Multi-objective optimization of hybrid microgrid for energy trilemma goals using slime mould algorithm

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.