A grid-informed multi-criteria techno-economic optimization framework for wave energy converter deployment

Wave energy is a promising yet underutilized marine resource whose techno-economic viability depends not only on resource availability and device performance but also on electrical grid constraints. Conventional site-selection approaches, typically based on single-criterion analysis or subjective weighting, fail to capture the complex trade-offs among energy production, cost, resource stability, and grid deliverability. To address these limitations, this study proposes a Grid-Informed Multi-Criteria Techno-Economic Optimization (GI-MC-TEO) framework that integrates high-resolution hydrodynamic modeling with multi-objective optimization. The framework couples a SWAN wave model with a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm to simultaneously maximize Annual Energy Production (AEP) and Resource Stability Index (RSI), while minimizing the Levelized Cost of Energy (LCOE). Grid-related constraints, including transmission distance and associated electrical losses, are explicitly incorporated into the optimization process. The framework is applied to the coastal waters of Sur, Oman, characterized by a steep continental shelf and moderate wave climate. Results indicate that grid integration significantly affects techno-economic performance, with electrical losses reducing delivered energy by approximately 12–14%, corresponding to 226–232 MWh/year. Among the evaluated technologies, the Oscillating Water Column (OWC) achieves the lowest LCOE (∼81 $/MWh) and highest RSI (∼0.8), while WaveDragon exhibits the highest energy production potential but at a higher cost (∼222 $/MWh). Other devices, including CETO and Oyster, show moderate performance, whereas Pelamis, CorPower, and AquaBuoy demonstrate comparatively lower techno-economic efficiency. The proposed framework provides a robust and practical decision-support tool for identifying optimal WEC deployment strategies under realistic grid constraints.