A multi-dimensional quantitative evaluation framework for resources complexity of new energy stations based on grey correlation analysis

In recent years, new energy has developed rapidly around the world and its role in promoting energy transformation is obvious to all, but it also poses a serious challenge to the safety and stability of the power system caused by the random fluctuation of output. New energy forecasting is currently considered to be the key technique to improve the operation level of the new power system. However, influenced by geographical location, installed capacity, forecasting scale, and other factors of the forecasting object, the power forecasting accuracy of each station varies significantly, which hinders the power generation group from delicacy management of owned new energy stations. Resource complexity evaluation is the key to solving the above challenges, but there are still gaps in related research currently. In this paper, a multi-dimensional quantitative evaluation framework for the resource complexity of new energy stations is proposed, aiming to realize the real-time dynamic evaluation of the complexity of new energy station resources. Firstly, wholeness and locality are defined as two dimensions to comprehensively evaluate the resources’ complexity. Secondly, complexity evaluation indicators such as overall range, frequency, and sampling loss area are designed under these two dimensions. Lastly, all indicators are assembled to obtain a comprehensive score based on the gray correlation analysis. The data from two real wind farms and two real photovoltaic power stations are adopted for simulation calculation. The results indicate that the proposed approach can accurately quantify the complexity of resources of a new power station, thus providing a reference for the establishment of the assessment basis of the power forecasting accuracy of the station.