Advanced methodology for wind resource assessment near hydroelectric dams in complex mountainous areas. (1st January 2020)

• Record Type: Journal Article
• Authors: Yang, Lin; Rojas, Jose I.; Montlaur, Adeline
• Is Part Of: Energy. Volume 190(2020)
• Publication Date: 2020-01-01
• Subjects: Wind energy -- Power density -- Wind turbine -- Hydroelectric dam -- CFD; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116487
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: To increase renewable energy generation in some hydroelectric dams, a solution consisting in installing wind turbines close to dams is proposed. Indeed, dam surroundings are prone to benefit from wind speed-up effect, extra wind generation associated with thermal winds, and existing electrical infrastructure. Identifying the most suitable locations for turbines, that is, areas of relatively high-speed and low-turbulence wind, is fundamental to maximize this complementary power. Easy accessibility to turbines and minimum distance to dam electrical infrastructure are also essential to reduce the costs. Thus, a methodology is proposed to improve wind resource assessment in complex mountainous areas. First, potentially interesting dams are chosen using statistical local wind data. Second, weighted results of wind speed and turbulence intensity, considering all wind directions are presented based on CFD simulations. Finally, wind power density and annual energy production maps are generated, along with accessibility maps, to identify suitable sites. The Camarasa dam in the north-east of the Iberian Peninsula is chosen as case study to show and test the proposed methodology. Error estimations are provided, along with validation against Wind Atlas data and WAsP simulations. Highlights: A low-cost methodology to install wind turbines close to dams is proposed. The proposed locations benefit from wind speed-up effect and thermal winds. The easy-to-follow methodology combines statistical wind data with CFD simulation. Case study shows the effectiveness of the methodology and provides error estimation.