Open Access Research Article Journal of Fundamentals of Renewable Energy and Applications J o u r n a l o f F u n d a m e n t a l s o f R e n e w a b l e E n e r g y a n d A p p li c a ti o n s ISSN: 2090-4541 J Fundam Renewable Energy Appl, an open access journal ISSN: 2090-4541 Cowen et al., J Fundam Renewable Energy Appl 2019, 9:2 Volume 9 • Issue 2 • 1000277 *Corresponding author: Torrey Wagner, Air Force Institute of Technology, Systems Engineering and Management, Wright-Patterson Air Force Base, OH, USA, Tel: +1 937-255-6565; E-mail: torrey.wagner@aft.edu Received August 06, 2019; Accepted August 23, 2019; Published August 30, 2019 Citation: Cowen L, Wagner T, Dudis D, (2019) Austere Location Wind Turbine Energy System Analysis. J Fundam Renewable Energy Appl 9: 277. Copyright: © 2019 Cowen L, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Abstract One promising technology to combat an energy shortage in austere locations is wind energy. In combination with battery storage and generator backup, we explore the feasibility of using a hybrid energy system to reduce the volume of diesel fuel required. Modeling the energy demands in austere locations will enable missions in remote settings to optimize their energy costs, increased their energy resiliency and assure their supply. For a modeled time-series energy requirement that varied between 2.4 MW and 5.1 MW, the optimal wind system size was 9.9 MW of installed wind power paired with a 741 kWh battery. Assuming an elevated price of fuel, the cost of operating the base with only fuel was greater than $55 million dollars. The total component and operational cost of the optimized wind, generator and battery system was cost-effective within one year and totaled $48 million dollars. Austere Location Wind Turbine Energy System Analysis Lukas Cowen, Torrey Wagner* ans Douglas Dudis Air Force Institute of Technology, Systems Engineering and Management, Wright-Patterson Air Force Base, OH, USA Keywords: Energy resilience; Energy efciency; Renewable energy; Wind energy Introduction Remote locations require power for communications, security, operational use, and the inhabitant’s quality of life; however a large portion of that power is currently generated through liquid fuels, which require overland transport or airlif. Wind turbines may increase the energy resiliency of a location by reducing logistical demand of frequent liquid fuels deliveries. Te Department of Defense stands to beneft from the implementation of wind turbines due to the costs of transporting liquid fuel. Tis could prevent ground casualties during convoy operations, which account for approximately nine percent of Army casualties in a deployed environment [1]. Literature Review In general, a successful transition to renewable energy will require addressing several challenges in areas of energy resilience, technology, and corporate culture [2-6]. Specifc to this work, factors related to wind as a resource, energy requirements and lifecycle considerations of implementing wind turbines at an austere location will be discussed. Wind resource Wind energy is derived from an uneven distribution of solar energy and was frst harnessed via wind turbine to generate electricity by James Blyth of Scotland in 1887 [7]. In general, wind turbine systems are composed of a rotor, generator, tower, and control equipment [8]. Te rotor converts wind energy into rotational energy. Tis rotational energy is directed to the generator which produces electricity, the tower provides structural support for the rotor and generator, and the control equipment optimizes the operation, power output, and safety of the system. Te minimum air speed required to generate power is called cut-of power. As wind speed increases, the power generated by the turbine increases, and generation ceases to protect the system from damage when the wind becomes too powerful. Wind resource data for a hypothetical location in East Asia is analyzed in this work, and the wind intensity data fts a Weibull distribution marked as a grey curve in Figure 1 [9]. Most wind resource data can be modeled through the Weibull distribution, given as: 1 (; , ) k k x fx k λ λ λ −   =     k x e λ −       (1) In the equation above, k is a shape parameter greater than zero, and λ is the scale parameter of the distribution. Te data fts the Weibull distribution with λ = 5.15 and k = 1.88. Te mean speed was 4.56 m/s. Tis is reasonable since because both in the Weibull distribution and wind data, values less than the mean are more common than extreme values including wind gusts. Energy requirement Energy requirements vary with the size, location, and operations of the base. Although it is useful to consider the average daily energy use of a deployed location, peak loads are important to consider when designing a power system. Tis analysis will utilize wind and electrical demand data modeled for a hypothetical outpost by McCaskey [10]. Figure 2 shows the seasonal variation of energy demand and will be used in conjunction with the base’s annual wind data to calculate the optimal number of wind turbines to meet the energy requirement. Te electricity demand showed daily and seasonal variation. Summer afernoons exhibited the most intense demands, and the evenings required the lowest electrical load. 24 18 12 6 0 Feb Apr Jun Aug Oct Dec kW 5,100 4,560 4,020 3,480 2,940 2,400 DMap Hour of Day Figure 1: Modeled annual electrical demand by hour.