A stochastic multi-objective enhanced energy management system in smart microgrid based on an improved grey wolf optimizer and reduced unscented transformation
A stochastic optimization framework reduces costs and emissions in smart microgrids with energy storage systems, indicating better energy management efficiency.
Key Points
This article investigates improving energy management in smart microgrids using renewable resources and optimization techniques.
Developed a mathematical model for energy management that accounts for real-time pricing, renewable resources, and grid interactions.
Examined a stochastic optimization framework with reduced unscented transformation to manage uncertainty in supply and demand.
Evaluated multiple optimization algorithms, including improved grey wolf optimizer, across various case studies.
Assessed performance under different scenarios including energy storage and grid limitations.
Improved grey wolf optimizer achieved the best solution with a cost of 2,366.88 cents/kWh and a low standard deviation of 72.43.
The best solutions for other algorithms were significantly higher, indicating lower efficiency compared to IGWO.
Total costs of the best solution under stochastic optimization were 893.93 cents/kWh with emissions of 736.48 kg/MWh.
Simulation results showed reductions in energy prices and environmental pollution, optimizing operations for the microgrid.