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author:

Jiang, Changxu (Jiang, Changxu.) [1] (Scholars:江昌旭) | Lin, Zheng (Lin, Zheng.) [2] | Liu, Chenxi (Liu, Chenxi.) [3] | Chen, Feixiong (Chen, Feixiong.) [4] (Scholars:陈飞雄) | Shao, Zhenguo (Shao, Zhenguo.) [5] (Scholars:邵振国)

Indexed by:

EI Scopus SCIE

Abstract:

The integration of distributed generations (DG), such as wind turbines and photovoltaics, has a significant impact on the security, stability, and economy of the distribution network due to the randomness and fluctuations of DG output. Dynamic distribution network reconfiguration (DNR) technology has the potential to mitigate this problem effectively. However, due to the non-convex and nonlinear characteristics of the DNR model, traditional mathematical optimization algorithms face speed challenges, and heuristic algorithms struggle with both speed and accuracy. These problems hinder the effective control of existing distribution networks. To address these challenges, an active distribution network dynamic reconfiguration approach based on an improved multi-agent deep deterministic policy gradient (MADDPG) is proposed. Firstly, taking into account the uncertainties of load and DG, a dynamic DNR stochastic mathematical model is constructed. Next, the concept of fundamental loops (FLs) is defined and the coding method based on loop-coding is adopted for MADDPG action space. Then, the agents with actor and critic networks are equipped in each FL to real-time control network topology. Subsequently, a MADDPG framework for dynamic DNR is constructed. Finally, simulations are conducted on an improved IEEE 33-bus power system to validate the superiority of MADDPG. The results demonstrate that MADDPG has a shorter calculation time than the heuristic algorithm and mathematical optimization algorithm, which is useful for real-time control of DNR.

Keyword:

active distribution network deep deterministic policy gradient Distribution network reconfiguration Distribution networks Encoding Heuristic algorithms Load modeling Mathematical models multi-agent deep reinforcement learning Optimization Power system dynamics Power system stability Real-time systems Uncertainty

Community:

  • [ 1 ] [Jiang, Changxu]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 2 ] [Lin, Zheng]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 3 ] [Liu, Chenxi]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 4 ] [Chen, Feixiong]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China
  • [ 5 ] [Shao, Zhenguo]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China

Reprint 's Address:

  • [Shao, Zhenguo]Fuzhou Univ, Sch Elect Engn & Automat, Fuzhou 350108, Peoples R China;;

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Source :

PROTECTION AND CONTROL OF MODERN POWER SYSTEMS

ISSN: 2367-2617

Year: 2024

Issue: 6

Volume: 9

Page: 143-155

8 . 7 0 0

JCR@2023

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

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