Existing Wind Farms Research Articles

Intelligent Integration of Renewable Energy Resources Review: Generation and Grid Level Opportunities and Challenges

This paper reviews renewable energy integration with the electrical power grid through the use of advanced solutions at the device and system level, using smart operation with better utilisation of design margins and power flow optimisation with machine learning. This paper first highlights the significance of credible temperature measurements for devices with advanced power flow management, particularly the use of advanced fibre optic sensing technology. The potential to expand renewable energy generation capacity, particularly of existing wind farms, by exploiting thermal design margins is then explored. Dynamic and adaptive optimal power flow models are subsequently reviewed for optimisation of resource utilisation and minimisation of operational risks. This paper suggests that system-level automation of these processes could improve power capacity exploitation and network stability economically and environmentally. Further research is needed to achieve these goals.

Hybrid offshore wind–solar energy farms: A novel approach through retrofitting

Due to the inherent variability of the power output of offshore wind farms, their integration into electrical grids poses a challenge to their stability and leads to significant balancing costs. Combining wind and solar power may be expected to mitigate the power output variability. In addition, a number of synergies can be realised—shared infrastructure, shared crews and vessels for operations and maintenance, and last but not least, optimum use of scarce marine space. The objective of this work is to investigate this hybrid approach and, in particular, to determine the optimum capacity of the solar energy subsystem given a certain installed capacity of the wind energy subsystem. Three case studies are considered in Europe and China. Each of them is an existing wind farm that could be retrofitted with floating solar PV. After assessing the local wind and solar energy resources, the optimal size of the floating solar array is calculated with a view to smooth the aggregated power output of each farm. Subsequently, the benefits of this hybrid approach are demonstrated from various perspectives. It is found that a much larger solar array is required for a wind farm with a large installed capacity, but the specific optimal size depends more on the local wind and solar resources. The novelty of this paper lies in its exploration of retrofitting existing offshore wind turbines with floating solar PV systems from the perspective of addressing the variability challenge for the currently operating wind farms. This study could serve as a guideline for project designs aiming to retrofit existing offshore wind farms with solar PV technology, thus reducing balancing costs and facilitating the penetration of offshore renewable energy into national power grids.

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability

The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020–2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.

Modelling wind farm effects in HARMONIE–AROME (cycle 43.2.2) – Part 1: Implementation and evaluation

Abstract. With increasing number and proximity of wind farms, it becomes crucial to consider wind farm effects (WFEs) in the numerical weather prediction (NWP) models used to forecast power production. Furthermore, these WFEs are also expected to affect other weather-related parameters at least locally. Thus, we implement the explicit wake parameterization (EWP) in the NWP model HARMONIE–AROME (hereafter HARMONIE) along-side the existing wind farm parameterization (WFP) by Fitch et al. (2012) (FITCH). We evaluate and compare the two WFPs against research flight measurements as well as against similar simulations performed with the Weather Research and Forecasting (WRF) model using case studies. The case studies include a case for WFEs above a wind farm as well as two cases for WFEs at hub height in the wake of farms. The results show that EWP and FITCH have been correctly implemented in HARMONIE. For the simulated cases, EWP underestimates the WFEs on wind speed and strongly underestimates the effect on turbulent kinetic energy (TKE). FITCH agrees better with the observations, and WFEs on TKE are particularly well captured by HARMONIE–FITCH. After this successful evaluation, simulations with all wind turbines in Europe will be performed with HARMONIE and presented in the second part of this paper series.

Repowering feasibility of Indian wind energy sector: A case study

Repowering wind farms entails upgrading or replacing old turbines with more efficient, capable, and profitable ones. This technique has the potential to boost energy production, improve grid integration, and lower operational costs. Repowering also makes use of the most recent breakthroughs in wind energy technology, ensuring that wind farms stay economically viable and contribute to the growth of renewable energy. The majority of the wind farms that are located in India were constructed in early 2000, and their capacity ratings range from 200 kW to 800 kW. The lifespans of these wind farms have finally come to an end. Repowering wind farms is a viable alternative in these regions due to the significant wind capacity that exists there. In order to find the wind potential in the site wind resource assessment is needed. This paper proposes a repowering scheme for the existing wind farm located in Kayathar, Tamil Nadu. The reduction in power loss due to the wake effect in the existing wind farm is analyzed using WAsP software and repowering scheme is proposed to increase the Annual Energy Production (AEP), Capacity Utilisation Factor (CUF). The Wind Atlas Analysis and Application Program (WAsP) is utilized in order to carry out the site’s wind resource evaluation. After the wind resource assessment, individual turbine wake loss is identified, and the viability of repowering the wind farm by raising the hub height of high wake-affected turbines was investigated. Another repowering study is also carried out by installing high-capacity turbines in place of the existing turbines.

Influence of platform dynamic yaw motions on wake, and subsequent impacts on downwind turbine power and loads.

The floating foundations of FOWTs cause turbine motions that introduce new unknowns to the field of wind turbine wake estimation, that again leads to uncertainties in the load and power estimation of wake affected turbines. Recent studies have shown significant effects of floater motions on power production and emphasizes the need for the effect to be integrated into existing wind farm energy yield tools. Also, field measurements in installed FOWT wind farms have shown that there are measurable effects of the wake on floater yaw motions and mooring line loads.This paper describes a study that aims to quantify the effect of time-varying yaw misalignment, from floater motion and/or turbine yaw control, on the power production and turbine loads. 2 IEA 15 MW wind turbines supported by the UMaine VolturnUS-S semisubmersible, serves as the case study. The wind field and power production was simulated including the effect of steady and time-varying yawed inflow. The response of the downwind turbine in the waked wind field was simulated.Power production and loads for the downwind turbine in yaw cases are benchmarked against results for the downwind turbine without time varying turbine yaw and show that including the upwind turbine yaw motion resulted in higher mean power (up to 4%), but also larger power fluctuations. It also lead to larger standard deviations in the mooring line tension. These effects seem to decrease with increasing wind speeds and turbine spacing, and increase with the amplitude of yaw motion.

Aerodynamic interference caused by wake effects of repowered wind farms on the annual energy production in neighboring wind farms

Worldwide, wind farms are updated due to technological advances and the end of life from different aspects: social, environmental, technical, and economical. In Brazil, by 2030, around 50 wind farms will reach the age of 20 years of operation, representing more than 600 wind turbines and a total power of around 1,000 MW. This study aims to develop a technical and economic analysis of the unaddressed topic of interference of repowering in neighboring wind farms by analyzing four wind farms in Brazil. Technical analysis of the wake is based on NOJ and BastanKhah models using the software PyWake, which is also employed to calculate the energy production of wind farms. Three wind turbine models with different hub heights and rated power were also considered. Results showed that the repowered wind turbine characteristics, micrositing, and distance between wind farms greatly influence the energy production of neighboring wind farms. The energy deficit in the existing wind farms varied from 433 MWh to 5391 MWh, indicating that, although repowering is a viable solution for wind farms reaching their end-of-life, careful technical analysis regarding the micrositing and wind turbine model should be considered to mitigate energy losses in neighboring existing wind farms.

The role of repowering India’s ageing wind farms in achieving net-zero ambitions

India’s ambitious net-zero climate goals include plans for a four-fold increase in current levels of wind energy generation by 2030. Many existing wind farms in India occupy sites with the best wind resources nationally but use older, smaller turbines that achieve lower capacity factors compared to modern turbine designs. A strategy of replacing existing wind turbines with state-of-the-art models (termed repowering) could boost capacity factors and ensure maximal use of available wind resources. However, a nationwide assessment of the potential wind generation increases resulting from repowering is currently lacking for India. Here, we present the first validated synthetic wind generation dataset for India based on reanalysis data and show that full repowering of the existing fleet of wind turbines could boost capacity factors by 82% nationwide (from 0.19 to 0.35). Our assessment of attainable capacity factors under full repowering exceeds equivalent estimates within the National Electricity Plan of India and national decarbonisation pathways compiled by the Intergovernmental Panel on Climate Change (IPCC), suggesting less total installed capacity is required to achieve specific generation outcomes than previously estimated. Ongoing technological progress, leading to increased turbine dimensions, will drive capacity factors beyond the levels estimated here, which could further add to the generation benefits of repowering. Yet, despite the higher average output from a repowered fleet of wind generators, substantial variability in generation across timescales persists, highlighting the increasing need for power system flexibility within a decarbonised energy system.

Spatially transferable machine learning wind power prediction models: [formula omitted]logit random forests

Wind power prediction models provide essential information to wind farm developers and power system operators on the power available at an undeveloped location. Traditionally, statistical models require recalibration of the model’s parameters in order to fit the model to a specific location’s dynamics. To mitigate this computational expense, this research develops a data-driven wind power prediction model that is spatially transferable, without the need for recalibration of the model’s parameters at a new location. This study also develops a wind direction prediction and interpolation model, as well as a transferability metric that demonstrates the model’s predictive accuracy at a new location. The transferability metric is evaluated in an exhaustive experimental setting. Results show that a v-logit random forest with minimum information requirements is the most transferable random forest variant for wind power prediction, with an error rate as low as 9% and never more than 20% when a transferred model is applied to a new location. The case study illustrates that the transferred model outperforms a site-specific, in-situ model for several existing wind farms. Clustering based on similarity, validated by cophenetic correlation and mapping, reveal that both the latitude of the farms, and the similarity of their turbine layouts, increase model transferability.

Synchronized optimization of wind farm start-stop and yaw control based on 3D wake model

In existing wind farms, the overall power output can be increased through yaw control. However, the cooperative control of start/stop, yaw and turbines positions is often overlooked, leading to wake superposition to downstream wind turbines and suboptimal power output. This paper proposes a synchronized optimized method that considers start/stop, yaw and turbines positions control based on a three-dimensional wake model and yaw flow superposition model. The objective function of the proposed strategy is to maximize the power output of the Chapman Ranch (CR) wind farm. Four cases are considered: start-stop, yaw control, start-stop & yaw control and start-stop & yaw & turbines positions control. The particle swarm algorithm is introduced to optimize the wind farm layout. According to the results, considering start-stop, yaw and turbines positions optimization can not only increase the annual power output of the wind farm by 8.85 %, but also avoid the colliding wake in the CR wind farm. However, the other three cases will cause colliding wake in some fields of the CR wind farm. This study provides important guidance on improving the overall power output of existing wind farms.

Flexible multi-fidelity framework for load estimation of wind farms through graph neural networks and transfer learning

Abstract With global wind energy capacity ramping up, accurately predicting damage equivalent loads (DELs) and fatigue across wind turbine populations is critical, not only for ensuring the longevity of existing wind farms but also for the design of new farms. However, the estimation of such quantities of interests is hampered by the inherent complexity in modeling critical underlying processes, such as the aerodynamic wake interactions between turbines that increase mechanical stress and reduce useful lifetime. While high-fidelity computational fluid dynamics and aeroelastic models can capture these effects, their computational requirements limits real-world usage. Recently, fast machine learning-based surrogates which emulate more complex simulations have emerged as a promising solution. Yet, most surrogates are task-specific and lack flexibility for varying turbine layouts and types. This study explores the use of graph neural networks (GNNs) to create a robust, generalizable flow and DEL prediction platform. By conceptualizing wind turbine populations as graphs, GNNs effectively capture farm layout-dependent relational data, allowing extrapolation to novel configurations. We train a GNN surrogate on a large database of PyWake simulations of random wind farm layouts to learn basic wake physics, then fine-tune the model on limited data for a specific unseen layout simulated in HAWC2Farm for accurate adapted predictions. This transfer learning approach circumvents data scarcity limitations and leverages fundamental physics knowledge from the source low-resolution data. The proposed platform aims to match simulator accuracy, while enabling efficient adaptation to new higher-fidelity domains, providing a flexible blueprint for wake load forecasting across varying farm configurations.

Pre-trained 1DCNN-BiLSTM Hybrid Network for Temperature Prediction of Wind Turbine Gearboxes

The safety and stability of a wind turbine is determined by the health condition of its gearbox. The temperature variation, compared with other characteristics of the gearbox, can directly and sensitively reflect its health conditions. However, the existing deep learning models (including the single model and the hybrid model) have their limitations in dealing with nonlinear and complex temperature data, making it challenging to achieve high-precision prediction results. In order to tackle this issue, this paper introduces a novel two-phase deep learning network for predicting the temperature of wind turbine gearboxes. In the first phase, a one-dimensional convolutional neural network (1DCNN) and a bidirectional long short-term memory (BiLSTM) network are separately trained using the same dataset. The two pre-trained networks are combined and fine-tuned to form the 1DCNN-BiLSTM model for the accurate prediction of gearbox temperatures in the second phase. The proposed model was trained and validated by measured datasets from gearboxes from an existing wind farm. The effectiveness of the model presented was showcased through a comparative analysis with five traditional models, and the result has clearly shown that the proposed model has a great improvement in its prediction accuracy.

Spatially-explicit optimization of an integrated wind-hydrogen supply chain network for the transport sector: The case study of Sicily

Almost half of the European railways are served by diesel-powered trains used for both cargo and passenger transport. Similar considerations apply to the bus sector in urban and suburban areas. The decarbonization of the transport sector can be achieved by using hydrogen as a fuel for trains and buses. However, the main barrier to adopting hydrogen fuel cell trains and buses is the construction of adequate infrastructure. Thus, to foster a sustainable energy transition, existing renewable plants should be considered when designing new hydrogen supply chains. In the proposed Sicilian case study, existing wind farms are chosen as electricity production plants. Most of them have been shut down due to grid unbalancing issues; still, the curtailed renewable electricity could be used to produce zero-carbon hydrogen via water electrolysis, significantly reducing costs. To account for this opportunity, this study models the hydrogen production, transportation, and refueling stages, employing a mixed-integer linear programming approach to find the optimal location and capacities of hydrogen infrastructure, while minimizing the total daily cost of the supply chain. The model is applied to Sicily and different scenarios for varying hydrogen demands for trains and buses are analyzed and discussed. Results show that capital expenses cover more than 90% of the total cost of the supply chain, with a hydrogen cost dispensed at the filling station ranging from 6.32 to 9.02 €/kgH2. The environmental analysis shows that as hydrogen demand rises, so do the carbon emissions from the distribution stage. However, the impact of using wind-generated electricity in terms of avoided emissions is notably greater. Finally, it is shown that transporting hydrogen in gaseous form using tube trailers is economically more attractive than transportation in liquid form with tanker trucks in the early stages of the network.

Do concerns about wind farms blow over with time? Residents’ acceptance over phases of project development and proximity

Social acceptance is a key issue for the continued expansion of onshore wind energy. Wind energy development targets increasingly rely on the assumption that residents' concerns related to new wind farms dissipate over time. The persistence of resistance to new wind farms has motivated efforts to investigate this effect. The ‘U-curve’ hypothesis proposes that acceptance is likely to decrease when residents are confronted with the planning of a wind farm in their neighbourhood, but that acceptance may later recover during construction and operation. In this study, relevant research is reviewed, discussed, and applied using a largescale experimental survey focused on residents living within 10 km of an existing wind farm in Ireland (n = 1109). It uses two indicators of how people experience wind farms to investigate willingness to accept further developments. The indicators include the proximity of existing wind farms and their development phase (i.e., planning, construction or operation). The findings show that experience is an important determinant of acceptance, as are an awareness of low-carbon energy initiatives and sense of community spirit. The study examines residents' expectations for participatory fairness and local benefits. Expected adverse impacts on local tourism or potential for discord within the community influence the acceptance for further development. Acceptance is also determined by trust in sources of information, including a designated community liaison officer. The concerns of residents living within the nearest 2 km radius of a wind farm and at the earliest and most uncertain phases of project planning can be crucial issues for acceptance.

Hybrid Generation of Existing Wind Power Plants with Additional Installation of Photovoltaic Power Plants

Abstract The paper describes the possibility of hybrid electricity generation in already existing wind power plants (WPP) by additional installation of photovoltaic power plants (PVPP) on individual wind turbines (WT). Wind turbines have the lowest generation of electricity in the summer months, while PVPPs have the opposite, the highest generation in those months. Connecting the PVPP to the existing WT (hybrid electricity generation) brings a more even generation and a greater degree of use of the infrastructure of the existing wind farms (HV/MV transformer station, medium voltage cable distribution...). This way the variation of generation in relation to the average annual generation is reduced, which reduces the problems of balancing electricity.

Local conflicts and citizen participation in the German energy transition: Quantitative findings on the relationship between conflict and participation

The expansion of wind energy is accompanied by numerous local conflicts. Dialogue-oriented and financial citizen participation processes are seen as instruments to increase local acceptance. So far, no information is available when, how often and under which circumstances conflicts arise. Moreover, there are no quantitative empirical findings on the relationship between conflicts and participation procedures. The article addresses this research gap. Based on quantitative Data on conflicts and participation procedures in the expansion of wind energy in Lower Saxony, the article shows that local conflicts often occur where new wind farms are planned. Repowering or the expansion of existing wind farms is less likely to be accompanied by conflicts. In addition, there seems to be habituation effects. This means that in municipalities where many wind turbines are already in operation, there are fewer conflicts than in municipalities with fewer wind turbines. The multiple correlation analysis shows a clear correlation between the occurrence of local conflicts and the availability of dialogue-oriented and financial participation procedures. The question of whether the conflict is a consequence of the participation process or whether the participation process is a consequence of the conflict cannot be answered. Further research is needed to answer this question.

Lightning risk assessment of offshore wind farms by semi-supervised learning

The wind turbine has rapidly developed worldwide with increasing height and scale, resulting in the increased risk of lightning strikes. When wind turbines were stroke by the lightning, they will be damaged, causing economic loss and outage. Lightning risk assessment can guide the improvement of lightning protection and the design of the wind farms to efficiently prevent lightning damages. The traditional lightning risk assessment methods rely on subjective features to some extent. The existing lightning risk assessment methods based on machine learning demand abundant labeled data. It is extremely difficult to label and acquire the data. This paper proposed a lightning risk assessment method based on semi-supervised learning to address the challenges of labeling negative samples and limited labeled data. The semi-supervised K-means algorithm is proposed to divide all data into three parts. The Laplacian support vector machine (LapSVM) with hyperparameters optimized by the particle swarm optimization (PSO) is used to assess the lightning risk. The proposed method has better performance than the standard SVM and neural network (NN). Moreover, previous researches did not consider lightning protection ability. This paper introduces the receptor number into lightning risk assessment. The assessment results suggest that there is higher lightning risk in areas with a great number of wind turbines, high lightning density, and strong lightning strength. The ocean area is more likely to have low lightning risk. The results are valuable for lightning protection optimization of existing wind farms and can give guidance for the plan of new wind farms.

Using logistic regression-cellular automata to project future sites for commercial wind energy development

Demands to decarbonize electricity production and improve energy security will continue to drive wind energy development. A Logistic Regression-Cellular Automata (LRCA) model is presented here to project timing and location of this future development and thus aid efforts to meet energy demands. The model's logistic regression equation is trained and tested using aggregated data from key predictors to correctly classify hexagonal grid cells covering the Conterminous United States (CONUS) as currently containing wind farms. The cellular automata component iteratively applies this equation, plus constraints and neighborhood effects, to project grid cells suited for future wind energy development out to the year 2050, with the model's sensitivity to constraint, neighborhood, and scenario definitions also examined. Projected wind farms are concentrated in high wind speed regions currently populated by wind farms (e.g., Central Plains, Midwest). State-level scale projections reveal local influences on future development, such as critical species habitats and infrastructure. Projected wind farm locations are trustworthy since the model correctly classifies over 85% of current grid cell states at CONUS and state-level scales. Current clusters of wind energy development across the CONUS will continue to expand in these projections, with these clusters growing earlier and faster given a larger neighborhood size and looser constraints. Model projections are less sensitive to scenario definitions, with modifications to predictors affecting when existing wind farm clusters expand rather than where new clusters form. Replacement of the wind farm location dataset would allow this model to project other decentralized land use changes, particularly solar energy development.

Investigating energy production and wake losses of multi-gigawatt offshore wind farms with atmospheric large-eddy simulation

Abstract. As a consequence of the rapid growth of the globally installed offshore wind energy capacity, the size of individual wind farms is increasing. This poses a challenge to models that predict energy production. For instance, the current generation of wake models has mostly been calibrated on existing wind farms of much smaller size. This work analyzes annual energy production and wake losses for future, multi-gigawatt wind farms with atmospheric large-eddy simulation. To that end, 1 year of actual weather has been simulated for a suite of hypothetical 4 GW offshore wind farm scenarios. The scenarios differ in terms of applied turbine type, installed capacity density, and layout. The results suggest that production numbers increase significantly when the rated power of the individual turbines is larger while keeping the total installed capacity the same. Even for turbine types with similar rated power but slightly different power curves, significant differences in production were found. Although wind speed was identified as the most dominant factor determining the aerodynamic losses, a clear impact of atmospheric stability and boundary layer height has been identified. By analyzing losses of the first-row turbines, the yearly average global-blockage effect is estimated to between 2 and 3 %, but it can reach levels over 10 % for stably stratified conditions and wind speeds around 8 m s−1. Using a high-fidelity modeling technique, the present work provides insights into the performance of future, multi-gigawatt wind farms for a full year of realistic weather conditions.

Optimal sizing of hybrid wind-photovoltaic plants: A factorial analysis

This research attempts to determine the optimal size (in terms of profitability) of a photovoltaic (PV) plant that is going to be added to an existing wind installation. The analysis carried out is based on a real sample of 62 wind facilities located in 25 Spanish provinces in 2021. Given the hourly energy generated throughout the year by the wind facility and its grid capacity, the optimal power of the PV plant in the hybrid facility will be the one that allows maximising the net present value of the investment, i.e., the one that allows to better adjust (in economic terms) the PV production to the characteristics of the wind farm. For our empirical analysis we need data on the day-ahead energy market price, on the grid capacity and hourly production (in the day-ahead energy market) of the 62 wind farms analysed, and on the hourly PV production in the Spanish provinces where those wind farms are located. In average terms, to maximise the Net Present Value (NPV) per € invested, the optimal PV power to be added to an existing wind farm should be 8% of the wind power already installed. The averages of the financial indicators for optimal PV sizing are promising (discount rate of 7%): NPV per € invested is 1.89, NPV is M€ 23.5, discounted payback is 4.85 years, and the internal rate of return index is 25.6%. To conclude our empirical analysis, we estimate a multilevel regression model for the hourly wind production. The regression model shows, among other results, that one more MW of wind power translates into an increase in wind generation of 0.31 MWh. Our findings will help the design of hybrid plants without neglecting the economic aspect.