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学者姓名：江昌旭
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The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node-edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method's effectiveness in this study.
Keyword ：
active distribution network active distribution network deep deterministic policy gradient deep deterministic policy gradient deep reinforcement learning deep reinforcement learning dynamic reconfiguration dynamic reconfiguration graph attention network graph attention network
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| GB/T 7714 | Guo, Chen , Jiang, Changxu , Liu, Chenxi . Dynamic Reconfiguration Method of Active Distribution Networks Based on Graph Attention Network Reinforcement Learning [J]. | ENERGIES , 2025 , 18 (8) . |
| MLA | Guo, Chen 等. "Dynamic Reconfiguration Method of Active Distribution Networks Based on Graph Attention Network Reinforcement Learning" . | ENERGIES 18 . 8 (2025) . |
| APA | Guo, Chen , Jiang, Changxu , Liu, Chenxi . Dynamic Reconfiguration Method of Active Distribution Networks Based on Graph Attention Network Reinforcement Learning . | ENERGIES , 2025 , 18 (8) . |
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The active distribution network (ADN) integrates a substantial amount of renewable energy sources (RESs), which exhibits considerable variability and uncertainty. To solve the challenges of renewable energy volatility and the lack of intelligence and flexibility in traditional distribution networks, this paper firstly constructs a bi-level optimization model for the dynamic reconfiguration of ADNs, incorporating soft open points (SOPs) and energy storage systems (ESSs). The mathematical model formulated in this paper is inherently characterized by its high-dimensionality, complexity, non-linearity, and stochastic optimization nature. Secondly, a double deep Q network algorithm embedded with physical knowledge (PK-DDQN) is developed to resolve the constructed model accurately and rapidly. The upper-level model optimizes the topology of the distribution network using the double deep Q network algorithm. In contrast, the lower-level model employs second-order cone programming to optimize the operation of ADNs incorporating SOPs and ESSs. This divide-and-conquer approach enhances the solution's efficiency. Finally, the superiority and scalability of the proposed algorithm are verified on the modified IEEE 33 and 69-bus distribution systems. The simulation results demonstrate that compared with the genetic algorithm (GA), the power losses are reduced by 3.77% and 23.47%, and the voltage deviations are reduced by 28.63% and 23.41%, respectively. Additionally, compared with the mixed-integer second-order cone programming (MISOCP), the computational efficiency is increased by 21.84 times and 36.15 times.
Keyword ：
Active distribution network Active distribution network Deep reinforcement learning Deep reinforcement learning Dynamic reconfiguration Dynamic reconfiguration Energy storage systems Energy storage systems Physical knowledge Physical knowledge Soft open points Soft open points
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| GB/T 7714 | Zhan, Hua , Jiang, Changxu , Lin, Junchi . A novel dynamic reconfiguration approach for active distribution networks with soft open points and energy storage systems [J]. | ENERGY REPORTS , 2025 , 13 : 1875-1887 . |
| MLA | Zhan, Hua 等. "A novel dynamic reconfiguration approach for active distribution networks with soft open points and energy storage systems" . | ENERGY REPORTS 13 (2025) : 1875-1887 . |
| APA | Zhan, Hua , Jiang, Changxu , Lin, Junchi . A novel dynamic reconfiguration approach for active distribution networks with soft open points and energy storage systems . | ENERGY REPORTS , 2025 , 13 , 1875-1887 . |
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为准确计算电动汽车充电需求,提出考虑站间交互影响的高速公路电动汽车动态充电需求模型.基于此,构建以充电设施投资成本和用户排队等待时间为目标,以充电站以及充电桩数量、站间距离、用户充电需求为约束的高速公路充电站多目标布局规划模型.为快速求解该模型,建立最短路径下候选站址集合,并将充电站间距离约束由较强的非线性约束转换为整数线性规划,采用快速非支配排序多目标遗传算法和证据推理方法进行求解.最后,以31节点沈海高速福建段实际出行数据为例对所提出的模型进行仿真验证.
Keyword ：
充电装置 充电装置 充电需求 充电需求 新能源 新能源 电动汽车 电动汽车 高速公路规划 高速公路规划
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| GB/T 7714 | 江昌旭 , 卢玥君 , 林铮 et al. 考虑站间交互影响的高速公路充电站布局规划 [J]. | 太阳能学报 , 2025 , 46 (4) : 11-21 . |
| MLA | 江昌旭 et al. "考虑站间交互影响的高速公路充电站布局规划" . | 太阳能学报 46 . 4 (2025) : 11-21 . |
| APA | 江昌旭 , 卢玥君 , 林铮 , 袁羽娟 , 郑文迪 . 考虑站间交互影响的高速公路充电站布局规划 . | 太阳能学报 , 2025 , 46 (4) , 11-21 . |
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To accurately calculate the charging demand of electric vehicles (EVs), a dynamic charging demand model of EVs on highway is proposed, which considers the interaction between stations. On this basis, a charging station layout planning model is constructed to minimize the investment cost of charging facilities and the waiting time of users in the queue, with the number of charging stations and piles, the distance between stations, and the charging demand of users as constraints. To solve the model quickly, this paper establishes a set of candidate sites under the shortest path and then linearizes the distance constraint between charging stations from nonlinear to integer linear programming. Moreover, the non-dominated sorting multi-objective genetic algorithm Ⅱ (NSGA-Ⅱ) and evidence reasoning are adopted to solve the proposed models. Finally, the proposed model is simulated and verified in the actual travel data of the Fujian section of the 31-node Shenhai Expressway. © 2025 Science Press. All rights reserved.
Keyword ：
Genetic algorithms Genetic algorithms Highway administration Highway administration Integer linear programming Integer linear programming Integer programming Integer programming Multiobjective optimization Multiobjective optimization
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| GB/T 7714 | Changxu, Jiang , Yuejun, Lu , Zheng, Lin et al. LAYOUT PLANNING OF EXPRESSWAY CHARGING STATIONS CONSIDERING INTERACTION BETWEEN CHARGING STATIONS [J]. | Acta Energiae Solaris Sinica , 2025 , 46 (4) : 11-21 . |
| MLA | Changxu, Jiang et al. "LAYOUT PLANNING OF EXPRESSWAY CHARGING STATIONS CONSIDERING INTERACTION BETWEEN CHARGING STATIONS" . | Acta Energiae Solaris Sinica 46 . 4 (2025) : 11-21 . |
| APA | Changxu, Jiang , Yuejun, Lu , Zheng, Lin , Yujuan, Yuan , Wendi, Zheng . LAYOUT PLANNING OF EXPRESSWAY CHARGING STATIONS CONSIDERING INTERACTION BETWEEN CHARGING STATIONS . | Acta Energiae Solaris Sinica , 2025 , 46 (4) , 11-21 . |
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With the rapid development of dual-carbon targets, many distributed power sources, represented by wind power and photovoltaics, are being connected to distribution networks. This will further exacerbate the intermittency and volatility of power output. Dynamic reconfiguration of active distribution networks constitutes a complex, high-dimensional, mixed-integer, nonlinear, and stochastic optimization problem. Traditional algorithms exhibit numerous shortcomings in addressing this issue. By integrating the advantages of both deep learning and reinforcement learning, the deep reinforcement learning algorithm is highly suitable for formulating dynamically reconfigurable strategies for active distribution networks, which are currently of great concern. This paper first summarizes the characteristics of the active distribution network of the new generation power system, and analyzes the progress and challenges of the current research on the dynamic reconfiguration of the active distribution network in mathematical models. Secondly, the coding method of the distribution network dynamic reconfiguration is discussed, and the deep reinforcement learning algorithm is systematically reviewed. Furthermore, the shortcomings of the existing algorithms in dealing with the dynamic reconfiguration of the active distribution network are analyzed, and the research status and advantages of the deep reinforcement learning algorithm in the dynamic reconfiguration of the active distribution network are summarized. Finally, the future research directions for the dynamic reconfiguration of active distribution networks are presented. © 2025 Science Press. All rights reserved.
Keyword ：
Active learning Active learning DC distribution systems DC distribution systems Deep learning Deep learning Deep reinforcement learning Deep reinforcement learning Learning algorithms Learning algorithms Network coding Network coding Power distribution networks Power distribution networks Reinforcement learning Reinforcement learning
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| GB/T 7714 | Jiang, Changxu , Guo, Chen , Liu, Chenxi et al. Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning [J]. | High Voltage Engineering , 2025 , 51 (4) : 1801-1816 . |
| MLA | Jiang, Changxu et al. "Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning" . | High Voltage Engineering 51 . 4 (2025) : 1801-1816 . |
| APA | Jiang, Changxu , Guo, Chen , Liu, Chenxi , Lin, Junjie , Shao, Zhenguo . Review of Active Distribution Network Dynamic Reconfiguration Based on Deep Reinforcement Learning . | High Voltage Engineering , 2025 , 51 (4) , 1801-1816 . |
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可再生能源具有较强的波动性和随机性，其大规模接入配电网将严重威胁系统安全稳定运行。基于风-光-储多能协同的动态优化方法展现出显著优势，但此方法因涉及随机优化理论与深度强化学习等前沿领域，存在内容复杂、多学科交叉性强等教学难点。为了有效改善传统教学实验的不足，激发学生创新思维和多学科交叉应用能力，该文搭建了基于风-光-储多能协同的主动配电网动态优化实验教学平台，构建了含可再生能源主动配电网双层随机动态优化模型，提出了一种嵌入数学模型的双深度Q网络算法，并从多个角度详细分析和比较了仿真实验结果。通过该实验平台建设和案例设计，有助于提升学生对主动配电网动态优化方法的认知与理解，显著提升其多学科知识融合能力与复杂工程问题解决能力，为新工科背景下培养高水平人才提供了有效实践载体。
Keyword ：
主动配电网 主动配电网 可再生能源 可再生能源 实验平台 实验平台 风-光-储多能协同 风-光-储多能协同
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| GB/T 7714 | 江昌旭 , 庄鹏威 , 林俊杰 et al. 基于风-光-储多能协同的主动配电网动态优化实验平台设计 [J]. | 实验技术与管理 , 2025 , 42 (05) : 105-114 . |
| MLA | 江昌旭 et al. "基于风-光-储多能协同的主动配电网动态优化实验平台设计" . | 实验技术与管理 42 . 05 (2025) : 105-114 . |
| APA | 江昌旭 , 庄鹏威 , 林俊杰 , 郑文迪 , 邵振国 . 基于风-光-储多能协同的主动配电网动态优化实验平台设计 . | 实验技术与管理 , 2025 , 42 (05) , 105-114 . |
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随着双碳目标的快速发展,大量以风电、光伏为代表的分布式电源接入配电网,这将进一步加剧电源出力的间歇性与波动性.主动配电网动态重构属于一个复杂的高维混合整数非线性随机优化问题,传统算法在解决该问题的过程中存在着诸多不足之处.而深度强化学习算法结合了深度学习与强化学习的优势,非常适用于制定当前备受关注的主动配电网动态重构策略.该文首先对新型电力系统主动配电网特征进行总结,并对当前主动配电网动态重构研究在构建数学模型方面所取得的进展以及所面临的挑战进行了深入分析.其次,对配电网动态重构编码方式进行了探讨,并对深度强化学习算法进行了系统性地综述.进而,重点分析了现有算法在处理主动配电网动态重构时的不足之处,并对深度强化学习算法在主动配电网动态重构方面的研究现状与优势进行了总结与概括.最后,对主动配电网动态重构的未来研究方向进行了展望.
Keyword ：
主动配电网 主动配电网 人工智能 人工智能 动态重构 动态重构 机器学习 机器学习 深度强化学习 深度强化学习 编码方式 编码方式
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| GB/T 7714 | 江昌旭 , 郭辰 , 刘晨曦 et al. 基于深度强化学习的主动配电网动态重构综述 [J]. | 高电压技术 , 2025 , 51 (4) : 1801-1816,中插16-中插20 . |
| MLA | 江昌旭 et al. "基于深度强化学习的主动配电网动态重构综述" . | 高电压技术 51 . 4 (2025) : 1801-1816,中插16-中插20 . |
| APA | 江昌旭 , 郭辰 , 刘晨曦 , 林俊杰 , 邵振国 . 基于深度强化学习的主动配电网动态重构综述 . | 高电压技术 , 2025 , 51 (4) , 1801-1816,中插16-中插20 . |
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Emergency control is essential for maintaining the stability of power systems, serving as a key defense mechanism against the destabilization and cascading failures triggered by faults. Under-voltage load shedding is a popular and effective approach for emergency control. However, with the increasing complexity and scale of power systems and the rise in uncertainty factors, traditional approaches struggle with computation speed, accuracy, and scalability issues. Deep reinforcement learning holds significant potential for the power system decision-making problems. However, existing deep reinforcement learning algorithms have limitations in effectively leveraging diverse operational features, which affects the reliability and efficiency of emergency control strategies. This paper presents an innovative approach for real-time emergency voltage control strategies for transient stability enhancement through the integration of edge-graph convolutional networks with reinforcement learning. This method transforms the traditional emergency control optimization problem into a sequential decision-making process. By utilizing the edge-graph convolutional neural network, it efficiently extracts critical information on the correlation between the power system operation status and node branch information, as well as the uncertainty factors involved. Moreover, the clipped double Q-learning, delayed policy update, and target policy smoothing are introduced to effectively solve the issues of overestimation and abnormal sensitivity to hyperparameters in the deep deterministic policy gradient algorithm. The effectiveness of the proposed method in emergency control decision-making is verified by the IEEE 39-bus system and the IEEE 118-bus system.
Keyword ：
Deep reinforcement learning Deep reinforcement learning Edge graph convolutional network Edge graph convolutional network Emergency control Emergency control Transient stability Transient stability
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| GB/T 7714 | Jiang, Changxu , Liu, Chenxi , Yuan, Yujuan et al. Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning [J]. | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 40 . |
| MLA | Jiang, Changxu et al. "Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning" . | SUSTAINABLE ENERGY GRIDS & NETWORKS 40 (2024) . |
| APA | Jiang, Changxu , Liu, Chenxi , Yuan, Yujuan , Lin, Junjie , Shao, Zhenguo , Guo, Chen et al. Emergency voltage control strategy for power system transient stability enhancement based on edge graph convolutional network reinforcement learning . | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 40 . |
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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 active distribution network deep deterministic policy gradient deep deterministic policy gradient Distribution network reconfiguration Distribution network reconfiguration Distribution networks Distribution networks Encoding Encoding Heuristic algorithms Heuristic algorithms Load modeling Load modeling Mathematical models Mathematical models multi-agent deep reinforcement learning multi-agent deep reinforcement learning Optimization Optimization Power system dynamics Power system dynamics Power system stability Power system stability Real-time systems Real-time systems Uncertainty Uncertainty
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| GB/T 7714 | Jiang, Changxu , Lin, Zheng , Liu, Chenxi et al. MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy [J]. | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2024 , 9 (6) : 143-155 . |
| MLA | Jiang, Changxu et al. "MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy" . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 9 . 6 (2024) : 143-155 . |
| APA | Jiang, Changxu , Lin, Zheng , Liu, Chenxi , Chen, Feixiong , Shao, Zhenguo . MADDPG-Based Active Distribution Network Dynamic Reconfiguration with Renewable Energy . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2024 , 9 (6) , 143-155 . |
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In the background of carbon neutrality and carbon peaking, provinces are actively promoting the construction of new-type power system. However, there is currently a lack of quantitative methods for evaluating the construction of new-type power system. In response to this issue, this paper proposes an evaluation method for statistical index of new-type power system based on hierarchical evidential reasoning. Firstly, the subjective and objective weights for the indicators are obtained through analytic hierarchy process and entropy weight method, and the combined weight is obtained through game theory as the final weight of the indicators. Then, the evaluation of each indicator is fused through hierarchical evidential reasoning. Subsequently, using the constructed statistical index system and indicator data of the new-type power system, simulation verification is conducted to quantitatively assess the construction status of the new-type power system in each province. Finally, by comparing and analyzing the construction of new-type power system in Zhejiang, Qinghai, and Fujian provinces, the future development directions of new-type power system construction in Fujian province are suggested. © 2024 IEEE.
Keyword ：
Analytic hierarchy process Analytic hierarchy process Carbon Carbon Entropy Entropy Game theory Game theory Statistics Statistics
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| GB/T 7714 | Wu, Xiangyu , Xun, Chao , Fu, Li et al. An Evaluation Method for Statistical Index of New-type Power System Based on Hierarchical Evidential Reasoning [C] . 2024 : 462-471 . |
| MLA | Wu, Xiangyu et al. "An Evaluation Method for Statistical Index of New-type Power System Based on Hierarchical Evidential Reasoning" . (2024) : 462-471 . |
| APA | Wu, Xiangyu , Xun, Chao , Fu, Li , Jiang, Xiaofu , Zhuang, Pengwei , Xu, Xun et al. An Evaluation Method for Statistical Index of New-type Power System Based on Hierarchical Evidential Reasoning . (2024) : 462-471 . |
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