Abstract
The design of electricity markets may be facilitated by simulating actors’ behaviors. Recent studies model human decision-makers within markets as agents which learn strategies that maximize expected profits. This work investigates the problem
of ’non-stationarity’ in the context of market simulations, a problem with the learning-algorithms used by such studies which results in agents behaving irrationally, thus limiting the studies’ applicability to real-world strategic behavior.
of ’non-stationarity’ in the context of market simulations, a problem with the learning-algorithms used by such studies which results in agents behaving irrationally, thus limiting the studies’ applicability to real-world strategic behavior.
| Original language | English |
|---|---|
| Title of host publication | 23rd Power Systems Computation Conference |
| Pages | 1-9 |
| Number of pages | 9 |
| Publication status | Published - 4 Jun 2024 |
| Event | 23rd Power Systems Computation Conference - Paris, France, Paris, France Duration: 4 Jun 2024 → 7 Jun 2024 Conference number: 23 https://pscc2024.fr/ |
Conference
| Conference | 23rd Power Systems Computation Conference |
|---|---|
| Abbreviated title | PSCC 2024 |
| Country/Territory | France |
| City | Paris |
| Period | 4/06/24 → 7/06/24 |
| Internet address |
Research Field
- Flexibility and Business Models
Keywords
- Deep learning
- Game theory
- Market design
- Market Simulation
- Reinforcement Learning