Online Learning-Based Optimal Bidding Approach for FTR Market Participants
We consider the problem of optimal bidding and portfolio optimization for bidders in the financial transmission rights (FTR) auction market. Based on the price-taker assumption, each FTR market participant aims to maximize the profit, which is the difference between the clearing price and FTR revenu...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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China electric power research institute
2025-01-01
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| Series: | CSEE Journal of Power and Energy Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10165643/ |
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| _version_ | 1849390522407321600 |
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| author | Guibin Chen Ye Guo Wenjun Tang Qinglai Guo Hongbin Sun Wenqi Huang |
| author_facet | Guibin Chen Ye Guo Wenjun Tang Qinglai Guo Hongbin Sun Wenqi Huang |
| author_sort | Guibin Chen |
| collection | DOAJ |
| description | We consider the problem of optimal bidding and portfolio optimization for bidders in the financial transmission rights (FTR) auction market. Based on the price-taker assumption, each FTR market participant aims to maximize the profit, which is the difference between the clearing price and FTR revenue. However, both clearing price and the FTR revenue are random and unknown. An online learning methodology is proposed to learn optimal bidding by updating its policy with the newest observations of clearing results. With bidding prices derived by the online learning algorithm, a budget-constrained portfolio optimization problem is solved to distribute the budget among profitable FTRs. Compared to other state-of-the-art online learning approaches, the proposed tree-based bid searching (TBS) algorithm converges faster to the optimal bidding price and has favourable linearithmic time complexity. |
| format | Article |
| id | doaj-art-e229d12d2cc5426f8a999adba28cb2de |
| institution | Kabale University |
| issn | 2096-0042 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | China electric power research institute |
| record_format | Article |
| series | CSEE Journal of Power and Energy Systems |
| spelling | doaj-art-e229d12d2cc5426f8a999adba28cb2de2025-08-20T03:41:34ZengChina electric power research instituteCSEE Journal of Power and Energy Systems2096-00422025-01-011141501151110.17775/CSEEJPES.2021.0539010165643Online Learning-Based Optimal Bidding Approach for FTR Market ParticipantsGuibin Chen0Ye Guo1Wenjun Tang2Qinglai Guo3Hongbin Sun4Wenqi Huang5Tsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,Guangdong,ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,Guangdong,ChinaTsinghua-Berkeley Shenzhen Institute, Tsinghua University,Shenzhen,Guangdong,ChinaTsinghua University,Department of Electrical Engineering,Beijing,ChinaTsinghua University,Department of Electrical Engineering,Beijing,ChinaDigital Grid Research Institute, China Southern Power Grid,Guangzhou,ChinaWe consider the problem of optimal bidding and portfolio optimization for bidders in the financial transmission rights (FTR) auction market. Based on the price-taker assumption, each FTR market participant aims to maximize the profit, which is the difference between the clearing price and FTR revenue. However, both clearing price and the FTR revenue are random and unknown. An online learning methodology is proposed to learn optimal bidding by updating its policy with the newest observations of clearing results. With bidding prices derived by the online learning algorithm, a budget-constrained portfolio optimization problem is solved to distribute the budget among profitable FTRs. Compared to other state-of-the-art online learning approaches, the proposed tree-based bid searching (TBS) algorithm converges faster to the optimal bidding price and has favourable linearithmic time complexity.https://ieeexplore.ieee.org/document/10165643/Conditional value at riskcongestionfinancial transmission rightsonline learning |
| spellingShingle | Guibin Chen Ye Guo Wenjun Tang Qinglai Guo Hongbin Sun Wenqi Huang Online Learning-Based Optimal Bidding Approach for FTR Market Participants CSEE Journal of Power and Energy Systems Conditional value at risk congestion financial transmission rights online learning |
| title | Online Learning-Based Optimal Bidding Approach for FTR Market Participants |
| title_full | Online Learning-Based Optimal Bidding Approach for FTR Market Participants |
| title_fullStr | Online Learning-Based Optimal Bidding Approach for FTR Market Participants |
| title_full_unstemmed | Online Learning-Based Optimal Bidding Approach for FTR Market Participants |
| title_short | Online Learning-Based Optimal Bidding Approach for FTR Market Participants |
| title_sort | online learning based optimal bidding approach for ftr market participants |
| topic | Conditional value at risk congestion financial transmission rights online learning |
| url | https://ieeexplore.ieee.org/document/10165643/ |
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