Data Quality Analysis Framework and Evaluation Methods for Power System Operation with High Proportion of Renewable Energy Penetration

Global climate crisis in 21st century pushed countries to move towards energy transformation in generation and consumption. To achieve green and low-carbon energy transformation goals, it is necessary that a large number of renewable energy resources such as wind and solar to be consumed. Renewable energy with intermittent fluctuations in time dimension and agglomerations in spatial dimension increases the complexity of green energy consumption friendly. Therefore, comprehensive data and advanced predictive analysis methods are required to guarantee safety of operation and transactions for renewable energy plants and stations. We can even say that quality of renewable energy data determines the accuracy of prediction and analysis. Firstly, this article analyzes the operation and transaction characteristics of distributed renewable energy plants, and data quality analysis framework for distributed renewable energy operations and transactions was built on the new energy cloud platform. Data information were classified into model parameter and status instance, which are related to dispatching and energy power transaction businesses such as equipment model management, operation monitoring and security analysis, measurement statistics etc. The importance between them is determined according to pairwise comparison. Finally, analytic hierarchy process (AHP) theory was applied to calculate weights for data integrity, accuracy, consistency and timeliness, data quality assessment process and calculation methods were designed, and load series data was used to verify its correctness.