A Chaos-Guided Slime Mould Algorithm for multi-energy dispatch in hydrogen–ammonia integrated multi-microgrid systems under dual trading constraints

The increasing global demand for low-carbon and flexible energy systems has accelerated the integration of renewable energy and hydrogen-based fuels into multi-energy systems. Multi-microgrid systems (MMGS) incorporating hydrogen and ammonia energy carriers have emerged as a promising architecture to achieve coordinated, efficient, and sustainable energy management. This paper proposes an advanced dispatch framework for a hydrogen–ammonia integrated MMGS under dual-market constraints, including Carbon Emission Trading (CET) and Green Certificate Trading (GCT). The objective is to minimize total operational costs while maximizing renewable energy utilization and reducing carbon emissions. The system integrates diverse energy carriers, including electricity, gas, heat, hydrogen, and ammonia, supported by advanced technologies such as electrolyzers (EL), hydrogen fuel cells (HFC), methane reactors (MR), ammonia production units (APE), and thermal/chemical energy storage. To efficiently solve the complex, nonlinear optimization problem, a novel Chaos-Guided Slime Mould Algorithm (C-SMA) is developed, integrating chaotic maps and adaptive parameter control to enhance convergence speed and global search capabilities. Simulation results demonstrate that the coordinated hydrogen–ammonia dispatch (Case 5) achieves a 35.2 % reduction in total operational cost and over 30 % CO2 emission reduction compared to the baseline (Case 1), while also improving wind energy utilization efficiency from 70.8 % to 95.8 %. Sensitivity analyses further confirm the system's robustness against uncertainties in hydrogen cost, wind forecast errors, and trading prices. The hybrid optimization method based on a C-SMA outperforms conventional metaheuristics in convergence speed and solution quality.