mlsanityData to 7 Oct 2026

Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learning

arXiv 1904.09374 ↗First posted 19 Apr 2019eess.SY cs.SYcited by 15

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Abstract (arXiv)

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 distribution grids by judiciously coupling data-driven with physicsbased optimization. On a faster 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, on the slower timescale (e.g., every hour), shunt capacitors are configured to minimize the longterm discounted voltage deviations using a deep reinforcement learning algorithm. Extensive numerical tests on a real-world 47- bus distribution network as well as the IEEE 123-bus test feeder using real data corroborate the effectiveness of the novel scheme.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 19 Apr 2019.

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Citing papers per year: 2021: 5; 2022: 5; 2023: 1; 2024: 3; 2025: 1.

15 papers.

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  1. 2021Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challengescited by 40
  2. 2021Action Set Based Policy Optimization for Safe Power Grid Managementcited by 18
  3. 2021PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power Systemscited by 15
  4. 2024Central Limit Theorem for Two-Timescale Stochastic Approximation with Markovian Noise: Theory and Applicationscited by 7
  5. 2024Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Mapcited by 6
  6. 2021Risk-Aware Learning for Scalable Voltage Optimization in Distribution Gridscited by 5
  7. 2023Multi-Agent Reinforcement Learning: Methods, Applications, Visionary Prospects, and Challengescited by 5
  8. 2024Powerformer: A Section-adaptive Transformer for Power Flow Adjustmentcited by 5
  9. 2022A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challengescited by 3
  10. 2021Bi-level Off-policy Reinforcement Learning for Volt/VAR Control Involving Continuous and Discrete Devicescited by 2
  11. 2022Transfer Deep Reinforcement Learning-based Large-scale V2G Continuous Charging Coordination with Renewable Energy Sourcescited by 2
  12. 2025Reinforcement Learning to Initialize Newton-Raphson for AC Power Flow with Quantum Annealing-Based Environment Updatescited by 1
3 more
  1. 2022Reducing Learning Difficulties: One-Step Two-Critic Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0
  2. 2022Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failurescited by 0
  3. 2022Reducing Action Space: Reference-Model-Assisted Deep Reinforcement Learning for Inverter-based Volt-Var Controlcited by 0