• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

ELECTRIC POWER CONSTRUCTION ›› 2022, Vol. 43 ›› Issue (12): 83-93.doi: 10.12204/j.issn.1000-7229.2022.12.009

• Energy Management and Scheduling • Previous Articles     Next Articles

A Novel Energy Management Method Based on Modified Deep Q Network Algorithm for Multi-park Integrated Energy System

XUE Mingfeng1, MAO Xiaobo1, XIAO Hao2(), PU Xiaowei2, PEI Wei2   

  1. 1. Wuxi Power Supply Company of State Grid Jiangsu Electric Power Co., Ltd., Wuxi 214000, Jiangsu Province, China
    2. Institute of Electrical Engineering, Chinese Academy of Sciences, Beijing 100190, China
  • Received:2022-06-30 Online:2022-12-01 Published:2022-12-06
  • Contact: XIAO Hao E-mail:xiaohao09@mail.iee.ac.cn
  • Supported by:
    State Grid Jiangsu Electric Power Co., Ltd. Research Program(J2021058);National Natural Science Foundation of China(52177124)


Multi-park integrated energy system can significantly improve the operation economy by complementing each other with multiple energy sources. However, the complex interactions between parks and multi-energy coupling decisions can bring challenging problems such as large decision space and difficult convergence of algorithms to the energy management of multi-park integrated energy system. To solve the above problems, an energy management method based on modified deep Q network (MDQN) algorithm for multi-park integrated energy systems is proposed. Firstly, the external meteorological data and historical interactive power data independent of the park are used to construct a long short-term memory (LSTM) deep network-based external interactive environmental equivalence model for each park integrated energy system, which reduces the computational complexity of the reinforcement learning reward function. Secondly, an improved DQN algorithm based on k-first sampling strategy is proposed to replace the greedy strategy with k-first sampling strategy to overcome the inefficiency of exploration in large-scale action spaces. Finally, the results are validated in an algorithm containing three integrated energy systems in the park, and show that the MDQN algorithm has better convergence and stability compared with the original DQN algorithm, while it can improve the economic efficiency of the park by 29.16%.

Key words: park integrated energy system, deep reinforcement learning, energy management, modified deep Q network (MDQN) algorithm

CLC Number: