Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas

Li, Jiangxia; Pan, Shunqi; Chen, Yongping; Yu, Yang; Xu, Conghao

doi:10.1016/j.energy.2022.125020

Cited by 19 publications

(7 citation statements)

References 46 publications

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“…In 2020, Li et al evaluated and converted offshore energy systems into sources of electrical energy based on the identification of equipment installation locations (Li et al, 2020). A recent study, on offshore wind and wave energy integration sites in the China Sea, was evaluated by Chen et al (Li et al, 2022), meta‐evaluation based on qualitative and quantitative indicators by using an unstructured‐grid finite‐volume surface wave model (FVCOM‐SWAVE). In addition, problems with the choice of locations for renewable energy extraction equipment are found by some DEA models (Costa et al, 2022; Kouaissah & Hocine, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

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A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Wang,

Nguyen,

Nhieu

et al. 2023

Manage Decis Econ

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Renewable energy sources have been utilized by countries worldwide, but offshore energy exploitation projects remain highly promising for both the world and New Zealand specifically. The selection of equipment installation locations can be challenging and requires significant investment, prompting the need for optimization in offshore energy exploitation. Wind and wave location (WWL) integrated sites offer a highly potential solution for reducing installation costs and enhancing efficiency in renewable energy exploitation. However, the decision‐making process for selecting equipment locations is crucial and directly affects the outcome. This study aims to evaluate and consider sustainable and effective equipment installation sites based on both quantitative indicators and behavioral psychology of decision making. To achieve this goal, the study utilizes the prospect theory Data Envelopment Analysis (PT‐DEA) model, which builds on PT to analyze and evaluate the effectiveness of offshore installation site selection with consideration for human psychology. By combining the results of quantitative indicators and decision‐making behavior, the study identifies sustainable and effective offshore installation sites, recommending appropriate equipment installation locations WWL. The study is also compared and referenced with relevant plans, projects, and development policies in New Zealand for confirmation.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Wang,

Nguyen,

Nhieu

et al. 2023

Manage Decis Econ

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“…The wind energy industry's cost‐effective expansion derives from accurate estimates of operating conditions (Krishnan et al., 2022; Takbash et al., 2019). Accurately estimating extreme wind speeds is paramount in optimizing the structural design of wind turbines, selecting appropriate turbines, and determining financing costs for a particular site (J. Li et al., 2022; Stern et al., 2021; Yaddanapudi et al., 2022). Extreme design loads, which are determined, in part, by the 50‐year return period sustained wind speed ( W 50 ), play a critical role in the selection of wind turbines and the associated costs (Pryor & Barthelmie, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…in optimizing the structural design of wind turbines, selecting appropriate turbines, and determining financing costs for a particular site (J. Li et al, 2022;Stern et al, 2021;Yaddanapudi et al, 2022). Extreme design loads, which are determined, in part, by the 50-year return period sustained wind speed (W 50 ), play a critical role in the selection of wind turbines and the associated costs (Pryor & Barthelmie, 2021).…”

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confidence: 99%

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

Zhao,

Huang,

et al. 2024

JGR Atmospheres

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Wind energy has grown rapidly in recent years as a measure to control carbon emissions and mitigate climate change. Extreme wind can damage wind turbines, cause losses to wind power plants, limit economic benefits of wind energy facilities, and disrupt regional grid balance. Therefore, an accurate assessment of extreme wind speeds at wind turbine hub height and their spatiotemporal variation under climate change is critical for the planning of wind energy and for guaranteeing regional energy security. In this study, the 100‐m extreme wind speeds in China are estimated using an empirical downscaling and Bayesian model averaging ensemble method with the latest ERA5 reanalysis and 20 global climate models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6). Two shared socioeconomic pathways (SSP), that is, SSP2‐4.5 and SSP5‐8.5, are considered to account for the uncertainty in anthropogenic emissions. According to the results, the highest extreme wind speeds are primarily found in Inner Mongolia, northeast China, western Tibet, and the eastern coastal region. Extreme wind speeds in central and southeastern China are projected to increase by approximately 2% in the middle (2031–2060) and the end (2071–2100) of the 21st century relative to the baseline period (1985–2014). Summer extreme wind speeds in northwestern Tibet are expected to increase by more than 9% at the end of the century. The findings of this study indicate that it is important to take the present and projected changes in local wind extremes into account when choosing locations for wind power plants and wind energy installations.

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“…The combined exploitation of offshore wind energy and wave energy has several advantages, including enhanced energy yields, improved predictability, smoothed output power, costeffectiveness, and environmental benefits [26][27][28]. In recent years, numerous countries have conducted comprehensive resource assessments of offshore wind and wave energy to develop combined wind-wave power generation systems [29][30][31][32][33][34][35][36][37]. Based on their foundation and layout, combined wind-wave power generation systems can be classified as follows [26]: co-located (Figure 1a), island systems (Figure 1b), and hybrid systems (Figure 1c).…”

Section: Introductionmentioning

confidence: 99%

The Energy Conversion and Coupling Technologies of Hybrid Wind–Wave Power Generation Systems: A Technological Review

Wang,

Sun,

Zhao

et al. 2024

Energies

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Based on the mutual compensation of offshore wind energy and wave energy, a hybrid wind–wave power generation system can provide a highly cost-effective solution to the increasing demands for offshore power. To provide comprehensive guidance for future research, this study reviews the energy conversion and coupling technologies of existing hybrid Wind–wave power generation systems which have not been reported in previous publications. The working principles of various wind and wave energy conversion technologies are summarised in detail. In addition, existing energy coupling technologies are specifically classified and described. All aforementioned technologies are comprehensively compared and discussed. Technological gaps are highlighted, and future development forecasts are proposed. It is found that the integration of hydraulic wind turbines and oscillating wave energy converters is the most promising choice for hybrid wind–wave power extraction. DC and hydraulic coupling are expected to become mainstream energy coupling schemes in the future. Currently, the main technological gaps include short their operating life, low energy production, limited economic viability, and the scarcity of theoretical research and experimental tests. The field offers significant opportunities for expansion and innovation.

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Assessment of combined wind and wave energy in the tropical cyclone affected region:An application in China seas

Cited by 19 publications

References 46 publications

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

A prospect theory extension of data envelopment analysis model for wave‐wind energy site selection in New Zealand

Ensemble Bayesian Model Averaging Projections of Wind‐Speed Extremes for Wind Energy Applications Over China Under Climate Change

The Energy Conversion and Coupling Technologies of Hybrid Wind–Wave Power Generation Systems: A Technological Review

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