Van‐Hai Bui
Biographic Data
| ID | 6902569 |
|---|---|
| NAME | Van‐Hai Bui |
| GIVEN NAMES | Van‐Hai |
| FAMILY NAME | Bui |
| SIGNATURE | BUI H |
| AFFILIATIONS | University of Michigan–Dearborn |
| ORCID | 0009-0002-4565-4780 |
| VERIFIED | Yes |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 3 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
FairMarket-RL
Peer-to-peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairness. This paper presents FairMarket-RL, a novel hybrid framework that combines Large Language Models (LLMs) with Reinforcement Learning (RL) to enable fairness-aware trading agents. In a simulated P2P microgrid with multiple sellers and buyers, the LLM acts as a real-time…
Scalable fairness shaping with LLM-guided multi-agent reinforcement learning for peer-to-peer electricity markets
Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and reinforcement learning(RL) designs emphasize efficiency or private profit and offer little real-time guidance to ensure equitable outcomes under uncertainty. To address this gap, we propose FairMarket-RL, a fairness-aware multiagent RL framework in which a large langua…
Designing effective demand response
Demand response (DR) programs help electricity systems balance supply and demand by encouraging consumers to adjust their usage in response to price signals or incentives. As population growth, urbanization, and the electrification of transportation and industry continue to increase energy needs, flexible and effective DR programs have become increasingly important. However, consumer participation remains difficult to secure because individuals a…
No prominent works on this page.
Designing effective demand response
Demand response (DR) programs help electricity systems balance supply and demand by encouraging consumers to adjust their usage in response to price signals or incentives. As population growth, urbanization, and the electrification of transportation and industry continue to increase energy needs, flexible and effective DR programs have become increasingly important. However, consumer participation remains difficult to secure because individuals a…
FairMarket-RL
Peer-to-peer (P2P) trading is increasingly recognized as a key mechanism for decentralized market regulation, yet existing approaches often lack robust frameworks to ensure fairness. This paper presents FairMarket-RL, a novel hybrid framework that combines Large Language Models (LLMs) with Reinforcement Learning (RL) to enable fairness-aware trading agents. In a simulated P2P microgrid with multiple sellers and buyers, the LLM acts as a real-time…
Scalable fairness shaping with LLM-guided multi-agent reinforcement learning for peer-to-peer electricity markets
Peer-to-peer (P2P) energy trading is becoming central to modern distribution systems as rooftop PV and home energy management systems become pervasive, yet most existing market and reinforcement learning(RL) designs emphasize efficiency or private profit and offer little real-time guidance to ensure equitable outcomes under uncertainty. To address this gap, we propose FairMarket-RL, a fairness-aware multiagent RL framework in which a large langua…
Smart Grid Energy Management (3 works) · Demand response (2 works) · Auction Theory and Applications (1 work) · Bidding (1 work) · Blockchain Technology Applications and Security (1 work) · Cognition (1 work) · Common value auction (1 work) · Consumer behaviour (1 work) · Consumer demand (1 work) · Consumption (sociology (1 work)