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学者姓名：刘丽军
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为了消解规模化电动汽车(electrical vehicle,EV)无序充电对交通路网、充电站和配电网运行稳定性带来的负面影响,提出一种基于"车-路-站-网"信息耦合的电动汽车有序充电策略.首先,构建"车-路-站-网"信息耦合模型和动态Floyd最短时间路径搜索模型,为EV用户搜寻最短耗时路径.其次,基于"车-路-站-网"实时状态预测EV用户选择不同路径前往各充电站快充产生的充电决策因素,通过层次分析法和改进CRITIC法综合EV用户充电决策因素的主客观权重,利用Topsis方法决策EV用户的最优充电路径.最后,提出EV用户慢充优化策略,对返程EV用户的慢充负荷进行优化,结合EV慢充和快充负荷,进一步实现配电网负荷的削峰填谷.仿真结果表明,所提出的EV有序充电策略能够同时提升"车-路-站-网"多方运行水平.
Keyword ：
Topsis决策 Topsis决策 交通路网 交通路网 动态Floyd搜索 动态Floyd搜索 有序充电策略 有序充电策略 电动汽车 电动汽车
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| GB/T 7714 | 刘丽军 , 陈昌 , 胡鑫 et al. 基于"车-路-站-网"信息耦合的电动汽车有序充电策略 [J]. | 高电压技术 , 2024 , 50 (2) : 709-721 . |
| MLA | 刘丽军 et al. "基于"车-路-站-网"信息耦合的电动汽车有序充电策略" . | 高电压技术 50 . 2 (2024) : 709-721 . |
| APA | 刘丽军 , 陈昌 , 胡鑫 , 林钰芳 . 基于"车-路-站-网"信息耦合的电动汽车有序充电策略 . | 高电压技术 , 2024 , 50 (2) , 709-721 . |
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With the massive participation of new energy in the operation of distribution network (DN), the uncertainties arising from both load and renewable generation have brought challenges to its secure and reliable operation. In this paper, a model of day-ahead and intraday optimal scheduling based on dynamic partitioning (DP) of DN is constructed to realize the refinement of operation in both temporal and spatial dimensions, as well as to improve the reliability of system operation. For the strong fluctuation characteristics of the source-load in DN, the DP is achieved under the premise that the partition structure in each period is tight and the internal power supply has the ability to balance the power fluctuations. In order to improve the regional autonomy of DN, a day-ahead multi-objective optimal scheduling model is constructed based on the DP with the optimization objectives of weak inter-partition power coupling, lowest deficiency rate of flexibility, and lowest operating cost. And a scheme for articulation is proposed to ensure the effective guidance of day-ahead for intraday. Then the intraday dispatch model is constructed by shifting the focus to the incorporate characteristics of DP and quickly smooth the net load fluctuations. Finally, the performance and reliability of the proposed models are verified via case studies.
Keyword ：
Dynamic partitioning Dynamic partitioning Prediction accuracy Prediction accuracy Two-time-scale Two-time-scale Uncertainty Uncertainty
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| GB/T 7714 | Liu, Lijun , Hu, Xin , Chen, Chang et al. Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning [J]. | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 160 . |
| MLA | Liu, Lijun et al. "Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning" . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 160 (2024) . |
| APA | Liu, Lijun , Hu, Xin , Chen, Chang , Wu, Ruixing , Wu, Tong , Huang, Huiyu . Research on day-ahead and intraday scheduling strategy of distribution network based on dynamic partitioning . | INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS , 2024 , 160 . |
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With the increasing penetration of renewable energy, short-term energy storage technology represented by electrochemical energy storage will make it difficult to meet the demand for new power systems to consume renewable energy, and at the same time, flexibility will become the core and key to the operational characteristics of the system. Therefore, this paper proposes a joint long-term and short-term energy storage planning method considering the flexibility of supply-demand balance. First, for the characteristics of energy storage technologies in different time scales, this paper integrates the dual regulation of short-term power and long-term energy, considers the flexibility of supply-demand balance combines the price-based demand response mechanism, and establishes a joint planning model of long-term and short-term energy storage with the lowest annualized comprehensive cost of the system as the optimization goal. Second, to simplify the problem size, typical scenarios are extracted each month to re-sculpt the whole year's time sequence using a deep convolutional embedding clustering algorithm, and the model is optimally solved through two stages of capacity planning of wind, photovoltaic, and energy storage and flexibility calibration. Finally, the effectiveness of the planning method proposed in this paper in considering the economics and operational flexibility of a new power system with high penetration of renewable energy in the future is verified by using a region in eastern China as an example. © 2024 Power System Technology Press. All rights reserved.
Keyword ：
Clean energy Clean energy
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| GB/T 7714 | Liu, Lijun , Huang, Weidong , Chen, Zekai et al. Joint Long-term and Short-term Energy Storage Planning for New Power System Considering Supply and Demand Balance of Flexibility [J]. | Power System Technology , 2024 , 48 (12) : 4908-4917 . |
| MLA | Liu, Lijun et al. "Joint Long-term and Short-term Energy Storage Planning for New Power System Considering Supply and Demand Balance of Flexibility" . | Power System Technology 48 . 12 (2024) : 4908-4917 . |
| APA | Liu, Lijun , Huang, Weidong , Chen, Zekai , Jiang, Yiqing , Huang, Junqiang , Chen, Feixiong . Joint Long-term and Short-term Energy Storage Planning for New Power System Considering Supply and Demand Balance of Flexibility . | Power System Technology , 2024 , 48 (12) , 4908-4917 . |
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The source and load uncertainties arising from increased applications of renewable energy sources such as wind and photovoltaic energy in the power system have had adverse effects on optimal planning and dispatching. Models for generating typical renewable energy and load scenarios are constructed to reduce such effects and improve the applicability of a planning and optimal dispatching model of power systems with a high proportion of renewable energy. The traditional clustering-based model for representing such scenarios cannot handle high-dimensional time-series data and consequently the feature-related information obtained cannot fully reflect the characteristics of the data. Thus, a deep convolutional embedded clustering model based on multi-head self-attention is proposed. First, a variational mode decomposition model is optimized to reduce the influence of noise-related signals on the feature extraction. The deep features are then extracted from the data using an improved convolutional autoencoder, and the appropriate number of clusters is determined using the elbow method. Following this, the network parameters are optimized based on the sum of losses during reconstruction and clustering. Subsequently, typical scenarios are then generated based on the optimized network model. Finally, the proposed method is evaluated based on data visualization and evaluation metrics. It is shown that the quality of features and the accuracy of clustering can be effectively improved by the proposed scenario generation method.
Keyword ：
Deep embedding for clustering Deep embedding for clustering extracting features of time-series data extracting features of time-series data multi-head self-attention mechanism multi-head self-attention mechanism scenario generation scenario generation uncertainty uncertainty
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| GB/T 7714 | Liu, Lijun , Hu, Xin , Chen, Junsheng et al. Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-head Self-attention [J]. | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2024 , 9 (1) : 122-132 . |
| MLA | Liu, Lijun et al. "Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-head Self-attention" . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS 9 . 1 (2024) : 122-132 . |
| APA | Liu, Lijun , Hu, Xin , Chen, Junsheng , Wu, Ruixing , Chen, Feixiong . Embedded Scenario Clustering for Wind and Photovoltaic Power, and Load Based on Multi-head Self-attention . | PROTECTION AND CONTROL OF MODERN POWER SYSTEMS , 2024 , 9 (1) , 122-132 . |
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In order to eliminate the negative impacts of disorderly charging of large-scale electric vehicles (EVs) on the operational stability of transportation road networks, charging stations, and distribution networks, we propose an orderly charging strategy for EVs based on 'vehicle-road-station-network' (VRSN) information coupling system. Firstly, a VRSN system and a dynamic floyd shortest time path search model are constructed to find the shortest time-consuming path for EV users. Secondly, based on the real-time state of VRSN, we predict the charging decision factors for EV users to choose different paths to each charging station for fast charging, synthesize the subjective and objective weights of the charging decision factors for EV users through hierarchical analysis and the improved CRITIC method, and decide the optimal charging paths for EV users by using Topsis method. Finally, the slow charging optimization strategy for EV users is proposed to optimize the slow charging load of EV users after their return trips, and the EV slow charging and fast charging loads are combined to further realize peak shaving and valley filling of the distribution network load. Simulation results show that the proposed EV orderly charging strategy can simultaneously improve the operation level of VRSN system. © 2024 Science Press. All rights reserved.
Keyword ：
Charging (batteries) Charging (batteries) Electric loads Electric loads Electric vehicles Electric vehicles Motor transportation Motor transportation Roads and streets Roads and streets Search engines Search engines
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| GB/T 7714 | Liu, Lijun , Chen, Chang , Hu, Xin et al. Ordered Charging Strategy for Electric Vehicles Based on the Information Coupling of Vehicle-Road-Station-Grid [J]. | High Voltage Engineering , 2024 , 50 (2) : 693-703 . |
| MLA | Liu, Lijun et al. "Ordered Charging Strategy for Electric Vehicles Based on the Information Coupling of Vehicle-Road-Station-Grid" . | High Voltage Engineering 50 . 2 (2024) : 693-703 . |
| APA | Liu, Lijun , Chen, Chang , Hu, Xin , Lin, Yufang . Ordered Charging Strategy for Electric Vehicles Based on the Information Coupling of Vehicle-Road-Station-Grid . | High Voltage Engineering , 2024 , 50 (2) , 693-703 . |
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As renewable energy penetration increases, flexibility will become the core and key of the operating characteristics. The proposal of the 'double carbon' target also makes the low-carbon transition an important goal for the future development. This paper firstly introduces a diversionary carbon capture plant and a liquid storage tank to establish a flexible carbon capture system. Secondly, a low-carbon economic dispatch model is established by combining the price-based demand response mechanism and taking into account the flexibility supply-demand balance. Finally, this dispatch method is verified to be able to take into account the flexibility in power systems, improve operation economy, and promote the system's transition to low carbon by referring to a case study in an eastern region of China. © 2024 IEEE.
Keyword ：
Carbon capture Carbon capture Electric load dispatching Electric load dispatching
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| GB/T 7714 | Huang, Weidong , Jiang, Yiqing , Lin, Junxuan et al. A Low-carbon Economic Dispatch Methodology that Considers Flexibility Supply-demand Balance and Carbon Capture [C] . 2024 : 134-139 . |
| MLA | Huang, Weidong et al. "A Low-carbon Economic Dispatch Methodology that Considers Flexibility Supply-demand Balance and Carbon Capture" . (2024) : 134-139 . |
| APA | Huang, Weidong , Jiang, Yiqing , Lin, Junxuan , Huang, Junqiang , Wang, Yuqiang , Liu, Lijun . A Low-carbon Economic Dispatch Methodology that Considers Flexibility Supply-demand Balance and Carbon Capture . (2024) : 134-139 . |
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In order to achieve the secure and economical dispatch of high-proportion renewable energy distribution systems, an interval optimization dispatching method that takes into account the uncertainty of wind, PV power and load demand prediction errors and the flexibility of the network is proposed. To avoid the information loss in traditional methods, the conservatism of traditional interval-based methods, and the aggressiveness of interval truncation methods, the paper describes the interval boundaries of uncertainty factors based on the probability box theory and conditional value-at-risk methods. Subsequently, the time coupling constraint of the dispatching plan is established based on the real-time adjustment capability of the flexibility resources, and the line transmission flexibility cross-limit constraint is established to improve the adaptability of the proposed method. The goal of the optimization is to minimize the total operating cost, and the genetic algorithm is used for solving. The examples based on the IEEE 33-bus test system, the actual distribution network of a region and the PG&E 69 node network are analyzed, and the results demonstrate that the proposed model ensures distribution lines to meet transmission capacity constraints while achieving optimal system operating costs. © 2024 Science Press. All rights reserved.
Keyword ：
Distributed power generation Distributed power generation Electric load dispatching Electric load dispatching Electric power transmission Electric power transmission Electric power transmission networks Electric power transmission networks Genetic algorithms Genetic algorithms Operating costs Operating costs Probability distributions Probability distributions Value engineering Value engineering
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| GB/T 7714 | Xu, Hanwei , Liu, Lijun , Huang, Huiyu et al. Interval Optimization Dispatching Method Considering Flexibility of the Distribution Net-work Based on P-Box and CVaR [J]. | High Voltage Engineering , 2024 , 50 (4) : 1478-1487 . |
| MLA | Xu, Hanwei et al. "Interval Optimization Dispatching Method Considering Flexibility of the Distribution Net-work Based on P-Box and CVaR" . | High Voltage Engineering 50 . 4 (2024) : 1478-1487 . |
| APA | Xu, Hanwei , Liu, Lijun , Huang, Huiyu , Xie, Feng , Zhang, Linyao , Chen, Feixiong . Interval Optimization Dispatching Method Considering Flexibility of the Distribution Net-work Based on P-Box and CVaR . | High Voltage Engineering , 2024 , 50 (4) , 1478-1487 . |
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随着可再生能源渗透率的不断提高,以电化学储能为代表的短期储能技术将难以满足新型电力系统消纳可再生能源的需求,与此同时,灵活性也将成为系统运行特性的核心和关键.因此,提出了一种考虑灵活性供需平衡的长短期储能联合规划方法.首先,针对不同时间尺度储能技术的特点,统筹短时功率和长期能量的双重调节,考虑灵活性供需平衡并结合价格型需求响应机制,建立了以系统年化综合成本最低为优化目标的长短期储能联合规划模型.其次,为简化问题规模,利用深度卷积嵌入聚类算法在各月份提取典型场景重新刻画全年时序,并通过风光储容量规划和灵活性校验2个阶段的迭代优化求解模型.最后,以中国东部某地区为算例,验证了所提规划方法在兼顾未来可再生能源高渗透的新型电力系统规划经济性和运行灵活性方面的有效性.
Keyword ：
新型电力系统 新型电力系统 深度嵌入聚类 深度嵌入聚类 灵活性 灵活性 长短期储能联合 长短期储能联合 风光储容量规划 风光储容量规划 高比例可再生能源 高比例可再生能源
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| GB/T 7714 | 刘丽军 , 黄伟东 , 陈泽楷 et al. 考虑灵活性供需平衡的新型电力系统长短期储能联合规划 [J]. | 电网技术 , 2024 , 48 (12) : 4908-4917,中插18-中插23 . |
| MLA | 刘丽军 et al. "考虑灵活性供需平衡的新型电力系统长短期储能联合规划" . | 电网技术 48 . 12 (2024) : 4908-4917,中插18-中插23 . |
| APA | 刘丽军 , 黄伟东 , 陈泽楷 , 蒋怡晴 , 黄俊强 , 陈飞雄 . 考虑灵活性供需平衡的新型电力系统长短期储能联合规划 . | 电网技术 , 2024 , 48 (12) , 4908-4917,中插18-中插23 . |
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针对大规模新能源及储能接入配电网导致碳排放溯源困难以及通信负担过重的问题,提出一种考虑碳排放分摊的配电网区域分布式控制方法.首先,基于配电网的拓扑结构及节点信息,采用谱聚类法对配电网进行集群划分;其次,量化新能源及储能元件对碳流分布的影响,提出适用于新型电力系统的碳流计算方法,解决储能运行导致碳排放时空转移的溯源问题,并结合碳势概念提出反映区域用电碳排放的动态碳排放因子;最后,基于集群边界耦合关系对配电网潮流、碳流进行解耦,并采用一致性交替方向乘子法对配电网的分布式调度模型并行求解.仿真结果表明,所提方法在保护各区域数据隐私、降低通信压力的前提下,实现区域碳排放的精准溯源以及碳排放责任的合理分摊.
Keyword ：
交替方向乘子法 交替方向乘子法 区域分布式 区域分布式 碳排放流 碳排放流 碳责任分摊 碳责任分摊 配电网 配电网
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| GB/T 7714 | 刘丽军 , 胡鑫 , 林锟 et al. 计及碳排放分摊的配电网分布式低碳调度策略 [J]. | 中国电机工程学报 , 2024 , 44 (24) : 9594-9606,中插8 . |
| MLA | 刘丽军 et al. "计及碳排放分摊的配电网分布式低碳调度策略" . | 中国电机工程学报 44 . 24 (2024) : 9594-9606,中插8 . |
| APA | 刘丽军 , 胡鑫 , 林锟 , 陈泽楷 , 陈飞雄 . 计及碳排放分摊的配电网分布式低碳调度策略 . | 中国电机工程学报 , 2024 , 44 (24) , 9594-9606,中插8 . |
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As new energy sources such as wind and photovoltaic connected to the distribution network in large quantities, how to describe the uncertainty of wind, photovoltaic and load (WPL) variations and generate typical operating scenarios is becoming more and more important for the operation scheduling and planning of the distribution network. Aiming at the problems of traditional clustering methods, which are difficult to deal with high-dimensional data, the separation of feature extraction process and clustering process, and the lack of clustering performance, this paper proposes a scenario generation method based on deep fuzzy k-means (DFKM) for WPL uncertainty sources. First, based on one-dimensional convolutional auto-encoder, we extract the temporal coupling features of the WPL data, and then use the fuzzy k-means with adaptive loss function to perform clustering in the embedded low-dimensional feature space. In the model optimization process, the feature extraction process and the clustering process are combined to obtain the final clustering results and generate typical scenarios based on different types of centers. The example takes the historical data of a region in southeast China as the research object, then analyzes the clustering indexes to verify the effectiveness and superiority of the proposed method. © 2023 IEEE.
Keyword ：
Cluster analysis Cluster analysis Extraction Extraction Feature extraction Feature extraction K-means clustering K-means clustering
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| GB/T 7714 | Hu, Xin , Huang, Huiyu , Liu, Lijun . A Scenario Generation Method for Wind, PV and Load Uncertainty Based on DFKM [C] . 2023 : 1536-1542 . |
| MLA | Hu, Xin et al. "A Scenario Generation Method for Wind, PV and Load Uncertainty Based on DFKM" . (2023) : 1536-1542 . |
| APA | Hu, Xin , Huang, Huiyu , Liu, Lijun . A Scenario Generation Method for Wind, PV and Load Uncertainty Based on DFKM . (2023) : 1536-1542 . |
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