Voronoi Diagrams and Perlin Noise for Simulation of Irregular Artefacts in Microscope Scans

Alreni, Atef; Momcheva, Galina; Pavlov, Stoyan

doi:10.5220/0010833000003123

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“…, p n }, 3 ≤ n ≤ ∞, d(p i , p j ) be the Euclidean distance between vertices p i and p j . The Voronoi diagram is defined as Equation ( 9) [32]:…”

Section: Voronoi Diagram Based On the Division Of The Scope Of Servic...mentioning

confidence: 99%

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Tian,

Yang,

et al. 2024

Energies

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In response to challenges in constructing charging and hydrogen refueling facilities during the transition from conventional fuel vehicles to electric and hydrogen fuel cell vehicles, this paper introduces an innovative method for siting and capacity determination of Electric Hydrogen Charging Integrated Stations (EHCIS). In emphasizing the calculation of vehicle charging and hydrogen refueling demands, the proposed approach employs the Voronoi diagram and the particle swarm algorithm. Initially, Origin–Destination (OD) pairs represent car starting and endpoints, portraying travel demands. Utilizing the traffic network model, Dijkstra’s algorithm determines the shortest path for new energy vehicles, with the Monte Carlo simulation obtaining electric hydrogen energy demands. Subsequently, the Voronoi diagram categorizes the service scope of EHCIS, determining the equipment capacity while considering charging and refueling capabilities. Furthermore, the Voronoi diagram is employed to delineate the EHCIS service scope, determine the equipment capacity, and consider distance constraints, enhancing the rationality of site and service scope divisions. Finally, a dynamic optimal current model framework based on second-order cone relaxation is established for distribution networks. This framework plans each element of the active distribution network, ensuring safe and stable operation upon connection to EHCIS. To minimize the total social cost of EHCIS and address the constraints related to charging equipment and hydrogen production, a siting and capacity model is developed and solved using a particle swarm algorithm. Simulation planning in Sioux Falls city and the IEEE33 network validates the effectiveness and feasibility of the proposed method, ensuring stable power grid operation while meeting automotive energy demands.

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“…, p n }, 3 ≤ n ≤ ∞, d(p i , p j ) be the Euclidean distance between vertices p i and p j . The Voronoi diagram is defined as Equation ( 9) [32]:…”

Section: Voronoi Diagram Based On the Division Of The Scope Of Servic...mentioning

confidence: 99%

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Tian,

Yang,

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

Voronoi Diagrams and Perlin Noise for Simulation of Irregular Artefacts in Microscope Scans

Cited by 1 publication

References 0 publications

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

Site Selection and Capacity Determination of Electric Hydrogen Charging Integrated Station Based on Voronoi Diagram and Particle Swarm Algorithm

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