With the increasing size of wind farms, the impact of the wake effect on wind farm energy yields become more and more evident. The arrangement of locations of the wind turbines (WTs) will influence the capital investment and contribute to the wake losses, which incur the reduction of energy production. As a consequence, the optimized placement of the WTs may be done by considering the wake effect as well as the components cost within the wind farm. In this paper, a mathematical model which includes the variation of both wind direction and wake deficit is proposed. The problem is formulated by using levelized production cost (LPC) as the objective function. The optimization procedure is performed by a particle swarm optimization (PSO) algorithm with the purpose of maximizing the energy yields while minimizing the total investment. The simulation results indicate that the proposed method is effective to find the optimized layout, which minimizes the LPC. The optimization procedure is applicable for optimized placement of WTs within wind farms and extendible for different wind conditions and capacity of wind farms.

There is more wind with less turbulence offshore compared with an onshore case, which drives the development of the offshore wind farm worldwide. Since a huge amount of money is required for constructing an offshore wind farm, many types of research have been done on the optimization of the offshore wind farm with the purpose of either minimizing the cost of energy or maximizing the total energy production. There are several factors that have an impact on the performance of the wind farm, mainly energy production of wind farm which is highly decided by the wind condition of construction area and micro-siting of wind turbines (WTs), as well as initial investment which is influenced by both the placement of WTs and the electrical system design, especially the scheme of cable connection layout. In this paper, a review of the state-of-art researches related to the wind farm layout optimization as well as electrical system design including cable connection scheme optimization is presented. The most significant factors that should be considered in the offshore wind farm optimization work is highlighted after reviewing the latest works, and the future needs have been specified.

Offshore wind farm has drawn more and more attention recently due to its higher energy capacity and more freedom to occupy area. However, the investment is higher. In order to make a cost-effective wind farm, the wind farm layout should be optimized. The wake effect is one of the dominant factors leading to energy losses. It is expected that the optimized placement of wind turbines (WT) over a large sea area can lead to the best tradeoff between energy yields and capital investment. This paper proposes a novel way to position offshore WTs for a regular shaped wind farm. In addition to optimizing the direction of wind farm placement and the spacing between WTs, the control strategy's impact on energy yields is also discussed. Since the problem is nonconvex and lots of optimization variables are involved, an evolutionary algorithm, the particle swarm optimization algorithm, is adopted to find the solution. In order to increase the probability of finding the global optimal solution, the adaptive parameter control strategy is utilized. Simulation results are given to verify the proposed approach and comparison is made with results obtained using other methods.