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2745. Resilience enhancement of distributions networks with robust optimal sizing and location under uncertainty and random contingencies
Invited abstract in session TD-35: Location and transportation problems under uncertainty, stream Stochastic, Robust and Distributionally Robust Optimization.
Tuesday, 14:30-16:00Room: 44 (building: 303A)
Authors (first author is the speaker)
| 1. | Pascal Quach
|
| CentraleSupélec | |
| 2. | Yiping Fang
|
| CentraleSupélec | |
| 3. | Anne Barros
|
| CentraleSupélec |
Abstract
The resilience of power systems is challenged by increasingly frequent and severe environmental events. Anticipation of threats and vulnerabilities is crucial to ensure adequate response and recovery from extreme events. In this context, the deployment of local generation can help absorb the impact of system disturbances. Size and location of distributed energy resources are critical factors in determining their economic and technical viability. In this paper, we develop robust and distributionally robust optimization models among others, under load and supply uncertainty, and random contingencies. An illustrative case study is presented to demonstrate the models’ performance on a benchmark distribution network with synthetic data combining a variety of representative days and failure scenarios in order to explore the trade-offs between reliability, costs, and resilience. System performance assessed under in-sample and out-sample scenarios constitute the resilience metrics.
Keywords
- Robust Optimization
- Mathematical Programming
- OR in Energy
Status: accepted
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