A Distributed Model Predictive Wind Farm Controller for Active Power Control

TL;DR

This paper introduces a distributed model predictive control strategy for wind farms that stabilizes power output and enables power reference tracking, addressing computational challenges for large-scale wind farm management.

Contribution

It develops a distributed MPC approach that accounts for wake dynamics, reducing computational complexity for real-time control of large wind farms.

Findings

Successfully stabilizes wind farm power output.

Enables power reference tracking for grid support.

Reduces computational complexity for large-scale applications.

Abstract

Due to the fluctuating nature of the wind and the increasing use of wind energy as a power source, wind power will have an increasing negative influence on the stability of the power grid. In this paper, a model predictive control strategy is introduced that not only stabilizes the power produced by wind farms, but also creates the possibility to perform power reference tracking with wind farms. With power reference tracking, it is possible for grid operators to adapt the power production to a change in the power demand and to counteract fluctuations that are introduced by other power generators. In this way, wind farms can actually contribute to the stabilization of the power grid when this is necessary instead of negatively influencing it. A low-fidelity control-oriented wind farm model is developed and employed in the developed distributed model predictive controller. In this control model, the wake dynamics are taken into account and consequently, the model's order is relatively large. This makes it, from a computational point of view, challenging for a centralized model predictive control to provide real-time control for large wind farms. Therefore, the controller proposed in this paper is a distributed model predictive control. Here, the central control problem is divided into smaller local control problems that are solved in parallel on local controllers, which significantly reduces the computational complexity and brings the application of model predictive control in a wind farm a step closer to practical implementation. The proposed control solution is tested in simulations on a 10 and 64 turbine wind farm.