Optimizing wind farm layout by addressing energy-variance trade-off: A single-objective optimization approach. (15th December 2019)

• Record Type: Journal Article
• Authors: Wang, Longyan; Zuo, Ming J.; Xu, Jian; Zhou, Yunkai; Tan, Andy C.
• Is Part Of: Energy. Volume 189(2019)
• Publication Date: 2019-12-15
• Subjects: Wind farm layout -- Wind power variability -- Weighted optimization -- Confidence interval optimization -- Monte Carlo method; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2019.116149
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Wind farm layout optimization results depend on the optimization objectives, such as power output and variance. This paper investigates an alternative strategy for wind farm layout optimization by trading off the mean wind power output and power variance. Two optimization schemes, the weighted optimization and confidence interval optimization are compared in term of their performances on trading off energy-variance. For the weighted optimization, the objective function weight value α varies from 0 to 1 (implying the optimization objective shifts its weight from the power variance to the power output), while for the confidence interval (CI) optimization, either the lower or the upper limit of power output CI is maximized/minimized. It is found that the CI maximization achieves a trade-off of mean power output and power variance similar to the weighted optimization with α ≥ 0.6, and the same individual power output range (192 kW–207 kW) is obtained with staggered placements of wind turbines. The CI minimization obtains a trade-off of average power output and power variance close to the weighted optimization with α ≤ 0.4, and the optimal wind turbine locations are aligned. The advantage of CI optimization lies on its capability of predicting the power output uncertainty. Highlights: Wind farm layout optimization trading-off the mean and the variance of the power output is conducted. Two optimization schemes (weighted and confidence interval optimization) are compared. CI maximization achieves a trade-off similar to the weighted optimization with α ≥ 0.6 . CI minimization obtains a trade-off close to the weighted optimization with α ≤ 0.4 . The advantage of CI optimization lies on its capability of predicting the power output uncertainty.