Abstract
This paper goes beyond the current state of the art related to Wasserstein distributionally robust optimal power flow problems, by adding dependence structure (correlation) and support information. In view of the space-time dependencies pertaining to the stochastic renewable power generation uncertainty, we apply a moment-metric-based distributionally robust optimization, which includes a constraint on the second-order moment of uncertainty. Aiming at further excluding unrealistic probability distributions from our proposed decision-making model, we enhance it by adding support information. We reformulate our proposed model, resulting in a semi-definite program, and show its satisfactory performance in terms of the operational
results achieved and the computational time.
results achieved and the computational time.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of 2021 IEEE PowerTech |
| Number of pages | 6 |
| Publisher | IEEE |
| Publication date | 2021 |
| ISBN (Print) | 978-1-6654-1173-8 |
| DOIs | |
| Publication status | Published - 2021 |
| Event | 2021 IEEE Madrid PowerTech - Virtual Event - from the Alberto Aguilera Campus of Comillas University, Madrid, Spain Duration: 28 Jun 2021 → 2 Jul 2021 Conference number: 14 https://www.powertech2021.com/ |
Conference
| Conference | 2021 IEEE Madrid PowerTech |
|---|---|
| Number | 14 |
| Location | Virtual Event - from the Alberto Aguilera Campus of Comillas University |
| Country/Territory | Spain |
| City | Madrid |
| Period | 28/06/2021 → 02/07/2021 |
| Internet address |
Keywords
- Distributionally robust optimization
- Space-time dependencies
- Optimal power flow
- Out-of-sample analysis
