Scalable Bilevel Optimization for Generating Maximally Representative OPF Datasets
New generations of power systems, containing high shares of renewable energy
resources, require improved data-driven tools which can swiftly adapt to
changes in system operation. Many of these tools, such as ones using machine
learning, rely on high-quality training datasets to construct probabilistic
models. Such models should be able to accurately represent the system when
operating at its limits (i.e., operating with a high degree of ``active
constraints"). However, generating training datasets that accurately represent
the many possible combinations of these active constraints is a particularly
challenging task, especially within the realm of nonlinear AC Optimal Power
Flow (OPF), since most active constraints cannot be enforced explicitly. Using
bilevel optimization, this paper introduces a data collection routine that
sequentially solves for OPF solutions which are ``optimally far" from
previously acquired voltage, power, and load profile data points. The routine,
termed RAMBO, samples critical data close to a system's boundaries much more
effectively than a random sampling benchmark. Simulated test results are
collected on the 30-, 57-, and 118-bus PGLib test cases.