  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-
and behavior-dependent resources. However, most methods are ignorant to data
quality, which may differ based on measurement and underlying
privacy-protection mechanisms. This paper addresses this shortcoming by (i)
proposing a practical data quality metric based on Wasserstein distance, (ii)
leveraging a novel modification of distributionally robust optimization using
information from multiple data sets with heterogeneous quality to valuate data,
(iii) applying the proposed optimization framework to an optimal power flow
problem, and (iv) 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.
