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    Multi-Layer Adaptive Power Demand Management For Behind The Meter Energy Management Systems
    21.
    发明申请
    Multi-Layer Adaptive Power Demand Management For Behind The Meter Energy Management Systems 审中-公开

    公开(公告)号：US20180268327A1

    公开(公告)日：2018-09-20

    申请号：US15789068

    申请日：2017-10-20

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ramin Moslemi ,  Ratnesh Sharma

    IPC: G06Q10/04 ,  G06Q50/06 ,  G05B15/02

    CPC classification number: G06Q10/04 ,  G05B15/02 ,  G05B19/042 ,  G05B19/048 ,  G06Q50/06

    Abstract: Systems and methods for adaptive demand charge management in a behind the meter energy management system. The system and method includes determining, in a first layer, an initial demand charge threshold (DCT), for a first period, based on historical DCT profiles, and generating recursively, in a second layer, a forecast of a power demand for a second period, wherein the second period is a subset of the first period. Further included is combining the first layer and the second layer to recursively modify the initial DCT with a DCT adjustment value to generate a modified DCT, wherein the DCT adjustment value is optimized according to the forecast of power demand for the second period, and controlling batteries according to the modified DCT, wherein the batteries are discharged if power demand is above the modified DCT, and the batteries are charged if the power demand is below the modified DCT.

    DYNAMIC PROBABILITY-BASED POWER OUTAGE MANAGEMENT SYSTEM
    22.
    发明申请
    DYNAMIC PROBABILITY-BASED POWER OUTAGE MANAGEMENT SYSTEM 审中-公开
    基于动态可靠性的功率管理系统

    公开(公告)号：US20160241031A1

    公开(公告)日：2016-08-18

    申请号：US15046110

    申请日：2016-02-17

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Babak Asghari ,  Ratnesh Sharma

    IPC: H02J3/00 ,  G06F17/18 ,  G05B13/04

    CPC classification number: H02J3/38 ,  H02J3/32 ,  H02J3/382

    Abstract: A method and system are provided for managing a power system having a grid portion, a load portion, a storage portion, and at least one of a renewable portion and a fuel-based portion. The method includes generating, by a scheduler responsive to an indication of an occurrence of a power outage, an outage duration prediction. The method further includes solving, by the scheduler, an economic dispatch problem using a long-term energy optimization model. The method also includes generating, by the scheduler based on an analysis of the long-term energy optimization model, an energy management directive that controls, for a time period of the outage duration prediction, the storage portion and at least one of the renewable portion and the fuel-based portion. The method additionally includes controlling, by a controller responsive to the directive, the storage portion and the at least one of the renewable portion and the fuel-based portion.

    Abstract translation: 提供了一种用于管理具有格栅部分，负载部分，存储部分以及可再生部分和基于燃料的部分中的至少一个的电力系统的方法和系统。 该方法包括响应于断电发生的指示，通过调度器生成中断持续时间预测。 该方法还包括通过调度器解决使用长期能量优化模型的经济调度问题。 该方法还包括通过调度器基于长期能量优化模型的分析生成能量管理指令，其在停电时间预测的时间段期间控制存储部分和可再生部分中的至少一个 和基于燃料的部分。 该方法还包括通过响应于该指令的控制器来控制存储部分和可再生部分和基于燃料的部分中的至少一个。

    OPTIMIZING SIZING OF GRID-SCALE BATTERIES FOR FREQUENCY REGULATION SERVICES
    23.
    发明申请
    OPTIMIZING SIZING OF GRID-SCALE BATTERIES FOR FREQUENCY REGULATION SERVICES 审中-公开
    用于频率调节服务的网格电池优化尺寸

    公开(公告)号：US20160092776A1

    公开(公告)日：2016-03-31

    申请号：US14846149

    申请日：2015-09-04

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ratnesh Sharma

    IPC: G06N5/04 ,  G01R31/36 ,  H01M10/42

    CPC classification number: G01R31/3651 ,  H01M10/4207 ,  H01M2220/10

    Abstract: Systems and methods for optimal sizing of one or more grid-scale batteries for frequency regulation service, including determining a desired battery output power for the one or more batteries for a particular period of time. A battery size is optimized for the one or more batteries for the particular period of time, and the optimizing is repeated using different time periods to generate a set of optimal battery sizes based on at least one of generated operational constraints or quality criteria constraints for the one or more batteries. A most optimal battery is selected from the set of optimal battery sizes.

    Abstract translation: 一种或多种用于频率调节服务的电网规模电池的最佳尺寸的系统和方法，包括在特定时间段内确定所述一个或多个电池的期望的电池输出功率。 电池尺寸针对特定时间段内的一个或多个电池进行了优化，并且使用不同的时间周期重复优化，以基于生成的操作约束或质量标准约束中的至少一个来生成一组最佳电池尺寸 一个或多个电池 从最佳电池尺寸的集合中选择最佳电池。

    Deep learning approach for battery aging model
    24.
    发明授权
    Deep learning approach for battery aging model 有权

    公开(公告)号：US11131713B2

    公开(公告)日：2021-09-28

    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 ,  H01M10/42 ,  G06N20/20 ,  G01R31/382 ,  G06F17/18

    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.

    Demand charge minimization in behind-the-meter energy management systems
    25.
    发明授权
    Demand charge minimization in behind-the-meter energy management systems 有权

    公开(公告)号：US10680455B2

    公开(公告)日：2020-06-09

    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.

    BATTERY CAPACITY FADING MODEL USING DEEP LEARNING
    26.
    发明申请
    BATTERY CAPACITY FADING MODEL USING DEEP LEARNING 审中-公开

    公开(公告)号：US20200011932A1

    公开(公告)日：2020-01-09

    申请号：US16458825

    申请日：2019-07-01

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Hossein Hosseini ,  Ratnesh Sharma

    IPC: G01R31/367 ,  G01R31/36 ,  H01M10/48 ,  G01R31/374

    Abstract: A battery management system is provided. The battery management system includes a memory for storing program code. The battery management system further includes a processor for running the program code to extract features from battery operation data. The processor further runs the program code to train a deep learning model to model a battery degradation process of a battery using the extracted features. The processor also runs the program code to generate, using the deep learning model, a prediction of a battery capacity degradation based on the battery operation data and a current battery capacity of the battery. The processor additionally runs the program code to control an operation of the battery responsive to the prediction of the battery capacity degradation.

    Service-based Approach Toward Management of Grid-Tied Microgrids
    28.
    发明申请
    Service-based Approach Toward Management of Grid-Tied Microgrids 审中-公开
    基于服务的网格化网格化管理方法

    公开(公告)号：US20150311713A1

    公开(公告)日：2015-10-29

    申请号：US14680848

    申请日：2015-04-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Babak Asghari ,  Ratnesh Sharma ,  Ali Hooshmand

    IPC: H02J3/00 ,  G05B15/02

    CPC classification number: H02J3/00 ,  H02J2003/007 ,  Y02E60/76 ,  Y04S40/22

    Abstract: Systems and methods are disclosed for providing service based interactions between a utility and a microgrid by adjusting power flow profile at a point of common coupling (PCC) between a microgrid and a utility, wherein the power flow profile is adjusted to achieve a predetermined objective function based on a utility request; delivering different services to the utility at different periods of time by altering its internal operation of distributed generators, energy storage units, and demands as a multi-purpose microgrid; and managing the microgrid to deliver services to the utility and reduce its operational cost simultaneously.

    Abstract translation: 公开了用于通过在微电网和公用设施之间调整公共耦合点（PCC）处的功率流分布来提供公用设施和微电网之间的基于服务的相互作用的系统和方法，其中调整功率流分布以实现预定的目标函数 基于效用请求; 通过改变分布式发电机，储能装置的内部运行和作为多用途微电网的需求，在不同时期向公用事业提供不同的服务; 并管理微电网向公用事业提供服务，同时降低运营成本。

    Decentralized Energy Management Platform
    29.
    发明申请
    Decentralized Energy Management Platform 有权
    权力下放能源管理平台

    公开(公告)号：US20150295410A1

    公开(公告)日：2015-10-15

    申请号：US14681027

    申请日：2015-04-07

    Applicant: NEC Laboratories America, Inc.

    Inventor： Ali Hooshmand ,  Ratnesh Sharma

    IPC: H02J3/12

    CPC classification number: G05B15/02 ,  G05B2219/2639 ,  G06Q10/04 ,  G06Q10/063 ,  G06Q50/06 ,  H02J3/12 ,  H02J3/382 ,  H02J7/34 ,  H02J2003/003 ,  Y04S10/54

    Abstract: A method for power management includes applying a decentralized control to manage a large-scale community-level energy system; obtaining a global optimal solution satisfying constraints between the agents representing the energy system's devices as a state-based potential game with a multi-agent framework; independently optimizing each agent's output power while considering operational constraints and assuring a pure Nash equilibrium (NE), wherein a state space helps coordinating the agents' behavior in energy system to deal with system-wide constraints including supply demand balance, battery charging power constraint and satisfy system-wide and device-level (local) operational constraints; and controlling distributed generations (DGs) and storage devices using the agent's output.

    Abstract translation: 电力管理的方法包括应用分散控制来管理大型社区一级的能源系统; 获得满足代表能量系统设备的代理之间的约束的全局最优解，作为具有多代理框架的基于状态的潜在游戏; 独立地优化每个代理的输出功率，同时考虑操作约束并确保纯粹的纳什均衡（NE），其中状态空间有助于协调代理在能量系统中的行为以处理系统范围的约束，包括供应需求平衡，电池充电功率约束和 满足系统范围和设备级（本地）操作限制; 并使用代理的输出来控制分布式代（DG）和存储设备。

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