A Resource Allocation Scheme for Energy Demand Management in 6G-enabled Smart Grid

TL;DR

This paper introduces a deep reinforcement learning-based resource allocation scheme for energy demand management in 6G-enabled smart grids, addressing challenges of data processing, latency, and security against false state injection attacks.

Contribution

It proposes a novel DRL-based resource allocation method for smart grid edge networks and develops a lightweight FSI detection mechanism to enhance security.

Findings

DRL effectively manages dynamic resource allocation in smart grids.

FSI attacks can significantly disrupt edge resource utilization.

The proposed detection mechanism successfully identifies false state injections.

Abstract

Smart grid (SG) systems enhance grid resilience and efficient operation, leveraging the bidirectional flow of energy and information between generation facilities and prosumers. For energy demand management (EDM), the SG network requires computing a large amount of data generated by massive Internet-of-things sensors and advanced metering infrastructure (AMI) with minimal latency. This paper proposes a deep reinforcement learning (DRL)-based resource allocation scheme in a 6G-enabled SG edge network to offload resource-consuming EDM computation to edge servers. Automatic resource provisioning is achieved by harnessing the computational capabilities of smart meters in the dynamic edge network. To enforce DRL-assisted policies in dense 6G networks, the state information from multiple edge servers is required. However, adversaries can "poison" such information through false state injection (FSI) attacks, exhausting SG edge computing resources. Toward addressing this issue, we investigate the impact of such FSI attacks with respect to abusive utilization of edge resources, and develop a lightweight FSI detection mechanism based on supervised classifiers. Simulation results demonstrate the efficacy of DRL in dynamic resource allocation, the impact of the FSI attacks, and the effectiveness of the detection technique.