This paper presents a general purpose platform for optimal open loop control of wind power plants as seen from a power production perspective. The general idea is to change the controller design criteria from greedy individual wind turbines to a controller design facilitating cooperative and interdependent elements of a wind power plant, with the overall aim to improve the wind power plant power production conditioned on ambient mean wind speed and mean wind direction. The flow within the wind power plant, including all essential interactions between the wind turbines, is modelled using a very fast linearized CFD RANS solver. The wind turbines are modelled as actuator discs, and two design variables per wind turbine – collective pitch, α, and tip speed ratio, λ – are initially defined for the optimization problem. However, a priory we expect one design variable to suffice – i.e. the unique set of (α, λ) representing the lowest thrust coefficient, CT , for a given power coefficient Cp . The conjectured collapse of the design space is justified in this paper. Optimized control schemes for the Lillgrund offshore wind farm are derived conditioned on ambient mean wind direction and wind speed. Aggregated over a year, using the site sector Weibull distributions, an increase in the annual energy production of 1% is demonstrated.
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Minimizing the cost of energy of a wind farm is a difficult task, which involves reducing the wake effects while satisfying several constraints. Due to its multidisciplinary nature, this problem is usually solved through numerical optimisers. TOPFARM is one of these tools, and in this paper, we have added to it a constraint on the fatigue loads. The efficiency of the implementation is guaranteed by an extensive use of gradients and load surrogate models. The paper is concluded by showing some case studies.
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.
In the preliminary phases of offshore wind farm development, very little information on project design are available for supporting financial valuation and site design. In this work, we develop a surrogate model for offshore wind support structure mass for input to techno-economic analysis that is based on a small set of input parameters. Using reference turbines and a broad set of met-ocean conditions, a large design spaced is developed from which a sampling of conditions is used to estimate the dimensions and mass of monopile support structures. The results are parameterized using statistical methods to create a functional model of costs relative to high-level site and technical inputs. To preserve the transparency of the model input-output relationships, a statistical surrogate model is used based on quadratic functions of the inputs. Overall, the rated power and rotor diameter of the turbine has the greatest influence on the mass, followed by the specific power. The water depth was the next most important environmental parameter, followed by wave period. The full surrogate model captures 98.5% of the variance of the monopile mass as a function of the above inputs. We present results related to monopile foundations, but the methodology is flexible and can be applied also in the case of other types of support structures.
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