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Updated: Apr 4, 2026

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Optimal scheduling study of microgrids based on multistrategy improved sardine algorithm.

Liming Wei1, Heng Zhong2

  • 1School of Electrical and Computer Engineering, Jilin Jianzhu University, Changchun, 130118, Jilin, China.

Scientific Reports
|April 2, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an improved optimization algorithm for microgrid scheduling, enhancing renewable energy use and reducing costs. The new method offers superior accuracy and speed for efficient power management.

Keywords:
Electricity transactionsEnergy storageImproving the sardine algorithmMicrogridOptimized scheduling

Related Experiment Videos

Last Updated: Apr 4, 2026

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Published on: February 14, 2025

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Area of Science:

  • Renewable Energy Systems
  • Optimization Algorithms
  • Microgrid Management

Background:

  • Microgrids face challenges in efficiently integrating wind and photovoltaic power due to operational constraints.
  • Existing meta-heuristic algorithms often suffer from premature convergence and low solution accuracy in complex scheduling models.
  • There is a need for advanced optimization techniques to improve the economic and environmental performance of microgrids.

Purpose of the Study:

  • To develop an optimal scheduling model for microgrids that minimizes combined economic and environmental costs.
  • To propose a novel multi-strategy improved sardine optimization algorithm (SOA) to overcome limitations of existing methods.
  • To enhance the utilization efficiency of wind and photovoltaic power generation within microgrid systems.

Main Methods:

  • Development of an optimal scheduling model incorporating power generation, energy storage, and electricity transaction constraints.
  • Proposal of a multi-strategy improved sardine optimization algorithm (SOA) by integrating SOA with particle swarm optimization (PSO) and four enhancement strategies.
  • Comparative evaluation of the proposed algorithm against eight other algorithms using twelve CEC2022 benchmark test functions.

Main Results:

  • The proposed multi-strategy improved SOA demonstrated superior performance in convergence speed and accuracy compared to eight other algorithms on benchmark test functions.
  • Application to the microgrid scheduling model resulted in a significant reduction in total system cost compared to traditional SOA, Grey Wolf Optimizer (GWO), and PSO.
  • The algorithm effectively addresses limitations of existing meta-heuristic approaches, offering enhanced precision and avoiding local optima.

Conclusions:

  • The developed multi-strategy improved SOA provides a novel and effective methodology for microgrid optimization dispatch.
  • The proposed approach significantly enhances the efficiency and cost-effectiveness of microgrid operations integrating renewable energy sources.
  • This study offers a robust solution for optimizing microgrid scheduling under various operational constraints.