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 ：
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 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) . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The proliferation of distributed energy resources and the introduction of new loads in distribution networks present significant challenges for monitoring and operation. To satisfy the enhanced observability and controllability requirements of modern distribution networks, there is an increasing demand for advanced monitoring devices. Distribution Network Phasor Measurement Units (DPMUs) offer high-precision measurement data with precise timestamps, thereby improving both the accuracy and redundancy of measurements within the distribution network.This paper introduces an optimization model for the strategic placement of PMUs within distribution networks, leveraging node metric indices. The indices considered are node degree, spatiotemporal correlation, and node power ratio. The relative importance of these indices is determined using an improved entropy weight method, which quantifies the differentiation of nodes within the network. This method facilitates the prioritized placement of DPMUs at critical nodes. The proposed model also incorporates constraints such as the depth of unobservability and zero injection nodes. Utilizing a 0–1 integer programming algorithm, the model derives a multi-stage optimal placement scheme for PMU placement. This scheme evolves from incomplete observability to critical observability and ultimately to full redundancy. Importantly, this approach allows for the monitoring of key nodes within the distribution network and enhances measurement redundancy without necessitating an increase in the number of placements. © 2024 Elsevier Ltd
Keyword ：
Measurement Redundancy Measurement Redundancy Node metric Index Node metric Index Observability Observability Optimization Placement Optimization Placement PMU PMU
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, J. , Chen, H. , Jiang, C. et al. Multi-stage optimization placement of DPMUs based on node metric indices [J]. | Sustainable Energy, Grids and Networks , 2024 , 39 . |
| MLA | Lin, J. et al. "Multi-stage optimization placement of DPMUs based on node metric indices" . | Sustainable Energy, Grids and Networks 39 (2024) . |
| APA | Lin, J. , Chen, H. , Jiang, C. , Han, K. , Wei, X. , Fang, C. . Multi-stage optimization placement of DPMUs based on node metric indices . | Sustainable Energy, Grids and Networks , 2024 , 39 . |
| 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. © 2024 Elsevier Ltd
Keyword ：
Deep reinforcement learning Deep reinforcement learning 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 and 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 and 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 and Networks , 2024 , 40 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
为了改善含大规模新能源电力系统电压稳定性，针对大电网系统提出了一种基于分区优化的静止同步补偿器（static synchronous compensator,STATCOM）动态无功多目标选址定容方法。提出了一种基于电气距离和暂态电压特性的分区方法，能够有效地考虑电力系统拓扑结构、线路参数以及励磁系统参数和稳定器参数等对电力系统分区的影响；采用电压控制指标辨识STATCOM最优安装节点；构建了包含投资成本、静态电压稳定指标和暂态电压严重性指标的多目标动态无功规划模型；采用基于自适应协方差矩阵和混沌搜索的多目标群搜索优化算法和熵权理想度排序法对多目标无功规划模型进行求解和决策推理。对所提方法在某省级规划电网中进行了测试，仿真结果表明，优化后的STATCOM配置方案能够有效改善电力系统静态和暂态电压稳定性。
Keyword ：
STATCOM优化 STATCOM优化 新能源 新能源 电压稳定 电压稳定 电气距离 电气距离 选址定容 选址定容
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | 黎萌 , 林章岁 , 林毅 et al. 含大规模新能源的电力系统STATCOM选址及容量优化 [J]. | 电网与清洁能源 , 2024 , 40 (05) : 139-150 . |
| MLA | 黎萌 et al. "含大规模新能源的电力系统STATCOM选址及容量优化" . | 电网与清洁能源 40 . 05 (2024) : 139-150 . |
| APA | 黎萌 , 林章岁 , 林毅 , 江昌旭 , 丁嘉鑫 , 欧阳富鑫 . 含大规模新能源的电力系统STATCOM选址及容量优化 . | 电网与清洁能源 , 2024 , 40 (05) , 139-150 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
The proliferation of distributed energy resources and the introduction of new loads in distribution networks present significant challenges for monitoring and operation. To satisfy the enhanced observability and controllability requirements of modern distribution networks, there is an increasing demand for advanced monitoring devices. Distribution Network Phasor Measurement Units (DPMUs) offer high-precision measurement data with precise timestamps, thereby improving both the accuracy and redundancy of measurements within the distribution network.This paper introduces an optimization model for the strategic placement of PMUs within distribution networks, leveraging node metric indices. The indices considered are node degree, spatiotemporal correlation, and node power ratio. The relative importance of these indices is determined using an improved entropy weight method, which quantifies the differentiation of nodes within the network. This method facilitates the prioritized placement of DPMUs at critical nodes. The proposed model also incorporates constraints such as the depth of unobservability and zero injection nodes. Utilizing a 0-1 integer programming algorithm, the model derives a multi-stage optimal placement scheme for PMU placement. This scheme evolves from incomplete observability to critical observability and ultimately to full redundancy. Importantly, this approach allows for the monitoring of key nodes within the distribution network and enhances measurement redundancy without necessitating an increase in the number of placements.
Keyword ：
Measurement Redundancy Measurement Redundancy Node metric Index Node metric Index Observability Observability Optimization Placement Optimization Placement PMU PMU
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Lin, Junjie , Chen, Haoyu , Jiang, Changxu et al. Multi-stage optimization placement of DPMUs based on node metric indices [J]. | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 39 . |
| MLA | Lin, Junjie et al. "Multi-stage optimization placement of DPMUs based on node metric indices" . | SUSTAINABLE ENERGY GRIDS & NETWORKS 39 (2024) . |
| APA | Lin, Junjie , Chen, Haoyu , Jiang, Changxu , Han, Kunyu , Wei, Xinchi , Fang, Chen . Multi-stage optimization placement of DPMUs based on node metric indices . | SUSTAINABLE ENERGY GRIDS & NETWORKS , 2024 , 39 . |
| Export to | NoteExpress RIS BibTex |
Version ：
Abstract ：
This article proposes a fault location method for distribution networks based on the characteristics and advantages of two measurement devices：fault indicators（FIs）and phasor measurement units（PMUs）. Firstly，a relationship matrix is established based on the power system’s network topology. The alarm vector is constructed using the alarm information from the FIs and the fault current direction，and the fault section is determined by solving the correspondence between the two. Secondly，the data of the voltage and current at both ends of the fault section are estimated by combining the PMU-configured node data and line parameters. Finally，the external circuits of the fault section are equivalently represented，and depending on the completeness of the node data，either the single-end impedance method or the double-end impedance method is selected to determine the fault distance，achieving accurate fault location. The simulation results show that the proposed method achieves high accuracy in short-circuit fault location under different fault locations，fault types，and transition resistance values. © 2024 Science Press. All rights reserved.
Keyword ：
fault indicators fault indicators fault location fault location fault section location fault section location phasor measurement units phasor measurement units power distribution networks power distribution networks
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, B. , Lin, J. , Jiang, C. et al. FAULT LOCATION METHOD FOR DISTRIBUTION NETWORK BASED ON PMU AND FI; [基于 PMU 和 FI 协同的配电网故障测距方法] [J]. | Acta Energiae Solaris Sinica , 2024 , 45 (12) : 659-666 . |
| MLA | Zhang, B. et al. "FAULT LOCATION METHOD FOR DISTRIBUTION NETWORK BASED ON PMU AND FI; [基于 PMU 和 FI 协同的配电网故障测距方法]" . | Acta Energiae Solaris Sinica 45 . 12 (2024) : 659-666 . |
| APA | Zhang, B. , Lin, J. , Jiang, C. , Shao, Z. , Fang, C. , Wei, X. . FAULT LOCATION METHOD FOR DISTRIBUTION NETWORK BASED ON PMU AND FI; [基于 PMU 和 FI 协同的配电网故障测距方法] . | Acta Energiae Solaris Sinica , 2024 , 45 (12) , 659-666 . |
| 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 ：
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 ：
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 ：
Export
| Results： |
Selected to |
| Format： |
