电力系统保护与控制2025，Vol.53Issue(20)：153-163,11.DOI:10.19783/j.cnki.pspc.241453
基于分层深度强化学习的多能虚拟电厂区域消纳优化策略
Regional consumption optimization strategy for multi-energy virtual power plants based on hierarchical deep reinforcement learning
摘要
Abstract
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.关键词
虚拟电厂/多能交易/多时间尺度/分层深度强化学习Key words
virtual power plant/multi-energy trading/multi-timescale/hierarchical deep reinforcement learning引用本文复制引用
张宁,杨凌霄,李炫浓,胡存刚,孙秋野..基于分层深度强化学习的多能虚拟电厂区域消纳优化策略[J].电力系统保护与控制,2025,53(20):153-163,11.基金项目
This work is supported by the National Natural Science Foundation of China(No.62203004 and No.62303006). 国家自然科学基金项目资助(62203004,62303006) （No.62203004 and No.62303006）
