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The integration of more renewable energy sources into the power system ispresenting system operators with various challenges. At the distribution systemlevel, voltage magnitudes that violate operating limits near large photovoltaicinstallations have been observed. While these issues can be partially mitigatedwith more advanced control, hardware upgrades are required at some point. Thiswork presents a scalable, optimization-based approach for deciding which linesin a network to upgrade. Compared to existing approaches, it explicitly takesthe operating policy of the system into account and provides both reasonablesolutions in short computation times as well as globally optimal solutions whenrun to completion. Compared to earlier work on the same topic, an extendedcomputational approach is taken that can simultaneously optimize for many loadscenarios across arbitrary configurations of machines and CPU cores per machinein a scalable manner by using the Benders decomposition. In addition to thetheory, numerical experiments are presented along with a discussion of thescaling properties of the Benders-based approach, giving potential users abetter basis to decide whether their problem is big enough for the approach tomake sense.

Scalable Power System Line Upgrade Planning With Policy Constraints: A "Branch and Benders" Approach