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Abstract ：
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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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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With the large-scale development of electric vehicles, the accuracy of electric vehicle charging load prediction is increasingly important for electric power system. Accurate EV charging load prediction is essential for the efficiency of electric system planning and economic operation of electric system. This paper proposes an electric vehicle charging load predicting method based on variational mode decomposition and Prophet-LSTM. Firstly, the variational mode decomposition algorithm is used to decompose the charging load into several intrinsic mode functions in order to explore the characteristics of EV charging load data. Secondly, in order to make full use of the advantages of various forecasting methods, the intrinsic mode functions are classified into low and high frequency sequences based on their over-zero rates. The high and low frequency sequences are reconstructed to obtain two frequency sequences. Then the LSTM neural network and Prophet model are used to predict the high and low frequency sequences, respectively. Finally, the prediction results obtained from the prediction of high frequency and low frequency sequences are combined to obtain the final prediction result. The assessment of the prediction results shows that the prediction accuracy of the prediction method proposed in this paper is improved compared to the traditional prediction methods, and the average absolute error is lower than that of ARIMA, LSTM and Prophet respectively by 7.57%, 8.73%, and 46.02%. The results show that the prediction method proposed in this paper has higher prediction accuracy than the traditional methods, and is effective in predicting EV charging load.
Keyword ：
electric vehicles charging load electric vehicles charging load neural network neural network prophet prediction model prophet prediction model time series prediction time series prediction variational mode decomposition variational mode decomposition
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| GB/T 7714 | Cheng, Nuo , Zheng, Peng , Ruan, Xiaofei et al. Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM [J]. | FRONTIERS IN ENERGY RESEARCH , 2023 , 11 . |
| MLA | Cheng, Nuo et al. "Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM" . | FRONTIERS IN ENERGY RESEARCH 11 (2023) . |
| APA | Cheng, Nuo , Zheng, Peng , Ruan, Xiaofei , Zhu, Zhenshan . Electric vehicle charging load prediction based on variational mode decomposition and Prophet-LSTM . | FRONTIERS IN ENERGY RESEARCH , 2023 , 11 . |
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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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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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针对可再生能源消纳与发电市场的博弈问题,研究不同场景中风电的调度策略,提出基于改进NashQ算法的风电调度策略模型.首先,在市场上博弈环境下建立风电优化调度模型,计及风电上网的预测偏差考核惩罚、风力发电经济效益与环境效益,考虑可再生能源的弃电限制,在这一基础上,对比风电独立运行、风—光、风—储联合运行下的风电调度策略;其次,采用JS散度优化各个智能体的学习率,提高多智能体强化学习的收敛效率;最后,在Matlab中搭建电网模型进行分析,仿真结果验证:改进NashQ方法相较于NashQ、NETRL算法的收敛速度有明显提升,风—车联合运行模式在多主体博弈下有较好吸引力.
Keyword ：
多主体博弈 多主体博弈 多智能体强化学习 多智能体强化学习 改进NashQ 改进NashQ 电动汽车充电站 电动汽车充电站 风电调度 风电调度
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| GB/T 7714 | 郑海林 , 朱振山 , 温步瀛 et al. 多主体博弈下基于改进NashQ算法的风电场调度策略 [J]. | 电力科学与技术学报 , 2022 , 37 (6) : 62-72 . |
| MLA | 郑海林 et al. "多主体博弈下基于改进NashQ算法的风电场调度策略" . | 电力科学与技术学报 37 . 6 (2022) : 62-72 . |
| APA | 郑海林 , 朱振山 , 温步瀛 , 翁智敏 . 多主体博弈下基于改进NashQ算法的风电场调度策略 . | 电力科学与技术学报 , 2022 , 37 (6) , 62-72 . |
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面向综合能源系统运行优化问题,建立了包含燃气轮机、余热回收、有机朗肯循环、空气源热泵和综合需求响应模型的电-热-气园区综合能源系统模型,并在此基础上提出一种基于柔性行动器-评判器的运行优化方法.首先,搭建综合能源系统框架和设备模型,针对传统综合需求响应建模不精确问题,结合历史数据和门控循环单元建立了反映用户真实响应能力的神经网络模型并应用于能源定价场景.其次,以最小化系统购能成本和弃风弃光成本为目标,建立综合能源系统经济调度模型,并采用深度强化学习框架进行表述,设置了柔性行动器-评判器智能体与环境交互过程的动作空间、状态空间、奖励函数等,训练收敛后的模型可直接用于实时决策,无需再重新训练.仿真结果表明所提方法可以有效进行能量管理和能源定价优化,降低系统的综合运行成本.
Keyword ：
新能源不确定性 新能源不确定性 柔性行动器-评判器 柔性行动器-评判器 综合能源系统 综合能源系统 综合需求响应 综合需求响应 运行优化 运行优化 门控循环单元 门控循环单元
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| GB/T 7714 | 朱振山 , 陈哲盛 , 盛明鼎 . 基于柔性行动器-评判器的园区综合能源系统运行优化 [J]. | 高电压技术 , 2022 , 48 (12) : 4949-4958 . |
| MLA | 朱振山 et al. "基于柔性行动器-评判器的园区综合能源系统运行优化" . | 高电压技术 48 . 12 (2022) : 4949-4958 . |
| APA | 朱振山 , 陈哲盛 , 盛明鼎 . 基于柔性行动器-评判器的园区综合能源系统运行优化 . | 高电压技术 , 2022 , 48 (12) , 4949-4958 . |
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提高供应侧风电、光伏等清洁能源的消纳,降低传统火电机组的碳排放是实现能源结构转型以及碳达峰、碳中和的重要手段.针对能源系统低碳运行的问题,提出了一种包含储液式碳捕集电厂、电转气、液化天然气气化站以及利用液化天然气冷能的液态空气储能的综合能源系统低碳经济调度策略,并结合需求侧的响应策略,从供需两侧联合调度以实现综合能源系统的经济性和低碳性.最后,以改进的IEEE 30节点电力系统与6节点天然气系统为例,验证了所提出的低碳经济调度策略可以提高系统的风电消纳,减少系统的碳排放量,削峰填谷效果更为明显,并可有效降低系统运行的总成本.
Keyword ：
Brayton循环 Brayton循环 液化天然气 液化天然气 液态空气储能系统 液态空气储能系统 碳捕集电厂 碳捕集电厂 综合需求响应 综合需求响应
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| GB/T 7714 | 朱振山 , 盛明鼎 , 陈哲盛 . 计及液态空气储能与综合需求响应的综合能源系统低碳经济调度 [J]. | 电力自动化设备 , 2022 , 42 (12) : 1-8 . |
| MLA | 朱振山 et al. "计及液态空气储能与综合需求响应的综合能源系统低碳经济调度" . | 电力自动化设备 42 . 12 (2022) : 1-8 . |
| APA | 朱振山 , 盛明鼎 , 陈哲盛 . 计及液态空气储能与综合需求响应的综合能源系统低碳经济调度 . | 电力自动化设备 , 2022 , 42 (12) , 1-8 . |
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To address the operation optimization problem of integrated energy system, an electric-thermal-gas integrated energy system model consisting of gas turbine, waste heat boiler, organic Rankine cycle, air source heat pump and integrated demand response model is established. Then, an operation optimization method based on soft actor-critic algorithm is proposed. Firstly, the integrated energy system framework and equipment model are built. For the problem that the traditional integrated demand response model is not accurate, a neural network model which can represent the real response capability of users is established by combining historical data and the gate recurrent unit. The neural network model is applied to energy pricing scenarios. Secondly, an integrated energy system economic dispatch model is established with the objective of minimizing system energy purchase cost and wind and photovoltaic power curtailment cost. A deep reinforcement learning framework is used to formulate the optimization problem. The action space, state space, and reward function of the soft actor-critic agent interaction process with the environment are set up. The trained model can be directly used for real-time decision making without further iterative computation. The simulation results show that the proposed method can effectively perform energy management and energy pricing optimization to reduce the overall operation cost of the system. © 2022 Science Press. All rights reserved.
Keyword ：
Costs Costs Decision making Decision making Electric load dispatching Electric load dispatching Iterative methods Iterative methods Neural network models Neural network models Optimization Optimization Rankine cycle Rankine cycle Recurrent neural networks Recurrent neural networks Reinforcement learning Reinforcement learning Waste heat Waste heat
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| GB/T 7714 | Zhu, Zhenshan , Chen, Zhesheng , Sheng, Mingding . Operation Optimization of Park-level Integrated Energy System Based on Soft Actor-critic [J]. | High Voltage Engineering , 2022 , 48 (12) : 4949-4958 . |
| MLA | Zhu, Zhenshan et al. "Operation Optimization of Park-level Integrated Energy System Based on Soft Actor-critic" . | High Voltage Engineering 48 . 12 (2022) : 4949-4958 . |
| APA | Zhu, Zhenshan , Chen, Zhesheng , Sheng, Mingding . Operation Optimization of Park-level Integrated Energy System Based on Soft Actor-critic . | High Voltage Engineering , 2022 , 48 (12) , 4949-4958 . |
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Increasing the consumption of clean energy such as wind power and photovoltaic at the supply sideand reducing the carbon emission of traditional thermal power units are important means to realize the transformation of energy structure as well as carbon peaking and carbon neutralization. As for the low-carbon operation of energy systema low-carbon economic dispatching strategy of the integrated energy system that includes reservoir-type carbon capture power plantpower-to-gasLNGLiquefied Natural Gas vaporizing stationand liquid air energy storage using cold energy of LNG is proposed. And combined with the demand-side response strategythe joint dispatching from both supply and demand sides is adopted to achieve the economy and low carbon of integrated energy system. Finallythe improved IEEE 30-bus power system and 6-node natural gas system is taken as an example to verify that the proposed low-carbon economic dispatching strategy can improve the consumption of wind powerreduce the carbon emission of systemenhance the performance of peak shavingand reduce the total operation cost of system effectively. © 2022 Electric Power Automation Equipment Press. All rights reserved.
Keyword ：
Carbon capture Carbon capture Economics Economics Electric load dispatching Electric load dispatching Fossil fuel power plants Fossil fuel power plants Liquefied natural gas Liquefied natural gas Wind power Wind power
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| GB/T 7714 | Zhu, Zhenshan , Sheng, Mingding , Chen, Zhesheng . Low-carbon economic dispatching of integrated energy system considering liquid air energy storage and integrated demand response [J]. | Electric Power Automation Equipment , 2022 , 42 (12) : 1-13 . |
| MLA | Zhu, Zhenshan et al. "Low-carbon economic dispatching of integrated energy system considering liquid air energy storage and integrated demand response" . | Electric Power Automation Equipment 42 . 12 (2022) : 1-13 . |
| APA | Zhu, Zhenshan , Sheng, Mingding , Chen, Zhesheng . Low-carbon economic dispatching of integrated energy system considering liquid air energy storage and integrated demand response . | Electric Power Automation Equipment , 2022 , 42 (12) , 1-13 . |
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