How many offshore wind turbines does New England need?

Livingston, Hannah; Lundquist, Julie K.

doi:10.1002/met.1969

Cited by 9 publications

(5 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Previous work (Livingston and Lundquist, 2020) assuming a constant 20% wake loss, shown here to be underestimated, suggested that 2,000 10-MW turbines could meet New England's demand 37% of the time. In all, the LA, with 1,418 12-MW turbines, supply 68 TWh year −1 and 71 TWh year −1 , or 1.72% (TKE_0) to 1.65% (TKE_100) of the nation's energy supply.…”

Section: Annual Energy Productionmentioning

confidence: 60%

Annual Variability of Wake Impacts on Mid-Atlantic Offshore Wind Plant Deployments

Rosencrans

Lundquist

Optis

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. The mid-Atlantic will experience rapid wind plant development due to its promising wind resource located near large population centers. Wind turbines and wind plants create wakes, or regions of reduced wind speed, that may negatively affect downwind turbines and plants. Long mid-Atlantic wakes are causing growing concern. We evaluate wake variability and annual energy production with the first year-long modeling assessment using the Weather Research and Forecasting Model, deploying 12-MW turbines across the domain at a density of 3.14 MW km−2, matching the planned density of 3 MW km−2. Using a series of simulations with no wind plants, one wind plant, and complete build-out of lease areas, we calculate wake effects and distinguish the effect of wakes generated internally within one plant from those generated externally between plants. The strongest wakes, propagating 58 km, occur in summertime stable stratification, just when New England’s grid demand peaks in summer. The seasonal variability of wakes in this offshore region is much stronger than diurnal variability of wakes. Overall, the mean year-long wake impacts reduce power output by 35.9 %. Internal wakes cause greater year-long power losses (27.4 %) compared to external wakes (14.1 %). Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy (TKE) from turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production (AEP) to New England grid demand and find that the lease areas can supply roughly 60 % of annual load.

show abstract

Section: Annual Energy Productionmentioning

confidence: 60%

Annual Variability of Wake Impacts on Mid-Atlantic Offshore Wind Plant Deployments

Rosencrans

Lundquist

Optis

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, the level of storage defined in the scenarios might be subject to over planting with respect to the renewable energy resource capacity. As shown by Livingston and Lundquist (2020) the ability to balance the potential of renewable generation with storage has a limit in the order of hours to a few days and additional storage will likely rarely be utilised by renewables.…”

Section: Limitations and Discussionmentioning

confidence: 99%

Linking Unserved Energy to Weather Regimes

Wuijts¹,

Stoop²,

Hu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In 2018, the New England region experienced its initial annual escalation in the average wholesale electricity load since 2013 which showed growth of 1.7% compared to the average of 2017. Nevertheless, when adjusting for the weather England region [80] which shows the summertime demonstrated a slightly elevated level of overall energy consumption compared to other seasons throughout the year. Air conditioning is a significant contributor to energy consumption during the warmer months as it is used maintain comfortable indoor temperatures.…”

Section: Time Series Analysismentioning

confidence: 99%

Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts

Sahin,

Udeh,

Wanik

et al. 2024

IEEE Access

View full text Add to dashboard Cite

This study aims to develop models for predicting hourly energy demand in the State of Connecticut, USA from 2011 to 2021 using machine learning algorithms inputted with airport weather stations' data from the Automated Surface Observing System (ASOS), demand data from ISO New England (ISO-NE). We built and evaluated nine different model experiments for each machine learning algorithm for each hour of the day addressing energy demand patterns, variations between workdays and weekends, and COVID-19 impacts. Error metrics analysis results highlighted the GBR model demonstrated better performance compared to the MPR and RFR models. Incorporating both temporal and weather features in the models resulted in a noticeable improvement in error metrics. A consistent overestimation trend was observed for all models during the validation period (2018-2019) which may be attributed to energy efficiency measures and integration of behind-the-meter generation, with a further notable increase in overestimation following the onset of COVID-19 due to a change of habits during the pandemic in addition to decarbonization initiatives in the State. This study emphasizes the need for adapting models to dynamic consumption and weather patterns for improved grid management.

show abstract

How many offshore wind turbines does New England need?

Cited by 9 publications

References 27 publications

Annual Variability of Wake Impacts on Mid-Atlantic Offshore Wind Plant Deployments

Annual Variability of Wake Impacts on Mid-Atlantic Offshore Wind Plant Deployments

Linking Unserved Energy to Weather Regimes

Predicting Energy Demand Using Machine Learning: Exploring Temporal and Weather-Related Patterns, Variations, and Impacts

Contact Info

Product

Resources

About