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GRP-180-147 Quantum Augmented Microgrids (QuAM) Simulator

Nitin Jha, Prateek Paudel

Published Apr 22, 2026• 0 citations
OpenAlex

Abstract

Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. To substantiate the possible benefits of using quantum augmented microgrids, we simulate a practical high-impact classical attack: a traffic analysis and priority-action spoofing campaign that can (1) deanonymize the anonymous notification for a high-priority action, (2) force excessive key usage, and (3) induce harmful allow/block operations at the control level. We quantify how these attacks affect information leakage, spoof acceptance, key sufficiency, and operational outcomes such as latency, deadline misses, unserved energy, etc. This quantum augmented microgrid (QuAM) framework lets us evaluate trade-offs between privacy, availability, and the operational cost of mitigations (cover traffic, verification delays, and key-rotation policies), further paving the path for the study of more nuanced attacks that arise due to the use of quantum-classical integrated frameworks.

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Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. To substantiate the possible benefits of using quantum augmented microgrids, we simulate a practical high-impact classical attack: a traffic analysis and priority-action spoofing campaign that can (1) deanonymize the anonymous notification for a high-priority action, (2) force excessive key usage, and (3) induce harmful allow/block operations at the control level. We quantify how these attacks affect information leakage, spoof acceptance, key sufficiency, and operational outcomes such as latency, deadline misses, unserved energy, etc. This quantum augmented microgrid (QuAM) framework lets us evaluate trade-offs between privacy, availability, and the operational cost of mitigations (cover traffic, verification delays, and key-rotation policies), further paving the path for the study of more nuanced attacks that arise due to the use of quantum-classical integrated frameworks.
Small modular nuclear reactors (SMRs) are redefining the energy generation landscape by enabling the deployment of modular, scalable, and pre-built power units that can be used to build distributed autonomous microgrids for critical infrastructure and burgeoning AI factories. Often, these microgrids are linked together to provide a resilient, decentralized power generation infrastructure. Consequently, the cybersecurity of microgrids is of critical importance. In this work, we propose a quantum augmented network framework for resilient microgrids. We integrate the ideas of secure quantum networking, quantum anonymous notification, and quantum random number generation to strengthen the integrity, confidentiality, and privacy of microgrid networks. To substantiate the possible benefits of using quantum augmented microgrids, we simulate a practical high-impact classical attack: a traffic analysis and priority-action spoofing campaign that can (1) deanonymize the anonymous notification for a high-priority action, (2) force excessive key usage, and (3) induce harmful allow/block operations at the control level. We quantify how these attacks affect information leakage, spoof acceptance, key sufficiency, and operational outcomes such as latency, deadline misses, unserved energy, etc. This quantum augmented microgrid (QuAM) framework lets us evaluate trade-offs between privacy, availability, and the operational cost of mitigation (cover traffic, verification delays, and key-rotation policies), further paving the path for the study of more nuanced attacks that arise due to the use of quantum-classical integrated frameworks.
The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the presence of several common channel noises. In this work, we propose an improved quantum anonymous notification (QAN) protocol that utilizes rotation operations on shared GHZ states to produce an anonymous notification in an n-user quantum-augmented network. We study the behavior of this modified QAN protocol under the dephasing noise model and observe stronger resilience to false notifications than earlier QAN approaches. The QAN framework is also proposed to be integrated with a machine-learning classifier, an enhanced quantum-augmented network. Finally, we discuss how this notification layer integrates with QuANets so that receivers can allow switch-bypass handling of quantum payloads, reducing header-based information leakage and vulnerability to targeted interference at compromised switches.
The scalability of current quantum networks is limited due to noisy quantum components and high implementation costs, thereby limiting the security advantages that quantum networks provide over their classical counterparts. Quantum Augmented Networks (QuANets) address this by integrating quantum components in classical network infrastructure to improve robustness and end-to-end security. To enable such integration, Quantum Anonymous Notification (QAN) is a method to anonymously inform a receiver of an incoming quantum communication. Therefore, several quantum primitives will serve as core tools, namely, quantum voting, quantum anonymous protocols, quantum secret sharing, etc. However, all current quantum protocols can be compromised in the presence of several common channel noises. In this work, we propose an improved quantum anonymous notification (QAN) protocol that utilizes rotation operations on shared GHZ states to produce an anonymous notification in an n-user quantum-augmented network. We study the behavior of this modified QAN protocol under the dephasing noise model and observe stronger resilience to false notifications than earlier QAN approaches. The QAN framework is also proposed to be integrated with a machine-learning classifier, an enhanced quantum-augmented network. Finally, we discuss how this notification layer integrates with QuANets so that receivers can allow switch-bypass handling of quantum payloads, reducing header-based information leakage and vulnerability to targeted interference at compromised switches.
The integration of information and communication technologies has introduced cybersecurity challenges to microgrid (MG) apart from bringing more flexibility, reliability, and resilience. Especially in the upcoming quantum era, the existing cryptographic systems are vulnerable to attacks from quantum computers. Motivated by these challenges, this article proposes a quantum-key-distribution (QKD)-based MG distributed control framework for cybersecurity enhancement. To defend the side-channel attacks and make the framework practical enough for industrial applications, measurement-device-independent QKD (MDI-QKD) is introduced. The following contributions are made in this work. 1) A novel QKD-based quantum-secure control architecture is established to assure the data transmission security in MG distributed control. 2) MDI-QKD with asymmetric protocol is used to form a scalable QKD network for MG control. 3) A method for fast parameter optimization based on deep neural network (DNN) is proposed to adjust the parameters in QKD systems in real-time. Simulations are carried on a 13-bus MG to demonstrate the working principles of the proposed control scheme. The test results also prove the feasibility of the DNN-based parameter optimizer.
The modernization of distributed energy systems introduces complex cybersecurity challenges as grid infrastructures become increasingly digitized, decentralized, and web-connected. This paper presents a novel security architecture that integrates quantum key distribution (QKD) with semantic web technologies to provide end-to-end secure, scalable, and adaptive protection for distributed energy resource (DER) networks. The proposed framework features a modular system design, incorporating BB84-based QKD protocols for quantum-resilient key generation, a metadata-driven policy layer using OWL ontologies and SWRL reasoning, and a web interface for operator access and real-time monitoring. Extensive performance evaluation in a simulated multi-domain microgrid environment demonstrates that the system achieves an average key generation rate of 2.3 kbps with quantum bit error rate (QBER) maintained below 5.2% across 40 km optical links. Session establishment latency averaged 435 ms, 29.8% lower than a traditional TLS/PKI baseline, while semantic access validation achieved 100% accuracy in 42 adversarial test cases. These cases were evaluated using automated semantic validation scripts simulating spoofed roles, malformed sessions, and unauthorized requests. The system sustained encrypted throughput of 110 messages per second per node and maintained service continuity under quantum noise and cross-domain attack simulations. Usability trials with six engineers yielded a system usability scale (SUS) score of 88.3, and the average DER onboarding time was reduced from 10.1 to 5.5 minutes. These findings affirm that QKD-enhanced, semantically governed web architectures can provide strong cryptographic guarantees while supporting dynamic policy enforcement and intuitive user workflows. The proposed solution demonstrates a viable path for deploying future-proof security mechanisms in next-generation smart grid environments.
The integration of information and communication technology in the smart grid has introduced cybersecurity challenges to traditional communication solutions, which threatens data integrity and confidentiality in microgrid control. This paper proposed a measurement-device-independent quantum key distribution (MDI-QKD) based microgrid control architecture to ensure the security of control and measurement dataflows in microgrids. The following contributions are made in this work: 1) A novel quantum secure microgrid control architecture is proposed to enhance the cybersecurity in microgrid. 2) MDI-QKD with asymmetric protocols is offered in the QKD-enabled microgrid control process. 3) A method of fast parameter optimization based on neural network is presented to enable the QKD network to rapidly adapt the changes of communication environments. Simulation results show how the quantum secure microgrid control architecture works in an islanded microgrid with 4 distributed generation units, and the testing of neural network proves the feasibility of our method.