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Updated: Sep 12, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm.

Alok Kumar Shrivastav1,2, Soham Dutta3,4

  • 1Department of Electrical Engineering, JIS College of Engineering, Kalyani, 741235, India.

Scientific Reports
|August 10, 2025
PubMed
Summary

The Slime Mould Algorithm optimizes hybrid microgrids for energy security, affordability, and sustainability. This approach significantly reduces energy loss and costs while improving power reliability.

Keywords:
Carbon emission reductionDistributed generationElectric vehicle integrationEnergy trilemmaIEEE 33-bus systemLevelized cost of energyMicrogrid optimizationPower system reliabilityRenewable energySlime mould algorithm

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Area of Science:

  • Electrical Engineering
  • Optimization Algorithms
  • Renewable Energy Systems

Background:

  • Hybrid microgrids (HMGs) are crucial for integrating diverse energy sources.
  • Balancing energy security, affordability, and sustainability (the energy trilemma) in HMGs is a complex challenge.
  • Existing optimization methods often fall short in addressing all trilemma goals simultaneously.

Purpose of the Study:

  • To present a multi-objective optimization of an HMG using the Slime Mould Algorithm (SMA).
  • To target the energy trilemma goals: energy security, affordability, and sustainability.
  • To evaluate the performance of SMA against conventional algorithms and benchmark tools.

Main Methods:

  • Multi-objective optimization using the Slime Mould Algorithm (SMA).
  • Integration of renewable energy sources, diesel generators, and electric vehicle (EV) batteries as distributed energy resources (DERs).
  • Modeling of bidirectional vehicle-to-grid (V2G) capabilities for EV batteries.

Main Results:

  • SMA achieved a 12.3% reduction in power loss and a 9.8% improvement in the levelized cost of energy (LCOE).
  • The loss of power supply probability (LPSP) was reduced to 0.012, outperforming HOMER (0.021) and Salp Swarm Algorithm (SSA) (0.017).
  • SMA demonstrated superior convergence speed and computational efficiency due to its exploration-exploitation balance.

Conclusions:

  • The Slime Mould Algorithm is highly effective for optimizing hybrid microgrids towards the energy trilemma.
  • The novel integration of bidirectional V2G-enabled EV batteries enhances HMG performance.
  • Future research should address scalability and real-time computational demands for practical implementation.