As a novel energy management paradigm, the virtual power plant (VPP) enables the intelligent integration and optimization of distributed energy resources, playing a significant role in promoting renewable energy consumption, optimizing energy structures, and facilitating greener energy systems. Focusing on multi-energy VPPs with the objective of achieving regional energy consumption, this paper proposes a regional consumption optimization and scheduling method based on hierarchical deep reinforcement learning. First, a non-direct regional consumption operation framework for multi-energy VPPs is proposed, which ensures user autonomy in participation while avoiding the disclosure of private information. Second, a joint trading mechanism within the VPP is designed, considering multi-energy coupling and multi-timescale characteristics. This avoids trading failures caused by neglecting energy transmission constraints, enables flexible matching across different energy types, and enhances VPP revenues while realizing regional self-consumption. Finally, an optimization strategy based on hierarchical deep reinforcement learning is developed to overcome the challenges posed by the large-scale state-action space and sparse reward characteristics of the model. Simulation case studies validate the effectiveness of the proposed method, demonstrating that the scheduling strategy can effectively achieve regional self-consumption.