Nondominated sorting grey wolf algorithm-based optimal scheduling for electric-hydrogen-heat multi-energy microgrid

Abstract

The multi-energy microgrid (MEMG) is a system in which multiple energy sources exist together, serving as a crucial approach to Accomplishing energy conservation, reducing emissions, and optimizing energy systems. Ensuring economic benefits while coordinating the utilization of multiple energy sources to balance environmental protection, and other objectives remains a critical issue that requires urgent resolution. This paper proposes a regional MEMG capable of absorbing CO₂ emissions and desalinating seawater. The alkaline solution generated during water electrolysis for hydrogen production can absorb CO₂ emitted by the system, while the desalination device produces fresh water. The system is capable of recovering waste heat from both water electrolysis and fuel cell processes. To enhance energy efficiency within the MEMG system, an optimization model is developed with the objectives of minimizing economic operating costs, reducing CO₂ emissions, and decreasing energy losses. Addressing the problem with an enhanced grey wolf optimization algorithm. Results show that the system performs excellently in handling complex multi-objective optimization problems, demonstrating significant advantages compared to various commonly used optimization algorithms. Specifically, the proposed system reduces operating costs by 24.08 %, CO₂ emissions by 13.25 %, and energy consumption by 8.60 %, achieving energy savings and emission reductions.