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    METHOD AND SYSTEM FOR CHARGING ELECTRIC VEHICLES IN AGGREGATION
    33.
    发明申请
    METHOD AND SYSTEM FOR CHARGING ELECTRIC VEHICLES IN AGGREGATION 有权
    电动汽车充电的方法和系统

    公开(公告)号：US20140125280A1

    公开(公告)日：2014-05-08

    申请号：US14072203

    申请日：2013-11-05

    Applicant: TSINGHUA UNIVERSITY

    Inventor： Hongbin Sun ,  Qinglai Guo ,  Boming Zhang ,  Wenchuan Wu ,  Zhengshuo Li ,  Shujun Xin

    IPC: B60L11/18

    CPC classification number: B60L11/184 ,  B60L11/1844 ,  Y02E60/721 ,  Y02T10/7005 ,  Y02T10/7072 ,  Y02T90/121 ,  Y02T90/128 ,  Y02T90/14 ,  Y02T90/163 ,  Y02T90/169 ,  Y04S10/126 ,  Y04S30/14

    Abstract: Method and system for charging electric vehicles in an aggregation is provided. The method includes: obtaining a plurality of first charge power curves of a plurality of electric vehicles in the aggregation; obtaining a coordinating information of each of the plurality of electric vehicles from the plurality of first charge power curves; obtaining a first feedback charge power curve of each of the plurality of electric vehicles from the coordinating information and a charging cost curve of each of the plurality of electric vehicles; judging whether the first feedback charge power curve is same with the first charge power curve of each of the plurality of electric vehicles; if yes, charging each of the plurality of electric vehicles in accordance with the first charge power curve.

    Abstract translation: 提供了一种在聚合中为电动汽车充电的方法和系统。 该方法包括：获得聚合中的多个电动车辆的多个第一充电功率曲线; 从所述多个第一充电功率曲线获取所述多个电动车辆中的每一个的协调信息; 从所述多个电动车辆的协调信息和充电成本曲线获得所述多个电动车辆中的每一个的第一反馈充电功率曲线; 判断所述第一反馈充电功率曲线是否与所述多个电动车辆中的每一个的所述第一充电功率曲线相同; 如果是，则根据第一充电功率曲线对多个电动车辆中的每一个进行充电。

    Full-distributed reactive voltage control method and device, and storage medium
    34.
    发明授权
    Full-distributed reactive voltage control method and device, and storage medium 有权

    公开(公告)号：US12294218B2

    公开(公告)日：2025-05-06

    申请号：US18188553

    申请日：2023-03-23

    Applicant: TSINGHUA UNIVERSITY

    Inventor： Wenchuan Wu ,  Siyun Li ,  Bin Wang ,  Hongbin Sun ,  Qinglai Guo

    IPC: H02J3/18 ,  G06F113/04

    Abstract: A fully-distributed reactive voltage control method, includes: establishing a power grid reactive voltage optimization model of a power grid; parting the power grid reactive voltage optimization model into a plurality of area reactive voltage optimization models of a plurality of areas of the power grid; converting a power flow equation constraint in each of the plurality of area reactive voltage optimization models to a linear regression model; solving the linear regression model by using a robust recursive regression algorithm to obtain a solution result of the linear regression model; solving each of the plurality of area reactive voltage optimization models by using the solution result of the linear regression model, a gradient projection algorithm, and an alternating direction multiplier algorithm, so as to realize a reactive voltage optimization control of each of the plurality of areas.

    Method for multi-time scale voltage quality control based on reinforcement learning in a power distribution network
    35.
    发明授权
    Method for multi-time scale voltage quality control based on reinforcement learning in a power distribution network 有权

    公开(公告)号：US12254385B2

    公开(公告)日：2025-03-18

    申请号：US17389558

    申请日：2021-07-30

    Applicant: Tsinghua University

    Inventor： Wenchuan Wu ,  Haotian Liu ,  Hongbin Sun ,  Bin Wang ,  Qinglai Guo

    IPC: G06N20/00

    Abstract: A method for multi-time scale reactive voltage control based on reinforcement learning in a power distribution network is provided, which relates to the field of power system operation and control. The method includes: constituting an optimization model for multi-time scale reactive voltage control in a power distribution network based on a reactive voltage control object of a slow discrete device and a reactive voltage control object of a fast continuous device in the power distribution network; constructing a hierarchical interaction training framework based on a two-layer Markov decision process based on the model; setting a slow agent for the slow discrete device and setting a fast agent for the fast continuous device; and deciding action values of the controlled devices by each agent based on measurement information inputted, so as to realize the multi-time scale reactive voltage control while the slow agent and the fast agent perform continuous online learning.

    Transient stability assessment method for an electric power system
    37.
    发明授权
    Transient stability assessment method for an electric power system 有权

    公开(公告)号：US12100954B2

    公开(公告)日：2024-09-24

    申请号：US17383764

    申请日：2021-07-23

    Applicant: Tsinghua University

    Inventor： Hongbin Sun ,  Yanzhen Zhou ,  Qinglai Guo ,  Bin Wang ,  Wenchuan Wu ,  Zhengcheng Wang ,  Jian Lan

    IPC: G06F113/04 ,  G06N3/04 ,  G06N3/08 ,  H02J3/24

    CPC classification number: H02J3/24 ,  G06N3/04 ,  G06N3/08 ,  G06F2113/04 ,  H02J2203/20

    Abstract: A transient stability assessment method for an electric power system is disclosed. Transient stability tags and steady-state data of the electric power system before a failure occurs are collected from transient stability simulation data. Data sets under different predetermined failures are obtained based on a statistical result of the transient stability tags and a maximum-minimum method. A similarity evaluation index between different predetermined failures is constructed based on a Jaccard distance and a Hausdorff distance. Different predetermined failures are clustered based on a clustering algorithm. A parameters-shared siamese neural network is trained for different predetermined failures in each cluster to obtain a multi-task siamese neural network for the transient stability assessment. Transient stability assessment results of the electric power system under all the predetermined failures are obtained based on the statistical result of the transient stability tags and the multi-task siamese neural network for the transient stability assessment.

    Bilateral stochastic power grid dispatching method
    38.
    发明授权
    Bilateral stochastic power grid dispatching method 有权

    公开(公告)号：US11689024B2

    公开(公告)日：2023-06-27

    申请号：US17383658

    申请日：2021-07-23

    Applicant: Tsinghua University

    Inventor： Wenchuan Wu ,  Shuwei Xu ,  Boming Zhang ,  Hongbin Sun ,  Bin Wang ,  Qinglai Guo

    IPC: H02J3/38 ,  G05B19/042

    CPC classification number: H02J3/381 ,  G05B19/042 ,  G05B2219/2639 ,  H02J2203/20 ,  H02J2300/28

    Abstract: The disclosure relates to a two-side stochastic dispatching method for a power grid. By analyzing historical data of wind power, the Gaussian mixture distribution is fitted by software. For certain power system parameters, a two-side chance-constrained stochastic dispatching model is established. The hyperbolic tangent function is used to analyze and approximate cumulative distribution functions of random variables in the reserve demand constraint and the power flow constraint, to convert the two-side chance constraint into a deterministic constraint. The disclosure can have the advantage of using the hyperbolic tangent function to convert the two-side chance constraint containing risk levels and random variables into the solvable deterministic convex constraint, effectively improving the solution efficiency of the model, and providing decision makers with a more reasonable dispatching basis.

    Method for calculating control parameters of heating supply power of heating network
    40.
    发明授权
    Method for calculating control parameters of heating supply power of heating network 有权

    公开(公告)号：US11262713B2

    公开(公告)日：2022-03-01

    申请号：US16842600

    申请日：2020-04-07

    Applicant: Tsinghua University

    Inventor： Hongbin Sun ,  Qinglai Guo ,  Bin Wang ,  Zhaoguang Pan

    IPC: G05B13/04 ,  F01K27/00 ,  G06F30/20 ,  H02J3/38

    Abstract: A method for calculating control parameters of a heating supply power of a heating network, pertaining to the technical field of operation and control of a power system containing multiple types of energy. The method: establishing a heating network simulation model that simulates a thermal dynamic process of the heating network; starting an upward simulation based on the heating network simulation model to obtain first control parameters from a set of up adjustment amounts; starting a downward simulation based on the heating network simulation model, to obtain second control parameters from a set of down adjustment amounts.

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