Techno-economic optimal sizing of hydrogen–ammonia hybrid storage for residential building multi-source energy systems via enhanced chaotic antlion optimization

The rapid growth of distributed renewable generation in residential sectors poses challenges in balancing variable supply with fluctuating demand. Photovoltaic (PV) and small-scale wind resources are inherently intermittent, often leading to surplus generation, curtailment losses, and increased reliance on the grid during low-production periods. This study proposes an optimal capacity planning framework for a hydrogen–ammonia hybrid energy storage system (HESS) integrated into a multi-source residential microgrid comprising PV, wind, and grid connections. Hydrogen is used for short-term balancing, while ammonia serves as long-term seasonal storage, improving renewable utilization and supply reliability. An Enhanced Chaotic Antlion Optimization (C-ALO) algorithm is developed to solve the multi-variable, constrained HESS sizing problem. The enhancement combines Logistic Map–based chaotic sequences, Latin Hypercube Sampling, and Opposition-Based Learning to improve population diversity and accelerate convergence. The objective function minimizes total net cost covering capital, operation and maintenance, grid purchase, and curtailment penalties while maximizing revenues from ammonia sales and grid exports. Simulation results for a typical residential building demonstrate that the proposed C-ALO algorithm outperforms conventional ALO, PSO, and GSA in both system performance and cost-effectiveness. Compared to the best alternative, C-ALO achieved a 7.8% increase in renewable energy utilization, a 12.5% reduction in grid dependency, and annual cost savings of up to 720$. The hybrid hydrogen–ammonia system also delivered a lower net annual cost (470$k in Case 1) than hydrogen-only (485$k) and ammonia-only configurations, highlighting its economic advantage. Under high renewable scenarios (50–70% wind increase), ammonia-related revenue rose by 15–18%, demonstrating the system’s scalability and effectiveness in harnessing surplus energy through long-duration storage.