Long‐term scenario generation of renewable energy generation using attention‐based conditional generative adversarial networks

Abstract

Long‐term scenario generation of renewable energy is regarded as an important part of the optimal planning of renewable energy systems. This study proposes a scenario generation method for generating long‐term correlated scenarios of wind and photovoltaic outputs from historical renewable energy data. The generation of scenarios was divided into two processes: long‐term yearly sequence generation and intraday scenario generation of wind‐solar energy. In the long‐term yearly sequence generation process, the k‐means clustering algorithm and Markov chain Monte Carlo simulation method were developed to capture the seasonal and long‐term features of wind and photovoltaic energies. Furthermore, an attention‐based conditional generative adversarial network (ACGAN) was proposed to capture short‐term features. An attention structure and conditional classifiers were developed to capture features in the generated scenarios. To accelerate the convergence process and improve the quality of the generated scenarios, a gradient penalty was included in the ACGAN model. Numerical case studies were conducted to verify the validity of the proposed method using a real‐world dataset.