As wind turbines account for a major proportion of the wind farm initial investment, previous researches mainly focused on optimizing the number and locations of wind turbines. In [6] and [7], the genetic algorithm was proposed to iteratively optimize gridded turbine layouts on flat terrains. In [8] and [9], the greedy algorithm was introduced to search for the optimal gridded turbine layouts on hilly wind farms. The modified particle swarm optimization algorithm was applied to solve the turbine layout optimization problem in a continuous solution space. The civil works include the construction of access roads, turbine foundations, crane hard-standings, cable-trenching, and substation buildings. Their cost can be substantial. Access roads are greatly influenced by topographic characteristics, and their cost is much higher for hilly or mountainous wind farms than plain ones. A comparative strategy and the minimum spanning tree were proposed to search for the shortest road network for wind farms in flat areas. In [11], the Euclidean Steiner tree was introduced to further reduce the length of the road network. However, these methods are not directly applicable to hilly or mountainous wind farms where the road gradient must be considered to build winding roads capable of accommodating significant weight. Wind farms are generally constructed in rural areas with challenging topography. With the rapid expansion of wind farms, black plastic planting pots new farms have to be built in hills or mountains, where designing suitable access roads for construction, turbine erection and maintenance is not straight forward.
It is necessary to know roughly which route should be chosen to connect turbines and how much they will cost, while placing turbines at the peaks for high energy production. The selection of appropriate access roads is essential for the overall planning of the wind farm construction. A well-chosen road network can also reduce the wind farm construction period and lower the environmental impact. In this paper, the constraints on the maximal gradient of access roads are guaranteed when developing an automatic contour-based road-network design model. The design of access roads and the evaluation of their cost are simultaneously considered in the process of optimizing turbine layouts, which results in a more technically feasible and economically beneficial micrositing of wind farms. The remainder of this paper is organized as follows. Section II proposes an automatic model for road network design. The problem of wind farm micrositing is formulated in Section III. Section IV gives the simulation results. Finally, Section V makes some concluding remarks.In civil engineering, a feasible route between the starting and end points is selected by establishing a series of control points on the terrain. Using these control points as an initial alignment, the horizontal and vertical curves are then located, subject to the road design constraints including geometric specifications and environmental requirements. Heuristic optimization algorithms such as the genetic algorithm, the Tabu search, and the simulated annealing algorithm have been applied to optimize horizontal and vertical alignments of roads. These algorithms depend on a high-resolution digital elevation model to support the analysis of road design features such as ground slopes and other landform characteristics.
The calculation of an optimal route between two given locations is time consuming and could even take several hours. The micrositing optimization process usually involves hundreds or thousands of iterations to evaluate the wake effects between turbines and to update the turbine layout. The process without road design is already intensive. If a complex road network for the whole farm needs to be designed in each iteration by the aforementioned heuristic optimization algorithms, the computation burden is prohibitive and even impossible. In this paper, only the control points of access roads are selected during the micrositing optimization process. Such a relatively simple and functional road design is suitable for low-volume access roads, which are mainly for wind farm construction and maintenance. It is also adequate for the design at the strategic and tactical level. Based on the above assumption, a fast automatic method for road-network design is developed to estimate the cost of access roads during the optimization of wind farm micrositing.Forest engineers usually use large-scale contour maps to select preliminary routes with dividers to connect two locations, a process known as route projection. Control points of a road can be selected during the route projection process. In this paper, contour lines as a group of nested closed curves are chosen to store topographic information of wind farms. A distinct advantage of the contour-line data over a digital elevation model is that the vector representation lends itself to the object-oriented modeling of terrains, which provides a natural mechanism for sorting terrains and facilitates the search through a contour map.
Given a technically feasible gradient, a route is projected from the starting point toward the end across adjacent contour lines. This process can be efficiently automated with a mathematical model. The basic idea of the route projection process is to determine the projected route segment between two neighboring contour lines, as the elevation of the terrain rises continuously. Every projected segment of the route must begin from a point on a contour line and end on another one.Two comparative cases are studied to investigate the effectiveness of the proposed road network design model and the significance of road network optimization for farms in hills or mountains. Due to the randomness of genetic algorithms, ten independent runs are performed for each case. Table IV summarizes the results, including the best, average, and worst values of the best solutions achieved in each independent run. In Case 1, the micrositing problem is solved in a two-stage manner, i.e., we first search for an optimized turbine layout without considering access roads and then design the road network for the given layout. The best configuration of the wind farm is shown in Fig. 7, where the blue triangle represents the entrance of the wind farm, red points mean the locations of wind turbines, and black polylines are the designed road network. In Case 2, the turbine layout and its corresponding road network are optimized simultaneously, i.e., in each optimization iteration, the road network design model is performed for every individual turbine layout generated by the genetic algorithm. The best configuration of the wind farm is shown in Fig. 8. The simulation results of both cases are summarized in Table IV. Note that, the simulations for both cases are time consuming. The simulations were carried out in an IBM Blade Center HS22 with 12 Intel Xeon X5650 CPUs at a frequency 2.66 GHz. Parallel computing was employed to speed up the simulation progress. In each run of the 10 independent simulations, 12 CPUs were utilized simultaneously and each CPU was responsible for each wind direction. Even with the parallel computing Fig. 7. Best wind farm configuration of Case 1. technique, plant plastic pots the running time for each simulation is still around 7–10 h. For both turbine layouts, reasonable road networks with feasible winding roads are obtained, which demonstrates the effectiveness of the proposed road network design model. Comparing Figs. 7 and 8, it is clear that the configuration of the road network depends on the turbine layout and the farm topology considerably. Therefore, to create a cost-effective road network, it is necessary to consider these two parts simultaneously during the microsting optimization process. Indeed, since the road network is simultaneously considered in Case 2, while the cost of the road network is ignored in Case 1, Case 2 consistently has a higher net present value than Case 1 in all independent runs. In the simulations, the net present value is determined by the annual energy production and the length of the road network.Methane released into the atmosphere as a result of agricultural activity, such as enteric fermentation and anaerobic digestion, significantly contributes to overall greenhouse gas emissions in the United States . The California Air Resources Board attributes approximately 60 % of recent anthropogenic CH4 emissions in California to agriculture, with 45 % of CH4 emissions directly related to dairy farm activity for 2013 . Reduction strategies proposed by CARB seek to lower California’s CH4 emissions to 40 % below 2013 rates by 2030 , thereby emphasizing the need for accurate methods to directly quantify the contribution of different CH4 sources within agricultural operations. Estimates of CH4 emissions due to dairy livestock can be calculated using inventory emission factors combined with activity data on animal populations, animal types, and details about feed intake in a particular country . Other methods to estimate CH4 emissions from ruminants involve direct atmospheric measurements.
Emissions from dairy farms have been estimated in the Los Angeles Basin, California, using downwind airborne flux measurements . Farmscale measurements of CH4 have been made using a variety of techniques and instruments, such as open-path infrared spectrometers , tunable-infrared direct absorption spectroscopy , and column measurements employing solar absorption spectrometers with comparisons to cavity ring-down spectrometers . Several studies of various CH4 sources assert that inventory-based calculations tend to underestimate emissions compared to atmospheric observations and modeling . Atmospheric studies have often used specific gases as tracers to distinguish a sample of interest from background conditions or interferences. Tracer gases released at known rates have been employed in experiments looking at chemical transport , dispersion , source allocation , and model verification using mobile laboratories , radiosondes, sampling towers, and ground-based equipment. Application of tracer gases in agricultural studies have involved insertion of a sulfur hexafluoride permeation tube into the rumen of a cow with subsequent collection of time-integrated breath samples . Inverse-dispersion techniques have employed line-source releases of SF6 within a dairy farm combined with open-path measurements to understand whole-site emissions . Release of a tracer gas directly into the atmosphere, 2–3 m above ground level, can be used to determine and distinguish CH4 emissions from various sources within a site . This study quantifies CH4 emissions using the well-established tracer flux ratio method at two dairy farms over the course of 8 summer days . Controlled releases of tracer gas from various areas on each farm mixed with site-derived emissions were observed by an instrumented aircraft and mobile laboratory . Using this technique provided the flexibility to estimate entire dairy farm emissions and apportion emissions among sources on multiple scales. Uncertainty in measurements from low-flying airborne studies has been attributed to the need to extrapolate results below the minimum safe flight heights as regulated by the Federal Aviation Administration . Prior to this study, Aerodyne Research, Inc. performed controlled ground releases of ethane in Colorado and Arkansas, while Scientific Aviation made measurements in a similar aircraft to the one used in this study . The original release rate of C2H6 was estimated via a refined mass balance technique, with a +2 % difference observed during tests in Colorado and +24 % difference in Arkansas as described in Conley et al. . These releases did not correspond to any CH4 source but demonstrated the feasibility of using a low-flying aircraft to successfully quantify flow rates from controlled tracer gas releases. Using tracer flux ratio in this study, we again utilized the aircraft to detect emitted tracer gas and then compared with dairy farm emissions to evaluate CH4 emission rates. This field study was originally focused on estimating CH4 emissions from dairy farms and distinguishing on-site sources using established techniques . An intentional effort was made to align measurement time windows of the mobile laboratory and aircraft for the purpose of inter-comparison between the tracer flux ratio and mass balance methods. As a result, the aircraft was exposed to several hours of ground-released tracer gas. Due to this overlap in time, we were able to further assess the viability of observing enhanced concentrations of a ground-released tracer gas from an aircraft at low flow rates, compare CH4 and C2H6 enhancements emitted from within dairy farms via tracer flux ratio to determine emission rates, and directly compare the application of tracer flux ratio methodology to simultaneous ground and airborne measurements of the same air mass.In a collaborative effort, SA and ARI attempted a flight-based tracer release experiment to quantify CH4 emissions from two dairy farms in central California. This study reanalyzes data collected as part of an Environmental Defense Fund coordinated project that occurred in June 2016 . ARI employed tracer flux ratio methodology with two tracer gases and a mobile laboratory, while SA conducted a mass balance experiment from a light aircraft.