research progress of wave energy...

Chapter 2Research Progress of Wave EnergyEvaluation

2.1 Assessment of Global Oceanic Wave Energy

2.1.1 Observation Data Stage

Under the condition that the observation wave data is extremely scarce, the pre-decessors have made great contributions to the wave energy evaluation. As early asthe 1970s, researchers began the calculation and evaluation of the distributioncharacteristics of wave energy resources along the global ocean coast by using thelimited marine ship reported data and buoy data. Tornkvist (1975) and Hulls (1977)pointed out that high-value areas of wave power density (WPD) in the global watersare located in the northeastern part of the North Atlantic, the Pacific west coast ofNorth America, the south coast of Australia, Chile in South America, and thesouthwest coast of South Africa. Denis (1986) presented the distribution of theWPD near coastal waters globally by analyzing limited observation data. Thorpe(1999) analyzed the distribution characteristics of WPD around the UK by utilisingobservation data. In 2002, the Centre for Renewable Energy Sources (CRES)(2002) made a distribution map of WPD in the global waters by utilizing theobserved wave data collected from all over the world. Lenee-bluhm et al. (2011)analyzed the wave energy resources in the Pacific Ocean in the northwest of theUnited States by using the data of National Data Buoy Center of the United States(NDBC) and Coastal Data Information Program (CDIP). They found that the waveenergy resources of these sea areas are mainly contributed by the significant waveheight (SWH) of 2–5 m and the wave period of 8–12 s.

Going to the scarcity of observation wave data, it is difficult to achieve thedetailed assessment of wave energy resources in a large sea areas, which is thereason why scientific basis cannot be provided for the macro site selection of waveenergy projects (Zheng et al. 2016a). In addition, the values of WPD are the mainconcerns in early wave resource studies. In the evaluation process of wave energyresources, a series of important indicators, such as resources enrichment,

© Springer Nature Singapore Pte Ltd. 2020C. Zheng et al., 21st Century Maritime Silk Road: Wave Energy ResourceEvaluation, Springer Oceanography, https://doi.org/10.1007/978-981-15-0917-9_2

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availability, long-term tendency, the storage (total storage, exploitable storage andtechnological storage), water depth and offshore distance, should also be consideredexcept the values of WPD and energy stability.

2.1.2 Satellite-Based Observation Stage

With the development of satellite technology, more and more satellite data are usedin wave energy research. Barstow et al. (1998) calculated the WPD of hundreds ofoffshore stations globally and obtained the distribution map by using the 2-yearSWH collected by TOPEX/Poseidon (T/P) satellite altimeter data and the statisticalrelationship between SWH of buoy station and wave energy period. Pontes (2008)also showed the effectiveness of two major microwave sensors, altimeter andsynthetic aperture radar (SAR), based on the wave energy assessment by utilizingremotely-sensed data. Wan et al. (2015a, b) analyzed the wave energy character-istics in the China seas and found that the wave energy in these waters was mainlycontributed by the sea state of 0.5 m � Hs � 4 m, 4 s � Te � 10, and usingAVISO data from 2009 to 2013.

There are certain shortcomings in time synchronization for satellite data. Forexample, the T/P altimeter has a 10-day repeat cycle, that is, it can return to thesame point after 10 days, which could result in the loss of significant weatherprocesses in some areas or sites and directly affect calculation results of monthlyaverage WPD, energy level occurrence. In addition, the time series of satellite datais relatively short, which cannot show the long-term trend of wave energy verywell.

2.1.3 Numerical Simulation Stage

With the rapid development of computer technology, some advanced numericalwave models have sprung up in Europe and America in recent years and have beenapplied in wave energy resource assessment. These wave numerical models arecommon: Wave Action Model (WAM), WAVEWATCH-III (WW3), SimulatingWAves Nearshore (SWAN). Liberti et al. (2013) analyzed the characteristics ofwave energy resource of the Mediterranean using the WAM model for recent10 years. Iglesias and Carballo (2010a, b) analyzed the characteristics of waveenergy resources in the offshore and inshore water of Asturias (northern Spain)using buoy data and WAM and SWAN simulation data for 44 years (1958–2001),finding that most wave energy was mainly contributed by the sea state of SWH 11–13 s wave period of 2–5 m. Cornett (2008) simulated the global wave field from1997 to 2006 by using NWW3 wave mode, and calculated the global wave energydistribution. Gunn and Stock-Williams (2012) once analyzed the characteristics ofwave energy in global sea areas using WW3 wave model, finding that wave energy

12 2 Research Progress of Wave Energy …

resources of the global coastline were 2.11 ± 0.05 TW (1.07 ± 0.03 TW in thenorthern hemisphere and 1.05 ± 0.02 in the southern hemisphere). Folley andWhittaker (2009) as a team as well as Iglesias and Carballo (2010a, b) for anothergroup calculated and analyzed the changes of wave energy near the coast ofScotland and Spain respectively by using the third-generation wave modelMIKE21 NSW and SWAN. In 2009, Roger (2009) successfully predicted the waveenergy of the east coast of the Pacific Ocean through WW3 wave model, andachieved a good simulation effect. Rusu and Onea (2013) simulated and analyzedthe characteristics of wave energy resources in the black sea by using the SWANmodel. Akpamar and Komurcu (2013) simulate the wave field of the Black Seafrom 1995 to 2009 by using the era-powered SWAN wave model from ECMWF(European Centre for medium-range Weather Forecasts) to, analyzed the waveenergy characteristics of the sea, and found that the southwest offshore of the blacksea is an appropriate area for wave energy development. Neill and Hashemi (2013)simulated wave fields in northwest Europe from 2005 to 2011 utilizing the SWANmodel. And then they analyzed climatic characteristics of wave energy in the areaand found there is close relationship between wave energy in winter and NAO.Gallagher et al. (2016) carried out a high resolution analysis of wave energy in theIreland based on a 14-year high resolution WW3 hindcast. Arinaga and Cheung(2012) contoured the wind-sea WPD and swell WPD in the global oceans by usinga 10-year WW3 hindcast. Chiri et al. (2013) carried out a high spatial analysis ofwave energy around the Canary Islands by using WAM hindcast. The results showthat only west and north edges of the archipelago in autumn and winter areavailable for wave energy development.

By virtue of numerical simulation method, we can not only grasp overall con-ditions of the wave energy in the global oceans and make an intensive assessmentof wave energy in the key sea area, but study some sea areas without observationdata. However, under the influence of orographic effect and others elements, themethod results of numerical models are usually poor in some sea areas with specialtopography. This means that the simulation data needs to be supplemented bysatellite data and buoy observations.

2.1.4 Reanalysis Data Stage

With the rapid development of ocean observation and computer technology, moreand more data are available for wave energy resource assessment. Besides, as theimprovement of variational assimilation technology, various reanalysis databecomes more and more widely used in this field. Pontes et al. (1997) and Pontes(1998) studied the distribution and seasonal changes of wave energy resources incoastal areas of Europe and drawn atlas, by using partial buoy data and wave fieldobtained by WAM mode. Rusu and Soares (2012), using satellite data, buoy dataand simulation data using SWAN model, analyzed the wave energy resourcecharacteristics in the waters around Madeira Islands. Based on waves reanalysis

2.1 Assessment of Global Oceanic Wave Energy 13

Fig. 2.1 Wave power density in global oceans (after Zheng et al. 2014). Note Color represents thewave power density and arrow represents the wave direction


data in past 61-year (1948–2008), Reguero et al. (2015) analyzed characteristics inseason and interannual variation and long-term trend of the wave energy resourceglobally, and found that the WPD in some waters is related to the Arctic Oscillation(AO), which will be beneficial to the related prediction of wave energy. Previousstudies have great contribution to the research on wave energy resources. But morebased on the size of the WPD. In the actual exploitation of wave energy resource, itis necessary to consider not only the size of the WPD, but the stability andlong-term trend of wave energy resources, storage and others. Based on the 45-year(1957.09–2002.08) European Centre for Medium-Range Weather Forecasts(ECMWF) Reanalysis (ERA-40) wave reanalysis derived from ECMWF, from apractical perspective, our team systematically studied on the global ocean waveenergy resource by analyzing the WPD (Fig. 2.1), energy level occurrence, thestability and long term trends of WPD, etc. (Zheng et al. 2014). Moreover, theauthor also takes the leading in the analysis to the global ocean swell energy (Zheng

Fig. 2.1 (continued)

2.1 Assessment of Global Oceanic Wave Energy 15

et al. 2014). The author (Zheng et al. 2013a), using ERA-40 wave reanalysis data,also calculated firstly total storage, exploitable storage and technical storage ofwave energy resources per unit area of the global oceans, as shown in Fig. 2.2.According to the exploitable storage of resources, engineers can know the yearlyenergy production of different regions intuitively.

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Fig. 2.2 Total storage (a) and exploitable storage (b) of wave energy resources per unit area ofthe global oceans (after Zheng et al. 2013a), unit: 105 kW h m−1


2.2 Wave Energy Assessment in the China Seas

The evaluation on wave energy in China started late, but thanks to the great con-tributions by early researchers under the condition of extremely lacking waveinformation, it has been progressing rapidly. In 1983, based on previous researchdata, Ma and Yu (1983) divided China’s offshore waters and its adjacent sea areasinto 332 parts to evaluate the total wave energy and total power of wave energy inthe China seas. You and Ma (2003) pointed out that the South China Sea is rich inwave energy resources, while the Bohai Sea has poorer wave energy resources. Buteven in some sea areas, where the wave energy is scarce, their wave energy canprovide enough electricity for the observation buoy system. According to Chu(2004), coastal waters which are windy or with stronger winds have higher wavepower density. Ren et al. (2008, 2009) implemented an information managementsystem to calculate and analyze the ocean wave data, as a way to evaluate waveenergy resources of the Shengshan sea area, Zhejiang Province. According to theresearch, the WPD in that area typically ranges from 0.5 to 8.8 kW/m with 2 kW/maround 60% of frequency distribution, and the wave energy could be used as a formof energy supply. Wang and Lu (2009) calculated the wave power density along theChina coast and found out that theoretically, the wave energy along the China coastis 7.0 � 107 kW with an uneven distribution. Coasts along the Xisha Islands; northof Haitan Island, Fujian province, Taiwan, areas of central Zhejiang province, aswell as Bohai Straits have higher wave energy power. Researchers like Chiu et al.(2013) analyzed the wave energy characteristics of the sea areas around Taiwan andfound that the northeast coastal areas have more abundant wave energy resources.According to the research, most sea areas around Taiwan have more wave energy inautumn and winter, and only the sea areas around southwest Taiwan have morewave energy in spring and summer.

The scarcity of observation marine data has always been a global problem, whichseverely limits the selection of sites to collect wave energy since it is impossible toevaluate the wave energy of sea areas without related data (Zheng et al. 2012a). Theauthor became the first to apply the SWAN model to simulate the value of waveenergy resources of the China seas, making more accurate researches on waveenergy in a wide range of sea areas (Zheng et al. 2011a, b) possible. In 2011, theauthor used the third-generation wave model WW3 to simulate the wave field of theChina seas from January 1988 to December 2009, with CCMP wind data as thedriving field. With the wave data from this study, the simulation analysis of waveenergy in the China seas was realized for the first time (Zheng and Li 2011).According to Fig. 2.3, except for the Bohai Sea and the northern part of the YellowSea, annual average WPD in most of China seas is above 2 kW/m, while the pre-vious estimation is 2–7 kW/m. In the evaluation on wave energy resources, the waveenergy is regarded as available when its WPD is over 2 kW/m, while the sea areashaving a WPD over 20 kW/m are considered to be rich wave energy areas, whichmeans the frequency of different WPD is an important measurement of whether thesea area has enough wave energy resources to develop. The author names the

2.2 Wave Energy Assessment in the China Seas 17

Fig. 2.3 Wave power density in January, April, July, October and annual mean value in the Chinaseas (after Zheng and Li 2011), unit: kW/m


measurement as energy level occurrence (Zheng and Li 2011), which has beenwidely recognized and used (Wen et al. 2013; Jiang 2013; Wan et al. 2015a, b).Based on the size of WPD, energy level occurrence, long-term trend and stability ofwaves, the China seas, except for the Bohai Sea and northern part of the Yellow Sea,has rich wave energy resources that can be developed, while the northern part of theSouth China Sea enjoys the richest wave energy resources. With the help of theSWAN model, Jiang (2013) simulated the sea waves in Zhejiang Province andanalyzed the characteristics. Together with other researchers, Liang et al. (2013)used the SWAN model to simulate and analyze the climatic characteristics of thewave energy resources in Shandong Peninsula from 1996 to 2011, and found that themaximum wave power density along the coast is 296 kW/m, and the average waveenergy, while the average wave density is 5.1 kW/m. The study contributed to theevaluation and development of wave energy resources in Shandong.

In the exploitation of wind energy, winds having effective wind speed (3–25 m/s)can be collected for use. In 2012, the author defined significant interval as a periodappropriate for energy collection when the wave height is from 0.5–4.0 m (Zhenget al. 2013b), the wind speed is less than 13.9 m/s and the wave power density isover 2 kW/m. The current wave power devices can work well when the wave heightis over 0.5 m, but when the wave height is over 4.0 m, wave power devices will beseriously damaged. Therefore, appropriate wave height is from 0.5 to 4.0 m, and thisstudy is based on significant intervals. It is found that most waters in East China Seaand South China Sea have more significant intervals, while the northern part of theSouth China Sea (from Hainan Island to Luzon Strait) have many significantintervals all year round. However, at present, only a few devices can capture waveenergy when the SWH is above 0.5 m. Most wave energy devices can only capturewave energy when the SWH is above 1.3 m. As a result, Zheng and Li (2018)redefined the effective wave height (SWH of 1.3–4.0 m). As the wave power devicesare becoming more and more advanced, the range of effective interval will continueto expand.

The energy storage is closely related to the amount of electricity than can begenerated. Ma and Yu (1983) found that the total storage of wave energy in China’soffshore waters and its adjacent sea areas is 5740 � 108 kW. Due to the scarcity ofrelated data, existing researches mainly estimate the total storage in a certain area,and do not show the regional differences in the value size of total storage, which isone of the primary bases for the site selection of wave energy projects. In 2013,with simulated wave data of high spatial and temporal resolution, the authoraccurately calculated the total storage, exploitable storage and technological storageof wave energy of per unit area in China seas (Zheng et al. 2013c). The authoranalyzed the long-term trend of wave energy resource (Zheng and Li 2015a),including the overall trend of WPD, as well as the change trends in different regionsand seasons. According to the study, during the past 24 years (from 1988 to 2011),the WPD in China seas increased at a rate of 0.2 (kW/m) every year as a whole(Fig. 2.4), which tends to benefit the exploiting of energy resources in China. Also,the trend in WPD exhibits obvious regional difference and seasonal difference. The


increasing trend of WPD in sea areas around Dongsha Islands is the strongest,which reaches over 0.6 kW/m per year, and its biggest change happens in winter.This study takes the lead in the studies of long-term tendency of wave energy in theChina seas, and its results are significant for long-term planning of wave energydevelopment.

As early as 2011, the author analyzed the wave climatic characteristics and waveenergy resources of the Xisha Islands and the Nansha Islands, and found that thesetwo regions enjoy abundant wave energy resources (Zheng et al. 2011a, b). Zhengand Li (2015b) systematically studied the wave energy and offshore wind energy insea areas around main islands in the South China Sea (Fig. 2.5), first demonstratedthe feasibility of wave power generation and offshore wind power generation on theSouth China Sea. When the wind power density is over 50 W/m2, the wind poweris exploitable; and when the wind power density is over 200 W/m2, the windenergy is abundant, which is different from the wave power density—when thewave power density is over 2 kW/m, the wave energy can be collected for use, andwhen the wave power density is over 20 kW/m, there is an abundant wave energy.It can be seen from Fig. 2.5 that wind energy and wave energy can be developed allthe year round in their studied sea area, and the wind energy resources are abundantin more than half of the year. The wind energy and wave energy in the sea area aremainly from two directions (Figures omitted), meaning a stable energy direction,which is beneficial to the collection and conversion of energy resources. Besides,Zheng and Li (2015c) also studied the relationship between the marine environmentcharacteristics around islands and the development of its wave energy and windenergy, as well as the safety of marine engineering. They found that in winter, whencold air flows south to the sea, the energy resources are sufficient but lessdestructive; and in summer, with the influence of the southwest monsoon, energyresources are sufficient, stable and less destructive. On the whole, the resources of

Fig. 2.4 Long-term trends of the China seas significant wave height (left, unit: cm/yr) and wavepower density (right, unit: (kW/m)/yr) (after Zheng and Li 2015a)


their researched sea area are rich, stable, and are safe for development. Recently,based on the ERA-Interim reanalysis data, Wan et al. (2015a, b) and otherresearchers found that the South China Sea has rich wave energy.

2.3 Wave Energy Assessment in the Maritime Silk Road

Although the previous researchers have contributed a lot to the wave energyevaluation in the global oceans, research on the Maritime Silk Road is rare, espe-cially the North Indian Ocean. As of today, only a few papers on the wave energyalong the Maritime Silk Road can be found online (Zheng et al. 2012b, 2016b,2019a; Zheng 2018; Zheng and Li 2018). In 2012, based on the WW3 hindcastwave data from September 1957 to August 2002 in the South China Sea and NorthIndian Ocean, Zheng et al. (2012b) carried out the first research on the wave energyevaluation along the Maritime Silk Road. They found that the sea area has rich andstable wave energy. In the South China Sea and North Indian Ocean sea area, bigWPD mainly occurs in the South China Sea and the Somalia waters. A relative bigarea of WPD is found in the central part of the Bay of Bengal, but much smallerthan that in the South China Sea and Somalia waters. The occurrence of WPDabove 2 kW/m (when the wave energy can be used) occurs frequently in the SouthChina Sea and North Indian Ocean, which means the overall wave energy resourcesalong the Maritime Silk Road is available. The result also found a good stability ofthe WPD along the Maritime Silk Road. And the wave energy is more stable inspring, autumn and winter than in summer, and is more stable in the South ChinaSea than the North Indian Ocean. In 2016, based on the data of ERA-Interim windfield from the European Center for Medium-Range Weather Forecasts (ECMWF),

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Fig. 2.5 Monthly characteristic of the wind power density and wave power density (after Zhengand Li 2015b)


Zheng et al. (2016b) reanalyzed the wave energy characteristics along the MaritimeSilk Road, and the average annual WPD is found to be largest in Arabian Sea (from8 to 18 kW/m), followed by the Bay of Bengal (from 6 to 16 kW/m), and smallestin the South China Sea (from 2 to 12 kW/m). The large WPD of the Arabian Sea isbrought by the strong southwest monsoon, while the WPD of the Bay of Bengal isdue to the swell of the South Indian Ocean and the WPD of the South China Sea issupposed to be mainly effected by cold air. It is worth noting that the two researchesare very different in the WPD in the Bay of Bengal—the research in 2012 showsthat the WPD in the Bay of Bengal is obviously smaller than that in the South ChinaSea and the Arabian Sea, but the reanalysis in 2016 shows that the WPD in the Bayof Bengal is larger than that in the South China Sea. It is necessary to furtherresearch on this issue, as a way to provide reliable basis for wave energydevelopment.

Currently, there is an extreme lack of wave energy researches along the MaritimeSilk Road. Besides, current researches have a relatively low spatial resolution,making it difficult for researchers to focus on the wave energy characteristics of keysea areas and islands, thus failing to support the wave energy development and theconstruction of maritime key points along the Maritime Silk Road. In addition,current researches are mainly focused on the value of WPD and coefficient ofvariation (Cv), but in wave energy development, long-term tendency, the availablerate, the richness, the storage, monthly variation index, seasonal variation index ofwave energy also matter. Therefore, it is necessary to conduct more systematicstudies on the wave energy to provide scientific and technological support for waveenergy projects such as wave power generation and seawater desalination.

2.4 Difficulties in Wave Energy Assessment

Previous researches are mostly concerned about the climatic spatial-temporal dis-tribution characteristics of wave energy, so there are still many difficulties in energyclassification, short-term forecasting, long-term trend, and long-term projection andso on, which are essential to the site selection for wave power generation andseawater desalination, as well as the systematic daily operation and long-termplanning. In addition, the relatively low spatial resolution of current researches cannot meet the needs for wave energy evaluation of key islands and sea areas, whilethe need for wave energy is more urgent at key nodes. Although the previousresearchers have contributed a lot to many sea areas around the whole world, but asof today, only a few papers on the wave energy in the South China Sea and NorthIndian Ocean can be found. The main difficulties in wave energy assessment of theMaritime Silk Road are as follows.


2.4.1 Climatic Characteristics of Wave Energy

The wave power density (WPD) is the most direct manifestation of wave energyintensity, so earlier researches are mainly concerned about this parameter. With theincreasing data of ocean waves and the continuous improvement of researchmethods, the WPD, the stability and storage of resource are all taken into con-sideration for the analysis of climatic characteristics of wave energy, as a way toprovide foundation for the wave power site selection. But these aspects are far fromenough. In wave energy evaluation, the available rate, richness, energy direction(co-occurrence of WPD and wave direction), contribution of sea condition to waveenergy (co-occurrence of SWH and wave period), etc. are also primary elements. Inaddition, the scarcity of data, the huge amount of operations and storage that will beneeded, and the high technical requirements have also greatly increased the diffi-culty of systematic research on wave energy.

2.4.2 Wave Energy Evaluation in the Maritime Silk Road

Although the previous researchers have contributed a lot to wave energy evaluationof many sea areas around the whole world, research about the wave energy alongthe Maritime Silk Road, especially on its dynamic characters of wave energy, isextremely rare. But these researches are urgent for infrastructure construction suchas electricity generation and seawater desalination along the Maritime Silk Road.

2.4.3 How to Focus on the Wave Energy of the MaritimeKey Points?

Maritime key points are important support for the deep-sea construction, which areusually far away from the mainland and based on islands. The development of wavepower generation according to local conditions is conducive to helping the maritimekey points to achieve self-sufficiency in electricity. But the premise is that we mustfully understand the resource characteristics. However, the wave energy research atpresent on the maritime key points along the Maritime Silk Road is rare, while itsdemand on energy is urgent. In 1979, the State Oceanic Administration (1979)published the Atlas of the Hydrographical Features in Indian Ocean, filling theblank of China’s wave data in the Indian Ocean, though the spatial resolution ofdata is relatively low (5° � 5°). In 1998, Liu and Yu (1998) sorted data of theNorth Indian Ocean from ship news report, and the spatial resolution of the datawas improved (2° � 2°). On the whole, the spatial resolution of the existing data isstill low, and it is difficult to focus on the features of wave energy at the key nodes,making the current wave energy research on maritime key points extremely scarce.

2.4 Difficulties in Wave Energy Assessment 23

Wave data of high spatial and temporal resolution is a prerequisite for the study ofthe wave energy research of the maritime key points. With all the related data as thebasis, it is urgent to create a universal wave energy evaluation system for islandsand reefs.

2.4.4 Wave Energy Classification

Reasonable energy classification is the main basis for site selection design of wavepower generation and seawater desalination. At present, a common classificationstandard has formed for wind energy, but wave energy has not yet formed areasonable classification scheme. The existing wave energy research mainlyintroduces the characteristics of each element of resources but can not providesufficient reliable reference for site selection. Therefore, creating a classificationscheme that can comprehensively consider resource characteristics, environmentalrisks and cost-effectiveness will contribute to improving collection efficiency,reducing construction costs, and expanding investment efficiency.

2.4.5 Short-Term Forecasting of Wave Energy

At present, there are abundant researches on the short-term forecasting of marineenvironment, but rare on the short-term forecasting products of marine energy,which is the urgent demand of wave energy engineering in the daily operation. Theshort-term forecasting of wave energy is helpful to ensure the daily operation of seawater desalination and improve the efficiency of resource acquisition andconversion.

2.4.6 Long-Term Variation of Wave Energy

At present, there are many studies on the climatic trends of marine and meteoro-logical elements, but there are few studies on the long-term variation of waveenergy, which is closely related to the long-term planning of wave power genera-tion and seawater desalination, and is also an important concern for global climatechange. The few studies on the long-term trends of resources are mainly concernedwith the trend of WPD. In actual resource development, energy stability is related toefficiency of acquisition and conversion, and equipment life. The effective waveheight occurrence (EWHO) reflects the availability of resources, and the energylevel occurrence reflects the resource enrichment. Therefore, to analyze thelong-term variation characteristics of wave energy, it is necessary to comprehen-sively analyze the changes of the above-mentioned series of key indicators.


2.4.7 Exploring the Relationship Between Wave Energyand Important Astronomical Earth Factors

Previous researchers have made great contribution to the studies on the relationshipbetween important factors and marine/meteorological elements (Li 2000; Li and Mu2001a, b; Li et al. 2003, 2004a, b, 2008a, b, 2016), but there are few studies on therelationship between wave energy and important factors, and the research along theMaritime Silk Road is rare. Exploring the relationship between wave energy andimportant factors (such as nino3, IOD index, etc.) and analyzing its internalphysical mechanism can provide a theoretical basis for improving the mid- andlong-term projection of wave energy, and it is also a scientific difficulty.

2.4.8 Long-Term Projection of Wave Energy

At present, there are many studies on long-term projection of marine and meteo-rological elements, but the research on the long-term projection of wave energy isscarce, which is the main basis for long-term planning of wave energy develop-ment. Zheng et al. (2017) pointed out that there are usually four method to realizethe long-term projection of wave energy: (1) climate prediction methods, such asregression analysis modeling, time series and grey system prediction, artificialneural networks (ANN), support vector machine (SVM) classification, autore-gressive integrated moving average (ARIMA) modeling (Beccali et al. 2008;Ahmad et al. 2014; Gairaa et al. 2016; Xiao et al. 2015; Cuadra et al. 2016),measure-correlate-prediction (MCP), and ensemble empirical mode decomposition(EEMD). (2) Long-term projection of wave energy based on correlation with cli-mate indices. (3) Numerical simulation methods: using the wave models such asWAM, WW3, and SWAN to carry out the long term projection driven by theCoupled Model Intercomparison Project (CMIP) data (Zheng et al. 2019b).(4) Projection for wave energy prediction based on swell propagation characteristicsand swell index (the proportion of swell in the mixed waves).

2.4.9 Swell Wave Characteristics Analysis

Swells have the feature of huge energy and good stability (Chen et al. 2002). Theexploration of sea areas with swells playing a dominant role in the mixed wave isconducive to the wave energy development. In addition, swells have strongdestructive power with huge energy, which can travel hundreds or even thousandsof kilometers with little energy dissipation (Munk et al. 1963; Snodgrass et al. 1996;Semedo et al. 2011; Alves 2006). Therefore, grasp how swell propagate will helpimprove the ability of short-term forecasting and medium—and long-term

2.4 Difficulties in Wave Energy Assessment 25

projection of wave and wave energy, enhance the capability of wave energy andimprove the early warning and monitoring capability for swell, all which can betterensure the development and utilization of wave energy. In the existing wave energyresearch, however, there is less data on wind-sea and wave separation, and most ofits study is on mixed wave. The research focusing on swell is extremely rare,especially the accurate research on the swell propagation path and speed is evenscarce.

2.4.10 Construction of Wave Energy Resource Dataset

In recent years, with the rapid development of observation means and numericalmodel, marine data is an exponential explosive growth and big data has come intopeople’s attention. How to extract useful information concerned by new energyassessment from marine original data with huge volume and low informationdensity, and establish marine new energy dataset, which crucial in rational andefficient resource development. In addition, the application and sharing of scientificdata has become an important symbol of a country’s scientific and technologicaldevelopment level and comprehensive national strength. Therefore, the establish-ment of an open access and public dataset of wave energy resources on theMaritime Silk Road is conducive to enhancing the international influence in thisdomain.

2.4.11 Construction of Wave Energy Integrated ApplicationPlatform

Due to the lack of systematic theoretical support, there is no professional integratedapplication platform of wave energy at home and abroad. In the future, wave energyresource dataset can be combined with geographic information system (such asGIS) to build a platform for comprehensive application of wave energy resources,so as to effectively ensure site selection design, daily operation, medium andlong-term planning and marine disaster early warning during the development ofmarine energy. In addition, to build a wave energy integrated application platformthat can meet the needs of diverse tasks and provide scientific and technologicalsupport for decision-making of important projects, the platform is equipped withreserved modules, through which special tasks can be added in the operationprocess according to the actual needs.


2.5 Research Content of the Book

The construction of the Maritime Silk Road will bring important opportunities tothe common prosperity and progress of human society. However, the capacity ofsupply power of countries and regions along the Maritime Silk Road is weak,seriously restricting the efficient construction of the Maritime Silk Road and mar-itime key points. The development of wave energy who has the characteristics oflarge reserves, renewable, all-weather and wide distribution and many otheradvantages is not only one of the best ways to solve the energy dilemma of theMaritime Silk Road, but also a good opportunity to fulfill its international obliga-tions, advance the connectivity and promote international cooperation, which willundoubtedly become a new highlight in the construction of the Maritime Silk Road.

Based on the urgent demand of electricity and fresh water for the construction ofthe Maritime Silk Road and a series of core problems faced in the assessment anddevelopment of wave energy resources, this book takes the lead in carrying outsystematic and detailed research on the wave energy resources of the Maritime SilkRoad at home and abroad. First, the authors discuss the important role of newmarine energy in the marine construction; then, the present situation and obstaclefaced in wave energy assessment is analyzed, and then countermeasures and sug-gestions are provided. During the study, the following aspects be considered: theinvestigation of climate characteristics of wave energy, the long-term trend of waveenergy along the Maritime Silk Road; the establishment of the short-term fore-casting and long-term prediction model of wave energy; the research in waveenergy characteristics of the maritime key points of the Maritime Silk Road; thecreation of wave energy resource dataset of the Maritime Silk Road. In this way,scientific reference could be provided for the sites election, daily operation andlong-term planning of wave energy development such as wave power generationand seawater desalination. Hoping that the aforementioned efforts can drive notonly the efficient development of the Maritime Silk Road, but the common pros-perity and progress of human society.

The book is organized as follows:Chapter 1 Introduction. The authors mainly discuss the following topics: the

importance of marine new energy in the marine construction, the comparison of theadvantages and disadvantages among the current main energy sources, and theoutlook for the application prospect of wave energy.

This chapter Analysis of current assessment status, difficulties and counter-measures of wave energy evaluation.

Chapter 3 At home and abroad, the authors opened the way for detailedinvestigations about climate characteristics of a series of key indicators of waveenergy resource along the entire Maritime Silk Road.

Chapter 4 The analysis of the historical trend of a series of key indicators ofwave energy along the Maritime Silk Road laid a theoretical foundation for theimprovement of medium—and long-term projection capacity for wave energyprediction.

2.5 Research Content of the Book 27

Chapter 5 A short-term forecasting scheme of wave energy was proposed. Basedon WW3 wave model, the current internationally advanced wave model, a rea-sonable method to select the extended region of wave energy in numerical analysisis proposed by comparing different boundary conditions. In the last part of thechapter, a short-term wave energy forecasting model for the Maritime Silk Road,which can fully consider the impact of swell generated from other regions, isestablished.

Chapter 6 A long-term projection scheme of wave energy resource was pro-posed. Using CMIP data to drive the WW3 wave model, the long-term wave energyprojection of the Maritime Silk Road for the future 40 years is carried out, whichincludes a series of key indicators, such as: the average WPD, available rate ofwave energy, the wave energy enrichment, stability, monthly difference in the next40 years and others.

Chapter 7 Based on the urgent need of electricity and fresh water in the con-struction of maritime key points and wave energy evaluation system of the islandreef constructed in the previous stage, the wave energy characteristics of the SriLanka, one of the maritime key points of the Maritime Silk Road, were refined forthe first time. And then decision-making advice is provided for wave energydevelopment.

Chapter 8 Creation of wave energy resource dataset along the Maritime SilkRoad.

Chapter 9 Focus of wave energy resource assessment into the future is given.

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2 Research Progress of Wave Energy Evaluation2.1 Assessment of Global Oceanic Wave Energy2.1.1 Observation Data Stage2.1.2 Satellite-Based Observation Stage2.1.3 Numerical Simulation Stage2.1.4 Reanalysis Data Stage

2.2 Wave Energy Assessment in the China Seas2.3 Wave Energy Assessment in the Maritime Silk Road2.4 Difficulties in Wave Energy Assessment2.4.1 Climatic Characteristics of Wave Energy2.4.2 Wave Energy Evaluation in the Maritime Silk Road2.4.3 How to Focus on the Wave Energy of the Maritime Key Points?2.4.4 Wave Energy Classification2.4.5 Short-Term Forecasting of Wave Energy2.4.6 Long-Term Variation of Wave Energy2.4.7 Exploring the Relationship Between Wave Energy and Important Astronomical Earth Factors2.4.8 Long-Term Projection of Wave Energy2.4.9 Swell Wave Characteristics Analysis2.4.10 Construction of Wave Energy Resource Dataset2.4.11 Construction of Wave Energy Integrated Application Platform

2.5 Research Content of the BookReferences

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