Edge-native intelligent scheduling for virtual power plants: A multi-scale perception and constrained reinforcement learning approach

Abstract

The proliferation of distributed energy resources at the edge of distribution networks provides substantial flexibility for virtual power plant (VPP) operation. However, existing methods often rely on aggregate load information and homogeneous scheduling policies. They, therefore, overlook device-specific response characteristics, heterogeneous response times, and operational safety constraints. This paper presents EDGE-VPP, an end-to-end scheduling framework that connects fine-grained load perception with safety-aware decision-making across multiple temporal scales. At the perception layer, a Load Decomposition Transformer (LDT) uses learnable multi-frequency positional encodings and device-specific attention heads. It jointly detects appliance states and disaggregates device power from aggregate measurements. At the coordination layer, a three-tier cloud–edge–device architecture assigns sub-second emergency response to devices, minute-level economic dispatch to edge controllers, and hour-ahead planning to the cloud. Bidirectional information exchange mitigates conflicts among these control layers. At the optimization layer, multi-constraint proximal policy optimization factorizes continuous and discrete actions. Adaptive Lagrange multipliers enforce voltage and current limits, while two value estimators stabilize policy learning. Experiments on REDD, UK-DALE, and a self-constructed VPP dataset show that LDT reduces mean absolute error by up to 6.86% and improves the F1-score by 3.51% over the Transformer baseline. The complete EDGE-VPP framework also achieves the lowest operating cost and the fewest constraint violations among the evaluated scheduling methods.