Site selection for hybrid offshore wind and wave power plants using a four-stage framework: A case study in Hainan, China

Abstract

The site selection for hybrid offshore wind and wave power plants (HOWWPP) is a critical step to a successful HOWWPP project. In this study, a four-stage framework is presented for determining the most suitable marine areas for the siting of HOWWPP. First, wind and wave energy potentials are assessed as a foundation for the implementation of a HOWWPP project. Next, unsuitable areas for the siting of HOWWPP are determined based on exclusion criteria to avoid any potential conflicts of marine spatial planning. Feasible areas (not satisfying the exclusion criteria) are classified and converted into spatial layers separately according to evaluation criteria. Then, the triangular fuzzy analytic hierarchy process is applied to calculate the evaluation criteria weights. Finally, the site suitability of feasible areas is calculated using the weighted overlay approach and then categorized into five classes. To validate the effectiveness of the proposed framework, a case study in Hainan Province of China was conducted. The results indicate that the marine areas with medium to very high suitability are approximately 1312 km2 (4.7% of the study area) for the deployment of HOWWPP. The obtained results of this study can support potential planners in selecting marine areas for the installation of HOWWPP.

Publication
Ocean & Coastal Management
Xiao Zhou
Xiao Zhou
Postdoctoral Researcher
2021.07 - Present

My research interests include sustainable development, marine spatial planning, and environmental policy analysis

Zhou Huang
Zhou Huang
Associate professor

Associate professor of GIScience

Han Wang
Han Wang
Grad Student
2020 - present

Interested in all things cool.

Ganmin Yin
Ganmin Yin
PhD Student
2020 - present

My research interests include Human Mobility, Transportation, Urban Data Mining, Social Sensing and GeoAI.

Yi Bao
Yi Bao
PhD Student
2018 - 2023

My research interests include Geographical Information Systems, Remote Sensing, Urban Data Mining, Deep Learning

Yu Liu
Yu Liu
Professor
1997 - present

Professor of GIScience