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In future power systems, widespread small-scale energy storage systems (ESSs) can be aggregated to provide ancillary services. In this context, this paper aims to integrate energy storage aggregators (ESAs) into the load frequency control (LFC) framework for power system frequency control. Firstly, a system disturbance observer is designed to supplement the secondary frequency control, where the ESA can respond to the estimated disturbance and accelerate the system frequency recovery. Then, within the ESA, a finite-time leader-follower consensus algorithm is proposed to control the small-scale ESSs via sparse communication network. This algorithm ensures that the ESAs can track the frequency control signals and the state-ofcharge balancing among each ESS in finite-time. The external characteristics of the ESA will resemble to that of one large-scale ESS. Numerical examples demonstrate the convergence of the ESA under different communication graphs. The effectiveness of the entire framework for power system frequency control is validated under a variety of scenarios.

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In order to smooth the system frequency disturbances caused by wind power fluctuation, this study proposes a novel load frequency control (LFC) strategy based on model predictive control (MPC) to regulate the system frequency. The proposed strategy mainly aims to improve the system frequency responses by considering the dynamics of wind turbine (WT) and forecasting the output power of WT. First, the frequency response model (FRM) of system including the power system LFC and WT is established. Second, through discretisation of the FRM, a predictive model of system is obtained. Then, system state and response in the next predictive period are forecasted by the predictive model. After obtaining the prediction information, the control signal of system is optimised for improving system frequency response. On this basis, a novel MPC-based LFC strategy is proposed based on the predictive model and the optimisation of control sequence. With the information of wind speed, this control strategy is able to forecast the system states and responses easily and adjust the control signal according to wind power fluctuation. Finally, numerical simulations are demonstrated to visualise the effectiveness and feasibility of the proposed MPCbased LFC strategy and its advantages over existing methods.

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