Multi-Objective Optimisation of the Benchmark Wind Farm Layout Problem

Wind farm layout optimisation has become a very challenging and widespread problem in recent years. In many publications, the main goal is to achieve the maximum power output and minimum wind farm cost. This may be accomplished by applying single or multi-objective optimisation techniques. In this paper, we apply a single objective hill-climbing algorithm (HCA) and three multi-objective evolutionary algorithms (NSGA-II, SPEA2 and PESA-II) to a well-known benchmark optimisation problem proposed by Mosetti et al., which includes three different wind scenarios. We achieved better results by applying single- and multi-objective algorithms. Furthermore, we showed that the best performing multi-objective algorithm was NSGA-II. Finally, an extensive comparison of the results of past publications is made.

A new three-dimensional wake model for the real wind farm layout optimization

The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.