Multi-objective Optimization with Uncertainty Costs for Green Hydrogen Systems in Colombian Urban Communities

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Chinese explanation / 中文解读

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Original abstract

This article presents an optimization model for the integration of hybrid photovoltaic-hydrogen-battery energy storage systems (PV-H2-BESS) applied to the urban context of Barranquilla, Colombia. The approach is based on modern methodologies employed in international studies on community energy systems with hydrogen which were specifically adapted to the tropical climatic conditions and typical demand patterns of the Colombian Caribbean. Uncertainty in PV generation and residential demand is modeled through Monte Carlo scenarios, while operational risk is incorporated using Conditional Value-at-Risk (CVaR). The multi-objective problem is formulated to minimize the Life Cycle Cost (LCC) and the Grid Interaction Level (GIL), and solved using a modified Multi-Objective Particle Swarm Optimization (MOPSO) algorithm capable of generating robust Pareto fronts under uncertainty. The results reveal significant differences compared to systems evaluated in North America (the reference study), largely due to Barranquilla’s low seasonality, high annual irradiance, and constant thermal loads. The proposed framework contributes substantially to the planning of future green hydrogen energy hubs in Colombia.

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