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    Semiconductor Device for Hybrid Energy Storage Systems
    43.
    发明申请
    Semiconductor Device for Hybrid Energy Storage Systems 审中-公开
    混合储能系统半导体装置

    公开(公告)号：US20160299195A1

    公开(公告)日：2016-10-13

    申请号：US15093263

    申请日：2016-04-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Feng Guo ,  Ratnesh Sharma

    IPC: G01R31/36 ,  H02M7/537 ,  H02M1/08 ,  H02J1/00

    CPC classification number: G01R31/3648 ,  G01R31/2601 ,  G06F17/5068 ,  G06N7/02 ,  H02J1/00 ,  H02M1/08 ,  H02M7/483 ,  H02M7/537 ,  H02M2001/0054 ,  H02M2007/4835 ,  Y02B70/1491

    Abstract: Systems and methods for semiconductor device selection, including identifying a worst operation condition for a plurality of semiconductor devices in a Modular Multilevel Converter (MMC). The identifying includes determining power losses for each of the semiconductor devices under a plurality of operation conditions, and calculating a maximum junction temperature for each of the plurality of semiconductor devices at each of the plurality of operation conditions. A maximum junction temperature under the identified worst operation condition is determined for each of a plurality of commercially available semiconductor devices which satisfy a threshold voltage rating, and all semiconductor devices which satisfy the threshold voltage rating and a maximum junction temperature threshold condition are compared to identify a semiconductor device with a lowest system cost.

    Abstract translation: 用于半导体器件选择的系统和方法，包括识别模块化多电平转换器（MMC）中的多个半导体器件的最差工作条件。 识别包括在多个操作条件下确定每个半导体器件的功率损耗，并且在多个操作条件中的每一个处计算多个半导体器件中的每一个的最大结温。 确定满足阈值电压额定值的多个市售半导体器件中的每一个的所识别的最差工作条件下的最大结温，并且将满足阈值电压额定值和最大结温阈值条件的所有半导体器件进行比较以识别 具有最低系统成本的半导体器件。

    OPTIMIZING POWER FLOWS USING HARMONY SEARCH WITH MACHINE LEARNING
    44.
    发明申请
    OPTIMIZING POWER FLOWS USING HARMONY SEARCH WITH MACHINE LEARNING 审中-公开
    优化使用和谐搜索与机器学习的功率流

    公开(公告)号：US20160125097A1

    公开(公告)日：2016-05-05

    申请号：US14933696

    申请日：2015-11-05

    Applicant: NEC Laboratories America, Inc.

    Inventor： Yanyi He ,  Ratnesh Sharma

    IPC: G06F17/30 ,  G05B19/042 ,  G06N99/00

    CPC classification number: G06F17/30979 ,  G05B19/042 ,  G05B2219/2639 ,  G06N99/005

    Abstract: Systems and methods for optimizing power flows using a harmony search, including decoupling phases in a multi-phase power generation system into individual phase agents in a multi-phase power flow model for separately controlling at least one of phase variables or parameters. One or more harmony segments from harmony memory are ranked and selected based on a utility value determined for each of the decoupled phases. A harmony search with gradient descent learning is performed to move the selected harmony segments to a better local neighborhood. A new utility value for each of the selected segments is determined based on historical performance, and the harmony memory is iteratively updated if one or more of the new utility values are higher than a utility value of a worst harmony segment stored in the harmony memory.

    Abstract translation: 用于使用和谐搜索优化功率流的系统和方法，包括在多相功率流模型中的多相发电系统中的去耦合相到单相相位试剂中，用于分别控制相位变量或参数中的至少一个。 根据为每个去耦合阶段确定的效用值，对来自和谐记忆的一个或多个和声段进行排名和选择。 执行具有梯度下降学习的和声搜索以将所选择的和声段移动到更好的本地邻域。 基于历史性能确定每个所选段的新效用值，并且如果一个或多个新效用值高于和谐存储器中存储的最差和谐段的效用值，则迭代地更新和谐存储器。

    Use of second battery life to reduce CO2 emissions
    45.
    发明授权
    Use of second battery life to reduce CO2 emissions 有权
    使用第二个电池寿命来减少二氧化碳排放

    公开(公告)号：US09183327B2

    公开(公告)日：2015-11-10

    申请号：US13764100

    申请日：2013-02-11

    Applicant: NEC Laboratories America, Inc.

    Inventor： Anupama Keeli ,  Ratnesh Sharma

    IPC: G06F7/60 ,  G06F17/10 ,  G06F17/50 ,  H01M10/44 ,  H01M10/48

    CPC classification number: G06F17/5009 ,  G06F2217/10 ,  G06F2217/78 ,  H01M10/44 ,  H01M10/48 ,  Y02E40/76 ,  Y02E60/76 ,  Y04S10/545 ,  Y04S40/22

    Abstract: A method for determining use of a second life battery under load conditions to reduce CO2 emissions includes using Monte Carlo simulations to modeling uncertainties of a load profile, a renewable energy profile, and CO2 emissions rate, determining an initial state of charge SOC of the second life battery based on a Gaussian distribution for determining a rate of charging during low emission hours and discharging during high CO2 emission hours of the second life battery and storage size of the second life battery and CO2 emissions reduction.

    Abstract translation: 用于确定在负载条件下使用第二寿命电池以减少二氧化碳排放的方法包括使用蒙特卡洛模拟来建模负载曲线，可再生能源曲线和二氧化碳排放率的不确定性，确定第二寿命电池的初始充电状态SOC 基于高斯分布的寿命电池，用于在第二寿命电池的高二氧化碳排放时段和第二寿命电池的储存尺寸以及二氧化碳排放减少期间确定低排放时间期间的充电速率和放电。

    TIERED POWER MANAGEMENT SYSTEM FOR MICROGRIDS
    46.
    发明申请
    TIERED POWER MANAGEMENT SYSTEM FOR MICROGRIDS 审中-公开
    微型电力管理系统

    公开(公告)号：US20140350743A1

    公开(公告)日：2014-11-27

    申请号：US14321931

    申请日：2014-07-02

    Applicant: NEC Laboratories America, Inc.

    Inventor： Babak Asghari ,  Ratnesh Sharma ,  Ali Hooshmand

    IPC: G05B13/04

    CPC classification number: G05B13/048 ,  H02J3/00 ,  H02J2003/003 ,  H02J2003/007

    Abstract: Systems and methods to perform multi-objective energy management of micro-grids include determining, by an advisory layer with Model Predictive Control (MPC) using a processor, long-term power management directives that include a charging threshold that characterizes one or more power sources, where the advisory layer provides optimal set points or reference trajectories to reduce a cost of energy; and determining real-time actions based on the charging threshold to adaptively charge a battery from the one or more power sources or to discharge the battery.

    Abstract translation: 执行微网格的多目标能量管理的系统和方法包括通过使用处理器的具有模型预测控制（MPC）的咨询层来确定长期功率管理指令，其包括表征一个或多个电源的充电阈值 ，其中咨询层提供最佳设定点或参考轨迹以降低能量成本; 以及基于所述充电阈值确定实时动作以对来自所述一个或多个电源的电池进行自适应充电或者对所述电池进行放电。

    Demand charge and response management using energy storage
    47.
    发明授权
    Demand charge and response management using energy storage 审中-公开

    公开(公告)号：US10673241B2

    公开(公告)日：2020-06-02

    申请号：US16185300

    申请日：2018-11-09

    Applicant: NEC Laboratories America, Inc.

    Inventor： Kiyoshi Nakayama ,  Ratnesh Sharma ,  Ali Hooshmand

    IPC: H02J3/32 ,  G06Q10/06 ,  G06Q50/06 ,  G05B13/04 ,  H02J13/00 ,  G06Q30/04 ,  H02J3/38 ,  H02J7/00 ,  H02J3/00 ,  G05B13/02

    Abstract: Systems and methods for controlling battery charge levels to maximize savings in a behind the meter energy management system include predicting a demand charge threshold with a power demand management controller based on historical load. A net energy demand is predicted for a current day with a short-term forecaster. A demand threshold maximizes financial savings using the net energy demand using a rolling time horizon optimizer by concurrently optimizing the demand charge savings and demand response rewards. A load reduction capability factor of batteries is determined with a real-time controller corresponding to an amount of energy to fulfill the demand response rewards. The net energy demand is compared with the demand threshold to determine a demand difference. Battery charge levels of the one or more batteries are controlled with the real time controller according to the demand difference and the load reduction capability factor.

    DEEP LEARNING APPROACH FOR BATTERY AGING MODEL
    50.
    发明申请
    DEEP LEARNING APPROACH FOR BATTERY AGING MODEL 审中-公开

    公开(公告)号：US20190257886A1

    公开(公告)日：2019-08-22

    申请号：US16273505

    申请日：2019-02-12

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Mehdi Assefi ,  Ratnesh Sharma

    IPC: G01R31/367 ,  G06N3/08 ,  G06N3/04 ,  G06F17/18 ,  G06N20/20 ,  G01R31/382 ,  H01M10/42

    Abstract: A computer-implemented method predicting a life span of a battery storage unit by employing a deep neural network is presented. The method includes collecting energy consumption data from one or more electricity meters installed in a structure, analyzing, via a data processing component, the energy consumption data, removing one or more features extracted from the energy consumption data via a feature engineering component, partitioning the energy consumption data via a data partitioning component, and predicting battery capacity of the battery storage unit via a neural network component sequentially executing three machine learning techniques.

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