WeChat Mini Program
Old Version Features

A Standardized Parametric Framework for Techno-economic Analysis of Renewable and Clean Energy Systems

Renewable Energy(2025)

Department of Management Science and Engineering

Cited 0|Views1
Abstract
To foster informed policy formulation and decision-making for the evolving energy transition and the rising demand for renewable and clean energy systems (REACES), it is imperative to establish a standardized and comprehensive Techno-Economic Analysis (TEA) method for widespread adoption. Existing TEA studies on REACES have been noted to contain significant inconsistencies and deficiencies in the economic frameworks, financial parameters, decision-making metrics, and methodological approaches. Key TEA parameters, such as balance of plant, land, and contingency cost, etc., were found underutilized, with implementation rates of only 36%, 28%, and 26%, respectively. This study addresses these critical gaps by developing a robust, comprehensive, and standardized TEA model specifically tailored to REACES. System-level macro- and micro parameters related to technical, economic, financial, and business risk were integrated. The impact of key parameters illustrated through a pilot case study emphasized that overlooking these parameters significantly skews the final decision metrics. Excluding incentives resulted in an 18% underestimation of the Levelized Cost of Energy (LCOE) and a 14% miscalculation in the Discounted Payback Period. Implementing this standardized model will enhance the accuracy, consistency, and credibility of TEA practices, enabling more precise evaluations and fostering meaningful cross-comparisons among various REACES.
More
Translated text
Key words
Economic analysis,Energy transition,Standardization,Techno-economic model
求助PDF
上传PDF
Bibtex
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
  • Pretraining has recently greatly promoted the development of natural language processing (NLP)
  • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
  • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
  • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
  • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined