  Stakeholders in electricity delivery infrastructure are amassing data about
their system demand, use, and operations. Still, they are reluctant to share
them, as even sharing aggregated or anonymized electric grid data risks the
disclosure of sensitive information. This paper highlights how applying
differential privacy to distributed energy resource production data can
preserve the usefulness of that data for operations, planning, and research
purposes without violating privacy constraints. Differentially private
mechanisms can be optimized for queries of interest in the energy sector, with
provable privacy and accuracy trade-offs, and can help design differentially
private databases for further analysis and research. In this paper, we consider
the problem of inference and publication of solar photovoltaic systems'
metadata. Metadata such as nameplate capacity, surface azimuth and surface tilt
may reveal personally identifiable information regarding the installation
behind-the-meter. We describe a methodology to infer the metadata and propose a
mechanism based on Bayesian optimization to publish the inferred metadata in a
differentially private manner. The proposed mechanism is numerically validated
using real-world solar power generation data.
