A surrogate model of offshore wind farm annual energy production to support financial evaluation

Abstract

The preliminary financial evaluation of wind farm profitability requires fast analysis of energy production and costs while having very little specific information around the project. Early in the design process, the selection of specific wind turbines and the layout design may not yet be defined. Techno-economic and financial analysis models have been developed to use input from a small set of high-level project characteristics to estimate major cost elements and energy production for a wind farm to support quick analysis of levelized cost of energy (LCoE), or other financial metrics. Such models are typically based on prior project data and/or very simple analytical models. However, as capabilities for financial analysis of wind farms advance, so does the desire to improve the accuracy of the physical and cost modelling of the system. In this work, we develop a surrogate model of Annual Energy Production (AEP) for offshore wind farms for financial analysis applications in the early stages of development. The surrogate is developed from an parameterized engineering model and covers a large potential wind farm design space addressing different technological and site conditions. The surrogate model uncovers the underlying structure in the model in terms of input-output relationships and achieves a coefficient of determination of 0.994. The method used to develop the surrogate model can be adapted for additional dimensions of inputs as needed.

Multidisciplinary Wind Farm Electrical Infrastructure Optimization

“…All the simulations were performed in PyWake [12,13] and TOPFARM [14], an open-source tool developed by DTU Wind and that has been used in previous works in the literature [15,16,17,18,19]. The case-study considered in this work is the Borselle wind farm, which has 74 wind turbines with 10MW each.…”

A two-step topology and layout wind farm optimization approach

“…These wind farms are designed to optimize energy production while minimizing costs associated with installation, operation, and maintenance. [19]. Designing a wind farm presents several challenges.…”

A bi‐level programming model and differential evolution for optimizing offshore wind farm layout

“…Moreover, unlike traditional wind farm layout optimization, Cazzaro et al solved the problem of selecting the shape and area of a wind farm from a larger area to maximize profitability, 21 that is, by taking macro, meso, and micro as three different scales and designing a heuristic evolutionary algorithm to choose the best shape of a wind farm for the above three scales. Other studies on offshore wind farms can be found in various studies 22,23 …”

“…Other studies on offshore wind farms can be found in various studies. 22,23 To reduce the investment cost, the optimization problem of offshore wind farm cable layout has received more attention from researchers. Early relevant studies focused on minimizing cable length and adopted the graph theory 24 or the classical evolutionary algorithm 25 to optimize cable length.…”