Cloud master station makes the power grid more efficient and intelligent

Electricity data is everywhere. The simple action of charging a mobile phone will generate data such as power consumption and charging time. Analyzing this data can accurately "profile" people's electricity usage behavior, tailor-make energy use plans, and help the power grid side to reasonably balance load peaks.

Big data is changing the ecology of traditional industries, and the power industry is no exception. Large-scale collection and systematic analysis of power data can optimize power production, transmission, consumption and other links, monitor power grid operation in real time, predict fault risks, and optimize operation and maintenance repair processes.

However, the smart grid distribution automation master station under the traditional operation and maintenance mode can only realize the operation and maintenance management of the power transmission and transformation equipment itself. With the access of massive data, the traditional master station architecture can no longer meet the higher requirements of safe, reliable, flexible and efficient power grids.

At the beginning of the year, State Grid Corporation of China fully deployed the construction of ubiquitous power Internet of Things. This has opened up a new path for safer grid operation, leaner management, more accurate investment, and better services. It can also give full play to the unique advantages of the grid and open up a blue ocean market for the digital economy.

In a sense, mobile Internet has accelerated the process of big data, and big data has promoted the development of cloud computing. What changes will it bring when the distribution network, which is closely related to people's lives, encounters the "cloud"?

Massive data collection, one-click analysis and processing

On May 28, State Grid Shanghai Electric Power Company built the first power distribution IoT cloud master station in China. This is an upgrade of the smart grid power distribution automation master station using IoT technology.

"If the power distribution Internet of Things is compared to the central nervous system of the human body, then the power distribution Internet of Things cloud master station is our brain," said Dr. Chen Ran, head of the research and development of the power distribution Internet of Things cloud master station.

The cloud-based power distribution IoT master station is connected to 17,858 medium and low voltage distribution stations, 123,989 public transformer substations, 10.653 million low voltage customers, and various distributed power sources and charging piles that provide electricity security for Shanghai residents and enterprises. The data it generates is no longer limited to power grid equipment such as transmission lines and substations, but includes IoT perception data from various smart terminals. These perception data, which are updated in units of 100 million per second, interact with information from maintenance, marketing, and control-related systems to achieve comprehensive collection and hierarchical processing.

At 10:00 on June 27, Gong Dai, an employee of Shanghai Pudong Power Supply Company, opened the cloud master station on her office computer. At the same time, her colleague Tang Li used a handheld terminal to scan the equipment labels of the distribution transformer and intelligent distribution transformer terminal at Haoyuan Zhangjiang Substation. Five seconds later, Gong Dai saw on the cloud master station that the terminal scanned by Tang Li had completed registration and configuration, and the corresponding information of the associated equipment also appeared.

Previously, it took at least three working days to complete the entry, release and association of equipment ledgers, and there were business risks such as entry errors and disordered association relationships.

After data collection, the most important thing is analysis. In-depth analysis and application of power data can not only improve the operational efficiency and level of the power grid, but also integrate with information such as macroeconomics, people's lives, social security, and road traffic to promote economic and social development. At present, the cloud master station has the primary analysis and judgment capabilities of IoT data, and can provide a practical solution for the whole process management of distribution network operation and inspection information.

At present, the cloud master station already has functions such as basic electrical data collection, environmental status collection, dynamic topology identification, low-voltage fault analysis, power quality monitoring, automatic reactive power compensation, and power outage and restoration information reporting.

It is worth mentioning that the intelligent distribution transformer terminal has a plug-and-play function. Taking a medium-sized substation as an example, each substation has more than 1,000 variables. The previous automation control system needed to configure point tables for various devices in advance and record the usage of each variable. An employee could only complete the point table configuration for one or two substations at most every day. The order and content of the intelligent distribution transformer terminals in different substations are different. Each substation must be configured separately and debugged and checked one by one when connected on site. The intelligent distribution transformer terminal is plug-and-play, so there is no need to configure point tables on the master station side, which reduces the equipment access time from 4 hours to 5 minutes.

At present, the plug-and-play intelligent distribution transformer terminal is being piloted in Shanghai Zhangjiang Park, China International Import Expo and other areas, and is expected to be promoted in Shanghai within the year.

Apply edge computing to actively perceive information

At 18:00 on May 30, the low-voltage outgoing line switch of an outdoor 10kV box-type transformer in Gutong Village 6, Pudong, Shanghai tripped. At 18:01, the emergency repair worker He Yongming received a notification on his mobile phone. At 18:27, the fault was eliminated.

When the fault occurred, the intelligent distribution transformer terminal installed on the side of the box-type transformer recorded the tripping event and reported it to the cloud master system in real time. The cloud master system immediately sent the emergency repair work order generated corresponding to the fault to the handheld terminal of the emergency repair personnel online. After receiving the work order, the emergency repair personnel found the fault point and handled it according to the name of the faulty transformer area and line name. It took less than 30 minutes from the discovery of the tripping to the resolution of the problem.

Two years ago, after a customer discovered a fault and reported it, it took at least an hour for the operation and maintenance repair commander to arrange a repair team to deal with it. "The equipment is large in quantity and wide in scope, and the single-point equipment information is small. Under the traditional technical environment, the efficiency of automatic fault monitoring and processing on the medium and low voltage distribution network side cannot meet the actual needs." said Ji Kunhua, director of the distribution department of the State Grid Shanghai Electric Power Equipment Department.

The time is obviously saved, mainly from the rapid grasp of fault information. This timely and efficient processing on the terminal side is edge computing. Because it is close to the data source, has good real-time performance, fast response, and high security, edge computing has become the core technology of the perception layer of the ubiquitous power Internet of Things. One of the advantages of the cloud master station is its compatibility with edge computing.

In the ubiquitous power Internet of Things scenario, relying on the huge number of intelligent distribution and transformation terminals in the power grid, through the combination of cloud master station computing analysis + local edge computing, the "distribution and transformation area-cable box-meter box-low-voltage customer" detection data can be combined and analyzed to achieve automatic analysis and rapid positioning of fault sections and power outage customers. It can even combine and analyze various equipment status data, environmental monitoring data and customer electricity consumption data to achieve early warning of distribution network failures.

On the customer side, the cloud master station analyzes various factors such as the charging load of electric vehicles and the use behavior, parking behavior, charging time, charging method, and vehicle type of electric vehicle customers, so that electric vehicles can be charged in an orderly manner, which can not only save energy for customers, but also make the power grid more efficient. There are many such applications. In the future, the cloud master station can meet various needs such as peak shaving, demand-side response, new energy consumption, and customized electricity.

The Shanghai Zhangjiang Science City Ubiquitous Power Internet of Things Demonstration Zone, which uses the cloud master station, is under construction. According to the plan, after completion, the proportion of renewable energy in the demonstration zone will increase from 43% to 50%, the energy consumption per unit of output value will drop from 0.27 tons/10,000 yuan to 0.2 tons/10,000 yuan, the energy utilization efficiency will increase from 47% to 51%, and the proportion of electricity in energy consumption will increase from 75% to 80%.

On the day when the cloud master station was completed, the ubiquitous power Internet of Things intelligent sensing device performance detection system in the same room entered the trial operation mode. The integrated development of the sensing device performance detection system and the cloud master station of the power distribution Internet of Things will further promote the extensive connection and interaction, in-depth information collection and data fusion analysis of power distribution and use equipment.