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The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
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Updated: Jul 5, 2025

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
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Efficient design of energy microgrid management system: A promoted Remora optimization algorithm-based approach.

Hua Zhang1, Yingying Ma1, Keke Yuan2

  • 1School of Computer Science and Engineering, Hunan University of Information Technology, ChangSha, 410151, China.

Heliyon
|January 15, 2024
PubMed
Summary

This study introduces an optimized energy management strategy for microgrids using the Promoted Remora Optimization (PRO) algorithm. The PRO algorithm enhances efficiency and cost-effectiveness in microgrid operations, particularly with renewable energy integration.

Keywords:
Battery storageFuel cellMicrogridsOptimal energy management strategyPhotovoltaic systemPromoted remora optimization algorithm

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Area of Science:

  • Electrical Engineering
  • Renewable Energy Systems
  • Optimization Algorithms

Background:

  • Microgrids offer decentralized power with enhanced reliability and resilience.
  • Integrating variable renewable energy sources into microgrids poses significant management challenges.
  • Effective energy management is crucial for balancing supply, demand, and cost in microgrids.

Purpose of the Study:

  • To propose an optimized and efficient energy management strategy for microgrids.
  • To address challenges in microgrids utilizing solar and green energy sources.
  • To ensure reliable power delivery while minimizing costs and protecting battery storage.

Main Methods:

  • Development of a novel energy management strategy based on the Promoted Remora Optimization (PRO) algorithm.
  • Application of the PRO algorithm to optimize microgrid operation in independent and grid-connected modes.
  • Comparative analysis with existing energy management strategies in microgrid literature.

Main Results:

  • The PRO algorithm effectively optimized battery charging, maintaining a State of Charge (SoC) between 33.37%-33.60%.
  • Achieved high system efficiency (average 87.99%) and optimizer efficiency (average 86.46%).
  • Demonstrated cost-effectiveness with a cost per power ranging from $0.1687/kW to $0.1699/kW.

Conclusions:

  • The PRO algorithm presents a promising approach for efficient and cost-effective microgrid energy management.
  • The proposed strategy effectively balances energy supply and demand while ensuring system stability and battery health.
  • Comparative analysis confirms the superiority of the PRO-based approach over existing methods for microgrid optimization.