Planning for sustainable development of energy infrastructure: fast – fast simulation tool

Energy management has significant impact on planning within local or regional scale. The consequences of the implementation of large-scale renewable energy source involves multifaceted analyses, evaluation of environmental impacts, and the assessment of the scale of limitations or exclusions imposed on potential urbanized structures and arable land. The process of site designation has to acknowledge environmental transformations by inclusion of several key issues, e.g. emissions, hazards for nature and/or inhabitants of urbanized zones, to name the most significant. The parameters of potential development of energy-related infrastructure of facility acquire its local properties – the generic development data require adjustment, which is site specific or area specific. FAST (Fast Simulation Tool) is a simple IT tool aimed at supporting sustainable planning on local or regional level in reference to regional or district scale energy management (among other issues). In its current stage, it is utilized – as a work in progress – in the assessment of wind farm structures located within the area of Poznan agglomeration. This paper discusses the implementation of FAST and its application in two conflicting areas around the agglomeration of Poznan

State of the Art in the Optimisation of Wind Turbine Performance Using CFD

Wind energy has received increasing attention in recent years due to its sustainability and geographically wide availability. The efficiency of wind energy utilisation highly depends on the performance of wind turbines, which convert the kinetic energy in wind into electrical energy. In order to optimise wind turbine performance and reduce the cost of next-generation wind turbines, it is crucial to have a view of the state of the art in the key aspects on the performance optimisation of wind turbines using Computational Fluid Dynamics (CFD), which has attracted enormous interest in the development of next-generation wind turbines in recent years. This paper presents a comprehensive review of the state-of-the-art progress on optimisation of wind turbine performance using CFD, reviewing the objective functions to judge the performance of wind turbine, CFD approaches applied in the simulation of wind turbines and optimisation algorithms for wind turbine performance. This paper has been written for both researchers new to this research area by summarising underlying theory whilst presenting a comprehensive review on the up-to-date studies, and experts in the field of study by collecting a comprehensive list of related references where the details of computational methods that have been employed lately can be obtained

A framework for the selection of optimum offshore wind farm locations for deployment

This research develops a framework to assist wind energy developers to select the optimum deployment site of a wind farm by considering the Round 3 available zones in the UK. The framework includes optimization techniques, decision-making methods and experts’ input in order to support investment decisions. Further, techno-economic evaluation, life cycle costing (LCC) and physical aspects for each location are considered along with experts’ opinions to provide deeper insight into the decision-making process. A process on the criteria selection is also presented and seven conflicting criteria are being considered for implementation in the technique for the order of preference by similarity to the ideal solution (TOPSIS) method in order to suggest the optimum location that was produced by the nondominated sorting genetic algorithm (NSGAII). For the given inputs, Seagreen Alpha, near the Isle of May, was found to be the most probable solution, followed by Moray Firth Eastern Development Area 1, near Wick, which demonstrates by example the effectiveness of the newly introduced framework that is also transferable and generic. The outcomes are expected to help stakeholders and decision makers to make better informed and cost-effective decisions under uncertainty when investing in offshore wind energy in the UK

A Review of Methodological Approaches for the Design and Optimization of Wind Farms

This article presents a review of the state of the art of the Wind Farm Design and Optimization (WFDO) problem. The WFDO problem refers to a set of advanced planning actions needed to extremize the performance of wind farms, which may be composed of a few individual Wind Turbines (WTs) up to thousands of WTs. The WFDO problem has been investigated in different scenarios, with substantial differences in main objectives, modelling assumptions, constraints, and numerical solution methods. The aim of this paper is: (1) to present an exhaustive survey of the literature covering the full span of the subject, an analysis of the state-of-the-art models describing the performance of wind farms as well as its extensions, and the numerical approaches used to solve the problem; (2) to provide an overview of the available knowledge and recent progress in the application of such strategies to real onshore and offshore wind farms; and (3) to propose a comprehensive agenda for future research

techno economic design of wind farms a methodology and multi scenario application

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Wind farm layout optimization under uncertainty with landowners\u27 financial and noise concerns

Current wind farm layout research focuses on advancing optimization methods. The research includes the assumption that a continuous piece of land is readily available. In reality, landowners\u27 decisions and concerns play a crucial role in wind projects, and some land parcels are more important to project success than others. During early farm development stages, developers must model many important factors, such as wind resource, land availability, topography, and etc. These factors are associated with great uncertainties. In this dissertation, three system-level optimization models, which include landowners\u27 concerns and optimization-under-uncertainty formulation, are developed progressively. System Model 1 applies a realistic cost model, including landowner remittances, to determine optimal turbine placement under three landowner participation scenarios and two land-plot shapes. The formulation represents landowner participation scenarios as a binary string variable, along with number of turbines. The optimal Cost-of-Energy results are compared to actual Cost-of-Energy data and found to be realistic. System Model 2 advances Model 1 with an optimization-under-uncertainty formulation. A farm layout is optimized under multiple sources of uncertainty including wind shear and farm cost. Landowner participation is represented as uncertain with a novel model of willingness-to-accept compensation. System Model 3 advances Model 2 by modeling landowners\u27 noise concerns and associated compensation. This uncertain model, together with a noise propagation model is then incorporated into the optimization-under-uncertainty system model. Including uncertain parameters and compensation models leads to a total farm cost estimate that is more accurate than the most current publicly-available model used by the National Renewable Energy Laboratory, which requires the addition of an arbitrary term to match industry-reported Cost-of-Energy data. Additionally, the framework presented here can help developers identify land plots that are worth the extra investment during early farm development. It can provide developers with a robust farm design that is not only profitable but also has minimal noise disturbance for landowners. It can also give landowners an idea of where turbines are likely to be placed, and the likely auditory impacts. This improved transparency-of-information can potentially facilitate the negotiation process between developers and landowners during early farm planning and ultimately improve the success rate of projects

Optimal capacity density of offshore wind farms : An analysis for the prospective wind energy projects in the North Sea

The North Sea and Norwegian continental shelf have been identified to possess some of the world's best wind resources. Nine countries, including Norway, have signed the Ostend Declaration and established their offshore wind development targets for 2050. However, space constraints, current consumption, siting regulations, and spatial planning risks accentuate the need for finding an optimum design parameter, i.e., capacity density (MW/km2) for offshore wind farms. A thorough understanding of this optimization problem seems to be missing in the offshore wind energy industry including leading offshore wind developers. Achieving an optimal capacity density involves a collaborative effort while also considering the potential economic and environmental benefits of the project. This master’s thesis aims to create guidelines and identify the levers that drive the optimal capacity density of an offshore wind farm in the North Sea by assessing wind characteristics, evaluating net annual energy production, computing economic indices, and performing sensitivity analysis. The work emphasizes better understanding of the input and output parameter sensitivities pertaining to techno-economic factors under eleven different scenarios using PyWake simulation and cross-linking the simulation results to create a sensitivity analysis tool for economic indices. The focus is on in-depth study to document the procedure involved in identifying the optimal windfarm capacity density and not simply objectifying the results based on the most accurate wake model. The study found that different offshore wind developers may reach different optimum capacity densities depending on their assumptions, methodologies and technologies used for estimation and reporting the financial metrics. For example, the study shows that the choice of wake model can lead to a significantly different optimum capacity density between 4.76 and 9.10 MW/km2 with the motive to maximize profit using a conservative and optimistic approach. Moreover, some developers may have more advanced or sophisticated methods for wind farm simulation and power production estimation, leading to more accurate and precise capacity density estimates. Based on a comprehensive analysis of various parameters and their impact on sensitivity, the optimal capacity density is anticipated to lie between 3.62 and 6.05 MW/km2 for a typical wind farm located in the North Sea. In some extreme cases where wind resources are scarce or strike prices are below levelized energy cost, the optimal capacity density could be as low as 2.64 MW/km2

Conceptual Design of Wind Farms Through Novel Multi-Objective Swarm Optimization

Wind is one of the major sources of clean and renewable energy, and global wind energy has been experiencing a steady annual growth rate of more than 20% over the past decade. In the U.S. energy market, although wind energy is one of the fastest increasing sources of electricity generation (by annual installed capacity addition), and is expected to play an important role in the future energy demographics of this country, it has also been plagued by project underperformance and concept-to-installation delays. There are various factors affecting the quality of a wind energy project, and most of these factors are strongly coupled in their influence on the socio-economic, production, and environmental objectives of a wind energy project. To develop wind farms that are profitable, reliable, and meet community acceptance, it is critical to accomplish balance between these objectives, and therefore a clean understanding of how different design and natural factors jointly impact these objectives is much needed. In this research, a Multi-objective Wind Farm Design (MOWFD) methodology is developed, which analyzes and integrates the impact of various factors on the conceptual design of wind farms. This methodology contributes three major advancements to the wind farm design paradigm: (I) provides a new understanding of the impact of key factors on the wind farm performance under the use of different wake models; (II) explores the crucial tradeoffs between energy production, cost of energy, and the quantitative role of land usage in wind farm layout optimization (WFLO); and (III) makes novel advancements on mixed-discrete particle swarm optimization algorithm through a multi-domain diversity preservation concept, to solve complex multi-objective optimization (MOO) problems. A comprehensive sensitivity analysis of the wind farm power generation is performed to understand and compare the impact of land configuration, installed capacity decisions, incoming wind speed, and ambient turbulence on the performance of conventional array layouts and optimized wind farm layouts. For array-like wind farms, the relative importance of each factor was found to vary significantly with the choice of wake models, i.e., appreciable differences in the sensitivity indices (of up to 70%) were observed across the different wake models. In contrast, for optimized wind farm layouts, the choice of wake models was observed to have no significant impact on the sensitivity indices. The MOWFD methodology is designed to explore the tradeoffs between the concerned performance objectives and simultaneously optimize the location of turbines, the type of turbines, and the land usage. More importantly, it facilitates WFLO without prescribed conditions (e.g., fixed wind farm boundaries and number of turbines), thereby allowing a more flexible exploration of the feasible layout solutions than is possible with other existing WFLO methodologies. In addition, a novel parameterization of the Pareto is performed to quantitatively explore how the best tradeoffs between energy production and land usage vary with the installed capacity decisions. The key to the various complex MO-WFLOs performed here is the unique set of capabilities offered by the new Multi-Objective Mixed-Discrete Particle Swarm Optimization (MO-MDPSO) algorithm, developed, tested and extensively used in this dissertation. The MO-MDPSO algorithm is capable of dealing with a plethora of problem complexities, namely: multiple highly nonlinear objectives, constraints, high design space dimensionality, and a mixture of continuous and discrete design variables. Prior to applying MO-MDPSO to effectively solve complex WFLO problems, this new algorithm was tested on a large and diverse suite of popular benchmark problems; the convergence and Pareto coverage offered by this algorithm was found to be competitive with some of the most popular MOO algorithms (e.g., GAs). The unique potential of the MO-MDPSO algorithm is further established through application to the following complex practical engineering problems: (I) a disc brake design problem, (II) a multi-objective wind farm layout optimization problem, simultaneously optimizing the location of turbines, the selection of turbine types, and the site orientation, and (III) simultaneously minimizing land usage and maximizing capacity factors under varying land plot availability

Application of an offshore wind farm layout optimization methodology at Middelgrunden wind farm

This is the author accepted manuscript. The final version is freely available from Elsevier via the DOI in this record.This article explores the application of a wind farm layout evaluation function and layout optimization framework to Middelgrunden wind farm in Denmark. This framework has been built considering the interests of wind farm developers in order to aid in the planning of future offshore wind farms using the UK Round 3 wind farms as a point of reference to calibrate the model. The present work applies the developed evaluation tool to estimate the cost, energy production, and the levelized cost of energy for the existing as-built layout at Middelgrunden wind farm; comparing these against the cost and energy production reported by the wind farm operator. From here, new layouts have then been designed using either a genetic algorithm or a particle swarm optimizer. This study has found that both optimization algorithms are capable of identifying layouts with reduced levelized cost of energy compared to the existing layout while still considering the specific conditions and constraints at this site and those typical of future projects. Reductions in levelized cost of energy such as this can result in significant savings over the lifetime of the project thereby highlighting the need for including new advanced methods to wind farm layout design.This work is funded in part by the Energy Technologies Institute (ETI) 699 and RCUK energy program for IDCORE (EP/J500847/1)

Techno-economic optimisation of offshore wind farms based on life cycle cost analysis on the UK

In order to reduce the cost of energy per MWh in wind energy sector and support investment decisions, an optimisation methodology is developed and applied on Round 3 offshore zones, which are specific sites released by the Crown Estate for offshore wind farm deployments, and for each zone individually in the UK. The 8-objective optimisation problem includes five techno-economic Life Cycle Cost factors that are directly linked to the physical aspects of each location, where three different wind farm layouts and four types of turbines are considered. Optimal trade-offs are revealed by using NSGA II and sensitivity analysis is conducted for deeper insight for both industrial and policy-making purposes. Four optimum solutions were discovered in the range between £1.6 and £1.8 billion; the areas of Seagreen Alpha, East Anglia One and Hornsea Project One. The highly complex nature of the decision variables and their interdependencies were revealed, where the combinations of site-layout and site-turbine size captured above 20% of total Sobol indices in total cost. The proposed framework could also be applied to other sectors in order to increase investment confidence