Final Technical Report Laramie County Community College: Utility-Scale Wind Energy Technology

2012 ◽  
Author(s):  
Douglas P. Cook
Keyword(s):  
Community College ◽  
Wind Energy ◽  
Energy Technology ◽  
Technical Report ◽  
Utility Scale

scholarly journals Large-eddy simulation of airborne wind energy farms

2021 ◽  
Author(s):  
Thomas Haas ◽  
Jochem De Schutter ◽  
Moritz Diehl ◽  
Johan Meyers
Keyword(s):  
Power Generation ◽  
Wind Energy ◽  
Large Scale ◽  
Flow Conditions ◽  
Model Predictive Controller ◽  
Large Eddy ◽  
Power Yield ◽  
Predictive Controller ◽  
Utility Scale ◽  
Turbulent Flow Conditions

Abstract. The future utility-scale deployment of airborne wind energy technologies requires the development of large-scale multi-megawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and large-scale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining large-eddy simulations to simulate turbulent flow conditions and optimal control techniques for flight-path generation and tracking. The two-way coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider ground-based power generation pumping-mode AWE systems (lift-mode AWES) and on-board power generation AWE systems (drag-mode AWES). For the lift-mode AWES, we additionally investigate different reel-out strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organized in 5 rows of 5 systems. For both lift- and drag-mode archetypes, we consider a moderate park layout with a power density of 10 MW km−2 achieved at a rated wind speed of 12 m s−1. For the drag-mode AWES, an additional park with denser layout and power density of 28 MW km−2 is also considered. The model predictive controller achieves very satisfactory flight-path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utility-scale AWE systems considered in the study. Wake-induced performance losses increase gradually through the downstream rows of systems and reach in the last row of the parks up to 17 % for the lift-mode AWE park and up to 25 % and 45 % for the moderate and dense drag-mode AWE parks, respectively. For an operation period of 60 minutes at a below-rated reference wind speed of 10 m s−1, the lift-mode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the drag-mode AWE parks, the moderate and dense layouts generate about 86.0 MW and 72.9 MW of power, respectively, corresponding to 89.2 % and 75.6 % of the ideal power yield.

scholarly journals Wind energy technology development and diffusion: a case study of Inner Mongolia, China

2001 ◽  
Vol 25 (1) ◽  
pp. 33-42 ◽  
Author(s):  
Xiliang Zhang ◽  
Shuhua Gu ◽  
Wenqiang Liu ◽  
Lin Gan
Keyword(s):  
Wind Energy ◽  
Inner Mongolia ◽  
Technology Development ◽  
Energy Technology ◽  
And Diffusion

Storage Options and Sizing for Utility Scale Integration of Wind Energy Plants

Solar Energy ◽  
2005 ◽  
Author(s):  
Allan E. Ingram
Keyword(s):  
Energy Storage ◽  
Wind Energy ◽  
Storage System ◽  
Electric Energy ◽  
Time Frame ◽  
Market Prices ◽  
Electric Energy Storage ◽  
Design Engineers ◽  
Scale Integration ◽  
Utility Scale

Electric energy storage has been discussed as an option for increasing the marketability of wind energy facilities by reducing output variation. Utility scale wind plants face economic exposure to tariff charges for output variation as well as depending on volatile market prices for success. Wind speed variability and associated changes in wind plant output raise specific challenges to design engineers sizing electric energy storage systems. Evaluation of prospective Wind/Storage applications depends on the characteristics of individual wind plant output and the choice of storage technology. Energy storage options range from traditional lead acid batteries and pumped hydro storage to recently commercialized electrochemical flow battery systems. Selection and sizing of energy storage for wind plants vary with the time frame for each application. Different time frames correspond with the utility definitions of regulation, load shaping and load factoring. Results from a storage system model are presented that differentiate appropriate storage system sizes for these applications.

Multi-expert wind energy technology selection using interval-valued intuitionistic fuzzy sets

Energy ◽  
2015 ◽  
Vol 90 ◽  
pp. 274-285 ◽  
Author(s):  
Sezi Cevik Onar ◽  
Basar Oztaysi ◽  
İrem Otay ◽  
Cengiz Kahraman
Keyword(s):  
Wind Energy ◽  
Fuzzy Sets ◽  
Intuitionistic Fuzzy Sets ◽  
Technology Selection ◽  
Energy Technology ◽  
Intuitionistic Fuzzy ◽  
Interval Valued

Research on the Influence of Wind Energy on New Energy Utilization Coefficient of EEDI

2013 ◽  
Vol 744 ◽  
pp. 561-565 ◽  
Author(s):  
Cai Ling Li ◽  
Song Zhou ◽  
Ye Han
Keyword(s):  
Energy Efficiency ◽  
Wind Energy ◽  
Alternative Energy ◽  
Energy Utilization ◽  
Calculation Formula ◽  
Energy Technology ◽  
Utilization Coefficient ◽  
New Energy ◽  
Energy Efficiency Design Index ◽  
Great Harm

Currently, the emissions discharged by ships are becoming more and more serious, which brings a great harm to the atmospheric and marine environment. In order to solve the emissions, especially of CO2 emission, the new ship energy efficiency design index (EEDI) has been introduced to reduce it. According to EEDI calculation formula, the use of new energy technology can reduce marine EEDI. Nowadays, wind energy as a clean and renewable energy, is an ideal alternative energy to be applied on ships. But until now, there is still no definite calculation formula and value regulation to parameters involved in the application of new energy on ships, including new energy utilization coefficient.

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