Analysis Of Offshore Wind Energy In Colombia: Current Status . - IJERT

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020area is given by the mass per second calculation into thestandard KE equation (1) resulting in the equation (3) [18]:1𝑃 𝜌𝐴𝑉 32(3)This research used the Reanalysis database of the NARRproject to determine the wind power density for two locationsalong the coast of Colombia. Barranquilla and La Guajira werethe two selected cities [19], [20]. The data was collected fromthe database of the study of Rueda Barona [6]. This data wasused to obtain information about the wind velocity and directionfrom January 1979 to December 2015 at 10 m of elevation forthe two strategic locations [19]. This historical data was firstconverted into a histogram. The histogram categorized each ofthe wind speeds over that time period in terms of bins. The binsize for the histograms at each site was chosen to be 1 m/s. Thebin range was chosen to be 0 to 20 m/s. This determined thetotal instances of wind speeds within a particular bin toconstruct the histogram.The total instances were then converted into a frequency todetermine the percent of time that the wind speed occurs in abin. This historical data was then used to find the Weibulldistribution [17]. The Weibull distribution is the most widelyaccepted function in the wind industry. This is because theInternational Electrotechnical Commission (IEC) recommendsthis function to estimate the wind speed data (via IEC standard61400-1 for large wind turbines).The Weibull distribution provides the most accuraterepresentation of the wind speed histograms. The Weibulldistribution probability density function (PDF) was utilized todetermine the best fit of the histogram's frequency data. ThePDF was then defined in terms of its shape and scale factor toestimate the best fit to the histogram data. Figure 2 showed theWeibull distribution in Barranquilla that exceeded the data,representing that this city is the best option compared to the cityin Figure 3 which showed that the data exceeded the Weibulldistribution.Fig. 3 Wind Speed Distribution based on the collected data and the best fitWeibull distribution in La GuajiraThis information was then used to determine the wind powerdensity at each site [17], [18].The wind power density (WPD) is generally defined as:1𝑊𝑃𝐷 𝜌𝑉 32(4)where WPD is given in Watts per square meters, 𝜌 is the airdensity (1.225 kg/m3), and 𝑉 is the wind speed in meters persecond. By increasing the WPD, the site is increasingly moresuitable for wind project development. By taking this equation,multiplying it by the frequency of the bins, and replacing thewind speed with the bin speed in the histogram, the WPD ateach bin can then be determined. By summing up each of thesebins, the total WPD at the site can, therefore, be estimated. Toquantify the wind power density in Barranquilla the shapefactor, scale factor, and average velocity (vave) were definedrespectively as:𝑎 2.25𝑏 7.177272 𝑚 𝑠𝑣𝑎𝑣𝑒 6.35712 𝑚 𝑠Fig. 2 Wind Speed Distribution based on the collected data and the best fitWeibull distribution in BarranquillaIJERTV9IS110277www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)612

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020TABLE 1 DATA USED TO QUANTIFY THE WIND POWER DENSITY (WPD) INBARRANQUILLAWPDDataWeibull3(W/𝑚 )WPDWeibull3(W/𝑚 )00.000E 000.000E 000.000E -024.600E-022.940E-013290.0659.200E-021.080E 031.514E 034350.0791.150E-013.090E 034.524E 035420.0951.280E-017.242E 039.804E 036350.0791.280E-011.043E 041.699E 047260.0591.180E-011.230E 042.480E 048440.0991.000E-013.108E 043.141E 049510.1157.900E-025.129E 043.518E 0410510.1155.700E-027.035E 043.527E 0411430.0973.900E-027.895E 043.193E 0412210.0472.500E-025.006E 042.627E 041350.0111.500E-021.515E 041.971E 0414008.000E-030.000E 001.354E 0415004.000E-030.000E 008.539E 0316002.000E-030.000E 004.939E 0317001.000E-030.000E 002.631E 0318001.000E-030.000E 001.296E 0319001.000E-030.000E 005.830E-0120004.960E-050.000E 002.430E-0100BinFrequencyData0014443.310E 05𝑣𝑎𝑣𝑒 6.1110053 𝑚 𝑠TABLE 2 DATA USED TO QUANTIFY THE WIND POWER DENSITY (WPD) IN LAGUAJIRABinFrequencyDataWeibullWPDData3(W/𝑚 )0000.000E 000.000E 000,00E 280E 004300.0681.230E-012.648E 034.808E 005350.0791.580E-016.031E 031.213E 016560.1261.710E-011.669E 042.272E 0171000.2251.570E-014.732E 043.304E 0181050.2361.210E-017.416E 043.802E 019490.1107.800E-024.928E 043.481E 0110190.0434.100E-022.621E 042.531E 011120.0051.700E-023.672E 031.451E 0112006.000E-030.000E 006.496E 0013001.000E-030.000E 002.243E 0014001.000E-030.000E 005.890E-0115005.620E-050.000E 001.160E-0116006.746E-050.000E 001.600E-0217005.951E-050.000E 001.790E-0318003.792E-050.000E 001.300E-0419001.714E-050.000E 007.203E 0020005.402E-050.000E 002.647E 002.260E 052.060E 022.684E 05444To quantify the wind power density in La Guajira the shapefactor, scale factor, and average velocity (vave) were definedrespectively as:𝑎 3𝑏 6.843388 𝑚 𝑠IJERTV9IS110277WPDWeibull3(W/𝑚 )IV.RESULTSBarranquilla, compared to La Guajira, reported the highestmonthly mean of wind power density. The expansion plan fortransmission and generation of electrical energy in Colombiawas studied. This plan covered the years 2015-2029 [3], [21].The map with the current interconnection and transmission linesof the country and the map with future interconnections wereobtained by Global Energy Network Institute as shows Figures4 and 5 [22], [23]. The Colombian electricity system hasinterconnections that allow electricity exchanges with Ecuadorand Venezuela.www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)613

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020Fig. 4 Current interconnections and transmission lines in the Colombian grid[22]When the graph was approached, the 220kV substationinstalled in Barranquilla was identified. This substation couldbe connected to the transmission line of the offshore windenergy farm. Figure 2 shows a zoom-in of figure 3 to identifyall the transmission lines in the local grid in Barranquilla.Fig. 6 Future interconnections and transmission lines to use with oil and gasfields [23]For future scenarios, Colombia considered an interconnectionbetween the Cerromatoso substation and the Panama IIsubstation. Figure 6 showed the branch that supports thiselectrical expansion.Barranquilla, ColombiaFig. 5 Current interconnections and transmission lines in the local grid inBarranquilla [22]On the map of interconnections, an oil field and a gas fieldwere observed near La Guajira [35]. However, Colombia doesnot yet have oil exploration or exploitation activities withfracking. A high court listens to advocates and opponents amida significant debate between lawmakers, activists, thegovernment, and citizens before deciding whether to allow thetechnique to be used.IJERTV9IS110277Fig. 7 Interconnections between Colombia and Panamá substations [24]System Advisor Model (SAM) was used to simulate ascenario of offshore wind energy in Barranquilla.SAM is an engineering tool designed for the techno-economicanalysis of renewable energy projects [25]. Basically, SAM is adecision-making tool for project developers, financial analysts,policymakers, and energy researchers. This research used SAMto model the offshore wind farm. The first step was to definewww.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)614

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020the wind resource using characteristics of the Weibulldistribution.The Weibull distribution represents the wind resource as astatistical distribution characterized by a single average annualwind speed and Weibull K factor [17], [18], [25]. SAM wasused to determine Weibull probability and turbine energy. TheWeibull PDF determines the probability that a given wind speedvalue will occur over a given period:𝑓(𝑉) 𝑘 (𝑘 1) (𝑉) 𝑘𝑉𝑒 𝜆𝜆𝑘(5)WhereFig. 9 Distance between the offshore wind farm and the 220kV substation inBarranquilla [26]𝑓(𝑉) Weibull wind speed probability distribution function𝑉 wind speed in m/sk dimensionless shape parameterGiven the information in Table 3 was implemented theproject in SAM.Table 3 Data on the specifications of the proposed offshore wind project inBarranquilla, Colombia𝜆 scale parameter in m/sTherefore, to get the wind speed Weibull distribution all windresources characteristics were defined such as:𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝑎𝑛𝑛𝑢𝑎𝑙 𝑤𝑖𝑛𝑑 𝑠𝑝𝑒𝑒𝑑 6.357 𝑚 𝑠𝑅𝑒𝑓𝑒𝑟𝑒𝑛𝑐𝑒 ℎ𝑒𝑖𝑔ℎ𝑡 𝑓𝑜𝑟 𝑤𝑖𝑛𝑑 𝑠𝑝𝑒𝑒𝑑 10𝑚𝑊𝑒𝑖𝑏𝑢𝑙𝑙𝐾 𝑓𝑎𝑐𝑡𝑜𝑟 2.25Fig. 8 Wind speed Weibull distribution and turbine energy distribution in SAMfor BarranquillaIn accordance to the information about Siemens’ SWT6.0-154, the offshore turbine’s power curve was inputtedinto SAM. The rated wind speed was 13m/s and wasdefined based on the maximum tip speed ratio [25], [27].Figure 9 indicated the location of the offshore wind farmproject with the 25km of approximate distance betweenoffshore and substation of 220kV.Fig. 10 Turbine power curve in SAMIJERTV9IS110277www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)615

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020According to the turbine layout map in Fig. 11, the totalproject area was calculated as 6,000 11,000 66,000,000𝑚2.Fig. 14 Financial parameters in SAM to define the economic analysisFig. 11 The proposed offshore wind project layout map in SAMThe capacity factor may theoretically vary from 0 to 100percent. However, they usually range from 20 to 70 percent andmostly be around 25-40 percent. This project, with a ratedcapacity of 360 megawatts and an efficiency factor of 0.37percent, would be expected to produce as follows: 365 * 24 *360000 (kW) * 0.37 1,166,832,000 kilowatt-hours per year.This calculation assumes wind availability at 24 hours a day allyear round. In practical application, this does not happen. SAMused the NREL wind maps to adjust your time figures for a moreaccurate location-specific figure. This value was 1,164,893,312kilowatt-hours per year.Fig. 12 Offshore wind farm wake, availability, electrical, and turbineperformance losses in SAMFig. 15 Monthly and annual energy output as well as the capacity factor for theproject in the first yearFig. 13 Cost model utilized in SAMFig. 16 After-tax Project cash flow in SAMIJERTV9IS110277www.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)616

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020A. Energy Storage SystemThe use of ESS has been extended to offshore wind energy.ESS can be utilized to store electricity during a period of lowmarket prices. Therefore, ESS can reduce the gap between peakand off-peak periods resulting in greater efficiency. During theself-discharge and large price variations, the electricity is notstored for long periods. The economic viability of an ESS in theoffshore wind project is dependent on the system's profitability.The total cost for the offshore wind farm can be determined incombination with an ESS [28]. The key performance indicatoris the net present value (NPV) of the project’s cash flow. In theanalysis, this project only considered onshore locations andaverage capacities of the ESS. The type of ESS considered inthis study was a lithium-ion battery. Based on previous studieswhich have determined the SUM model with prices and winddata for New York during 2010-13, the researchers evaluatedfour designs of offshore wind and battery storage systems suchas an offshore wind farm without BESS, an onshore BESS, anoffshore BESS, and a hybrid. A system that BESS uses both onland and at sea - to assess the impacts of the battery systemlocation on its overall profitability [28], [29]. Basically, one ofthe most important attributes of the “SUM” model is that itinvolves degradation as a sum of the capacity fades in the batterycells produced by the battery charging and discharging process,in addition to the deterioration in time function, regardless ofbattery usage. According to these criteria, the battery is cycled ifthe income supports the costs of loss of capacity. In addition toadding other different decision factors, such as wind reduction,cable size, and BESS shipping, this research concludes that theincreased revenue potential and may offset some of the costsrelated to degradation occurs when installing the system oflithium battery grounded while operating within its state ofcharge window [28].Fig. 17 Topology of the connection between the offshore wind energy andenergy storageV. DISCUSSION AND PROSPECTSAfter getting the financial model in SAM of the offshore windenergy farm in Colombia, the annual output was1,164,893,312kWh. This value represented the capacity of theIJERTV9IS110277turbines array working for the first year. The capacity factor wascalculated to be approximately 37%. The levelized cost ofenergy (LCOE) was 12.3cents/kWh it measures lifetime costsdivided by energy production and calculates the present value ofthe total cost of building and operating a power plant over anassumed lifetime. LCOE was useful because it could allow thecomparison of different technologies that the Colombiangovernment could implement in Barranquilla (e.g., wind, solar,natural gas) of unequal life spans, project size, different capitalcost, risk, return, and capacities [30]. The net present value(NPV) of the project was 103,433,664. The results of thisproject indicate that the Colombian government should considerinstalling an offshore wind energy farm because of the positiveeconomic impact it would have on the country. Not only is theNPV positive, but the sensitivity analysis shows that it remainspositive under a wide variety of conditions including varying thediscount rate, costs, and quantity of electricity generated. Thereare several grant programs in the U.S. that could potentially fundsome or all the initial capital costs. It should be re-emphasizedthat SAM is one decision-making tool among many. Variouspositive externalities such as an offset of future CO2 emissions,reduced pollution, increased energy security and negativeexternalities such as possible bird or bat deaths. There is thepossibility of the inclusion and diversification of renewableenergy on coasts around the world, which can be supported bygovernments. Also, investment decision making about offshorewind energy requires significant capital. Still, it may have lowoperating costs compared with technologies based on fossilfuels, which do not require too much capital, but the cost ofoperation is high [25].The essential advantage that offshore wind energy has is thespeed of wind resources in the sea, where the wind speed isusually very high [3], [10]. Other advantages presented by thetechnology are related to the fact of being installed in suitablefree areas in the sea. However, the technology requires moreinstallations. This technology gives many benefits to theenvironment because can reduce carbon dioxide emissions, andalso there are many benefits to the population due to thereduction of the visual and auditory impact, as well as the easeof transport of wind turbines which can allow more generationper install unit [5], [6], [9]. The disadvantage of offshore windenergy technology is the cost of the authorizing and detailengineering process, as well as the building and operationphases, which require a high price [25]. Furthermore, there arenot usually under the sea electrical infrastructures that connectthe highest wind resource areas with the consumer centers,leading to the construction of transmission lines and electricalnetworks. In other cases, the existing power system needs to bestrengthened.A greater body of water is the Caribbean Sea which isdelimited by Colombia, Venezuela, and Panama to the south, towest Costa Rica delimits it, Nicaragua, Guatemala, Honduras,and Belize; as the Greater Antilles Cuba, Jamaica, DominicanRepublic, and Puerto Rico, delimit it to the north, the LesserAntilles does it to the east. Colombia has 928,660 km2 of oceanswww.ijert.org(This work is licensed under a Creative Commons Attribution 4.0 International License.)617

Published by :http://www.ijert.orgInternational Journal of Engineering Research & Technology (IJERT)ISSN: 2278-0181Vol. 9 Issue 11, November-2020and 2,900 km of coastline, and it is a unique South Americancountry that has coasts on the Pacific Ocean and the CaribbeanSea. A big population lives in the Caribbean zone because thereare main cities located along the coast. The Colombian powersystem has a big dependence on hydropower production,approximately 70% of the installed capacity of the country ismade up of hydropower plants due to that Colombia is one of thecountries with the most significant water wealth both globallyand in Latin America [5], [12].A. Opportunities for offshore wind energy in ColombiaWind speed in Barranquilla, Colombia, is significantly highand less unstable caused by the roughness of the Caribbean Seasurface, which is smaller than land surfaces [3], [6].According to the Colombian energy market, the Colombiangovernment could implement a tax reduction or exemption toinvestors of the project to support the inclusion anddiversification of offshore energy farms [12], [31].Offshore energy technology could be implemented for severalpurposes; one of these important purposes, when applied to theenvironment, could be to reduce carbon dioxide emissions in thecountry. Therefore, Colombia could accomplish the goal ofreducing 20% of carbon dioxide emissions by 2050 [3].B. Challenges of offshore wind energy to ColombiaThe main challenges of offshore wind energy areconstruction and maintenance costs. Furthermore, the offshoreturbines need an efficient and resilient structural design to facecritical conditions in the sea. Colombia has analyzed thedevelopment of political proposals to boost renewable energiesas an opportunity of the energy transition. Every proposal wasthought in other renewable energy sources such as solar, windenergy, and biomass. It could represent a challenge to offshorewind energy because offshore is a relatively new technologycompared with other sources that the Colombian government,private, and public sector knows. The private and public sectorfacilitates the accomplishment of renewable energy projects.The private sector must focus on supply resources for theimplementation of the project, while the public sector must focuson determining the right regulations to increase energy servicesaround the country, stimulating the development of otherrenewable energy systems in Colombi

offshore wind energy in Colombia. Predominant factors that allow the development of offshore wind energy in this country can be political, technical, and economical. The purpose of this study is to explore the opportunities and challenges at a technical and economic levels to include offshore wind energy to Colombia's energy needs.