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学者姓名：江昌旭
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配电网同步相量测量单元（PMU）可以提供带有时标的高精度量测数据，配电网PMU线性状态估计高准确性和实时性的特点可以满足新型配电网的要求。该文重点研究了PMU配置成本与基于配电网PMU状态估计性能之间的权衡关系，提出了一种基于NSGA-II和熵权理想度排序法的多目标PMU优化配置方法，PMU优化配置的目标函数为最小化PMU配置成本和最小化PMU状态估计精度，约束条件考虑了PMU因发生意外事故而中断的情况，以应对因故障造成的量测不足的问题。基于NSGA-II算法和熵权理想度排序法，从Pareto解集中筛选出权衡多目标的最优解。算例表明，所提算法能得到权衡PMU配置成本和状态估计精度的最优解。
Keyword ：
NSGA-Ⅱ NSGA-Ⅱ PMU优化配置 PMU优化配置 熵权理想度排序法 熵权理想度排序法 状态估计 状态估计 配电网 配电网
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| GB/T 7714 | 陈浩宇 , 林俊杰 , 江昌旭 et al. 基于熵权理想度排序法的配电网PMU多目标优化配置 [J]. | 电网与清洁能源 , 2024 , 40 (08) : 36-45 . |
| MLA | 陈浩宇 et al. "基于熵权理想度排序法的配电网PMU多目标优化配置" . | 电网与清洁能源 40 . 08 (2024) : 36-45 . |
| APA | 陈浩宇 , 林俊杰 , 江昌旭 , 涂明权 , 林铮 , 卢玥君 . 基于熵权理想度排序法的配电网PMU多目标优化配置 . | 电网与清洁能源 , 2024 , 40 (08) , 36-45 . |
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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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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
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| 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 . |
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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
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| 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 . |
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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. © 2024 Elsevier Ltd
Keyword ：
Deep reinforcement learning Deep reinforcement learning 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 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 . |
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Grid-connected voltage source converters (VSCs) have been broadly applied in modern power system. However, instability issues may be triggered by the integration of grid-connected VSCs, jeopardising the operation of the power grid. Conventional stability analysis methods can be utilised to derive system stability margins under nominal conditions. Whereas grid-connected VSCs inevitably operate under multiparameter uncertainty, which may result in overly optimistic or even incorrect estimations of stability margins, thereby posing potential risks to system operation. To address this issue, an interval small-signal stability analysis approach is proposed to investigate the system stability under multiparameter uncertainty. First, the interval state-space model of the grid-connected VSC system is constructed based on interval symbolic linearisation. Furthermore, the interval eigenvalue decomposition is introduced to calculate the interval eigenvalue distribution of the interval state-space model. Eventually, the upper bounds of the real part of the dominant interval eigenvalues are utilised for deriving interval stable parameter regions. Results of Monte Carlo analysis and time-domain simulations of the grid-connected VSC system are utilised to verify the effectiveness of the proposed interval stability analysis method.
Keyword ：
power convertors power convertors power system interconnection power system interconnection power system stability power system stability
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| GB/T 7714 | Ouyang, Fuxin , Shao, Zhenguo , Jiang, Changxu et al. Interval analysis of the small-signal stability of grid-connected voltage-source converter system considering multiparameter uncertainty [J]. | IET ENERGY SYSTEMS INTEGRATION , 2024 , 6 (2) : 144-161 . |
| MLA | Ouyang, Fuxin et al. "Interval analysis of the small-signal stability of grid-connected voltage-source converter system considering multiparameter uncertainty" . | IET ENERGY SYSTEMS INTEGRATION 6 . 2 (2024) : 144-161 . |
| APA | Ouyang, Fuxin , Shao, Zhenguo , Jiang, Changxu , Zhang, Yan , Chen, Feixiong . Interval analysis of the small-signal stability of grid-connected voltage-source converter system considering multiparameter uncertainty . | IET ENERGY SYSTEMS INTEGRATION , 2024 , 6 (2) , 144-161 . |
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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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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
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| 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 . |
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With the advancement of carbon peak and carbon neutrality process, the new generation power system will change in terms of energy structure, load characteristics, power grid form, technical basis and operation characteristics. This brings new requirements and changes to the statistical work of the power grid. To adapt to the new situation of the new generation power system, this paper constructs a scientific and comprehensive set of statistical indicators for the new generation based on the principles of feasibility, universality, systematization and science. Corresponding to the five characteristics of the new generation power system, the statistical indicator system constructed in this paper focuses on the five dimensions as its core: clean and low-carbon, safe and reliable, flexible and intelligent, open and interactive, economic and efficient. It includes a total of 18 secondary indicators and 75 tertiary indicators. The proposed statistical indicator system for the new generation power system covers various aspects of production, operation and decision-making, to accurately reflect the construction of provincial demonstration areas for the new generation power system, as well as a reference value for guiding the efficient and scientific construction of the new electricity system.
Keyword ：
clean and low-carbon clean and low-carbon flexible and intelligent flexible and intelligent New generation power system New generation power system statistical indicator system statistical indicator system
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| GB/T 7714 | Li, Jucong , Li, Rongming , Xun, Chao et al. Statistical Indicator System for New Generation Power System Construction [J]. | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 2, ICWPT 2023 , 2024 , 1159 : 66-78 . |
| MLA | Li, Jucong et al. "Statistical Indicator System for New Generation Power System Construction" . | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 2, ICWPT 2023 1159 (2024) : 66-78 . |
| APA | Li, Jucong , Li, Rongming , Xun, Chao , Wu, Xiangyu , Jiang, Xiaofu , Tang, Zhijun et al. Statistical Indicator System for New Generation Power System Construction . | PROCEEDINGS OF 2023 INTERNATIONAL CONFERENCE ON WIRELESS POWER TRANSFER, VOL 2, ICWPT 2023 , 2024 , 1159 , 66-78 . |
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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
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| 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) . |
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