Cost Of Wind Farm Research Articles

An Intelligent 5G Unmanned Aerial Vehicle Path Optimization Algorithm for Offshore Wind Farm Inspection

In recent years, clean energy has gained increasing attention, with offshore wind power playing a crucial role in global energy production. However, the high operating and maintenance costs of offshore wind farms remain a significant challenge. The advent of 5G technology provides a solution for efficiently monitoring and controlling wind power equipment. The use of 5G unmanned aerial vehicles (UAVs) for blade inspections is a promising development. A key challenge is efficiently planning UAV flight paths for fast and effective inspections in complex offshore environments. To address this problem, we conduct an in-depth study of the 5G UAV path optimization method. In this paper, the UAV inspection path problem is modeled as an obstacle avoidance traveling salesman problem (TSP), taking into full account UAV flight constraints and complex sea environment factors, particularly the impact of sea wind on UAV flight speed. We propose a novel Sea Wind-Aware Improved A*-Guided Genetic Algorithm (SWA-IAGA), which integrates an improved A* algorithm to guide the genetic algorithm for efficient path planning, with the assistance of relevant graphical knowledge. This algorithm overcomes the limitations of traditional single-path planning methods, enabling more accurate and efficient path planning.

Structural damage detection of floating offshore wind turbine blades based on Conv1d-GRU-MHA network

Due to the global commitment to mitigate climate warming by replacing fossil fuels, the global wind power installed capacity continues to increase. The reliability of wind turbines is a vital factor in ensuring their safety and profitability. Early fault detection can significantly reduce the maintenance costs of wind farms. This paper constructs a Conv1d-GRU-MHA model for detecting blade structural damage in floating offshore wind turbines (FOWTs) operating under complex conditions. This model combines a one-dimensional convolutional neural network (Conv1d), gated recurrent units (GRU), and multi-head attention mechanism (MHA) to provide accurate detection of blade structural damage. This amalgamation enables the model to comprehend diverse scales of sequence data, effectively capturing intricate patterns and abstract features. The NREL OpenFAST software was used to model a 5 MW wind turbine, where structural damage was introduced on the blades. Acceleration data was collected from blades and tower in different directions and under different wind turbulence intensities (WTI). The Conv1d-GRU-MHA network predicts blade health with an accuracy of up to 97.58 % under the 5 % turbulence level background, with both precision and recall being superior to the comparative models.

Geospatial analysis of post-protection scour development in offshore wind farm

Scour is one of the most serious challenges affecting the normal operation of offshore wind power. Whether measures for scour protection are effective directly affects the safety and economical efficiency of offshore wind farms. Data mining techniques and analytical tools based on monitoring data analysis play a critical role in making appropriate strategic decisions. Post-protection scour inspection was regularly monitored on a typical offshore wind farm containing 72 turbines installed with monopile foundations thrice over the course of two years. Geospatial properties of scour, including maximum scour depth (MSD), maximum scour extension and scouring and siltation volume, were analysed in detail. The possible autocorrelation of scour data measured for each turbine was identified using a data-driven decision making process. The characteristics of the investigated scour and their spatial dependence on the turbines of the inspected offshore wind farm generally showed an upward trend over time. A high–high area in the local geospatial dependency map of MSD was identified, and an equation was derived for predicting development of MSD. These findings show indications to reduce the operation and maintenance costs of offshore wind farms and improve the efficiency for upgrading scour protection on specific offshore wind foundations.

Deep-Learning-based damage detection of an experimentally tested full-scale wind turbine blade.

Reducing the Operation and Maintenance (O&M) costs of Wind Farms can substantially contribute towards adopting environmentally sustainable and cost effective energy sources. O&M costs in Wind Turbines can account up to 50% of the Turbine’s whole life cycle, with the majority of it being the frequent manual inspection needed for Wind Turbine Blades (WTB). Vibration-based structural health monitoring (SHM) approaches can be used for detecting WTB damages in an early state, before they lead to catastrophic failure, while they can be used to perform inspection on an informed basis, reducing O&M costs. In the present study, a data-driven damage detection framework is developed for WTBs. A Finite Element Model (FEM) of a full-scale WTB (the Aventa AV-7 “Light Wind Turbine”, sold under the Leichtwindanlagen© name) is developed in ABAQUS FEA using sparce material properties and dimensions and subsequently modified to match a physical WTB. Damage is modelled in the form of a Trailing Edge (TE) longitudinal crack propagated in a stepwise manner. The FE model is updated based on the geometry altering and material property degrading effects of the TE crack. Numerical simulations are performed to obtain the dynamic responses of the blade, under white noise and chirp excitation. A damage detection framework is then developed using Auto-Regressive, with exogenous excitation (ARX) or without (AR), models and its Nonlinear counterparts NARX and NAR models. In addition, model dimensionality reduction is performed using Principal Component Analysis (PCA) and Deep-Autoencoders (D-AE). The results are presented in terms of Receiver Operating Characteristics (ROC) curves.

Technical and economic challenges for floating offshore wind deployment in Italy and in the Mediterranean Sea

AbstractOffshore wind is nowadays already well developed in the North European countries. Ninety‐nine percent of the offshore wind turbines are installed on fixed foundations in shallow waters. For areas with water depth greater than 50–60 m, the floating wind is the cheapest and mostly used technology. This technology is going to reach the commercial phase in a few years, thus disclosing the potential of all marine areas with deep waters close to the coast, including the Mediterranean basin. One of the main challenges for floating offshore wind deployment in this area is the achievement of its economic feasibility. The offshore wind resource in the Mediterranean is generally lower than the one in the North Sea and in Oceans and the cost of offshore wind farms, especially with floating technology, is higher than the present offshore wind farm installations also because this industrial sector has not yet started in this area. However, in the Mediterranean area, the potential of offshore wind to contribute to the decarbonization pathway and reduce the dependence on imported fuel supply is substantial. Numerous studies, examined in this article, have already performed a technical‐economic assessment of offshore wind farms in different countries and geographical areas within the basin. A significant number of offshore wind projects are already in different stages of development, confirming the industrial interest and readiness of the Mediterranean offshore wind energy sector. The article provides a comprehensive review of various factors influencing the future deployment of offshore wind in the Mediterranean. It covers a range of topics including technology advancements, resource assessment, wind energy potential, ongoing projects, costs, and economic aspects. Additionally, it discusses environmental sustainability, regulatory frameworks, supply chain logistics, and system integration. The updated review presented in this article could assist decision‐makers and stakeholders in gaining a better understanding of the characteristics of this promising sector and accelerating its development.This article is categorized under: Sustainable Energy > Wind Energy

Maintenance & failure data analysis of an offshore wind farm

Offshore Wind (OW) continues to grow globally at a rapid pace, with growth estimates of 630GW by 2050. To facilitate this rapid growth, costs must continue to be reduced. Reducing operations and maintenance (O&M) costs, which are estimated at 30% of the lifetime costs of wind farms, offers opportunity. This could be achieved by moving current maintenance strategies to a prescriptive strategy. Prescriptive strategies use the turbine monitoring data to determine component remaining useful lifetimes or predict failure windows and then provide an optimised maintenance plan. The first stage of a framework, that can be applied to operational assets, for improving maintenance schedules with failure predictions is presented. Analysis of the SCADA system and the maintenance logs, at an operational offshore wind farm (OWF), with the purpose of identifying turbine failure rates, availabilities and losses and costs from maintenance and failures has been performed. The analysis has revealed two types of maintenance actions, one is cost of maintenance driven and the other cost of downtime driven. It is proposed that, given different characteristics, they should be approached differently in the context of failure predictions. It is also revealed that electrical components are critical to the failure rate and energy losses due to maintenance at the OWF. Electrical components represent approximately 28% of all failures and nearly 40% of revenue loss due downtimes from failure for the period analysed. The power converters drive most electrical failures and are of key commercial interest to the farm. As a result, the power converters should be the target for future prognostic model development. The analysis also shows that with perfect prediction and maintenance scheduling, this OWF could generate an extra 0.26% revenue and a generic 1GW OWF could generate an extra 0.6% extra energy and approximately £1.4m in revenue. This analysis did not reveal the benefit of taking fewer maintenance actions, which should be assessed in future work. Producing a combined prognostic maintenance scheduling method will generate extra wind farm revenues, reduce the number of maintenance actions taken and facilitate the work of maintenance teams.

Research on the Technical and Economic Development of Large Megawatt Wind Turbines Based on Medium-Voltage Electrical System

INTRODUCTION: With the advent of the "bidding era" and "parity era" in the wind power market, the competition of the whole machine factory is becoming more and more fierce, and the capacity of the single fan is getting larger and larger, which becomes the key to the design of the fan electrical system of the large-capacity unit (5.XMW or more). At present, the low-voltage wind power system (690V,1140V) is the common solution for wind turbines. However, due to the limitation of the cable section of low-voltage electrical system and the increase of the rated current of the generator, the increase of the capacity of a single machine makes more cables from the generator side to the grid, and the cost also increases.
 OBJECTIVES: Aiming at the future large megawatt wind power market, the medium voltage Doubly-Fed electrical system solution is proposed to increase the higher generation and electricity income of wind farms and reduce the manufacturing cost of wind farms.
 METHODS: The technology and economy of medium voltage and low voltage electrical system are compared.
 RESULTS: With the gradual increase of single capacity, the economy of medium pressure wind power generation system is getting better and better, and the higher the height of the tower, the better the economy. At the same time, the reduction of the rated current of the generator brings about the reduction of line loss and the increase of power generation. The number of cables is greatly reduced, and the construction cost and difficulty of cable laying will be greatly reduced.
 CONCLUSION: In response to the technical trend of large-capacity wind turbines in the future, the medium-voltage wind power generation system has a good application prospect, both from the economic and technical point of view.

Cooperative game-based energy storage planning for wind power cluster aggregation station

It is possible to cut down the investment costs in energy storage and enhance the utilization of energy storage by planning the shared energy storage in the wind farm collection station to replace the dispersed energy storage of each wind farm. Considering the cluster complementary effects of multiple wind farms, this article proposes a cooperative game-based plan for the hybrid energy storage of battery and supercapacitor in the wind power cluster. Firstly, charging and discharging strategy for batteries follows the peak shaving and valley filling approach, while the strategy for supercapacitors aims to flatten power fluctuations and track power generation plans. Then, a dual-layer planning model for the shared energy storage station is established, and evaluation indicators for the energy storage configuration results are constructed. Finally, based on the improved Shapley value method, the profits of each wind farm are allocated, and the impact of energy storage investment costs on the results is analyzed. The case study results demonstrate that under this cooperative scheme, not only can the real-time output deviation of wind power be reduced, but also the investment costs of wind farms in energy storage systems can be reduced.

Condition Monitoring of Wind Turbine Drivetrain Bearings

Abstract As an extremely valuable and reliable energy source, the renewable energy is developing around the world at an unprecedented pace. By the end of 2022, the global installed capacity of offshore wind turbines is expected to reach 46.4 GW, among which 33.9 GW in Europe. Efficiencies in Operations and Maintenance (O&M) offer potential to achieve significant cost savings as it accounts for around 20%–30% of overall offshore wind farm costs. A recent study of approximately 350 offshore wind turbines indicates that gearboxes might have to be replaced as early as 6.5 years. Therefore sensing and condition monitoring (CM) systems are needed in order to obtain reliable information on the state and condition of different critical parts. The development and use of such technologies will allow companies to schedule actions at the right time. The reduced costs of O&M enable the wind energy at a competitive price thus strengthening productivity of the wind energy sector. At the academic level, a plethora of methodologies have been proposed during the last decades focusing toward early and accurate fault detection. Among others, Envelope Analysis is one of the most important methodologies, where the envelope of the vibration signal is estimated, usually after filtering around a selected frequency band excited by impacts due to the faults. Different tools, such as Kurtogram, have been proposed in order to accurately select the optimum filter parameters (center frequency and bandwidth). Cyclostationary analysis and corresponding methodologies, i.e., the cyclic spectral correlation and the cyclic spectral coherence, have been proved as powerful tools for CM. On the other hand the application, test, and evaluation of such tools in general industrial cases is still rather limited. Therefore the main aim of this paper is the application and evaluation of advanced diagnostic techniques and diagnostic indicators, including the Enhanced Envelope Spectrum and the Spectral Flatness on real-world vibration data collected from vibration sensors on gearboxes in multiple wind turbines over an extended period of time of nearly four years. The diagnostic indicators are compared with classical statistic time and frequency indicators, i.e., Kurtosis, Crest Factor, etc. and their effectiveness is evaluated based on the successful detection of two failure events.

Convolutional Neural Network for Wind Turbine Failure Classification Based on SCADA Data

As a renewable energy source and an alternative tofossil fuels, the wind power industry is growing rapidly. However,due to harsh weather conditions, wind turbines (WT) still face manyfailures that raise the price of energy produced and reduce thereliability of wind energy. Hence, the use of reliable monitoringand diagnostic systems of WTs is of great importance. Operationand maintenance expenses represent 30% of the total cost of largewind farms. The installation of offshore and remote wind farmshas increased the need for efficient fault detection and conditionmonitoring systems. In this work, without using specific customdevices for monitoring conditions, but only increasing the samplingfrequency in the sensors already available (in all commercial WT) ofthe supervisory control and data acquisition system (SCADA), data-driven multiple fault detection is performed, and a classificationstrategy is developed. The data is processed, and subsequently,using a convolutional neural network (CNN), six faults are classifiedand evaluated with different metrics. Finally, it should be noted thatthe classification speed allows the implementation of this strategyto monitor conditions online in real under-production WTs.

Sustainable Operation and Maintenance of Offshore Wind Farms Based on the Deep Wind Forecasting

Offshore wind farms are becoming a pivotal solution to address the increasing energy demand worldwide and reduce carbon emissions to achieve a sustainable energy sector. Considering the higher operational and maintenance cost of offshore wind farms, it is important to make a good maintenance plan to guarantee the system’s reliability and reduce the total cost related to maintenance activities at the same time. Because maintenance planning is a long-term decision problem and the wind force is random, long-term wind force prediction is needed to help managers evaluate the loss caused by maintenances to be executed in the future. However, long-term wind force prediction is naturally complicated, which is much harder than the short-term (e.g., day-ahead) prediction widely investigated in the literature. In order to overcome this difficulty, we design a deep learning framework combining variational mode decomposition, a convolution neural network, long short-term memory network, and full-connected network. Using the public data from the city of Leeds, the prediction accuracy of the above framework is validated by comparing it with other prediction techniques. Then, the predicted wind force is input into the established optimization model determining preventive maintenances during a predefined period. Because the uncertainty of wind force is replaced by the prediction value, the optimization model can be established as a mixed-integer linear programing model, which only contains limited variables and can be solved quickly. Lastly, an abundance of numerical experiments are conducted to validate the effectiveness of the proposed optimization model, based on which some managerial insights are provided to the managers of offshore wind farms about the optimal operations and maintenance strategy. The research outcome will greatly promote the development of the wind power industry in the future.

A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

Concept design and floating installation method study of multi-bucket foundation floating platform for offshore wind turbines

Efficient installation technologies for foundations and integrated offshore wind turbines play an important role in reducing the construction costs of offshore wind farms. The paper proposes a concept of a multi-bucket foundation floating platform (MBFFP) for the simultaneous transport of several bucket foundation-tower-turbine (BFTT) units, which relies on the fact that the bucket foundation can be prefabricated in bulk onshore and has reasonable floating stability. In addition, preliminary feasibility studies are carried out on static stability characteristics, hydrodynamic characteristics, multi-body coupling characteristics, and structural reliability. Firstly, the MBFFP concept is developed by drawing on our team's existing engineering experience of floating platforms and on-site monitoring data, with the objective of large-scale transport and installation of BFTTs. Next, the BFTTs transportation and installation method are presented. The static stability, hydrodynamic and multi-body coupling characteristics of the MBFFP are systematically studied based on numerical simulation methods, and the strategies of foundation shipment, integrated transportation, and multi-body coupling are proposed. Finally, a SESAM-ABAQUS co-simulation method is used to investigate the spatial distribution of structural stresses and the time history of nacelle vibration acceleration at different drafts, wave heights, and wave periods. The suitable operating sea conditions are summarized based on the nacelle acceleration limits and material ultimate strength. The results show that the roll and pitch of the MBFFP during floating do not exceed 2.5° and the stresses in all parts of the structure during installation are below 250 MPa at 2.5 m significant wave height and 11 s peak period, which indicates preliminary technical feasibility. Compared with traditional installation techniques, the proposed structure and method will contribute to the efficient transportation and installation of BFTTs.

Fault Diagnosis of Wind Turbines using Scada Data by ML Techniques

Fault diagnosis technology is crucial for the safety and stability of the operation of wind turbines. Efficient wind turbine fault diagnosis technology quickly identifies the types of failures in order to reduce the operating and maintenance costs of wind farms and improve the efficiency of power generation. The majority of wind farms currently use supervisory control and data acquisition (SCADA) systems to acquire operation and maintenance data. SCADA systems are rich in data pertaining to the working parameters of wind turbines. Moreover, fault diagnostic functionality is not common in SCADA systems. SVMs are a common intelligence technique used in the fault diagnostics of wind turbines. The choice of SVM parameters is essential for precise model classification. The penalty factor and kernel function parameter of SVM were optimi zed in this study along with the construction of the SSA-ABC wind turbine defect detection model using the Artificial Bee Colony (ABC), a brand-new and very effective optimization technique. Data are obtained from a wind farm SCADA system and, after pre-processing and feature selection, entail a faulting set. Evidence suggests that the ABC-SVM diagnostic model, which has a quick convergence rate and a potent optimization capability, effectively enhances the accuracy of wind turbine fault diagnosis when compared to the GS-SVM, GA-SVM, and PSO-SVM models. The ABC-SVM diagnostic model may also be utilized in real-world engineering applications to identify defects.

Wind turbine fault detection and identification through self-attention-based mechanism embedded with a multivariable query pattern

Condition monitoring (CM) of wind turbines (WTs) is commonly accepted as an effective way to increase the availability and reduce the operation and maintenance (O&M) costs of wind farms. CM methods based on data from Supervisory Control and Data Acquisition (SCADA) system have garnered a great deal of interest due to their accessibility and low cost. Existing studies based on SCADA data provide whether the WT is normal or abnormal, and further the fault locations if an anomaly occurs. Nevertheless, few studies have attempted to identify the underlying causes of the anomaly. In this paper, a novel WTCM method utilizing a self-attention-based mechanism embedded with a multivariable query pattern is proposed for the anomaly detection and underlying causes identification. Firstly, an anomaly detection model composed of multiple cascaded encoders and decoders with a multi-head self-attention mechanism is proposed to extract cross-variable correlations. Furthermore, the multivariable query pattern is designed to evaluate the influences of different features on the target, which is essential for fault identification. Finally, the relative anomaly index (RAI) is proposed for each feature to quantify the impact of each feature on the anomaly. RAIs are adopted to analyze the underlying causes of anomalies. Experimental results confirmed the effectiveness of the proposed method for the WT fault early detection and identification of anomaly causes.

Assessment of energy production and costs associated with a massive exploitation of wind power in the North Sea

A study to assess the potential of a massive exploitation of offshore wind power in the North Sea has been performed. The study includes both a prediction of the available wind resources and the costs associated with such a large-scale exploitation of the wind power in the North Sea. The main parameters of the model are wind turbine size, average distance between the turbines (determined from area and number of turbines), as well as wind resources (i.e. mean wind speed Weibull distribution) and water depths conditioned on specific locations within the North Sea. The cost model includes expressions for the most essential wind farm cost elements, such as costs of wind turbines, support structures, cables and electrical substations, as well as costs of operation and maintenance - all as function of wind climate, rotor size, interspatial distance between the turbines and water depth. A main outcome of the study, is that exploiting an area of 180.000 km2, corresponding to about 1/3 of the North Sea, covered with 60.000 20 MW wind turbines, may in average produce electricity corresponding to the consumption of Europe at a price of 6.2 €cents/kWh (in 2020 prices). Furthermore, more energy could actually be captured by installing the wind turbines closer to each other. As an example, doubling the average power by installing more wind turbines on the same area will increase the levelized cost of energy from 6.2 €cent/kWh to 10.1 €cent/kWh. Still, this may be relevant in case of scarcity of suitable sites for a massive exploration.

Developing the optimal vessel fleet size and mix model to minimize the transportation cost of offshore wind farms

It is crucial for the present generation of offshore wind farms to develop ways to reduce their operation and maintenance expenses to sell electricity at competitive market pricing. A wind farm located offshore will typically consist of a huge number of turbines, each of which must be maintained regularly. It is vital to make maintenance plans that reduce production losses and downtime. Also, it is essential to effectively use expensive resources such as service vessels and technician hours. This article starts with an overview of the vessel fleet size and mix problem that arises while maintaining offshore wind farms using a fleet of maintenance vessels. The main emphasize of this work is to establish the best assignments of vessels to the routes and technicians on the vessels in terms of cost. In this paper an integer programming model is formulated taking into consideration the relevant characteristic peculiarities and limits encountered in the vessel management of offshore wind farm. An algorithm is developed that has been implemented using a commercial solver. A case study is done to show the computational and dimensional perspectives of the problems in real-life settings. The findings give not only the optimal cost but also the comprehensive arrangements, both of which are directly applicable to the process of planning for maintenance vessel fleet size and mix selection.

Design and Analysis of an Offshore Wind Power to Ammonia Production System in Nova Scotia

Green ammonia has potential as a zero-emissions energy vector in applications such as energy storage, transmission and distribution, and zero-emissions transportation. Renewable energy such as offshore wind energy has been proposed to power its production. This paper designed and analyzed an on-land small-scale power-to-ammonia (P2A) production system with a target nominal output of 15 tonnes of ammonia per day, which will use an 8 MW offshore turbine system off the coast of Nova Scotia, Canada as the main power source. The P2A system consists of a reverse osmosis system, a proton exchange membrane (PEM) electrolyser, a hydrogen storage tank, a nitrogen generator, a set of compressors and heat exchangers, an autothermal Haber-Bosch reactor, and an ammonia storage tank. The system uses an electrical grid as a back-up for when the wind energy is insufficient as the process assumes a steady state. Two scenarios were analyzed with Scenario 1 producing a steady state of 15 tonnes of ammonia per day, and Scenario 2 being one that switched production rates whenever wind speeds were low to 55% the nominal capacity. The results show that the grid connected P2A system has significant emissions for both scenarios, which is larger than the traditional fossil-fuel based ammonia production, when using the grid in provinces like Nova Scotia, even if it is just a back-up during low wind power generation. The levelized cost of ammonia (LCOA) was calculated to be at least 2323 CAD tonne−1 for both scenarios which is not cost competitive in this small production scale. Scaling up the whole system, reducing the reliance on the electricity grid, increasing service life, and decreasing windfarm costs could reduce the LCOA and make this P2A process more cost competitive.

Power Prediction of Wind Turbine Based on The Presumed Shape of Power Curve

An accurate model of power plays a crucial role in turbine energy assessment, wind turbine condition monitoring, estimation of wind energy potential, warranty formulations, power forecasting, wind turbine selection, optimization of the operational cost and expansion of windfarm. To achieve all these, algorithms of linear and cubic law models are used to predict the output power of BWC Excel 10 wind turbine. The comparative results show that the considered models can approximate and satisfactorily predicts the output power of wind turbines when compared with fundamental equation of wind turbine that depends on stringent factors like air density, turbine blade parameters, mechanical and control issues etc to yield similar results.

Quantifying the land-based opportunity carbon costs of onshore wind farms

The development of onshore wind energy impacts the land where it is constructed, together with competition for natural resources between the energy and land sector. The loss of terrestrial carbon stocks and ecosystem services from land use change to wind farms can be interpreted as the opportunity cost that landowners give up by choosing to construct wind farms on their land. Here, we spatially quantify the impact onshore wind farms have on land when we factor in the opportunity carbon (C) costs. We found that, the construction of 3848 wind turbines in Scotland generated 4.9 million tonnes of carbon dioxide (CO 2 ) emissions from land use change. On average the emission intensity of land use change in peatland is 560 g CO 2 kWh −1 , in forestry is 88 g CO 2 kWh −1 , in cropland is 45 g CO 2 kWh −1 , and in pastureland is 30 g CO 2 kWh −1 . In the worst land use change scenario, the displacement of Dystrophic basin peat habitats generated 1760 g CO 2 kWh −1 , which is comparable to the life cycle emissions of fossil-fuel technologies such as coal and gas-fired electricity generation. In arable land, the loss of harvestable crop to wind power was forfeited for a gain in opportunity costs up to £15.4 million over a 25 year operating life. Considering the short-term value of CO 2 in the trading market, the opportunity carbon costs of onshore wind farms can range from £0.3 to £65.0 per MWh of electricity generated per year. These findings highlight that the preservation of terrestrial carbon stocks and crop production in the land sector require the development of new payment schemes that can compete economically against the monetary benefits that landowners can access from lease agreements agreed with energy companies. This ensures also that wind turbines are geographically placed to protect ecosystem C stocks, and to minimize the carbon intensity of the electricity generated. • The displacement of terrestrial carbon stocks is crucial to quantifying the environmental impact of onshore wind energy. • Direct and indirect greenhouse gas emissions are quantified spatially based on land cover types and wind farm characteristics. • Emissions of land use change from the construction of 3848 wind turbines across Scotland vary from 16 g CO 2 kWh −1 in shrubland to 1760 g CO 2 kWh −1 in peatland. • Opportunity costs of onshore wind farms range from £0.30 to £65.0 per MWh of electricity generated per year.