| Citation:Changxu Jiang,Zheng Lin,Chenxi Liu,Feixiong Chen,Zhenguo Shao.MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy[J].Protection and Control of Modern Power Systems,2024,V9(6):143-155[Copy] |
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| 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. |
| Key words: Distribution network reconfiguration, active distribution network, deep deterministic policy gradient, multi-agent deep reinforcement learning. |
| DOI：10.23919/PCMP.2023.000283 |
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| Fund:This work is supported by the Natural Science Foundation of Fujian Province (No. 2022J0512 and No. 2021J05134), and the National Natural Science Foundation of China (No. 52377087). |
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