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Investigation of offshore wind farm layouts regarding wake effects and cable topology Bryce Wade [email protected] Instituto Superior Técnico, Universidade de Lisboa, Portugal October 2018 Abstract— Offshore wind energy is emerging as a large rectilinear form where turbines are spaced evenly along contributor to installed renewable energy capacity. In order to columns and rows. This is the case for many notable large continue the momentum of its development, the offshore wind projects such as Horns Rev, London Array, Gemini Wind Farm industry is looking to continually lower the levelized cost of and Dudgeon. electricity (LCOE). One area being explored in an effort to lower Wind farm designers have considered the value of the LCOE of offshore wind generation is the optimization of the minimizing wake effects, as the rows and columns of wind wind farm layout. Many of the offshore wind farm layout designs that exist today are structured in a rectilinear form where turbines farms are often oriented into an optimal direction based on the are spaced evenly along columns and rows. This thesis explores the local wind climate. However, the inconsistent nature of economic advantages of removing rectilinear constraints and environmental factors such as the local wind climate, optimizing the positions of the individual turbines within an bathymetry, and soil types would conclude that these are not offshore wind farm. driving factors in the structured layouts. At the core of achieving the thesis objective was the development It has been hypothesized through many academic research of a model that is capable of simulating an existing offshore wind efforts that keeping rectilinear formats may cost a great deal farm by converting representative wind farm data into an LCOE. when measuring the annual energy production over the lifetime The positions of the turbines within the wind farm can be modified of the windfarm [3]. using an optimization framework with the intent to minimize the LCOE. The model comprised of the Jensen Wake Model, a hybrid When determining the spacing between turbines in an cable layout heuristic and an NREL cost scaling model. The wind offshore windfarm layout, there are two key parameters that farm layout was optimized using a genetic algorithm. oppose with regards to profitability; wake effects and the The cost estimation model and optimization framework were collection cable length. As the turbine spacing increases, the applied into two case studies to analyze the results of the wind farm wake effects decrease which results in an increase in the annual layout optimization of two wind farms, Horns Rev and Borssele. energy production. Though increased space between turbines In both case studies the optimized layouts provided higher AEP, results in longer collection cables and in turn, higher shorter intra-array collection cable lengths and ultimately a lower transmission losses and increased cable costs. The opposing LCOE than the baseline rectilinear layouts. nature of these design variables result in some layouts being more profitable than others. Abandoning the rectilinear constraints described above and Keywords- Offshore Wind Energy, Wind Farm Layout Optimization Problem, Wake Effects, Cable Topology, Genetic Algorithm understanding how the wake effect and cable cost variables are related can be utilized in the wind farm design process to I. INTRODUCTION improve the economic viability of offshore wind development. A. Background B. Project Objective Developing carbon free sources of electricity has become a This work explores the advantages of optimizing the global focus in recent decades. As the understanding of the positions of the individual turbines within an offshore wind importance of this shift within the energy sector spreads, farm. The optimization seeks to minimize the objective increased interest and investment have been directed towards function which is LCOE of an offshore wind farm. At the core renewable energy sources in an effort to expedite the transition. of this work was the development of a model that is capable of Wind energy is a leading technology in the development of the following: large-scale renewable energy generation and is a significant • Simulating an existing offshore wind farm by percentage of the electricity generation of many nations. [1] [2] converting necessary input data into a representative Offshore wind energy in particular is an emerging technology LCOE. that is becoming increasingly viable as improvements in technology are driving down the levelized cost of electricity • Providing the ability to optimize the positions of the (LCOE) making it more competitive with onshore wind individual turbines within the wind farm in order to technology and other forms of electricity generation. lower the LCOE. One particular area being explored in an effort to lower the • Providing the ability to perform sensitivity analysis for LCOE of offshore wind generation is the optimization of the different input parameters for the offshore wind farm wind farm layout. There are multiple parameters that are that can provide insight to the importance of the considered when designing a wind farm array including but not different design variables. limited to: wake effects, wind profile, bathymetry, soil types, The model was validated using measured data from an cable installation costs, transmission losses, environmental existing wind farm. Upon validation, the model was used to concerns, permitting restrictions and aesthetics. While estimate the difference in LCOE’s for different wind farms with acknowledging these parameters, many of the resulting wind the existing and optimized wind farm layouts and conclusions farm layout designs that exist today are structured in a were drawn from the results. 1 II. STATE OF THE ART in WFLOP are focused on improving the formulations A wind farm consists of an array of wind turbines that describing the expected energy conversion of the wind farm, the convert the kinetic energy from the wind to electricity. Wind wind turbine wakes and turbulence impacts, the wind turbine turbines can be arranged within a wind farm in a certain structural fatigue and degradation, the various types of orientation in order to increase the energy the wind farm yields. environmental impacts, the overall electrical system losses and Traditional layouts are typically in a rectilinear format, reliability and the uncertainty and risk management. identifiable by the turbine’s arrangement in equally spaced rows and columns. The majority of the largest wind farms in III. MODEL DEVELOPMENT operation today are in a grid format, rotated in an orientation with respect to the wind direction to maximize energy yield (for The objective of the model optimization, as previously the set formation). However there has been a growing field of stated, is to optimize the positions of the turbines within a wind research into truly maximizing the energy yield for a given farm in an effort to minimize the LCOE. There are two variables wind farm site by abandoning the rectilinear constraints. The within Equation 1 that are dependent on the positions of the area of this research has been termed the Wind Farm Layout turbines (all else were considered independent within this Optimization Problem (WFLOP) where interested parties are work); Initial Capital Costs (ICC) and the annual energy creating models and using optimization algorithms to better production (AEP). More specifically within the ICC, it is the understand how different wind farm orientations can provide intra-array cable costs that are dependent on the wind turbine higher energy yields under different environmental and positions. physical circumstances. [3] 퐼퐶퐶 ∗ 퐹퐶푅 (1) 퐿퐶푂퐸 = + 퐴푂퐸 퐴퐸푃 A. Wind Farm Layout Optimization Problem The WFLOP requires a defined objective function and an The modelling framework developed in this work is explained optimization strategy. The objective function is calculated easiest by breaking down the three core components. The based on the design parameters, and is minimized through physical component included sub-models that provided optimization of the wind farm design. collection cable lengths and annual energy production for a The optimization strategies use algorithms that iterate given wind farm based on its layout. The economic component through various scenarios in search of satisfying constraints and utilized the cable length and AEP to generate an LCOE. The minimizing the objective function. The optimization algorithms optimization component used the LCOE as its objective can be categorized into gradient methods [4], genetic function and modified the turbine positions in search of an algorithms [5] [6], viral algorithms [7], particle swarm optimal layout. algorithms [8] and greedy heuristic algorithms [9]. [10] A. Physical Component The WFLOP was first explored by Mosetti et al. [5] in 1994 The physical component accounts for the environmental and by considering a wind farm that did not have a defined desired physical characteristics of the wind farm and provides the power output, but a fixed number of potential turbine positions. annual energy production and collection cable layout. The two The wind farm consisted of a 10x10 grid where the wind turbine core sub-models within the physical component pertain to the could be placed in the center of each quadrant or not, in search wake effects and the cable topology. of the most profitable design based on the objective function. Since the work of Mosetti et al. there have been numerous 1) Wake Effects attempts to determine the best methods for designing a wind As a turbine extracts energy from the wind and converts it to farm layout. The research that followed Mosetti et al. can electrical energy, it leaves a wake behind which is characterized largely be categorized into two paths: (1) a track focused on the by reduced wind speeds and increased turbulence in the flow development and testing of different optimization strategies i.e. [13]. The wake from a turbine expands and gradually returns to optimization algorithms, [6] [8] [9] and (2) a track focused on the free stream condition as it continues downstream.

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