In the new energy grid

the intermittent impact of natural resources such as wind and light makes the power of the grid stochastic

leading to the problem of multiple objectives needing to be optimized simultaneously. Traditional particle swarm optimization combined with a single model often needs to give up the consideration of load

which is a key factor

to ensure that the single model solution process will not fall into the local optimal solution. In this regard

a research on phased operation optimization of new energy grid resources considering load constraints is proposed. By using the Differential Autoregressive Moving Average (ARIMA) model to predict the low-frequency part of the load

and using a Deep Belief Network (DBN) to predict the high-frequency part of the load

the two parts are superimposed and reconstructed to generate the load prediction results for the new energy grid. Design a two-stage optimization operation model for power grid resources

including day ahead and real-time. Finally

using the adaptive mutation particle swarm optimization algorithm

a global optimal solution is performed for the two-stage configuration model to achieve optimization of resource operation in the new energy grid. The experimental results show that the proposed algorithm has a lower amount of abandoned wind and solar power in each time period within 24 hours compared to the comparative algorithm. The new energy consumption rate reaches the highest 97.6% at 3 hours

and the controllable load time shift rate and operating cost are much lower than the comparative algorithm

improving the automation and intelligence level of the power grid.