Wind Energy Initiative (WEI) A New Model for Wind Farm Layout Optimization with Landowner Decisions Problem Formula,on • Current wind farm layout optimization research assumes that a continuous piece of land is readily available for the development of a wind farm. • In reality, wind farm projects rely on the permission of landowners for success. • Landowners play a crucial role in the development of a wind farm, and some land parcels are more important to the success of the project than others. • Our research relaxes the assumption that a continuous piece of land is available, developing a novel approach that includes landowners’ decisions on whether or not to participate in the project. Identify the most crucial landowners and the optimal positions of turbines for specific wind farm cases in order to minimize costs while maximizing the total power output. Landowners and their plots of land are considered as design variables for the wind farm layout optimization problem. The optimization algorithm (genetic algorithm) will automatically “select” the most crucial landowners based on the objective function. Objec,ve Three cases are considered in this study: a) 4 out of 9 landowners agree to participate (44%) b) 5 out of 9 landowners agree to participate (56%) c) 6 out of 9 landowners agree to participate (67%) Each case takes into account three wind scenarios: (1) unidirectional uniform wind (12 m/s) (2) multidirectional uniform wind (12 m/s from four directions) (3) multidirectional non-uniform wind as shown below Wake Loss Model When a turbine in a wind farm is extracting energy from wind, it will develop a turbulent wake that reduces the downstream wind speed [1-3]. Op,mal Layouts for Mul,direc,onal Non‐Uniform Wind Scenarios Land plots 1 and 9 are included in all three optimal results as they do not have upstream or downstream plots in the prevailing wind direction ( ), while land plot 5 is not included in all the three results. If only x% of landowners agree to participate, which landowners are the most crucial to the success of the project? Reference: [1] Du Pont, B., and Cagan, J., 2010, "An Extended Pattern Search Approach to Wind Farm Layout Optimization," ASME IDETC Conference Proceedings, Aug 15-18, 2010, Montreal, Canada. [2] Jensen, N., 1983, "A Note on Wind Generator Interaction," Risø National Laboratory, DK-4000 Roskilde, Denmark. [3] Mosetti, G., Poloni, C., and Diviacco, B., 1994, "Optimization of Wind Turbine Positioning in Large Windfarms by Means of a Genetic Algorithm," Journal of Wind Engineering and Industrial Aerodynamics, 51(1), pp. 105-116. Case (a) Case (b) Case (c) 310 ! Minimize: Cost of Energy X ( ) = Cost X ( ) P tot X ( ) = N X ( ) 2 3 + 1 3 e !0.00174 NX ( ) 2 " # $ % & ' P i X ( ) i =1 NX ( ) ( Binary Representation of X: 101111010 landowners ' decisions ! " # $ # 0101011010... ... ...10111000 turbine locations ! " ##### $ #####  Mo,va,on Conclusion • More landowner participation does not necessarily guarantee less “Cost of Energy” • Some landowners are more crucial than others • Multiple optimal layouts make the recruiting process easier Using our approach, a site developer can spend more resources on persuading the most-important landowners to take part in the project. This will ultimately increase the efficiency of wind farm projects, increasing energy output and saving time and money in the development stages. Le Chen, lechen@iastate.edu Erin MacDonald, erinmacd@iastate.edu The objective function of the optimization program is defined as: The problem considers a plot of land 3696 by 3696 meters, owned by 9 landowners. Each landowner owns a small square plot of land with 16 cells. Each cell can have 2 possible states: contains a turbine or does not contains a turbine.