Techno-economic analysis of hybrid wave energy and floating photovoltaic systems in remote islands: A case study in Indonesia

Abstract

Remote islands in Indonesia continue to face significant challenges in achieving reliable and sustainable electricity access, with diesel-based systems dominating energy supply despite high operational costs, limited availability, and environmental drawbacks. This research investigated the techno-economic feasibility of two renewable energy configurations for achieving 24-hour electrification on remote islands, using Pulau Enggano as a representative case. Scenario 1 combines a floating photovoltaic (FPV) system, a wave energy converter (WEC), and battery storage, while Scenario 2 relies solely on FPV and battery systems. Using the System Advisor Model (SAM) of NREL for performance simulation and an annuitizing method for Levelized Cost of Electricity (LCOE) analysis, both systems were designed to meet hourly and annual energy demands. Scenario 1 achieved an LCOE of USD 306/MWh, offering a more stable supply profile with reduced battery cycling. Scenario 2, though technically sufficient, resulted in a higher LCOE of USD 382/MWh due to larger storage requirements. Both scenarios were compared against the adjusted diesel generation cost of USD 246/MWh. Sensitivity analysis revealed that WACC and CAPEX are the most influential factors on economic performance, particularly for Scenario 2. Battery cost uncertainty also significantly impacted the LCOE of the battery-dependent system. This study concludes that hybrid renewable energy systems leveraging both solar and marine resources can deliver continuous power more economically and reliably than solar-only alternatives, especially when supported by appropriate financing mechanisms. The research highlights the need for targeted policy support—such as subsidy reforms and capital incentives—to enhance the competitiveness of clean energy in Indonesia's remote regions. Future research is recommended to assess the role of bioenergy alternatives like palm oil biodiesel and to expand real-world resource validation using long-term time series data.