Modern distribution grids are currently being challenged by frequent and sizable voltage fluctuations, due mainly to the increasing deployment of electric vehicles and renewable generators. Existing approaches to maintaining bus voltage magnitudes within the desired region can cope with either traditional utility-owned devices (e.g., shunt capacitors), or contemporary smart inverters that come with distributed generation units (e.g., photovoltaic plants). The discrete on-off commitment of capacitor units is often configured on an hourly or daily basis, yet smart inverters can be controlled within milliseconds, thus challenging joint control of these two types of assets. In this context, a novel two-timescale voltage regulation scheme is developed for radial distribution grids by judiciously coupling data-driven with physics-based optimization. On a fast timescale, say every second, the optimal setpoints of smart inverters are obtained by minimizing instantaneous bus voltage deviations from their nominal values, based on either the exact alternating current power flow model or a linear approximant of it; whereas, at the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the long-term discounted voltage deviations using a deep reinforcement learning algorithm. Numerical tests on a real-world 47-bus distribution feeder using real data corroborate the effectiveness of the novel scheme.
from cs updates on arXiv.org http://bit.ly/2PpJYyA
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