Characterizing the Influence of Land Configuration on the Optimal Wind Farm Performance

2011 ◽  
Author(s):  
Souma Chowdhury ◽  
Jie Zhang ◽  
Achille Messac ◽  
Luciano Castillo
Keyword(s):  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Simultaneous Optimization ◽  
Cost Of Energy ◽  
Wind Farm Design ◽  
Farm Performance ◽  
The Cost ◽  
Crucial Part

The development of large scale wind farms that can produce energy at a cost comparable to that of other conventional energy resources presents significant challenges to today’s wind energy industry. The consideration of the key design and environmental factors that influence the performance of a wind farm is a crucial part of the solution to this challenge. In this paper, we develop a methodology to account for the configuration of the farm land (length-to-breadth ratio and North-South-East-West orientation) within the scope of wind farm optimization. This approach appropriately captures the correlation between the (i) land configuration, (ii) the farm layout, and (iii) the selection of turbines-types. Simultaneous optimization of the farm layout and turbine selection is performed to minimize the Cost of Energy (COE), for a set of sample land configurations. The optimized COE and farm efficiency are then represented as functions of the land aspect ratio and the land orientation. To this end, we apply a recently developed response surface method known as the Reliability-Based Hybrid Functions. The overall wind farm design methodology is applied to design a 25MW farm in North Dakota. This case study provides helpful insights into the influence of the land configuration on the optimum farm performance that can be obtained for a particular site.

scholarly journals Noise assessment of the small-scale wind farm

2019 ◽  
Vol 112 ◽  
pp. 02011
Author(s):  
Cristian-Gabriel Alionte ◽  
Daniel-Constantin Comeaga
Keyword(s):  
Power Systems ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Small Scale ◽  
Large Power ◽  
Small Power ◽  
Noise Assessment ◽  
The Impact

The importance of renewable energy and especially of eolian systems is growing. For this reason, we propose the investigation of an important pollutant - the noise, which has become so important that European Commission and European Parliament introduced Directive 2002/49/CE relating to the assessment and management of environmental noise. So far, priority has been given to very large-scale systems connected to national energy systems, wind farms whose highly variable output power could be regulated by large power systems. Nowadays, with the development of small storage capacities, it is feasible to install small power wind turbines in cities of up to 10,000 inhabitants too. As a case study, we propose a simulation for a rural locality where individual wind units could be used. This specific case study is interesting because it provides a new perspective of the impact of noise on the quality of life when the use of this type of system is implemented on a large scale. This option, of distributed and small power wind turbine, can be implemented in the future as an alternative or an adding to the common systems.

Probabilistic Models for Wind Turbine and Wind Farm Performance

2011 ◽  
Vol 133 (4) ◽  
Author(s):  
Sanjay R. Arwade ◽  
Matthew A. Lackner ◽  
Mircea D. Grigoriu
Keyword(s):  
Markov Model ◽  
Wind Turbine ◽  
Large Scale ◽  
Wind Farm ◽  
Probabilistic Models ◽  
Performance Metrics ◽  
Wind Farms ◽  
Component Reliability ◽  
Mc Simulation ◽  
Farm Performance

A Markov model for the performance of wind turbines is developed that accounts for component reliability and the effect of wind speed and turbine capacity on component reliability. The model is calibrated to the observed performance of offshore turbines in the north of Europe, and uses wind records obtained from the coast of the state of Maine in the northeast United States in simulation. Simulation results indicate availability of 0.91, with mean residence time in the operating state that is nearly exponential and has a mean of 42 days. Using a power curve typical for a 2.5 MW turbine, the capacity factor is found to be beta distributed and highly non-Gaussian. Noticeable seasonal variation in turbine and farm performance metrics are observed and result from seasonal fluctuations in the characteristics of the wind record. The input parameters to the Markov model, as defined in this paper, are limited to those for which field data are available for calibration. Nevertheless, the framework of the model is readily adaptable to include, for example: site specific conditions; turbine details; wake induced loading effects; component redundancies; and dependencies. An on-off model is introduced as an approximation to the stochastic process describing the operating state of a wind turbine, and from this on-off process an Ornstein–Uhlenbeck (O–U) process is developed as a model for the availability of a wind farm. The O–U model agrees well with Monte Carlo (MC) simulation of the Markov model and is accepted as a valid approximation. Using the O–U model in design and management of large wind farms will be advantageous because it can provide statistics of wind farm performance without resort to intensive large scale MC simulation.

A Levelized Cost of Energy (LCOE) Model for Wind Farms That Includes Power Purchase Agreement (PPA) Energy Delivery Limits

2016 ◽  
Author(s):  
Maira Bruck ◽  
Navid Goudarzi ◽  
Peter Sandborn
Keyword(s):  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Cost Models ◽  
Renewable Energy Resources ◽  
Supply Source ◽  
Levelized Cost ◽  
Cost Of Energy ◽  
Expected Performance ◽  
The Cost

The cost of energy is an increasingly important issue in the world as renewable energy resources are growing in demand. Performance-based energy contracts are designed to keep the price of energy as low as possible while controlling the risk for both parties (i.e., the Buyer and the Seller). Price and risk are often balanced using complex Power Purchase Agreements (PPAs). Since wind is not a constant supply source, to keep risk low, wind PPAs contain clauses that require the purchase and sale of energy to fall within reasonable limits. However, the existence of those limits also creates pressure on prices causing increases in the Levelized Cost of Energy (LCOE). Depending on the variation in capacity factor (CF), the power generator (the Seller) may find that the limitations on power purchasing given by the utility (the Buyer) are not favorable and will result in higher costs of energy than predicted. Existing cost models do not take into account energy purchase limitations or variations in energy production when calculating an LCOE. A new cost model is developed to evaluate the price of electricity from wind energy under a PPA contract. This study develops a method that an energy Seller can use to negotiate delivery penalties within their PPA. This model has been tested on a controlled wind farm and with real wind farm data. The results show that LCOE depends on the limitations on energy purchase within a PPA contract as well as the expected performance characteristics associated with wind farms.

scholarly journals Complementarity Roses Evaluating Spatial Complementarity in Time between Energy Resources

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1918 ◽  
Author(s):  
Alfonso Risso ◽  
Alexandre Beluco ◽  
Rita Marques Alves
Keyword(s):  
Power Plants ◽  
Large Scale ◽  
Wind Farms ◽  
Renewable Resource ◽  
Energy Resources ◽  
Complete Evaluation ◽  
Hybrid Energy ◽  
Available Energy ◽  
Cost Of Energy ◽  
The Cost

Hybrid energy systems have higher initial costs than systems that are based on only one renewable resource, but allow for the fulfillment of the demands of consumer loads with lower values for the cost of energy. The possible complementarity between the resources used can contribute to a better use of the available energy. On a large scale, complementarity between power plants can serve as a tool for the management of energy resources. A complete evaluation of complementarity needs to consider three components: time complementarity, energy complementarity, and complementarity between amplitudes of variation. Complementarity can also be assessed between energy resources in one place (which may be termed temporal complementarity) and between resources at different sites (termed spatial complementarity). This paper proposes a method for quantifying spatial complementarity over time and for its expression through maps. The method suggests the establishment of a hexagonal network of cells and the determination of complementary roses for each cell that contains power plants. This article also applies the method proposed to some hydroelectric plants and wind farms in the State of Rio Grande do Sul, in southern Brazil, and present the map of spatial complementarity in time obtained.

scholarly journals Optimal Micro-Siting of Weathervaning Floating Wind Turbines

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 886
Author(s):  
Javier Serrano González ◽  
Manuel Burgos Payán ◽  
Jesús Manuel Riquelme Santos ◽  
Ángel Gaspar González Rodríguez
Keyword(s):  
Wind Direction ◽  
Wind Farm ◽  
Single Point ◽  
Wind Farms ◽  
Offshore Wind ◽  
Classical Solutions ◽  
Feasible Region ◽  
Offshore Wind Farms ◽  
Cost Of Energy ◽  
Wind Farm Design

This paper presents a novel tool for optimizing floating offshore wind farms based on weathervaning turbines. This solution is grounded on the ability of the assembly (wind turbine plus floater) to self-orientate into the wind direction, as this concept is allowed to freely pivot on a single point. This is a passive yaw potential solution for floating wind farms currently in the demonstration phase. A genetic algorithm is proposed for optimizing the levelised cost of energy by determining the geographical coordinates of the pivot points (i.e., the position over which the assembly can rotate to self-orient to the incoming wind direction). A tailored evaluation module is proposed to take into account the weathervaning motion around the pivot point depending on the incoming wind direction. The results obtained show the suitability of the proposed method to solve the addressed problem under realistic conditions. Additionally, the influence of the feasible region defined by the plot and the maximum area occupied on floating offshore wind farm design are also analysed in the proposed test cases. These deployable area constraints are of great importance for the viability of this technology, as it requires more space than classical solutions anchored to a fixed point.

scholarly journals Optimization of Power Collector System for Large-scale Offshore Wind Farm Based on Topological Redundancy Assessment

2020 ◽  
Vol 194 ◽  
pp. 03025
Author(s):  
Wei Shurong ◽  
Feng Yuyao ◽  
Liu Kunlun ◽  
Fu Yang
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Large Scale ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Collector System ◽  
Definition Of ◽  
The Cost

Because of the bad environment of wind farms in the far -reaching sea, the cost of power collector system is high. The contradiction between economy and reliability of the power collector system planning is particularly prominent. According to the particularity of the wind farm in the far-reaching sea and the demand of the power collector system on higher reliability, this paper proposes the definition of topological redundancy of the power collector system and develops a multi-objective optimization model based on the topological redundancy. Thus, the contradictory variables of economy and reliability are optimized. Taking a large-scale offshore wind farm as an example, based on the topological redundancy assessment, the topology of its power collector system is optimized from the perspective of life cycle cost. The results show that, although the initial cost of the optimal redundancy topology is slightly higher than that of the radial structure, the advantage of life cycle cost after 8 years of operations is obvious, which can meet the actual engineering requirements of the power collector system for the wind farm in the far-reaching sea.

scholarly journals The Influence of Climate Parameters on Maintenance of Wind Farms—A Galician Case Study

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 40
Author(s):  
José A. Orosa ◽  
Ángel M. Costa ◽  
Diego Vergara ◽  
Feliciano Fraguela
Keyword(s):  
Wind Farm ◽  
Extreme Values ◽  
Wind Farms ◽  
Working Hours ◽  
Weather Conditions ◽  
Electrical Network ◽  
Economic Implications ◽  
Power Regulation ◽  
The Cost

There are different monitoring procedures in wind farms with two main objectives: (i) to improve energy production by the capability of the national electrical network and (ii) to reduce the stooped hours due to preventive and or corrective maintenance activities. In this sense, different sensors are employed to sample in real-time the working conditions of equipment, the electrical production and the weather conditions. Despite this, just the anemometer measurement can be related to the more important errors of interruption of power regulation and anemometer errors. Both errors are related to gusty winds and contribute to more than 33% of the cost of a wind farm. The present paper reports some mathematical relations between weather and maintenance but there are no extreme values of each variable that let us predict a near failure and its corresponding loss of working hours. To achieve this, statistical analysis identifies the relation between weather variables and errors and different models are obtained. What is more, due to the difficulty and economic implications involving the implementation of complex algorithms and techniques of artificial intelligence, it is still a challenge to optimize this process. Finally, the obtained results show a particular case study that can be extrapolated to other wind farms after different case studies to adjust the model to different weather regions, and serve as a useful tool for weather maintenance.

Study on the Importance of Wind Energy Development

2012 ◽  
Vol 268-270 ◽  
pp. 958-961
Author(s):  
Zhi Qiang Xu ◽  
Kai Guo Qian
Keyword(s):  
Renewable Energy ◽  
Wind Power ◽  
Electricity Market ◽  
Large Scale ◽  
Wind Farm ◽  
New Technologies ◽  
Wind Farms ◽  
Reducing Costs ◽  
Wind Energy Development ◽  
The Cost

with the development of new technologies, investment costs and operatingcosts of large-scale wind farms has dropped significantly, the market has begun to enter the operational phase. As global demand for energy and environmental pollution in the form of increasingly severe, the development of renewable energy has become a strategic arrangement of the world scientific and technological developmen. Wind power renewable energy in the near future will be the most promising-scale energy.. Foreign large-scale construction of wind power reached 8,000 yuan/kW 0. 4 yuan/kWh is exoected in the next 15years there is a 40% price cut. The large-scale wind farm is about to become a competitive option in the electricity market. The key prospects depend on the cost of the wind power market, and reducing costs is the key to scientific and technological progress and industrial scale.

scholarly journals Coupled wind turbine design and layout optimization with nonhomogeneous wind turbines

2019 ◽  
Vol 4 (1) ◽  
pp. 99-114 ◽  
Author(s):  
Andrew P. J. Stanley ◽  
Andrew Ning
Keyword(s):  
Wind Turbines ◽  
Wind Shear ◽  
Shell Thickness ◽  
Wind Farm ◽  
Wind Farms ◽  
Layout Optimization ◽  
Structural Constraints ◽  
Cost Of Energy ◽  
The Cost ◽  
Turbine Design

Abstract. In this study, wind farms were optimized to show the benefit of coupling complete turbine design and layout optimization as well as including two different turbine designs in a fixed 1-to-1 ratio in a single wind farm. For our purposes, the variables in each turbine optimization include hub height, rotor diameter, rated power, tower diameter, tower shell thickness, and implicit blade chord-and-twist distributions. A 32-turbine wind farm and a 60-turbine wind farm were both considered, as well as a variety of turbine spacings and wind shear exponents. Structural constraints as well as turbine costs were considered in the optimization. Results indicate that coupled turbine design and layout optimization is superior to sequentially optimizing turbine design, then turbine layout. Coupled optimization results in an additional 2 %–5 % reduction in the cost of energy compared to optimizing sequentially for wind farms with turbine spacings of 8.5–11 rotor diameters. Smaller wind farms benefit even more from coupled optimization. Furthermore, wind farms with closely spaced wind turbines can greatly benefit from nonuniform turbine design throughout the farm. Some of these wind farms with heterogeneous turbine design have an additional 10 % cost-of-energy reduction compared to wind farms with identical turbines throughout the farm.

Evaluating Community Engagement and Benefit-Sharing Practices in Australian Wind Farm Development

2017 ◽  
Vol 1 (1) ◽  
pp. 1-6 ◽  
Author(s):  
Nina Lansbury Hall ◽  
Jarra Hicks ◽  
Taryn Lane ◽  
Emily Wood
Keyword(s):  
Community Engagement ◽  
Community Involvement ◽  
Wind Farm ◽  
Social Acceptance ◽  
Wind Farms ◽  
Clean Energy ◽  
Benefit Sharing ◽  
Delphi Panel ◽  
Government Organizations

The wind industry is positioned to contribute significantly to a clean energy future, yet the level of community opposition has at times led to unviable projects. Social acceptance is crucial and can be improved in part through better practice community engagement and benefit-sharing. This case study provides a “snapshot” of current community engagement and benefit-sharing practices for Australian wind farms, with a particular emphasis on practices found to be enhancing positive social outcomes in communities. Five methods were used to gather views on effective engagement and benefit-sharing: a literature review, interviews and a survey of the wind industry, a Delphi panel, and a review of community engagement plans. The overarching finding was that each community engagement and benefit-sharing initiative should be tailored to a community’s context, needs and expectations as informed by community involvement. This requires moving away from a “one size fits all” approach. This case study is relevant to wind developers, energy regulators, local communities and renewable energy-focused non-government organizations. It is applicable beyond Australia to all contexts where wind farm development has encountered conflicted societal acceptance responses.

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