This paper proposes an electromechanical transient method to build a battery energy storage system-based virtual synchronous generator model, suitable for a large-scale grid. This model consists of virtual synchronous generator control, system limitation and the model interface. The equations of a second-order synchronous machine, the characteristics of charging/discharging power, state of charge, operating efficiency, dead band and inverter limits are also considered. By equipping the energy storage converter into an approximate synchronous voltage source with an excitation system and speed regulation system, the necessary inertia and damping characteristics are provided for the renewable energy power system with low inertia and weak damping. Based on the node current injection method by the power system analysis software package (PSASP), the control model is built to study the influence of different energy storage systems. A study on the impact of renewable energy unit fluctuation on frequency and the active power of the IEEE 4-machine 2-area system is selected for simulation verification. Through reasonable control and flexible allocation of energy storage plants, a stable and friendly frequency environment can be created for power systems with high-penetration renewable energy.

At present, the back-propagation (BP) network algorithm widely used in the short-term output prediction of photovoltaic power stations has the disadvantage of ignoring meteorological factors and weather conditions in the input. The existing traditional BP prediction model lacks a variety of numerical optimization algorithms, such that the prediction error is large. The back-propagation (BP) neural network is easy to fall into local optimization thus reducing the prediction accuracy in photovoltaic power prediction. In order to solve this problem, an improved grey wolf optimization (GWO) algorithm is proposed to optimize the photovoltaic power prediction model of the BP neural network. So, an improved grey wolf optimization algorithm optimized BP neural network for a photovoltaic (PV) power prediction model is proposed. Dynamic weight strategy, tent mapping and particle swarm optimization (PSO) are introduced in the standard grey wolf optimization (GWO) to construct the PSO–GWO model. The relative error of the PSO–GWO–BP model predicted data is less than that of the BP model predicted data. The average relative error of PSO–GWO–BP and GWO–BP models is smaller, the average relative error of PSO–GWO–BP model is the smallest, and the prediction stability of the PSO–GWO–BP model is the best. The model stability and prediction accuracy of PSO–GWO–BP are better than those of GWO–BP and BP.