A More Meticulous Method for Modeling Renewable Energy Storage Capacity
A More Meticulous Method for Modeling Renewable Energy Storage Capacity lead image
Solar and wind energy, which require efficient storage systems, will increasingly serve energy needs in the coming decades. Compared to others, compressed air energy storage (CAES) is a viable option for utility scale applications. However, when configured with typical daily data models, they can produce storage capacity inaccuracies, which can hinder commercial applicability.
For more accurate capacity configurations, Yu et al. proposed to use a nonparametric density estimation technique to extract features from historical data and produce distributions of typical weekly wind power, solar power, and load.
“We wanted to improve the accuracy of typical weekly data by comparing the Parzen window estimation method to parametric aggregation,” said author Guoxin Sun. “It evaluates the capacity configuration of wind-solar energy storage systems obtained by different methods based on one-year time series data as the evaluation basis.”
The study involved simulations for both “islanded” and “grid-connected” modes with considerations of renewable energy rate, system purchasing rate, and environmental benefits.
Its findings revealed the method to more accurately reflect true data characteristics, promising more robust economic benefits for energy providers.
Indeed, such improvements were shown to save electricity purchase costs of $6.55 million per week for an average energy user load of 699.26 MW when the system is connected to the grid; and $6.74 million per week in island mode.
In grid-connected mode, the method was shown to reduce the power shortage rate by 27.24% and increase the wind-solar utilization rate by 11.68%. In island mode, a wind-solar utilization rate increase of more than 38 percent was revealed.
“We hope this study helps produce future capacity configurations that are more in line with reality,” said Sun.
Source: “Optimization of wind and solar energy storage system capacity configuration based on the Parzen window estimation method,” by Qihui Yu, Shengyu Gao, Guoxin Sun, and Ripeng Qin, Journal of Renewable and Sustainable Energy (2023). The article can be accessed at http://doi.org/10.1063/5.0172720 .
