Query：
学者姓名：江昌旭
Refining：
Year
Type
Indexed by
Source
Complex
Former Name
Co-
Language
Clean All
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 et al. "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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
Most of the existing electric vehicle (EV) charging navigation methods do not simultaneously take into account the electric vehicle charging destination optimization and path planning. Moreover, they are unable to provide online real-time decision-making under a variety of uncertain factors. To address these problems, this paper first establishes a bilevel stochastic optimization model for EV charging navigation considering various uncertainties, and then proposes an EV charging navigation method based on the hierarchical enhanced deep Q network (HEDQN) to solve the above stochastic optimization model in real-time. The proposed HEDQN contains two enhanced deep Q networks, which are utilized to optimize the charging destination and charging route path of EVs, respectively. Finally, the proposed method is simulated and validated in two urban transportation networks. The simulation results demonstrate that compared with the Dijkstra shortest path algorithm, single-layer deep reinforcement learning algorithm, and traditional hierarchical deep reinforcement learning algorithm, the proposed HEDQN algorithm can effectively reduce the total charging cost of electric vehicles and realize online realtime charging navigation of electric vehicles, that shows excellent generalization ability and scalability.
Keyword ：
Charging navigation Charging navigation Destination optimization Destination optimization Electric vehicle Electric vehicle Hierarchical reinforcement learning Hierarchical reinforcement learning Route planning Route planning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Jiang, Changxu , Zhou, Longcan , Zheng, J. H. et al. Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| MLA | Jiang, Changxu et al. "Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 157 (2024) . |
| APA | Jiang, Changxu , Zhou, Longcan , Zheng, J. H. , Shao, Zhenguo . Electric vehicle charging navigation strategy in coupled smart grid and transportation network: A hierarchical reinforcement learning approach . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 157 . |
| Export to | NoteExpress RIS BibTex |
Version ：
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 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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The dynamic reconfiguration of active distribution networks (ADNDR) essentially belongs to a complex high-dimensional mixed-integer nonlinear stochastic optimization problem. Traditional mathematical optimization algorithms tend to encounter issues like slow computational speed and difficulties in solving large-scale models, while heuristic algorithms are prone to fall into local optima. Furthermore, few scholars in the existing research on distribution network (DN) reconfiguration have considered the graph structure information, resulting in the loss of critical topological information and limiting the effect of optimization. Therefore, this paper proposes an ADNDR approach based on the graph convolutional network deep deterministic policy gradient (GCNDDPG). Firstly, a nonlinear stochastic optimization mathematical model for the ADNDR is constructed, taking into account the uncertainty of sources and loads. Secondly, a loop-based encoding method is employed to reduce the action space and complexity of the ADNDR. Then, based on graph theory, the DN structure is transformed into a dynamic network graph model, and a GCNDDPG-based ADNDR approach is proposed for the solution. In this method, graph convolutional networks are used to extract features from the graph structure information, and the state of the DN, and the deep deterministic policy gradient is utilized to optimize the ADNDR decision-making process to achieve the safe, stable, and economic operation of the DN. Finally, the effectiveness of the proposed approach is verified on an improved IEEE 33-bus power system. The simulation results demonstrate that the method can effectively enhance the economy and stability of the DN, thus validating the effectiveness of the proposed approach.
Keyword ：
active distribution network active distribution network deep deterministic policy gradient deep deterministic policy gradient deep reinforcement learning deep reinforcement learning distribution network dynamic reconfiguration distribution network dynamic reconfiguration graph convolutional network graph convolutional network
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhan, Hua , Jiang, Changxu , Lin, Zhen . A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy [J]. | ENERGIES , 2024 , 17 (24) . |
| MLA | Zhan, Hua et al. "A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy" . | ENERGIES 17 . 24 (2024) . |
| APA | Zhan, Hua , Jiang, Changxu , Lin, Zhen . A Novel Graph Reinforcement Learning-Based Approach for Dynamic Reconfiguration of Active Distribution Networks with Integrated Renewable Energy . | ENERGIES , 2024 , 17 (24) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
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
Cite：
Copy from the list or Export to your reference management。
| 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 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Export
| Results： |
Selected to |
| Format： |
