Assessing long-term metocean data variability for optimal energy system planning via static robust optimization approach

Small, non-interconnected island systems are at the forefront of the energy transition but their isolated condition makes them exposed to the natural variability of renewable resources. This work develops a static robust optimization framework to design multi-technology portfolios that explicitly account for the inter-annual variability of the wave climate. The methodology couples the EnergyPLAN simulation tool with a MATLAB-based NSGA-II algorithm. The design vector includes the installed capacities of onshore and offshore wind, photovoltaics, two different wave energy converters (Pelamis and CorPower) and a battery energy storage system (BESS). Robustness is assessed over a discrete uncertainty set composed of twenty years of hourly meteomarine data from Copernicus reanalysis for La Gomera (Canary Islands). Three system-level indicators are optimized in a worst-case sense: annual CO2 emissions, a demand-generation mismatch metric ϕ, and the BESS exploitation index. A post-processing step selects all robust portfolios that satisfy a stringent emissions target, corresponding to roughly a 70% reduction with respect to the validated reference configuration. Within this low-carbon subset, CO2 is treated as a saturated objective and the remaining trade-offs are explored in the two-dimensional ϕ-RBESS plane, where a secondary Pareto front is identified. The resulting portfolios reveal a clear interaction between storage use and temporal balancing, with different BESS levels but a quite constant wave contribution. The framework is generic and transferable to other island systems and renewable technology combinations, providing a practical tool for integrating long-term resource variability and explicit decarbonization targets into energy system planning.