## 4 Research Needs and Trends in Wind Farm Design and Optimization
### Commercial Software
Wake losses are calculated with the use of the DNV GL's CFD model, which is based on the Reynolds-Averaged Navier-Stokes equations complemented with an eddy viscosity turbulence closure. The optimization procedures are based on greedy heuristics, which can either maximize the annual energy production or the financial balance of the project. The software includes the possibility of adding a customized financial model and actively updates the selected performance metric during the optimization process. The optimization process includes environmental impacts (noise, visual impact, shadow flicker and turbulence intensity) as constraints with predefined thresholds. Meteodyn WT v4 [51] is a CFD-based tool for wind resource assessment and wind farm design. The wind flow modeling approach is based on the Reynolds-Averaged Navier-Stokes equations, which are complemented with a one-equation turbulence model (_i.e._, the turbulent kinetic energy equation). The software is able to assess the annual energy production of the wind farm while accounting for wake effects (which are modeled using the Jensen-Katic wake model). However, it currently lacks an optimization framework for automated micro-siting as well as of additional modules to perform environmental impact and uncertainty assessments. Wind Farm v4.2 [52] is a general tool, made by ReSoft, for the analysis, design and optimization of wind farms. The software is capable of performing wind resource assessments based on the linearized steady-state flow model MS-Micro/3 (having the same technical background as WAsP), developed by the Atmospheric Environment Service of Environment Canada and Zephyr, and predict long term variations in wind speed using the Measure-Correlate-Predict (MCP) method. The wind farm energy yield is estimated based on pre-constructed wind maps while accounting for wake losses, which are modeled using different wake models including the Ainslie and the WAsP Park models, and includes additional losses caused by turbulence. Similarly to WindFarmer and WindPro, Wind Farm is capable of performing noise estimations (based on the ISO 9613 standard [315]), as well as visual and shadow flicker assessments. The optimization module of WindFarm determines the optimum layout of WTs based on a pre-defined set of constraints such as the WT minimum separation, separation from houses, noise level and ground slope. The performance metric can be either the AEP\({}_{\text{Farm}}\) or the CoE (considering a simple form of the capital costs as the value to be minimized) of the wind farm. The optimization strategy is based on a greedy local search heuristic. The optimization process is repeatedly restated in order to avoid local minima or maxima. At each restart, each turbine might be moved randomly within a specified movement limit, which is reduced by a specified step size on each restart until the movement limit is reduced to zero. WindSim v6.2 [47] is a CFD-based tool for wind resource assessment and wind farm design and optimization. The wind flow modeling approach is based on the Reynolds-Averaged Navier-Stokes equations, which are complemented with different kind of turbulence models (e.g., standard _k_-\(\varepsilon\), RNG _k_-\(\varepsilon\) and the standard _k_-\(\omega\)). The wind farm energy yield is estimated based on pre-constructed wind maps while accounting for wake losses (which can be quantified using different wake models such as the Jensen-Katic [23], Larsen [25] or the Ishihara [288] models). The objective performance metric of the proposed optimization framework is the Net Present Value (NPV) of the project.