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Related Concept Videos

Maximum Power Flow and Line Loadability01:23

Maximum Power Flow and Line Loadability

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The maximum power flow for lossy transmission lines is derived using ABCD parameters in phasor form. These parameters create a matrix relationship between the sending-end and receiving-end voltages and currents, allowing the determination of the receiving-end current. This relationship facilitates calculating the complex power delivered to the receiving end, from which real and reactive power components are derived.
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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Power flow problem analysis is fundamental for determining real and reactive power flows in network components, such as transmission lines, transformers, and loads. The power system's single-line diagram provides data on the bus, transmission line, and transformer. Each bus k in the system is characterized by four key variables: voltage magnitude Vk​, phase angle δk​, real power Pk​, and reactive power Qk​. Two of these four variables are inputs, while the...
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Imagine a bucket of water. It contains many molecules, of the order of 1026 molecules. Thus, although it contains discrete elements (molecules) at the microscopic level, macroscopically, it can be considered continuous. Small volume elements of water, infinitesimal compared to the bulk of the bucket's volume, still contain many molecules. Under this framework, quantized matter is approximated as continuous for practical purposes.
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Electric vehicles charging station allocation based on load profile forecasting and Dijkstra's algorithm for optimal

Sahbi Boubaker1, Sameer Al-Dahidi2, Souad Kamel3

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Electric vehicle (EV) charging is optimized using a new framework that forecasts demand and plans routes. Drones provide real-time data, reducing EV wait times and travel distances to charging stations (CSs).

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Transportation Systems

Background:

  • Widespread Electric Vehicle (EV) adoption creates challenges for accessing Charging Stations (CSs) due to limited availability and variable demand.
  • Efficient allocation of EVs to optimal CSs is crucial for managing charging infrastructure.

Purpose of the Study:

  • To propose an integrated framework for optimizing EV charging station allocation.
  • To enhance decision-making for guiding EVs to the most suitable CSs based on real-time and forecasted data.

Main Methods:

  • Utilized a Nonlinear Auto-Regressive with Exogenous inputs (NARX) model for predicting future load profiles at CSs.
  • Applied Dijkstra's algorithm for shortest-path computation to determine optimal routes from EVs to CSs.
  • Integrated drone-assisted edge computing for real-time data exchange on slot availability and local conditions.

Main Results:

  • The NARX model achieved a 90% correlation coefficient for CS real data forecasting.
  • Dijkstra's algorithm effectively optimized EV routing to nearest charging stations.
  • Simulations demonstrated significant enhancements in EV allocation efficiency, reduced waiting times, and shorter travel distances.

Conclusions:

  • The proposed framework effectively addresses EV charging station allocation challenges.
  • Drone-assisted edge computing and predictive modeling improve charging infrastructure management.
  • Further research is recommended for regulatory and logistical aspects of drone deployment.