This paper presents a time efficient finite-control-set model predictive control (FCS-MPC) scheme for the doubly fed induction generator system. In this scheme, the switching states of the rotor side converter are directly taken as control inputs. This way, the optimized control action can be directly applied to the converter. Compared with the existing FCS-MPC approaches, the salient feature of the proposed scheme is the reduction of the computation time. By introducing a set of augmented decision variables, the original intractable binary quadratic programming problem in FCS-MPC can be analytically transformed to a binary linear programming problem, which can be solved efficiently. By this means, the computation time of the proposed scheme is much less than that of the existing schemes. This reduction in computation time enables FCS-MPC with longer prediction horizons, thus yielding better control performance.

The injection of a significant amount of wind power tends to increase the rate of change of the grid frequency. Therefore, it is a trend for wind farm to participate in the grid frequency regulation. However, during the frequency control process, individual wind generators in a wind farm may prone to instability due to possible over-deceleration. To address this issue, this paper presents a new nonlinear model predictive control (NMPC) scheme for the wind farm frequency response. By incorporating the nonlinear dynamics of each individual wind generator into the NMPC design, it achieves both objectives of dynamically optimal frequency response and wind generator stability. This scheme has a three-layer structure. Based on the linear model predictive control and moving horizon estimation, a top-layer controller computes the overall wind farm power reference to support the frequency control. This overall power reference is fed to the middle- layer NMPC, and is further distributed among multiple wind generators. The dispatched power references are then sent to the bottom-layer wind generators local controllers for execution. Simulation results verify the effectiveness of the proposed scheme.

DC collection grid and high-voltage direct-current (HVDC) transmission lines provide promising solutions for offshore wind farms (OWF). However, due to the decoupling effect of the dc collection grid and HVDC link, an HVDC-connected dc-grid OWF cannot directly participate in the frequency regulation of the onshore ac grid. To address this issue, this paper presents a communication-free control scheme, which allows an HVDC-connected all-dc OWF to contribute to the onshore ac grid fre-quency regulation. This scheme consists of making the offshore dc collection grid voltage and onshore frequency deviations pro-portional. It relies on the HVDC voltage control at the onshore converter, together with the dc collection grid voltage control at the offshore converter. Furthermore, by continuously adjusting the droop of each wind generator in response to its local wind speed, this scheme takes into account the wind speed difference among wind generators, thereby improving the frequency nadir / peak and guaranteeing the wind generator stability. Simulation results verify the effectiveness of the proposed scheme.

Microgrids have emerged as a promising paradigm to integrate the renewable generation units, energy storage systems, and dispersed loads. However, the inherent fluctuation of renewable generations adds significant uncertainty to the supply side of microgrid. On the other hand, due to the small scale of the microgrid, the load demand in microgrid also has higher uncertainty than that observed in utility grids. Furthermore, the supply uncertainty and demand uncertainty may have different distribution characteristics, e.g., Gaussian and non-Gaussian uncertainties may exist simultaneously. These uncertainties pose significant challenges to the microgrid energy management. To address these challenges, this paper establishes a new stochastic energy scheduling scheme for microgrids. In this scheme, the energy scheduling is formulated as a stochastic model predictive control problem, which incorporates the uncertainties in both sides of supply and demand. Using machine-learning techniques, the corresponding stochastic optimization problem is converted to a standard convex quadratic programming, and thus can be solved efficiently. A key feature of this scheme is that it handles the coexistence of Gaussian and non-Gaussian uncertainties. Simulation results validate the effectiveness of the proposed scheme.