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Related Experiment Video

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SZOA: An Improved Synergistic Zebra Optimization Algorithm for Microgrid Scheduling and Management.

Lihong Cao1,2, Qi Wei3

  • 1School of Management, Guangzhou College of Technology and Business, Guangzhou 510850, China.

Biomimetics (Basel, Switzerland)
|October 28, 2025
PubMed
Summary
This summary is machine-generated.

A new Synergistic Zebra Optimization Algorithm (SZOA) improves microgrid scheduling for economic efficiency and low-carbon goals. This algorithm outperforms others in simulations, demonstrating practical benefits for sustainable energy management.

Keywords:
Zebra Optimization Algorithmeconomic cost optimizationglobal optimizationinnovation managementmicrogrid scheduling

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Area of Science:

  • * Electrical Engineering
  • * Optimization Algorithms
  • * Sustainable Energy Systems

Background:

  • * Microgrid scheduling faces challenges balancing economic costs and low-carbon objectives.
  • * Traditional optimization algorithms like Zebra Optimization Algorithm (ZOA) have limitations in complex scenarios.

Purpose of the Study:

  • * To propose a Synergistic Zebra Optimization Algorithm (SZOA) for enhanced microgrid scheduling.
  • * To integrate SZOA with innovative management concepts for improved performance.
  • * To address the limitations of traditional algorithms in complex economic and environmental optimization problems.

Main Methods:

  • * Development of SZOA with a multi-population cooperative search, vertical crossover-mutation, and leader-guided boundary control.
  • * Application of SZOA to a bi-objective optimization model for grid-connected microgrids.
  • * Integration of renewable energy sources (PV, WT), controllable sources (FC, MT, GS), battery storage (BT), and the main grid.

Main Results:

  • * SZOA demonstrated superior optimization accuracy and stability on benchmark datasets (CEC2017, CEC2022) compared to nine state-of-the-art algorithms.
  • * Friedman tests confirmed SZOA's superiority with best average rankings.
  • * Simulations showed optimized microgrids achieved significantly lower operating costs and standard deviations, outperforming comparison algorithms.

Conclusions:

  • * The SZOA effectively balances economic efficiency and low-carbon operation in microgrid scheduling.
  • * It provides a reliable and practical technical solution for innovative microgrid management.
  • * The algorithm successfully coordinates diverse energy sources to minimize costs and emissions.