Optimized Placement of Onshore Wind Farms Considering Topography

Xiawei Wu,Frede Blaabjerg,Cong Chen,Qi Huang,Weihao Hu,Zhe Chen

doi:10.3390/en12152944

Abstract

As the scale of onshore wind farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore wind farms should take terrain into consideration including height change and slope curvature. However, optimized wind turbine (WT) placement for onshore wind farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore wind farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore wind farms. The optimization method is applicable for optimized placement of onshore wind farms and can be extended to different topographic conditions.

Highlights

With the high recent demand for clean energy, wind energy has become increasingly important because of its many advantages, such as environmental friendliness, safety, and high utilization.According to the Global Wind Energy Council (GWEC) 2018, the worldwide newly installed capacity of wind energy will exceed 60 GW in 2020, distributed over about 100 countries, and it is estimated that the globally installed wind energy capacity will exceed 800 GW [1].The wake effect is a phenomenon where the wind turbine (WT) draws energy from the wind and forms a wake where the wind velocity of downstream wind turbines (WTs) is reduced [2]
This paper proposes an optimization methodology for three-dimensional onshore wind farms to find the optimized WTs placement
Two test simulations are adopted to verify the feasibility of the proposed model and to find the optimized placement of the onshore wind farm with different topography using the particle swarm optimization (PSO) algorithm

Summary

Introduction

With the high recent demand for clean energy, wind energy has become increasingly important because of its many advantages, such as environmental friendliness, safety, and high utilization.According to the Global Wind Energy Council (GWEC) 2018, the worldwide newly installed capacity of wind energy will exceed 60 GW in 2020, distributed over about 100 countries, and it is estimated that the globally installed wind energy capacity will exceed 800 GW [1].The wake effect is a phenomenon where the wind turbine (WT) draws energy from the wind and forms a wake where the wind velocity of downstream WT is reduced [2]. WT and the wake effect causes an uneven distribution of wind power and a decline in production. The wake effect results in energy losses which reduce the annual energy production by about 10–15%, causing great financial losses to wind farm owners [3]. Due to the gradually advanced wind power technology, more and more large-scale wind farms have been built and the wake losses have become more evident at the same time. This affects the WTs control strategy and operating reserve [4,5]

Methods

Results

Conclusion