Abstract:

Information freshness conducts an important impact on the training accuracy of the distributed energy dispatching and control model.Poor dispatching and control information freshness will increase the loss function of the training model, reduce the reliability and economy of dispatching and control, and effect the real-time balance of energy supply and demand.Simplified power Internet of things can provide plug-and-play and multi-mode fusion communication support for distributed energy dispatching and control, but it still faces challenges of the inadaptability between cross-domain resource optimization and model training, and the difficulty in guaranteeing dispatching and control information freshness.To solve the above challenges, an information freshness aware-based communication-and-computation collaborative optimization algorithm (IFAC³O) was proposed, and the information freshness deviation was regulated by the awareness of deficit virtual queue evolution.On this basis, IFAC³O leveraged deep Q network and dispatching and control information freshness awareness to learn the channel allocation and batch size joint optimization strategy, thereby minimizing model loss function while guaranteeing long-term dispatching and control information freshness constraints.Compared with the federated DRL based low-latency resource allocation algorithm and adaptive federated learning-based batch size optimization algorithm, IFAC³O can reduce global loss function by 63.29% and 38.88% as well as improve information freshness by 20.59% and 57.69%.

Key words: simplified power Internet of things, distributed energy dispatching and control, dispatching and control information freshness, multi-mode communication, cross-domain resource cooperation