Considering Low-Carbon Economic Dispatch of Power Systems with the Participation of Large-Scale Energy Storage and Multiple Types of Regulation Resources

In response to the low-carbon strategic goals, the proportion of intermittent renewable energy resources in the power system has been increasing in recent years, which has significantly increased the load uncertainty of the power system. In order to solve the negative impact of the instability of new energy generation on the power grid, a low-carbon economic dispatching method of the power system with the participation of large-scale energy storage and multiple types of regulating resources is proposed. Firstly, a neural network algorithm is used to simulate and predict the new energy output, and an economic dispatching architecture is constructed with the participation of large-scale energy storage and multiple types of regulating resources; secondly, a stepped carbon trading mechanism is considered, and a mathematical model of the power system with large-scale energy storage and multiple types of regulating resources such as scenery and fire is established with the operation and maintenance cost, carbon emission cost, and operation penalty cost of renewable energy output as the objective function. At the same time, the average change rate index of scenery power output is used to evaluate the power fluctuation leveling ability of the energy storage system, so as to obtain the system operation scheme with the optimal comprehensive fluctuation leveling ability, economy and low carbon. Finally, the optimization model is solved by using the CPLEX solver under different operation scenarios and the feasibility of the proposed scheme is verified.