Hitachi, Ltd.

Panitarn Chongfuangprinya

Bo Yang

Yanzhu Ye

Masanori Abe

Yasushi Tomita

Sumito Tobe

Anthony Hoang

Natsuhiko Futamura

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.

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San Jose, CA, United States of America

Panitarn Chongfuangprinya