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Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system.

Dongmei Yan1,2,3, Hongkun Wang4,5,6, Yujie Gao1,2,3

  • 1College of Mechanical and Electrical Engineering, Shihezi University, Shihezi, 832003, China.

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|October 22, 2024
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Summary
This summary is machine-generated.

This study introduces a new algorithm to optimize multi-microgrid scheduling, improving accuracy and speed for renewable energy integration. The method enhances revenue and reduces emissions for all participants.

Keywords:
Crayfish optimization algorithmMulti-microgrid systemOptimal schedulingShared energy storageStackelberg game

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Renewable Energy Systems

Background:

  • High penetration of new energy in Multi-Microgrid (MMG) systems complicates optimal scheduling.
  • Interactions among subjects in MMG systems affect solution accuracy and speed.

Purpose of the Study:

  • To develop an optimized scheduling model for MMG systems with shared energy storage.
  • To address the challenges posed by complex inter-subject interactions and high renewable energy penetration.

Main Methods:

  • Established a bi-level optimal scheduling Stackelberg game model considering shared energy storage and inter-subject interactions.
  • Proposed the Chaotic Gaussian Quantum Crayfish Optimization Algorithm (CGQCOA) by integrating Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy.
  • Applied the improved algorithm to solve the optimization scheduling model.

Main Results:

  • The CGQCOA demonstrated superior initial solutions and enhanced search capabilities compared to the original algorithm, with significant reductions in relative errors (up to 98.74%).
  • The proposed methodology increased revenues for Microgrid 1 (0.73%), Microgrid 2 (1.17%), and Microgrid 3 (1.04%), and for shared storage (1.91%).
  • Pollutant emission penalty costs decreased significantly across all microgrids (5.9% to 12.68%).

Conclusions:

  • The developed Stackelberg game model and the CGQCOA effectively solve the optimal scheduling problem for MMG systems.
  • The methodology enhances operational revenue for microgrids and shared storage while substantially reducing pollutant emissions.
  • The findings validate the proposed approach for efficient and sustainable MMG operation under high renewable energy penetration.