Peer-to-peer energy trading optimization in energy communities using multi-agent deep reinforcement learning
importance 4/10
ai assisted
Smart Grid Energy Management energy trading stage: pilot
Problem
Prosumers in energy communities need to optimize how much energy to trade and at what price in peer-to-peer markets.
Why it matters
Inefficient peer-to-peer trading raises energy costs and hinders decentralized, renewable-based energy markets.
What the AI did
Two reinforcement learning algorithms, used separately, optimize transacted energy amounts and purchase/sale prices within a competitive multi-agent double-auction ecosystem.
Results
not stated in source
Impact
Methodology was integrated into an agent-based ecosystem, but the abstract gives no quantitative outcomes.
Record
| Venue | Energy Informatics |
|---|---|
| Published | 2022 |
| Work type | article |
| Authors | Helder Pereira, Luís Gomes, Zita Vale |
| Citations | 33 (OpenAlex, at import time) |
| References | 26 |
| Model or method | multi-agent deep reinforcement learning (algorithms unnamed) |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s42162-022-00235-2 |
| Open access | gold |
| Keywords | peer-to-peer energy trading, reinforcement learning, multi-agent, double auction, energy communities |
Importance rating
4
real but narrower advance
P2P energy trading optimization supports decentralized renewable markets, but demonstrated impact is small: 50-player community, 44.65 EUR weekly savings for trained players.
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