Technical, economic and uncertainty modelling of a wind farm project

Afanasyeva, Svetlana; Saari, Jussi; Kalkofen, Martin; Partanen, Jarmo; Pyrhönen, Olli

doi:10.1016/j.enconman.2015.09.048

Cited by 69 publications

(28 citation statements)

References 8 publications

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“…Despite significant technological improvements, the average cost of wind generated electricity is higher than that from traditional energy resources such as coal and natural gas [14,15], and it is subject to a larger variability [16][17][18]. It is not clear whether further upscaling beyond the existing 5-7 MW range is both technically feasible and economically attractive.…”

Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Ashuri

Zaaijer

Martins

et al. 2016

Energy Conversion and Management

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a b s t r a c tWind energy has experienced a continuous cost reduction in the last decades. A popular cost reduction technique is to increase the rated power of the wind turbine by making it larger. However, it is not clear whether further upscaling of the existing wind turbines beyond the 5-7 MW range is technically feasible and economically attractive. To address this question, this study uses 5, 10, and 20 MW wind turbines that are developed using multidisciplinary design optimization as upscaling data points. These wind turbines are upwind, 3-bladed, pitch-regulated, variable-speed machines with a tubular tower. Based on the design data and properties of these wind turbines, scaling trends such as loading, mass, and cost are developed. These trends are used to study the technical and economical aspects of upscaling and its impact on the design and cost. The results of this research show the technical feasibility of the existing wind turbines up to 20 MW, but the design of such an upscaled machine is cost prohibitive. Mass increase of the rotor is identified as a main design challenge to overcome. The results of this research support the development of alternative lightweight materials and design concepts such as a two-bladed downwind design for upscaling to remain a cost effective solution for future wind turbines.

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Section: Introductionmentioning

confidence: 99%

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Ashuri

Zaaijer

Martins

et al. 2016

Energy Conversion and Management

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show abstract

“…Although wind turbine technology has mature supply chain and good development outlook worldwide, its investment faces the financial risk due to uncertainties of wind speed, direction and density. The naturally stochastic variability of wind resource may lead to low power production and profitability of wind farm project [89]. Specifically, in Taiwan, the payback period of this technology is approximately 10 to 18 years due to its greater capital cost and power production expenditure [90].…”

Section: Economic Barriersmentioning

confidence: 99%

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Theo

Lim

et al. 2017

Renewable and Sustainable Energy Reviews

242

105

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“…Other important factors that impact the actual costs of an individual technology or project relate to market pricing, auction designs, technological developments and geographical aspects. [21][22][23] The approach described in this paper focuses on the latter aspects. For that reason, the example of onshore wind turbines is selected for this following analysis.…”

Section: Investment Cost Analysismentioning

confidence: 99%

Cost Uncertainties in Energy System Optimisation Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

Lopion¹,

Markewitz²,

Stolten³

et al. 2019

Preprint

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Designing the future energy supply in accordance with ambitious climate change mitigation goals is a challenging issue. Common tools for planning and calculating future investments in renewable and sustainable technologies are often linear energy system models based on cost optimisation. However, input data and the underlying assumptions of future developments are subject to uncertainties that negatively affect the robustness of results. This paper introduces a quadratic programming approach to modifying linear, bottom-up energy system optimisation models in order to take cost uncertainties into account. This is accomplished by implementing specific investment costs as a function of the installed capacity of each technology. In contrast to established approaches like stochastic programming or Monte Carlo Simulation, the computation time of the quadratic programming approach is only slightly higher than that of linear programming. The model’s outcomes were found to show a wider range as well as a more robust allocation of the considered technologies than the linear model equivalent.

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Technical, economic and uncertainty modelling of a wind farm project

Cited by 69 publications

References 8 publications

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Multidisciplinary design optimization of large wind turbines—Technical, economic, and design challenges

Review of distributed generation (DG) system planning and optimisation techniques: Comparison of numerical and mathematical modelling methods

Cost Uncertainties in Energy System Optimisation Models: A Quadratic Programming Approach for Avoiding Penny Switching Effects

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