Holistic Offshore Wind Farm Optimization Approach

Irina Cortizo, T. Hodgson, Tom Hiorns, David Aqui, L. Jones
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Abstract

There are multiple stakeholders involved in the successful development of offshore wind farm projects. There are also numerous datasets that evolve along the lifecycle of the project. Understanding how all the components of a wind farm act on each other before costs are committed can reduce overall costs and timescales and produce an optimized development. This paper will describe an innovative offshore wind farm optimization approach that evaluates various development concepts to provide indicative farm design and comparative levelized cost of energy (LCOE). A digital approach has been developed to evaluate the influence of various attributes to provide indicative farm design and comparative LCOE. The optimization goal can be tailored to suit developer's preferences such as minimizing LCOE, efficient use of upfront capital expenditure (CAPEX), and development planning / phasing amongst others. This enables information such as farm layout and array spacing or identifying the optimal substructure type across a field to be determined. Input attributes such as water depth, ground conditions, wind resource, and distance from prospective grid interconnection are considered during the optimization approach. It can also consider lease financing such as royalties for unused lease area. The proposed approach can be used to inform decisions such as the capacity of the turbines to be used and overall reduce project development risk. Typical results will be shown demonstrating the power of the holistic optimization. Wind farm CAPEX, Operational Expenditure (OPEX) and LCOE tend to increase for sites that are more distant from shore, are in deeper water, or have less favorable ground conditions. The shape of the available site can also affect CAPEX and LCOE. The relationship between LCOE, CAPEX and array spacing can be inconsistent between various sites. The reductions in LCOE and CAPEX are greatly influenced by parameters such as wind resource, the bathymetry and shape of each site. Typically increasing wind farm capacity tends to improve LCOE due to economies in scale as site wide costs (permitting, design, mobilization, etc.) are distributed over more turbines counteracting detrimental effects associated with increasing farm footprints extending further offshore. LCOE reduces as turbine capacity increases within a competitive supply chain. This levels off as supply and demand diverges for turbines that require specialist providers in the supply chain. The substructures required to support the larger turbines often need some innovation which can introduce technical risks. An offshore wind farm optimization approach utilizes data from many components of a wind farm. The ability to process this efficiently enables developers to explore many configurations using various sensitivity studies. The approach is implemented through deep optimization technology, simulation and modeling methodologies to deal with high system complexity and constantly expanding data to enable rapid and powerful optimization.
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整体海上风电场优化方法
海上风电场项目的成功开发涉及多个利益相关者。还有许多数据集在项目的生命周期中不断发展。在确定成本之前,了解风力发电场的所有组成部分是如何相互作用的,可以降低总体成本和时间尺度,并产生优化的开发。本文将描述一种创新的海上风电场优化方法,该方法评估各种开发概念,以提供指示性风电场设计和比较平准化能源成本(LCOE)。已经开发了一种数字方法来评估各种属性的影响,以提供指示性农场设计和比较LCOE。优化目标可以根据开发人员的偏好进行调整,例如最小化LCOE、有效使用前期资本支出(CAPEX)以及开发计划/分阶段等。这样就可以确定农场布局和阵列间距等信息,或者确定整个油田的最佳子结构类型。在优化方法中考虑了水深、地面条件、风力资源和与预期电网连接的距离等输入属性。它也可以考虑租赁融资,如未使用租赁面积的特许权使用费。所提出的方法可以用来为决策提供信息,例如要使用的涡轮机的容量,并整体降低项目开发风险。典型的结果将展示整体优化的力量。风电场CAPEX、运营支出(OPEX)和LCOE倾向于在距离海岸较远、水深较深或地面条件较差的地点增加。可用场地的形状也会影响CAPEX和LCOE。LCOE、CAPEX和阵列间距之间的关系在不同的站点之间可能不一致。LCOE和CAPEX的降低很大程度上受到风资源、测深和每个站点形状等参数的影响。通常,增加风电场容量往往会提高LCOE,这是由于规模经济,因为现场范围内的成本(许可、设计、动员等)分布在更多的涡轮机上,抵消了与不断增加的农场足迹进一步延伸到海上相关的有害影响。在竞争激烈的供应链中,LCOE随着涡轮机容量的增加而降低。随着供需分化,这种情况趋于平稳,因为涡轮机需要供应链中的专业供应商。支持大型涡轮机所需的子结构通常需要一些创新,这可能会带来技术风险。海上风电场优化方法利用来自风电场许多组件的数据。有效处理这一问题的能力使开发人员能够使用各种灵敏度研究来探索许多配置。该方法通过深度优化技术、仿真和建模方法来实现,以处理高系统复杂性和不断扩展的数据,从而实现快速而强大的优化。
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