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    METHOD AND SYSTEM FOR CHARGING ELECTRIC VEHICLES IN AGGREGATION
    32.
    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。

    Method of accessing dynamic flexibility for virtual power plant
    33.
    发明授权
    Method of accessing dynamic flexibility for virtual power plant 有权

    公开(公告)号：US12136817B2

    公开(公告)日：2024-11-05

    申请号：US17446642

    申请日：2021-09-01

    Applicant: TSINGHUA UNIVERSITY

    Inventor： Wenchuan Wu ,  Siyuan Wang ,  Hongbin Sun ,  Bin Wang ,  Qinglai Guo

    IPC: H02J3/28 ,  G06F17/11 ,  H02J3/38

    Abstract: The present invention relates to a method of assessing dynamic flexibility for a virtual power plant, which belongs to the technical field of operating and controlling a power system. The method equals a virtual power plant to an equivalent energy storage device and an equivalent generator and decouples a network constraint condition between the two types of devices through a Robust optimization method. Subsequently, by using a two-stage Robust optimization algorithm, parameters of the equivalent energy storage device and the equivalent generator are calculated and finally accurate depiction is realized on adjusting ability of a distributed resource, so as to provide a scientific decision basis for the virtual power plant to participate in grid control, such that it has a great value in an actual application.

    Power grid reactive voltage control model training method and system
    34.
    发明授权
    Power grid reactive voltage control model training method and system 有权

    公开(公告)号：US11689021B2

    公开(公告)日：2023-06-27

    申请号：US17025154

    申请日：2020-09-18

    Applicant: TSINGHUA UNIVERSITY

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

    IPC: H02J3/18 ,  G06F30/20

    CPC classification number: H02J3/18 ,  G06F30/20 ,  H02J2203/20

    Abstract: A power grid reactive voltage control model training method. The method comprises: establishing a power grid simulation model; establishing a reactive voltage optimization model, according to a power grid reactive voltage control target; building interactive training environment based on Adversarial Markov Decision Process, in combination with the power grid simulation model and the reactive voltage optimization model; training the power grid reactive voltage control model through a joint adversarial training algorithm; and transferring the trained power grid reactive voltage control model to an online system. The power grid reactive voltage control model trained by using the method according to the present disclosure has transferability as compared with the traditional method, and may be directly used for online power grid reactive voltage control.

    Method, apparatus, and storage medium for planning power distribution network
    35.
    发明授权
    Method, apparatus, and storage medium for planning power distribution network 有权

    公开(公告)号：US11514206B2

    公开(公告)日：2022-11-29

    申请号：US17136941

    申请日：2020-12-29

    Applicant: Tsinghua University

    Inventor： Wenchuan Wu ,  Boming Zhang ,  Zihao Li ,  Hongbin Sun ,  Bin Wang ,  Qinglai Guo

    IPC: G05B19/042 ,  H02J3/00 ,  G06F30/13

    Abstract: The disclosure provides a method for planning a power distribution network, an apparatus for planning a power distribution network, and a storage medium. The method includes: establishing a model for planning the power distribution network, the model including a target function and constraints, the target function for minimizing a cost of the power distribution network when branches and nodes are installed into the power distribution network, the nodes including transformers and substations, the constraints including a power balance constraint of the power distribution network, a power constraint of the branches, a power constraint of the transformers, a radial operation constraint of the power distribution network, a fault constraint, a calculation constraint of indices of a reliability, a constraint of the indices of the reliability, and a logic constraint; and solving the model to determine whether the branches and the nodes are installed into the power distribution network.

    Power grid reactive voltage control method based on two-stage deep reinforcement learning
    36.
    发明授权
    Power grid reactive voltage control method based on two-stage deep reinforcement learning 有权

    公开(公告)号：US11442420B2

    公开(公告)日：2022-09-13

    申请号：US17026364

    申请日：2020-09-21

    Applicant: TSINGHUA UNIVERSITY

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

    IPC: G05B19/00 ,  G05B19/042 ,  G06N20/00

    Abstract: A power grid reactive voltage control method and control system based on two-stage deep reinforcement learning, comprising steps of: building interactive training environment based on Markov decision process, according to a regional power grid simulation model and a reactive voltage optimization model; training a reactive voltage control model offline by using a SAC algorithm, in the interactive training environment based on Markov decision process; deploying the reactive voltage control model to a regional power grid online system; and acquiring operating state information of the regional power grid, updating the reactive voltage control model, and generating an optimal reactive voltage control policy. As compared with the existing power grid optimizing method based on reinforcement learning, the online control training according to the present disclosure has costs and safety hazards greatly reduced, and is more suitable for deployment in an actual power system.

    POWER GRID REACTIVE VOLTAGE CONTROL METHOD BASED ON TWO-STAGE DEEP REINFORCEMENT LEARNING
    38.
    发明申请
    POWER GRID REACTIVE VOLTAGE CONTROL METHOD BASED ON TWO-STAGE DEEP REINFORCEMENT LEARNING 有权

    公开(公告)号：US20210356923A1

    公开(公告)日：2021-11-18

    申请号：US17026364

    申请日：2020-09-21

    Applicant: TSINGHUA UNIVERSITY

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

    IPC: G05B19/042 ,  G06N20/00

    Abstract: A power grid reactive voltage control method and control system based on two-stage deep reinforcement learning, comprising steps of: building interactive training environment based on Markov decision process, according to a regional power grid simulation model and a reactive voltage optimization model; training a reactive voltage control model offline by using a SAC algorithm, in the interactive training environment based on Markov decision process; deploying the reactive voltage control model to a regional power grid online system; and acquiring operating state information of the regional power grid, updating the reactive voltage control model, and generating an optimal reactive voltage control policy. As compared with the existing power grid optimizing method based on reinforcement learning, the online control training according to the present disclosure has costs and safety hazards greatly reduced, and is more suitable for deployment in an actual power system.

    Method and device for controlling active distribution network
    40.
    发明授权
    Method and device for controlling active distribution network 有权

    公开(公告)号：US10291027B2

    公开(公告)日：2019-05-14

    申请号：US15015225

    申请日：2016-02-04

    Applicant: Tsinghua University

    Inventor： Wenchuan Wu ,  Boming Zhang ,  Hongbin Sun ,  Zhuang Tian ,  Qinglai Guo ,  Bin Wang

    IPC: H02J3/38 ,  G05B17/02 ,  H02J3/06 ,  H02J3/16 ,  H02J3/48 ,  H02J3/50 ,  G05F1/66 ,  H02J3/18 ,  H02J3/00

    Abstract: The present disclosure provides a method and a device for controlling an active distribution network, relating to the field of power system operation and control technology. The method includes: creating a power loss objective function; determining first power flow equations; obtaining second power flow equations by performing linearization on the first power flow equations; determining a sub-scale adjustment model of a transformer; obtaining a linearized model of the transformer by performing linearization on the sub-scale adjustment model; obtaining control parameters by solving the power loss objective function according to the second power flow equations, the linearized model of the transformer, an operation constraint of the continuous reactive power compensator, an operation constraint of the grouping switching capacitor, an operation constraint of the distributed generator and a safety operation constraint in the active distribution network, such that the active distribution network is controlled by the obtained parameters to minimize power loss.

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