OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms

Perr-Sauer, Jordan; Optis, Mike; Fields, M. Jason; Bodini, Nicola; Lee, Joseph C. Y.; Todd, Austin C.; Simley, Eric; Hammond, Robert; Phillips, Caleb; Lunacek, Monte; Kemper, Travis; Williams, Lindy; Craig, Anna; Agarwal, Nathan; Sheng, Shuangwen; Meissner, John

doi:10.21105/joss.02171

Cited by 9 publications

(7 citation statements)

References 6 publications

(5 reference statements)

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“…Next, we removed any data that are flagged as curtailed or not fully operational for any of the relevant wind turbines. The data were then further filtered to remove power production anomalies using the filtering toolkit in NREL's OpenOA software (Perr-Sauer et al, 2021). Specifically, the power production data for each wind turbine was divided into 50-kW bins.…”

Section: Resultsmentioning

confidence: 99%

Experimental results of wake steering using fixed angles

Fleming

Sinner

Young

et al. 2021

Preprint

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Abstract. In this article, the authors present a test of wake steering at a commercial wind farm. A single fixed yaw offset, rather than an optimized offset schedule, is alternately applied to an upstream wind turbine and the effect on downstream turbines is analyzed. This experimental design allows for comparison with engineering wake models independent of the controller's ability to track a varying offset and correctly measure wind direction. Additionally, by applying the same offset in beneficial and detrimental conditions, we are able to collect important data for assessing second-order wake model predictions. Results of the article from collected data show good agreement with the FLOw Redirection and Induction in Steady State (FLORIS) engineering model and offer support for the asymmetry of wake steering predicted by newer models, such as the Gauss-curl hybrid model.

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Section: Resultsmentioning

confidence: 99%

Experimental results of wake steering using fixed angles

Fleming

Sinner

Young

et al. 2021

Preprint

Self Cite

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“…However, pure gross energy cannot be obtained from operational data because wake losses, turbine performance losses, and blade degradation losses are inherently present in the reported SCADA data from each wind turbine. Therefore, we begin our gap analysis by calculating the turbine ideal energy 9 (TIE), which we define as the gross energy with the abovementioned losses included, from both the EYA and operational data. Next, we continue the gap analysis by progressively adding electrical losses, availability losses, and any remaining losses to the EYA‐derived and operational TIE values, finally leading to a comparison in terms of net energy.…”

Section: Methodsmentioning

confidence: 99%

“…To analyze the wind plant operational data, we use an open‐source package for operational analysis (OpenOA, 9 v2.0.1). The OpenOA functionality implemented for the operational data in this study includes data quality control, calculation of operational energy production estimates, estimation of losses from SCADA data, and gap analysis routines.…”

Section: Methodsmentioning

confidence: 99%

An independent analysis of bias sources and variability in wind plant pre‐construction energy yield estimation methods

et al. 2022

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The wind resource assessment community has long had the goal of reducing the bias between wind plant pre-construction energy yield assessment (EYA) and the observed annual energy production (AEP). This comparison is typically made between the 50% probability of exceedance (P50) value of the EYA and the long-term corrected operational AEP (hereafter OA AEP) and is known as the P50 bias. The industry has critically lacked an independent analysis of bias investigated across multiple consultants to identify the greatest sources of uncertainty and variance in the EYA process and the best opportunities for uncertainty reduction. The present study addresses this gap by benchmarking consultant methodologies against each other and against operational data at a scale not seen before in industry collaborations. We consider data from 10 wind plants in North America and evaluate discrepancies between eight consultancies in the steps taken from estimates of gross to net energy. Consultants tend to overestimate the gross energy produced at the turbines and then compensate by further overestimating downstream losses, leading to a mean P50 bias near zero, still with significant variability among the individual wind plants. Within our data sample, we find that consultant estimates of all loss categories, except environmental losses, tend to reduce the project-to-project variability of the P50 bias. The disagreement between consultants, however, remains flat throughout the addition of losses. Finally, we find that differences in consultants' estimates of project performance can lead to differences up to $10/MWh in the levelized cost of energy for a wind plant.

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“…The WP3 initiative has led to the development of OpenOA, 135,36 an open-source Python package for wind power plant data operational analysis.…”

Section: Wind Plant Power Curve Approachmentioning

confidence: 99%

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

et al. 2021

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Many operational analyses of wind power plants require a statistical relationship, which can be called the wind plant power curve, to be developed between wind plant energy production and concurrent atmospheric variables. Currently, a univariate linear regression at monthly resolution is the industry standard for post-construction yield assessments. Here, we evaluate the benefits in augmenting this conventional approach by testing alternative regressions performed with multiple inputs, at a finer time resolution, and using nonlinear machine-learning algorithms. We utilize the National Renewable Energy Laboratory's open-source software package OpenOA to assess wind plant power curves for 10 wind plants. When a univariate generalized additive model at daily or hourly resolution is used, regression uncertainty is reduced, in absolute terms, by up to 1.0% and 1.2% (corresponding to a À59% and À80% relative change), respectively, compared to a univariate linear regression at monthly resolution; also, a more accurate assessment of the mean long-term wind plant production is achieved. Additional input variables also reduce the regression uncertainty: when temperature is added as an input to the conventional monthly linear regression, the operational analysis uncertainty connected to regression is reduced, in absolute terms, by up to 0.5% (À43% relative change) for wind power plants with strong seasonal variability. Adding input variables to the machine-learning model at daily resolution can further reduce regression uncertainty, with up to a À10% relative change. Based on these results, we conclude that a multivariate nonlinear regression at daily or hourly resolution should be recommended for assessing wind plant power curves.

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OpenOA: An Open-Source Codebase For Operational Analysis of Wind Farms

Cited by 9 publications

References 6 publications

Experimental results of wake steering using fixed angles

Experimental results of wake steering using fixed angles

An independent analysis of bias sources and variability in wind plant pre‐construction energy yield estimation methods

Lowering post‐construction yield assessment uncertainty through better wind plant power curves

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