Predictive control of wind farms based on lexicographic minimizers for power reserve maximization

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American Control Conference (ACC)





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This paper presents a model predictive control (MPC) strategy aimed to regulate the total power delivered to the grid while maximizing the power reserve. Nowadays, the

high participation of wind energy in the electricity generation requires that wind power plants (WPPs) also provide ancillary services. This fact implies that WPPs must be capable of temporally increasing the power generation to help, for instance, the primary-frequency control. To this end, WPPs work below the maximum generation capacity keeping some power reserves (difference between available and generated powers). The available power depends on the wind conditions that each turbine is facing but these conditions are also affected by the wakes produced by upstream turbines. In order to satisfy the aforementioned objectives, this work proposes to cast the MPC strategy as a multi-objective optimization problem solved using a lexicographic approach in order to consider the hierarchy of the control objectives. The performance of the control scheme is evaluated by simulations for the case of a WPP with three turbines taking into account the variation of wind speed faced by downstream turbines due to the wake effect.


automation, control theory, optimisation.

Author keywords

predictive control, lexicographic minimizers, wind farms control

Scientific reference

S. Siniscalchi, F. Bianchi and C. Ocampo-Martínez. Predictive control of wind farms based on lexicographic minimizers for power reserve maximization, 2018 American Control Conference, 2018, Milwaukee, USA, pp. 701-706.