Potential of Offshore Wind Energy in Malaysia: An Investigation into Wind and Bathymetry Conditions and Site Selection

Li, Mingxin; Carroll, James; Ahmad, Ahmad Sukri; Hasan, Nor Shahida; Zolkiffly, M. Zaid B.; Falope, Gboyega Bishop; Sabil, Khalik Mohamad

doi:10.3390/en17010065

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…Notable progress has been made in offshore wind energy, tidal energy, and wave energy development. (Li et al, 2023a). However, the assessment of the impact posed by these marine renewable energy sources holds paramount importance for the environment, economy, and society.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

Song,

Gao

2024

Front. Energy Res.

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As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal’s marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN-Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. “ACO-TCN-Attention” is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy.

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Section: Introductionmentioning

confidence: 99%

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

Song,

Gao

2024

Front. Energy Res.

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Techno-Economic Analysis Incorporating Intelligent Operation and Maintenance Management: A Case Study of An Integrated Offshore Wind and Hydrogen Energy System

Li,

Carroll

et al. 2024

J. Phys.: Conf. Ser.

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In this study, a comprehensive examination of wind-hydrogen energy systems is conducted through detailed techno-economic analysis and sensitivity analysis. The primary emphasis is on optimizing operation and maintenance (O&M) strategies and understanding the impacts of market dynamics. Utilizing Monte Carlo simulations, we first identify the optimal intelligent O&M plan, leading to significant reductions in annual O&M costs ($39.9/MW) and downtime (6.59 days per turbine) compared to conventional methods. The incorporation of prognostics and health management (PHM) further demonstrate a notable impact, leading to a 9.9% reduction in O&M costs and a 10.7% decrease in downtime. In the broader context, these outcomes translate into reductions in the O&M expenditures, total lifecycle costs of the system, Levelized Cost of Hydrogen (LCOH) and Levelized Cost of Energy (LCOE) by 3.9%, 0.75%, 2.4%, and 1.8%, respectively, highlighting the economic benefits of intelligent O&M strategies. The extensive sensitivity analysis, encompassing 54 scenarios, delves into the effects of maintenance strategies, hydrogen prices, wind energy share, and subsidies, revealing nuanced insights into cost savings and operational efficiencies. Notably, intelligent maintenance and favorable hydrogen subsidies effectively reduce LCOH, while the interplay between wind energy share and hydrogen pricing influences system profitability and efficiency, underscoring the complex dynamics at play in optimizing renewable energy systems.

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Winds of Change: A Study on the Resource Viability of Offshore Wind Energy in Montenegro

Bogdanović,

Ivošević

2024

Energies

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The energy produced from renewable sources (solar, wind, hydro, geothermal, and biomass) provides direct access to clean and safe energy. Offshore wind energy, generated through wind farms, has traditionally relied on fixed structures, whereas innovative floating structures have been commercially applied since 2017. This study investigates offshore areas in Montenegro suitable for wind farm construction. Research on average annual wind speeds has successfully identified a surface area deemed suitable for constructing a wind farm in the Montenegrin part of the Adriatic Sea. Analysis of available bathymetric databases has pinpointed technical solutions for the supporting structures of wind turbines required to construct an offshore wind farm. Applying an assessment method to the defined surface of Montenegrin waters, seven blocks have been identified as suitable for wind farm construction. The research results indicate that wind farms can be built in Montenegrin waters with a technical potential corresponding to a total capacity of 2299.794 MW, which includes 2034.48 MW for floating structures, 126.759 MW for fixed structures, and 138.555 MW for jacket-fixed structures.

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Potential of Offshore Wind Energy in Malaysia: An Investigation into Wind and Bathymetry Conditions and Site Selection

Cited by 3 publications

References 56 publications

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

Techno-Economic Analysis Incorporating Intelligent Operation and Maintenance Management: A Case Study of An Integrated Offshore Wind and Hydrogen Energy System

Winds of Change: A Study on the Resource Viability of Offshore Wind Energy in Montenegro

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