Improved Pied Kingfisher Optimization Algorithm for optimal scheduling of microgrids with hybrid energy storage

Area of Science:

• Electrical Engineering
• Optimization Algorithms
• Renewable Energy Systems

Background:

• Microgrids are crucial for integrating renewable energy sources and energy storage.
• Renewable intermittency and hybrid energy storage create complex, nonlinear scheduling problems.
• Conventional methods struggle with the nonconvexity and coupled constraints of microgrid scheduling.

Purpose of the Study:

• To propose an Improved Pied Kingfisher Optimization (IPKO) algorithm for optimal microgrid scheduling.
• To enhance population diversity, search adaptability, and convergence stability in optimization.
• To address the challenges of nonlinear and nonconvex scheduling in microgrids.

Main Methods:

• Developed an Improved Pied Kingfisher Optimization (IPKO) algorithm with four enhancement strategies.
• Integrated Chebyshev-chaotic initialization, multi-source guidance, differential-spiral development, and periodic dynamic adjustment.
• Modeled a grid-connected microgrid with battery and hydrogen storage, incorporating demand response.

Main Results:

• The IPKO-based framework consistently reduced economic and environmental costs.
• IPKO achieved 4-10% lower mean daily operating costs compared to other algorithms.
• Demonstrated superior solution quality and robustness in microgrid scheduling.

Conclusions:

• The proposed IPKO algorithm offers a robust and efficient solution for microgrid optimal scheduling.
• IPKO effectively handles the complexities of renewable energy integration and hybrid storage systems.
• This approach significantly improves cost reduction and solution stability for microgrids.