FELIPE NEVES PIANCÓ
Data: 07/02/2024, 13h.
Sala Zoom: https://puc-rio.zoom.us/j/98789887722?pwd=YXhRVVJBK24zTXpKeXRSTEdFODg1QT09
Orientadores: Bruno Fânzeres dos Santos | PUC-Rio & Alexandre Moreira da Silva | Lawrence Berkeley National Laboratory
Resumo:
Wildfires can be a source of vulnerability for power systems operations. Those events can especially affect the operation of transmission and distribution systems. It can interrupt energy supply, increase costs, and decrease grid reliability. Numerous approaches can be executed to prevent this. Planning decisions that consider the relationship between operative actions and the probability of wildfire disruption hasn’t been properly evaluated by academia. By not recognizing this aspect, the operation of power systems may be impaired. Properly modeling this dependency could lower wildfire disruption and loss of load. In this thesis, a two-stage, distributionally robust optimization problem with decision-dependent uncertainty is developed to consider distribution system multiperiod operation. The first stage determines the optimal switching actions and lines investments, and the second stage evaluates the worst-case expected operation cost. It is designed a decision-dependent uncertainty framework where the line failure probabilities are a function (dependent) of its power flow levels. An iterative method is proposed to solve this model and an out-of-sample analysis is developed to validate it through different case studies. Results showed that, by neglecting the uncertainty dependency on operative decisions, the way is developed here, there could be a higher expected loss of load and a higher operational cost. By considering this new approach when operating power lines, the grid’s resilience could be improved and wildfires consequences can be mitigated with lesser costly actions.