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Home My WebLink About2017 Wind Published ArticleWind power project size and component costs: An Alaska case study Jeremy VanderMeer, Marc Mueller-Stoffels, and Erin Whitney Citation: Journal of Renewable and Sustainable Energy 9, 061703 (2017); View online: https://doi.org/10.1063/1.4986579 View Table of Contents: http://aip.scitation.org/toc/rse/9/6 Published by the American Institute of Physics Articles you may be interested in Preface: Technology and cost reviews for renewable energy in Alaska: Sharing our experience and know-how Journal of Renewable and Sustainable Energy 9, 061501 (2017); 10.1063/1.5017516 An Alaska case study: Solar photovoltaic technology in remote microgrids Journal of Renewable and Sustainable Energy 9, 061704 (2017); 10.1063/1.4986577 An Alaska case study: Diesel generator technologies Journal of Renewable and Sustainable Energy 9, 061701 (2017); 10.1063/1.4986585 An Alaska case study: Energy storage technologies Journal of Renewable and Sustainable Energy 9, 061708 (2017); 10.1063/1.4986580 An Alaska case study: Biomass technology Journal of Renewable and Sustainable Energy 9, 061705 (2017); 10.1063/1.4986578 Heat pump technology: An Alaska case study Journal of Renewable and Sustainable Energy 9, 061706 (2017); 10.1063/1.4986584 Wind power project size and component costs: An Alaska case study Jeremy VanderMeer,Marc Mueller-Stoffels,and Erin Whitney Alaska Center for Energy and Power, University of Alaska Fairbanks, P.O. Box 755910, Fairbanks, Alaska 99775-5910, USA (Received 6 June 2017; accepted 4 December 2017; published online 21 December 2017) Many communities in Alaska, especially along the coast, have excellent wind resources and serve as ideal laboratories for microgrids around the Arctic and the world. Wind power systems have been installed in a number of locations in Alaska, both in remote areas and along the road system. As more isolated microgrids are developed worldwide, understanding the cost and performance of wind power in these systems is of increased importance. In this review of wind power project sizes and costs in Alaska, the costs per kilowatt for the different components of wind turbine installations generally were found to decrease with increasing sizes. Capacity factors ranged from approximately 10% to 40%, with variability from rated wind speeds, wind turbine heights, and the resolution of the wind power class map. Due to the size of communities in Alaska as well as other factors, turbines installed in Alaska are smaller than the trend in the larger market. Evidence indicates that installing overcapacity wind farms, together with energy storage and signiﬁcant diversion into thermal loads, would allow communities to achieve diesel-off and least-cost energy when considering displacement of diesel fuel for both electricity and heat. However, the size of wind turbines that can be installed in Alaska is limited by the small size of the state’s communities and the challenges of integrating high penetrations of wind power into microgrids. Demonstrations of different high-penetration wind power integration techniques are still needed. Published by AIP Publishing.https://doi.org/10.1063/1.4986579 INTRODUCTION The purpose of this paper is to review the cost and performance of wind power installations in Alaska. Speciﬁc metrics in this review include capital costs, operation and maintenance costs, expected life, capacity factors, diesel offsets, costs per kilowatt-hour, conditions for great- est efﬁciency, cost curves over time, installed costs by major components, transportation costs, technology trends, refurbishment/upgrade markets, and realized cost savings. This is the only paper of its kind speciﬁcally for Alaska. The Alaskan wind market is unique from the rest of the United States, due to the large number of isolated microgrids and extreme weather condi- tions. The isolated microgrids are often not connected to a road system, making it difﬁcult to transport components. As more isolated microgrids are developed worldwide, understanding the cost and performance of wind power in these systems is of increased importance. Many communities in Alaska, especially along the coast, have excellent wind resources. Wind power systems have been installed in several locations in Alaska, both in remote areas and along the road system. Total installed capacity exceeds 60MW, with installations ranging from 40kW to 24MW (McMahon, 2015). The levelized cost of energy (LCOE) of wind power has been decreasing due to improved technology, siting techniques, and maturity of the industry; wind is now one of the most afford- able sources of energy based on unsubsidized LCOE in the United States (http://www.awea.org/ falling-wind-energy-costs). In Alaska, the cost of energy is much higher than that in the rest of the nation due to the remoteness, harsh climate, and small size of remote Alaska microgrids. 1941-7012/2017/9(6)/061703/19/$30.00 Published by AIP Publishing.9, 061703-1 JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 9, 061703 (2017) (Remote microgrids are not connected to a larger electrical grid, which means that they must generate all electricity consumed within the grid.) Although the cost of wind power is also affected by these conditions, this energy source has helped to reduce the cost of energy in many Alaska communities and more wind power potential remains in locations around the state. Alaska currently has 62MW of installed wind power capacity (http://apps2.eere.energy. gov/wind/windexchange/wind_installed_capacity.asp), with installations in 28 remote microgrid communities. Kodiak, Alaska, is an example of a wind power success story. In 2016, this com- munity generated 99.7% of its electricity from renewable energy sources, with 33.8% from wind and 65.9% from hydro. The cost of generating electricity from wind power in Kodiak is less than half the cost of generating electricity from diesel (http://www.kodiakelectric.com/ generation.html). This review of wind power technology in Alaska is a result of Alaska Senate Bill (SB) 138. In this bill, the Alaska State Legislature created an uncodiﬁed section of law entitled: “Plan and Recommendations to the Legislature on Infrastructure Needed to Deliver Affordable Energy of the State to Areas That Do Not Have Direct Access to a [proposed] North Slope Natural Gas Pipeline.” To support the Alaska Energy Authority (AEA) in its development of an Alaska Affordable Energy Strategy, the Alaska Center for Energy and Power (ACEP) con- tracted with AEA to document technology development needs speciﬁc to Alaska with regard to renewable and sustainable energy technologies. The intention was to determine what targeted, energy technology development solutions could be implemented in Alaska to make energy more affordable in the Alaska Affordable Energy Study area. While the focus was on technol- ogy research solutions, other factors such as logistics, labor, and training were also addressed. Drafts of technology reviews were vetted by expert roundtables in late February and early March 2016. These reviews are not meant to be exhaustive discussions of energy technologies in Alaska or proper designs for each technology, and they should not be used as guides for the choice and installation of speciﬁc systems. As such, not all possible issues with power production and each technology are addressed. Data for each technology were collected from surveys and pub- lically available databases. Only completed projects, or projects with clearly reported data, were included in each technology analysis. These distinctions and descriptions of data sources are included in each technology review. METHODS This analysis of wind power technology in Alaska largely relies on data extracted from applications to the AEA’s Renewable Energy Fund (REF), Rounds 1–8. This dataset includes the costs that were budgeted for different wind power projects in Alaska. There are 103 projects spanning the time frame from 2008 to 2016. The costs were separated into the categories of Energy Analysis, Conceptual Design, Final Design, Hardware, Transportation, Foundation and Infrastructure, Turbine Installation, Transmission, and Integration. These categories were neces- sary since individual applications often only included a subset of costs. Linear regressions were used to determine whether the installed capacity, year, or region in Alaska of the installation signiﬁcantly impacted the different cost categories. The only factor that signiﬁcantly affected the costs was the installed capacity. This effect corresponds to the well docu- mented effect of economies of scale in wind power projects (Wiser and Bolinger, 2017). Linear regressions were calculated for the capital expenditure (CAPEX) per installed capac- ity ($/kW) for each individual cost with respect to the installed capacity of the wind power pro- ject. The regressions of individual costs were added together to estimate the total cost (in $/ kW) with respect to the installed capacity of the wind power projects. A coefﬁcient was consid- ered signiﬁcant if its P-value (the probability of there being no relationship between the depen- dent variable and regressors compared with the given regression) was less than 0.05. The results were compared with as-built costs from AEA (see Appendix C) and are a good prediction of as-built costs. Costs are in 2015 dollars. 061703-2 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) The general form of the equation showing the correlation between the cost Yi;c (in $/kW) with respect to the installed capacity Xi (in kW) for cost category c of wind power project i is given by Eq.(1). The general form of the equation showing the total cost Yi (in $/kW) with respect to the installed capacity Xi (in kW) for project i is given by Eq.(2). The variable i;c is the error term between the actual cost of a particular cost category of a project and what is pre- dicted by the linear equation with respect to installed capacity. The variable i similarly gives the error for the predicted total cost of a project. Table I shows the values of the coefﬁcients for Eqs.(1)and (2)for the different cost categories and the total cost of the wind power proj- ects considered in this paper log Yi;cðÞ¼b0;i;c þ b1;i;c log XiðÞþi;c ;(1) log YiðÞ¼b0;i þ b1;i log XiðÞþb2;i log XiðÞ2þ i :(2) In addition to these cost analyses, we considered data from REF applications and the litera- ture to analyze other cost and performance factors for wind power installation in Alaska. RESULTS AND DISCUSSION Capital costs Tables I and II show the coefﬁcients (b0 ,b1 ;and b2 ) of the linear regressions for the indi- vidual cost categories and total costs of a wind power project with respect to the installed capacity. The general form of the regression equations is given by Eqs.(1)and (2). The aster- isks (*) beside the coefﬁcient values indicate the p-value for that coefﬁcient, with * for p <0.05, ** for p <0.01, and *** for p <0.001. Beneath the value for each coefﬁcient, the standard error is shown in parentheses. The bottom row shows the adjusted R-squared (R2 ) value of the ﬁt, which is a value between 0 and 1 that indicates how much of the variability in the cost is explained by the linear regression with respect to the installed capacity. The plots showing linear regression for each cost category are shown in Appendix A (Figs.8–16). Based on data from REF applications, the costs per kilowatt for the different cost catego- ries of wind turbine installations were found to decrease with the increasing size of the installa- tion. Exceptions were the costs for transmission and integration, which did not vary signiﬁ- cantly with the capacity of the installed system. Individual costs were tested for correlations TABLE I. The coefﬁcients and adjusted R-squared values for the cost categories and the total cost of wind power projects with respect to installed capacity. Equations (1)and (2)show the general form of the equations. Energy analysis Conceptual design Final design Hardware Transportation b2 b1 0.36* (0.14)0.84*** (0.11)0.71*** (0.17)0.38*** (0.05)0.35** (0.11) b0 6.6*** (0.83) 10.5*** (0.63) 10.5*** (1.0) 10.5*** (0.35) 9.0*** (0.69) R2 0.11 0.56 0.30 0.53 0.27 TABLE II. The coefﬁcients and adjusted R-squared values for the cost categories and the total cost of wind power projects with respect to installed capacity, continued. Equations (1)and (2)show the general form of the equations. Foundation and infrastructure Turbine installation Transmission lines Integration Total project cost b2 0.028*** (7.5 10 5) b1 0.75*** (0.10)0.55*** (0.14)0.77*** (9.4 10 4) b0 12.1*** (0.63) 10.6*** (0.91) 6.6*** (0.24) 6.6*** (0.23) 12.9*** (2.8 10 3) R2 0.69 0.29 1 061703-3 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) with the region and year, but in most cases, the relationship was not signiﬁcant or did not signiﬁcantly improve the ﬁt of installed capacity without taking these relationships into consider- ation. Figure 1 shows the incremental costs of each individual category for different installed capacity systems. Each line represents the vertical summation of the lines below it. Thus, the top line (integration) represents the total cost. Note that the x-axis is plotted on a log scale. Table III shows the cost per installed capacity for several system sizes, which have been calculated based on the regression analysis. The costs are grouped as analysis and design (energy analysis and conceptual and ﬁnal design),hardware and transport (turbine hardware and transport), and bal- ance of system (foundations and infrastructure, turbine installation, transmission, and integration). Operation and maintenance $/kW Many communities in Alaska have performance-based operation and maintenance (O&M) contracts with the turbine supplier. The supplier performs O&M (often in collaboration with the utility) while guaranteeing a certain level of availability. Operation and maintenance does not include repairs and replacements. Figure 2 shows the predicted maintenance costs based on REF applications, with an average of $0.036/kWh. Predicted maintenance costs tend to be lower than O&M calculations from other sources. The points are plotted against the annual wind generation and sorted by the annual electric consumption of the grid. No clear trends are indicated in the data. Expected life The minimum design lifetime for wind turbines must be 20years (based on IEC 61400–1, Wind Turbines-Part 1: Design requirements). Table IV shows average wind turbine lifetimes as FIG. 1. Cumulative cost ($/kW) of a wind power system. Each line is added to the line below it, with the top line represent- ing the total cost per kilowatt of installed capacity. These costs are based on regressions calculated from REF applications, shown with the data and equations in Appendix A. Very few applications included all cost categories; thus, regressions were found for each category and added to determine an estimate for the total project cost. Costs are in 2015 dollars. TABLE III. Costs per installed capacity taken from the regressions shown in Fig.1 (costs in 2015 dollars). Size (kW) Analysis and design ($/kW) Hardware and transport ($/kW) Balance of system ($/kW) Total ($/kW) 50 3805 10661 15353 29819 100 2284 8251 10145 20680 500 715 4552 4438 9705 1000 439 3523 3357 7319 2000 273 2728 2676 5676 5000 148 1945 2143 4236 061703-4 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) FIG. 2. Operation and maintenance costs per kWh of wind energy production. The points are sorted by the annual electric- ity demand of the grid. These data are from REF applications. FIG. 3. Capacity factor plotted against the wind power class. The wind power classes are from the Alaska Energy Data Inventory (AEDI) wind power class map (http://www.arcgis.com/home/item.html?id¼6aaef4ce5821459cad757bf9adda3079). Capacity factors above 40% were removed since they are possible but unlikely. The capacity factor was calculated as the total wind energy used to supply electrical loads (excluding diversion loads) in 1year, divided by the energy that would be harvested from the wind if the turbines were outputting their rated (maximum) power the entire year. In general, the capacity factor should depend primarily on the wind power class of the installation site for low and medium penetration hybrid-diesel systems. Wind power class refers to the available energy from the wind as outlined by DOE. TABLE IV. Wind turbine lifetime (source data from NREL, 2016). Wind turbine size (kW) Lifetime (yr) Lifetime standard deviation (yr) <10 14 9 10–100 19 5 100–1000 16 0 1000–10000 20 7 061703-5 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) reported by the National Renewable Energy Laboratory in 2013 (NREL, 2016). For planning purposes, 20years is typically used. However, considering that many wind turbines installed in Alaska fall in the 10-to-1000-kW range, it might be prudent to revise the values given in Table II or work with the vendor on sufﬁcient warranties. Capacity factor Figure 3 shows predicted capacity factors based on REF applications. Capacity factors above 40% were removed since they are possible but unlikely. The capacity factor was calcu- lated as the total wind energy used to supply electrical loads (excluding diversion loads) in 1year, divided by the energy that would be harvested from the wind if the turbines were out- putting their rated (maximum) power the entire year. In general, the capacity factor should depend primarily on the wind power class of the installation site for low- and medium- penetration hybrid-diesel systems. Wind power class refers to the available energy from the wind, as outlined by the U.S. Department of Energy (DOE). At high wind power penetrations, it might not be possible to use all the energy from wind for electrical loads. In such cases, thermal loads can be supplied, but this is not reﬂected in the capacity factor. Thus, the capacity factor might not be as good of a metric as reduction in die- sel consumption, and even then, a distinction will have to be made between the displacement of diesel fuel slated for electricity production and heat production. Factors affecting the variance of capacity factors at wind power classes include the rated wind speed and the height of the wind turbines and the resolution of the wind power class map. A turbine rated for low wind speeds will have a higher capacity factor at low wind speeds than a turbine rated for high wind speeds. At high wind speeds, a turbine rated for high wind speeds may have a higher capacity factor due to a higher cutoff wind speed. Taller wind turbines expe- rience higher wind speeds, which usually results in higher capacity factors. The wind power classes in Fig.3 are from the Alaska Energy Data Inventory (AEDI) wind power class map (http://www.arcgis.com/home/item.html?id¼6aaef4ce5821459cad757bf9adda3079). Localized wind speeds can be higher than what is shown by the resolution of the wind power class map, resulting in higher capacity factors used in the REF applications than what is shown in Fig.3. Diesel offset Figure 4 shows the predicted improvement in grid diesel electric efﬁciency based on REF applications. This improvement is calculated as the total electric consumption divided by the FIG. 4. System diesel electric efﬁciency before and after installing wind turbines, plotted against wind energy penetration. Corresponding before-and-after values are connected by a dashed line. Wind energy penetration is the amount of wind energy in kilowatt-hours that can be generated (assuming no diversion), divided by grid electrical consumption. 061703-6 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) diesel consumed. Wind power supplies some of the electric load, reducing diesel consumption. At higher penetrations, not all wind energy can be used for electric loads. This “excess” genera- tion can be used to supply thermal loads. Figure 4 only accounts for electric loads. Wind energy displaces more diesel by supplying electric loads than thermal loads. Assuming a boiler that has an efﬁciency of 110000Btu/gal and a diesel generator that has an efﬁciency of 13kWh/gal, 33 kWh of wind energy will displace around 1 gal of diesel if it sup- plies a heating load and 2.5 gal of diesel if it supplies an electrical load. Thus, since diesel is more efﬁcient at supplying thermal loads than electric loads, wind energy displaces more diesel by supplying electric loads. Figure 5 shows the predicted ratio of diesel offset for thermal loads to electric loads supplied by wind energy at different energy penetrations. These ratios simply show what the project plan was for using thermal and electric loads and do not necessarily reﬂect what is fea- sible or optimal. For example, a project may plan to install a boiler or several masonry thermo- electric heaters that use some but not all of the excess wind generation. One project below 20% wind energy penetration included a plan to use a large portion of the wind energy to supply thermal loads. Typically, this plan is not economical due to the amount of wind energy required to displace diesel for thermal loads. In general, only projects above around 25% energy penetra- tion included a plan to supply thermal loads with wind energy. Cost per kilowatt-hour Figure 6 shows the resulting levelized cost of electricity (LCOE), assuming yearly O&M costs of $0.036/kWh of wind energy generation that increase with an inﬂation rate of 2%, an interest rate of 5%, a lifetime of 20years, and different average capacity factors; LCOE equa- tions are given in Appendix B. These costs do not take into account subsidies; thus, they are higher than the actual cost to the utility and not directly comparable to subsidized diesel gener- ating costs. Table V shows LCOE values for various installed wind capacities and average capacity factors. Conditions for greatest efficiency Consistent, high-speed, non-turbulent winds result in the best wind farm performance. Turbines are rated by IEC 61400-1 design requirements for average wind speed, extreme 50- FIG. 5. The predicted ratio of diesel offset for thermal loads to electric loads supplied by wind energy. Wind energy pene- tration is the amount of wind energy in kilowatt-hours that can be generated (assuming no diversion), divided by the grid electrical consumption. 061703-7 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) year gust, and turbulence. The wind power at heights of 10 m and 50m is classiﬁed by DOE using “Wind Power Class 1–7,” with Class 3 and above usually suitable for utility wind power (Elliot et al., 1986). A grid’s ability to accept power from a wind farm at a given moment may result in having to divert or curtail excess generation. Excess generation can be used for either energy storage or controllable loads. Cost curve over time The REF applications did not show any statistically signiﬁcant change in costs over time. As-built costs from Alaska (outlined in Appendix C) did not show a signiﬁcant change in cost over time either. In Fig.7, as-built costs for projects in Alaska are plotted in 2015 dollars against the year the projects were installed. Installed costs by major components Information on installed costs by major components is given in the “Capital Costs” section and in Appendix A. Transportation Information on transportation costs is given in the “Capital Costs” section and in Appendix A. TABLE V. LCOE values for various installed wind capacities and average capacity factors. Installed wind capacity (kW) 20% average capacity factor ($/kWh) 30% average capacity factor ($/kWh) 40% average capacity factor ($/kWh) 50 1.41 0.96 0.73 100 0.99 0.67 0.52 200 0.71 0.49 0.38 500 0.49 0.34 0.27 1000 0.38 0.27 0.21 2000 0.30 0.22 0.17 5000 0.24 0.17 0.14 FIG. 6. Levelized cost of energy for different installed capacities and capacity factors, assuming yearly O&M costs of $0.036/kWh of wind energy generation that increase with an inﬂation rate of 2%, an interest rate of 5%, and a lifetime of 20years. 061703-8 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) Technology trends The wind market is moving toward larger and more powerful wind turbines, often with gearless direct-drive generators (Wiser and Bolinger, 2017). Due to the size of communities in Alaska as well as other factors, turbines installed in Alaska are smaller than the trend in the larger market. Evidence indicates that installing overcapacity wind farms together with energy storage and signiﬁcant diversion into thermal loads allows communities to achieve diesel-off and least-cost energy when considering the displacement of diesel fuel for both electricity and heat (Simpkins et al., 2015). Robust direct drive turbines are currently popular in Alaska since they require less maintenance. Refurbishment/upgrade market The refurbished wind turbine market is signiﬁcant. As wind farms upgrade to larger wind turbines, companies such as Windmatic purchase the old wind turbines to refurbish and resell them (Fauy et al., 2010). Upgrades in new and refurbished models that are popular for Alaska include marine-grade paint on the tower and black Teﬂon paint and heaters on the blades. Modiﬁcations to turbines, such as hub extensions, and control and drive upgrades are consid- ered at times. Realized cost savings Cost savings from integrating renewable power are difﬁcult to gauge due to technical and incentive impacts at the entire power system level. At the technical level, for example, the effects of diminished losses of secondary services, such as recovered waste heat and reductions in fuel efﬁciency, are hard to gauge, as they depend not only on average reductions in load but also on speciﬁc operating schemes regarding the minimum allowable load on diesels and spin- ning reserve kept. CONCLUSIONS Wind power systems have been installed in Alaska, both in remote areas and along the road system. Total installed capacity exceeds 60MW, with installations ranging from 40kW to 24MW. The costs per kilowatt for the different components of wind turbine installations were found to decrease with increasing size of installation. Exceptions were the costs for transmis- sion and integration, which did not vary signiﬁcantly with the capacity of the installed system. FIG. 7. As-built costs for projects in Alaska plotted against the year of installation. Costs are converted to 2015 dollars. 061703-9 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) Individual costs were tested for correlations with the region and year, but in most cases, the relationship was not signiﬁcant. Capacity factors range from approximately 10% to 40%. The rated wind speed and height of the wind turbines and the resolution of the wind power class map affect the variance of capacity factors at wind power classes. A turbine rated for low wind speeds will have a higher capacity factor at low wind speeds than a turbine rated for high wind speeds. At high wind speeds, a turbine rated for high wind speeds may have a higher capacity factor due to a higher cutoff wind speed. Taller wind turbines experience higher wind speeds, which usually result in higher capacity factors. Wind power technology is relatively mature internationally, nationally, and in Alaska. Turbines have minimal down time in a year, often for their entire design life and sometimes longer. The remoteness of communities in Alaska leads to higher transportation, infrastructure, and maintenance costs. Signiﬁcant economies of scale could be gained with larger wind tur- bines. The size of wind turbines that can be installed in Alaska is limited by the small size of the state’s communities and the challenges of integrating high penetrations of wind power into microgrids. Excess wind generation and the need for more complex integration equipment increase with higher penetrations of wind power. Demonstrations of different high-penetration wind power integration techniques are needed, including demand-side management and wind to heat. Improvements and reduction in costs of integration equipment such as energy storage systems will help to achieve higher penetrations of wind power. An integrated approach is needed that includes analysis to understand low- hanging fruit and mechanisms to allow collaboration between government and industry. Funding is a major issue in implementing grid improvements, and state, federal, and private funds could help with this implementation. ACKNOWLEDGMENTS The authors wish to thank the Alaska Energy Authority for its funding to support this project. Many people have contributed information and insight to this report. For their review and comments, we wish to thank David Burlingame of Electric Power Systems, Inc.; Rob Bensin, Energy Efﬁciency and Renewable Energy Division Manager at Bering Straits Development Company; Ian Baring-Gould of the National Renewable Energy Laboratory; Steve Gilbert of the Alaska Village Electric Cooperative, Inc.; and Dan Smith, Josh Craft, and Neil McMahon of the Alaska Energy Authority. APPENDIX A: LINEAR REGRESSION PLOTS FOR EACH COST CATEGORY FIG. 8. Graph of data and best-ﬁt equation for energy analysis costs of installing wind power. 061703-10 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) FIG. 9. Graph of data and best-ﬁt equation for conceptual design costs of installing wind power. FIG. 10. Graph of data and best-ﬁt equation for ﬁnal design costs of installing wind power. FIG. 11. Graph of data and best-ﬁt equation for turbine component costs of installing wind power. 061703-11 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) FIG. 12. Graph of data and best-ﬁt equation for turbine installation costs of installing wind power. FIG. 13. Graph of data and best-ﬁt equation for transmission costs of installing wind power. FIG. 14. Graph of data and best-ﬁt equation for foundation and supporting infrastructure costs of installing wind power. 061703-12 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) APPENDIX B: LEVELIZED COST OF ENERGY EQUATIONS Equations for LCOE: LCOE ¼NPVc CRF AnnualEnergyProduction ; NPVc ¼CAPEX þ XN j¼1 1 þ i 1 þ r j OM; CRF ¼r 1 ð 1 þ rÞ N ; where NPVc is the net present value of the annual cost of the system,CAPEX is the capital expen- diture,i is the inﬂation rate,r is the interest rate, and N is the system lifetime in years. FIG. 15. Graph of data and best-ﬁt equation for the integration cost of installing wind power. FIG. 16. Graph of data and best-ﬁt equation for the turbine transportation cost of installing wind power. 061703-13 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) APPENDIX C: COMPARISON OF REFAPPLICATION COST ESTIMATES WITH AS-BUILT COSTS Figure 17 shows the total project costs and installed capacity predicted by REF applications compared to future as-built costs and installed capacity from the same community. The projects can be seen in Table VI. Note that the REF data do not necessarily represent all costs. For exam- ple, many projects do not include the engineering and energy assessment. Other projects do not include transport, foundation, installation, integration, and/or transmission costs. It is not always clear whether these costs are included with other costs. Also, it is not known what exactly the as- built costs cover. That being said, a trend of lower power and higher CAPEX/Power for as-built projects compared with the associated REF application is apparent. Figure 18 shows a comparison of the best ﬁts for as-built costs and the costs predicted in the REF applications. The ﬁts are rela- tively similar, indicating that the costs calculated from the REF applications and used in this paper are representative of actual costs. FIG. 17. REF costs and associated as-built costs and power are connected with dashed lines. The projects can be seen in Table VI. The ﬁt to the REF data is shown by the solid line. FIG. 18. As-built costs with the as-built ﬁt and REF ﬁt. The ﬁt for both datasets is similar. This indicates that the costs cal- culated from the REF applications and used in this paper are representative of actual costs. The as-built costs show a more dramatic curvature, but for absolute values, they are relatively similar. 061703-14 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) TABLE VI. Comparison of REF application costs and as-built costs for the same location. Not all as-built costs are for the same project as the REF application. The ones with a dark grey backgroundwere built before the REF application.REF applicationsAs built costsNames YearSize(kW)Assessment($/kW)Designconceptual($/kW)Designfinal($/kW)Hardware($/kW)Transport-ation($/kW)Foundatio-n ($/kW)Installatio-n ($/kW)Transmiss-ion ($/kW)Integration($/kW)Total($/kW)Size(kW) DateCAPE-X ($/kW)Powerratio(kW/kW)CAPEXratio ($/$)Unalakleet WindFarmConstruction2008 1200 0 24 291 2238 748 980 770 504 1858 7414 600 1November200910000 0.50 1.35Bethel WindPower ProjectTimes 42008 400 0 0 0 7980 0 0 0 0 0 7980 100 1 April201424450 0.25 3.06Bethel Wind FarmConstruction(BNC land)2008 2000 0 0 0 1500 0 1750 650 350 0 4250 100 1 April201424450 0.05 5.75Bethel 2011 1000 62 190 433 3865 0 0 0 0 111 4661 100 1 April201424450 0.10 5.25Kongiganak WindFarmConstruction2008 450 0 0 889 4436 0 0 0 0 1652 6977 450 15December20127111 1.00 1.02Quinhagak WindFarmConstruction2008 300 0 0 0 7707 550 0 5422 0 0 13679 300 1November201014379 1.00 1.05Mekoryuk WindFarmConstruction2008 200 0 0 0 4097 900 3816 2720 0 6000 17532 200 1 February201117532 1.00 1.00Toksook BayWind FarmExpansionConstruction2008 100 0 0 315 4750 650 3959 1856 0 0 11531 100 1 October201011531 1.00 1.00300 1 June200611149 3.00 0.97Hooper Bay WindFarmConstruction2008 200 0 0 569 6876 0 0 3655 0 0 11101 300 1 July20099530 1.50 0.86 061703-15 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) TABLE VI. (Continued.)REF applicationsAs built costsNames YearSize(kW)Assessment($/kW)Designconceptual($/kW)Designfinal($/kW)Hardware($/kW)Transport-ation($/kW)Foundatio-n ($/kW)Installatio-n ($/kW)Transmiss-ion ($/kW)Integration($/kW)Total($/kW)Size(kW) DateCAPE-X ($/kW)Powerratio(kW/kW)CAPEXratio ($/$)Kotzebue WindFarm ExpansionConstruction2008 3250 0 0 0 2407 268 481 241 431 0 3828 195 1 July1997#N/A 0.06 #N/A455 1 May1999#N/A 0.14 #N/A100 1 May2002#N/A 0.03 #N/A130 1 May2005#N/A 0.04 #N/A65 1 May2006#N/A 0.02 #N/A195 1 October2006#N/A 0.06 #N/A1800 1 August20125975 0.55 1.5625 1 May2014#N/A 0.01 #N/AKotzebue 2010 1800 0 0 56 2033 1163 987 384 0 1352 5975 195 1 July1997#N/A 0.11 #N/A455 1 May1999#N/A 0.25 #N/A100 1 May2002#N/A 0.06 #N/A130 1 May2005#N/A 0.07 #N/A65 1 May2006#N/A 0.04 #N/A195 1 October2006#N/A 0.11 #N/A 061703-16 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) TABLE VI. (Continued.)REF applicationsAs built costsNames YearSize(kW)Assessment($/kW)Designconceptual($/kW)Designfinal($/kW)Hardware($/kW)Transport-ation($/kW)Foundatio-n ($/kW)Installatio-n ($/kW)Transmiss-ion ($/kW)Integration($/kW)Total($/kW)Size(kW) DateCAPE-X ($/kW)Powerratio(kW/kW)CAPEXratio ($/$)1800 1 August20125975 1.00 1.0025 1 May2014#N/A 0.01 #N/ABuckland WindFarmConstruction2008 300 0 0 0 4138 1287 4136 275 7758 0 17596 200 1 May201529674 0.67 1.69Deering WindFarmConstruction2008 200 0 0 0 4138 1287 4136 396 3491 0 13449 100 1 October201525000 0.50 1.86Eva Creek WindFarmConstruction2008 24000 39 125 40 1633 500 719 334 479 58 3928 24600 20 October20123821 1.03 0.97Eva Creek 2011 24000 0 0 125 1571 0 0 2192 0 0 3888 24600 20 October20123821 1.03 0.98Delta JunctionWind FarmConstruction2008 2000 0 0 0 2532 0 541 681 108 0 3862 100 1September20083970 0.05 1.03900 1 June20103970 0.45 1.03900 1September20132717 0.45 0.70Sand Point Wind 2009 1000 0 0 0 896 377 234 469 153 214 2341 1000 1 February20112978 1.00 1.27Sand Point 2016 1000 0 0 65 748 0 0 1007 0 0 1820 1000 1 February20112978 1.00 1.64Tuntutuliak HighPenetration WindDiesel2009 475 0 0 611 3436 0 0 1335 0 1693 7074 450 1 July20127467 0.95 1.06 061703-17 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) TABLE VI. (Continued.)REF applicationsAs built costsNames YearSize(kW)Assessment($/kW)Designconceptual($/kW)Designfinal($/kW)Hardware($/kW)Transport-ation($/kW)Foundatio-n ($/kW)Installatio-n ($/kW)Transmiss-ion ($/kW)Integration($/kW)Total($/kW)Size(kW) DateCAPE-X ($/kW)Powerratio(kW/kW)CAPEXratio ($/$)Shaktoolik Wind 2009 200 0 0 1050 3591 1853 0 4645 0 2500 13640 200 1 February201214200 1.00 1.04Emmonak/Alakanuk Wind &Trans2009 800 0 0 263 3470 720 0 3727 0 1563 9741 400 1September201122222 0.50 2.28Selawik turbineupgrade2011 300 83 228 00000003122601October20035990 0.87 19.22Wales 2013 100 400 1600 00000002000 130 1 July1998#N/A 1.30 #N/A 061703-18 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017) APPENDIX D: DIESEL OFFSET PER INSTALLED CAPACITY CALCULATION The total electrical energy produced in a year by a wind farm is Ee ¼8760h X3 P, where X3 is the average capacity factor and P is the nameplate capacity of the windfarm. Assuming that the energy content of diesel is 38kWh/gal and an average diesel efﬁciency of g will displace Ee =ð38 gÞ gallons of diesel. Thus, the offset per installed capacity in gal/kW is OFF ¼230 g X3 . Elliot, D., Holladay, C., Barchet, W., Foote, H., and Sandusky, W., Wind Energy Resource Atlas of United States, Report No. DOE/CH 10093-4 (1986). Fauy, G., Schworer, T., and Keith, K.,http://www.uaf.edu/ﬁles/acep/Alaska-Isolated-Wind-Diesel-Systems.pdf for Alaska Isolated Wind-Diesel Systems: Performance and Economic Analysis, Alaska Energy Authority; accessed June 2010. McMahon, N., personal communication (2015). NREL,http://www.nrel.gov/analysis/tech_lcoe_re_cost_est.html for Distributed Generation Renewable Energy Estimate of Costs. NREL, Updated August 2013; accessed 28 January 2016. Simpkins, T., Cutler, D., Hirsch, B., Olis, D., and Anderson, K., “Cost-optimal pathways to 75% fuel reduction in remote Alaskan villages,” Report No. NREL/CP-7A40-64491; accessed 28 October 2015. Wiser, R. and Bolinger, M., 2016 Wind Technologies Market Report, Report No. DOE-GO/102017-5033 (2017). 061703-19 VanderMeer, Mueller-Stoffels, and Whitney J. Renewable Sustainable Energy 9, 061703 (2017)