Response Surface Based Cost Model for Onshore Wind Farms Using Extended Radial Basis Functions

2010 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Luciano Castillo ◽  
Jose Lebron
Keyword(s):  
Response Surface ◽  
Radial Basis Functions ◽  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Labor Cost ◽  
Basis Functions ◽  
Onshore Wind ◽  
Radial Basis ◽  
The Cost

This paper develops a cost model for onshore wind farms in the U.S.. This model is then used to analyze the influence of different designs and economic parameters on the cost of a wind farm. A response surface based cost model is developed using Extended Radial Basis Functions (E-RBF). The E-RBF approach, a combination of radial and non-radial basis functions, can provide the designer with significant flexibility and freedom in the metamodeling process. The E-RBF based cost model is composed of three parts that can estimate (i) the installation cost, (ii) the annual Operation and Maintenance (O&M) cost, and (iii) the total annual cost of a wind farm. The input parameters for the E-RBF based cost model include the rotor diameter of a wind turbine, the number of wind turbines in a wind farm, the construction labor cost, the management labor cost and the technician labor cost. The accuracy of the model is favorably explored through comparison with pertinent real world data. It is found that the cost of a wind farm is appreciably sensitive to the rotor diameter and the number of wind turbines for a given desirable total power output.

Surrogate Modeling of Complex Systems Using Adaptive Hybrid Functions

2011 ◽  
Author(s):  
Jie Zhang ◽  
Souma Chowdhury ◽  
Achille Messac ◽  
Junqiang Zhang ◽  
Luciano Castillo
Keyword(s):  
Radial Basis Functions ◽  
Wind Farm ◽  
Surrogate Modeling ◽  
Surrogate Models ◽  
Modeling Method ◽  
Basis Functions ◽  
Sampling Techniques ◽  
Hybrid Functions ◽  
Model Complex ◽  
Radial Basis

This paper explores the effectiveness of the recently developed surrogate modeling method, the Adaptive Hybrid Functions (AHF), through its application to complex engineered systems design. The AHF is a hybrid surrogate modeling method that seeks to exploit the advantages of each component surrogate. In this paper, the AHF integrates three component surrogate models: (i) the Radial Basis Functions (RBF), (ii) the Extended Radial Basis Functions (E-RBF), and (iii) the Kriging model, by characterizing and evaluating the local measure of accuracy of each model. The AHF is applied to model complex engineering systems and an economic system, namely: (i) wind farm design; (ii) product family design (for universal electric motors); (iii) three-pane window design; and (iv) onshore wind farm cost estimation. We use three differing sampling techniques to investigate their influence on the quality of the resulting surrogates. These sampling techniques are (i) Latin Hypercube Sampling (LHS), (ii) Sobol’s quasirandom sequence, and (iii) Hammersley Sequence Sampling (HSS). Cross-validation is used to evaluate the accuracy of the resulting surrogate models. As expected, the accuracy of the surrogate model was found to improve with increase in the sample size. We also observed that, the Sobol’s and the LHS sampling techniques performed better in the case of high-dimensional problems, whereas the HSS sampling technique performed better in the case of low-dimensional problems. Overall, the AHF method was observed to provide acceptable-to-high accuracy in representing complex design systems.

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.

Wind Energy

2020 ◽  
pp. 185-227
Author(s):  
Paul F. Meier
Keyword(s):  
Renewable Energy ◽  
Wind Farm ◽  
Wind Farms ◽  
Direct Impact ◽  
High Wind ◽  
Onshore Wind ◽  
Fast Growing ◽  
Transmission Equipment ◽  
Impact Area ◽  
The Cost

A wind farm is a collection of wind turbines, sufficiently spaced to avoid wind interference between turbines. Onshore and offshore are the two basic types of wind farms. The cost of building an offshore farm is greater because of the need for turbines that withstand high wind and corrosive conditions of the sea, plus the expense of installing underwater transmission cables to shore. For an onshore wind farm, the land area for the farm is large, but the direct impact area is relatively small. The direct impact area includes the turbine pads, roads, substations, and transmission equipment, and only makes up about 2% of the total wind farm area. Since the direct impact area is small compared to the total wind farm area, agriculture and ranching can coexist with the wind farm. Wind is a very fast growing renewable energy technology. In the ten years since 2009, the worldwide capacity for wind power increased 276% while US capacity increased 175%.

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

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 448
Author(s):  
Jens Nørkær Sørensen ◽  
Gunner Christian Larsen
Keyword(s):  
Prediction Model ◽  
Wind Turbines ◽  
Water Depth ◽  
Energy Production ◽  
Wind Farm ◽  
Cost Model ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farms ◽  
The Cost

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.

Study on Offshore Wind Farm Maintenance Strategies Optimization

2011 ◽  
Vol 347-353 ◽  
pp. 795-799 ◽  
Author(s):  
Xiao Yan Bian ◽  
Guang Yue Li ◽  
Yang Fu
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Planning Method ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Onshore Wind ◽  
Maintenance Strategies ◽  
Total Cost ◽  
The Cost ◽  
Maritime Environment

With the harsh maritime environment, the accessibility of the site leads to the high cost directly. The cost on Operation & Maintenance for offshore wind farm is about twice of equivalent onshore wind farms. Consequently the study on the maintenance strategies is necessary. The objective of this work is to optimize the offshore wind farm maintenance strategies by network planning method, to minimize the total cost, by considing the effect of several factors, such as weather, boat availability, the location of the wind farm and ect.

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