SNA: A Network-Aware Framework for Decentralized Inverter-Based Voltage Control

Overview

This project develops a Scalable Network-Aware (SNA) learning framework for decentralized secondary voltage control in inverter-dominated distribution grids. Instead of relying on centralized critics that ingest global states/actions, SNA leverages the grid’s network structure so each critic only depends on local and κ-hop neighbor information, while actors are guided by their neighbors’ critics. This respects physical coupling, reduces input dimensionality, and preserves cooperation—enabling practical learning-based voltage regulation in large systems.

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Why This Research

• Operational pressure is rising. Variable renewables and prosumers cause fast, uncertain power fluctuations that defeat traditional capacitor/regulator-based schemes and leave primary droop control with steady-state voltage deviations. Decentralized secondary control is needed to restore nominal voltages at scale.
• Dominant MARL training is not scalable. Centralized Training & Decentralized Execution (CTDE) makes each critic depend on global observations/actions; dimension grows with the number of agents, causing sample inefficiency and poor training in large grids.
• Untapped prior: grid topology. Voltage control naturally has local interactions on the network. If we exploit this locality, we can cut critic inputs without losing the signal actors need to cooperate.
• Goal. Achieve scalable, theoretically justified decentralized voltage control that keeps voltages within limits under stochastic loads and model imperfections, while requiring only neighbor-level information exchange during training.

New Measures

• Networked-MDP formulation of voltage control. The grid is modeled so that a DG’s next-state distribution depends only on its neighbors, aligning with EMTP-style discretization where interactions are effectively local.
• Exponential-decay property ⇒ truncated critics. We prove that an agent’s Q-function sensitivity to distant nodes decays exponentially with graph distance; hence a critic that only sees κ-hop observations/actions approximates the true Q with a bounded error $\le c \rho^{\kappa+1}$.
• Distributed actor guidance. Each actor is updated using the sum of its neighbors’ truncated critics, promoting local cooperation that aligns with global welfare—without any global critic.
• Algorithm-agnostic integration. SNA plugs into standard multi-agent actor-critic methods (e.g., MASAC, MATD3) by replacing the critic inputs and actor objectives accordingly.
• Communication-efficient training. Training requires only neighbor exchanges (tunable by κ), reducing bandwidth and improving privacy, with κ=1 often sufficient in practice.

Impact

• Scalability from tens to 100+ DGs. SNA trains effectively on systems with up to 114 DGs, surpassing prior decentralized voltage-control studies limited to ≈40 agents.
• Better training performance at scale. Compared to CTDE baselines, SNA attains higher final training rewards as agent count grows (e.g., 84/114 DGs), evidencing improved sample efficiency and critic learnability.
• Voltage quality improvements. Under identical disturbances, SNA reduces voltage-magnitude excursions versus PI and CTDE-trained policies; the proportion of nodes exceeding ±0.05 pu shrinks notably in large cases.
• Tunable locality for accuracy vs. complexity. Increasing κ tightens the approximation but enlarges inputs; on 114-DG systems, κ=2 can improve rewards, while κ=3 may hurt due to dimensionality—guiding practical κ selection.
• Plug-and-play across algorithms. Integrating SNA with MATD3 similarly boosts final rewards, showing method generality beyond MASAC.
• Deployment-friendly. Because SNA relies on neighbor-level data and distributed training, it aligns with realistic utility communications, easing field integration.

Publications