Game Theoretic Resilience Recommendation Framework for CyberPhysical Microgrids Using Hypergraph MetaLearning

TL;DR

This work addresses the resilience of cyber-physical microgrids under coordinated cyberattacks by integrating a Hypergraph Neural Network (HGNN) with Model-Agnostic Meta-Learning (MAML) to rapidly adapt attacker predictions. A distributed defender is optimized via NSGA-II with an ADMM feasibility layer, framed as a Stackelberg game to jointly balance load served, operational cost, and voltage stability while enforcing AC constraints. Key contributions include probabilistic vulnerability assessment, feeder-aware generalized resilience rules, and end-to-end validation on IEEE 69-, 123-, and 300-bus systems, demonstrating scalability and actionable guidance such as pre-arming critical tie-switches to protect Feeder 2. The framework’s end-to-end pipeline—from topology-aware attacker modeling to Pareto-optimal defense and Monte Carlo-based recommendations—offers a practical, system-level approach for real-world cyber-physical microgrid resilience and decision support.

Abstract

This paper presents a physics-aware cyberphysical resilience framework for radial microgrids under coordinated cyberattacks. The proposed approach models the attacker through a hypergraph neural network (HGNN) enhanced with model agnostic metalearning (MAML) to rapidly adapt to evolving defense strategies and predict high-impact contingencies. The defender is modeled via a bi-level Stackelberg game, where the upper level selects optimal tie-line switching and distributed energy resource (DER) dispatch using an Alternating Direction Method of Multipliers (ADMM) coordinator embedded within the Non-dominated Sorting Genetic Algorithm II (NSGA-II). The framework simultaneously optimizes load served, operational cost, and voltage stability, ensuring all post-defense states satisfy network physics constraints. The methodology is first validated on the IEEE 69-bus distribution test system with 12 DERs, 8 critical loads, and 5 tie-lines, and then extended to higher bus systems including the IEEE 123-bus feeder and a synthetic 300-bus distribution system. Results show that the proposed defense strategy restores nearly full service for 90% of top-ranked attacks, mitigates voltage violations, and identifies Feeder 2 as the principal vulnerability corridor. Actionable operating rules are derived, recommending pre-arming of specific tie-lines to enhance resilience, while higher bus system studies confirm scalability of the framework on the IEEE 123-bus and 300-bus systems.

Figures (6)

• Figure 1: Proposed cyber–physical resilience framework integrating physical and cyber layers with attacker modeling, defender optimization, game-theoretic analysis, and Monte Carlo–based recommendation extraction.
• Figure 2: Single-line diagram of the IEEE 69-bus distribution network
• Figure 3: Load served for the top vulnerable lines: post-attack vs. post-defense.
• Figure 4: Attacker ranking (normalized 0–100). Highest-risk lines concentrate on Feeder 2.
• Figure 5: Left: top vulnerable lines. Right: most-used defensive ties across those attacks.