Research Article
Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model
@ARTICLE{10.4108/ew.7243, author={Shu Li and Junyong Xiang and Rong Li and Duo Wang}, title={Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model}, journal={EAI Endorsed Transactions on Energy Web}, volume={11}, number={1}, publisher={EAI}, journal_a={EW}, year={2024}, month={9}, keywords={Clean Energy, Investment Risk, Investment Return, ISM, Monte Carlo}, doi={10.4108/ew.7243} }- Shu Li
Junyong Xiang
Rong Li
Duo Wang
Year: 2024
Risk-return analysis of clean energy grid project investment based on integrated ISM and Monte Carlo model
EW
EAI
DOI: 10.4108/ew.7243
Abstract
To solve the problem of complex and difficult to quantify factors affecting investment returns and risks in clean energy power grids, this study comprehensively applies the interpretive structural model and Monte Carlo model to the analysis of investment risk-returns in clean energy power grid projects. The interpretive structural model is utilized to analyze project investment returns, while the Monte Carlo model is used to analyze project investment risks. The project investment risk is based on the factor analysis of project investment returns, and key risk factors are identified through 1000 simulations, and the impact of these risks on project returns is quantified. By combining the two, the investability of the project is analyzed. The results showed that grid electricity prices, kilowatt hour subsidies, technology learning rates, total annual sunshine hours, and system power generation efficiency were key factors driving investment returns. The average expected value of investment return was about 20%, and the probability of investment return below 6% was close to 0. The overall project is worth investing in. From this, it can be seen that the research designed investment risk-return analysis methods for clean energy grid projects can effectively distinguish the main factors affecting investment returns and risks, and pre simulate the risk situation of returns. This study can provide reference for investor decision-making.
Copyright © 2024 S. Li et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
