Wind Farm Layout Optimization Research Articles

Optimization of wind farm layout on a limited area

Optimization of wind farm layout on a limited area

Study on offshore wind farm layout optimization based on decommissioning strategy

In recent years, along with the first generation of wind power reaching retirement age, the high decommissioning cost arises with more and more attention all around the word. To reduce this huge cost, an innovative offshore wind farm layout optimization method based on decommissioning strategy is presented in this paper. In the optimization method, the decommissioning strategy means that the foundations can be reused after the retirement of the first generation of wind turbines, and then smaller second-generation wind turbines will be installed on the original foundations. The optimization process is based on the Multi-Population Genetic Algorithm (MPGA). A conceptual two-dimensional (2D) wake model is adopted to calculate wind losses caused by wake effect. The Cost of Energy (COE) is regarded as the criteria to judge the effectiveness of this new method. The way to estimate costs will also be introduced in this study. Finally, a case study in Waglan sea area in Hong Kong is analyzed and discussed. From the case results, Hong Kong is an ideal region to develop the offshore wind industry, and the proposed optimization method can reduce the COE down to 1.02 HK$/kWh.

Economics- and Reliability-Based Design for an Offshore Wind Farm

This study examines the optimal layout of an offshore wind farm for a fixed number of wind turbines (WTs) and a fixed area of coverage. After the optimal layout of the turbines is determined, the topology of the cable connections is optimized. The wind decay that is associated with the wake effect is considered and the charged system search algorithm is utilized to obtain the optimal WT layout that maximizes the power output of the wind farm. The Wind Atlas Analysis and Application Program software is utilized to verify the optimality of the layout. Mixed integer linear programming is utilized to optimize the topology of radial cable connections. The system reliabilities of the proposed layout and the traditional regular layout are considered. Finally, the optimal wind-farm layout that maximizes wind power output, minimizes cable cost, and maximizes reliability is obtained.

Design optimization of a wind farm layout for maximum wind energy capture: A new constructive approach

Wind energy is becoming an attractive source of clean energy. However, this type of power source is subject to power reductions due to losses in wind energy conversion system and to frequent changes in wind velocity. For that reason, the important phase of a wind farm design is solving the wind farm layout optimization problem, which consists in optimally positioning the turbines within the wind farm so that the wake effects are minimized and therefore the expected power production is maximized. This problem has been receiving increasing attention from the scientific community. In this article, a mathematical optimization scheme is employed to optimize the locations of wind turbines with respect to maximizing the wind farm power production. To formulate the mathematical optimization problem, we used Jensen’s wake model. We calculate the wake loss and express the expected wind farm power as a differentiable function in terms of the locations of the wind turbines. Furthermore, we develop a new constructive approach to find the best solution to the wind turbines placement problem. Finally, our results are compared with those in some other ealier studies.

Decomposition based multi-objective evolutionary algorithm for windfarm layout optimization

An efficient windfarm layout to harness maximum power out of the wind is highly desirable from technical and commercial perspectives. A bit of flexibility on layout gives leeway to the designer of windfarm in planning facilities for erection, installation and future maintenance. This paper proposes an approach where several options of optimized usable windfarm layouts can be obtained in a single run of decomposition based multi-objective evolutionary algorithm (MOEA/D). A set of Pareto optimal vectors is obtained with objective as maximum output power at minimum wake loss i.e. at maximum efficiency. Maximization of both output power and windfarm efficiency are set as two objectives for optimization. The objectives thus formulated ensure that in any single Pareto optimal solution the number of turbines used are placed at most optimum locations in the windfarm to extract maximum power available in the wind. Case studies with actual manufacturer data for wind turbines of same as well as different hub heights and with realistic wind data are performed under the scope of this research study.

Three-dimensional structure of wind turbine wakes as measured by scanning lidar

Abstract. The lower wind speeds and increased turbulence that are characteristic of turbine wakes have considerable consequences on large wind farms: turbines located downwind generate less power and experience increased turbulent loads. The structures of wakes and their downwind impacts are sensitive to wind speed and atmospheric variability. Wake characterization can provide important insights for turbine layout optimization in view of decreasing the cost of wind energy. The CWEX-13 field campaign, which took place between June and September 2013 in a wind farm in Iowa, was designed to explore the interaction of multiple wakes in a range of atmospheric stability conditions. Based on lidar wind measurements, we extend, present, and apply a quantitative algorithm to assess wake parameters such as the velocity deficits, the size of the wake boundaries, and the location of the wake centerlines. We focus on wakes from a row of four turbines at the leading edge of the wind farm to explore variations between wakes from the edge of the row (outer wakes) and those from turbines in the center of the row (inner wakes). Using multiple horizontal scans at different elevations, a three-dimensional structure of wakes from the row of turbines can be created. Wakes erode very quickly during unstable conditions and can in fact be detected primarily in stable conditions in the conditions measured here. During stable conditions, important differences emerge between the wakes of inner turbines and the wakes of outer turbines. Further, the strong wind veer associated with stable conditions results in a stretching of the wake structures, and this stretching manifests differently for inner and outer wakes. These insights can be incorporated into low-order wake models for wind farm layout optimization or for wind power forecasting.

Trading off sound pressure level and average power production for wind farm layout optimization

This research explores the trade-offs between a wind farm's average power production and noise impact on nearby observers. Two specific wind farm designs were studied and optimized using the FLORIS wake model and an acoustic model based on semi-empirical turbine noise calculations. It was found in the two wind farms that the average power production could be increased, up to 8.01% in one and 3.63% in the other, ignoring sound level considerations. Including a noise restriction in the optimization had a minimal impact on the optimal average power production within about a five-decibel range. Past this range, sound limitations decreased the wind farm's power production significantly. By analyzing average power production and sound pressure level together, we can take advantage of the multi-modality of the optimization to find solutions were noise impact can be improved with an insignificant effect on power production.

Decomposition based multi-objective optimization to simultaneously determine the number and the optimum locations of wind turbines in a wind farm

Wind energy is one of the most progressive types of renewable energies deployed using wind turbines, which convert wind power into electrical energy. Various methodologies and layouts have been proposed to determine the optimal layout (numbers and locations) of wind turbines inside a wind farm to extract maximum energy. However, limited availability of land area has resulted in the construction of these wind farms near to human habitats causing a negative impact on human health. Compared to other factors, noise has become an immense concern for wind farm designers, as it needs to be constrained within the mandatory limits. Using a well-established wake model and ISO-9613-2 noise calculation, this study performs a wind farm layout optimization (WFLO) based on a multi-objective trade-off between minimization of noise propagation and maximization of energy generation. A novel hybrid methodology is proposed as a combination of proposed probabilistic variable decomposed multi-objective evolutionary algorithm (VdRBNSGA-II) and a newly developed gradient-based non-dominated normalized normal constraint (nD-NNC) method. As opposed to previous works, generated non-dominated front provides various alternative energy-noise solutions along with an additional information on turbine layouts, which allows a decision maker to select among different competing solutions based on the existing noise and other regulations.

Wind farm power production in the changing wind: Robustness quantification and layout optimization

Wind farms operate often in the changing wind. The wind condition variations in a wide range of time scales lead to the variability of wind farms’ power production. This imposes a major challenge to the power system operators who are facing a higher and higher penetration level of wind power. Thus, wind farm developers/owners need to take the variability into consideration in the designing/planning stage, in addition to the conventional main objective of maximizing the expected power output under a fixed wind distribution. In this study, we first propose a new metric to evaluate the variability of wind power based on the characteristics of the wind farm and its local wind conditions. Then a series of robustness metrics are proposed to quantify wind farm’s ability to produce power with high mean value and low variability under changing wind, considering both short-term and long-term wind condition variations. Based on these metrics, wind farm layout optimization is performed to maximize the robustness of a real offshore wind farm in Denmark. The results demonstrate that the robustness metrics are more flexible and complete than the conventional metrics for characterizing wind farm power production, such as mean power output or wind power variability alone, and it is feasible to design wind farms to produce power with high mean value and low variability.

Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm

This article explores the application of a wind farm layout evaluation function and layout optimization framework to Middelgrunden wind farm in Denmark. This framework has been built considering the interests of wind farm developers in order to aid in the planning of future offshore wind farms using the UK Round 3 wind farms as a point of reference to calibrate the model. The present work applies the developed evaluation tool to estimate the cost, energy production, and the levelized cost of energy for the existing as-built layout at Middelgrunden wind farm; comparing these against the cost and energy production reported by the wind farm operator. From here, new layouts have then been designed using either a genetic algorithm or a particle swarm optimizer. This study has found that both optimization algorithms are capable of identifying layouts with reduced levelized cost of energy compared to the existing layout while still considering the specific conditions and constraints at this site and those typical of future projects. Reductions in levelized cost of energy such as this can result in significant savings over the lifetime of the project thereby highlighting the need for including new advanced methods to wind farm layout design.

A tight upper bound for quadratic knapsack problems in grid-based wind farm layout optimization

ABSTRACTThe 0-1 quadratic knapsack problem (QKP) in wind farm layout optimization models possible turbine locations as nodes, and power loss due to wake effects between pairs of turbines as edges in a complete graph. The goal is to select up to a certain number of turbine locations such that the sum of selected node and edge coefficients is maximized. Finding the optimal solution to the QKP is difficult in general, but it is possible to obtain a tight upper bound on the QKP's optimal value which facilitates the use of heuristics to solve QKPs by giving a good estimate of the optimality gap of any feasible solution. This article applies an upper bound method that is especially well-suited to QKPs in wind farm layout optimization due to certain features of the formulation that reduce the computational complexity of calculating the upper bound. The usefulness of the upper bound was demonstrated by assessing the performance of the greedy algorithm for solving QKPs in wind farm layout optimization. The results show that the greedy algorithm produces good solutions within 4% of the optimal value for small to medium sized problems considered in this article.

A novel geometric pattern-based approach to maximize power output of a wind farm

The placement of wind turbines is crucial for a wind farm because the power generation of wind turbine is greatly affected by the wake effect produced by the upstream turbines. The optimum placement of wind turbines in a wind farm will give the maximum total power output. The effective methodology is imperative to place turbines in such a way that effect of the wake is minimum on a performance of turbines and has a proper utilization of the wind farm area. The present study proposes a novel approach based on the geometrical pattern-inspired placement methodology to locate wind turbines and maximize the power output of a wind farm. In the proposed approach, various geometrical patterns made from circle, hexagon, pentagon and triangle are considered for the numerical investigation. Further, wind behavior is modeled for uniform and variable wind speed from all directions. The present approach is also experimented numerically with and without land availability constraints. Heat transfer search algorithm is used to solve the wind farm layout optimization problem with the proposed approach and the results are compared with other approaches available in the literature. Results show that the proposed geometrical pattern-based approach produces the higher power generation (~4–8%) compared to other approaches for the variable wind speed scenario. In the case of land availability constraints, about 4% higher power generation compared to the grid-based approach is achieved. In order to place turbines with an optimum performance of a wind farm, the present approach can be helpful to wind farm developers.

Optimal Design for Offshore Wind Farm considering Inner Grid Layout and Offshore Substation Location

This paper introduces an efficient methodology to design optimally cable layout of inner grid as well as location of offshore substation. The methodology is composed of two optimizers: one is inner grid layout optimizer, which consists of several algorithms such as the k-clustering-based genetic algorithm, the minimum spanning tree, and the cable selection; the other one is offshore substation location optimizer based on the pattern search method. While performing iteratively two optimizers, alternatives for cable layout and candidates for location of offshore substation are generated and evaluated in the economic and reliability aspects, respectively. Optimal layout of offshore wind farm is determined so as to minimize the total cost which is a sum of construction, power loss, maintenance, and reliability costs.

Layout optimization of a wind farm to maximize the power output using enhanced teaching learning based optimization technique

The global warming is a major concern in the present era that arises a need of cleaner production of energy. The wind energy is a major source of contribution for such clean energy demands. The Wind farm layout optimization (WFLO) problem deals with the optimum placement of wind turbines in a wind farm so as to maximize the total power output with minimum cost of energy. The placement of turbines is crucial for a wind farm because the power generation of wind turbine decreases if it is in the wake effect produced by the upstream turbines. So, WFLO problem is a challenging combinatorial optimization problem for which many direct search and local optimization method fails to attend the global optimum solution. The Meta-heuristic method often provides the effective solution for such problems in terms of convergence and the quality of the solution. In this work two, different metaheuristics algorithms are proposed to solve WFLO problem. These algorithms are developed by incorporating changes in the basic Teaching-learning based optimization (TLBO) algorithm. The proposed algorithms eliminate the limitations of basic TLBO algorithm to enhance its exploration and exploitation by incorporating effective search techniques. The implementations of the proposed algorithm are effective to optimize the position of wind turbines in a wind farm to maximize the expected power output of a wind farm with a minimum investment cost. The proposed algorithm is investigated for WFLO problem and a set of 10 challenging real life benchmark problems. The numerical results indicate that the proposed method is an effective technique to solve the WFLO problem compared to its basic algorithm and other state of the art methods. The optimum design of the wind farm results in the economical utilization of the wind resource and leads to clean energy production.

Gradient-based multidisciplinary design of wind farms with continuous-variable formulations

In addressing the multi-criteria Wind Farm Layout Optimization (WFLO) problem, the literature has been focused on the simple weighted sum approach using single-objective stochastic and evolutionary algorithms, in addition to Pareto formulations using evolutionary algorithms. There is no single solution to a multi-criteria problem with conflicting objectives; therefore, the Pareto approach is useful to provide the developer with a non-dominated set of solutions. However, the evolutionary optimization algorithms tend to be computationally prohibitive, especially when optimizing large-scale wind farms. Additionally, most of WFLO problems are highly constrained, where many unfeasible zones can exist inside the proposed wind farm boundaries, which in turn complicates the optimization process. To remedy these drawbacks, we propose a gradient-based approach to Pareto optimization of the multi-criteria WFLO problems considering land footprint, energy output, electrical infrastructure and environmental impact. Mathematical functions and their derivatives are developed to represent the four objectives, land-based constraints, and their gradients. The developed models were validated by devised numerical experiments; and the optimized layouts using the proposed algorithm were compared to those by the Non-Dominated Sorting Genetic Algorithm (NSGA-II). Our results provide some evidence regarding the inability of the NSGA-II to cover the objective space when optimizing wind farms with large power-densities. In contrast, our proposed approach succeeds in obtaining high-density layouts efficiently. Furthermore, we demonstrated the superiority of the developed algorithm, in the aspects of coverage, spread, and computational cost.

Aesthetic Local Search of Wind Farm Layouts

The visual impact of wind farm layouts has seen little consideration in the literature on the wind farm layout optimisation problem to date. Most existing algorithms focus on optimising layouts for power or the cost of energy alone. In this paper, we consider the geometry of wind farm layouts and whether it is possible to bi-optimise a layout for both energy efficiency and the degree of visual impact that the layout exhibits. We develop a novel optimisation approach for solving the problem which measures mathematically the degree of visual impact of a layout. The approach draws inspiration from the field of architecture. To evaluate our ideas, we demonstrate them on three benchmark problems for the wind farm layout optimisation problem in conjunction with two recently-published stochastic local search algorithms. Optimal patterned layouts are shown to be very close in terms of energy efficiency to optimal non-patterned layouts.

A Two-Stage Placement Algorithm with Multi-Objective Optimization and Group Decision Making

Abstract Atwo-stage placement algorithm with multi-objective optimization and group decision making is proposed. The first stage aims to determineaset of design alternatives for objects placement by multi-objective combinatorial optimization. The second stage relies on business intelligence via group decision-making based on solution of optimization task to makeachoice of the most suitable alternative. The design alternatives are determined by means of weighted sum and lexicographic methods. The group decision making is used to evaluate determined design alternatives toward the design parameters. The described algorithm is used for wind farm layout optimization problem. The results of numerical testing demonstrate the applicability of the proposed algorithm.

A Novel Decomposition-Based Evolutionary Algorithm for Engineering Design Optimization

In recent years, evolutionary algorithms based on the concept of “decomposition” have gained significant attention for solving multi-objective optimization problems. They have been particularly instrumental in solving problems with four or more objectives, which are further classified as many-objective optimization problems. In this paper, we first review the cause-effect relationships introduced by commonly adopted schemes in such algorithms. Thereafter, we introduce a decomposition-based evolutionary algorithm with a novel assignment scheme. The scheme eliminates the need for any additional replacement scheme, while ensuring diversity among the population of candidate solutions. Furthermore, to deal with constrained optimization problems efficiently, marginally infeasible solutions are preserved to aid search in promising regions of interest. The performance of the algorithm is objectively evaluated using a number of benchmark and practical problems, and compared with a number of recent algorithms. Finally, we also formulate a practical many-objective problem related to wind-farm layout optimization and illustrate the performance of the proposed approach on it. The numerical experiments clearly highlight the ability of the proposed algorithm to deliver the competitive results across a wide range of multi-/many-objective design optimization problems.

Wind farm layout optimization using a Gaussian-based wake model

The wind farm layout optimization problem has received considerable attention over the past two decades. The objective of this problem is to determine the wind farm layout that maximizes the annual energy generated. The majority of studies that have solved this problem simulated the velocity deficit using the Jensen wake model. However, this model is not in agreement with field measurements and computational fluid dynamics simulations. In this study, an approach to solve the wind farm layout optimization problem based on a Gaussian wake model is proposed. The Gaussian wake model uses an exponential function to evaluate the velocity deficit, in contrast to the Jensen wake model that assumes a uniform velocity profile inside the wake. The proposed approach minimizes the annual cost of energy of a wind farm using a genetic algorithm. The application of the proposed approach yields higher annual generation and a lower computational time for all wind scenarios under study. Under a more complex wind scenario, the improvement was relatively small. This suggests that the use of a more robust wake model in the WFLO problem, does not lead to greater efficiency in real wind cases.

Wind farm layout using biogeography based optimization

Wind energy is one of the most promising option for the renewable energy. Finding optimum set of locations for wind turbines in a wind farm so that the total energy output of the farm is maximum, is usually referred as the wind farm layout optimization problem (WFLOP). This article presents the solution of WFLOP using a recent unconventional optimization algorithm, Biogeography Based Optimization (BBO). In this article, for a given wind farm not only the optimum locations of the wind turbines are obtained but also the maximum number of turbines is recommended. Experiments have been carried out for wind farms of various sizes. BBO has shown to outperform as compare to earlier methodologies of solving WFLOP.