Optimal Joint Bidding and Pricing of Electricity Retailers Using Multi-agent Deep Reinforcement Learning-Reference-Cited by-同舟云学术

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Optimal Joint Bidding and Pricing of Electricity Retailers Using Multi-agent Deep Reinforcement Learning

Published:2023-12-15 Issue: Volume: Page:4850-4855
ISSN:
Container-title:2023 IEEE 7th Conference on Energy Internet and Energy System Integration (EI2)
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Author:

Ye Shankun¹,Lu Jixiang¹,Huang Hua¹,Wang Ke²,Xu Hongsheng³

Affiliation:

1. State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Nari Technology Co., Ltd,Nanjing,China

2. College of Energy and Electrical Engineering Hohai University,Nanjing,China

3. State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks, Nari Technology Co., Ltd. College of Energy and Electrical Engineering, Hohai University,Nanjing,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10511272/10512350/10513344.pdf?arnumber=10513344

Reference24 articles.

1. Optimal Price-Energy Demand Bids for Aggregate Price-Responsive Loads

2. Decision-Making for Electricity Retailers: A Brief Survey

3. The values of market-based demand response on improving power system reliability under extreme circumstances

4. Deep Reinforcement Learning for Joint Bidding and Pricing of Load Serving Entity

5. Strategic Bidding for a Large Consumer

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