Data Valuation From Data-Driven Optimization

With the ongoing investment in data collection and communication technology in power systems, data-driven optimization has been established as a powerful tool for system operators to handle stochastic system states caused by weather-dependent and behavior-dependent resources. However, most methods are ignorant to data quality, which may differ based on measurement and underlying privacy-protection mechanisms. This article addresses this shortcoming by proposing a practical data quality metric based on Wasserstein distance, leveraging a novel modification of distributionally robust optimization using information from multiple datasets with heterogeneous quality to valuate data, applying the proposd optimization framework to an optimal power flow problem, and, finally, showing a direct method to valuate data from the optimal solution. We conduct numerical experiments to analyze and illustrate the proposed model and publish the implementation open source.