A two-stage optimization model for floating offshore wind farm layout and control

The effective planning of wind turbine (WT) positions in a wind farm (WF) plays a crucial role in capturing wind energy and increasing power generation. Control optimization of WT attitudes can also help reduce wake effects and increase power generation, thereby maximizing the annual electricity production (AEP) of a WF. In this paper, a two-stage optimization model is proposed for planning the stationary layout of floating offshore wind farms (FOWFs), as well as the positions and attitudes of the floating offshore wind turbines (FOWTs) via optimal control. In the first stage, a competitive particle swarm optimizer (CPSO) is employed to generate multiple FOWF layouts. In the second stage, the genetic algorithm (GA) is utilized to optimize the yaw angles and axial induction factors of the FOWTs for operation control. Unlike the existing approaches that address WT layout design and operation control sequentially and separately, the proposed method integrates these aspects to simplify the FOWTs operation and to improve their performances. Additionally, taking the movable characteristics of the FOWTs into consideration, the proposed model incorporates the positional movement of the FOWTs under varying wind conditions, making the power calculations more realistic. Case studies demonstrate that compared with the traditional layout-only optimization, the proposed two-stage optimization model can help increase the AEP of the FOWFs by 4.72 %.