From Wave Energy Converter Optimisation to Hybrid Offshore Integration: A Critical Review with the TALOS WEC as a Case Study

Optimising standalone wave energy converters (WECs) does not establish their performance within hybrid offshore wind–wave systems. This review develops an evidence-based framework linking WEC optimisation to hybrid integration through critical appraisal of modelling, validation, performance boundaries, and uncertainty. It synthesises 179 publications (96 primary studies) spanning WECs, offshore wind, coupled modelling, hybrid integration, and machine learning (ML) and artificial intelligence (AI), using the Technologically Advanced Learning Ocean System (TALOS) as a literature-derived case study. Published TALOS optimisation increased simulated mechanical power take-off (PTO)-level annual energy production (AEP) from approximately 345 MWh to 1311 MWh (3.8-fold) within the investigated numerical design space. This does not demonstrate electrical AEP, experimentally validated full-scale performance, or beneficial TALOS–wind integration. ML and AI can support prediction, monitoring, control, and optimisation, but require representative data, physical consistency, and validation. The synthesis identifies coupled dynamics, wave-to-wire conversion, structural and mooring constraints, reliability, and techno-economic performance as key evidence gaps. A proposed pathway prioritises uncertainty-aware modelling, experimental validation, coupled hybrid-system assessment, and offshore demonstration. Device-level optimisation gains must be independently evaluated at system level before hybrid performance or economic benefits can be claimed.