Shining Light on Electrical Energy Burden: Affordability and Equity in Rate Design
2024 IEEE TEXAS POWER AND ENERGY CONFERENCE, TPEC（2024）
Abstract
This study explores the concept of electrical energy burden minimization from a utility's perspective. It proposes an equitable rate design that minimizes the electrical energy burden on residential customers while ensuring the economic viability of the utility. We do this by analyzing data on household income, energy consumption, and utility rates. The model incorporates electrical energy burden as a key metric in designing rate structures. Results from the analysis indicate that the proposed rate design can reduce the electrical energy burden for low-income households without imposing significant financial impacts on higher-income households. The highest income census tract in the study experiences a maximum of 11.34% increase in yearly energy costs for a 10 census tract case study.
MoreTranslated text
Key words
electricity tariffs,energy transition,energy in-security,energy equity,energy burden
求助PDF
上传PDF
View via Publisher
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
Related Papers
2005
被引用11127 | 浏览
2016
被引用6 | 浏览
2019
被引用31 | 浏览
2020
被引用21 | 浏览
2020
被引用27 | 浏览
2020
被引用75 | 浏览
2022
被引用16 | 浏览
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