Genetic-algorithm-based smart sizing of hybrid renewable energy systems for stochastic high-power cold ironing loads

Cold Ironing (CI) is a proven strategy for reducing ship emissions at berth; however, its large, stochastic electricity demand creates significant technical and economic stress on port energy systems when supplied exclusively by the utility grid. This study proposes a smart sizing framework for a grid-connected Hybrid Renewable Energy System integrating photovoltaic and wind generation to sustainably supply CI operations. Using real operational data from a Mediterranean port, a high-resolution energy model is coupled with a Genetic Algorithm–based capacity optimization to determine the optimal renewable mix that minimizes both the levelized cost of energy and the carbon footprint of shore-side electrification. The resulting hybrid system is designed to maximize on-site renewable penetration while using the grid only as a balancing resource under load and resource uncertainty. Results show that the optimized configuration substantially reduces grid dependency and delivers major emission abatement compared with both grid-only CI and auxiliary-engine operation. The study demonstrates that smart capacity sizing, rather than real-time dispatch control, is the critical enabler of techno-economic and environmental viability for renewable-powered CI, and it provides a scalable, process-oriented decision-support framework for the design of sustainable port energy infrastructures. Development of a Genetic Algorithm–based smart sizing framework for hybrid photovoltaic–wind systems tailored to Cold Ironing demand profiles. Integration of real vessel traffic data and site-specific renewable resources to derive an optimal capacity mix that balances technical reliability with economic efficiency. Demonstration that the smart-sized hybrid system produces over 57 GWh/year and reduces grid dependence by more than 54% relative to grid-only Cold Ironing. Quantification of deep decarbonization, with the optimized Hybrid Renewable Energy System achieving a 72.01% reduction in carbon footprint compared to auxiliary engine operation. Evidence that algorithmic smart sizing outperforms heuristic and mono-source approaches, providing a scalable methodology for sustainable port electrification.