Wind Plants Research Articles

Measuring wake deflection from SCADA data during wake steering using machine learning

As wake steering is implemented on large wind plants, methods for evaluating how well these systems optimize plant performance are needed. The data analysis from experiments conducted so far generally focuses on evaluating the improvement in power or energy production of the plant and comparing that to the predicted improvement. In this study, we explore a new approach to provide additional insight and validation of optimization tools by measuring the wake deflection experimentally using only the downstream turbine as a sensor. This approach is demonstrated using the SMARTEOLE wind plant wake steering experimental data. Light gradient boosting machines (LGBM) and Gaussian process (GP) machine learning models are trained and then interrogated by making a set of predictions under defined conditions to determine the wake deflection observed at the downstream turbine. The data set is sparse, particularly for larger yaw angles, and noisy due to factors not captured in the SCADA leading to relatively high uncertainty in the predictions. The GP model is better suited to smoothly fit this data. Using the GP model, a wake deflection of 0.35 rotor diameters at 3.7 rotor diameters downwind is estimated for a yaw error of 20 degrees on the upwind turbine.

Estimating Uncertainties from Dual-Doppler Radar Measurements of Onshore Wind Plants Using LES

Towards the ongoing work of improving the capability of flow modeling within and around wind plants, an onshore model validation benchmark campaign is underway based on a field experiment involving multiple wind plants in Oklahoma, U.S.A. Dual-Doppler radar is being leveraged to provide flowfield information for the benchmarking owing to the unparalleled capability of such radar to capture minute-by-minute horizontal wind fields over a scale of tens of kilometers. However, dual-Doppler radar exhibits sampling artifacts that must be considered during model validation, and these are due to probe-volume averaging, coarse azimuthal/elevational resolution, non-ideal stereo angles, and coarse temporal sampling. Such sources of error in radar-reconstructed flowfields can be quantified using virtual radar sampling in the high-fidelity simulation environment (i.e., large-eddy simulation (LES)) where the true velocity field is known with confidence, and this is the uncertainty quantification approach adopted in this article. We leverage a virtual radar tool designed to replicate the specific sampling strategy of the X-band dual-Doppler instrument installed in the field campaign. This tool is featured in LES of an expansive 100 km by 100 km region of Oklahoma including hundreds of wind turbines modeled as actuator disks. In agreement with the sampling principles of radar, the results show that large-scale flow structures are qualitatively well-resolved by the instrument, though more simulation time and analysis are needed to determine the accuracy of the radar’s integral lengthscale estimates. At the turbine scale, the radar struggles to capture all of the features of the turbine wakes. The process of probe-volume averaging, as well as the subsequent interpolation to a Cartesian grid, biases the reconstruction of the peak near-wake deficit by an average of 2 m/s, or around 20% of the freestream velocity. Errors in this quantity of interest, as well as one characterizing the magnitude of the free-flow wind speed around the wakes, are found to be sensitive to the radar beam-crossing angle and beam range.

Research on wind farm power forecasting based on IGWO-CNN

Abstract An enhanced convolutional neural network (CNN) has been proposed to address the issue of low accuracy in wind farm power prediction using traditional methods, which leverages an improved version of the Gray Wolf Optimizer (IGWO). The standard Gray Wolf Optimizer (GWO) exhibits a linear change in the search range, which can limit its dynamic convergence capabilities. By incorporating nonlinear convergence factors and dynamic convergence strategies, the improved algorithm modulates the global search pace, effectively preventing it from getting stuck in local optima. The refined IGWO is subsequently applied to fine-tune the optimal CNN architecture, mitigating the uncertainty typically associated with the structural configuration of CNNs. When predicting the actual power measurements from a wind plant in Inner Mongolia, the results indicate that the IGWO-based CNN method outperforms both the conventional GWO-CNN approach and the standalone CNN regarding prediction accuracy.

Verification and Validation of Wind Farm Flow Models

Wind farm flow models allow for the analysis of wind power plant wake effects. There are several mathematical models available to describe this complex flow, and some of them are implemented across multiple code bases. This manuscript presents a framework for verification and validation of these models of different complexities. As part of this work, an API was developed to interface the different flow tools using the WindIO wind plant schema. Then, verification techniques are applied to the embedded tools as a test to make sure there are no mistakes in the codes, and across different flow models. This reveals similarities and differences in stochastic trends given realistic measurement uncertainty. Validation is performed using a database of large eddy simulations to quantify model agreement with high fidelity data and develop a predictor of what this implies for future simulations.

An international benchmark for wind plant wakes from the American WAKE ExperimeNt (AWAKEN)

This article introduces the first benchmark study within the International Energy Agency Wind Task 57 framework, focusing on wind plant wakes. Leveraging data from the American WAKE ExperimeNt (AWAKEN), the benchmark aims to assess the accuracy of simulation tools in modeling wind plant wakes and their impact on the downstream flow under diverse inflow conditions. The AWAKEN field campaign, conducted in Oklahoma from 2022 to 2024, provides unprecedented observations of wind plant-atmosphere interactions, thus offering a large dataset to validate numerical models of different complexity. The benchmark will include three phases—code calibration, blind comparison, and iteration—allowing participants to refine their numerical models based on the feedback from the benchmark team. This article describes the benchmark case study selected from observations providing details on atmospheric conditions, wake evidence, and wind turbine operation. The benchmark’s structure and timeline, along with the expected publication of results, are discussed as well. This collaborative effort aims to enhance the accuracy of wind plant wake simulations, thus contributing to the improvement of wind energy production estimates.

How do technological choices affect the economic and environmental performance of offshore wind farms?

The ongoing energy transition towards fully sustainable energy systems requires designing wind farms looking beyond the sole levelized cost of energy, in order to concurrently ensure not only the economic profitability but also the environmental friendliness of future plants. Within this new approach to design, it becomes necessary to understand the effects that various possible technological choices have on both the economic and the environmental performance of wind farms. This study presents a framework designed to support these coupled economic-environmental assessments. The capabilities of the code are showcased by analysing the impact of different choices in terms of support structure type, specific power, tower height, powertrain type, and array and export voltage level for an exemplary offshore farm, chosen here as the IEA Wind 740-10-MW Reference Offshore Wind Plant with irregular layout. While the effects of many technological choices on the cost of energy are already well understood by industry, the present analysis shows that — at least in this specific case — climate change impacts are mainly driven by steel production, due to the massive amount of required material, but also, interestingly, by vessel activities. A low specific power, tall towers, and a high export cable voltage appear to offer the greatest potential for the concurrent improvement of the environmental and economic performance of the plant.

Offshore Wind Energy Validation Experiment Hierarchy

This paper provides a summary of planning work for experiments that will be necessary to address the long-term model validation needs required to meet offshore wind energy deployment goals. Conceptual experiments are identified and laid out in a validation hierarchy for both wind turbine and wind plant applications. Instrumentation needs that will be required for the offshore validation experiments to be impactful are then listed. The document concludes with a nominal vision for how these experiments can be accomplished.

Co-optimization of power and spinning reserve in multi-area electrical networks with wind farms

Integrating wind farms into electrical networks reconfigures energy matrices and poses new challenges to system operators. For instance, wind farm owners must now guarantee spinning reserves stipulated in grid codes. This prompts an urgent quest for optimizing power and reserve allocation in liberalized markets using advanced models. In this sense, this paper introduces an optimal power flow (OPF) model to co-optimize power and spinning reserve in multi-area electrical networks with wind farms. The granularity and layout of the wind plant are considered for a correct calculation of the spinning reserve in each variable speed wind turbine. Unlike classical OPF studies, where generator participation is free and mandatory in system reserves, this new approach considers both power and reserve as variable factors in the optimization process, which depend on reserve costs, system load, and wind speed. The approach is evaluated through comparative analyzes on two power networks: a small 12-bus, two-area network with two wind farms and a larger 100-bus, four-area network with four wind farms. Multi-period 12-hour look-ahead analyzes are conducted with variable conditions of demand and wind speed. The studies demonstrate the advantages of the new model compared to the classical OPF, showcasing its ability to optimize power dispatch with wind farms actively contributing to the spinning reserve requirements of the power network.

Feature Selection by Binary Differential Evolution for Predicting the Energy Production of a Wind Plant

We propose a method for selecting the optimal set of weather features for wind energy prediction. This problem is tackled by developing a wrapper approach that employs binary differential evolution to search for the best feature subset, and an ensemble of artificial neural networks to predict the energy production from a wind plant. The main novelties of the approach are the use of features provided by different weather forecast providers and the use of an ensemble composed of a reduced number of models for the wrapper search. Its effectiveness is verified using weather and energy production data collected from a 34 MW real wind plant. The model is built using the selected optimal subset of weather features and allows for (i) a 1% reduction in the mean absolute error compared with a model that considers all available features and a 4.4% reduction compared with the model currently employed by the plant owners, and (ii) a reduction in the number of selected features by 85% and 50%, respectively. Reducing the number of features boosts the prediction accuracy. The implication of this finding is significant as it allows plant owners to create profitable offers in the energy market and efficiently manage their power unit commitment, maintenance scheduling, and energy storage optimization.

Numerical Simulation of the Plant Shelterbelt Configuration Based on Porous Media Model

Low-coverage line-belt-pattern protective forests offer significant advantages in terms of wind and sand control measures. It is important to study the windbreak effectiveness of sand-fixing forests with different spacing for the construction and optimization of plant shelterbelt configurations. The effect of plant spacing on the flow field around a row of trees was investigated using the k-ε turbulence model coupled with the porous media model. In order to accurately simplify the complex and stochastic plant constitutive features, we simplify the plant canopy to a circular platform geometry, which introduces a porous media model, and the plant trunk is simulated as a solid cylinder. The simulation results show that windbreaks only affect wind profiles up to 1.25-times the height of the tree; on the leeward side of the canopy, large-spaced shelterbelts provide greater protection in the near-wake zone, while small-spaced shelterbelts are more effective at reducing velocity in the re-equilibration zone. The flow field recovery properties of the trunk and canopy indicate that the canopy wake zone is longer. In this study, we also quantitatively analyze the relationship between average wind protection effectiveness as a function of plant spacing and streamwise distance from the leeward side of the canopy, and the given parameterized scheme shows a power exponential relationship between wind protection effectiveness and plant spacing and a logarithmic relationship with streamwise distance. This scheme can provide a predictive assessment of the effects during the implementation of the plant shelterbelt.

Developing onshore wind farms in Aotearoa New Zealand: carbon and energy footprints

ABSTRACT In recognition of deeper insights into the implications of wind farm deployments, this paper addresses the need for an updated Life Cycle Assessment (LCA) for onshore wind generation systems, using 4.3 MW wind turbines and direct drive permanent magnet synchronous generators. The environmental and energy performances were estimated through an LCA for an onshore wind plant under construction in Aotearoa New Zealand with a total nameplate capacity of 176 MW. This study used real construction data showing literature data overestimates civil works and underestimates transportation contributions in the wind farm footprint. Further, different end-of-life management alternatives for turbine blades are analysed: landfill, mechanical recycling, and chemical recycling. The results indicate a carbon footprint of 10.8–9.7 gCO2eq/kWh, a greenhouse gas payback time of 1.5–1.7 years for avoided combined cycle gas turbines, and an energy payback time of 0.4–0.5 years, in which the chemical recycling of the blades is the lower emission solution overall. The outcomes underscore the environmental efficiency of onshore wind farms and their important role in the energy transition. Notably, the manufacturing of wind turbines is the primary contributor to the carbon and energy footprints, highlighting a critical area for targeted environmental mitigation strategies.

Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering

Artificial intelligence-aided wind plant optimization for nationwide evaluation of land use and economic benefits of wake steering

HyDesign: a tool for sizing optimization of grid-connected hybrid power plants including wind, solar photovoltaic, and lithium-ion batteries

Abstract. Hybrid renewable power plants consisting of collocated wind, solar photovoltaic (PV), and lithium-ion battery storage connected behind a single grid connection can provide additional value to the owners and society in comparison to individual technology plants, such as those that are only wind or only PV. The hybrid power plants considered in this article are connected to the grid and share electrical infrastructure costs across different generation and storing technologies. In this article, we propose a methodology for sizing hybrid power plants as a nested-optimization problem: with an outer sizing optimization and an internal operation optimization. The outer sizing optimization maximizes the net present values over capital expenditures and compares it with standard designs that minimize the levelized cost of energy. The sizing problem formulation includes turbine selection (in terms of rated power, specific power, and hub height), a wind plant wake loss surrogate, simplified wind and PV degradation models, battery degradation, and operation optimization of an internal energy management system. The problem of outer sizing optimization is solved using a new parallel “efficient global optimization” algorithm. This new algorithm is a surrogate-based optimization method that ensures a minimal number of model evaluations but ensures a global scope in the optimization. The methodology presented in this article is available in an open-source tool called HyDesign. The hybrid sizing algorithm is applied for a peak power plant use case at different locations in India where renewable energy auctions impose a monetary penalty when energy is not supplied at peak hours. We compare the hybrid power plant sizing results when using two different objective functions: the levelized cost of energy (LCoE) or the relative net present value with respect to the total capital expenditure costs (NPV/CH). Battery storage is installed only on NPV/CH-based designs, while the hybrid design, including wind, solar, and battery, only occurs on the site with good wind resources. Wind turbine selection on this site prioritizes cheaper turbines with a lower hub height and lower rated power. The number of batteries replaced changes at the different sites, ranging between two or three units over the lifetime. A significant oversizing of the generation in comparison to the grid connection occurs on all NPV/CH-based designs. As expected LCoE-based designs are a single technology with no batteries.

An Onshore Deployment of Advanced Dual-Doppler Radar for Wind Energy Applications

Two advanced X-band radars have been developed and deployed by Texas Tech University to provide continuous wind measurements serving project AWAKEN, including the construction of near real-time dual-Doppler synthesized wind fields covering a large 35 km x 35 km analysis domain. This deployment represents the first overland long duration utilization of the technology, which was configured to document a wide range of wind energy relevant flows spanning turbine inflow/wake flow to regional wind flows and wind plant interactions. The overland environment coupled with technology innovation has led to very high data availability yielding a growing archive of measurements documenting diverse atmospheric phenomena including numerous instances of wind plant interaction. Through the first few project months, data availability greater than 90%, 80% and 70% has been assessed at the ranges of 8 km, 16 km, and 25 km, respectively. Robust data availability coupled with fast radar scan speeds and high along beam measurement resolution position advanced Doppler radar technology to broadly contribute to diverse wind energy measurement applications covering domains sizes that far exceed existing industry standard measurement systems.

Exploring the capacity and economic viability of green hydrogen production by utilising surplus energy from wind farms and small hydropower plants in Southern Brazil

This study seeks to estimate the potential hydrogen production from the remaining energy in the small hydropower plants (SHPs). SHPs are plants with a reservoir of three-square miles and an installed capacity between 1 and 30 MW and wind plants in the south of Brazil. Subsequently, to evaluate the generation of chemical and electrical energy and the amount of stored hydrogen and to determine possible applications in the transport sector and its injection into the natural gas network of the regional mesh. Moreover, scenarios 1 and 2 represented 5% and 3% of the surplus power for hydrogen production. The results indicated that green hydrogen production from the remaining energy in the small hydroelectric and wind power plants proved viable. There was an average potential of 5.95 MNm3/day in SHPs and 7.66 MNm3/day in wind farms. Furthermore, the wind farms could generate an average of 16,000 MWh/day through H2 production, while the SHPs have the potential to produce an average of 9340 MWh/day. Further, the proposed scenarios were economically viable since the profit estimated ranged from 27.13 to 617.92 U$/kg in wind farms and from 162.87 to 342.42 U$/kg in SHPs.

Life cycle environmental analysis of offshore wind power: A case study of the large-scale offshore wind farm in China

China's decarbonization is indispensable for the large-scale utilization of renewable energy. In this process, the development of offshore wind energy has become an important support, because in the eastern region of China, especially the coastal areas, there's the most load requirements but limited onshore renewable resources. Under the goals of both carbon neutral and sustainable development, however, there's still a research gap in evaluating the environmental impacts of large-scale offshore wind plants in China. In this study, the research performed a comprehensive process-based life cycle environmental analysis of a large-scale (400 MW) offshore wind farm with large wind turbine units (5 MW) in China. Global Warming Potential is 25.73 g CO2-eq/kWh and greenhouse gas payback time is calculated as 12.05 months. Fossil fuel consumption amounts to 0.31 MJ/kWh with an energy payback period of 25.56 months. Material resources and ecotoxicity to freshwater need more attention with the values of 1.68 CTUe/kWh and 3.32 10−6 kg Sb-eq/kWh, respectively. The manufacturing stage contributes most to all the impacts examined. The disposal and recycling process shows significant environmental benefits for most impacts but exerts burdens on the particulate matter. Focusing on Global Warming Potential, the wind turbine generation set has the highest effect, among which the blades contribute 73.2% of it. Further, the evolution of electricity and heating mix of China and use of biomass-based precursors could decrease 57% of the blades' greenhouse gas emissions in 2050. This study could provide guidance on the sustainable design and policy-making of offshore wind farms.

Seasonal variability of wake impacts on US mid-Atlantic offshore wind plant power production

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. We evaluate wake variability and annual energy production with the first yearlong 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. We also provide a first step towards uncertainty quantification by testing the amount of added turbulence kinetic energy (TKE) by 0 % and 100 %. We provide a sensitivity analysis by additionally comparing 25 % and 50 % for a short case study period. The strongest wakes, propagating 55 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 the diurnal variability of wakes. Overall, yearlong simulated wake impacts reduce power output by a range between 38.2 % and 34.1 % (for 0 %–100 % added TKE). Internal wakes cause greater yearlong power losses, from 29.2 % to 25.7 %, compared to external wakes, from 14.7 % to 13.4 %. The overall impact is different from the linear sum of internal wakes and external wakes due to non-linear processes. Additional simulations quantify wake uncertainty by modifying the added amount of turbulent kinetic energy from wind turbines, introducing power output variability of 3.8 %. Finally, we compare annual energy production to New England grid demand and find that the lease areas can supply 58.8 % to 61.2 % of annual load. We note that the results of this assessment are not intended to make nor are they suitable to make commercial judgments about specific wind projects.

Impact and Integration of Electric vehicles on renewable energy based Microgrid: Frequency profile improvement by a-SCA optimized FO-Fuzzy PSS approach

The modelling of an electric vehicle along with its integration and impact over a renewable energy based microgrid topology is well addressed in this manuscript. The frequent charging and discharging of the electric vehicle makes an oscillation over grid frequency. The performance especially frequency of an islanded AC microgrid is also affected seriously under the actions of different uncertainties like load dynamics, wind fluctuation in wind plant, solar intensity variation of PV plant etc. In order to maintain standard frequency, this research work aims to regulate the net power generation of the system in response to total demand. To monitor net generation, this work has intended a fractional order fuzzy power system stabilizer (FO-Fuzzy PSS) control scheme in several dynamic situations. The proposed FO-Fuzzy PSS control scheme acts as most potential candidate to pertain stability in system frequency in above discussed disturbances. The controller gains are tuned optimally with suggesting an advanced- Sine Cosine Algorithm (a-SCA) under different conditions. The performance of the optimal FO- Fuzzy PSS controller is compared over standard fuzzy controller and PID controller in regard to frequency regulation of microgrid system. It is observed that proposed FO-Fuzzy PSS control scheme has the credential to reduce settling time of ΔF1 (area1 microgrid frequency) by 98.60% and 250.82% over fuzzy controller & PID controller correspondingly. Further, the dynamic optimal performance of the proposed a-SCA is compared over original SCA and PSO techniques to justify its superiority.

Multi-modular bridge-type FCL for enhancing FRT capability of VSC-HVDC connected offshore wind plants during AC faults

This paper presents a novel concept Multi-Modular Bridge-Type Fault Current Limiter (MMBFCL) for enhancing the fault ride-through (FRT) capability in Voltage Source Converter (VSC)-based High-Voltage Direct-Current (HVDC) systems. The MMBFCL can be directly connected to HVDC system in ac grid side without series connection of semiconductor switches and series injection transformers. Also, the limiting resistor of each module is determined based on binary arrangements to provide a multi-step insertion resistor BFCL to limit the fault current and enhance the FRT capability under different voltage sag levels. The principle operation and control system of the MMBFCL is described. Then, the performance of the MMBFCL for enhancing the FRT is validated through time domain simulations by PSCAD/EMTDC software in a HVDC integrated wind farm system. Also, the performance of the MMBFCL is compared with the single-modular BFCL (SMBFCL). Simulation results reveal that the MMBFCL compensate the voltage sag for each voltage sag levels without any over voltage at the HVDC terminal. Also, it has superior performance than SMBFCL.

Coordinating Demand Response and Wind Turbine Controls for Alleviating the First and Second Frequency Dips in Wind-Integrated Power Grids

This paper introduces a method employing demand response (DR) and wind turbine (WT) to decrease the first and second frequency dips in power systems with the massive proliferation of wind resources. Inverter air conditioners (IACs) are utilized to participate in the DR program. The buildings' thermal model and the IACs' electrical model are integrated to characterize a relation between power system frequency and IACs regulation power. An adaptive delay controller (ADC) is presented to capture the command communication delays from the DR controller to IACs. In addition, a controller is suggested to adopt wind power plants in system frequency regulation. This controller rises the wind plant output power for some seconds and returns it, following frequency recovery, to the initial power. The recovery period causes the second frequency dip, in response to which, a method is introduced to identify the moment of the second dip and alleviate it via DR.