An Optimal Storage Capacity Allocation Method for Wind and Solar Energy Bases Through Production Simulation

The large-scale integration of wind, solar and other renewable energy power generation into the grid poses challenges in terms of consumption. The rational allocation for energy storage in new energy bases is conducive to improving the consumption rate. However, the high cost of energy storage seriously restricts the system economy. How to determine the optimal energy storage capacity has become an urgent issue in need of resolution. Based on the method of production simulation, in this paper we establish a bi-level optimal configuration model, aiming to investigate the optimal energy storage capacity of the wind and solar energy bases. Firstly, we employ the Generative Adversarial Network for unsupervised learning on the historical data of wind, photovoltaic and loads, and generate massive sample data that conform to the data distribution law. These data are then used as input for production simulation. The energy storage device’s cost-benefit is evaluated within the inner configuration model to optimize its operation with the goal of maximizing the net present value. The results are then feed into the outer layer, which optimizes the energy storage capacity with the goal of maximizing the energy storage return on investment throughout its entire life cycle. The entire life cycle simulation calculation is completed through the inner and outer layer iterations, and the configuration results are evaluated by the life cycle benefits and power abandonment penalty costs. Finally, a large-scale new energy base in Gansu Province is taken as an example to conduct simulation research, thereby verifying the rationality and effectiveness of the method in this paper.