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Zhe Chen - One of the best experts on this subject based on the ideXlab platform.
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Optimal power dispatch strategy of onshore Wind Farms considering environmental impact
International Journal of Electrical Power & Energy Systems, 2020Co-Authors: Qi Huang, Cong Chen, Zhou Liu, Zhe ChenAbstract:Abstract The power dispatch strategy for onshore Wind Farms generally focuses on maximizing the captured power or minimizing the investment cost. However, as for onshore Wind Farms, there are some environmental impacts that need to be considered if needed. Among them, the Wind turbine (WT) noise is a fairly obvious environmental impact. Noise caused by Wind Farms may cause interference to the surrounding living environment and the power dispatch strategy should combine power production and environmental factors. Besides, the amount of electricity generated by onshore Wind Farms is also affected by topography and power losses. In this paper, an optimal power dispatch strategy of Wind Farms with limited WTs noise impact is proposed for a better environmental performance as well as maximizing the power generation. The new method is compared with the traditional MPPT method for single WT and the improved global MPPT method for the whole Wind farm within two terrain scenarios. The case results show the feasibility and effectiveness of this novel strategy.
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Optimized Placement of Onshore Wind Farms Considering Topography
Energies, 2019Co-Authors: Xiawei Wu, Weihao Hu, Qi Huang, Cong Chen, Zhe ChenAbstract:As the scale of onshore Wind Farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore Wind Farms should take terrain into consideration including height change and slope curvature. However, optimized Wind turbine (WT) placement for onshore Wind Farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore Wind Farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore Wind Farms. The optimization method is applicable for optimized placement of onshore Wind Farms and can be extended to different topographic conditions.
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A Novel Active Power Dispatch Method for Onshore Wind Farms to Reduce Wind Turbine Noise
2019 IEEE Power & Energy Society General Meeting (PESGM), 2019Co-Authors: Qi Huang, Cong Chen, Zhe ChenAbstract:The Maximum Power Point Tracking (MPPT) is the most common conventional control method to maximize power generation in Wind Farms. On the other hand, Wind turbines (WTs) noise is also an important concern especially for onshore Wind Farms. In this paper, an environmentally friendly active power dispatch method is proposed to ensure the energy productivity while reducing WTs noise. The proposed method is evaluated by a typical reference Wind farm case. Simulation results demonstrate the effectiveness of the proposed method which may be used for Wind Farms operation and design.
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wake effects on lifetime distribution in dfig based Wind Farms
IEEE International Future Energy Electronics Conference and ECCE Asia, 2017Co-Authors: Jie Tian, Chi Su, Dao Zhou, Zhe ChenAbstract:With the increasing size of the Wind Farms, the impact of the wake effect on the energy yields and lifetime consumption of Wind turbine can no longer be neglected. In this paper, the affecting factors like the Wind speed and Wind direction are investigated in terms of the single wake and multiple wakes. As the power converter is the most fragile component among the turbine system, its lifetime estimation can be calculated seen from the thermal stress of the power semiconductor. On the basis of the relationship of the power converter in a 5 MW Doubly-Fed Induction Generator (DFIG) Wind turbine system and the Wind speed, the lifetime consumption of the individual turbine in a 10-turbine and an 80-turbine Wind Farms can be calculated by considering the real distributions of the Wind speed and direction. It can be seen that there is significant lifetime difference among individual turbines, and it guides to the optimized control strategies for the balanced reliability.
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Cable Connection Optimization for Onshore Wind Farms Considering Restricted Area and Topography
IEEE Systems Journal, 2024Co-Authors: Junxian Li, Xiawei Wu, Weihao Hu, Qi Huang, Cong Chen, Zhe ChenAbstract:For onshore Wind Farms where all Wind turbine (WT) locations are fixed, it is necessary to design corresponding cable connections to collect electricity generated by each WT. The cable connection system should ensure the collection of electricity successfully and reduce the cable cost optimally. Cables are prohibited from crossing restricted land areas, such as reserves, oil wells, and rocky areas. Therefore, in this article, a restricted area is added to the planning area of onshore Wind Farms. To obtain more accurate final optimized results, we consider topographic factors of onshore Wind Farms in the optimization process. The algorithm used for cable connection optimization is the dynamic minimum spanning tree. Results indicate the nonnegligible effects of the restricted area and topographic factors on the cable connection topology. The total cable cost savings can exceed 1.8% compared with the reference cable connection topology, in which the topographic factors and restricted area are not considered.
Charles Meneveau - One of the best experts on this subject based on the ideXlab platform.
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flow structure and turbulence in Wind Farms
Annual Review of Fluid Mechanics, 2017Co-Authors: Richard J A M Stevens, Charles MeneveauAbstract:Similar to other renewable energy sources, Wind energy is characterized by a low power density. Hence, for Wind energy to make considerable contributions to the world's overall energy supply, large Wind Farms (on- and offshore) consisting of arrays of ever larger Wind turbines are being envisioned and built. From a fluid mechanics perspective, Wind Farms encompass turbulent flow phenomena occurring at many spatial and temporal scales. Of particular interest to understanding mean power extraction and fluctuations in Wind Farms are the scales ranging from 1 to 10 m that comprise the wakes behind individual Wind turbines, to motions reaching 100 m to kilometers in scale, inherently associated with the atmospheric boundary layer. In this review, we summarize current understanding of these flow phenomena (particularly mean and second-order statistics) through field studies, Wind tunnel experiments, large-eddy simulations, and analytical modeling, emphasizing the most relevant features for Wind farm design and operation.
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coupled wake boundary layer model of Wind Farms
Journal of Renewable and Sustainable Energy, 2015Co-Authors: Richard J A M Stevens, Dennice F Gayme, Charles MeneveauAbstract:We present and test a coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a Wind-farm. This model couples the traditional, industry-standard wake model approach with a “top-down” model for the overall Wind-farm boundary layer structure. The wake model captures the effect of turbine positioning, while the “top-down” portion of the model adds the interactions between the Wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the “top-down” model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing Wind-Farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations show that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted Wind-Farms shows that the model can also be successfully applied to real Wind-Farms
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coupled wake boundary layer model of Wind Farms
arXiv: Fluid Dynamics, 2014Co-Authors: Richard J A M Stevens, Dennice F Gayme, Charles MeneveauAbstract:We present and test the coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a Wind-farm. The model couples the traditional, industry-standard wake model approach with a "top-down" model for the overall Wind-farm boundary layer structure. This wake model captures the effect of turbine positioning, while the "top-down" portion of the model adds the interactions between the Wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the "top-down" model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing Wind-Farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations (LES) shows that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted Wind-Farms shows that the model can also be successfully applied to real Wind-Farms.
Richard J A M Stevens - One of the best experts on this subject based on the ideXlab platform.
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flow structure and turbulence in Wind Farms
Annual Review of Fluid Mechanics, 2017Co-Authors: Richard J A M Stevens, Charles MeneveauAbstract:Similar to other renewable energy sources, Wind energy is characterized by a low power density. Hence, for Wind energy to make considerable contributions to the world's overall energy supply, large Wind Farms (on- and offshore) consisting of arrays of ever larger Wind turbines are being envisioned and built. From a fluid mechanics perspective, Wind Farms encompass turbulent flow phenomena occurring at many spatial and temporal scales. Of particular interest to understanding mean power extraction and fluctuations in Wind Farms are the scales ranging from 1 to 10 m that comprise the wakes behind individual Wind turbines, to motions reaching 100 m to kilometers in scale, inherently associated with the atmospheric boundary layer. In this review, we summarize current understanding of these flow phenomena (particularly mean and second-order statistics) through field studies, Wind tunnel experiments, large-eddy simulations, and analytical modeling, emphasizing the most relevant features for Wind farm design and operation.
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coupled wake boundary layer model of Wind Farms
Journal of Renewable and Sustainable Energy, 2015Co-Authors: Richard J A M Stevens, Dennice F Gayme, Charles MeneveauAbstract:We present and test a coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a Wind-farm. This model couples the traditional, industry-standard wake model approach with a “top-down” model for the overall Wind-farm boundary layer structure. The wake model captures the effect of turbine positioning, while the “top-down” portion of the model adds the interactions between the Wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the “top-down” model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing Wind-Farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations show that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted Wind-Farms shows that the model can also be successfully applied to real Wind-Farms
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coupled wake boundary layer model of Wind Farms
arXiv: Fluid Dynamics, 2014Co-Authors: Richard J A M Stevens, Dennice F Gayme, Charles MeneveauAbstract:We present and test the coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a Wind-farm. The model couples the traditional, industry-standard wake model approach with a "top-down" model for the overall Wind-farm boundary layer structure. This wake model captures the effect of turbine positioning, while the "top-down" portion of the model adds the interactions between the Wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the "top-down" model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing Wind-Farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations (LES) shows that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted Wind-Farms shows that the model can also be successfully applied to real Wind-Farms.
R J Barthelmie - One of the best experts on this subject based on the ideXlab platform.
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Cost performance and risk in the construction of offshore and onshore Wind Farms
Wind Energy, 2016Co-Authors: Benjamin K Sovacool, Peter Enevoldsen, Christian Koch, R J BarthelmieAbstract:This article investigates the risk of cost overruns and underruns occurring in the construction of 51 onshore and offshore Wind Farms commissioned between 2000 and 2015 in 13 countries. In total, these projects required about $39 billion in investment and reached about 11 GW of installed capacity. We use this original dataset to test six hypotheses about construction cost overruns related to (i) technological learning, (ii) fiscal control, (iii) economies of scale, (iv) configuration, (v) regulation and markets and (vi) manufacturing experience. We find that across the entire dataset, the mean cost escalation per project is 6.5% or about $63 million per Windfarm, although 20 projects within the sample (39%) did not exhibit cost overruns. The majority of onshore Wind Farms exhibit cost underruns while for offshore Wind Farms the results have a larger spread. Interestingly, no significant relationship exists between the size (in total MWor per individual turbine capacity) of a Windfarm and the severity of a cost overrun. Nonetheless, there is an indication that the risk increases for larger Wind Farms at greater distances offshore using new types of turbines and foundations. Overall, the mean cost escalation for onshore projects is 1.7% and 9.6% for offshore projects, amounts much lower than those for other energy infrastructure.
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modelling and measuring flow and Wind turbine wakes in large Wind Farms offshore
Wind Energy, 2009Co-Authors: R J Barthelmie, Sten Tronaes Frandsen, K Rados, O Rathmann, Kurt Schaldemose Hansen, W Schlez, J Phillips, J G Schepers, Arthouros Zervos, E S PolitisAbstract:Average power losses due to Wind turbine wakes are of the order of 10 to 20% of total power output in large offshore Wind Farms. Accurately quantifying power losses due to wakes is, therefore, an important part of overall Wind farm economics. The focus of this research is to compare different types of models from computational fluid dynamics (CFD) to Wind farm models in terms of how accurately they represent wake losses when compared with measurements from offshore Wind Farms. The ultimate objective is to improve modelling of flow for large Wind Farms in order to optimize Wind farm layouts to reduce power losses due to wakes and loads. The research presented is part of the EC-funded UpWind project, which aims to radically improve Wind turbine and Wind farm models in order to continue to improve the costs of Wind energy. Reducing wake losses, or even reduce uncertainties in predicting power losses from wakes, contributes to the overall goal of reduced costs. Here, we assess the state of the art in wake and flow modelling for offshore Wind Farms, the focus so far has been cases at the Horns Rev Wind farm, which indicate that Wind farm models require modification to reduce under-prediction of wake losses while CFD models typically over-predict wake losses. Further investigation is underway to determine the causes of these discrepancies. Copyright © 2009 John Wiley & Sons, Ltd.
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flow and wakes in large Wind Farms in complex terrain and offshore
European Wind Energy Conference & Exhibition (EWEC 2008) | European Wind Energy Conference & Exhibition (EWEC 2008) | 31 03 2008 - 03 04 2008 | Brusse, 2008Co-Authors: R J Barthelmie, E S Politis, John Prospathopoulos, Sten Tronaes Frandsen, K Rados, O Rathmann, Kurt Schaldemose Hansen, W Schlez, D Cabezon, J PhillipsAbstract:Power losses due to Wind turbine wakes are of the order of 10 and 20% of total power output in large Wind Farms. The focus of this research carried out within the EC funded UPWind project is Wind speed and turbulence modelling for large Wind Farms/Wind turbines in complex terrain and offshore in order to optimise Wind farm layouts to reduce wake losses and loads.
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analytical modelling of Wind speed deficit in large offshore Wind Farms
Wind Energy, 2006Co-Authors: Sten Tronaes Frandsen, R J Barthelmie, O Rathmann, Sara C. Pryor, Soren Ejling Larsen, J Hojstrup, Morten Lybech ThogersenAbstract:The proposed model for the Wind speed deficit in Wind Farms is analytical and encompasses both small Wind Farms and Wind Farms extending over large areas. As is often the need for offshore Wind Farms, the model handles a regular array geometry with straight rows of Wind turbines and equidistant spacing between units in each row and equidistant spacing between rows. Firstly, the case with the flow direction being parallel to rows in a rectangular geometry is considered by defining three flow regimes. Secondly, when the flow is not in line with the main rows, solutions are suggested for the patterns of Wind turbine units corresponding to each Wind direction. The presentation is an outline of a model complex that will be adjusted and calibrated with measurements in the near future. Copyright © 2006 John Wiley & Sons, Ltd.
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A Review of the Economics of Offshore Wind Farms
Wind Engineering, 2001Co-Authors: R J Barthelmie, Sara C. PryorAbstract:This paper describes the economics of offshore Wind energy in the transition from prototype offshore Wind Farms, developed and installed during the 1990's, to the commercial Wind Farms that are currently being installed. Since 1990, 96 MW of offshore Wind have been installed in Europe and the costs of electricity produced have fallen from almost twice that at equivalent land sites to the point where the best offshore Wind Farms compete with moderate onshore locations. We summarise the transition to increasing economic viability and document some factors that may influence the feasibility of offshore developments in the future.
Yunhe Hou - One of the best experts on this subject based on the ideXlab platform.
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probabilistic forecast for multiple Wind Farms based on regular vine copulas
IEEE Transactions on Power Systems, 2018Co-Authors: Zhao Wang, Weisheng Wang, Chun Liu, Zheng Wang, Yunhe HouAbstract:The uncertain nature of Wind power causes difficulties in power system operation scheduling. Probabilistic descriptions of the uncertainty have been studied for decades. However, probabilistic forecasts designed for the regional multiple Wind Farms are few. Although the traditional methods for the single Wind farm can still be used, they have the limitations in capturing the spatial correlations among Wind Farms, and they are less robust when multivariate observations are not so complete. To improve the forecast quality in this case, we combine the multivariate distribution modeling and probabilistic forecasts in this paper. An advanced model—the regular vine copula, which can describe the Wind Farms’ dependence structure precisely and flexibly with various bivariate copulas as blocks, is used in this paper. Enough simulation data can be generated from the model, which can be easily used to form the conditional forecast distributions under multiple forecast conditions. A case of 10 Wind Farms in East China has been used to compare the proposed method with its competitors. The results showed the method's advantages of providing reliable and sharp forecast intervals, especially in the case with limited observations available.
