it is particularly important to detect and remove foreign objects attached to transmission lines, as soon as possible. In this context, the widespread promotion and application of smart robots in the power industry can help address the increasingly complex challenges faced by the industry and ensure the efficient, economical, and safe operation of the power grid system. This article proposes a bionic-based UAV pose estimation and target perception strategy, which aims to address the lack of pattern recognition and automatic tracking capabilities of traditional power line inspection UAVs, as well as the poor robustness of visual odometry. Compared with the existing UAV environmental perception solutions, the bionic target perception algorithm proposed in this article can efficiently extract point and line features from infrared images and realize the target detection and automatic tracking function of small multi-rotor drones in the power line scenario, with low power consumption.

Strategic deployment of energy storage systems in the Indian power grid

PurposeThe transition from centralized thermal power plants to distributed renewable energy sources complicates the balance between power supply and load demand in electrical networks. Energy storage systems (ESS) offer a viable solution to this challenge. This research aims to analyze the factors influencing the implementation of ESS in the Indian smart grid.Design/methodology/approach To analyze the factors affecting ESS deployment in the grid, the SAP-LAP framework (situation-actor-process and learning-action-performance) integrated with e-IRP (efficient-interpretive ranking process) was used. The variables of SAP-LAP elements were selected from expert opinion and a literature review. Here, e-IRP was utilized to prioritize elements of SAP-LAP (actors in terms of processes and actions in terms of performance).Findings This analysis prioritized five stakeholders in the Indian power industry for energy storage implementation: government agencies/policymakers, ESS technology developers/manufacturers, private players, research and development/academic institutions, and contractors. Furthermore, the study prioritized the necessary actions that these stakeholders must take.Research limitations/implications The study’s findings help identify actors and manage different actions in implementing grid energy storage integration. Ranking these variables would help develop a strategic roadmap for ESS deployment and decisions about adopting new concepts.Originality/value It is one of the first attempts to analyze factors influencing ESS implementation in the power grid. Here, qualitative and quantitative methodologies are used to identify and prioritize various aspects of ESS implementation. As a result, the stakeholder can grasp the concept much more quickly.

Robustness Against Data Integrity Attacks in Decentralized Federated Load Forecasting

This study examines the impact of data integrity attacks on Federated Learning (FL) for load forecasting in smart grid systems, where privacy-sensitive data require robust management. While FL provides a privacy-preserving approach to distributed model training, it remains susceptible to attacks like data poisoning, which can impair model performance. We compare Centralized Federated Learning (CFL) and Decentralized Federated Learning (DFL), using line, ring and bus topologies, under adversarial conditions. Employing a three-layer Artificial Neural Network (ANN) with substation-level datasets (APEhourly,PJMEhourly, and COMEDhourly), we evaluate the system’s resilience in the absence of anomaly detection. Results indicate that DFL significantly outperforms CFL in attack resistance, achieving Mean Absolute Percentage Errors (MAPEs) of 0.48%, 4.29% and 0.702% across datasets, compared to the CFL MAPEs of 6.07%, 18.49% and 10.19%. This demonstrates the potential of DFL as a resilient, secure solution for load forecasting in smart grids, minimizing dependence on anomaly detection to maintain data integrity.

Optimization of grid-connected hybrid renewable energy system for the educational institutes in Pakistan

To foster resilient energy transition systems, the deployment of renewable energy sources is potentially helpful in the acceleration towards sustainable, reliable, and diversified solutions. In this feasibility study, aims to pursuit the best optimal solution for renewable energy resources, integrated into the local university's energy mix, to meet the electrical load demand of 8500 kWhr/day and a peak load of 815 kW. A model of a hybrid grid-connected renewable energy (PV/Biomass/Hydel/Diesel generator/Grid) system was implemented in HOMER software, to obtain the most efficient and cost-effective hybrid energy generation. The outcomes of the simulation seem more viable based on sensitivity analysis, as the cost of energy (COE) for an energy system with a hybrid combination is 0.13.$/kWh, meanwhile energy price with the grid system is 0.18 $/kWh. According to the HOMER analysis, a hybrid grid-connected PV/biomass/hydro system demonstrates both technological feasibility and economic viability, meanwhile it will also save 1575 tons of CO2 per year. Therefore, this proposed hybrid energy system, a step towards sustainable development, can be applied at institutional levels, especially in developing countries.

Adaptive fuzzy PID integrated renewable power management system for off grid and on grid conditions

Adaptive fuzzy PID integrated renewable power management system for off grid and on grid conditions

Optimal planning for high renewable energy integration considering demand response, uncertainties, and operational performance flexibility

The operational performance of the grid needs to be considered and incorporated with other verified system flexibility measures, such as energy storage system and demand response, when planning a hybrid renewable energy system with a high fraction of renewable energy sources. Therefore, this study developed an integrated model that considers the effects of time-of-use demand response and risk-based uncertainty analysis on the operational performance of a grid-connected hybrid energy system for high renewable power penetration. A multi-stage approach for optimal scheduling of the energy systems with time-of-use pricing considering uncertainties’ impacts on grid and energy storage systems operations is modeled. Techno-economic metrics are developed to assess the decision-making effectiveness of the energy system planning and grid operation under four scenarios based on the information gap decision theory to quantify the energy system and grid operation performance flexibility. The considered scenarios are without and with demand response using risk-neutral, risk-averse, and risk-seeking strategies. The analysis of the results indicates that the risk-averse approach achieves the best techno-economic tradeoff, guaranteeing improved technical performances compared to the other risk strategies for uncertainty management in renewable power outputs and grid operational performances within the cost deviation tolerance limit. The risk-averse strategy further justified the substantial inclusion of energy storage and the adoption of the demand response to provide better operational flexibility to ensure improved grid performance, as seen in the reduced grid loss and improved voltage profile with higher power contribution from the RES. Significantly, the considered model can help system planners manage the tradeoff for efficient system performance under substantial renewable energy integration, considering the impacts of uncertainties.

Collaborative water-electricity operation optimization of a photovoltaic/pumped storage-based aquaculture energy system considering water evaporation effects

The increasing global population, economic development, and urbanization trends have led to a heightened demand for seafood, presenting a significant challenge to the growth trajectory of the food industry. As the most rapidly expanding segment within the food industry, aquaculture presents a sustainable solution to mitigate the overexploitation of marine resources and address the growing demand for food. Aquaculture's sufficient aquatic expanses offer considerable potential for PV deployment, thereby relieving the demand for limited land resources. However, existing research rarely addresses the collaborative operation of water and electricity in aquaculture systems. The effects of water evaporation from PV panel-covered water surfaces on the collaborative water-electricity operation are generally neglected. Hence, this work proposes a collaborative water-electricity operation of a photovoltaic (PV)-pumped storage-based aquaculture energy system considering the water evaporation effects. This optimization method accounts for the reduced water evaporation due to PV panel shading. Water surface PV and pumped storage are integrated into the system to fulfill electricity needs, with pumped storage serving as a dual-purpose device for water and electricity supply. Environmentally sustainable water-electricity generation and aquaculture energy system requirements are established. Water surface PV electricity generation is modeled based on climate and engineering conditions, while aquaculture pond electricity requirement includes oxygenation, feeding, and water replenishment. Finally, a collaborative water-electricity operation model is introduced to optimize operation strategies for various devices. A case study on a fish-light complementation project demonstrates that this optimization method can reduce reliance on the utility grid and overall system operational costs.

Understanding Tidal Jet Vortices Over Complex Bathymetry via Numerical Modeling and Drone Observation: Match and Mismatch in the Vortex Dynamics Under Idealized and Realistic Topographic Settings

AbstractThe formation and evolution of tidal jet vortices over complex bathymetry were investigated using numerical modeling and in situ observation. Delft3D FM simulation on an unstructured grid system, complemented by field measurements using recreational drones captured tidal dynamics in the Uldolmok Strait, known for its strong tidal currents up to 6.0 m/s and turbulent whirlpool formation. This study particularly focused on temporal changes in whirlpools near their narrowest points. Modeling across the entire strait showed current velocity fields consistent with field observations, revealing significant temporal and spatial variability influenced by strait geometry and bathymetry. Whirlpools induced by tides were identified by setting a swirl strength threshold, with their centroids and equivalent spherical diameters pinpointed. It was observed that larger whirlpools, upon reaching critical size, were entrained and shifted with the tidal jet at about half its maximum velocity, while smaller vortices separated from the nearshore boundary layer remained nearly stationary. Focusing on the initiation of whirlpools and their relations with the coastline and bathymetry, a targeted field survey using a recreational drone measured surface flow fields in detail near the strait's narrowing region. During the ebb phase, shallow regions exhibited numerous smaller eddies due to increased energy dissipation, showing a dual power‐law scaling in the size distribution of eddies, contrasting with the single scaling exponent observed during the flood phase. The study underscored the role of coastline and bathymetry in developing whirlpools and shaping their dynamics, providing insights into complex tidal interactions in the natural coast.

Soft X-ray tomography analysis of mitochondria dynamics in Saccharomyces cerevisiae

BackgroundMitochondria are highly dynamic organelles that constantly undergo processes of fission and fusion. The changes in mitochondrial dynamics shape the organellar morphology and influence cellular activity regulation. Soft X-ray tomography (SXT) allows for three-dimensional imaging of cellular structures while they remain in their natural, hydrated state, which omits the need for cell fixation and sectioning. Synchrotron facilities globally primarily use flat grids as sample carriers for SXT analysis, focusing on adherent cells. To investigate mitochondrial morphology and structure in hydrated yeast cells using SXT, it is necessary to establish a method that employs the flat grid system for examining cells in suspension.ResultsWe developed a procedure to adhere suspended yeast cells to a flat grid for SXT analysis. Using this protocol, we obtained images of wild-type yeast cells, strains with mitochondrial dynamics defects, and mutant cells possessing distinctive mitochondria. The SXT images align well with the results from fluorescent microscopy. Optimized organellar visualization was achieved by constructing three-dimensional models of entire yeast cells.ConclusionsIn this study, we characterized the mitochondrial network in yeast cells using SXT. The optimized sample preparation procedure was effective for suspended cells like yeast, utilizing a flat grid system to analyze mitochondrial structure through SXT. The findings corresponded with the mitochondrial morphology observed under fluorescence microscopy, both in regular and disrupted dynamic equilibrium. With the acquired image of unique mitochondria in Δhap2 cells, our results revealed that intricate details of organelles, such as mitochondria and vacuoles in yeast cells, can be characterized using SXT. Therefore, this optimized system supports the expanded application of SXT for studying organellar structure and morphology in suspended cells.

Nonlinear Analysis and Solution for an Overhead Line Magnetic Energy Harvester with an Active Rectifier

Recently, there has been a significant focus on developing various energy harvesting technologies to power remote electronic sensors, data loggers, and communication devices for smart grid systems. Among these technologies, magnetic energy harvesting stands out as one straightforward method to extract substantial power from current-carrying overhead lines. Due to the relatively small size of the harvester, the high currents in the distribution system quickly saturate its magnetic core. Consequently, the magnetic harvester operates in a highly nonlinear manner. The nonlinear nature of the downstream AC to DC converters further complicates the process, making precise analytical modeling a challenging task. In this paper, a clamped type overhead line magnetic energy harvester with a controlled active rectifier generating significant DC output power is investigated. A piecewise nonlinear analytical model of the magnetic harvester is derived and reported. The modeling approach is based on the application of the Froelich equation. The chosen approximation method allowed for a complete piecewise nonlinear analytical treatise of the harvester’s behavior. The main findings of this study include a closed-form solution that accounts for both the core and rectifiers’ nonlinearities and provides an accurate quantitative prediction of the harvester’s key parameters such as the transfer window width, optimal pulse location, average DC output current, and average output power. To facilitate the study, a nonlinear model of the core was developed in simulation software, based on parameters extracted from core experimental data. Furthermore, theoretical predictions were verified through comparison with a computer simulation and experimental results of a laboratory prototype harvester. Good agreement between the theoretical, simulation, and experimental results was found.

A modified and extended genetic algorithm for optimal distributed generation grid-integration solutions in direct current power grids

The integration of distributed generation into direct current power grids presents a critical challenge in modern energy systems, as it directly impacts grid reliability, efficiency, and the successful transition to renewable energy. This study addresses the problem of optimizing distributed generation placement and sizing in direct current grids, a key issue for reducing power losses and improving energy distribution. To tackle this, a modified and extended genetic algorithm was developed, capable of handling both continuous and discrete variables simultaneously. The algorithm was tested on two standard direct current grid systems, a 21-bus microgrid and a 69-bus network. The results demonstrated significant improvements over existing methods, reducing power losses by 84.5% in the 21-bus microgrid and by 95% in the 69-bus direct current network, with notably reduced computation times. These findings indicate that the proposed algorithm not only optimizes distributed generation integration effectively but also offers superior performance compared to traditional approaches, without the need for additional methods or software. The novelty of this work lies in its ability to handle complex, nonlinear optimization problems within direct current grids using a single, efficient approach, advancing beyond previous efforts by achieving better results with fewer computational resources.

Holistic approach to energy storage management aspects in sustainable community

Energy management is nowadays key topic for synchronic operation of renewable sources of energy and their recipients. Contemporary national electrical power grid systems more often cannot supply efficiently electrical energy and cannot receive energy produced by renewable sources. The common approach to the problem is to meet energy demands supplying from electrical grid and renewable power sources with energy storage feature. From the other side, off-grid solutions based on the co-generation biogas plants are commonly aimed on small local communities as power supply supported by renewable energy systems like photovoltaic (PV) systems, wind power plants or small water plants with energy storage to support self-consumption of electrical energy. Integration of intermittent renewable power sources, such as solar, wind and biogas plant, increases the difficulty of managing the electricity grid and maintaining the balance of electricity supply and demand, especially in small communities. The holistic approach to the energy storage management takes all above aspects and presents the concept where municipal waste is used to produce energy in biogas plant supported by PV systems and community shared electrical energy storage to provide uninterrupted power supply. The study also presents how energy storage management can be used in whole process to adjust the size and manage energy supply and demand within the community based on energy self-consumption optimization. It is also shown that by utilizing municipal waste produced by the community we can meet the goals of circular economy and sustainable development of local communities as the waste will be used in full without necessity of recycling it outside the community. The novelty of the study is the foundation for energy storage capacity and renewable energy sources size evaluation to balance energy management process without the need of on-grid power supply and with use only municipal biodegradable waste for biogas fuel supply and solar energy for energy production.

Drivers of global irrigation expansion: the role of discrete global grid choice

Abstract. Global statistical irrigation modeling relies on geospatial data and traditionally adopts a discrete global grid based on longitude–latitude reference. However, this system introduces area distortion, which may lead to biased results. We propose using the ISEA3H geodesic grid based on hexagonal cells, enabling efficient and distortion-free representation of spherical data. To understand the impact of discrete global grid choice, we employ a non-parametric statistical framework, utilizing random forest methods, to identify the main drivers of historical global irrigation expansion using, among other data, outputs from the global dynamic vegetation model Lund-Potsdam-Jena managed Land (LPJml). Irrigation is critical for food security amidst growing populations, changing consumption patterns, and climate change. It significantly boosts crop yields but also alters the water cycle and global water resources. Understanding past irrigation expansion and its drivers is vital for global change research, resource assessment, and the prediction of future trends. We compare predictive accuracy, simulated irrigation patterns, and identification of irrigation drivers between the two grid systems. Using the ISEA3H geodesic grid system increases the predictive accuracy by up to 28 % compared to the longitude–latitude grid. The model identifies population density, potential productivity increase, evaporation, precipitation, and water discharge as key drivers of historical global irrigation expansion. Gross domestic product (GDP) per capita also shows some influence. We conclude that the geodesic discrete global grid system significantly affects predicted irrigation patterns and identification of drivers and thus has the potential to enhance statistical modeling, which warrants further exploration in future research across related fields. This analysis lays the foundation for comprehending historical global irrigation expansion.

Liverwort (Marchantiophyta) and Hornwort (Anthocerotophyta) Flora of Yenice District (Çanakkale): New Floristic Findings from the Northern Part of Mount Ida (Kaz Dağı)

The Yenice district of Çanakkale stands out due to its diverse land features, being part of the Kaz Dağı (Mount Ida), its expansive coniferous and deciduous forests, and its potential for high biological diversity and endemism. It also provides highly suitable habitats for liverworts. However, the absence of legally protected areas highlights the need for a detailed assessment of the region's biodiversity. Liverworts are key components of floristic biological richness. Nevertheless, studies on the liverwort flora in both Çanakkale province and Yenice district are quite limited. Based on these considerations, this study aimed to investigate the liverwort flora of the Yenice district. During the study, a total of 295 liverwort specimens were collected from 74 different sites representative of the district’s various regions. The collected specimens were identified using appropriate methods and preserved as herbarium material. As a result of the study, 29 liverwort species from 17 families and 1 hornwort species were identified from the area. Of the identified species, 7 taxa are new records for the B6 grid square according to Henderson’s grid system (1961), and 13 taxa are new records for Çanakkale province.

Fractional-order long short-term memory network for forecasting of solar irradiance

Abstract The unpredictable nature of solar energy presents a significant obstacle to its effective incorporation into current grid systems. Global Horizontal Irradiance (GHI) is a critical factor in solar energy technology, as it directly influences the effectiveness of photovoltaic systems and solar thermal plants. Precise GHI forecasts are essential for this challenge and facilitate prompt and efficient involvement in the energy market. However, traditional neural network models often struggle to accurately predict GHI due to their time series data's nonlinear and nonstationary nature. Thus, this research proposes fractional-order LSTM (FOLSTM) models by accurately incorporating the fractional activation functions to predict GHI using the National Renewable Energy Laboratory data. The fractional activation functions, including sigmoid, tanh, and rectified linear units, have been developed using the fractional derivatives. The performance of FOLSTM models will be compared with the conventional LSTM models during training, testing, and validation in mean square errors and R-squared values. The results demonstrate the proposed FOLSTM models' effectiveness compared to conventional networks.

Recent Advances on the Challenges of Resilient Automation Systems of the Power Grid: Critical Review

Recent Advances on the Challenges of Resilient Automation Systems of the Power Grid: Critical Review

The Applications and Challenges of Digital Twin Technology in Smart Grids: A Comprehensive Review

This comprehensive review explores the applications and challenges of Digital Twin (DT) technology in smart grids. As power grid systems rapidly evolve to meet the increasing energy demands and the new requirements of renewable source integration, DTs offer promising solutions to enhance the monitoring, control, and optimization of these systems. In this paper, we examine the concept of DTs in the context of smart grids, and their requirements, challenges, and integration with the Internet of Things (IoT) and Artificial Intelligence (AI). We also discuss different applications in asset management, system operation, and disaster response. This paper analyzes current challenges, including data management, interoperability, cost, and ethical considerations. Through case studies from various sectors in Canada, we illustrate the real-world implementation and impact of DTs. Finally, we discuss emerging trends and future directions, highlighting the potential of DTs to revolutionize smart grid networks and contribute to more efficient, reliable, and sustainable power systems.

Using a discrete global grid system for a scalable, interoperable, and reproducible system of land-use mapping

ABSTRACT An accurate and timely land-use map of an area is a valuable geospatial dataset. Making one requires assembling heterogeneous geospatial input data. This becomes computationally expensive as the number of inputs grow or the combinational classification logic becomes more convoluted. We present as a solution an analytical framework based on a DGGS. This provides a spatial data structure that: facilitates horizontal scaling; improves geospatial data interoperability; and makes classified geospatial data easier to reproduce. We demonstrate a benchmark of land-use assignment with a DGGS against the vector and raster geospatial data models. Significant performance benefits are apparent over the vector case, while performance is equivalent to raster. Yet a DGGS has other compelling benefits that makes it more convenient to use in the domain of land-use assignment than the raster data model.