Nec Corporation

Hitachi, Ltd.

Yanzhu Ye

Ratnesh Kumar Sharma

Bo Yang

Panitarn Chongfuangprinya

Masanori Abe

Di Shi

Yasushi Tomita

Sumito Tobe

Natsuhiko Futamura

Anthony Hoang

Feng Guo

Jian Gao

A method for visualization of an end device. The method may include setting an object display limit on a display map, the display map displaying a plurality of objects, and wherein each of the plurality of objects is assigned a priority status; receiving data from the end device; classifying the data and assigning priority status to an object with the data, wherein the object is added to the plurality of objects; and displaying the plurality of objects based on priority status and the object display limit.

Behind-the-meter devices visualization method and computing system

Example implementations described herein provide systems and methods for visualization of power distribution feeder networks. The systems and methods involve setting a limit of objects of the power distribution network to be displayed on a map, the objects comprising at least nodes and links between the nodes, the links comprising at least conductive lines; when the total number of the objects exceeds a limit, selecting one or more subsets of the conductive lines based on one or more conditions related to the conductive lines; and generating a first visualization displaying the map comprising a subset of the objects including the one or more subsets of the conductive lines, wherein a number of displayed objects is less than or equal to the limit.

Distribution system visualization method and computing system

Example implementations described herein are directed to managing the operation of a plurality of electric vehicles (EVs) for transportation service, power system operating reserve service and operation planning, which is utilized to determine whether to use each EV for transportation travel or power system operating reserve supply for a given time period and location. Such management allows for the increase of the total operation value by transportation travel and power system operation reserve supply.

Electric vehicles operation management equipment

Example implementations described herein involve a multi-layer hybrid model power generation prediction method and computing system. The example implementations described herein are configured to predict power generation that can be utilized for short-term planning by utilities to optimize operation planning to ensure economic and stable operation of the electricity.

Multi-layer hybrid model power generation prediction method and computing system

Example implementations described herein involve a hybrid spatial-temporal event probability prediction method and system configured to predict spatial-temporal event probability that can be utilized for short term planning. For systems such as utility systems that manage multiple locations over time, the example implementations utilize both a spatial model and a temporal model to create a hybrid spatial-temporal model that accounts for data taken over time as well as data across the locations to more accurately forecast the probability of an event occurrence over a period of time.

Hybrid spatial-temporal event probability prediction method

The multi-time scale battery energy/storage system (BES) management framework proposed in the present disclosure can be used for day-ahead capacity scheduling and real-time BES operation control in the latest performance-based frequency regulation market. This multi-time scale BES management framework includes three control layers: long-term battery performance planning layer, day-ahead regulation capacity scheduling layer, real-time battery operation control layer. These three layers will coordinate with each other, and operate at different time scales.

Multi-time scale energy storage management framework for performance-based frequency regulation (FR) market

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.

Modular multilvel converter and control framework for hybrid energy storage

A method of managing power between the multiple components of a hybrid electrical energy storage system (HESS) that includes providing at least two power storage elements, and at least one renewable power source. The method further includes managing the power flow among the at least two power storage elements with a fuzzy logic controller. The fuzzy logic controller uses a hardware processor that is configured to increase or decrease current to each of the at least two power storage elements using a fuzzy rule base that is dependent upon at least one of a state of charge for each of the at least two power storage elements, and a requested power demand of the hybrid electrical storage system.

Fuzzy logic based integrated power coordination system for hybrid energy storage system

Systems and methods are disclosed to control a power system with an energy generator and a hybrid energy storage system. The system includes two or more energy storage system, each with different energy storage capacity and energy discharge capacity. The system includes developing data for one or more control variables refined from expert knowledge, trials and tests; providing the control variables to a fuzzy logic controller with a rule base and membership functions; and controlling the energy generator and the hybrid energy storage system using the fuzzy logic controller.

Power system with an energy generator and a hybrid energy storage system

A multilayer control framework for a power system includes a hybrid storage system (HSS) to store energy using a plurality of energy storage devices; a local controller coupled to the HSS to smooth output power of wind or photovoltaic energy sources while regulating a State of Charge (SoC) of the HSS; and a system-wide controller coupled to the HSS activated upon an occurrence of one or more energy disturbances with a control strategy designed to improve system dynamics to address the one or more energy disturbances.

Multi-layer control framework for an energy storage system

A hybrid ultracapacitor-battery energy storage system is integrated with a photovoltaic system to help solve fluctuations. A fuzzy-logic-based adaptive power management system enables optimization of the power/energy distributions and a filter-based power coordination layer serving as a rudimentary step for power coordination among the hybrid storage system and a fuzzy-logic-based control adjustment layer that keeps monitoring the operation status of all the energy storage devices, taking into account their dynamic characteristics, and fine-tuning the control settings adaptively.

Adaptive control of hybrid ultracapacitor-battery storage system for photovoltaic output smoothing

A method of controlling power in a hybrid energy storage system that may include controlling power through the at least one battery storage element and the capacitor storage element using a multi-level power management system. In some embodiments, the multi-level power management system includes at least a long term battery management layer and a real time power management layer. The long term battery management layer can be for estimating and managing a life cycle for the battery. The real time power management layer can be for managing power sharing between the at least one battery storage element and the at least one capacitor storage element at each time instant dependent upon adjustments to battery performance based upon the long term battery management layer.

Power coordination system for hybrid energy storage system

System and methods are disclosed to handle power imbalance in one or more distributed generation (DG) units: detecting islanding at time t; selecting Phasor Measurement Unit (PMU) measurements during a pre-defined time window [t-&#x3c4;, t]; checking the time window for an abrupt voltage change; if no sudden change of voltage is detected, determining an average value of sbetween t-&#x3c4; and tand using the average as a best estimate of a system power imbalance; if a sudden change of voltage is detected at time instant t, determining the average value of sbetween t-&#x3c4; and tand using the average as a best estimate of the system power imbalance; and adjusting a power reference of the DG units based on the best estimated system power imbalance.

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San Jose, CA, United States of America

Yanzhu Ye