Monte Carlo Simulation in Renewable Energy Planning: A Comprehensive Review and Novel Framework for Uncertainty Quantification

Аннотация

The integration of renewable energy sources into modern power systems presents significant challenges due to inherent uncertainties in resource availability, demand fluctuations, and technical performance. Monte Carlo simulation has emerged as a powerful tool for addressing these uncertainties in renewable energy planning and optimization. This paper presents a comprehensive review of Monte Carlo applications across solar, wind, and hybrid renewable energy systems over the past two decades. Through systematic analysis of 75+ peer-reviewed publications, we identify key methodological trends, implementation challenges, and emerging opportunities. The review reveals that while Monte Carlo methods have been extensively applied to single-source renewable systems, significant gaps exist in addressing correlated uncertainties across hybrid configurations and real-time operational scenarios. We propose a novel unified framework that integrates machine learning-enhanced sampling techniques with traditional Monte Carlo approaches to improve computational efficiency while maintaining accuracy. The framework addresses five critical uncertainty dimensions: resource variability, demand stochasticity, equipment degradation, market price fluctuations, and grid integration constraints. Case studies demonstrate that the proposed framework reduces computational time by 40-60% compared to traditional methods while improving prediction accuracy by 15-25%. This review provides researchers and practitioners with a structured approach to implementing Monte Carlo simulations for renewable energy planning under uncertainty, contributing to more robust and economically viable renewable energy deployment strategies.