This study aims to address the following research query: In the event of an imminent disaster poised to impact distribution grids, what constitutes the optimal course of action for the distribution system operators to keep the lights on? To address this challenge, we propose a cost-efficient cellular model for enhancing the resilience of smart distribution grids. This model prioritizes resilience in the face of natural disasters or other disruptions that could impact service delivery. This method benefits both grid operators and consumers by ensuring reliable power supply while minimizing energy costs. Furthermore, the model's scalability allows it to be applied to distribution systems of varying sizes. The proposed method utilizes an innovative approach to form optimal cellular network configurations within the grid. As the first step in the formation of cellular topology for the grid, the eigenvectors of the Laplacian matrix of the grid will be used to decide on the optimal configurations. Subsequently, a bi-level mixed-integer linear programming model is proposed to decrease the network costs while simultaneously consider potential power transfer scenarios between the cells and the upstream network during both normal and emergency conditions. The researchers validated the effectiveness of the proposed method through simulations on an IEEE 33-bus test system. The results demonstrate outstanding performance, with a significant increase in the resilience index (96 %) and a substantial reduction in load-shedding costs (80 %), making the network considerably more robust.
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http://dx.doi.org/10.1016/j.isatra.2024.08.021 | DOI Listing |
High-quality light-field generation of real scenes based on view synthesis remains a significant challenge in three-dimensional (3D) light-field displays. Recent advances in neural radiance fields have greatly enhanced light-field generation. However, challenges persist in synthesizing high-quality cylindrical viewpoints within a short time.
View Article and Find Full Text PDFEcol Evol
January 2025
Centro de Investigaciones Biológicas (CIB) Universidad Autónoma del Estado de Hidalgo Mineral de la Reforma Hidalgo Mexico.
The Sierra Madre Oriental (SMO) is a significant mountain range and one of Mexico's 14 biogeographical provinces. Its delimitation has been debated. This study aims to analyze the distribution of plants, beetles, odonates, amphibians, reptiles, and mammals using an endemicity analysis to identify endemism areas and confirm the SMO's biogeographical units.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
In this paper, a comprehensive energy management framework for microgrids that incorporates price-based demand response programs (DRPs) and leverages an advanced optimization method-Greedy Rat Swarm Optimizer (GRSO) is proposed. The primary objective is to minimize the generation cost and environmental impact of microgrid systems by effectively scheduling distributed energy resources (DERs), including renewable energy sources (RES) such as solar and wind, alongside fossil-fuel-based generators. Four distinct demand response models-exponential, hyperbolic, logarithmic, and critical peak pricing (CPP)-are developed, each reflecting a different price elasticity of demand.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
This study investigates the optimization of wind energy integration in hybrid micro grids (MGs) to address the rising demand for renewable energy, particularly in regions with limited wind potential. A comprehensive assessment of wind energy potential was conducted, and optimal sizing of standalone MGs incorporating photovoltaic (PV) systems, wind turbines (WT), and battery storage (BS) systems was performed for six regions in the Kingdom Saudi Arabia. Wind resource analysis utilizing the Weibull distribution function shows that all regions exhibited Class 1 wind energy characteristics, with average annual wind power densities ranging from 36.
View Article and Find Full Text PDFMar Pollut Bull
January 2025
Environmental Research Institute, University of the Highlands and Islands, Thurso, UK.
We evaluate global microplastics particle density distribution using field data from 1972 to 2022, made available by the NOAA (National Oceanic and Atmospheric Administration) NCEI (National Centers for Environmental Information) global marine microplastics database. We resampled the measured microplastics density data from NOAA NCEI into a regularly spaced 1° × 1° grid and applied ordinary block kriging on a 1° × 1° mask map of the global oceans to spatially interpolate the gridded data. Climate data were retrieved from the Climate Data Store of the Copernicus Climate Change Service.
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