Wind Farm Layout Optimization Research Articles

Nature-Inspired Wind Farm Layout Optimization: Harnessing Smart Patterns for Sustainable Energy

This research investigates the transformative realm of wind farm layout optimization, with a specific focus on harnessing the efficiency of bio-inspired patterns. The positioning of wind turbine is play a vital role in the maximizing the energy output of a wind farm. If a wind turbine is placed in the wake region of the upstream turbine, the energy produced by the downstream turbine is reduced. Hence, it is imperative to place turbines in such a way that effect of the wake is minimum on a performance of turbines. The conventional grid-based approaches, commonly employed in wind farm layouts, face limitations in capturing the inherent complexity of wind flow dynamics, especially in varied terrains. In contrast, bio-inspired layouts, inspired by patterns observed in natural ecosystem, offer the promising results over the conventional Grid based approach. Our investigation involves the development and implementation of a novel bio-inspired wind farm layout positioning pattern. In the proposed approach, various nature inspired pattern like honeycomb and sunflower seeds pattern is explored for the turbine positioning. Further, the modeling of wind behavior includes both uniform wind speeds and variable wind speeds originating from all directions. The modified passing vehicle search (mPVS) optimization algorithm is used for optimizing the wind turbine placement. The results are obtained for the different wind scenario and compare it with the available results in the literature. The anticipated outcomes of this research include a deeper understanding of the potential benefits and challenges associated with bio-inspired wind farm layouts. The findings aim to contribute valuable insights into optimizing wind turbine placements, maximizing energy capture, and fostering sustainable practices in the wind energy sector. Results shows that the turbine positioning in the proposed bio-inspired pattern produce the higher power output (6%) compared to the conventional grid based approach. Hence, it can be concluded that the approach present in this study can assist wind farm designers and developers in optimally placing turbines for better performance.

A new analytical wind turbine wake model considering the effects of coriolis force and yawed conditions

Wind turbine wakes significantly affect power production and impose higher loads on downstream turbines. Therefore, the development of accurate and efficient wake models is important for optimizing wind farm layouts and predicting wind turbine performance. This study introduces a novel analytical wake model for yawed wind turbines that incorporates the effects of the Coriolis force. The wake deflection in the far wake region is derived through the application of the principles of mass and momentum conservation. In the near wake, the deflection is assumed to be linear with distance. A Gaussian distribution is assumed for the velocity deficit within the wind turbine wake. Two approaches have been proposed to estimate the onset of the far wake region. While the first approach employs a simplified empirical formula, the second approach utilizes an iteration-based method. The proposed analytical wake model has been validated against computational fluid dynamics (CFD) results. Subsequently, the effects of several important parameters on the wake deflection have been systematically investigated. Overall, the simulation results showed a satisfactory agreement between the CFD results and those obtained from the proposed model. Furthermore, the study concluded that the Coriolis force can exert significant effects on wake deflection, particularly in the far wake region, confirming previous findings from numerical simulations. Due to its simplicity and computational efficiency, the proposed model can be readily used in several applications, including wind farm layout optimization, control and risk assessment.

Multi-objective layout optimization for wind farms based on non-uniformly distributed turbulence and a new three-dimensional multiple wake model

Wind farm layout optimization (WFLO) can reduce the turbulence intensity on the wind turbines while maximizing power generation. To accurately compute turbulence intensity distribution in the wind farm, we utilize the innovative three-dimensional cosine-shaped turbulence intensity (3D-COTI) model. By integrating the three-dimensional cosine-shaped linear entrainment (3DCLE) wake model previously developed by the authors, we introduce a new multiple wind turbine wake model (3DCLE-M). This model considers the wake superposition effect, enabling precise calculation of power generation. Building upon the 3D-COTI and 3DCLE-M models, we propose a multi-objective WFLO approach to maximize power generation and minimize streamwise turbulence intensity in the wind-turbine-swept area. The results indicate that under maximized power generation, this approach significantly reduces turbulence intensity and its non-uniform distribution in the wind-turbine-swept area. In two ideal cases, the maximum turbulence intensity in all wind-turbine-swept areas of the optimized layout is 25 % and 3.8 % lower than that of the selected benchmark layout. By optimizing the layout of the Horns Rev offshore wind farm, a new layout can be obtained with increased total power output by 1.58 % and the reduced total duration of high-fatigue-load wind turbines under high turbulence intensity by 25 % compared to the benchmark.

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

An Adaptive Strategy-incorporated Integer Genetic Algorithm for Wind Farm Layout Optimization

Optimization of Non-Uniform Onshore Wind Farm Layout Using Modified Electric Charged Particles Optimization Algorithm Considering Different Terrain Characteristics

Designing an onshore wind farm layout poses several challenges, including the effects of terrain and landscape characteristics. An accurate model should be developed to obtain the optimal wind farm layout. This study introduces a novel metaheuristic algorithm called Modified Electric Charged Particles Optimization (MECPO) to maximize wind farms’ annual energy production (AEP) by considering the different terrain and landscape characteristics of the sites. Some non-uniform scenarios are applied to the optimization process to find the best combination of decision variables in the wind farm design. The study was initiated by a uniform wind farm layout optimization employing identical wind turbine hub heights and diameters. Following this, these parameters underwent further optimization based on some non-uniform scenarios, with the optimal layout from the initial uniform wind farm serving as the reference design. Three real onshore sites located in South Sulawesi, Indonesia, were selected to validate the performance of the proposed algorithm. The wind characteristics for each site were derived from WAsP CFD, accounting for the terrain and landscape effects. The results show that the non-uniform wind farm performs better than its uniform counterpart only when using varying hub heights. Considering the impacts of the terrain and landscape characteristics, it is observed that sites with a higher elevation, slope index, and roughness length exhibit a lower wake effect than those with lower ones. Moreover, the proposed algorithm, MECPO, consistently outperforms other algorithms, achieving the highest AEP across all simulations, with a 100% success rate in all eight instances. These results underscore the algorithm’s robustness and effectiveness in optimizing wind farm layouts, offering a promising avenue for advancing sustainable wind energy practices.

Wind Farm Layout Optimization Subject to Cable Cost, Hub Height, and a Feasible 3D Gaussian Wake Model Implementation

We address the Wind Farm Layout Optimization (WFLO) problem and tackle the optimal placement of several turbines within a specific (wind farm) area by incorporating additional aspects of an economically driven target function. With this, we contribute three refinements for WFLO research: First, while many research contributions optimize the turbines’ locations subject to maximum energy production or energy efficiency, we instead pursue a strategy of maximizing a profit objective. This enables us to incorporate inner-farm wiring costs (underground cable installation). For this, we explore the impact of using MSTs (Minimum Spanning Trees) and adding junction (so-called “Steiner”) points to the terrain plane. Second, while most research focuses on finding optimal x and y coordinates (i.e., address two-dimensional turbine placement), we also optimize the turbines’ hub heights z. Third, we also provide a software implementation of the Gaussian wake model. The latter finds entrance to the open-source WFLO research framework that comes as package <strong>wflo</strong> for statistical software R. We find that taking wiring cost into account may lead to very different turbine placements, however, increasing overall profit significantly. Allowing the optimizer to vary the hub heights may have an ambiguous impact on the wind farm profit.

Gradient-based wind farm layout optimization with inclusion and exclusion zones

Abstract. Wind farm layout optimization is usually subjected to boundary constraints of irregular shapes. The analytical expressions of these shapes are rarely available, and, consequently, it can be challenging to include them in the mathematical formulation of the problem. This paper presents a new methodology to integrate multiple disconnected and irregular domain boundaries in wind farm layout optimization problems. The method relies on the analytical gradients of the distances between wind turbine locations and boundaries, which are represented by polygons. This parameterized representation of boundary locations allows for a continuous optimization formulation. A limitation of the method, if combined with gradient-based solvers, is that wind turbines are placed within the nearest polygons when the optimization is started in order to satisfy the boundary constraints; thus the allocation of wind turbines per polygon is highly dependent on the initial guess. To overcome this and improve the quality of the solutions, two independent strategies are proposed. A case study is presented to demonstrate the applicability of the method and the proposed strategies. In this study, a wind farm layout is optimized in order to maximize the annual energy production (AEP) in a non-uniform wind resource site. The problem is constrained by the minimum distance between wind turbines and five irregular polygon boundaries, defined as inclusion zones. Initial guesses are used to instantiate the optimization problem, which is solved following three independent approaches: (1) a baseline approach that uses a gradient-based solver; (2) approach 1 combined with the relaxation of the boundaries, which allows for a better design space exploration; and (3) the application of a heuristic algorithm, “smart-start”, prior to the gradient-based optimization, improving the allocation of wind turbines within the inclusion polygons based on the potential wind resource and the available area. The results show that the relaxation of boundaries combined with a gradient-based solver achieves on average +10.2 % of AEP over the baseline, whilst the smart-start algorithm, combined with a gradient-based solver, finds on average +20.5 % of AEP with respect to the baseline and +9.4 % of AEP with respect to the relaxation strategy.

Wind Farm Layout Optimization Based on Dynamic Opposite Learning-Enhanced Sparrow Search Algorithm

In recent years, the proportion of wind power in new energy generation has gradually increased. The natural wind in wind farms is subject to velocity attenuation by the wake effect, so improving the efficiency of wind farm power generation has become a problem that must be solved for wind power generation. Considering the uncertainty of wind farms, we regard wind farm layout optimization (WFLO) as a strongly nonlinear problem. In this paper, we improve the sparrow search algorithm (SSA) using dynamic opposite learning (DOL) strategy. Twenty-eight benchmark test results prove that compared with other algorithms, the improved algorithm DOLSSA has excellent robustness and the ability of searching for a better solution when solving a strongly nonlinear optimization problem, and the DOL strategy effectively improves the shortcomings of the original algorithm which is prone to local optimization and space limitation. In this paper, the authors establish the dynamic rotational coordinates of wind farms and set six different physical scenarios by considering the wind direction and wind speed variables, and the results prove that the performance of DOLSSA is optimal.

MORSA: Multi-objective reptile search algorithm based on elite non-dominated sorting and grid indexing mechanism for wind farm layout optimization problem

The proposal of the wind farm layout optimization (WFLO) problem aims to better utilize wind energy. A multi-objective reptile search algorithm (MORSA) based on elite non-dominated sorting and grid indexing mechanism was proposed to solve the multi-objective optimization problem of wind farm layout under the Jansen wake model to maximize power generation while minimizing costs. Firstly, the elite non-dominated sorting method was used to sort the population, and then the crowding distance method was used to maintain the diversity between the optimal sets. By calculating the crowding distance, the same level of non-dominated populations are sorted. Then, the grid index mechanism is added to the archive for selection and deletion. Leaders can use the roulette wheel to select and delete some solutions in high-density grids. By obtaining the optimal Pareto solution set while maintaining the diversity of the population, the effectiveness of solving multi-objective optimization problems is improved. The improved algorithm solves the WFLO problem based on the Jansen wake model by using two different initial self-flow winds for performance testing while considering the effects of single wake and multiple wake with or without partial wake occlusion. The performance of the WFLO (output power and cost) was compared without considering wake obstruction, MORSA can obtain the minimum turbine cost of 26.3601 and the maximum power generation of 2.3078E-08 with a single wake flow of 16 m/s. The experimental results show that the proposed MORSA has better convergence and applicability, and has shown good results in multi-objective layout optimization of wind farms.

A novel techno-economical layout optimization tool for floating wind farm design

Abstract. Over the past few years, the offshore wind sector has been subject to renewed yet growing interest from the industry and from the research sphere, with a particular focus on a recently developed concept, the floating offshore wind (FOW). Because of its novelty, floating research material is found in limited quantity. This paper focuses on the layout optimization of a floating offshore wind farm (FOWF) considering multiple parameters and engineering constraints, combining floating-specific parameters together with economic indicators. Today’s common wind farm layout optimization codes do not take into account either floating-specific technical parameters (anchors, mooring lines, inter-array cables (IACs), etc.) or non-technical parameters (operational expenditure, OPEX; capital expenditure, CAPEX; and other techno-economic project parameters). In this paper, a multi-parametric objective function is used in the optimization of the layout of a FOWF, combining the annual energy production (AEP) together with the costs that depend on the layout. The mooring system and the collection system including the inter-array cables and the offshore substation are identified as layout-dependent and therefore modeled in the optimization loop. Using ScotWind site 10 as a study case, it was found with the predefined technical and economic assumptions that the profit was increased by EUR 34.5 million compared to a grid-based layout. The main drivers were identified to be the AEP, followed by the anchors and the availability associated with the failures of inter-array cables.

Predicting wind farm wake losses with deep convolutional hierarchical encoder–decoder neural networks

Wind turbine wakes are the most significant factor affecting wind farm performance, decreasing energy production and increasing fatigue loads in downstream turbines. Wind farm turbine layouts are designed to minimize wake interactions using a suite of predictive models, including analytical wake models and computational fluid dynamics simulations (CFD). CFD simulations of wind farms are time-consuming and computationally expensive, which hinder their use in optimization studies that require hundreds of simulations to converge to an optimal turbine layout. In this work, we propose DeepWFLO, a deep convolutional hierarchical encoder–decoder neural network architecture, as an image-to-image surrogate model for predicting the wind velocity field for Wind Farm Layout Optimization (WFLO). We generate a dataset composed of image representations of the turbine layout and undisturbed flow field in the wind farm, as well as images of the corresponding wind velocity field, including wake effects generated with both analytical models and CFD simulations. The proposed DeepWFLO architecture is then trained and optimized through supervised learning with an application-tailored loss function that considers prediction errors in both wind velocity and energy production. Results on a commonly used test case show median velocity errors of 1.0%–8.0% for DeepWFLO networks trained with analytical and CFD data, respectively. We also propose a model-fusion strategy that uses analytical wake models to generate an additional input channel for the network, resulting in median velocity errors below 1.8%. Spearman rank correlations between predictions and data, which evidence the suitability of DeepWFLO for optimization purposes, range between 92.3% and 99.9%.

Optimal placement of fixed hub height wind turbines in a wind farm using twin archive guided decomposition based multi-objective evolutionary algorithm

Harnessing maximum wind energy's power output and efficiency is vital to combat environmental challenges tied to conventional fossil fuels. Wind power's cost-effectiveness and emission reduction potential underscore its significance. Efficient wind farm layout plays a pivotal role, both technically and commercially. Evolutionary algorithms show their potential while solving multi-objective wind farm layout optimization problems. However, due to the large-scale nature of the problems, existing algorithms are getting trapped into local optima and fail to explore the search space. To address this, the TAG-DMOEA algorithm is upgraded with an adaptive offspring strategy (AOG) for better exploration. The proposed algorithm is employed on a wind farm layout problem with real-time data of wind speed and direction from two different locations. Unlike mixed hub heights, fixed hub heights such as 60, 67, and 78 m are adopted to conduct the case studies at two potential locations with real-time statistical data for the investigation of improved results. The results obtained by TAG-DMOEA-AOG on six cases are compared with 10 state-of-the-art algorithms. Statistical tests such as Friedman test and Wilcoxon signed rank test along with post hoc analysis (Nemenyi test) confirmed the superiority of the TAG-DMOEA-AOG on all cases of the considered multi-objective wind farm layout optimization problem.

An optimization framework for wind farm layout design using CFD-based Kriging model

Wind farm layout optimization (WFLO) seeks to alleviate the wake loss and maximize wind farm power output efficiency, and is a crucial process in the design and planning of wind energy projects. Since the optimization algorithms typically require thousands of numerical evaluations of the wake effects, conventional WFLO studies are usually carried out with the low-fidelity analytical wake models, while the higher-fidelity computational-fluid-dynamics-based (CFD-based) methods are seldom used due to the excessive computational cost. In this paper, we develop a self-adaptive optimization framework for wind farm layout design using CFD-based Kriging model to maximize the annual energy production (AEP) of wind farms. This surrogate-based optimization (SBO) framework uses latin hypercube sampling to generate a group of wind farm layout samples, based on which CFD simulations with the turbines modeled as actuator disks are carried out to obtain the corresponding AEPs. This initial wind farm layout dataset is used to train the Kriging model, which is then integrated with an optimizer based on genetic algorithm (GA). As the optimization progresses, the intermediate optimal layout designs are again fed into the dataset to re-train the Kriging model. Such adaptive update of wind farm layout dataset continues until the algorithm converges to the optimal layout design. To evaluate the performance of the proposed SBO framework, we apply it to four representative wind farm cases. Compared to the conventional staggered layout along the dominant wind direction, the optimized wind farm produces significantly higher total AEP, which is more evenly distributed among the turbines. In particular, the SBO framework requires significantly smaller number of CFD calls to yield the optimal layouts that generates almost the same AEP with the direct CFD-GA method. Further analysis on the velocity fields show that the optimization framework always attempts to locate the downstream turbines away from the wakes of upstream ones along the dominant wind directions. The proposed CFD-based surrogate model provides a more accurate and flexible alternative to the conventional analytical-wake-model-based methods in WFLO tasks, and has the potential to be used for designing efficient wind farm projects.

A novel binary genetic differential evolution optimization algorithm for wind layout problems

<abstract><p>This paper addresses the increasingly critical issue of environmental optimization in the context of rapid economic development, with a focus on wind farm layout optimization. As the demand for sustainable resource management, climate change mitigation, and biodiversity conservation rises, so does the complexity of managing environmental impacts and promoting sustainable practices. Wind farm layout optimization, a vital subset of environmental optimization, involves the strategic placement of wind turbines to maximize energy production and minimize environmental impacts. Traditional methods, such as heuristic approaches, gradient-based optimization, and rule-based strategies, have been employed to tackle these challenges. However, they often face limitations in exploring the solution space efficiently and avoiding local optima. To advance the field, this study introduces LSHADE-SPAGA, a novel algorithm that combines a binary genetic operator with the LSHADE differential evolution algorithm, effectively balancing global exploration and local exploitation capabilities. This hybrid approach is designed to navigate the complexities of wind farm layout optimization, considering factors like wind patterns, terrain, and land use constraints. Extensive testing, including 156 instances across different wind scenarios and layout constraints, demonstrates LSHADE-SPAGA's superiority over seven state-of-the-art algorithms in both the ability of jumping out of the local optima and solution quality.</p></abstract>

Optimization of Wind Farm Layout using Genetic Algorithms

In order to increase the economic feasibility, sustainability, and efficiency of energy production, this research proposes an improved optimization framework for hybrid wind-solar energy systems that use an augmented Genetic Algorithm (GA). Wind turbine size and photovoltaic (PV) panel orientation were optimized using historical data on wind and solar resources, system load profiles, and component specifications. There was an 18% increase in energy production, a 14% improvement in wind turbine efficiency, and a 16% increase in solar panel output because to the GA's outstanding performance. An 18% reduction in the payback time and a 12% reduction in the Levelized Cost of Energy (LCOE) were achieved. Results from the evaluation of the project's social and environmental consequences showed that community acceptability increased by 9 percentage points and land-use efficiency by 12 percentage points. A sensitivity study verified that the system could withstand several economic and environmental scenarios. The results demonstrate the promise of GA-based optimization in improving the efficiency of renewable energy hybrid systems.

Wind Farm Layout Optimization Problem Using Nature‐Inspired Algorithms

The wind farm layout optimization problem (WFLOP) is a significant problem in the field of renewable energy. In this development of wind farm, solving the WFLOP is a crucial task, which entails placing the turbines in the wind farm in the best locations to reduce wake effects and enhance predicted power generation. In recent decades, the WFLOP problem has been solved mathematically. Meanwhile, the growing load demand led to an increase in the complexity of WFLOP. The results obtained by mathematical methods were not accurate enough to suit complexity of WFLOP. Nowadays, researchers from a variety of fields are developing nature‐inspired algorithms (NIAs) to solve difficult real‐world problems. This study is an attempt to review the most important innovations in the field of NIAs to solve the WFLOP problem. The classification of the reviewed literature is based on different applied approaches and models. In addition to specific proposals, the advantages and disadvantages of certain domains are also discussed. This study provides a future direction to the fellow researchers who are working in the field of WFLOP.

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

GNN Representation Learning and Multi-Objective Variable Neighborhood Search Algorithm for Wind Farm Layout Optimization

Optimal floating offshore wind farms for Mediterranean islands

Offshore wind will play an important role in achieving the Green Deal's goals of green transition and renewable energy generation. The European Union (EU) has launched an initiative to support a clean energy transition towards sustainable and renewable energy production in the EU islands. In fact, the Mediterranean islands obtain most of their energy supply from imported fossil fuels, making electricity costs generally very high due to their remote location. In this paper, we compare the optimal offshore wind farm layout, type and number of wind turbines for four selected sites next to Mediterranean islands. Each layout is evaluated in terms of levelised cost of energy, taking into account aerodynamic wake and transmission losses in the productivity estimation and a detailed cost model. Aerodynamic losses are modelled using a Jensen kinematic wake model and an area overlapping model to consider partial wake shadowing between wind turbines, while transmission losses are estimated as Ohmic losses of the inter-array and export transmission cables. Compared to the state-of-the-art PyWake code, the in-house MATLAB wake model demonstrated higher computational efficiency and is integrated into an efficient optimisation algorithm consisting of several iterations of the Interior-point optimisation algorithm. Results show that offshore wind turbines can provide renewable energy at a competitive price, especially at the most energetic site with an LCOE about 80 €/MWh. The normalised optimal LCOE and capacity factor as a function of the number of wind turbines are not significantly influenced by the chosen location and reach a maximum difference of 2–3 %, showing that they mainly depend on the type of wind turbine. The lowest optimal LCOE depending on the number of wind turbines is reduced by about 10 % and 25 % by the higher rated wind turbine.

A teaching-learning-based optimization algorithm with reinforcement learning to address wind farm layout optimization problem

As the global demand for renewable energy continues to rise, wind energy has received widespread attention as an eco-friendly energy source. Wind power generation is regarded as one of the key means to reduce carbon emissions and achieve sustainable development. Usually, a mass of turbines works together to produce electricity in a wind farm. However, downstream turbines will inevitably be influenced by the wake generated by upstream turbines, resulting in unused wind energy being lost. To reduce the negative effects of the wake, maximization of wind farm output power, and minimization of wind farm cost, a teaching-learning-based optimization algorithm with reinforcement learning is proposed in this paper. The improvements of the proposed algorithm mainly include the following three points: i) the original serial structure of the algorithm is changed to a parallel structure to accelerate the convergence and improve the efficiency of the algorithm. ii) the parameter F, which is adjusted by RL, is proposed to adjust the selection of the updating phase due to the design of a parallel structure. iii) in the modified learner phase, an individual is added to participate in the update, and a selection probability is proposed to improve the ability of the algorithm to retain the information of superior individuals. To study the performance of the modified algorithm, it was first tested against 10 other advanced algorithms on a benchmark testing suite. They then ran numerical experiments on four hypothetical wind farm cases under two simulated wind conditions. Finally, the superiority of improved algorithm over others and the effectiveness of addressing wind farm layout problem are demonstrated by experimental results.

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models

Accelerated wind farm yaw and layout optimisation with multi-fidelity deep transfer learning wake models