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An optimization framework for simultaneous design of wind turbines (WTs) and cable layout for collection systemof offshore wind farms (OWFs) is presented in this paper. The typical approach used in both research and practical design is sequential, with an initial annual energy production (AEP) maximization, followed then by the collection system design. The sequential approach is robust and effective, however it fails to exploit the synergies between optimization blocks. Intuitively,one of the strongest trade-offs is between the WTs and cable layout, as they generally compete, i.e. spreading out WTs mitigates wake losses for larger AEP, but also results in longer submarine cables in the collection system and higher costs. The proposedo ptimization framework implements a gradient-free optimization algorithm to smartly move the WTs within the project area subject to minimum distance constraint, while a fast heuristic algorithm is called in every function evaluation in order to calculate a cost estimation of the cable layout. In a final stage, a refined cable layout design is obtained by iteratively solving amixed integer linear program (MILP), modelling all typical engineering constraints of this particular problem. A comprehensive performance analysis of the cost estimation from the fast heuristic algorithm with respect to the exact model is carried out.The applicability of the method is illustrated through a large-scale real-world case study. Results shows that: (i) the quality of the cable layout estimation is strongly dependent on the separation between WTs, where dense WTs layouts present better performance parameters in terms of error, correlation and computing time, and (ii) the proposed simultaneous design approach provides up to 6% of improvement on the quality of fully feasible wind farm designs, and broadly, a statistically significant enhancement is ensured in spite of the stochasticity of the optimization algorithm.

An optimization framework for simultaneous design of wind turbines (WTs) and cable layout for collection systemof offshore wind farms (OWFs) is presented in this paper. The typical approach used in both research and practical design is sequential, with an initial annual energy production (AEP) maximization, followed then by the collection system design. The sequential approach is robust and effective, however it fails to exploit the synergies between optimization blocks. Intuitively,one of the strongest trade-offs is between the WTs and cable layout, as they generally compete, i.e. spreading out WTs mitigates wake losses for larger AEP, but also results in longer submarine cables in the collection system and higher costs. The proposedo ptimization framework implements a gradient-free optimization algorithm to smartly move the WTs within the project area subject to minimum distance constraint, while a fast heuristic algorithm is called in every function evaluation in order to calculate a cost estimation of the cable layout. In a final stage, a refined cable layout design is obtained by iteratively solving amixed integer linear program (MILP), modelling all typical engineering constraints of this particular problem. A comprehensive performance analysis of the cost estimation from the fast heuristic algorithm with respect to the exact model is carried out.The applicability of the method is illustrated through a large-scale real-world case study. Results shows that: (i) the quality of the cable layout estimation is strongly dependent on the separation between WTs, where dense WTs layouts present better performance parameters in terms of error, correlation and computing time, and (ii) the proposed simultaneous design approach provides up to 6% of improvement on the quality of fully feasible wind farm designs, and broadly, a statistically significant enhancement is ensured in spite of the stochasticity of the optimization algorithm.

The electrical systems needed for offshore wind farms to collect power from wind turbines-and transmit it to shore- will be a significant cost element of these systems. This paper describes the development of a simplified model of the cost and performance of such systems. The performance prediction accounts for losses as a function of the power produced in the wind farm and the length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore. The collection system-the medium-voltage electrical grid within the wind farm, and the transmission system-the high-voltage electrical connection to an on-shore transmission line-are treated independently in the model. Data sources for the model and limitations of the data are discussed, and comparison is made to costs reported by others. The choice of transmission system technology is also addressed. This electrical system model is intended for integration into a more comprehensive model of offshore wind farm design, cost, and performance that will be used for parametric studies and optimization of wind farm configurations. Because some concepts for future offshore wind installations in deep water use floating platforms, this paper briefly discusses the application of submarine cable technology to nonfixed termination points, a departure from current practice. Introduction The National Wind Technology Center (NWTC) of the National Renewable Energy Laboratory (NREL) in Golden, Colorado, has undertaken a series of concept studies to evaluate the cost and performance of offshore wind farms. The product of these studies will be a comprehensive model of offshore wind farm design, cost, and performance suitable for parametric studies and optimization of wind farmconfigurations. The overall goal of this effort is to helpidentify technology pathways for offshore wind energy development and deployment in the United States. Offshore wind farms present an attractive option because they allow for larger wind turbines that operate in higher wind resources than land-based [1,2]. Offshore installations are also more expensive, so an understanding of their performance, cost, and optimal configurations is needed. This paper is an overview of one of these concept studies. It focuses on the power losses in wind farm electrical power collection and transmission systems, as well as the costs of the system components and their installation. A hypothetical system was based loosely on the Horns Rev offshore wind farm in Denmark. Inquiries were made with manufacturers about electrical and cost data on the required components, which were then compiled in a spreadsheet model. The performance prediction accounts for losses as a function of power output of the wind farm and length and size of the cables. The cost prediction is flexibly formulated so wind farm configurations can be evaluated by parameters such as the number of wind turbines, wind turbine size, turbine array configuration and spacing, and distance from shore.

Offshore wind energy development along the East Coast of the US is proceeding quickly as a result of large areas with an excellent wind resource, low water depths and proximity to large electricity markets. Careful planning of wind turbine deployments in these offshore wind energy lease areas (LA) is required to maximize power output and to minimize wake losses between neighboring wind farms as well as those internal to each wind farm. Here, we used microscale wind modeling with two wake parameterizations to evaluate the potential annual energy production (AEP) and wake losses in the different LA areas, and we developed and applied a levelized cost of energy (LCoE) model to quantify the impact of different wind turbine layouts on LCoE. The modeling illustrated that if the current suite of LA is subject to deployment of 15 MW wind turbines at a spacing of 1.85 km, they will generate 4 to 4.6% of total national electricity demand. The LCoE ranged from $68 to $102/MWh depending on the precise layout selected, which is cost competitive with many other generation sources. The scale of the wind farms that will be deployed greatly exceed those currently operating and mean that wake-induced power losses are considerable but still relatively poorly constrained. AEP and LCoE exhibited significant dependence on the precise wake model applied. For the largest LA, the AEP differed by over 10% depending on the wake model used, leading to a $10/MWh difference in LCoE for the wind turbine layout with 1.85 km spacing.

During recent years, wind energy has moved from an emerging technology to a nearly competitive technology. This fact, coupled with an increasing global focus on environmental concern and a political desire of a certain level of diversification in the energy supply, ensures wind energy an important role in the future electricity market. For this challenge to be met in a cost-efficient way, a substantial part of new wind turbine installations is foreseen to be erected in big onshore or offshore wind farms. This fact makes the production, loading and reliability of turbines operating under such conditions of particular interest.

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

A MILP program for integrated global optimization of electrical cables systems in Offshore Wind Farms (OWFs) is presented. Electrical cables encompass the cable layout in collection systems to interconnect Wind Turbines (WTs), and transmission systems to couple Offshore Substations (OSSs) to the Onshore Connection Point (OCP). The program is solved through a modern branch-and-cut solver, demonstrating the ability to tackle large-scale instances with hundreds of WTs and several OSSs. The model supports as objective function the initial investment plus economic losses due to total electrical power losses. The importance and functionality of incorporating electrical losses is demonstrated, along with the need to simultaneously optimize the cable layout, OSSs location, and transmission cables. The method is tested for three case studies. The results show that (i) points near the global optimum, with an imposed maximum tolerance, are calculable within reasonable computational time and effort, and (ii) the integrated model can be much more efficient than a benchmark approach based on enumeration, i.e., exhaustive evaluation of all possible optimization problems derived from unique OSSs locations.

Floating offshore wind energy will play a key role in the clean energy transition scenario. The number of projects deploying large-scale wind farms is growing in multiple regions, from Northern Europe to the East Coast of the United States, and extending to the Mediterranean Sea. Offshore wind farms face fewer constraints in layout design, as they do not need to consider orography and can generally be situated in vast, open sea areas. Consequently, offshore wind turbines could be arranged in simple layouts, such as grid patterns or staggered rows, spaced uniformly. However, this regular turbine arrangement would result in significant annual energy production (AEP) losses due to wake-rotor interaction. Although increasing spacing between turbines can mitigate this issue, it is not always feasible due to marine space availability. Moreover, when feasible, it can lead to higher costs for additional cabling and maintenance. This paper aims to introduce a multi-objective wind farm optimization framework that employs a genetic algorithm (NSGA II). This framework seeks to maximize the energy production while minimizing OPEX and CAPEX costs, taking into account the wind resource and bathymetry of a specific region. In the paper, two case studies are presented for the optimization of wind farms in the Mediterranean Sea assuming 15MW wind turbines. AEP evaluation of each individual wind farm is obtained with the open-source library FLORIS, while the optimization algorithm relies on the library PyMoo for multi-objective optimization.

A state-of-the-art review of the optimization of electrical cables in offshore wind farms (OWFs) is presented in this paper. One of the main contributions of this paper is to propose a general classification of this problem, framed in the general context of the OWFs design and optimization (OWiFDO). The classification encompasses two complementary aspects. First, the optimum sizing of electrical cables, with the three main approaches used nowadays, static-rated sizing, dynamic load cycle profile, and dynamic full time series, is conceptually analyzed and compared. The latest techniques and advances are described, along with the presentation of potential research areas not thoroughly addressed today, such as dynamic cable rating, and cable’s lifetime estimation under time-varying conditions. Second, the network optimization of large OWFs is thoroughly presented, dividing the problem with a bottom–top approach: cable layout of the collection system, wind turbines (WTs) allocation to offshore substations (OSSs), number and location of OSSs, and interconnection between OSSs and onshore connection points (OCPs). A comparison among different methods is performed, taking into consideration the main engineering constraints. Global optimization, specifically, binary programming (BIP) or mixed-integer linear programming (MILP), is envisaged as the best way to tackle this topic. The full combinatorial problem is found to be better addressed following a top–bottom approach, combining exact formulations with high-level heuristics, or holistically with evolutionary algorithms.

The development of offshore wind power has become a pressing energy issue, driven by the need to find new electrical power sources and to reduce the use of fossil fuels. Offshore wind farms can harness tremendous wind resources without annoying citizens and with a comparatively low environmental impact. They are thus becoming a central pillar of a carbon free energy system. However offshore turbines and wind farms are costly to install and maintain, making reliability and cost-effectiveness key issues. This work covers reliability of offshore wind farms as a whole, starting from weather and wind conditions, dealing with wind turbine technology, farm layout, monitoring, safety and maintenance. The thoroughly revised second edition additionally covers turbines of up to 10 MW, turbine design changes, turbine converters, HVDC converter stations and DC links, offshore sub-sea collector and export cables, and the structures supporting large offshore wind farms. Offshore Wind Power is essential reading for scientists, engineers, technicians and advanced students interested or engaged in the design of wind turbines, drivetrain technology and power mechatronics, in academia and industry.

Wind energy is a promising alternate energy resource. However, the on-land wind farms are limited by space, noise, and visual pollution and, therefore, many countries build wind farms near the shore. Until now, most offshore wind farms have been built in relatively shallow water (less than 30 m) with fixed tower type wind turbines. Recently, several countries have planned to move wind farms to deep water offshore locations to find stronger and steadier wind fields as compared to near shore locations. For the wind farms in deeper water, floating platforms have been proposed to support the wind turbine. The model tests described in this paper were performed at MARIN (maritime research institute netherlands) with a model setup corresponding to a 1:50 Froude scaling. The wind turbine was a scaled model of the national renewable energy lab (NREL) 5 MW horizontal axis reference wind turbine supported by three different generic floating platforms: a spar, a semisubmersible, and a tension-leg platform (TLP). The wave environment used in the tests is representative of the offshore in the state of Maine. In order to capture coupling between the floating platform and the wind turbine, the 1st bending mode of the turbine tower was also modeled. The main purpose of the model tests was to generate data on coupled motions and loads between the three floating platforms and the same wind turbine for the operational, design, and survival seas states. The data are to be used for the calibration and improvement of the existing design analysis and performance numerical codes. An additional objective of the model tests was to establish the advantages and disadvantages among the three floating platform concepts on the basis of the test data. The paper gives details of the scaled model wind turbine and floating platforms, the setup configurations, and the instrumentation to measure motions, accelerations, and loads along with the wind turbine rpm, torque, and thrust for the three floating wind turbines. The data and data analysis results are discussed in the work of Goupee et al. (2012, “Experimental Comparison of Three Floating Wind Turbine Concepts,” OMAE 2012-83645).

An optimization framework for global optimization of the cable layout topology for offshore wind farm (OWF) is presented. The framework designs and compares closed‐loop and radial layouts for the collection system of OWFs. For the former, a two‐stage stochastic optimization program based on a mixed integer linear programming (MILP) model is developed, while for the latter, a hop‐indexed full binary model is used. The purpose of the framework is to provide a common base for assessing both designs economically, using the same underlying contingency treatment. A discrete Markov model is implemented for calculating the cable failure probability, useful for estimating the time under contingency for multiple power generation scenarios. The objective function supports simultaneous optimization of (i) initial investment (network topology and cable sizing), (ii) total electrical power loss costs and (iii) operation costs due to energy curtailment from cable failures. Constraints are added accounting for common engineering aspects. The applicability of the full method is demonstrated by tackling three differently sized real‐world OWFs. Results show that (i) the profitability of either topology type depends strongly on the project size and wind turbine rating. Closed loop may be a competitive solution for large‐scale projects where large amounts of energy are potentially curtailed. (ii) The stochastic model presents low tractability to tackle large‐scale instances, increasing the required computing time and memory resources. (iii) Strategies must be adopted in order to apply stochastic optimization for modern OWFs, intending analytically or numerically simplification of mathematical models.

Kuwait plans to produce 15 % of its electricity from renewable resources by 2030. This paper aims at designing a 300-MW wind farm in six different sites in Kuwait. The study uses the measured wind data at Kuwait International Airport to predict the wind profile (speed and power density) at the selected sites. To validate the work, two different types of wind generation technologies are used in the design. The first type is Full-Converter Wind Turbine Generators (FCWTGs), and the second type is Doubly Fed Induction Generators (DFIGs). The designed wind farms are simulated using the Wind Atlas Analysis and Application Program (WAsP®) to estimate the Annual Energy Production (AEP) for each type of wind farms in each selected site. The location of every wind turbine in each wind farm (both types) for every selected site is accurately determined while considering needed spacing between rows and columns of turbines. The wake effect for downstream wind turbines is considered and estimated in the design. A comparison between the different wind farms in the six sites using the DIFGs and the FCWTGs generators is carried out. The economic feasibility of the designed wind farms is studied using the estimated Annual Energy Production (AEP) in each one of the designed wind farms.

<strong class="journal-contentHeaderColor">Abstract.</strong> Offshore wind farms are more commonly installed in wind farm clusters, where wind farm interaction can lead to energy losses; hence, there is a need for numerical models that can properly simulate wind farm interaction. This work proposes a Reynolds-averaged Navier-Stokes (RANS) method to efficiently simulate the effect of neighboring wind farms on wind farm power and annual energy production. First, a novel steady-state atmospheric inflow is proposed. This inflow model is well suited for RANS simulations of large wind farms because it does not lead to the development of nonphysical wind farm wakes. Second, a RANS-based wind farm parametrization is introduced, the actuator wind farm (AWF) model, which represents the wind farm as a forest canopy and allows to use of coarser grids compared to modeling all wind turbines as actuator disks (ADs). When the horizontal resolution of the RANS-AWF model is increased, the model results approach the results of the RANS-AD model. A double wind farm case is simulated with RANS to show that replacing an upstream wind farm with an AWF model only causes a deviation less than 1 % in terms of wind farm power of the downstream wind farm. Most importantly, a reduction in CPU hours of 74.4 % is achieved, provided that the AWF inputs are known, namely, wind farm thrust and power coefficients. The reduction in CPU hours is further reduced when all wind farms are represented by AWF models; namely 89.3 % and 99.9 %, for the double wind farm case and for a wind farm cluster case consisting of three wind farms, respectively. For the double wind farm case, the RANS models predict different wind speed flow fields compared to output from simulations performed with the mesoscale Weather Research and Forecasting model (WRF), but the models are in agreement with the inflow wind speed of the downstream wind farm. The double wind farm case is also simulated with the TurbOPark engineering wake model. Similar wake shapes are reproduced by TurbOPark but the model predicts a larger wind farm wake magnitude compared to RANS and WRF. TurbOPark predicts much better results when its ground model is switched off and a wake expansion coefficient of 0.06 is used. The RANS-AD-AWF model is also validated with SCADA measurements in terms of wind farm shape; the model captures the trend of the measurements for a wide range of wind directions, although the SCADA measurements indicate more pronounced wind farm wake shapes for certain wind directions.

Offshore wind turbines in a wind farm are affected by wakes of upstream turbines and adjacent wind farms depending on the park layout and wind direction. As a result the power output may decrease, while structural loads are increasing. In this research a coupled numerical approach based on multi-body system and free vortex methods is used to simulate shadow effects on the Alpha Ventus wind farm. The AV5 is operating at 12 m/s wind speed at half wake conditions with enabled control system and flexible blades and tower. Results of power output, rotor speed, blade pitch and blade root moment over time and azimuth demonstrate the high impact of the half wake condition on the wind turbine performance and loads.