A hybrid extended pattern search/genetic algorithm for multi-stage wind farm optimization

2016 ◽  
Vol 17 (1) ◽  
pp. 77-103 ◽  
Author(s):  
Bryony DuPont ◽  
Jonathan Cagan
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Pattern Search ◽  
Wind Farm Optimization ◽  
Multi Stage ◽  
Extended Pattern Search

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.

Optimization of Floating Offshore Wind Energy Systems Using an Extended Pattern Search Method

2016 ◽  
Author(s):  
Caitlin Forinash ◽  
Bryony DuPont
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Power Production ◽  
Pattern Search ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Power Development ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Extended Pattern Search

An Extended Pattern Search (EPS) approach is developed for offshore floating wind farm layout optimization while considering challenges such as high cost and harsh ocean environments. This multi-level optimization method minimizes the costs of installation and operations and maintenance, and maximizes power development in a unidirectional wind case by selecting the size and position of turbines. The EPS combines a deterministic pattern search algorithm with three stochastic extensions to avoid local optima. The EPS has been successfully applied to onshore wind farm optimization and enables the inclusion of advanced modeling as new technologies for floating offshore wind farms emerge. Three advanced models are incorporated into this work: (1) a cost model developed specifically for this work, (2) a power development model that selects hub height and rotor radius to optimize power production, and (3) a wake propagation and interaction model that determines aerodynamic effects. Preliminary results indicate the differences between proposed optimal offshore wind farm layouts and those developed by similar methods for onshore wind farms. The objective of this work is to maximize profit; given similar parameters, offshore wind farms are suggested to have approximately 24% more turbines than onshore farms of the same area. EPS layouts are also compared to those of an Adapted GA; 100% efficiency is found for layouts containing twice as many turbines as the layout presented by the Adapted GA. Best practices are derived that can be employed by offshore wind farm developers to improve the layout of platforms, and may contribute to reducing barriers to implementation, enabling developers and policy makers to have a clearer understanding of the resulting cost and power production of computationally optimized farms; however, the unidirectional wind case used in this work limits the representation of optimized layouts at real wind sites. Since there are currently no multi-turbine floating offshore wind farm projects operational in the United States, it is anticipated that this work will be used by developers when planning array layouts for future offshore floating wind farms.

Optimization of Wind Farm Layout and Wind Turbine Geometry Using a Multi-Level Extended Pattern Search Algorithm That Accounts for Variation in Wind Shear Profile Shape

2012 ◽  
Author(s):  
Bryony L. DuPont ◽  
Jonathan Cagan ◽  
Patrick Moriarty
Keyword(s):  
Wind Speed ◽  
Wind Shear ◽  
Wind Farm ◽  
Search Algorithm ◽  
Atmospheric Stability ◽  
Pattern Search ◽  
Power Development ◽  
Profile Shape ◽  
Multi Level ◽  
Extended Pattern Search

This paper presents a multi-level Extended Pattern Search algorithm (EPS) to optimize both the local positioning and geometry of wind turbines on a wind farm. Additionally, this work begins to draw attention to the effects of atmospheric stability on wind farm power development. The wind farm layout optimization problem involves optimizing the local position and size of wind turbines such that the aerodynamic effects of upstream turbines are reduced, thereby increasing the effective wind speed at each turbine, allowing it to develop more power. The extended pattern search, employed within a multi-agent system architecture, uses a deterministic approach with stochastic extensions to avoid local minima and converge on superior solutions compared to other algorithms. The EPS presented herein is used in an iterative, hierarchical scheme — an overarching pattern search determines individual turbine positioning, then a sub-level EPS determines the optimal hub height and rotor for each turbine, and the entire search is iterated. This work also explores the wind shear profile shape to better estimate the effects of changes in the atmosphere, specifically the changes in wind speed with respect to height on the total power development of the farm. This consideration shows how even slight changes in time of day, hub height, and farm location can impact the resulting power. The objective function used in this work is the maximization of profit. The farm installation cost is estimated using a data surface derived from the National Renewable Energy Laboratory (NREL) JEDI wind model. Two wind cases are considered: a test case utilizing constant wind speed and unidirectional wind, and a more realistic wind case that considers three discrete wind speeds and varying wind directions, each of which is represented by a fraction of occurrence. Resulting layouts indicate the effects of more accurate cost and power modeling, partial wake interaction, as well as the differences attributed to including and neglecting the effects of atmospheric stability on the wind shear profile shape.

An Extended Pattern Search Approach to Wind Farm Layout Optimization

2012 ◽  
Vol 134 (8) ◽  
Author(s):  
Bryony L. Du Pont ◽  
Jonathan Cagan
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Search Algorithm ◽  
Pattern Search ◽  
Global Evaluation ◽  
Layout Problem ◽  
Pattern Search Algorithm ◽  
Wind Farm Layout ◽  
Search Approach ◽  
Extended Pattern Search

An extended pattern search approach is presented for the optimization of the placement of wind turbines on a wind farm. Problem-specific extensions infuse stochastic characteristics into the deterministic pattern search, inhibiting convergence on local optima and yielding better results than pattern search alone. The optimal layout for a wind farm is considered here to be one that maximizes the power generation of the farm while minimizing the farm cost. To estimate the power output, an established wake model is used to account for the aerodynamic effects of turbine blades on downstream wind speed, as the oncoming wind speed for any turbine is proportional to the amount of power the turbine can produce. As turbines on a wind farm are in close proximity, the interaction of turbulent wakes developed by the turbines can have a significant effect on the power development capability of the farm. The farm cost is estimated using an accepted simplified model that is a function of the number of turbines. The algorithm develops a two-dimensional layout for a given number of turbines, performing local turbine movement while applying global evaluation. Three test cases are presented: (a) constant, unidirectional wind, (b) constant, multidirectional wind, and (c) varying, multidirectional wind. The purpose of this work is to explore the ability of an extended pattern search (EPS) algorithm to solve the wind farm layout problem, as EPS has been shown to be particularly effective in solving multimodal layout problems. It is also intended to show that the inclusion of extensions into the algorithm can better inform the search than algorithms that have been previously presented in the literature. Resulting layouts created by this extended pattern search algorithm develop more power than previously explored algorithms using the same evaluation models and objective functions. In addition, the algorithm’s resulting layouts motivate a heuristic that aids in the manual development of the best layout found to date. The results of this work validate the application of an extended pattern search algorithm to the wind farm layout problem, and that its performance is enhanced by the use of problem-specific extensions that aid in developing results that are superior to those developed by previous algorithms.

Investigation of Two Analytical Wake Models Using Data From Wind Farms

2011 ◽  
Author(s):  
Anshul Mittal ◽  
Lafayette K. Taylor ◽  
Kidambi Sreenivas ◽  
Abdollah Arabshahi
Keyword(s):  
Genetic Algorithm ◽  
Wind Farm ◽  
Wind Farms ◽  
Maximum Error ◽  
Wind Regime ◽  
Wind Farm Optimization ◽  
Wake Model ◽  
Using Data ◽  
The Cost ◽  
Large Wind

A code ‘Wind Farm Optimization using a Genetic Algorithm’ (referred as WFOG) was developed for optimizing the placement of wind turbines in large wind farms. It utilizes an analytical wake model (by Jensen et al.) to minimize the cost per unit power for the wind farm. In this study, a new wake model by Ishihara et al. is tested in WFOG. The wake model takes into account the effect of atmospheric turbulence and rotor generated turbulence on the wake recovery. Results of the two wake models are compared with data from Horns Rev and Nysted wind farm. The maximum error (Horns Rev wind farm) for Ishihara’s wake model was 7% as compared to 15% for Jensen’s wake model. The optimal results obtained in earlier studies (using Jensen’s wake model) are compared to wind farm configurations obtained for Ishihara’s wake model. The optimization is carried out for the simplest wind regime: Constant wind speed and fixed wind direction.

An Extended Pattern Search Approach to Wind Farm Layout Optimization

2010 ◽  
Author(s):  
Bryony L. Du Pont ◽  
Jonathan Cagan
Keyword(s):  
Wind Farm ◽  
Search Algorithm ◽  
Turbine Blades ◽  
Pattern Search ◽  
Total Power ◽  
Local Optima ◽  
Wake Model ◽  
Power Capability ◽  
Search Approach ◽  
Extended Pattern Search

An extended pattern search approach is presented for optimizing the placement of wind turbines on a wind farm. The algorithm will develop a two-dimensional layout for a given number of turbines, employing an objective function that minimizes costs while maximizing the total power production of the farm. The farm cost is developed using an established simplified model that is a function of the number of turbines. The power development of the farm is estimated using an established simplified wake model, which accounts for the aerodynamic effects of turbine blades on downstream wind speed, to which the power output is directly proportional. The interaction of the turbulent wakes developed by turbines in close proximity largely determines the power capability of the farm. As pattern search algorithms are deterministic, multiple extensions are presented to aid escaping local optima by infusing stochastic characteristics into the algorithm. This stochasticity improves the algorithm’s performance, yielding better results than purely deterministic search methods. Three test cases are presented: a) constant, unidirectional wind, b) constant, multidirectional wind, and c) varying, multidirectional wind. Resulting layouts developed by this extended pattern search algorithm develop more power than previously explored algorithms with the same evaluation models and objective functions. In addition, the algorithm’s layouts motivate a heuristic that yields the best layouts found to date.

Multi-Stage Optimization of Wind Farms With Limiting Factors

2013 ◽  
Author(s):  
Bryony L. DuPont ◽  
Jonathan Cagan
Keyword(s):  
Wind Farm ◽  
Solution Space ◽  
Wind Farms ◽  
Objective Evaluation ◽  
Pattern Search ◽  
Limiting Factors ◽  
Limiting Factor ◽  
Test Case ◽  
Wind Speeds ◽  
Multi Stage

Larger onshore wind farms are often installed in phases, with discrete smaller sub-farms being installed and becoming operational in succession until the farm as a whole is completed. An extended pattern search (EPS) algorithm that selects both local turbine position and geometry is presented that enables the installation of a complete farm in discrete stages, exploring optimality of both incremental sub-farm solutions and the completed project as a whole. The objective evaluation is the maximization of profit over the life of the farm, and the EPS uses modeling of cost based on an extensive cost analysis by the National Renewable Energy Laboratory (NREL). The EPS uses established wake modeling to calculate the power development of the farm, and allows for the consideration of multiple or overlapping wakes. A limiting factor is used to determine the size of wind farm stages: optimization stages based on the number of turbines currently available for development (representative of limitations in initial capital, which is commonly encountered in wind farm stage development). Two wind test cases are considered: a unidirectional test case with constant wind speed and a single wind direction, and a multidirectional test case, with three wind speeds and a defined probability of occurrence for each. The test case shown in the current work is employed on a 4000 km by 4000 km solution space. In addition, two different methods are performed: the first uses the optimal layout of a complete farm and then systematically “removes” turbines to create smaller sub-farms; the second uses a weighted multi-objective optimization over sequential, adjacent land that concurrently optimizes each sub-farm and the complete farm. The exploration of these resulting layouts indicates the value of full-farm optimization (in addition to optimization of the individual stages) and gives insight into how to approach optimality in sub-farm stages. The behavior exhibited in these tests cases suggests a heuristic that can be employed by wind farm developers to ensure that multi-stage wind farms perform at their peak throughout their completion.

scholarly journals Research and Application of a New Hybrid Forecasting Model Based on Genetic Algorithm Optimization: A Case Study of Shandong Wind Farm in China

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Ping Jiang ◽  
Xiaofei Li ◽  
Yao Dong
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Hybrid Model ◽  
Hybrid System ◽  
Wind Farm ◽  
Evaluation Criteria ◽  
Renewable Resource ◽  
Original Data ◽  
Determine Parameter ◽  
Forecasting Method

With the increasing depletion of fossil fuel and serious destruction of environment, wind power, as a kind of clean and renewable resource, is more and more connected to the power system and plays a crucial role in power dispatch of hybrid system. Thus, it is necessary to forecast wind speed accurately for the operation of wind farm in hybrid system. In this paper, we propose a hybrid model called EEMD-GA-FAC/SAC to forecast wind speed. First, the Ensemble empirical mode decomposition (EEMD) can be applied to eliminate the noise of the original data. After data preprocessing, first-order adaptive coefficient forecasting method (FAC) or second-order adaptive coefficient forecasting method (SAC) can be employed to do forecast. It is significant to select optimal parameters for an effective model. Thus, genetic algorithm (GA) is used to determine parameter of the hybrid model. In order to verify the validity of the proposed model, every ten-minute wind speed data from three observation sites in Shandong Peninsula of China and several error evaluation criteria can be collected. Through comparing with traditional BP, ARIMA, FAC, and SAC model, the experimental results show that the proposed hybrid model EEMD-GA-FAC/SAC has the best forecasting performance.

Sign in / Sign up

Export Citation Format

Share Document