A Gramian angular field-based data-driven approach for multiregion and multisource renewable scenario generation

Abstract

Scenario generation is a pivotal method for providing system operators with a reasonable quantity of power scenarios that are capable of reflecting uncertainties and spatiotemporal processes to make exact and effective decisions for power systems. Aiming at improving the forecasting performance of renewable generation and capturing uncertainty as well as dependency over renewable site groups in different regions, this paper proposes a data-driven approach for parallel scenario generation. To capture the complex spatiotemporal dynamics of renewable energy sources (RESs), the proposed approach utilizes Gramian angular field (GAF) to process time sequences and constructs style-based super-resolution models that correspond with the idea of multi-model ensembles. Thereafter, a two-stage stochastic optimization strategy is adopted to accomplish scenario forecasting using point forecasts and historical error information as input. Based on two real-world datasets from the National Renewable Energy Laboratory (NREL) and the Belgian transmission operator ELIA, the effectiveness of the proposed approach is verified by methods including statistical analysis, spatiotemporal correlations, power system scheduling, and out-of-sample evaluations. Compared with three advanced benchmarks, the proposed approach has superior forecasting performance and spatiotemporal dynamic capture capability. At a 24-h lead time, the proposed model achieves continuous ranked probability scores (CRPSs) of 4–14% over the other models with consistent performance during the economic dispatching of actual power system operations.