  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.
