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A study of scheduling strategies for microgrids based on the non-dominated sorting dung beetle optimization

Yutong Chen1, Wu Ning2, Wei Du1

  • 1Department of Electronic and Information Engineering, Liaoning University of Technology, Jinzhou, 121001, China.

Scientific Reports
|May 20, 2025
PubMed
Summary

This study introduces a novel microgrid scheduling strategy using the Non-Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO). The NSDBO algorithm enhances economic benefits and operational efficiency for microgrids by improving renewable energy integration.

Keywords:
Dung beetle algorithmMicrogrid scheduling strategyNon-dominated sortingOverall operating cost

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Renewable Energy Systems

Background:

  • Microgrids offer self-operation and management for renewable energy, enhancing power supply reliability.
  • Microgrids face challenges with low economic benefits and inefficient operations.
  • Optimizing microgrid scheduling is crucial for maximizing their potential.

Purpose of the Study:

  • To develop an advanced microgrid scheduling strategy to overcome economic and operational inefficiencies.
  • To introduce the Non-Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO) for microgrid applications.
  • To enhance the global search capability and avoid local optima in multi-objective microgrid optimization.

Main Methods:

  • Implementation of the Non-Dominated Sorting Dung Beetle Optimization Algorithm (NSDBO).
  • Integration of a non-dominated sorting mechanism to tier solution sets.
  • Evaluation of NSDBO against Grey Wolf Optimizer (GWO) and standard Dung Beetle Optimization (DBO).

Main Results:

  • NSDBO demonstrated superior performance in hypervolume (HV) and generational distance (GD) compared to other algorithms.
  • The algorithm effectively avoids local optima and improves global search capabilities.
  • NSDBO reduced overall operation costs by 52% compared to GWO and 8.1% compared to DBO.

Conclusions:

  • The proposed NSDBO algorithm is effective in improving microgrid economic benefits.
  • NSDBO enhances operational efficiency and reduces environmental pollution in microgrids.
  • This strategy offers a promising solution for optimizing microgrid performance and integration of renewables.