Loading…
New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method
Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the cr...
Saved in:
Published in: | Mathematical problems in engineering 2022-08, Vol.2022, p.1-12 |
---|---|
Main Authors: | , , , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
cited_by | |
---|---|
cites | cdi_FETCH-LOGICAL-c294t-1436f5c775a293ef4c63ceff667af8d541640552a0fd9695dac21f83395ae4313 |
container_end_page | 12 |
container_issue | |
container_start_page | 1 |
container_title | Mathematical problems in engineering |
container_volume | 2022 |
creator | Liu, Wei Guo, Liang Kou, Yan Wang, Yuan Li, Bingkang Zhao, Huiru Li, Chenhui |
description | Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support. |
doi_str_mv | 10.1155/2022/4096088 |
format | article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2704754664</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2704754664</sourcerecordid><originalsourceid>FETCH-LOGICAL-c294t-1436f5c775a293ef4c63ceff667af8d541640552a0fd9695dac21f83395ae4313</originalsourceid><addsrcrecordid>eNp9kMtOwzAQRS0EEqWw4wMssYRQv5MsaZWWSuWxAMEuspIxTdUmxXZa2g2_jqt0zWpm7OM71xeha0ruKZVywAhjA0FSRZLkBPWoVDySVMSnoSdMRJTxz3N04dyCEEYlTXro9xm2OKvBfu3wa7MFiycQJu2rpg7nHuzaVg7wyEJZeZxt9LLtLofaQYlDM273-x0egvPRR2Odx7ou8XS1ts0mAE_aB5EoW8IK6iDw46F2h_dP4OdNeYnOjF46uDrWPnofZ2-jx2j2MpmOHmZRwVLhIyq4MrKIY6lZysGIQvECjFEq1iYppaBKECmZJqZMVSpLXTBqEs5TqUFwyvvoptMNtr7b4DVfNK2tw8qcxUTEUiglAnXXUYVtnLNg8vD7lba7nJL8EHF-iDg_Rhzw2w6fV3Wpt9X_9B9zsXvh</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2704754664</pqid></control><display><type>article</type><title>New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method</title><source>Wiley_OA刊</source><source>Publicly Available Content (ProQuest)</source><creator>Liu, Wei ; Guo, Liang ; Kou, Yan ; Wang, Yuan ; Li, Bingkang ; Zhao, Huiru ; Li, Chenhui</creator><contributor>Ma, Junhai ; Junhai Ma</contributor><creatorcontrib>Liu, Wei ; Guo, Liang ; Kou, Yan ; Wang, Yuan ; Li, Bingkang ; Zhao, Huiru ; Li, Chenhui ; Ma, Junhai ; Junhai Ma</creatorcontrib><description>Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support.</description><identifier>ISSN: 1024-123X</identifier><identifier>EISSN: 1563-5147</identifier><identifier>DOI: 10.1155/2022/4096088</identifier><language>eng</language><publisher>New York: Hindawi</publisher><subject>Credit management ; Decision making ; Electric power generation ; Electricity distribution ; Empirical analysis ; Energy ; Markets ; Performance indices</subject><ispartof>Mathematical problems in engineering, 2022-08, Vol.2022, p.1-12</ispartof><rights>Copyright © 2022 Wei Liu et al.</rights><rights>Copyright © 2022 Wei Liu et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c294t-1436f5c775a293ef4c63ceff667af8d541640552a0fd9695dac21f83395ae4313</cites><orcidid>0000-0002-0779-7011</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2704754664/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2704754664?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><contributor>Ma, Junhai</contributor><contributor>Junhai Ma</contributor><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Guo, Liang</creatorcontrib><creatorcontrib>Kou, Yan</creatorcontrib><creatorcontrib>Wang, Yuan</creatorcontrib><creatorcontrib>Li, Bingkang</creatorcontrib><creatorcontrib>Zhao, Huiru</creatorcontrib><creatorcontrib>Li, Chenhui</creatorcontrib><title>New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method</title><title>Mathematical problems in engineering</title><description>Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support.</description><subject>Credit management</subject><subject>Decision making</subject><subject>Electric power generation</subject><subject>Electricity distribution</subject><subject>Empirical analysis</subject><subject>Energy</subject><subject>Markets</subject><subject>Performance indices</subject><issn>1024-123X</issn><issn>1563-5147</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><recordid>eNp9kMtOwzAQRS0EEqWw4wMssYRQv5MsaZWWSuWxAMEuspIxTdUmxXZa2g2_jqt0zWpm7OM71xeha0ruKZVywAhjA0FSRZLkBPWoVDySVMSnoSdMRJTxz3N04dyCEEYlTXro9xm2OKvBfu3wa7MFiycQJu2rpg7nHuzaVg7wyEJZeZxt9LLtLofaQYlDM273-x0egvPRR2Odx7ou8XS1ts0mAE_aB5EoW8IK6iDw46F2h_dP4OdNeYnOjF46uDrWPnofZ2-jx2j2MpmOHmZRwVLhIyq4MrKIY6lZysGIQvECjFEq1iYppaBKECmZJqZMVSpLXTBqEs5TqUFwyvvoptMNtr7b4DVfNK2tw8qcxUTEUiglAnXXUYVtnLNg8vD7lba7nJL8EHF-iDg_Rhzw2w6fV3Wpt9X_9B9zsXvh</recordid><startdate>20220810</startdate><enddate>20220810</enddate><creator>Liu, Wei</creator><creator>Guo, Liang</creator><creator>Kou, Yan</creator><creator>Wang, Yuan</creator><creator>Li, Bingkang</creator><creator>Zhao, Huiru</creator><creator>Li, Chenhui</creator><general>Hindawi</general><general>Hindawi Limited</general><scope>RHU</scope><scope>RHW</scope><scope>RHX</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TB</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CWDGH</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>KR7</scope><scope>L6V</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><orcidid>https://orcid.org/0000-0002-0779-7011</orcidid></search><sort><creationdate>20220810</creationdate><title>New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method</title><author>Liu, Wei ; Guo, Liang ; Kou, Yan ; Wang, Yuan ; Li, Bingkang ; Zhao, Huiru ; Li, Chenhui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c294t-1436f5c775a293ef4c63ceff667af8d541640552a0fd9695dac21f83395ae4313</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Credit management</topic><topic>Decision making</topic><topic>Electric power generation</topic><topic>Electricity distribution</topic><topic>Empirical analysis</topic><topic>Energy</topic><topic>Markets</topic><topic>Performance indices</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Wei</creatorcontrib><creatorcontrib>Guo, Liang</creatorcontrib><creatorcontrib>Kou, Yan</creatorcontrib><creatorcontrib>Wang, Yuan</creatorcontrib><creatorcontrib>Li, Bingkang</creatorcontrib><creatorcontrib>Zhao, Huiru</creatorcontrib><creatorcontrib>Li, Chenhui</creatorcontrib><collection>Hindawi Publishing Complete</collection><collection>Hindawi Publishing Subscription Journals</collection><collection>Hindawi Publishing Open Access Journals</collection><collection>CrossRef</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Middle East & Africa Database</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><jtitle>Mathematical problems in engineering</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Wei</au><au>Guo, Liang</au><au>Kou, Yan</au><au>Wang, Yuan</au><au>Li, Bingkang</au><au>Zhao, Huiru</au><au>Li, Chenhui</au><au>Ma, Junhai</au><au>Junhai Ma</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method</atitle><jtitle>Mathematical problems in engineering</jtitle><date>2022-08-10</date><risdate>2022</risdate><volume>2022</volume><spage>1</spage><epage>12</epage><pages>1-12</pages><issn>1024-123X</issn><eissn>1563-5147</eissn><abstract>Under the background of the new power system, the proportion of new energy power generation enterprises in the power market is gradually increasing. With the further expansion of China’s power trading scale, the increasingly fierce market competition, and the instability of new energy output, the credit problems of new energy power generation enterprises in the trading process cannot be ignored. Therefore, improving and perfecting the credit system of new energy power generation enterprises is necessary for building a modern market system. Firstly, the credit indexes of new energy power generation enterprises are constructed from the three dimensions of performance ability, performance behavior, and performance willingness. Then, a credit index evaluation model of new energy power generation enterprises is proposed based on the fuzzy best-worst and improved matter-element extension method. Finally, an empirical study is carried out. The analysis results show that scheduling discipline compliance, historical credit, and participation rate of the market-oriented transaction have a more significant impact on the recognition of new energy power generation enterprises. They should focus on market transactions. The model proposed in this paper can effectively deal with the ambiguity of indexes in the credit evaluation of new energy power companies. Through comparison with other models, the effectiveness of the model proposed in this paper in the credit evaluation of power companies is further verified. Construction provides method support.</abstract><cop>New York</cop><pub>Hindawi</pub><doi>10.1155/2022/4096088</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-0779-7011</orcidid><oa>free_for_read</oa></addata></record> |
fulltext | fulltext |
identifier | ISSN: 1024-123X |
ispartof | Mathematical problems in engineering, 2022-08, Vol.2022, p.1-12 |
issn | 1024-123X 1563-5147 |
language | eng |
recordid | cdi_proquest_journals_2704754664 |
source | Wiley_OA刊; Publicly Available Content (ProQuest) |
subjects | Credit management Decision making Electric power generation Electricity distribution Empirical analysis Energy Markets Performance indices |
title | New Energy Power Generation Enterprise Credit Evaluation Based on Fuzzy Best-Worst and Improved Matter-Element Extension Method |
url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T04%3A30%3A36IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=New%20Energy%20Power%20Generation%20Enterprise%20Credit%20Evaluation%20Based%20on%20Fuzzy%20Best-Worst%20and%20Improved%20Matter-Element%20Extension%20Method&rft.jtitle=Mathematical%20problems%20in%20engineering&rft.au=Liu,%20Wei&rft.date=2022-08-10&rft.volume=2022&rft.spage=1&rft.epage=12&rft.pages=1-12&rft.issn=1024-123X&rft.eissn=1563-5147&rft_id=info:doi/10.1155/2022/4096088&rft_dat=%3Cproquest_cross%3E2704754664%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c294t-1436f5c775a293ef4c63ceff667af8d541640552a0fd9695dac21f83395ae4313%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2704754664&rft_id=info:pmid/&rfr_iscdi=true |