Wind Farm Wakes Simulated Using WRF

S C Pryor,P Volker,A N Hahmann,R J Barthelmie,T J Shepherd

doi:10.1088/1742-6596/1256/1/012025

S C Pryor, P Volker + Show 3 more

Open Access

https://doi.org/10.1088/1742-6596/1256/1/012025

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Abstract

Optimization of wind turbine (WT) arrays to maximize system-wide power production (i.e. minimize ‘wind-theft’) requires high-fidelity simulations of array-array interactions at the regional scale. This study systematically compares two parameterizations (Fitch and EWP) developed to describe wind farm impacts on atmospheric flow in the Weather Research and Forecasting (WRF) model. We present new year-long simulations for a nested domain centred on Iowa (the state with highest WT density) in the US Midwest that employ real WT characteristics and locations. Simulations with Fitch and EWP indicate similar seasonality in system-wide gross capacity factors (CF) for WT operating in Iowa, but the gross CF are systematically higher in simulations using EWP. The mean gross CF from the Fitch scheme is 44.1%, while that from EWP is 46.4%. These differences in CF are due to marked differences in the intensity and vertical profile of wakes simulated by the two approaches. Output from EWP also indicates much smaller near-surface climate impacts from WT. For example, when summertime hourly near-surface temperature (T2m) from the 299 WT grid cells are compared (i.e. EWP or Fitch minus noWT) the results show warming of nocturnal temperatures (lowest decile of T2m) but the maximum warming is considerably larger in simulations with the Fitch scheme.

Highlights

Wind turbine (WT) deployments in the USA have shown strong growth over the last few years and remain dominated by the onshore market (Fig. 1) [1]
Simulations with Fitch and Explicit Wake Parameterization (EWP) indicate similar seasonality in system-wide gross capacity factors (CF) for wind turbine (WT) operating in Iowa, but the gross CF are systematically higher in simulations using EWP
3.1 Power production and capacity factors Gross capacity factors (CF) are computed as the ratio the sum of electrical power produced by each scheme in each 10-minute period to the maximum possible as determined by the cumulative nameplate capacity for all WT in Iowa

Summary

Introduction

Wind turbine (WT) deployments in the USA have shown strong growth over the last few years and remain dominated by the onshore market (Fig. 1) [1]. A recent report by the U.S Federal Energy Regulatory Commission concluded that proposed generation and retirements by December 2021 include net capacity additions by renewable sources of 169,914 MW. The projected expansion of the WT fleet and installed capacity may have implications for local/regional climates in the deployment locations that can only be assessed a priori using high resolution numerical simulations [3]. Addressing these questions and evaluating tools available for regional scale simulations of array effects are the motivations for the research reported

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Results

Conclusion