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    Detection of false data injection attacks in power systems using multiplex invariant networks and domain knowledge
    1.
    发明授权
    Detection of false data injection attacks in power systems using multiplex invariant networks and domain knowledge 有权

    公开(公告)号：US10585123B2

    公开(公告)日：2020-03-10

    申请号：US16151544

    申请日：2018-10-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kiyoshi Nakayama ,  Chenrui Jin ,  Ratnesh Sharma ,  Nikhil Muralidhar

    IPC: G01R19/25 ,  G06F17/16 ,  G06K9/72 ,  G06Q50/06

    Abstract: A computer-implemented method, system, and computer program product are provided for anomaly detection in a power system. The method includes receiving, by a processor-device, a plurality of measurements from a plurality of meters throughout the power system. The method also includes generating, by the processor-device, temporal causal networks based on pair-wise relationships between the plurality of measurements from the plurality of meters over time. The method additionally includes determining, by the processor-device, invariant relationships for the plurality of meters between the temporal causal networks. The method further includes predicting, by the processor-device, an anomaly from the invariant relationships for the plurality of meters with a residual anomaly threshold. The method also includes disabling one of the plurality of meters that originated the anomaly.

    Machine learning based demand charge
    2.
    发明授权
    Machine learning based demand charge 有权

    公开(公告)号：US10333307B2

    公开(公告)日：2019-06-25

    申请号：US15833343

    申请日：2017-12-06

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ratnesh Sharma ,  Ramin Moslemi

    IPC: H02J3/14 ,  H02J3/32 ,  H02J7/00 ,  G06F17/18 ,  G06Q10/04 ,  G06N20/00 ,  G05B15/02 ,  H02J3/00

    Abstract: A computer-implemented method, system, and computer program product are provided for demand charge management. The method includes receiving an active power demand for a facility, a current load demand charge threshold (DCT) profile for the facility, and a plurality of previously observed load DCT profiles. The method also includes generating a forecast model from a data set of DCT values based on the current load DCT profile for the facility and the plurality of previously observed load DCT profiles. The method additionally includes forecasting a monthly DCT value for the facility using the forecast model. The method further includes preventing actual power used from a utility from exceeding the next month DCT value by discharging a battery storage system into a behind the meter power infrastructure for the facility.

    DEMAND CHARGE MINIMIZATION IN BEHIND-THE-METER ENERGY MANAGEMENT SYSTEMS
    3.
    发明申请
    DEMAND CHARGE MINIMIZATION IN BEHIND-THE-METER ENERGY MANAGEMENT SYSTEMS 审中-公开

    公开(公告)号：US20190140465A1

    公开(公告)日：2019-05-09

    申请号：US16180415

    申请日：2018-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ratnesh Sharma ,  Korosh Vatanparver

    IPC: H02J7/00 ,  G06Q50/06

    Abstract: Systems and methods for controlling behind-the meter energy storage/management systems (EMSs) for battery-optimized demand charge minimized operations, including determining an optimal monthly demand charge threshold based on a received customer load profile and a customer load profile and savings. The determining of the monthly demand charge threshold includes iteratively performing daily optimizations to determine a daily optimal demand threshold for each day of a month, selecting a monthly demand threshold by clustering the daily optimal demand thresholds for each day of the month into groups, and determining a dominant group representative of a load pattern for a next month. A mean demand threshold for the dominant group is selected as the monthly demand threshold, and continuous battery-optimized demand charge minimized EMS operations are provided based on the monthly demand threshold using a real-time controller configured for overriding the optimal charging/discharging profiles when a monthly demand threshold violation is detected.

    SYSTEM AND METHOD FOR REDUCING TIME-AVERAGED PEAK CHARGES
    7.
    发明申请
    SYSTEM AND METHOD FOR REDUCING TIME-AVERAGED PEAK CHARGES 审中-公开

    公开(公告)号：US20170310140A1

    公开(公告)日：2017-10-26

    申请号：US15498034

    申请日：2017-04-26

    Applicant: NEC Laboratories America, Inc.

    Inventor： Babak Asghari ,  Ali Hooshmand ,  Ratnesh Sharma

    IPC: H02J7/00 ,  G05B13/02

    CPC classification number: H02J3/32 ,  H02J2003/003 ,  H02J2003/146 ,  Y04S20/224

    Abstract: Systems and methods for minimizing demand charges, including determining one or more optimal monthly demand charge thresholds based on historical load data, time of use charges, demand charges, and energy storage unit size for one or more end users. A grid power dispatch setpoint is calculated for a particular time step based on a daily load forecast and a daily economic dispatch solution based on the determined optimal monthly demand charge thresholds. A grid power dispatch setpoint for a subsequent time step is determined by iteratively solving the daily energy dispatch for the subsequent time step to determine an optimal grid power dispatch setpoint. Energy and demand charges are minimized by controlling charging and discharging operations for the energy storage unit in real-time based on the determined optimal grid power dispatch setpoint.

    MODULAR MULTILVEL CONVERTER AND CONTROL FRAMEWORK FOR HYBRID ENERGY STORAGE
    9.
    发明申请
    MODULAR MULTILVEL CONVERTER AND CONTROL FRAMEWORK FOR HYBRID ENERGY STORAGE 有权
    用于混合动力储存的模块化多变换器和控制框架

    公开(公告)号：US20160105020A1

    公开(公告)日：2016-04-14

    申请号：US14878817

    申请日：2015-10-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Feng Guo ,  Yanzhu Ye ,  Ratnesh Sharma

    IPC: H02J1/00

    CPC classification number: H02J7/0054 ,  H02J3/32 ,  H02J7/345 ,  H02J2007/0059 ,  H02M2001/0003 ,  H02M2007/4835

    Abstract: A modular multilevel converter for hybrid energy storage includes three phases connectable in series to a battery, and in parallel to one another. Each phase includes at least two arms of sub-modules and buffer inductors, and each of the sub-modules comprises a half-bridge and an ultracapacitor. A two layer controller, including a coordination layer and a converter layer, is configured to independently control battery output power and ultracapacitor output power, and to distribute a power load between the battery and the ultracapacitor to optimize the performance of a hybrid energy storage system.

    Abstract translation: 用于混合能量存储的模块化多电平转换器包括与电池串联连接并且彼此并联的三相。 每相包括子模块和缓冲电感器的至少两个臂，并且每个子模块包括半桥和超级电容器。 包括协调层和转换器层的双层控制器被配置为独立地控制电池输出功率和超级电容器输出功率，并且在电池和超级电容器之间分配功率负载以优化混合能量存储系统的性能。

    INTEGRATED OPTIMAL PLACEMENT, SIZING, AND OPERATION OF ENERGY STORAGE DEVICES IN ELECTRIC DISTRIBUTION NETWORKS
    10.
    发明申请
    INTEGRATED OPTIMAL PLACEMENT, SIZING, AND OPERATION OF ENERGY STORAGE DEVICES IN ELECTRIC DISTRIBUTION NETWORKS 审中-公开
    电力分配网络中的能源存储设备的集成最佳放置，尺寸和操作

    公开(公告)号：US20150261892A1

    公开(公告)日：2015-09-17

    申请号：US14562915

    申请日：2014-12-08

    Applicant: NEC Laboratories America, Inc.

    Inventor： Mohammad C. Bozchalui ,  Ratnesh Sharma ,  Ali Hooshmand

    IPC: G06F17/50 ,  G06F17/10

    CPC classification number: H02J3/32 ,  Y02E70/30

    Abstract: A method and system are provided. The method includes co-optimizing a placement, a sizing, and an operation schedule of at least one energy storage system in an energy distribution system. The energy distribution system further has at least one renewable energy resource and at least one distributed energy resource. The co-optimizing step includes generating a placement-sizing-scheduling co-optimization model of the at least one energy storage system by integrating therein a distribution optimal power flow optimization model of the energy distribution system and components thereof. The distribution optimal power flow optimization model integrates therein at least an energy storage system model, a renewable energy resource model, and a distributed energy resource model. The co-optimizing step further includes optimally determining, using a processor-based placement-sizing-scheduling optimizer, the placement, the sizing, and the operation schedule of the at least one energy storage system based on the placement-sizing-scheduling co-optimization model.

    Abstract translation: 提供了一种方法和系统。 该方法包括共同优化能量分配系统中的至少一个能量存储系统的位置，尺寸和操作进度。 能量分配系统还具有至少一个可再生能源和至少一个分布式能源。 共同优化步骤包括通过在其中集成能量分配系统的分布最优功率流优化模型及其组件来生成至少一个能量存储系统的放置调度协同优化模型。 分布式最优潮流优化模型至少在其中集成了能量存储系统模型，可再生能源资源模型和分布式能源资源模型。 共同优化步骤还包括基于放置调整调度共享来最优地确定使用基于处理器的布局调度优化器，所述至少一个能量存储系统的布局，尺寸和操作调度， 优化模型。

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