Greenhouse gas emissions from electricity generated by offshore wind farms

For wind power generation offshore sites offer significantly better wind conditions compared to onshore. At the same time, the demand for raw materials and therefore the related environmental impacts increase due to technically more demanding wind energy converters and additional components (e.g. substructure) for the balance of plant. Additionally, due to environmental concerns offshore wind farms will be sited farshore (i.e. in deep water) in the future having a significant impact on the operation and maintenance efforts (O&M). Against this background the goal of this analysis is an assessment of the specific GHG (greenhouse gas) emissions as a function of the site conditions, the wind mill technology and the O&M necessities. Therefore, a representative offshore wind farm is defined and subjected to a detailed LCA (life cycle assessment). Based on parameter variations and modifications within the technical and logistical system, promising configurations regarding GHG emissions are determined for different site conditions. Results show, that all parameters related to the energy yield have a distinctive impact on the specific GHG emissions, whereas the distance to shore and the water depth affect the results marginally. By utilizing the given improvement potentials GHG emissions of electricity from offshore wind farms are comparable to those achieved onshore.     

Life cycle assessment of a multi-megawatt wind turbine

At the present moment in time, renewable energy sources have achieved great significance for modern day society. The main reason for this boom is the need to use alternative sources of energy to fossil fuels which are free of CO₂ emissions and contamination. Among the current renewable energy sources, the growth of wind farms has been spectacular. Wind power uses the kinetic energy of the wind to produce a clean form of energy without producing contamination or emissions. The problem it raises is that of quantifying to what extent it is a totally clean form of energy. In this sense we have to consider not only the emissions produced while they are in operation, but also the contamination and environmental impact resulting from their manufacture and the future dismantling of the turbines when they come to the end of their working life. The aim of this study is to analyse the real impact that this technology has if we consider the whole life cycle. The application of the ISO 14040 standard [ISO. ISO 14040. Environmental management – life cycle assessment – principles and framework. Geneva, Switzerland: International Standard Organization; 1998.] allows us to make an LCA study quantifying the overall impact of a wind turbine and each of its components.Applying this methodology, the wind turbine is analysed during all the phases of its life cycle, from cradle to grave, with regard to the manufacture of its key components (through the incorporation of cut-off criteria), transport to the wind farm, subsequent installation, start-up, maintenance and final dismantling and stripping down into waste materials and their treatment.     

Life cycle cost assessment of wind power–hydrogen coupled integrated energy system

Aiming at the economic evaluation of wind power-hydrogen coupled integrated energy system (WPHCIES), a life-cycle economic assessment method of integrated energy system is proposed. Firstly, the integrated scheme and operation mode of integrated energy system are given. Secondly, with the net profit of the integrated energy system in the whole life cycle as objectives and the energy flow and stable operation of the system as constraints, a mathematical model for the economic evaluation of the given integrated energy system in the whole life cycle is constructed, and the payback period and net profit of life cycle of the given system can be reckoned by the model. Finally, taking a wind farm in South China as background, the life cycle economic evaluation model of the proposed system is simulated and calculated, and the effects of both the ratio of hydrogen production from wind power and the ratio of hydrogen to fuel cell on the net profit of wind power-hydrogen coupled integrated energy system during the payback period and the whole life cycle are further analyzed. In addition, the comparison demonstrated that the capital return period of the wind farm can be reduced from 11 years to 8.13 years, and the cumulative net income can be increased from 0.67 billion yuan to 0.93 billion yuan by reasonably choosing the power ratio of hydrogen production from wind power.     

Life cycle energy and greenhouse emissions analysis of wind turbines and the effect of size on energy yield

Wind turbines, used to generate renewable energy, are typically considered to take only a number of months to produce as much energy as is required in their manufacture and operation. With a life expectancy of upwards of 20 years, the energy produced by wind turbines over their life can be many times greater than that embodied in their production. Many previous life cycle energy studies of wind turbines are based on methods of assessment now known to be incomplete. These studies may underestimate the energy embodied in wind turbines by more than 50%, potentially overestimating the energy yield of those systems and possibly affecting the comparison of energy generation options. With the increasing trend towards larger scale wind turbines, comes a respective increase in the energy required for their manufacture. It is important to consider whether or not these increases in wind turbine size, and thus embodied energy, can be adequately justified by equivalent increases in the energy yield of such systems. This paper presents the results of a life cycle energy and greenhouse emissions analysis of two wind turbines and considers the effect of wind turbine size on energy yield. The issue of incompleteness associated with many past life cycle energy studies is also addressed. Energy yield ratios of 21 and 23 were found for a small and large scale wind turbine, respectively. The embodied energy component was found to be more significant than in previous studies, emphasised here due to the innovative use of a hybrid embodied energy analysis approach. The life cycle energy requirements were shown to be offset by the energy produced within the first 12 months of operation. The size of wind turbines appears to not be an important factor in optimising their life cycle energy performance.     

Life cycle assessment of the offshore wind farm alpha ventus

Due to better wind conditions at sea, offshore wind farms have the advantage of higher electricity production compared to onshore and inland wind farms. In contrast, a greater material input, leading to increased energy consumptions and emissions during the production phase, is required to build offshore wind farms. These contrary effects are investigated for the first German offshore wind farm alpha ventus in the North Sea. In a life cycle assessment its environmental influence is compared to that of Germany’s electricity mix.In comparison to the mix, alpha ventus had better indicators in nearly every investigated impact category. One kilowatt-hour electricity, generated by the wind farm, was burdened with 0.137 kWh Primary Energy-Equivalent and 32 g CO₂-Equivalent, which represented only a small proportion of the accordant values for the mix. Furthermore, the offshore foundations as well as the submarine cable were the main energy intensive components. The energetic and greenhouse gas payback period was less than one year.Therefore, offshore wind power, even in deep water, is compatible with the switch to sustainable electricity production relying on renewable energies. Additional research, taking backup power plants as well as increasingly required energy storage systems into account, will allow further calculation.     

Analysis of wind power generation and prediction using ANN: A case study

Many developing nations, such as India have embarked upon wind energy programs for areas experiencing high average wind speeds throughout the year. One of the states in India that is actively pursuing wind power generation programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind farm concentration is high. Wind energy engineers are interested in studies that aim at assessing the output of wind farms, for which, artificial intelligence techniques can be usefully adapted. The present paper attempts to apply this concept for assessment of the wind energy output of wind farms in Muppandal, Tamil Nadu (India). Field data are collected from seven wind farms at this site over a period of 3 years from April 2002 to March 2005 and used for the analysis and prediction of power generation from wind farms. The model has been developed with the help of neural network methodology. It involves three input variables—wind speed, relative humidity and generation hours and one output variable-energy output of wind farms. The modeling is done using MATLAB toolbox. The model accuracy is evaluated by comparing the simulated results with the actual measured values at the wind farms and is found to be in good agreement.     

Aggregated dynamic model for wind farms with doubly fed induction generator wind turbines

As a result of increasing wind farms penetration in power systems, the wind farms begin to influence power system, and thus the modelling of wind farms has become an interesting research topic. Nowadays, doubly fed induction generator based on wind turbine is the most widely used technology for wind farms due to its main advantages such as high-energy efficiency and controllability, and improved power quality. When the impact of a wind farm on power systems is studied, the behavior of the wind farm at the point common coupling to grid can be represented by an equivalent model derived from the aggregation of wind turbines into an equivalent wind turbine, instead of the complete model including the modelling of all the wind turbines. In this paper, a new equivalent model of wind farms with doubly fed induction generator wind turbines is proposed to represent the collective response of the wind farm by one single equivalent wind turbine, even although the aggregated wind turbines operate receiving different incoming winds. The effectiveness of the equivalent model to represent the collective response of the wind farm is demonstrated by comparing the simulation results of equivalent and complete models both during normal operation and grid disturbances.     

Wind farms on undegraded peatlands are unlikely to reduce future carbon emissions

Onshore wind energy is a key component of the renewable energies used by governments to reduce carbon emissions from electricity production, but will carbon emissions be reduced when wind farms are located on carbon-rich peatands? Wind farms are often located in uplands because most are of low agricultural value, are distant from residential areas, and are windy. Many UK uplands are peatlands, with layers of accumulated peat that represent a large stock of soil carbon. When peatlands are drained for construction there is a higher risk of net carbon loss than for mineral soils. Previous work suggests that wind farms sited on peatlands can reduce net carbon emissions if strictly managed for maximum retention of carbon. Here we show that, whereas in 2010, most sites had potential to provide net carbon savings, by 2040 most sites will not reduce carbon emissions even with careful management. This is due to projected changes in the proportion of fossil fuels used to generate electricity. The results suggest future policy should avoid constructing wind farms on undegraded peatlands unless drainage of peat is minimal and the volume excavated in foundations can be significantly reduced compared to energy output.     

A wind-hydrogen energy storage system model for massive wind energy curtailment

With wind energy penetration rate increasing, wind energy curtailment turns severe in some wind farms nowadays and new wind farm construction trends to aggregate this situation. Therefore the need for massive energy storage technology such as “Power to gas” is growing. In this study, a model of integrating curtailed wind energy with hydrogen energy storage is established based on real time data in term of 10 min avg. throughout a whole year in a wind farm. Two wind/hydrogen production scenarios via water electrolysis are given and the influence exerted on payback period by electrolyser power and hydrogen price is talked in tandem as well as the model validity is specified in the conclusion section. Our results further stress the importance of hydrogen energy storage technology on addressing wind energy curtailment and disclose some regularities from an economical perspective.     

Monte Carlo analysis of wind farm surge arresters risk of failure due to lightning surges

This paper presents a Monte Carlo procedure intended for the assessment of the metal-oxide (MO) surge arresters risk of failure in onshore wind farms. It focuses on the energy withstand (absorption) capability of the MO surge arresters in relation to lightning surges and in terms of their risk of failure assessment. Presented methodology accounts for the fact that the lightning itself is stochastic in nature and that the MO surge arrester energy capability is a statistical quantity. The well-known backsurge phenomenon is employed as a means for studying the MO surge arresters energy stresses due to lightning surge transients (in onshore wind farms), where the associated transient (i.e. high-frequency) models of particular wind farm components feature prominently. Necessary numerical simulations are carried-out with the well-known EMTP-ATP software package. This procedure could be seen as beneficial in selection of the optimal MO surge arrester energy withstand capability for wind farm projects situated in areas marked with high keraunic levels and/or having high soil resistivity.     

Dynamic models of wind farms with fixed speed wind turbines

The increasing wind power penetration on power systems requires the development of adequate wind farms models for representing the dynamic behaviour of wind farms on power systems. The behaviour of a wind farm can be represented by a detailed model including the modelling of all wind turbines and the wind farm electrical network. But this detailed model presents a high order model if a wind farm with high number of wind turbines is modelled and therefore the simulation time is long. The development of equivalent wind farm models enables the model order and the computation time to be reduced when the impact of wind farms on power systems is studied. In this paper, equivalent models of wind farms with fixed speed wind turbines are proposed by aggregating wind turbines into an equivalent wind turbine that operates on an equivalent wind farm electrical network. Two equivalent wind turbines have been developed: one for aggregated wind turbines with similar winds, and another for aggregated wind turbines under any incoming wind, even with different incoming winds.The proposed equivalent models provide high accuracy for representing the dynamic response of wind farm on power system simulations with an important reduction of model order and simulation time compare to that of the complete wind farm modelled by the detailed model.     

Development of a viability assessment model for hydrogen production from dedicated offshore wind farms

Dedicated offshore wind farms for hydrogen production are a promising option to unlock the full potential of offshore wind energy, attain decarbonisation and energy security targets in electricity and other sectors, and cope with grid expansion constraints. Current knowledge on these systems is limited, particularly the economic aspects. Therefore, a new, integrated and analytical model for viability assessment of hydrogen production from dedicated offshore wind farms is developed in this paper. This includes the formulae for calculating wind power output, electrolysis plant size, and hydrogen production from time-varying wind speed. All the costs are projected to a specified time using both Discounted Payback (DPB) and Net Present Value (NPV) to consider the value of capital over time. A case study considers a hypothetical wind farm of 101.3 MW situated in a potential offshore wind development pipeline off the East Coast of Ireland. All the costs of the wind farm and the electrolysis plant are for 2030, based on reference costs in the literature. Proton exchange membrane electrolysers and underground storage of hydrogen are used. The analysis shows that the DPB and NPV flows for several scenarios of storage are in good agreement and that the viability model performs well. The offshore wind farm – hydrogen production system is found to be profitable in 2030 at a hydrogen price of €5/kg and underground storage capacities ranging from 2 days to 45 days of hydrogen production. The model is helpful for rapid assessment or optimisation of both economics and feasibility of dedicated offshore wind farm – hydrogen production systems.     

Mitigating wind exposure with zero-cost collar insurance

Renewable energy generation worldwide has relied increasingly on wind farms where wind energy is transformed into electricity. On the other hand, electricity prices are uncertain and wind speeds are highly variable, which exposes the producer to risks. Typically wind power producers enter into long term fixed price contracts in order to hedge against energy price risk, but these contracts expose the wind farm to energy volume risk, as they require delivery of the full amount of energy contracted, even if energy production falls short due to low wind speeds. To mitigate this risk, wind producers can purchase insurance. This article proposes a zero-cost collar insurance and develop a stochastic model to determine the feasible range of wind strikes for both the wind farm and the insurer. The results indicate there is a set of possible strike combinations that meets the objectives of both parties.     

Hydrogen production from wind energy in Western Canada for upgrading bitumen from oil sands

Hydrogen is produced via steam methane reforming (SMR) for bitumen upgrading which results in significant greenhouse gas (GHG) emissions. Wind energy based hydrogen can reduce the GHG footprint of the bitumen upgrading industry. This paper is aimed at developing a detailed data-intensive techno-economic model for assessment of hydrogen production from wind energy via the electrolysis of water. The proposed wind/hydrogen plant is based on an expansion of an existing wind farm with unit wind turbine size of 1.8 MW and with a dual functionality of hydrogen production and electricity generation. An electrolyser size of 240 kW (50 Nm³ H₂/h) and 360 kW (90 Nm³ H₂/h) proved to be the optimal sizes for constant and variable flow rate electrolysers, respectively. The electrolyser sizes aforementioned yielded a minimum hydrogen production price at base case conditions of $10.15/kg H₂ and $7.55/kg H₂. The inclusion of a Feed-in-Tariff (FIT) of $0.13/kWh renders the production price of hydrogen equal to SMR i.e. $0.96/kg H_2, with an internal rate of return (IRR) of 24%. The minimum hydrogen delivery cost was $4.96/kg H₂ at base case conditions. The life cycle CO₂ emissions is 6.35 kg CO₂/kg H₂ including hydrogen delivery to the upgrader via compressed gas trucks.     

The impact of land use constraints in multi-objective energy-noise wind farm layout optimization

Recently the environmental impact of onshore wind farms is receiving major attention from both governments and wind farm designers. As land is more extensively exploited for wind farms, it is more likely for wind turbines to be in proximity with human dwellings, infrastructure (e.g. roads, transmission lines), and natural habitats (e.g. rivers, lakes, forests). This proximity makes significant portions of land unusable for the designers, introducing a set of land-use constraints. In this study, we conduct a constrained and continuous-variable multi-objective optimization that considers energy and noise as its objective functions, based on Jensen's wake model and the ISO-9613-2 noise standard. A stochastic evolutionary algorithm (NSGA-II) solves the optimization problem, while the land-use constraints are handled with static and dynamic penalty functions. Results of this study illustrate the effect of constraint severity and spatial distribution of unusable land on the trade-off between energy generation and noise production.     

Transfer learning for short-term wind speed prediction with deep neural networks

As a type of clean and renewable energy source, wind power is widely used. However, owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model for large-scale wind power penetration. Numerical weather prediction (NWP) and data-driven modeling are two typical paradigms. NWP is usually unavailable or spatially insufficient. Data-driven modeling is an effective candidate. As to some newly-built wind farms, sufficient historical data is not available for training an accurate model, while some older wind farms may have long-term wind speed records. A question arises regarding whether the prediction model trained by data coming from older farms is also effective for a newly-built farm. In this paper, we propose an interesting trial of transferring the information obtained from data-rich farms to a newly-built farm. It is well known that deep learning can extract a high-level representation of raw data. We introduce deep neural networks, trained by data from data-rich farms, to extract wind speed patterns, and then finely tune the mapping with data coming from newly-built farms. In this way, the trained network transfers information from one farm to another. The experimental results show that prediction errors are significantly reduced using the proposed technique.     

Portfolio theory application in wind potential assessment

Development of a wind farm project includes a lot of interconnected steps and one of the most important ones is the proper energy yield assessment. Wind energy yield assessment is typically based on wind measurements on a measurement mast that are later used in one of the wind flow software models. In cases where there are multiple wind measurements on the potential wind farm site, a question arises on how to optimally use all the available data. This paper shows a method of using such data through the application of the portfolio theory, a well-established theory in economics and frequently used in other scientific disciplines. The method shown is very flexible in terms of input data and software models, and the results of its application show that it is possible to increase accuracy and reduce uncertainty of energy yield assessment. The key result of the method is the possibility to achieve better quality of input data for the energy yield assessment without spending additional resources. The method opens up a wide space for further research and improvements, all with the objective of achieving better results of energy yield assessment and finally, better prepared wind project.     

The role of energy storage in accessing remote wind resources in the Midwest

Replacing current generation with wind energy would help reduce the emissions associated with fossil fuel electricity generation. However, integrating wind into the electricity grid is not without cost. Wind power output is highly variable and average capacity factors from wind farms are often much lower than conventional generators. Further, the best wind resources with highest capacity factors are often located far away from load centers and accessing them therefore requires transmission investments. Energy storage capacity could be an alternative to some of the required transmission investment, thereby reducing capital costs for accessing remote wind farms. This work focuses on the trade-offs between energy storage and transmission. In a case study of a 200 MW wind farm in North Dakota to deliver power to Illinois, we estimate the size of transmission and energy storage capacity that yields the lowest average cost of generating and delivering electricity ($/MW h) from this farm. We find that transmission costs must be at least $600/MW-km and energy storage must cost at most $100/kW h in order for this application of energy storage to be economical.     

Wind energy potential assessment for the offshore areas of Taiwan west coast and Penghu Archipelago

The average wind speed and wind power density of Taiwan had been evaluated at 10 m, 30 m and 50 m by simulation of mesoscale numerical weather prediction model (MM5). The results showed that wind energy potential of this area is excellent. Taiwan has offered funds to encourage the founding of offshore wind farms in this area. The purpose of this study is to make a high resolution wind energy assessment for the offshore area of Taiwan west coast and Penghu archipelago by using WAsP. The result of this study has been used to the relative financial planning of offshore wind farm projects in Taiwan. The basic inputs of WAsP include wind weather data and terrain data. The wind weather data was from a monitoring station located on a remote island, Tongi, because that all of weather stations in the area of Taiwan west coast are affected by urbanization. SRTM was selected to be used as terrain data and downloaded from CGIAR-CSI for voids problem. The coverage of considered terrain area in this assessment work is about 300 km × 400 km that made some difficulties to run wind energy assessment of the whole area with a high resolution of 100 m. So the interested area of this study is divided into 19 areas for the wind energy assessment and mapping. The assessment results show the Changhua area has best wind energy potential in the area of Taiwan west coast which power density is above 1000 W/m² height and the areas of Penghu archipelago are above 1300 W. These results are higher than the expected from NWP. 180 of 3 MW wind turbines were used in the study of micro sitting in the Changhua area.The type and number of the wind turbines and the layout of the wind farm is similar to the prior study of Taipower Company for demonstrating the reliability of this study. The assessment result of average net annual energy production (AEP) of the wind farm is about 11.3 GWh that is very close to the prior study. The terrain effect is also studied. The average net annual energy production will decrease about 0.7 GWh if the wind turbines were moved eastward 3600 m closer to the coast because of terrain effect. As the same reason, the average net annual energy production would be increased to 11.392 GWh if the wind farm is moved westward 3600 m away from the coast.     

A geographic analysis of wind turbine placement in Northern California

The development of new wind energy projects requires a significant consideration of land use issues. An analytic framework using a Geographic Information System (GIS) was developed to evaluate site suitability for wind turbines and to predict the locations and extent of land available for feasible wind power development. The framework uses rule-based spatial analysis to evaluate different scenarios. The suitability criteria include physical requirements as well as environmental and human impact factors. By including socio-political concerns, this technique can assist in forecasting the acceptance level of wind farms by the public. The analysis was used to evaluate the nine-county region of the Greater San Francisco Bay Area. The model accurately depicts areas where large-scale wind farms have been developed or proposed. It also shows that there are many locations available in the Bay Area for the placement of smaller-scale wind turbines. The framework has application to other regions where future wind farm development is proposed. This information can be used by energy planners to predict the extent that wind energy can be developed based on land availability and public perception.