HVDC Connection of Offshore Wind Farms to the Transmission System

This paper presents a technical and economic analysis to evaluate the benefits and drawbacks of grid connecting offshore wind farms through a dc link. A first case, concerning a 100-MW wind farm, is thoroughly investigated and cases of larger wind farms (200 and 500 MW) are presented. Three different transmission solutions are compared: 150-kV ac, 400-kV ac, and high-voltage dc based on voltage sourced converters (VSC-HVDC). After a brief overview of the features of these connection solutions, the related operational aspects are evaluated. An economic assessment compares the dc connection option to the ac alternatives, taking into account the investment, operation, and maintenance costs, and the negative valorization of losses and energy not supplied. Economic assessment includes sensitivity analyses of parameters, which could impact the 100-MW wind farm: distance, component costs, dc converter reliability, and dc converter losses     

Site selection of offshore wind farms around the Korean Peninsula through economic evaluation

We have conducted a feasibility study on the development of offshore wind farms around the Korean Peninsula as part of the national plan. This study deals with the selection of the optimal site for an offshore wind farm. We set rating indices in order to select an optimal site of the candidate coasts, which include the expected B/C (benefit to cost) ratio, the possible installation capacity of the wind farm, the convenience of grid connection, and so on, for each candidate site. The expected B/C ratio is described as the benefit from the annual energy production compared to the costs that correspond to the construction of the turbine foundation, and the grid connection between the offshore wind farm and the substation on land. It can be found from the evaluation that the construction costs associated with the substructure and grid connection are crucial in determining the location of the first offshore wind farm in Korea. Consequently, we could select a top site among the candidate sites to be implemented as the first national project of offshore wind farm development.     

Rapid sizing of a hydrogen-battery storage for an offshore wind farm using convex programming

This paper carries out a comprehensive analysis on an offshore wind farm equipped with a hybrid storage comprised of hydrogen and battery, from the perspective of economic effectiveness. To rapidly evaluate the system economy, a computationally efficient convex program that takes the nonlinear storage efficiencies into account is provided, which can simultaneously and synergistically optimize the storage sizing and energy management over a long offshore wind cycle. In the analysis, a case study on the optimal configuration and operation of the hybrid storage is thoroughly investigated, answering what the scalings are and how the storage functions in the offshore wind farm. Comparisons to other offshore wind farms with none or only one storage type further demonstrate the advantage of combining hydrogen plant and battery. Influences of the offshore wind electricity price of grid parity and hydrogen price on the system economies, in the terms of total annual cost, net annual profit and hydrogen production cost, are also discussed, revealing sensitivity and dependency of the scalings. Finally, this paper presents the future potential of applying hydrogen plant in the offshore wind farm, from the angles of hydrogen production cost and energy saving.     

ENDOW (efficient development of offshore wind farms): modelling wake and boundary layer interactions

While experience gained through the offshore wind energy projects currently operating is valuable, a major uncertainty in estimating power production lies in the prediction of the dynamic links between the atmosphere and wind turbines in offshore regimes. The objective of the ENDOW project was to evaluate, enhance and interface wake and boundary layer models for utilization offshore. The project resulted in a significant advance in the state of the art in both wake and marine boundary layer models, leading to improved prediction of wind speed and turbulence profiles within large offshore wind farms. Use of new databases from existing offshore wind farms and detailed wake profiles collected using sodar provided a unique opportunity to undertake the first comprehensive evaluation of wake models in the offshore environment. The results of wake model performance in different wind speed, stability and roughness conditions relative to observations provided criteria for their improvement. Mesoscale model simulations were used to evaluate the impact of thermal flows, roughness and topography on offshore wind speeds. The model hierarchy developed under ENDOW forms the basis of design tools for use by wind energy developers and turbine manufacturers to optimize power output from offshore wind farms through minimized wake effects and optimal grid connections. The design tools are being built onto existing regional‐scale models and wind farm design software which was developed with EU funding and is in use currently by wind energy developers. Copyright © 2004 John Wiley & Sons, Ltd.     

The value of energy storage in optimal non-firm wind capacity connection to power systems

Wind is a variable and uncontrollable source of power with a low capacity factor. Using energy storage facilities with a non-firm connection strategy is the key to maximum integration of distant wind farms into a transmission-constrained power system. In this paper, we explore the application of energy storage in optimal allocation of wind capacity to a power system from distant wind sites. Energy storage decreases transmission connection requirements, smoothes the wind farm output and decreases the wind energy curtailments in a non-firm wind capacity allocation strategy. Specifically, we examine the use of compressed air energy storage (CAES) technology to supplement wind farms and downsize the transmission connection requirements. Benders decomposition approach is applied to decompose this computationally challenging and large-scale mixed-integer linear programming (MILP) into smaller problems. The simulation results show that using energy storage systems can decrease the variation of wind farms output as well as the total cost, including investment and operation costs, and increase the wind energy penetration into the power system.     

Optimal selection of time windows for preventive maintenance of offshore wind farms subject to wake losses

The maintenance of wind farms is one of the major factors affecting their profitability. During preventive maintenance, the shutdown of wind turbines causes downtime energy losses. The selection of when and which turbines to maintain can significantly impact the overall downtime energy loss. This paper leverages a wind farm power generation model to calculate downtime energy losses during preventive maintenance for an offshore wind farm. Wake effects are considered to accurately evaluate power output under specific wind conditions. In addition to wind speed and direction, the influence of wake effects is an important factor in selecting time windows for maintenance. To minimize the overall downtime energy loss of an offshore wind farm caused by preventive maintenance, a mixed-integer nonlinear optimization problem is formulated and solved by the genetic algorithm, which can select the optimal maintenance time windows of each turbine. Weather conditions are imposed as constraints to ensure the safety of maintenance personnel and transportation. Using the climatic data of Cape Cod, Massachusetts, the schedule of preventive maintenance is optimized for a simulated utility-scale offshore wind farm. The optimized schedule not only reduces the annual downtime energy loss by selecting the maintenance dates when wind speed is low but also decreases the overall influence of wake effects within the farm. The portion of downtime energy loss reduced due to consideration of wake effects each year is up to approximately 0.2% of the annual wind farm energy generation across the case studies—with other stated opportunities for further profitability improvements.     

Development of a viability assessment model for hydrogen production from dedicated offshore wind farms

Dedicated offshore wind farms for hydrogen production are a promising option to unlock the full potential of offshore wind energy, attain decarbonisation and energy security targets in electricity and other sectors, and cope with grid expansion constraints. Current knowledge on these systems is limited, particularly the economic aspects. Therefore, a new, integrated and analytical model for viability assessment of hydrogen production from dedicated offshore wind farms is developed in this paper. This includes the formulae for calculating wind power output, electrolysis plant size, and hydrogen production from time-varying wind speed. All the costs are projected to a specified time using both Discounted Payback (DPB) and Net Present Value (NPV) to consider the value of capital over time. A case study considers a hypothetical wind farm of 101.3 MW situated in a potential offshore wind development pipeline off the East Coast of Ireland. All the costs of the wind farm and the electrolysis plant are for 2030, based on reference costs in the literature. Proton exchange membrane electrolysers and underground storage of hydrogen are used. The analysis shows that the DPB and NPV flows for several scenarios of storage are in good agreement and that the viability model performs well. The offshore wind farm – hydrogen production system is found to be profitable in 2030 at a hydrogen price of €5/kg and underground storage capacities ranging from 2 days to 45 days of hydrogen production. The model is helpful for rapid assessment or optimisation of both economics and feasibility of dedicated offshore wind farm – hydrogen production systems.     

Life cycle assessment of a wind farm and related externalities

This paper concentrates on the assessment of energy and emissions related to the production and manufacture of materials for an offshore wind farm as well as a wind farm on land based on a life cycle analysis (LCA) model. In Denmark a model has been developed for life cycle assessments of different materials. The model is able to assess the energy use related to the production, transportation and manufacture of 1 kg of material. The energy use is divided into fuels used in order to estimate the emissions through the life cycle. In the paper the model and the attached assumptions are described, and the model is demonstrated for two wind farms. The externalities for the wind farms are reported, showing the importance of life cycle assessment for renewable energy technologies.     

Feasibility study of off-shore wind farms: an application to Puglia region

Recent environmental constraints and new secure technologies have enforced the development of comprehensive programmes for renewable energy. Wind energy is one of the most promising solutions, especially considering its technological advancements and its growth over the last years. In particular, off-shore wind energy is a key element in the EU White Paper target of 10% contribution of Renewable energy by 2010.In this paper, the technical and economical feasibility of off-shore wind farms is reviewed, in order to evaluate profitability and investment opportunities. In particular, a pre-feasibility study of off-shore wind farms to some selected sites in Puglia Region is provided. The study indicates the best sites in Puglia Region for off-shore plants. For each site, the cost of energy and the profitability of the investment are calculated. Moreover, in the most promising site, different wind turbine generators (WTGs) models are compared in order to evaluate the best performances. In the best site, which presents an average wind speed at 35 m height of 7.66 m/s, the cost of energy ranges between 5.2 and 6.0 c€/kWh. Moreover, the analysis shows that the use of large size WTGs allows reducing the cost of energy and increasing the profitability of the wind farm.     

Optimizing hybrid offshore wind farms for cost-competitive hydrogen production in Germany

Nearly 96% of the world's current hydrogen production comes from fossil-fuel-based sources, contributing to global greenhouse gas emissions. Hydrogen is often discussed as a critical lever in decarbonizing future power systems. Producing hydrogen using unsold offshore wind electricity may offer a low-carbon production pathway and emerging business model. This study investigates whether participating in an ancillary service market is cost competitive for offshore wind-based hydrogen production. It also determines the optimal size of a hydrogen electrolyser relative to an offshore wind farm. Two flexibility strategies for offshore wind farms are developed in this study: an optimal bidding strategy into ancillary service markets for offshore wind farms that build hydrogen production facilities and optimal sizing of Power-to-Hydrogen (PtH) facilities at wind farms. Using empirical European power market and wind generation data, the study finds that offshore-wind based hydrogen must participate in ancillary service markets to generate net positive revenues at current levels of wind generation to become cost competitive in Germany. The estimated carbon abatement cost of “green” hydrogen ranges between 187 EUR/tonCO₂e and 265 EUR/tonCO₂e. Allowing hydrogen producers to receive similar subsidies as offshore wind farms that produce only electricity could facilitate further cost reduction. Utilizing excess and intermittent offshore wind highlights one possible pathway that could achieve increasing returns on greenhouse gas emission reductions due to technological learning in hydrogen production, even under conditions where low power prices make offshore wind less competitive in the European electricity market.     

Towards a mature offshore wind energy technology—guidelines from the opti‐OWECS project

The article reviews the main results of the recent European research project Opti‐OWECS (‘Structural and Economic Optimisation of Bottom‐Mounted Offshore Wind Energy Converters'), which has significantly improved the understanding of the requirements for a large‐scale utilization of offshore wind energy. An integrated design approach was demonstrated for a 300 MW offshore wind farm at a demanding North Sea site. Several viable solutions were obtained and one was elaborated to include the design of all major components. Simultaneous structural and economic optimization took place during the different design stages. An offshore wind energy converter founded on a soft–soft monopile was tailored with respect to the distinct characteristics of dynamic wind and wave loading. The operation and maintenance behaviour of the wind farm was analysed by Monte Carlo simulations. With an optimized maintenance strategy and suitable hardware a high availability was achieved. Based upon the experience from the structural design, cost models for offshore wind farms were developed and linked to a European database of the offshore wind energy potential. This enabled the first consistent estimate of cost of offshore wind energy for entire European regions. Copyright © 1999 John Wiley & Sons, Ltd.     

Overall Optimization for Offshore Wind Farm Electrical System

Based on particle swarm optimization (PSO), an optimization platform for offshore wind farm electrical system (OWFES) is proposed in this paper, where the main components of an offshore wind farm and key technical constraints are considered as input parameters. The offshore wind farm electrical system is optimized in accordance with initial investment by considering three aspects: the number and siting of offshore substations (OS), the cable connection layout of both collection system (CS) and transmission system (TS) as well as the selection of electrical components in terms of voltage level and capacity. Because hundreds of optimization variables, continuous or discrete, are involved in the problem, a mix integer PSO (MIPSO) is required to obtain the solution. The fuzzy C‐means clustering (FCM) algorithm is used to partition the wind farm into several sub regions. The collection system layout in each sub region as well as the connection scheme between offshore substations are optimized by an adaptive PSO‐minimum spanning tree algorithm (APSO‐MST) which has been proposed in a previous work. The simulation results show that the proposed optimization platform can find an optimized layout that save 3.01% total cost compared with the industrial layout, and can be a useful tool for OWFES design and evaluation. Copyright © 2016 John Wiley & Sons, Ltd.     

The impact of wind resource spatial variability on floating offshore wind farms finance

Wind resource availability determines the financial performance of wind farms as it is directly related to production. Offshore wind developers require great investments to design, build, operate and dismantle offshore wind farms. Furthermore, the investments in the offshore floating wind sector are expected to increase in the future. Because of that, the assessment of the variability of the investments, mainly because of the wind resource variability, seems to be a crucial step in the design methodology. Consequently, a flexible methodology for supporting offshore floating wind farm optimal location assessment is presented in this paper. The proposed methodology is focused on including the offshore wind resource variability and its influence on the power production of floating wind farms, as well as on the main financial indicators (internal rate of return, net present value, pay‐back period and cost of energy). The methodology is applied to the north coast of Spain, and it allows to identify the most promising sites for offshore wind farms deployment. Differences on the cost of energy up to 100% can be found in the area under study. The methodology proposed has been conceived to be site‐independent and applied at any spatial and time horizon. Copyright © 2017 John Wiley & Sons, Ltd.     

生态系统理念在海上风电项目管理中的应用研究

  [目的]  中国海上风电行业已迈入快速发展期,如何创新项目开发管理模式,有效应对海上风电造价高、界面多、工期长等问题,实现成本降低、效能提升,是推动海上风电行业健康快速发展的重要保障。  [方法]  通过将生态系统理念应用到海上风电场项目开发建设和运营管理中,探索构建海上风电场生态系统,以搭建风电场神经网络为核心,提高整体关联效能,实现最优开发目标;利用层次分析法对系统影响因子权重进行定量分析。  [结果]  通过海上风电场生态系统的构建,使海上风电场具备信息快速的收集、存储、共享、分析和反馈调节功能,降本增效,提升风电场稳定性。  [结论]  生态系统的引入为海上风电项目的开发管理和提供了新的思路,借助人工智能技术等前沿科技的发展,海上风电场生态系统也将不断完善和广泛应用。     

Coordinate control strategy for stability operation of offshore wind farm integrated with Diode-rectifier HVDC

Due to low investment cost and high reliability, a new scheme called DR-HVDC (Diode Rectifier based HVDC) transmission was recently proposed for grid integration of large offshore wind farms. However, in this scheme, the application of conventional control strategies for stability operation face several challenges due to the uncontrollability of the DR. In this paper, a coordinated control strategy of offshore wind farms using the DR-HVDC transmission technology to connect with the onshore grid, is investigated. A novel coordinated control strategy for DR-HVDC is proposed based on the analysis of the DC current control ability of the full-bridge-based modular multilevel converter (FB-MMC) at the onshore station and the input and output characteristics of the diode rectifier at the offshore. Considering the characteristics of operation stability and decoupling between reactive power and active power, a simplified design based on double-loop droop control for offshore AC voltage is proposed after power flow and voltage–current (I–V) characteristics of the offshore wind farm being analyzed. Furthermore, the impact of onshore AC fault to offshore wind farm is analyzed, and a fast fault detection and protection strategy without relying on communication is proposed. Case studies carried out by PSCAD/EMTDC verify the effectiveness of the proposed control strategy for the start up, power fluctuation, and onshore and offshore fault conditions.     

深海漂浮式风力机的概念设计与气动—水动力耦合特性评述

海上风电场建设是风力发电技术的重要方向之一,随着近海风场深入开发,风场建设从近海到远海是其未来必然发展趋势.但海水深度增加将导致深海风电场建设成本急剧上升,漂浮式风力机(发电)技术是解决这一问题的有效途径.较为系统地介绍了工作在深海的漂浮式风力机的各种概念设计,并依据水动力学特性对其进行评价,指出漂浮式的设计计算核心是...     

A comparison of methods for assessing power output in non‐uniform onshore wind farms

Wind resource assessments are used to estimate a wind farm's power production during the planning process. It is important that these estimates are accurate, as they can impact financing agreements, transmission planning, and environmental targets. Here, we analyze the challenges in wind power estimation for onshore farms. Turbine wake effects are a strong determinant of farm power production. With given input wind conditions, wake losses typically cause downstream turbines to produce significantly less power than upstream turbines. These losses have been modeled extensively and are well understood under certain conditions. Most notably, validation of different model types has favored offshore farms. Models that capture the dynamics of offshore wind conditions do not necessarily perform equally as well for onshore wind farms. We analyze the capabilities of several different methods for estimating wind farm power production in 2 onshore farms with non‐uniform layouts. We compare the Jensen model to a number of statistical models, to meteorological downscaling techniques, and to using no model at all. We show that the complexities of some onshore farms result in wind conditions that are not accurately modeled by the Jensen wake decay techniques and that statistical methods have some strong advantages in practice.     

Efficiency and effectiveness of wind farms—keys to cost optimized operation and maintenance

Operation and maintenance (O&M) cost will be the key to the economic viability of large offshore wind farms planned worldwide. In order to support investment decisions a systematic mathematical approach to the O&M cost contributions is required prior to detailed engineering or even construction of the wind farm.Adopting the general terms of efficiency, productivity and effectiveness defined for production processes we introduce the Wind Farm Process and its Total Overall Equipment Effectiveness (TotalOEE) by considering wind farms performing a transformation of produced electrical energy to delivered (sold) electrical energy. This transformation process consists of an installation, i.e. properly selected wind energy converters and their arrangement to form a wind farm, and of a process comprising operation and maintenance. Both are the subject of optimization to maximize the annual energy output by minimizing the different kinds of losses.In a systematic approach to the causes and nature of losses in wind farms the terms theoretical production time, available production time and valuable production time are redefined in unit full load hours. Then, a calculation scheme is developed to quantify wind farm production losses in terms of planned or unplanned downtimes and speed losses and to relate the associated reduction of revenues ΔR to the theoretical maximum of annual wind park revenues R_theo(park). It leads to the simple equation ΔR/R_theo = TotalOEE – 1 < 0.     

Optimisation of electrical system for offshore wind farms via genetic algorithm

An optimisation platform based on genetic algorithm (GA) is presented, where the main components of a wind farm and key technical specifications are used as input parameters and the electrical system design of the wind farm is optimised in terms of both production cost and system reliability. The power losses, wind power production, initial investment and maintenance costs are considered in the production cost. The availability of components and network redundancy are included in the reliability evaluation. The method of coding an electrical system to a binary string, which is processed by GA, is developed. Different GA techniques are investigated based on a real example offshore wind farm. This optimisation platform has been demonstrated as a powerful tool for offshore wind farm design and evaluation.     

Load flow analysis for variable speed offshore wind farms

A serial AC?DC integrated load flow algorithm for variable speed offshore wind farms is proposed. It divides the electrical system of a wind farm into several local networks, and different load flow methods are used for these local networks sequentially. This method is fast, more accurate, and many factors such as the different wind farm configurations, the control of wind turbines and the power losses of pulse width modulation converters are considered. The DC/DC converter model is proposed and integrated into load flow algorithm by modifying the Jacobian matrix. Two iterative methods are proposed and integrated into the load flow algorithm: one takes into account the control strategy of converters and the other considers the power losses of converters. In addition, different types of variable speed wind turbine systems with different control methods are investigated. Finally, the method is demonstrated using an 80-MW offshore wind farm.