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.

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.

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.

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 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.

scholarly journals Electric power generation in wind farms with pumping kites: levelized cost of energy and sensitivity analysis

2020 ◽  
Vol 9 (7) ◽  
pp. e666974528
Author(s):  
Anny Key de Souza Mendonça ◽  
Antonio Cezar Bornia
Keyword(s):  
Power Generation ◽  
Sensitivity Analysis ◽  
High Altitude ◽  
Electric Power ◽  
Energy Cost ◽  
Wind Farm ◽  
Wind Farms ◽  
Level Energy ◽  
Levelized Cost ◽  
Cost Of Energy

This research aims to analyze the levelized level energy cost of energy (LCOE) of wind farms with tethered airfoils. For this, it was considering the technical characteristics of the system, the location of operation, the necessary investments and the characteristics of the Brazilian market, to analyze the levelized cost of energy of three wind farm scenarios: Classic wind farm, Wind farm with tethered airfoils operating in Pumping Kite mode and a hybrid park with the two park configurations studied. The research makes use of the LCOE method. The results indicate that the technology with wired airfoils requires less investment and that wind farms with this technology can generate more energy than a classic wind farm of the same nominal power, since the wired airfoils can exploit high altitude winds, where they are more frequent and strong. The results also indicate that wind farms with wired airfoils are not only economically viable, but produce energy at a level cost, well below the values currently practiced for the sale of energy in the domestic market.

scholarly journals Analysis levelized cost of energy from the perspective of an power purchase agreements for airborne wind power technology

2020 ◽  
Vol 24 ◽  
pp. e53
Author(s):  
Anny Key De Souza Mendonça ◽  
Antonio Cezar Bornia
Keyword(s):  
Wind Energy ◽  
Wind Farm ◽  
New Technologies ◽  
Wind Farms ◽  
Capacity Factor ◽  
Innovative Technology ◽  
Cost Models ◽  
Energy Delivery ◽  
Cost Of Energy ◽  
Wind Potential

With the need to diversify the national electric matrix to expand the generation capacity, the searches for new technologies capable of contributing to supply the national demand are fundamental. In this sense, the development of wind energy technology, but specifically high-altitude wind energy using wired airfoils, is an attractive possibility, given the high national wind potential. This research aimed to analyze the cost models used in the literature to assess the leveled cost of energy (LCOE) from the perspective of an power purchase agreements (PPAs) and to simulate the leveled cost of energy for a wind farm with the innovative technology of wired airfoils. For this, we chose two cities Fortaleza (CE) and Florianópolis (SC) to carry out the simulations. The wind farms are identical, with the same number of wind turbines, the parameters that have been modified are, the amount of energy supplied by each of the scenarios, to visualize the influence of the capacity factor of each scenario within the real and nominal LCOE and the PPA real and nominal after 20 years of analysis. As wind energy is not a constant source of supply, the simulation considered the limits of energy delivery that are above or below the minimum limit of energy delivery. The results demonstrated economic viability in almost all scenarios, with greater attractiveness for scenarios with an increase in the capacity factor. When financial parameters such as federal and state charges are varied downwards, all scenarios investigated are attractive to development with an IRR greater than the reference value.

An Extended Pattern Search Method for Offshore Floating Wind Layout and Turbine Geometry Optimization

2016 ◽  
Author(s):  
Caitlin Forinash ◽  
Bryony DuPont
Keyword(s):  
Wind Farm ◽  
Cost Model ◽  
Geometry Optimization ◽  
Interaction Model ◽  
Solution Space ◽  
Wind Farms ◽  
Power Production ◽  
Pattern Search ◽  
Offshore Wind ◽  
Extended Pattern Search

An Extended Pattern Search (EPS) method is developed to optimize the layout and turbine geometry for offshore floating wind power systems. The EPS combines a deterministic pattern search with stochastic extensions. Three advanced models are incorporated: (1) a cost model considering investment and lifetime costs of a floating offshore wind farm comprised of WindFloat platforms; (2) a wake propagation and interaction model able to determine the reduced wind speeds downstream of rotating blades; and (3) a power model to determine power produced at each rotor, and includes a semi-continuous, discrete turbine geometry selection to optimize the rotor radius and hub height of individual turbines. The objective function maximizes profit by minimizing cost, minimizing wake interactions, and maximizing power production. A multidirectional, multiple wind speed case is modeled which is representative of real wind site conditions. Layouts are optimized within a square solution space for optimal positioning and turbine geometry for farms containing a varying number of turbines. Resulting layouts are presented; optimized layouts are biased towards dominant wind directions. Preliminary results will inform developers of best practices to include in the design and installation of offshore floating wind farms, and of the resulting cost and power production of wind farms that are computationally optimized for realistic wind conditions.

scholarly journals GASOL: Gas Analysis and Optimization for Ethereum Smart Contracts

2020 ◽  
pp. 118-125 ◽  
Author(s):  
Elvira Albert ◽  
Jesús Correas ◽  
Pablo Gordillo ◽  
Guillermo Román-Díez ◽  
Albert Rubio
Keyword(s):  
Upper Bound ◽  
Cost Model ◽  
Gas Analysis ◽  
Smart Contracts ◽  
Cost Models ◽  
Automatic Optimization ◽  
Gas Consumption ◽  
The Cost ◽  
Storage Patterns ◽  
Program Line

Abstract We present the main concepts, components, and usage of Gasol, a Gas AnalysiS and Optimization tooL for Ethereum smart contracts. Gasol offers a wide variety of cost models that allow inferring the gas consumption associated to selected types of EVM instructions and/or inferring the number of times that such types of bytecode instructions are executed. Among others, we have cost models to measure only storage opcodes, to measure a selected family of gas-consumption opcodes following the Ethereum’s classification, to estimate the cost of a selected program line, etc. After choosing the desired cost model and the function of interest, Gasol returns to the user an upper bound of the cost for this function. As the gas consumption is often dominated by the instructions that access the storage, Gasol uses the gas analysis to detect under-optimized storage patterns, and includes an (optional) automatic optimization of the selected function. Our tool can be used within an Eclipse plugin for which displays the gas and instructions bounds and, when applicable, the gas-optimized function.

Optimized Dynamic Operation of Fixed-Speed Wind Farms Using Classical and Advanced Controllers

2020 ◽  
Vol 21 (2) ◽  
Author(s):  
Othman A. Omar ◽  
Niveen M. Badra ◽  
Mahmoud A. Attia ◽  
Ahmed Gad
Keyword(s):  
Renewable Energy ◽  
Output Power ◽  
Pitch Angle ◽  
Wind Farm ◽  
Wind Farms ◽  
Optimization Techniques ◽  
Energy Resources ◽  
Voltage Regulator ◽  
Renewable Energy Resources ◽  
Proportional Integral

AbstractElectric power systems are allowing higher penetration levels of renewable energy resources, mainly due to their environmental benefits. The majority of electrical energy generated by renewable energy resources is contributed by wind farms. However, the stochastic nature of these resources does not allow the installed generation capacities to be entirely utilized. In this context, this paper attempts to improve the performance of fixed-speed wind turbines. Turbines of this type have been already installed in some classical wind farms and it is not feasible to replace them with variable-speed ones before their lifetime ends. A fixed-speed turbine is typically connected to the electric grid with a Static VAR Compensator (SVC) across its terminal. For a better dynamic voltage response, the controller gains of a Proportional-Integral (PI) voltage regulator within the SVC will be tuned using a variety of optimization techniques to minimize the integrated square of error for the wind farm terminal voltage. Similarly, the controller gains of the turbine’s pitch angle may be tuned to enhance its dynamic output power performance. Simulation results, in this paper, show that the pitch angle controller causes a significant minimization in the integrated square of error for the wind farm output power. Finally, an advanced Proportional-Integral-Acceleration (PIA) voltage regulator controller has been proposed for the SVC. When the PIA control gains are optimized, they result in a better performance than the classical PI controller.

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