scholarly journals Improving accuracy of wind resource assessment through feedback loops of operational performance data: A South African case study

2019 ◽  
Vol 30 (3) ◽  
pp. 1-10
Author(s):  
D. Pullinger ◽  
A. Ali ◽  
M. Zhang ◽  
N. Hill ◽  
T. Crutchley
Keyword(s):  
South African ◽  
Wind Farm ◽  
Wind Farms ◽  
Resource Assessment ◽  
Performance Data ◽  
Operational Performance ◽  
Energy Yield ◽  
Cause Of Error ◽  
Improving Accuracy ◽  
Wind Resource

This study addresses two key objectives using operational performance data from most of the Round 1 wind farms connected to the grid in South Africa: benchmarking of wind farm performance and validation of the pre-construction energy yield assessments. These wind farms were found to perform in line with internationally reported levels of wind farm availability, with a mean energy-based availability of 97.8% during the first two years of operation. The pre-construction yield assessments used for financing in 2012 were found to over-predict project yield (P50) by 4.9%. This was consistent with other validation studies for Europe and North America. It was also noted that all projects exceed the pre-construction P90 estimate. The reasons for this discrepancy were identified, with the largest cause of error being wind flow and wake-modelling errors. Following a reassessment using up to date methodologies from 2018, the mean bias in pre-construction predictions was 1.4%.

Offshore Wind Power Assessment on the East Coast of Saudi Arabia

2005 ◽  
Vol 29 (5) ◽  
pp. 409-419 ◽  
Author(s):  
Shafiqur Rehman
Keyword(s):  
Saudi Arabia ◽  
Wind Speed ◽  
Ghg Emissions ◽  
Resource Assessment ◽  
Offshore Wind ◽  
Energy Yield ◽  
North West ◽  
North East ◽  
Prevailing Wind Direction ◽  
Wind Resource

This paper, to the best of author's knowledge, presents the first wind resource assessment for offshore-wind energy off the mainland coasts of Saudi Arabia, despite the onshore wind resource being known. The study utilized wind speed data from, in effect, an offshore meteorological station to study the annual and seasonal variation of wind speed, wind speed frequency distribution, energy yield and consequent opportunity for reduction in green house gases (GHG) emissions. These results were compared with contemporaneous data from a mainland location ∼ 10 km inland at the same longitude Energy yields were calculated using HOMER and RetScreen models. The annual mean wind measured at Abu Ali Island, the offshore location, was 5.43 m/s. This is larger than the 4.9 m/s measured over the same period at Abu Kharuf, the nearby inland location. Larger wind speeds were found in winter months than in summer months at both locations. At Abu Ali Island, the power of the wind could be extracted for 75% of the time at hub-height of 60 meters using modern wind machines of cut-in-speed 4 m/s, in comparison with 60% of time at Abu Kharuf. The prevailing wind direction was found to be North (N), North West (NNW) and North East (NNE).

scholarly journals An Optimization Framework for Wind Farm Design in Complex Terrain

2018 ◽  
Vol 8 (11) ◽  
pp. 2053 ◽  
Author(s):  
Ju Feng ◽  
Wen Shen ◽  
Ye Li
Keyword(s):  
Complex Terrain ◽  
Wind Farm ◽  
Search Algorithm ◽  
Random Search ◽  
Wind Farms ◽  
Minimal Distance ◽  
Original Design ◽  
Optimization Framework ◽  
Random Search Algorithm ◽  
Wind Resource

Designing wind farms in complex terrain is an important task, especially for countries with a large portion of complex terrain territory. To tackle this task, an optimization framework is developed in this study, which combines the solution from a wind resource assessment tool, an engineering wake model adapted for complex terrain, and an advanced wind farm layout optimization algorithm. Various realistic constraints are modelled and considered, such as the inclusive and exclusive boundaries, minimal distances between turbines, and specific requirements on wind resource and terrain conditions. The default objective function in this framework is the total net annual energy production (AEP) of the wind farm, and the Random Search algorithm is employed to solve the optimization problem. A new algorithm called Heuristic Fill is also developed in this study to find good initial layouts for optimizing wind farms in complex terrain. The ability of the framework is demonstrated in a case study based on a real wind farm with 25 turbines in complex terrain. Results show that the framework can find a better design, with 2.70% higher net AEP than the original design, while keeping the occupied area and minimal distance between turbines at the same level. Comparison with two popular algorithms (Particle Swarm Optimization and Genetic Algorithm) also shows the superiority of the Random Search algorithm.

scholarly journals Offshore wind energy potential for Bahrain via multi-criteria evaluation

2020 ◽  
pp. 0309524X2092539
Author(s):  
Mohamed Elgabiri ◽  
Diane Palmer ◽  
Hanan Al Buflasa ◽  
Murray Thomson
Keyword(s):  
Wind Energy ◽  
Geographical Information Systems ◽  
Wind Farm ◽  
Electricity Consumption ◽  
Pairwise Comparison ◽  
Wind Farms ◽  
Offshore Wind ◽  
Geographical Information ◽  
Energy Yield ◽  
Energy Potential

Current global commitments to reduce the emissions of greenhouse gases encourage national targets for renewable generation. Due to its small land mass, offshore wind could help Bahrain to fulfil its obligations. However, no scoping study has been carried out yet. The methodology presented here addresses this research need. It employs analytical hierarchy process and pairwise comparison methods in a geographical information systems environment. Publicly available land use, infrastructure and transport data are used to exclude areas unsuitable for development due to physical and safety constraints. Meteorological and oceanic opportunities are ranked and then competing uses are analyzed to deliver optimal sites for wind farms. The potential annual wind energy yield is calculated by dividing the sum of optimal areas by a suitable turbine footprint to deliver maximum turbine number. In total, 10 favourable wind farm areas were identified in Bahrain’s territorial waters, representing about 4% of the total maritime area, and capable of supplying 2.68 TWh/year of wind energy or almost 10% of the Kingdom’s annual electricity consumption. Detailed maps of potential sites for offshore wind construction are provided in the article, giving an initial plan for installation in these locations.

scholarly journals 3-D shear-layer model for the simulation of multiple wind turbine wakes: description and first assessment

2017 ◽  
Vol 2 (2) ◽  
pp. 569-586 ◽  
Author(s):  
Davide Trabucchi ◽  
Lukas Vollmer ◽  
Martin Kühn
Keyword(s):  
Wind Turbine ◽  
Shear Layer ◽  
Wind Farm ◽  
Wind Farms ◽  
Theoretical Background ◽  
Offshore Wind ◽  
Energy Yield ◽  
Main Concern ◽  
Layer Model ◽  
New Model

Abstract. The number of turbines installed in offshore wind farms has strongly increased in the last years and at the same time the need for more precise estimations of the wind farm efficiency too. In this sense, the interaction between wakes has become a relevant aspect for the definition of a wind farm layout, for the assessment of its annual energy yield and for the evaluation of wind turbine fatigue loads. For this reason, accurate models for multiple overlapping wakes are a main concern of the wind energy community. Existing engineering models can only simulate single wakes, which are superimposed when they are interacting in a wind farm. This method is a practical solution, but it is not fully supported by a physical background. The limitation to single wakes is given by the assumption that the wake is axisymmetric. As an alternative, we propose a new shear-layer model that is based on the existing engineering wake models but is extended to also simulate non-axisymmetric wakes. In this paper, we present the theoretical background of the model and four application cases. We evaluate the new model for the simulation of single and multiple wakes using large-eddy simulations as reference. In particular, we report the improvements of the new model predictions in comparison to a sum-of-squares superposition approach for the simulation of three interacting wakes. The lower deviation from the reference considering single and multiple wakes encourages the further development of the model and promises a successful application for the simulation of wind farm flows.

scholarly journals Wind Speed using Improved Mixture Weibull Distribution

2019 ◽  
Vol 8 (6) ◽  
pp. 1871-1874
Keyword(s):  
Wind Speed ◽  
Probability Distribution ◽  
Weibull Distribution ◽  
Wind Farms ◽  
Resource Assessment ◽  
Estimation Of Parameters ◽  
Basic Properties ◽  
Wind Resource Assessment ◽  
Proposed Model ◽  
Wind Resource

Wind is random in nature both in space and in time. Several technologies are used in wind resource assessment (WRA).The appropriate probability distribution used to calculate the available wind speed at that particular location and the estimation of parameters is the essential part in installing wind farms. The improved mixture Weibull distribution is proposed model which is the mixture of two and three parameter Weibull distribution with parameters including scale, shape, location and weight component. The basic properties of the proposed model and estimation of parameters using various methods are discussed.

scholarly journals Comparing and validating intra-farm and farm-to-farm wakes across different mesoscale and high-resolution wake models

2021 ◽  
Author(s):  
Jana Fischereit ◽  
Kurt Schaldemose Hansen ◽  
Xiaoli Guo Larsén ◽  
Maarten Paul van der Laan ◽  
Pierre-Elouan Réthoré ◽  
...  
Keyword(s):  
High Resolution ◽  
Wind Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Wake Effect ◽  
Disk Model ◽  
Computational Costs ◽  
Rans Simulations ◽  
Wind Resource

Abstract. Numerical wind resource modelling across scales from mesoscale to turbine scale is of increasing interest due to the expansion of offshore wind energy. Offshore, wind farm wakes can last several tens kilometres downstream and thus affect the wind resources of a large area. So far, scale-specific models have been developed and it remains unclear, how well the different model types can represent intra-farm wakes, farm-to-farm wakes as well as the wake recovery behind a farm. Thus, in the present analysis the simulation of a set of wind farm models of different complexity, fidelity, scale and computational costs are compared among each other and with SCADA data. In particular, two mesoscale wind farm parameterizations implemented in the mesoscale Weather Research and Forecasting model (WRF), the Explicit Wake Parameterization (EWP) and the Wind Farm Parameterization (FIT), two different high-resolution RANS simulations using PyWakeEllipSys equipped with an actuator disk model, and three rapid engineering wake models from the PyWake suite are selected. The models are applied to the Nysted and Rødsand II wind farms, which are located in the Fehmarn Belt in the Baltic Sea. Based on the performed simulations, we can conclude that average intra-farm variability can be captured reasonable well with WRF+FIT using a resolution of 2 km, a typical resolution of mesoscale models for wind energy applications, while WRF+EWP underestimates wind speed deficits. However, both parameterizations can be used to estimate median wind resource reduction caused by an upstream farm. All considered engineering wake models from the PyWake suite simulate intra-farm wakes comparable to the high fidelity RANS simulations. However, they considerably underestimate the farm wake effect of an upstream farm although with different magnitudes. Overall, the higher computational costs of PyWakeEllipSys and WRF compared to PyWake pay off in terms of accuracy for situations when farm-to-farm wakes are important.

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