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2047. Data value at the grid-edge
Invited abstract in session WA-35: Data Valuation from Data-driven Optimization, stream Stochastic, Robust and Distributionally Robust Optimization.
Wednesday, 8:30-10:00Room: 44 (building: 303A)
Authors (first author is the speaker)
| 1. | Robert Mieth
|
| Industrial and Systems Engineering, Rutgers University |
Abstract
This talk presents an approach to quantify the value of data sets available to the operator of an electric distribution system that seeks to compute optimal control setpoints for distributed energy resources. We leverage the ability of the Wasserstein metric to encode the quality of available data sets and present a modification of a data-driven AC optimal power flow problem with stochastic load and renewable energy injections that can internalizes this data quality information. The proposed approach offers an effective tool for the system operator to handle data sets of varying quality, e.g., due to the necessary privacy protection or aggregation of data collected at the grid edge. We show that this not only helps the system operator to improve operations with existing data, but also informs planning decisions for investments in advanced data collection and communication technology.
Keywords
- Stochastic Optimization
- Energy Policy and Planning
Status: accepted
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