Existing Wind Farms Research Articles

Tidal energy site characterisation in a large tidal channel in Banks Strait, Tasmania, Australia

Banks Strait is a 15 km wide tidal channel in Tasmania, Australia, and has been identified as a prospective site for tidal energy extraction, however flow characteristics have never been investigated. We present detailed field measurements from campaigns which deployed multiple Acoustic Doppler Current Profiles (ADCPs) across Banks Strait to analyze energy resources. The deployment period lasted for almost a year including a rigorous technical assessment of tidal stream resource that exceeded IEC site characterisation standards. Our results reveal that the Banks Strait channel has sufficient depth but that tidal currents generally do not exceed 2.5 m/s at the mooring sites. Large areas reveal an active wave climate and significant wave current-interaction. Maximum values of in-situ tidal kinetic power densities were approximately 0.85 kW/m2 on the southern side of the channel. The northern side suggests even more energy potential although ADCPs could not be recovered due to strong currents and sediment movement. The results will feed into numerical models to further investigate the energy potential. Based on our in-situ estimates, a tidal turbine farm could bring significant advantages to the region by reducing intermittency and increasing constant energy extraction to support an already existing wind farm located in north eastern Tasmania.

Formulation of a wind farm control strategy considering lifetime of DC‐link capacitor bank of type IV wind turbines

The cost of energy should be minimised to increase market penetration of wind energy conversion systems. The existing wind farm control strategies are not helpful since they speed up the damage in upstream wind turbines. Therefore, a control strategy is proposed to maximise the energy yield while equalising lifetime of one of the most fragile sub-system of the wind turbines. The DC-link capacitor bank of the back-to-back converters is considered to this end. The proposed control strategy can be directly applied in systems based on type 4 wind turbines with full power converters. This exposition reveals the formulation of the control strategy by analysing the strengths and weaknesses of the existing strategies based on the maximum power output. The lifetime-based control strategy is extended to formulate a multi-stage reactive power dispatch method with the help of a total energy-based strategy. The effectiveness of all control strategies is validated using the model-based simulations.

Economic investigation of repowering of the existing wind farms with hybrid wind and solar power plants: a case study

In Tamil Nadu, India, the old wind turbines installed in 1988 have to be repowered and replaced by modern/advanced technological wind turbines. A solar power plant can be set up using the vast area between the wind turbines on the farm. The economic evaluation must be carried out to determine whether this hybrid project is economically beneficial. This article aims to examine the financial viability of repowering old wind turbines with solar power plants’ inclusion. According to the initial investment costs specified within fixed areas and the distance between turbines, the issue is articulated as optimizing the net present value (NPV) of the project. In this study, the economic investigation on the wind farm located in Kayathar was explored by replacing 30 numbers of 200 kW old wind turbines with modern wind turbines of 2000–3000 kW and installing the solar power plant between wind turbines, excluding the shadow region of wind turbines. The HOMER Pro software and Excel spreadsheet were used to analyze the optimal allocation, economic, electricity production, and sensitivity analyses of the effective hybrid systems. The value of electricity prices was measured using the levelized cost of energy and NPV methods. Additionally, a sensitivity analysis of the chosen parameters for the internal rate of return and debt service coverage ratio was estimated for wind and solar hybridization. The economic analysis suggests satisfactory profitability for the project, even without depending on subsidies. Furthermore, sensitivity analysis helps one see the consequences of the risk on the life of the wind farm and its impact on the project’s productivity and performance.

A Minimalistic Prediction Model to Determine Energy Production and Costs of Offshore Wind Farms

A numerical framework for determining the available wind power and associated costs related to the development of large-scale offshore wind farms is presented. The idea is to develop a fast and robust minimal prediction model, which with a limited number of easy accessible input variables can determine the annual energy output and associated costs for a specified offshore wind farm. The utilized approach combines an energy production model for offshore-located wind farms with an associated cost model that only demands global input parameters, such as wind turbine rotor diameter, nameplate capacity, area of the wind farm, number of turbines, water depth, and mean wind speed Weibull parameters for the site. 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—as function of rotor size, interspatial distance between the wind turbines, and water depth. The numbers used in the cost model are based on previous but updatable experiences from offshore wind farms, and are therefore, in general, moderately conservative. The model is validated against data from existing wind farms, and shows generally a very good agreement with actual performance and cost results for a series of well-documented wind farms.

Optimalno dimenzionisanje mikromreže sa obnovljivim izvorima energije u Srbiji

The paper analyzes the formation of the microgrid in South Banat. The paper contains three scenarios related to determining the size of the distribution area of the part of the electric power system of Serbia that can potentially be powered only from renewable energy sources. Based on real data on the production of four wind farms in Serbia (Čibuk, Kovačica, Alibunar and Košava) for the period January - June 2019 and real consumption data, the first scenario was formed which refers to determining the part of distribution area that can be covered from existing wind farms. In this scenario, with respect to the criteria of uninterrupted power supply, the percentage share of energy from the electricity network was determined for different sizes of distribution area. In order to reduce the share of energy from the electricity network, a new scenario was considered, which refers to the addition of production capacities of solar energy. The analysis of the second scenario showed reduced cumulative intermittency of wind and solar power plants compared to the first scenario. The complementary nature of the sun and wind contributes to a better correlation between consumption and production. The third scenario involves adding storage capacity to the second scenario. A genetic algorithm was applied for the optimal determination of the storage capacity of a microgrid in both grid-connected and islanded modes. A correlation coefficient of production and consumption was formed for each scenario. A complete analysis was performed in order to consider the possibility of forming a microgrid in the part of the Serbian power system near wind farms. This analysis showed a reduction in transmission losses, a reduction in CO2 emissions and better voltage conditions.

Optimizing the layout of wind turbines by upgrading a wind farm

The optimization of the size of wind farms is little studied in the literature. The objective of this study is to renew the existing wind farms by inserting new wind turbines with different characteristics. To evaluate our approach, a genetic algorithm was chosen to optimize our objective function, which aims to maximize the power of the wind farm studied at a reasonable cost, the Jensen wake model was chosen for the power calculation of the park. The results obtained from the simulation on the Horns-rev wind farm showed a significant increase in energy and a relatively reasonable cost of energy.

Data investigation of installed and output power densities of onshore and offshore wind turbines worldwide

Differing estimates have emerged about how much land or water area is used by existing wind farms and how much power can be obtained from that area. Whereas, no single unique method exists to define wind farm spacing area, the spacing area (thus installed and output power densities) of a wind farm can be determined in a way to meet specific logical criteria, This study proposes a new, intuitive, data based, automatized method of estimating spacing areas occupied by existing onshore and offshore wind farms worldwide. The method eliminates the erroneous counting of space outside of wind farm boundaries, space between clusters of turbines, and overlapping space that results when assuming a large fixed area around each turbine. At least one of three types of extra space has incorrectly been included in all calculations of wind farm areas to date. Unlike most previous methods, this method also ensures that the addition of a wind turbine to a farm increases the overall required spacing area of the farm. The study then uses data from over 1600 operating wind turbines in 16 onshore and 7 offshore wind farms in 13 countries across 5 continents during the period 2016–2018 to quantify installed and output power densities of these farms. Finally, it compares results with estimates from other studies. Results indicate that the mean (range) installed and output power densities of onshore wind farms in Europe are 19.8 (6.2–46.9) MW/km2 and 6.64 (2.3–8.2) W/m2, respectively; of onshore wind farms outside of Europe are similarly 20.5 (16.5–48) MW/km2 and 6.84 (4.81–11.2) W/m2, respectively; and of offshore wind farms in Europe are 7.2 (3.3–20.2) MW/km2 and 2.94 (1.15–6.32) W/m2, respectively. The mean capacity factors in each case are thus 33.5%, 33.4%, and 40.8%, respectively. These results indicate substantially higher installed and output power densities than previously reported, based simply on different definitions of land area, with no impact on capacity factor. Thus, existing wind turbines may extract more wind power over less land or water than previously thought.

Farmers vs. lakers: Agriculture, amenity, and community in predicting opposition to United States wind energy development

Utility-scale wind energy is now the largest source of renewable electricity in the US. Wind energy’s continued growth remains contingent upon finding adequate resource potential and transmission capacity, along with communities willing to host turbines. While previous research on the social acceptance of wind has relied predominantly on case studies, resident surveys, and reviews of development practices and strategies, here we use a new method. We use a wind contention survey of energy professionals (n = 46) to assess the contention associated with 69 existing wind farms in four US Midwest states and identify underlying characteristics, i.e., agricultural, land-use, and demographic characteristics, that may have predisposed communities to either support or oppose wind farm development. We then use publicly available data to parameterize and model those characteristics using wind farm contention as our dependent variable. Our analysis shows that a greater proportion of production-oriented farming and fewer natural amenities in a community are associated with reduced opposition to wind farm development. Additionally, and perhaps counterintuitively, communities with a greater percentage of residents that voted Republican in the 2016 Presidential election demonstrate less opposition. Rather than negating the need for employing best practices in community engagement, stakeholder development, and participatory decision-making processes, this study can help prepare developers for the type of reception that might await them in potential host communities.

Variability in Wind Energy Generation across the Contiguous United States

AbstractERA5 provides high-resolution, high-quality hourly wind speeds at 100 m and is a unique resource for quantifying temporal variability in likely wind-derived power production across the United States. Gross capacity factors (CF) in seven independent system operators (ISOs) are estimated using the location and rated power of each wind turbine, a simplified power curve, and ERA5 output from 1979 to 2018. Excluding the California ISO, the marginal probability of a calm (zero power production) is less than 0.1 in any ERA5 grid cell. When a calm occurs, the mean co-occurrence across wind-turbine-containing grid cells ranges from 0.38 to 0.39 for ISOs in the Midwest and central plains [Midcontinent (or Midwest) ISO (MISO), Southwest Power Pool (SPP), and the Electric Reliability Council of Texas (ERCOT) region], increasing to 0.54–0.58 for ISOs in the eastern United States [Pennsylvania–New Jersey–Maryland interconnection (PJM), New York ISO (NYISO), and New England ISO (NEISO)]. Periods with low gross CF have a median duration of ≤6 h, except in California, and are most likely during summer. Gross CF exhibit highest variance at periods of 1 day in ERCOT and SPP; on synoptic scales in MISO, NEISO, and NYISO; and on interannual time scales in PJM. This implies differences in optimal strategies for ensuring resilience of supply. Theoretical scenarios show adding wind energy capacity near existing wind farms is advantageous even in areas with high existing installed capacity (IC), while expanding into areas with lower IC is more beneficial to reducing ramps and the probability of gross CF falling below 20%. These results emphasize the benefits of large balancing areas and aggregation in reducing wind power variability and the likelihood of wind droughts.

Public preferences for wind farms involving electricity trade and citizen engagement in Ireland

International trade in electricity from renewable sources can offer a flexible means by which EU member states can meet their country targets but to date in Ireland initiatives to build wind farms for export have been resolutely contested by affected communities. Using a discrete choice experiment (DCE), a survey of respondents living within 10 km of an existing wind farm is conducted to estimate the social impact of wind turbines designed to produce electricity for export under different levels of financial benefit and participation. Most respondents are willing to make trade-offs to allow for an export market but financial compensation required is greater for exports compared to domestic supply. Respondents prefer greater levels of participation than currently offered but favour moderate levels of participation in the absence of financial benefits. Respondents that had a positive experience of living next to an existing wind farm in terms of financial benefits, employment and community representation are generally more accepting of wind farms. Conversely, subjects with a strong sense of place attachment or a less convincing experience in terms of financial benefits and participation were less tolerant of wind farms and more likely to pick the status quo of no new wind farm development.

Wind Farm Loads under Wake Redirection Control

Active wake control (AWC) is a strategy for operating wind farms in such a way as to reduce the wake effects on the wind turbines, potentially increasing the overall power production. There are two concepts to AWC: induction control and wake redirection. The former strategy boils down to down-regulating the upstream turbines in order to increase the wind speed in their wakes. This has generally a positive effect on the turbine loading. The wake redirection concept, which relies on intentional yaw misalignment to move wakes away from downstream turbines, has a much more prominent impact and may lead to increased loading. Moreover, the turbines are typically not designed and certified to operate at large yaw misalignments. Even though the potential upsides in terms of power gain are very interesting, the risk for damage or downtime due to increased loading is seen as the main obstacle preventing large scale implementation of this technology. In order to provide good understanding on the impacts of AWC on the turbine loads, this paper presents the results from an in-depth analysis of the fatigue loads on the turbines of an existing wind farm. Even though for some wind turbine components the fatigue loads do increase for some wind conditions under yaw misalignment, it is demonstrated that the wake-induced loading decreases even more so that the lifetime loads under AWC are generally lower.

Stochastic assessment of aerodynamics within offshore wind farms based on machine-learning

Abstract Wind turbine flow field prediction is difficult as it requires computationally expensive computational fluid dynamics (CFD) models. The contribution of this paper is to propose and develop a method for stochastic analysis of an offshore wind farm using CFD and a non-intrusive stochastic expansion. The approach is developed through testing a range of machine-learning methods, evaluating dataset requirements and comparing the accuracy against site measurement data. The approach used is detailed and the results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation models compared are Artificial Neural Networks, Gaussian Process, Radial Basis Function, Random Forest and Support Vector Regression. RBF achieves a mean absolute error relative to the CFD model of only 0.54% and the error of the SVR predictions relative to the real data, with scatter, was 12%, compared to 16% from Jensen. This approach has the potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

Site selection of wind farms using GIS and multi-criteria decision making method in Wafangdian, China

Abstract Site selection is one of the most significant decision making activities for development of wind energy resource. In this study, a novel method integrating Geographic Information System (GIS), interval Analytic Hierarchy Process (IAHP) and stochastic VIKOR was proposed to tackle site selection issue of wind farms in the Wafangdian region, China. Two major factors, i.e. biodiversity conservation and production safety, were utilized to determine feasible areas. Afterwards, the weight of evaluation criteria, including the social impact, economic benefit, terrain and eco-environment protection, were identified by using IAHP. Finally, the suitability indexes of various alternatives were calculated by stochastic VIKOR and their ranking was used to determine high-suitability areas for wind farm locations. The obtained results indicated that 30.2% of studied region were suitable for installing the wind-power facilities, but only 3.36% were determined to be highly suitable. By comparing the optimized results to the actual locations of existing wind farms, it was revealed that the ideological framework of this study were practical and effective in guiding site selection of wind farms, and they were also useful for applications in site selection of other renewable energy forms involving complex spatial analysis and multiple criteria evaluation, including solar, hydroelectric, geothermal and biomass.

Dynamic rating assists cost-effective expansion of wind farms by utilizing the hidden capacity of transformers

Dynamic rating of power transmission devices is a technology that allows better equipment utilization through real-time monitoring of the weather conditions and the load. Dynamic rating of transformers is a fairly new technology if compared to the dynamic rating of power lines, and has a high potential for significantly improving component utilization while lowering investment costs on installing new transformers.The following work investigates how to utilize already operational transformers, which are used for wind farm connection, for expanding wind generation capacity. Also, this paper shows improvements that dynamic transformer rating can bring to both power grid operators and wind farm owners by exploring the economic benefits of expanding wind parks without investing in new power transformers. Connecting additional wind turbines at sites with high wind potential after the wind park is already in exploitation can assist in lowering electricity price and provide a possibility of less risky investment in the wind energy sector. This paper uses transformer thermal modelling and wind farm expansion techniques such as convolution method and product method to investigate to which extent existing wind farms can be expanded using already installed transformer units.Five transformer locations and nine units are studied for finding the potential of dynamic transformer rating for network expansion applications. The analysis shows that the optimal expansion of wind power from a generator perspective is around 30% to 50%, although, it can be limited further by network restrictions. A possibility to use a large component, such as power transformer, closer to its full potential can provide material and cost savings for building new devices and decrease investment costs on manufacturing, transportation and installation of new units. Dynamic rating of power transformers can also increase the socio-economic benefits of renewable energy by lowering electricity price from renewables and incentivize an increased share of green power in electricity markets.

Wind Energy Implementation to Mitigate Wildfire Risk and Preemptive Blackouts

Damages caused by wildfires in California due to transmission line failures have increased significantly in recent years. Curtailment of electric service in areas under fire threat has been implemented to avoid these wildfires. Results from this research indicated that 24% of California’s cities are at risk of wildfire, while 52% are at risk of blackout. These blackouts have resulted in significant financial losses and risk to life and health. Undergrounding current transmission lines has been proposed as a long-term solution. However, undergrounding lines would take decades to complete and increase average monthly electric bills from $80 to $260. This research investigated shortening the length of the electricity supply chain, supplying affected communities with onshore and offshore wind energy. Results showed good wind energy potential in locations near affected cities. Distance analyses revealed that more than two hundred cities (population 5.5 million) can be served by existing wind farms located at less than 50 km. Future offshore wind turbines could generate high power output (capacity factor >50% for significant periods). An analysis of diverse locations along California’s coast indicated that just one offshore wind farm could serve more than a hundred cities with cumulative population larger than one million.

Application of a GIS-Based Fuzzy Multi-Criteria Evaluation Approach for Wind Farm Site Selection in China

The development and utilization of wind energy has alleviated the problems of energy shortage and environmental pollution; however, it caused many negative impacts due to suboptimal site selections. This study proposes an innovative method integrating Geographic Information System (GIS), fuzzy Analytic Hierarchy Process (FAHP), and fuzzy VIšekriterijumsko KOmpromisno Rangiranje (VIKOR) for site selection of wind farms in the Wafangdian region, China. The uncertainties caused by subjective judgments of the stakeholders were tackled by the FAHP method firstly, where weight values of six criteria were identified. Next, the fuzzy VIKOR method and GIS tool were used to generate the Qi value of each location for ranking their appropriate degrees for wind energy development. The results demonstrated that the middle and upper parts of the studied area are suitable for the exploitation of wind energy, while the central and eastern areas are unfavorable. The influences exerted by various weight combinations and climate change on a site suitability assessment were examined. The resulting comparison with existing wind farms reflected the practicability and reliability of the proposed method; the estimation of climate change impacts on site selection provided the suggestion and support of a long-term plan for wind power development, and even the energy structure adjustment scheme adapted to climate change.

At What Cost Can Renewable Hydrogen Offset Fossil Fuel Use in Ireland’s Gas Network?

The results of a techno-economic model of distributed wind-hydrogen systems (WHS) located at each existing wind farm on the island of Ireland are presented in this paper. Hydrogen is produced by water electrolysis from wind energy and backed up by grid electricity, compressed before temporarily stored, then transported to the nearest injection location on the natural gas network. The model employs a novel correlation-based approach to select an optimum electrolyser capacity that generates a minimum levelised cost of hydrogen production (LCOH) for each WHS. Three scenarios of electrolyser operation are studied: (1) curtailed wind, (2) available wind, and (3) full capacity operations. Additionally, two sets of input parameters are used: (1) current and (2) future techno-economic parameters. Additionally, two electricity prices are considered: (1) low and (2) high prices. A closest facility algorithm in a geographic information system (GIS) package identifies the shortest routes from each WHS to its nearest injection point. By using current parameters, results show that small wind farms are not suitable to run electrolysers under available wind operation. They must be run at full capacity to achieve sufficiently low LCOH. At full capacity, the future average LCOH is 6–8 €/kg with total hydrogen production capacity of 49 kilotonnes per year, or equivalent to nearly 3% of Irish natural gas consumption. This potential will increase significantly due to the projected expansion of installed wind capacity in Ireland from 5 GW in 2020 to 10 GW in 2030.

An Induction Curve Model for Prediction of Power Output of Wind Turbines in Complex Conditions

Power generation from wind farms is traditionally modeled using power curves. These models are used for assessment of wind resources or for forecasting energy production from existing wind farms. However, prediction of power using power curves is not accurate since power curves are based on ideal uniform inflow wind, which do not apply to wind turbines installed in complex and heterogeneous terrains and in wind farms. Therefore, there is a need for new models that account for the effect of non-ideal operating conditions. In this work, we propose a model for effective axial induction factor of wind turbines that can be used for power prediction. The proposed model is tested and compared to traditional power curve for a 2.5 MW horizontal axis wind turbine. Data from supervisory control and data acquisition (SCADA) system along with wind speed measurements from a nacelle-mounted sonic anemometer and turbulence measurements from a nearby meteorological tower are used in the models. The results for a period of four months showed an improvement of 51% in power prediction accuracy, compared to the standard power curve.

Optimization of reliable cyclic cable layouts in offshore wind farms

ABSTRACT A novel approach for optimizing reliable cable layouts in offshore wind farms is presented. While optimization models traditionally are designed to suggest acyclic cable routes, those developed in this work recognize that cyclic layouts reduce the consequences of cable failures. The models under study take into account that cables cannot cross each other, which, particularly in instances with restrictive cable capacity, can make it attractive to let cables follow a joint trajectory, and visit turbines without connecting to them. A two-layered optimization process is developed. The outer layer is associated with an integer programming problem, which is subject to simultaneous generation of rows and columns representing cable paths. In the inner layer, a problem identifying feasible low cost paths is solved, guided by optimal dual variable values in the continuous relaxation of the former problem. Results from experimental applications to existing wind farms show good promise of the method.

“Wind Theft” from Onshore Wind Turbine Arrays: Sensitivity to Wind Farm Parameterization and Resolution

AbstractHigh-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.