  The importance of reactive power compensation for power factor (PF)
correction will significantly increase with the large-scale integration of
distributed generation interfaced via inverters producing only active power. In
this work, we focus on co-optimizing energy storage for performing energy
arbitrage as well as local power factor correction. The joint optimization
problem is non-convex, but can be solved efficiently using a McCormick
relaxation along with penalty-based schemes. Using numerical simulations on
real data and realistic storage profiles, we show that energy storage can
correct PF locally without reducing arbitrage profit. It is observed that
active and reactive power control is largely decoupled in nature for performing
arbitrage and PF correction (PFC). Furthermore, we consider a real-time
implementation of the problem with uncertain load, renewable and pricing
profiles. We develop a model predictive control based storage control policy
using auto-regressive forecast for the uncertainty. We observe that PFC is
primarily governed by the size of the converter and therefore, look-ahead in
time in the online setting does not affect PFC noticeably. However, arbitrage
profit are more sensitive to uncertainty for batteries with faster ramp rates
compared to slow ramping batteries.
