Mathematical Analysis Methods for Quantitative Scenario Generation of Renewable Power Output: A Comprehensive Review
Year: 2026
Type: article
Abstract: As the proportion of renewable power continues to increase, its inherent intermittency and volatility pose serious challenges to the security and stability of power systems. Scenario generation technology serves as a key tool supporting decision-making methods such as stochastic optimization and risk analysis. By generating representative power output scenarios, it can effectively characterize the uncertainty of renewable power output. This paper systematically reviews mainstream methods for the scenario generation of renewable power output, categorizing them into two major classes: sampling-based methods and model-based methods. Among them, sampling-based methods include Monte Carlo sampling, Latin hypercube sampling (LHS), Markov chains (MCs), and Copula functions. Model-based methods encompass artificial neural networks (ANNs), long short-term memory networks (LSTMs), autoregressive moving average models (ARMAs), generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models and transformer-based models. This paper elaborates on the principles and characteristics of each type of method. Moreover, scenario quality is evaluated from three dimensions: output-based metrics for numerical accuracy, distribution-based metrics for statistical consistency, and event-based metrics for key operational event representation. The current research challenges and future research directions are also summarized to provide a reference for modeling the uncertainty of renewable output.
Source: Energies
DOI: 10.3390/en19071701
Authors: Tong Ma, Boyu Qin, Shidong Hong, Yiwei Su
Institutions: Xi'an Jiaotong University, Qinghai University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W7143677566