A Study on Site Selection and Capacity Allocation of a Four-Energy Complementary Power Generation System in the Zhoushan Archipelago Based on the Analytic Hierarchy Process

ABSTRACT: Marine renewable energy (MRE) is a vital component of emerging energy systems, playing a key role in the low-carbon transition and enhancing energy self-sufficiency in coastal regions. The Zhoushan Archipelago possesses favorable conditions for wind, wave, tidal-current, and solar energy, providing a resource foundation for multi-energy complementary systems. However, due to resource intermittency, spatiotemporal heterogeneity, and marine-use constraints, single MRE sources cannot independently ensure the long-term stable power supply required for isolated island grids. This study develops a comprehensive decision-making framework for wind-wave-tidal-solar integration by combining logical veto screening, the Analytic Hierarchy Process (AHP), and capacity allocation optimization. First, a multi-level evaluation system is established across resource, natural-engineering, socio-economic, and environmental dimensions, utilizing exclusionary factors—such as nature reserves, cultural heritage, and existing marine engineering—as preliminary veto criteria. Second, a “four-energy complementarity–synergy index” is introduced to characterize temporal availability, ensuring that site selection accounts for the contribution of multi-energy combinations to supply stability. Third, AHP is applied to determine weights and rank candidate sites, while a minimum-variance model optimizes capacity ratios for preferred locations. Furthermore, the TOPSIS method is introduced as an alternative multi-criteria decision-making approach for comparative analysis, to test the sensitivity of the ranking results to the choice of evaluation method. Based on the shortlisted priority candidate sites, a minimum variance capacity allocation model is established to analyse the synergy relationships between different energy types. Results indicate that multi-criteria evaluation effectively reveals suitability differences that single-resource metrics miss. Additionally, optimized capacity allocation significantly reduces combined-output fluctuations and enhances supply stability. The proposed framework is structured, verifiable, and adaptable, providing a methodological reference for the siting and preliminary capacity planning of multi-energy offshore power stations.