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Power system stability has been suffering increasing threats with the ever-growing penetration of intermittent renewable generation such as wind power and solar power. Battery energy storage systems (BESSs) could mitigate frequency fluctuation of the power system because of their accurate regulation capability and rapid response. By dividing the Area Control Error (ACE) signal and the State of Charge (SOC) of battery energy storage systems into different intervals, the frequency control task of BESSs could be determined by considering the frequency control demand of the power grid in each interval and SOC self-recovery. The well-developed model predictive control can be employed to attain the optimal control variable sequence of BESSs in the following time, which can determine the output depths of BESSs and action timing at different intervals. The simulation platform MATLAB/Simulink is used to build two secondary frequency control scenarios of BESSs for providing frequency regulation service. The feasibility of the presented strategy is demonstrated by simulation results of a sample system.

At present, battery energy storage systems (BESS) have become an important resource for improving the frequency control performance of power grids under the situation of high penetration rates of new energy. Aiming at the problem that the existing control strategy is not sufficient for allocating the frequency regulation power instructions, a hierarchical distributed coordinated control strategy for BESS to participate in the automatic generation control (AGC) of a regional power grid is proposed. At the upper layer, the state of charge (SOC) of BESS and the technical characteristics of different frequency regulation power sources are comprehensively considered to complete the coordinated distribution of frequency regulation commands between BESS and traditional generators; at the lower layer, for the purpose of optimizing the economic operation of the regional power grid, the distributed consistency algorithm is used to control the distributed BESS in order to realize the fine management of power output of BESS. The simulation results indicate that this control strategy can give full play to the technical characteristics of different frequency power sources and improve the frequency regulation of the power grid. The excessive power consumption of BESS is successfully avoided, and the continuous operation of BESS is realized.

The increased penetration of renewable energy sources (RES) and electric vehicles (EVs) is resulting in significant challenges to the stability, reliability, and resiliency of the electrical grid due to the intermittency nature of RES and uncertainty of charging demands of EVs. There is a potential for significant economic returns to use vehicle-to-grid (V2G) technology for peak load reduction and frequency control. To verify the effectiveness of the V2G-based frequency control in a microgrid, modeling and simulations of single- and multi-vehicle-based primary and secondary frequency controls were conducted to utilize the integrated components at the Canadian Centre for Housing Technology (CCHT)-V2G testing facility by using MATLAB/Simulink. A single-vehicle-based model was validated by comparing empirical testing and simulations of primary and secondary frequency controls. The validated conceptual model was then applied for dynamic phasor simulations of multi-vehicle-based frequency control with a proposed coordinated control algorithm for improving frequency stability and facilitating renewables integration with V2G-capable EVs in a microgrid. This proposed model includes a decentralized coordinated control of the state of charge (SOC) and charging schedule for five aggregated EVs with different departure times and SOC management profiles preferred by EV drivers. The simulation results showed that the fleet of 5 EVs in V2B/V2G could effectively reduce frequency deviation in a microgrid.

The extensive development of renewable energies in power systems causes several problems due to intermittent output power generation. To tackle the challenge, demand response contribution to ancillary service is currently well recognized under the smart grid infrastructure. The application of the heat pump water heater (HPWH) as a controllable load in primary frequency control is well presented in the literature; however, the motivation of this paper is to use HPWHs for secondary frequency control. To this end, a model predictive control (MPC) method for a two-area power system incorporating HPWHs to contribute to secondary frequency control is proposed in this paper. A detailed model of HPWH is employed as a controllable load to control the power consumption during water heating. The MPC method predicts the future control signals using a quadratic programming-based optimization. It uses the system model, past inputs and outputs, as well as system control signals to predict the next signals. The effective performance of the proposed method for the two-area power system with HPWH is demonstrated for different scenarios of load changes, intermittent renewable power generation and parameter variations as the sensitivity analysis.