NREL's Balance-of-System Cost Model for Land-Based Wind

Eberle, Annika; Roberts, Owen; Key, Alicia; Bhaskar, Parangat; Dykes, Katherine

doi:10.2172/1569457

Cited by 18 publications

(33 citation statements)

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“…LandBOSSE requires input to the model to estimate the costs within the six categories. Additional details for each of these BOS cost categories are presented in the LandBOSSE documentation (Eberle et al 2019). A summary of these categories and their input values to the model are presented in this section.…”

Section: Baseline Model Inputs and Assumptionsmentioning

confidence: 99%

“…The baseline wind turbine foundation type considered in Section 2.2.3 was the spread-foot, reinforced concrete foundation (Eberle et al 2019). This gravity-based foundation is one of the most common in the market.…”

Section: Wind Turbine Foundationmentioning

confidence: 99%

“…The higher cost associated with the deep foundation type for mid and large-size wind turbines is a result of the added cost of (1) renting and mobilizing additional equipment on-site, such as a pile driver, for digging holes in the earth, (2) assembling rebar cages that are lowered into the hole, (3) injecting concrete into these foundation holes, and (4) including additional wait time until the concrete in the in-situ piles cures (roughly an additional week of wait time). These steps are in addition to the steps required for constructing the reinforced concrete pile caps, which follow a similar construction process as that of the shallow, spread-foot foundation considered in LandBOSSE (Eberle et al 2019). Therefore, the technologies we explored for midsize wind turbines and up were limited to innovations within the shallow, spread-foot foundation technology type, and were focused on innovations that lower the amount of material and labor required per foundation.…”

Section: Wind Turbine Foundationmentioning

confidence: 99%

“…When considering the conventional erection strategy (Eberle et al 2019) discussed in Section 2.2.4, running LandBOSSE for the 650-kW wind turbine scenarios, the total tower erection installation cost is estimated to be $76,401 (or $116/kW). Similarly, when considering the conventional erection strategy, running LandBOSSE for the single 1,500-kW wind turbine scenario, the total tower erection installation cost is estimated to be $251,603 (or $168/kW).…”

Section: Image From Northwestern Energy (2015)mentioning

confidence: 99%

“…This exploratory analysis characterizes the balance-of-system (BOS) cost reduction opportunity for small, commercial, medium, and large distributed wind systems. To do this, we used the National Renewable Energy Laboratory's (NREL's) Land-based Balance of System Systems Engineering (LandBOSSE) model (Eberle et al 2019). This model calculates the capital expenditures (CapEx) associated with installation and the system components (e.g., foundation and electrical infrastructure) other than the rotor nacelle assembly and tower.…”

Section: Introductionmentioning

confidence: 99%

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Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

Bhaskar¹,

Stehly²

2021

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Section: Baseline Model Inputs and Assumptionsmentioning

confidence: 99%

Section: Wind Turbine Foundationmentioning

confidence: 99%

Section: Wind Turbine Foundationmentioning

confidence: 99%

Section: Image From Northwestern Energy (2015)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

Bhaskar¹,

Stehly²

2021

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Collective wind farm operation based on a predictive model increases utility-scale energy production

Howland

Quesada

Martínez

et al. 2022

Preprint

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Wind turbines located in wind farms are operated to maximize only their own power production. Individual operation results in wake losses that reduce farm energy. In this study, we operate a wind turbine array collectively to maximize total array production through wake steering. The selection of the farm control strategy relies on the optimization of computationally efficient flow models. We develop a physics-based, data-assisted flow control model to predict the optimal control strategy. In contrast to previous studies, we first design and implement a multi-month field experiment at a utility-scale wind farm to validate the model over a range of control strategies, most of which are suboptimal. The flow control model is able to predict the optimal yaw misalignment angles for the array within ±5 • for most wind directions (11-32% power gains). Using the validated model, we design a control protocol which increases the energy production of the farm in a second multi-month experiment by 2.7% and 1.0%, for the wind directions of interest and for wind speeds between 6 and 8 m/s and all wind speeds, respectively. The developed and validated predictive model can enable a wider adoption of collective wind farm operation.

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National‐scale impacts on wind energy production under curtailment scenarios to reduce bat fatalities

et al. 2022

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Wind energy often plays a major role in meeting renewable energy policy objectives; however, increased deployment can raise concerns regarding the impacts of wind plants on certain wildlife. Particularly, estimates suggest hundreds of thousands of bat fatalities occur annually at wind plants across North America, with potential implications for the viability of several bat species. One approach to reducing bat fatalities is shutting down (or curtailing) turbines when bats are most at risk, such as at night during relatively low wind speed periods throughout summer and early autumn.While curtailment has consistently been shown to reduce bat fatalities, the lost power production reduces revenues for wind plants. This study conducted simulations with a range of curtailment scenarios across the contiguous United States to examine sensitivities of annual energy production (AEP) loss and potential impacts on economic metrics for future wind energy deployment. We found that AEP reduction can vary across the country from less than 1% to more than 10% for different curtailment scenarios. From an estimated 2891 gigawatts (GW) of simulated economically viable wind capacity (measured by a positive net present value), we found the mid curtailment scenario (6.0 m/s wind speed cut-in from July 1 through October 31) reduced the quantity of economic wind capacity by 274 GW or 9.5%. Our results indicate that high levels of curtailment could substantially reduce the future footprint of financially viable wind energy. In this context, future work that illuminates cost-effective strategies to minimize curtailment while reducing bat fatalities would be of value.

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NREL's Balance-of-System Cost Model for Land-Based Wind

Cited by 18 publications

References 0 publications

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

Technology Innovation Pathways for Distributed Wind Balance-of-System Cost Reduction

Collective wind farm operation based on a predictive model increases utility-scale energy production

National‐scale impacts on wind energy production under curtailment scenarios to reduce bat fatalities

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