Security defense strategy for distribution-microgrids based on dynamic programming

奚泽立; 陈聪; 杨新森; 詹细妹; 郭国伟; 张乾

doi:10.11930/j.issn.1004-9649.202507101

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Security defense strategy for distribution-microgrids based on dynamic programming

更新时间：2026-02-03

- Security defense strategy for distribution-microgrids based on dynamic programming
- Vol. 59, Issue 1, Pages: 97-104(2026)
- 作者机构：
  
  1. 广东电网有限责任公司佛山顺德供电局,广东,佛山,528300
  2. 武汉大学电气与自动化学院,湖北,武汉,430000
- 作者简介：
- 基金信息：
- DOI：10.11930/j.issn.1004-9649.202507101
  CLC：
- Published Online：13 January 2026，
  
  Published：13 January 2026
- 稿件说明：
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奚泽立, 陈聪, 杨新森, 詹细妹, 郭国伟, 张乾. 基于动态规划的配微网安全防御策略[J]. 中国电力, 2026, 59(1): 97-104.

奚泽立, 陈聪, 杨新森, et al. Security defense strategy for distribution-microgrids based on dynamic programming[J]. 2026, 59(1): 97-104.
奚泽立, 陈聪, 杨新森, 詹细妹, 郭国伟, 张乾. 基于动态规划的配微网安全防御策略[J]. 中国电力, 2026, 59(1): 97-104. DOI： 10.11930/j.issn.1004-9649.202507101.

奚泽立, 陈聪, 杨新森, et al. Security defense strategy for distribution-microgrids based on dynamic programming[J]. 2026, 59(1): 97-104. DOI： 10.11930/j.issn.1004-9649.202507101.

摘要

基于信息物理融合结构的配微协同新型配电系统，为多能互补的能源优化调度提供了新的技术路径。然而，多种能源出力的强不确定性以及未知扰动导致发电难以精准调控，严重威胁系统的安全稳定运行。为应对这一挑战，提出了一种融合非对称结构建模的动态规划最优安全防御策略。将未知不确定性扰动与防御侧的非对称结构问题统一建模为非合作博弈框架，并利用自适应评判学习网络进行求解，实现防御资源的最优分配。仿真结果表明：所提策略能有效缓解非对称结构下的不确定性安全风险。

Abstract

The new distribution system based on the information-physical fusion structure provides a new technical path for the optimization and dispatch of multi-energy complementary energy

the strong uncertainty of multi-energy output and unknown disturbances make it difficult for power generation to be precisely regulated

which seriously threatens the safe and stable operation of the system. address this challenge

an optimal security defense strategy with dynamic programming is proposed by integrating asymmetric structure modeling. The unknown uncertainty disturbance and the asymmetric structure problem on the defense side are unified into non-cooperative game framework and are solved by using an adaptive evaluation learning network

which implements the optimal allocation of defense resources. Simulation results show that the proposed strategy can effectively the uncertainty security risk under asymmetric structure.

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references

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