A Scenario Generation Method for Wind/PV Power Outputs and Load Sequences Preserving Extreme Scenario Characteristics

ABSTRACT Generating a substantial set of long‐term operational scenarios for wind power, photovoltaic (PV) power, and load sequences is the data foundation for planning high‐penetration renewable energy power systems. The existing scenario generation methods (SGMs) have some defects, such as neural network–based approaches requiring a large amount of historical data and lack of preservation of the characteristics of extreme scenarios. In response to the above challenge, this paper proposes an SGM for wind/PV power outputs and load sequences, which is able to preserve the characteristics of extreme scenarios. Specifically, the method extracts extreme scenarios via an iterative procedure and generates conventional scenarios using a double‐layer Markov chain model. By combining the iterative extraction process with the double‐layer model, the proposed framework effectively incorporates extreme scenario characteristics into the scenario generation process. The results of the case study from a northern province of China demonstrate that the proposed method effectively preserves the statistical characteristics of the original data and extracts representative extreme scenarios, providing diverse scenarios for evaluating high‐penetration renewable energy power systems.