Holistic Offshore Wind Farm Optimization Approach

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.