'How Much is Enough?' Determining Adequate Levels of Environmental Compensation for Wind Power Impacts Using Equivalency Analysis: An Illustrative & Hypothetical Case Study of Sea Eagle Impacts at the Smøla Wind Farm, Norway

2011 ◽  
Author(s):  
Scott Cole
Keyword(s):  
Wind Power ◽  
Wind Farm ◽  
Hypothetical Case

Offshore Wind Turbines Operation and Maintenance in China: A Case Study of Donghai Bridge Offshore Wind Farm

2013 ◽  
Vol 448-453 ◽  
pp. 1871-1874
Author(s):  
Yuan Xie
Keyword(s):  
Wind Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Power ◽  
Offshore Wind Turbines ◽  
Operation And Maintenance ◽  
Steady Stage ◽  
Power Project

China has great potential in offshore wind energy and makes an ambitious target for offshore wind power development. Operation and Maintenance (O&M) of offshore wind turbines become more and more important for China wind industry. This study introduces the current offshore wind power projects in China. Donghai Bridge Offshore Demonstration Wind Farm (Donghai Bridge Project) is the first commercial offshore wind power project in China, which was connected to grid in June 2010. O&M of Donghai Bridge Project represent the state-of-the-art of China offshore O&M. During the past two and half years, O&M of Donghai Bridge Project has gone through three phases and stepped into a steady stage. Its believed that analysis of O&M of Donghai Bridge Project is very helpful for Chinas offshore wind power in the future.

scholarly journals On the wake effect in wind farm power forecasting: a new data-driven approach

2020 ◽  
Vol 197 ◽  
pp. 08016
Author(s):  
Fabio Famoso ◽  
Sebastian Brusca ◽  
Antonio Galvagno ◽  
Michele Messina ◽  
Rosario Lanzafame
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Wind Power Generation ◽  
Physical Models ◽  
Production Forecast ◽  
Innovative Method ◽  
Power Forecasting

Wind power generation differs from other energy sources, such as thermal, solar or hydro, due to the inherent stochastic nature of wind. For this reason wind power forecasting, especially for wind farms, is a complex task that cannot be accurately solved with traditional statistical methods or needs large computational systems if physical models are used. Recently, the so-called learning approaches are considered a good compromise among the previous methods since they are able to integrate physical phenomena such as wake effects without presenting heavy computational loads. The present work deals with an innovative method to forecast wind power generation in a wind farm with a combination of GISbased methods, neural network approach and a wake physical model. This innovative method was tested with a wind farm located in Sicily (Italy), used as a case study. It consists of 30 identical wind turbines (850 kW each one), located at different heights, for an overall Power peak of 25 MW. The time series dataset consists of one year with a sampling time of 10 minutes considering wind speeds and wind directions. The output of this innovative model leaded to good results, especially for medium-term overall energy production forecast for the case study.

scholarly journals Qatar’s Wind Energy Potential with Associated Financial and Environmental Benefits for the Natural Gas Industry

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3329 ◽  
Author(s):  
Carlos Méndez ◽  
Yusuf Bicer
Keyword(s):  
Wind Speed ◽  
Power Plant ◽  
Natural Gas ◽  
Wind Turbine ◽  
Wind Power ◽  
Wind Farm ◽  
Oil And Gas ◽  
Environmental Benefits ◽  
Wind Power Plant

This study analyzes the possibility to use the wind’s kinetic energy to produce electricity in Northern Qatar for the natural gas processing industry. An evaluation of the wind potentiality is performed based on a thorough analysis of parameters such as wind speed and direction, temperature, atmospheric pressure, and air density. In addition, based on the measured parameters, a commercial wind turbine is selected, and a case study is presented in order to quantify the energy that a wind farm could produce and its environmental benefits. Furthermore, an economical assessment is made to quantify the repercussions that it could produce if this wind farm substitutes a fraction of the energy demand (within the oil and gas field) that is currently generated by traditional hydrocarbons. The results indicate that the environmental parameters, led by a 5.06 m/s wind speed mean, allow the production of wind energy in the area with an annual CO2 savings of 6.813 tons in a 17 MW wind power plant. This enables Qatar to reduce its internal oil and gas consumption. As a result, the amount of hydrocarbon (natural gas) saved could be used for exportation purposes, generating a positive outcome for the economy with a cost savings of about 3.32 million US$ per year through such a small size wind power plant. From the energy production point of view, the natural parameters enable a single wind turbine to produce an average of 6995.26 MWh of electricity. Furthermore, the wind farm utilized in the case study is capable of generating an average of 34.976 MWh in a year.

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

Analysis on the Penetration Level of Wind Farm Considering Transient Stability Constraint and Uncertainty of Wind Power

2020 ◽  
Vol 13 (7) ◽  
pp. 1078-1086
Author(s):  
Bai Hao ◽  
Huang Andi ◽  
Zhou Changcheng
Keyword(s):  
Wind Power ◽  
Energy Function ◽  
Wind Farm ◽  
Transient Stability ◽  
Programming Model ◽  
Forecast Error ◽  
Credibility Theory ◽  
Mixed Integer ◽  
Stability Constraint ◽  
Level Problem

Background: The penetration level of a wind farm with transient stability constraint and static security constraint has been a key problem in wind power applications. Objective: The study explores maximum penetration level problem of wind considering transient stability constraint and uncertainty of wind power out, based on credibility theory and corrected energy function method. Methods: According to the corrected energy function, the transient stability constraint of the power grid is transferred to the penetration level problem of a wind farm. Wind speed forecast error is handled as a fuzzy variable to express the uncertainty of wind farm output. Then this paper builds a fuzzy chance-constrained model to calculate wind farm penetration level. To avoid inefficient fuzzy simulation, the model is simplified to a mixed integer linear programming model. Results: The results validate the proposed model and investigate the influence of grid-connection node, wind turbine characteristic, fuzzy reliability index, and transient stability index on wind farm penetration level. Conclusion: The result shows that the model proposed in this study can consider the uncertainty of wind power out and establish a quantitative transient stability constraint to determine the wind farm penetration level with a certain fuzzy confidence level.

scholarly journals Numerical Weather Prediction and Artificial Neural Network Coupling for Wind Energy Forecast

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 338
Author(s):  
Lorenzo Donadio ◽  
Jiannong Fang ◽  
Fernando Porté-Agel
Keyword(s):  
Machine Learning ◽  
Wind Energy ◽  
Wind Power ◽  
Numerical Weather Prediction ◽  
Wind Farm ◽  
Weather Prediction ◽  
Wind Power Forecasting ◽  
Numerical Weather ◽  
Artificial Neural ◽  
Power Forecasting

In the past two decades, wind energy has been under fast development worldwide. The dramatic increase of wind power penetration in electricity production has posed a big challenge to grid integration due to the high uncertainty of wind power. Accurate real-time forecasts of wind farm power outputs can help to mitigate the problem. Among the various techniques developed for wind power forecasting, the hybridization of numerical weather prediction (NWP) and machine learning (ML) techniques such as artificial neural networks (ANNs) are attracting many researchers world-wide nowadays, because it has the potential to yield more accurate forecasts. In this paper, two hybrid NWP and ANN models for wind power forecasting over a highly complex terrain are proposed. The developed models have a fine temporal resolution and a sufficiently large prediction horizon (>6 h ahead). Model 1 directly forecasts the energy production of each wind turbine. Model 2 forecasts first the wind speed, then converts it to the power using a fitted power curve. Effects of various modeling options (selection of inputs, network structures, etc.) on the model performance are investigated. Performances of different models are evaluated based on four normalized error measures. Statistical results of model predictions are presented with discussions. Python was utilized for task automation and machine learning. The end result is a fully working library for wind power predictions and a set of tools for running the models in forecast mode. It is shown that the proposed models are able to yield accurate wind farm power forecasts at a site with high terrain and flow complexities. Especially, for Model 2, the normalized Mean Absolute Error and Root Mean Squared Error are obtained as 8.76% and 13.03%, respectively, lower than the errors reported by other models in the same category.

scholarly journals Miniaturization of an Offshore Platform with Medium-Frequency Offshore Wind Farm and MMC-HVDC Technology

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2058
Author(s):  
Zheren Zhang ◽  
Yingjie Tang ◽  
Zheng Xu
Keyword(s):  
Wind Power ◽  
Computer Aided Design ◽  
Wind Farm ◽  
Offshore Platform ◽  
Offshore Wind ◽  
Operating Frequency ◽  
Offshore Wind Farm ◽  
Medium Frequency ◽  
Offshore Wind Power ◽  
System Frequency

Offshore wind power has great development potential, for which the key factors are reliable and economical wind farms and integration systems. This paper proposes a medium-frequency wind farm and MMC-HVDC integration system. In the proposed scheme, the operating frequency of the offshore wind farm and its power collection system is increased from the conventional 50/60 Hz rate to the medium-frequency range, i.e., 100–400 Hz; the offshore wind power is transmitted to the onshore grid via the modular multilevel converter-based high-voltage direct current transmission (MMC-HVDC). First, this paper explains the principles of the proposed scheme in terms of the system topology and control strategy aspects. Then, the impacts of increasing the offshore system operating frequency on the main parameters of the offshore station are discussed. As the frequency increases, it is shown that the actual value of the electrical equipment, such as the transformers, the arm inductors, and the SM capacitors of the rectifier MMC, can be reduced, which means smaller platforms are required for the step-up transformer station and the converter station. Then, the system operation characteristics are analyzed, with the results showing that the power losses in the system increase slightly with the increase of the offshore AC system frequency. Based on time domain simulation results from power systems computer aided design/electromagnetic transients including DC (PSCAD/EMTDC), it is noted that the dynamic behavior of the system is not significantly affected with the increase of the offshore AC system frequency in most scenarios. In this way, the technical feasibility of the proposed offshore platform miniaturization technology is proven.

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