Algorithmic intelligence for industrial decarbonization: a comparative analysis of meta-heuristic optimization versus commercial solvers in designing resilient hybrid microgrids

Reliable electricity is a prerequisite for industrial competitiveness in developing economies, yet frequent grid instabilities force manufacturers toward carbon-intensive backup generation. While Hybrid Renewable Energy Systems (HRES) offer a solution, commercial tools like HOMER Pro often converge on capital-intensive architectures due to discrete search space constraints. This study proposes a continuous-variable Artificial Gorilla Troops Optimizer (GTO) to design a techno-economically optimal HRES for a 35 MW textile facility in Bangladesh. The comparative analysis reveals a fundamental architectural divergence: while the HOMER Pro benchmark utilizes a capital-intensive 75 MWh pure storage configuration, the proposed GTO framework discovers a ’Hybrid Resilience’ optimum that integrates an 11.9 MW waste-to-energy biogas system . This diversification reduced battery requirements by 38.3% and Solar PV by 21.4%, lowering the Net Present Cost (NPC) to USD 103.8 million, representing a 6.5% reduction over the commercial benchmark. Economically, the optimized system achieves an LCOE of USD 0.0316/kWh and a rapid 5.4-year payback period, offering a 70% cost advantage over grid tariffs. Furthermore, the design mitigates 99,044 tons of CO 2 annually. These findings validate that meta-heuristic AI algorithms can identify complex trade-offs standard solvers overlook, providing a robust, circular-economy model for industrial decarbonization in weak-grid environments.