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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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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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Distribution reliability in electrical power systems is critical for ensuring an uninterrupted power supply to consumers at minimal cost. According to IEEE Standard Terms, reliability is the probability that a device will function without failure over a specified time period or amount of usage. For electric power distribution, this translates to maintaining continuous power supply and addressing customer concerns over power outages. Several indices, as defined by IEEE Standard 1366-2012, are...
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Transformers in distribution systems can be broadly categorized into distribution substation transformers and other distribution transformers. They are crucial for stepping down high transmission voltages to levels suitable for distribution and end-user applications.
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Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Resilience driven EV coordination in multiple microgrids using distributed deep reinforcement learning.

Yuxin Wu1, Ting Cai2, Xiaoli Li1

  • 1School of Computer Engineering, Hubei University of Arts and Science, Xiangyang, 441053, China.

Scientific Reports
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

Integrating electric vehicles (EVs) into multi-microgrids (MMGs) enhances resilience. A novel attention-based deep reinforcement learning approach optimizes EV coordination to minimize load-shedding costs, improving grid stability.

Keywords:
Coupled power-transportation networkDistributed deep reinforcement learningEV coordinationMicrogridsResilience-driven

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Multi-microgrids (MMGs) can improve operational resilience by integrating electric vehicles (EVs).
  • Existing methods struggle with optimal EV routing and scheduling due to spatial-temporal uncertainties and incomplete information.

Purpose of the Study:

  • To address the coordination challenges of EVs for enhancing MMG resilience.
  • To minimize load-shedding costs through optimized EV routing and scheduling.

Main Methods:

  • A coupled power-transportation network (CPTN) model was developed to integrate power and transportation dynamics.
  • A partially observable Markov decision process (POMDP) formulation was used for EV coordination.
  • An attention-based distributed multi-agent deep deterministic policy gradient (AD-MADDPG) method was proposed.

Main Results:

  • The AD-MADDPG method demonstrated superior performance in load restoration and fairness compared to baseline methods.
  • The approach effectively handled uncertainties in distributed renewables, load profiles, and traffic flow.
  • Simulation results on a modified IEEE 33-bus test feeder validated the method's efficiency.

Conclusions:

  • The proposed AD-MADDPG approach offers an effective solution for coordinating EVs to enhance MMG resilience.
  • Optimized EV routing and scheduling significantly reduce load-shedding costs and improve grid stability.
  • The method shows promise for practical implementation in smart grids with high EV penetration.