Optimal design and techno-economic analysis of wave-integrated hybrid energy systems for coastal communities

Wave energy has the potential to improve renewable complementarity in coastal microgrids; however, its technoeconomic competitiveness under moderate resource conditions remains uncertain. This study develops a two-stage stochastic mixed-integer linear programming (MILP) framework to evaluate the integration of wave energy converters (WECs) into hybrid offshore microgrids under joint resource and demand uncertainty. Renewable generation and demand are modeled using ARIMA-based forecasting, with interdependencies captured via a Student-t copula and reduced through k-mediods clustering. The framework optimally sizes wind, floating photovoltaics, battery energy storage, and WEC systems by minimizing the expected annual system cost. A case study in coastal New Jersey indicates that, under current cost assumptions, WEC deployment is not economically viable, despite the potential to reduce storage requirements through resource complementarity. Sensitivity analysis identifies capital cost reduction thresholds of approximately 70%–80%, depending on device type and operating policy, to achieve cost parity and system-level benefits. Demand-side management improves curtailment outcomes but provides limited economic gains without substantial WEC cost reductions. Results demonstrate that the capital cost, rather than resource variability, is the primary barrier to wave integration in moderate Atlantic wave climates. The proposed framework provides a generalizable tool for evaluating deployment thresholds of emerging marine technologies within multi-resource energy systems under uncertainty.