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学者姓名：朱振山
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Abstract ：
This paper addresses the issue of energy interaction between offshore fish farms and islands with surplus wind and solar resources by developing an energy transportation strategy involving fully electric ships for the island-fish farm-coast system. The proposed strategy utilizes deep reinforcement learning, which is well-suited to managing the uncertainties of offshore wind and solar resources and can accommodate large-scale energy transfer models. First, the mobile energy storage battery group is detailed into fully charged, unloaded, and partially charged batteries. Then, the energy transportation problem is modeled as a Markov Decision Process with a hybrid action space. To solve the hybrid action space issue, a parameterized dual deep Q-network based on multi-batch forward propagation is proposed. This method decouples the unrelated discrete and continuous actions using a multi-step forward pass strategy, reducing volatility during the agent's training process and converging to a more optimal solution. Finally, simulation results verify that the proposed strategy effectively facilitates energy transfer between locations. Compared to traditional deep reinforcement learning methods suited for discrete action spaces, the proposed algorithm demonstrates greater flexibility and achieves superior performance in the target scenario. Additionally, comparative analysis in expanding model scales further validates the advantages of the proposed method in addressing large-scale energy transportation challenges. ©2025 Chin.Soc.for Elec.Eng.
Keyword ：
all-electric ship (AES) all-electric ship (AES) deep reinforcement learning deep reinforcement learning hybrid action space hybrid action space mobile energy storage battery mobile energy storage battery
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| GB/T 7714 | Zhu, Z. , Chen, H. , Chen, W. et al. Research on Energy Transportation Strategies Between Islands and Offshore Fish Farms for Ships With Energy Storage Based on Deep Reinforcement Learning; [基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究] [J]. | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (7) : 2486-2499 . |
| MLA | Zhu, Z. et al. "Research on Energy Transportation Strategies Between Islands and Offshore Fish Farms for Ships With Energy Storage Based on Deep Reinforcement Learning; [基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究]" . | Proceedings of the Chinese Society of Electrical Engineering 45 . 7 (2025) : 2486-2499 . |
| APA | Zhu, Z. , Chen, H. , Chen, W. , Huang, Y. . Research on Energy Transportation Strategies Between Islands and Offshore Fish Farms for Ships With Energy Storage Based on Deep Reinforcement Learning; [基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究] . | Proceedings of the Chinese Society of Electrical Engineering , 2025 , 45 (7) , 2486-2499 . |
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针对海上渔排与风光资源富余岛屿能源交互问题,该文提出含全电力船舶(all-electric ship,AES)的岛屿-海上渔排-海岸能源运输策略,利用能够很好处理海面风光不确定性问题以及适应较大规模能源转移模型的深度强化学习方法对上述能源运输模型进行求解.首先,将移动式储能电池组细化为满充电池、空载电池以及不完全充电电池;其次,将上述能源运输问题建模为含混合动作空间的马尔可夫决策过程;考虑到针对混合动作空间问题,提出一种适用于混合动作空间的基于多批次前向传播的参数化双深度Q网络,该方法通过多步前向传递策略对不相关离散与连续动作进行解耦,减少了智能体训练过程中的波动性并能够收敛于更优的解;最后,通过算例仿真可知,所提策略能够有效实现各地点间能量转移,所提算法相较于传统适用于离散动作空间的深度强化学习方法更加灵活,在目标场景下能够实现更优运行.此外,在模型逐渐扩大的情况下,将该文方法与传统方法求解效果进行对比,验证所提方法在解决大规模能源运输问题的优势.
Keyword ：
全电力船舶 全电力船舶 深度强化学习 深度强化学习 混合动作空间 混合动作空间 移动式储能电池 移动式储能电池
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| GB/T 7714 | 朱振山 , 陈豪 , 陈炜龙 et al. 基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究 [J]. | 中国电机工程学报 , 2025 , 45 (7) : 2486-2499,中插4 . |
| MLA | 朱振山 et al. "基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究" . | 中国电机工程学报 45 . 7 (2025) : 2486-2499,中插4 . |
| APA | 朱振山 , 陈豪 , 陈炜龙 , 黄缨惠 . 基于深度强化学习的含储能船舶的海岛-海上渔排能源运输策略研究 . | 中国电机工程学报 , 2025 , 45 (7) , 2486-2499,中插4 . |
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目前用户侧电动汽车（EV）等新型应急资源尚未形成系统化调配机制。对此，提出一种考虑EV与电网互动（V2G）潜力模糊评估与移动式储能协调调度的灾后供电恢复策略。在固定式可调配能源有限的前提下，将V2G加入配电网孤岛主动划分与移动式储能对电能的时空转移协调调度过程中，建立孤岛间的能量连接，保证供电连续性与稳定性。考虑主客观影响因素，运用模糊模型评估EV响应潜力，引导EV集群参与供电恢复，提高可调度容量利用率。结合配电网运行约束，建立以停电损失与恢复资源调度成本最小化、电动汽车聚合商收益最大化为目标的灾后恢复模型，兼顾电网侧、电动汽车聚合商、用户的利益，降低灾后停电损失。设立评估指标，在仿真算例中验证所提策略的有效性与经济性。
Keyword ：
V2G V2G 模糊推理 模糊推理 电动汽车 电动汽车 移动储能 移动储能 配电网供电恢复 配电网供电恢复 配电网重构 配电网重构
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| GB/T 7714 | 刘俊琳 , 朱振山 , 温步瀛 . 基于V2G潜力模糊评估与移动储能协调调度的灾后供电恢复策略 [J]. | 电力自动化设备 , 2024 , 44 (09) : 89-97 . |
| MLA | 刘俊琳 et al. "基于V2G潜力模糊评估与移动储能协调调度的灾后供电恢复策略" . | 电力自动化设备 44 . 09 (2024) : 89-97 . |
| APA | 刘俊琳 , 朱振山 , 温步瀛 . 基于V2G潜力模糊评估与移动储能协调调度的灾后供电恢复策略 . | 电力自动化设备 , 2024 , 44 (09) , 89-97 . |
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In the current research landscape，there lacks a systematic deployment mechanism for new emergency resources such as electric vehicle（EV）from user side. A post-disaster power supply restoration strategy that considers the fuzzy assessment of vehicle-to-grid（V2G） potential and the coordinated scheduling of mobile energy storage is proposed to address the above issues. Under the premise of limited fixed dispatchable energy，V2G is added to the coordinated scheduling process including the active division of distribution network islands and the spatio-temporal transfer of electric energy by mobile energy storage，so as to establish the energy connection between islands and ensure the continuity and stability of power supply. Considering subjective and objective influencing factors，the fuzzy model is used to evaluate the EV response potential，in order to guide EVs to participate in power supply restoration and improve the utilization rate of schedulable capacity. Combined with the operation constraints of the distribution network，a post-disaster restoration model is established with the goal of minimizing the power failure loss and restoration resource scheduling cost，and maximizing the profit of electric vehicle aggregation，taking into account the interests of the grid side，the electric vehicle aggregation and users，and reducing the loss of post-disaster power failure. The evaluation indicators are set up to verify the effectiveness and economy of the proposed strategy in a simulation example. © 2024 Electric Power Automation Equipment Press. All rights reserved.
Keyword ：
distribution network reconfiguration distribution network reconfiguration electric vehicles electric vehicles fuzzy reasoning fuzzy reasoning mobile energy storage mobile energy storage power supply restoration of distribution network power supply restoration of distribution network vehicle-to-grid vehicle-to-grid
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| GB/T 7714 | Liu, J. , Zhu, Z. , Wen, B. . Post-disaster power supply restoration strategy based on V2G potential fuzzy evaluation and mobile energy storage coordinated scheduling; [基于 V2G 潜力模糊评估与移动储能协调调度的灾后供电恢复策略] [J]. | Electric Power Automation Equipment , 2024 , 44 (9) : 89-97 . |
| MLA | Liu, J. et al. "Post-disaster power supply restoration strategy based on V2G potential fuzzy evaluation and mobile energy storage coordinated scheduling; [基于 V2G 潜力模糊评估与移动储能协调调度的灾后供电恢复策略]" . | Electric Power Automation Equipment 44 . 9 (2024) : 89-97 . |
| APA | Liu, J. , Zhu, Z. , Wen, B. . Post-disaster power supply restoration strategy based on V2G potential fuzzy evaluation and mobile energy storage coordinated scheduling; [基于 V2G 潜力模糊评估与移动储能协调调度的灾后供电恢复策略] . | Electric Power Automation Equipment , 2024 , 44 (9) , 89-97 . |
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大量分布式新能源接入给配电网运行带来了电压越限和网损增加等一系列问题。提出了一种基于多智能体强化学习的无模型电压控制策略，通过协调光伏逆变器、分布式储能和智能软开关以降低网损、消除电压越限。针对传统电压控制策略对配电网精确的模型参数依赖性强的问题，提出了基于高斯过程回归的潮流替代模型，通过多智能体与潮流替代模型交互实现无模型的离线训练和在线应用。同时提出了一种基于随机加权三重Q学习的多智能体深度强化学习算法，能够进一步降低柔性演员-评论家算法的高低估误差，提升算法探索能力和收敛结果。最后在IEEE33节点系统上的仿真结果，验证了所提方法在解决配电网分布式电压优化控制问题上的有效性。
Keyword ：
多智能体 多智能体 智能软开关 智能软开关 深度强化学习 深度强化学习 电压控制 电压控制 配电网 配电网 高斯过程回归 高斯过程回归
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| GB/T 7714 | 朱振山 , 张新炳 , 陈豪 . 基于深度强化学习的含智能软开关配电网电压控制方法 [J]. | 高电压技术 , 2024 , 50 (03) : 1214-1224 . |
| MLA | 朱振山 et al. "基于深度强化学习的含智能软开关配电网电压控制方法" . | 高电压技术 50 . 03 (2024) : 1214-1224 . |
| APA | 朱振山 , 张新炳 , 陈豪 . 基于深度强化学习的含智能软开关配电网电压控制方法 . | 高电压技术 , 2024 , 50 (03) , 1214-1224 . |
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The widespread integration of distributed renewable energy sources has brought a series of problems to the operation of distribution networks, including voltage violations and increase in network losses. This paper proposes a model-free voltage control strategy based on multi-agent reinforcement learning. By coordinating photovoltaic inverters, distributed energy storages, and soft open points, the strategy aims to reduce network losses and eliminate voltage violations. To tackle the problem that traditional voltage control strategies have strong dependence on accurate distribution network model parameters, a power flow surrogate model based on Gaussian process regression is proposed. The model enables offline training and online application through interactions between multi-agents and the power flow surrogate model. Additionally, a multi-agent deep reinforcement learning algorithm based on random weighted triple Q-learning is proposed to further reduce the overestimation and underestimation errors of the soft actor-critic algorithm. The proposed method improves the algorithm exploration capability and results quality. Finally, simulation results on the IEEE 33-node system verify the effectiveness of the proposed method in solving the distributed voltage control problem of distribution networks. © 2024 Science Press. All rights reserved.
Keyword ：
Deep learning Deep learning Electric load flow Electric load flow Gaussian distribution Gaussian distribution Gaussian noise (electronic) Gaussian noise (electronic) Learning algorithms Learning algorithms Learning systems Learning systems Power quality Power quality Reinforcement learning Reinforcement learning Renewable energy Renewable energy Voltage control Voltage control
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| GB/T 7714 | Zhu, Zhenshan , Zhang, Xinbing , Chen, Hao . Voltage Control Method of Distribution Network with Soft Open Point Based on Deep Reinforcement Learning [J]. | High Voltage Engineering , 2024 , 50 (3) : 1214-1225 . |
| MLA | Zhu, Zhenshan et al. "Voltage Control Method of Distribution Network with Soft Open Point Based on Deep Reinforcement Learning" . | High Voltage Engineering 50 . 3 (2024) : 1214-1225 . |
| APA | Zhu, Zhenshan , Zhang, Xinbing , Chen, Hao . Voltage Control Method of Distribution Network with Soft Open Point Based on Deep Reinforcement Learning . | High Voltage Engineering , 2024 , 50 (3) , 1214-1225 . |
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为解决源荷不确定性和风电相关性导致区域综合能源系统调度结果可信度低的问题,以综合运行成本最小为目标,提出一种计及多时间尺度的区域综合能源系统调度模型.在日前阶段,提出计及风电相关性的两阶段鲁棒优化模型,使用列和约束生成法进行迭代求解.日内调度阶段考虑了冷热电响应速率的不同,提出基于模型预测控制的冷热电分层滚动优化模型,进一步消除源荷功率波动.仿真结果表明:计及风电相关性的鲁棒优化方法降低了保守性,提高了经济性;在冷热电分层优化时使用模型预测控制,实现了区域综合能源系统的经济及稳定运行.
Keyword ：
冷热电分层 冷热电分层 区域综合能源系统 区域综合能源系统 多时间尺度 多时间尺度 模型预测控制 模型预测控制 线性多面体集合 线性多面体集合
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| GB/T 7714 | 陈志颖 , 温步瀛 , 朱振山 . 计及风电相关性的区域综合能源系统多时间尺度优化调度 [J]. | 电力自动化设备 , 2023 , 43 (8) : 25-32 . |
| MLA | 陈志颖 et al. "计及风电相关性的区域综合能源系统多时间尺度优化调度" . | 电力自动化设备 43 . 8 (2023) : 25-32 . |
| APA | 陈志颖 , 温步瀛 , 朱振山 . 计及风电相关性的区域综合能源系统多时间尺度优化调度 . | 电力自动化设备 , 2023 , 43 (8) , 25-32 . |
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In order to deal with the voltage fluctuation caused by the high penetration of photovoltaics (PV) and realize the coordinated control of various voltage regulating devices in the distribution network, a two-stage voltage coordinated control strategy is proposed. In the day-ahead stage, stochastic optimization based scheduling strategy for OLTC and CB is proposed to adapt to the uncertainty of PV generation and load. In the real time stage, voltage is managed by PV inverters and distributed energy storages (DESS). Considering the decentralized location of PV inverters and DESS, a control method based on partition is proposed to reduce the communication and computational cost. and a partitioning strategy based on Gray Wolf Optimization-Affinity Propagation algorithm (GWO-AP) is proposed to search for subareas with the best structural and power margin. Finally, the proposed strategy is verified in the IEEE33 bus system. © 2023 IEEE.
Keyword ：
Electric inverters Electric inverters Electric power distribution Electric power distribution Optimization Optimization Voltage control Voltage control
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| GB/T 7714 | Li, Manwei , Zhu, Zhenshan , Weng, Kailiang . A Two-Stage Voltage Coordination Control Strategy for Distribution Networks Based on GWO-AP Partitioning Algorithm [C] . 2023 : 593-599 . |
| MLA | Li, Manwei et al. "A Two-Stage Voltage Coordination Control Strategy for Distribution Networks Based on GWO-AP Partitioning Algorithm" . (2023) : 593-599 . |
| APA | Li, Manwei , Zhu, Zhenshan , Weng, Kailiang . A Two-Stage Voltage Coordination Control Strategy for Distribution Networks Based on GWO-AP Partitioning Algorithm . (2023) : 593-599 . |
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The complementary and coordinated operation of comprehensive energy multi-microgrid groups belonging to different subjects is considered to be an effective means of cascade utilization and deep coupling between different energies, which is of great significance for the consumption of new energy and the realization of the goal of dual carbon. Firstly, considering the distant transportation of hydrogen energy and the demand-side carbon emission reduction, this paper establishes a comprehensive energy multi-microgrid system scheduling model considering the gas network hydrogen doping to further strengthen the coupling relationship between different energy sources. Secondly, a dynamic hydrogen price mechanism based on the time distribution of electric load and the level of new energy consumption is proposed to guide hydrogen fuel cell vehicles to charge in an orderly manner. While the demand for electricity is flattened, the users will also charge their vehicles more during the period of high curtailment. Then, by decoupling the comprehensive energy multi-microgrid model, the system is distributed to obtain the global optimal operation strategy by the regularized alternating direction multiplier algorithm with the adaptive step link. Finally, the analysis shows that the proposed dispatch strategy is able to increase the consumption of renewable energy by 81.48%, reduce the carbon emissions by 35.21%, and decrease the charging cost of hydrogen fuel cell vehicle users by 1.58% under the premise of regional autonomy. © 2023 Power System Technology Press. All rights reserved.
Keyword ：
distributed optimization distributed optimization dynamic hydrogen valence dynamic hydrogen valence gas network doped with hydrogen gas network doped with hydrogen hydrogen fuel cell vehicles hydrogen fuel cell vehicles integrated energy multi-microgrid system integrated energy multi-microgrid system
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| GB/T 7714 | Zhu, Z. , Liu, B. , Guo, L. . Distributed Optimal Dispatch of Integrated Energy Multi-microgrid System Considering Dynamic Hydrogen Price Mechanism; [考虑动态氢价机制的综合能源多微网系统分布式优化调度] [J]. | Power System Technology , 2023 , 47 (12) : 5036-5045 . |
| MLA | Zhu, Z. et al. "Distributed Optimal Dispatch of Integrated Energy Multi-microgrid System Considering Dynamic Hydrogen Price Mechanism; [考虑动态氢价机制的综合能源多微网系统分布式优化调度]" . | Power System Technology 47 . 12 (2023) : 5036-5045 . |
| APA | Zhu, Z. , Liu, B. , Guo, L. . Distributed Optimal Dispatch of Integrated Energy Multi-microgrid System Considering Dynamic Hydrogen Price Mechanism; [考虑动态氢价机制的综合能源多微网系统分布式优化调度] . | Power System Technology , 2023 , 47 (12) , 5036-5045 . |
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The volatility and uncertainty of high-penetration renewable energy pose significant challenges to the stability of the power system. Current research often fails to consider the insufficient system flexibility during real-time scheduling. To address this issue, this paper proposes a flexibility scheduling method for high-penetration renewable energy power systems that considers flexibility index constraints. Firstly, a quantification method for flexibility resources and demands is introduced. Then, considering the constraint of the flexibility margin index, optimization scheduling strategies for different time scales, including day-ahead scheduling and intra-day scheduling, are developed with the objective of minimizing total operational costs. The intra-day optimization is divided into 15 min and 1 min time scales, to meet the flexibility requirements of different time scales in the power system. Finally, through simulation studies, the proposed strategy is validated to enhance the system's flexibility and economic performance. The daily operating costs are reduced by 3.1%, and the wind curtailment rate is reduced by 4.7%. The proposed strategy not only considers the economic efficiency of day-ahead scheduling but also ensures a sufficient margin to cope with the uncertainty of intra-day renewable energy fluctuations.
Keyword ：
battery energy storage battery energy storage flexibility index flexibility index hydrogen energy storage hydrogen energy storage optimal scheduling optimal scheduling renewable energy renewable energy
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| GB/T 7714 | Lin, Yi , Lin, Wei , Wu, Wei et al. Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility [J]. | ENERGIES , 2023 , 16 (14) . |
| MLA | Lin, Yi et al. "Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility" . | ENERGIES 16 . 14 (2023) . |
| APA | Lin, Yi , Lin, Wei , Wu, Wei , Zhu, Zhenshan . Optimal Scheduling of Power Systems with High Proportions of Renewable Energy Accounting for Operational Flexibility . | ENERGIES , 2023 , 16 (14) . |
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