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学者姓名：江昌旭
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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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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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The increasing integration of distributed generation into high-penetration renewable distribution networks challenges conventional single-dimensional optimization strategies. These strategies struggle to balance multi-objective requirements, such as reducing network losses, ensuring voltage stability, and accommodating renewable energy. Existing approaches often neglect critical interactions among network topology, reactive power compensation, and active power regulation, leading to compromised performance under high DG penetration. This paper proposes a multi-agent graph reinforcement learning framework for multi-timescale coordinated rolling optimization of topology, reactive power, and active power. Our method integrates graph convolutional networks to model grid-structured data and double deep Q-networks to address complex decision-making. By hierarchically coordinating slow-and fast-timescale resources through rolling optimization, it adaptively manages stochastic fluctuations in DG outputs and loads. Case studies demonstrate enhanced crosstimescale coordination, achieving higher renewable utilization, stabilized voltage profiles, improved operational flexibility, and robust uncertainty handling capabilities. Furthermore, the framework exhibits excellent scalability and computational efficiency, enabling effective optimization even for large-scale distribution networks, which is critical for practical deployments in complex high-renewable scenarios. The framework offers a scalable solution for intelligent grid management in high-renewable scenarios.
Keyword ：
Decarbonization-driven operational optimization Decarbonization-driven operational optimization Energy storage-integrated flexibility enhancement Energy storage-integrated flexibility enhancement Graph convolutional network Graph convolutional network High-penetration renewable distribution network High-penetration renewable distribution network Multi-agent reinforcement learning Multi-agent reinforcement learning Multi-energy coordination Multi-energy coordination
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| GB/T 7714 | Jiang, Changxu , Guo, Chen , Lin, Junchi et al. Enhancing economic efficiency and operational stability of high-penetration renewable distribution networks: A multi-timescale coordinated optimization method leveraging multi-agent graph reinforcement learning [J]. | RENEWABLE ENERGY , 2025 , 256 . |
| MLA | Jiang, Changxu et al. "Enhancing economic efficiency and operational stability of high-penetration renewable distribution networks: A multi-timescale coordinated optimization method leveraging multi-agent graph reinforcement learning" . | RENEWABLE ENERGY 256 (2025) . |
| APA | Jiang, Changxu , Guo, Chen , Lin, Junchi , Lin, Junjie , Zheng, Shunlin . Enhancing economic efficiency and operational stability of high-penetration renewable distribution networks: A multi-timescale coordinated optimization method leveraging multi-agent graph reinforcement learning . | RENEWABLE ENERGY , 2025 , 256 . |
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In order to address the challenge of electric vehicle charging decisions under multiple uncertain factors in the power-transportation coupling system, this paper proposes a graph multi-agent reinforcement learning algorithm to optimize the electric vehicle charging guidance strategy. Firstly, second-order cone optimization is employed to solve the optimal power flow of the distribution network, deriving the marginal node prices of the distribution network for updating the charging prices. Then, based on the graph theory, the information of vehicles, charge stations (CSs), traffic roads, and the distribution network is transformed into a dynamic graph. An attention-based graph neural network multi-agent reinforcement learning algorithm is adopted to optimize the electric vehicle charging strategy. Finally, the proposed algorithm is simulated and validated in a transportation network. The simulation results indicate that the proposed graph multi-agent reinforcement learning algorithm can effectively reduce the time and economic costs of electric vehicle users, promote balanced operation of CSs, and exhibit good adaptability and scalability of the model. © Beijing Paike Culture Commu. Co., Ltd. 2025.
Keyword ：
Graph theory Graph theory Reinforcement learning Reinforcement learning
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| GB/T 7714 | Yuan, Yujuan , Jiang, Changxu , Liu, Chenxi et al. Graph Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Guidance in Coupled Power and Transportation Networks [C] . 2025 : 642-654 . |
| MLA | Yuan, Yujuan et al. "Graph Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Guidance in Coupled Power and Transportation Networks" . (2025) : 642-654 . |
| APA | Yuan, Yujuan , Jiang, Changxu , Liu, Chenxi , Zhuang, Pengwei . Graph Multi-Agent Reinforcement Learning Method for Electric Vehicle Charging Guidance in Coupled Power and Transportation Networks . (2025) : 642-654 . |
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To better simulate the irrational decision-making behavior of electric vehicle (EV), this paper proposes an innovative EV charging navigation strategy integrating user regret psychology and deep reinforcement learning considering multiple uncertain factors. Firstly, a mathematical optimization model for EV charging navigation based on user regret psychology is established. It calculates the regret value of strategy by time and charging cost based on historical data. Then, the proposed model is optimized by the Double Deep Q Network (DDQN) reinforcement learning algorithm to minimize the regret value of the strategy. Finally, the proposed method is simulated in an urban traffic network. The simulation results show that the proposed method can effectively solve problems with multiple uncertainties. Compared with the nearest charging strategy based on the Dijkstra algorithm, the proposed strategy can effectively reduce the regret value, thereby lowering the total cost of EV charging, and it is more acceptable to user. Furthermore, the proposed strategy maintains good adaptability and effectiveness in different simulation environments. © Beijing Paike Culture Commu. Co., Ltd. 2025.
Keyword ：
Deep reinforcement learning Deep reinforcement learning
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| GB/T 7714 | Zhuang, Pengwei , Jiang, Changxu , Xu, Hao et al. An Innovative Electric Vehicle Charging Navigation Strategy: Integrating User Regret Psychology and Deep Reinforcement Learning [C] . 2025 : 463-477 . |
| MLA | Zhuang, Pengwei et al. "An Innovative Electric Vehicle Charging Navigation Strategy: Integrating User Regret Psychology and Deep Reinforcement Learning" . (2025) : 463-477 . |
| APA | Zhuang, Pengwei , Jiang, Changxu , Xu, Hao , Zhou, Longcan . An Innovative Electric Vehicle Charging Navigation Strategy: Integrating User Regret Psychology and Deep Reinforcement Learning . (2025) : 463-477 . |
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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 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) . |
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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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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 et al. "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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Current electric vehicle charging navigation (EVCN) models typically treat the problem as a static, one-shot decision, neglecting the user regret psychology and myriad uncertainties that unfold throughout the charging journey. Moreover, by restricting decision-making to a predefined roster of conventional charging strategies and simply selecting the option with the lowest retrospective regret, these methods risk overlooking novel or dynamic policies that could yield even greater satisfaction. To address these problems, a user regret psychology-driven EVCN strategy based on deep reinforcement learning (DRL) and transfer learning (TL) is proposed. In the EVCN problem, multiple uncertainties are considered, and several commonly used charging strategies are selected as comparison strategies to construct a regret-theory-based EVCN model. Subsequently, to accurately estimate the attributes of comparison strategies under uncertain conditions, a TL-based attribute estimation method is developed, utilizing the replay buffer and an improved Jaccard similarity. Based on this, a DRL algorithm is used for fast and efficient model optimization. Finally, the proposed method is simulated on the transportation network, and the results demonstrate that the proposed method improves the driving time estimation accuracy of comparison strategies, significantly lowers the charging strategy regret, and exhibits excellent adaptability and scalability.
Keyword ：
Charging navigation strategy Charging navigation strategy Deep reinforcement learning Deep reinforcement learning Electric vehicle Electric vehicle Regret theory Regret theory
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| GB/T 7714 | Zhuang, Pengwei , Jiang, Changxu , Xu, Hao et al. User regret psychology-driven electric vehicle charging navigation strategy based on deep reinforcement learning and transfer learning [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 172 . |
| MLA | Zhuang, Pengwei et al. "User regret psychology-driven electric vehicle charging navigation strategy based on deep reinforcement learning and transfer learning" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 172 (2025) . |
| APA | Zhuang, Pengwei , Jiang, Changxu , Xu, Hao , Lin, Junjie . User regret psychology-driven electric vehicle charging navigation strategy based on deep reinforcement learning and transfer learning . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2025 , 172 . |
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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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