Name
Abstract | ||
|---|---|---|
Given the importance of handling high levels of uncertainty from renewables in the unit commitment problem, there has been increased attention given to the use of stochastic programming methods. Since these are computationally very demanding, there is a need for new approximations. We propose to use a point forecast of energy from wind and loads, where the point forecast is chosen and adapted by simulation to produce a robust solution. Traditionally, point forecasts represent an expectation. In our work, we suggest that we can use an appropriately chosen quantile of the forecast distribution which is optimized within a stochastic environment. The result is a policy search algorithm built around a point forecast, which is easily implement able using standard industry models and algorithms. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/HICSS.2013.468 | HICSS |
Keywords | Field | DocType |
stochastic programming,approximation theory | Mathematical optimization,Search algorithm,Renewable energy,Load modeling,Computer science,Power system simulation,Approximation theory,Stochastic process,Quantile,Stochastic programming | Conference |
ISSN | Citations | PageRank |
1060-3425 | 0 | 0.34 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Defourny | 1 | 25 | 6.26 |
| Hugo Simão | 2 | 106 | 8.38 |
| Warren B. Powell | 3 | 1614 | 151.46 |