In this paper, a complete comparison analysis of two advanced control algorithms, namely robust model predictive control (MPC) and stochastic MPC, is performed in order to optimize the operation of a wind power generation system (WPGS). The power maximization often conflicts with the mechanical load experienced by the turbine in the full-load region (i.e., the higher the power extracted, the higher the load) under the wind speed disturbance, thereby leading to high maintenance cost resulting from the fatigue damage. Thus, a typical 5 MW wind turbine operating in a high-speed region is considered to guarantee system security and economy. The robust MPC is designed by utilizing the min–max framework to track steady-state optimum operating reference trajectory with the deterministic constraint of output power, while the stochastic MPC is constructed by incorporating the invariant set theory to also ensure the system security subjecting to the probabilistic constraint of output power. The relation between the constraints and the implications on optimal performance are also studied. Comprehensive simulations on a mechanism model and FAST simulator are carried out to demonstrate the validation of the two control methods under various scenarios. It is discovered that when wind speed in the near future can be predicted and utilized in controller design, the stochastic MPC can effectively reduce the maintenance cost by suppressing the constraint violation rate compared to robust MPC with a similar energy utilization due to the incorporation of the stochastic characteristics of wind speed.
The frequency stability of interconnected power systems becomes quite challenging when incorporating renewable energy sources (mostly wind power). Distributed model predictive control (DMPC) is an effective method to maintain stable grid frequency and realize power system load frequency control (LFC). This paper proposes a two-layer robust DMPC for the LFC of an interconnected power system. In the scheme, the wind power penetrating the power grid is largely affected by the environment condition, and it is taken as an uncertain disturbance to the power system. The two-layer robust DMPC consists of a nominal DMPC controller and an ancillary DMPC controller. The nominal DMPCs coordinate with each other in achieving the systemwide LFC objective, where the systemwide objective is a strict convex combination of the local LFC objectives. The nominal optimization problems are solved supposing the wind power fluctuation is zero. The ancillary DMPC generates the actual control signal for each generation unit based on signals which are transmitted from the nominal DMPC controller. The simulation on a four-area interconnected power system demonstrates the effectiveness of the proposed algorithm in alleviating the frequency deviation caused by varying the load and uncertain wind power fluctuation.
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