At present, the application research of battery energy storage systems in power systems, especially in new energy power systems, mainly includes load characteristic optimization, auxiliary frequency regulation, auxiliary peak shaving, dealing with uncertainty in new energy sources, emergency energy control, and black start. When considering improving the peak valley difference characteristics of the power grid load, that is, enhancing the demand side response capability of the load through the coordination of battery energy storage, it is necessary for the battery to have an Operation Control Strategy (OCS) that can meet load fluctuations, as well as power ramp and State of Charge (SOC) control strategies; When participating in power grid frequency regulation and peak shaving, the battery energy storage system needs to have regulation accuracy and response speed that meet the energy imbalance regulation needs of the power grid in a specific time scale; When dealing with the uncertainty of new energy generation systems such as photovoltaics and wind power, it is necessary for battery energy storage to have different input-output characteristics at multiple time scales to cope with the fluctuations and uncertainties of new energy output, and to effectively improve the multi time scale power balance ability of the new energy grid.
Therefore, when the battery energy storage system is configured at different locations such as photovoltaic power plants, wind farms, grid nodes or load sides, for different purposes such as optimizing energy or power balance under steady-state operating conditions of the grid, increasing the proportion of new energy consumption, and fast energy control of the grid, different requirements are put forward for the energy characteristics of the battery energy storage system, including battery energy storage charging and discharging control strategies and State of Charge optimization. Domestic researchers have conducted extensive research on energy characteristic models of battery energy storage systems in different application scenarios. Scholars and researchers have proposed a Constant Power Control Strategy (CPCS) based on the constant charging and discharging power of battery energy storage systems. The optimization objective is to minimize the daily charging and discharging times of the battery energy storage system, and to optimize the peak valley difference caused by the imbalance between the demand and supply of power grid source load energy. Due to the optimization goal of this method being the number of battery charges and discharges, it brings significant limitations to the full utilization of battery system capacity, while also suppressing the consumption of new energy to a certain extent. The optimization goal of maximizing the consumption of new energy within the power grid and minimizing energy fluctuations within a certain time scale of the battery energy storage system is to optimize the energy balance problem caused by the dual fluctuations of new energy and load in the battery energy storage system. The charging and discharging and state of charge control strategy of the battery energy storage system can to some extent better solve the new energy consumption problem and peak valley difference optimization problem of large-scale grid connected new energy.
On the other hand, the specific application scenarios and energy characteristic models of battery energy storage systems in the power grid directly affect the operating costs and revenue levels of battery energy storage systems. With the goal of maximizing the benefits of battery energy storage systems participating in grid operation and regulation, and with constraints on the operating status of new energy sources, traditional power sources, power network, load characteristics, reliability indicators, and stability performance, and considering the mutual influence between the marginal cost of grid electricity prices and spot electricity prices at different time periods, an optimization model for the operating cost and economic benefits of battery energy storage systems is established. In terms of research on algorithms for solving energy control strategy models in battery energy storage systems, there are mainly single objective and multi-objective optimization problems represented by particle swarm optimization algorithms, as well as solving methods represented by game theory.
Heuristic algorithms such as genetic algorithm, particle swarm algorithm, and ant colony algorithm were used to study the single objective or multi-objective energy control strategy solving algorithms for battery energy storage systems in different application scenarios in the power grid. Due to the algorithmic mechanism of heuristic numerical optimization algorithms, it is difficult to utilize real-time state information of new energy power systems and the real-time solving speed of algorithms. Therefore, these algorithms still have certain limitations in solving real-time dynamic optimization problems of energy control strategies in power grids and battery energy storage systems.
The study of energy control strategies for power grid battery energy storage systems based on game theory can calculate the energy input and output characteristics and battery charge state of battery energy storage at different time scales in the future, according to the different energy characteristics required by the power grid for energy storage, achieving better battery energy storage utilization efficiency and economy. Based on the theories of non cooperative and cooperative games, battery energy storage, power supply, power grid, and load are considered as individual participants in the game. Under the constraints of power supply operation status, new energy utilization rate, and battery operation status, a fully dynamic information game optimization model is studied to coordinate the benefits of new energy sources and the cost of load electricity consumption. This effectively improves the energy balance level of the power grid at multiple time scales, And can achieve good economic efficiency and carbon reduction capacity while maintaining a high level of energy balance.