mlsanityData to 14 Oct 2026

Deep Reinforcement Learning for Voltage Control and Renewable Accommodation Using Spatial-Temporal Graph Information

arXiv 2401.15848 ↗First posted 29 Jan 2024eess.SY cs.SYcited by 1

On the map: Deep reinforcement learning methods → RL for energy, networks and operations

Abstract (arXiv)

Renewable energy resources (RERs) have been increasingly integrated into distribution networks (DNs) for decarbonization. However, the variable nature of RERs introduces uncertainties to DNs, frequently resulting in voltage fluctuations that threaten system security and hamper the further adoption of RERs. To incentivize more RER penetration, we propose a deep reinforcement learning (DRL)-based strategy to dynamically balance the trade-off between voltage fluctuation control and renewable accommodation. To further extract multi-time-scale spatial-temporal (ST) graphical information of a DN, our strategy draws on a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN), consisting of ST attention mechanism and ST convolution to explore the node correlations in the spatial and temporal views. The continuous decision-making process of balancing such a trade-off can be modeled as a Markov decision process optimized by the deep deterministic policy gradient (DDPG) algorithm with the help of the derived ST information. We validate our strategy on the modified IEEE 33, 69, and 118-bus radial distribution systems, with outcomes significantly outperforming the optimization-based benchmarks. Simulations also reveal that our developed MG-ASTGCN can to a great extent accelerate the convergence speed of DDPG and improve its performance in stabilizing node voltage in an RER-rich DN. Moreover, our method improves the DN's robustness in the presence of generator failures.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for energy, networks and operations, one of 15 directions in Deep reinforcement learning methods. Builds on 15 of its 52 references that are in the map. First posted on arXiv 29 Jan 2024.

Builds on

15 of 52 references.

References · 15

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  2. 2017Proximal Policy Optimization Algorithmscited by 15,215
  3. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
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  7. 2016Sample Efficient Actor-Critic with Experience Replaycited by 188
  8. 2019Two-Timescale Voltage Control in Distribution Grids Using Deep Reinforcement Learningcited by 20
  9. 2020Deep Reinforcement Learning Based Volt-VAR Optimization in Smart Distribution Systemscited by 17
  10. 2020Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networkscited by 12
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  12. 2013Distributed reactive power feedback control for voltage regulation and loss minimizationcited by 3
3 more
  1. 2020Model-Free Voltage Regulation of Unbalanced Distribution Network Based on Surrogate Model and Deep Reinforcement Learningcited by 3
  2. 2014Photovoltaic Inverter Controllers Seeking AC Optimal Power Flow Solutionscited by 2
  3. 2017Linear/Quadratic Programming-Based Optimal Power Flow using Linear Power Flow and Absolute Loss Approximationscited by 1

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Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024Graph Reinforcement Learning for Power Grids: A Comprehensive Surveycited by 7

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  4. 2026Utilizing Adversarial Training for Robust Voltage Control: An Adaptive Deep Reinforcement Learning Method
  5. 2024End-to-End Reinforcement Learning of Curative Curtailment with Partial Measurement Availability
  6. 2025Distributed Reinforcement Learning using Local Smart Meter Data for Voltage Regulation in Distribution Networks
  7. 2024Learning a local trading strategy: deep reinforcement learning for grid-scale renewable energy integration
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