Abstract:As the penetration level of renewable distributed generations such as wind turbine generator and photovoltaic stations increases, the load frequency control issue of a multi-area interconnected power system becomes more challenging. This paper presents an adaptive model predictive load frequency control method for a multi-area interconnected power system with photovoltaic generation by considering some nonlinear features such as a dead band for governor and generation rate constraint for steam turbine. The dynamic characteristic of this system is formulated as a discrete-time state space model firstly. Then, the predictive dynamic model is obtained by introducing an expanded state vector, and rolling optimization of control signal is implemented based on a cost function by minimizing the weighted sum of square predicted errors and square future control values. The simulation results on a typical two-area power system consisting of photovoltaic and thermal generator have demonstrated the superiority of the proposed model predictive control method to these state-of-the-art control techniques such as firefly algorithm, genetic algorithm, and population extremal optimization-based proportional-integral control methods in cases of normal conditions, load disturbance and parameters uncertainty.

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Abstract:Fractional order proportional-integral-derivative(FOPID) controllers have attracted increasing attentions recently due to their better control performance than the traditional integer-order proportional-integral-derivative (PID) controllers. However, there are only few studies concerning the fractional order control of microgrids based on evolutionary algorithms. From the perspective of multi-objective optimization, this paper presents an effective FOPID based frequency controller design method called MOEO-FOPID for an islanded microgrid by using a Multi-objective extremal optimization (MOEO) algorithm to minimize frequency deviation and controller output signal simultaneously in order to improve finally the efficient operation of distributed generations and energy storage devices. Its superiority to nondominated sorting genetic algorithm-II (NSGA-II) based FOPID/PID controllers and other recently reported single-objective evolutionary algorithms such as Kriging-based surrogate modeling and real-coded population extremal optimization-based FOPID controllers is demonstrated by the simulation studies on a typical islanded microgrid in terms of the control performance including frequency deviation, deficit grid power, controller output signal and robustness.

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A numerical simulation for the wake deviation effect in a wind farm is carried out using the full rotor model of the National Renewable Energy Laboratory 5 MW wind turbine. The downstream wind turbine decreases its performance significantly due to the upstream wake interference. To reduce this effect, a control strategy based on the yaw angle is adopted to skew the trajectory of an upstream wake, thereby avoiding the downstream wind turbine and improving the efficiency of whole wind farm power generation. In this paper, the commercial CFD software STAR-CCM+ was used to simulate the wind farm which contains two tandem wind turbines operating in the atmospheric boundary layer condition. The results show that the wind farm's total power increases when the upstream wind turbine applies a yaw angle intentionally. According to the analysis of velocity contours, wake centerlines, and vortex structures, a counter-rotating blade tip vortex pair is observed to be responsible for the wake deviation effects concentrated on the hub height, which reveals that the influence of a yawed wake on the downstream wind turbine may be slightly underestimated.

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