The effective application of power big data can provide a large number of value-added services with high added value for both inside and outside the industry, which is of great value for the improvement of the profitability and control level of power enterprises. Power grid experts say that a 10 percent increase in data utilization will increase the profit of the power grid by 20 to 49 percent.

A McKinsey report predicted that worldwide, widespread use of big data analytics could cut electricity bills by $300 billion a year. The effective application of power big data can provide a large number of value-added services with high added value for both inside and outside the industry, which is of great value for the improvement of the profitability and control level of power enterprises. Power grid experts say that a 10 percent increase in data utilization will increase the profit of the power grid by 20 to 49 percent.

The data sources of the power industry mainly come from the various links of power generation, transmission, transformation, distribution, power consumption and dispatch of power production and use, which can be roughly divided into three categories: first, the data of power grid operation and equipment detection or monitoring; Second, marketing data of power enterprises, such as transaction price, electricity sold, electricity customers and other data; Third, the management data of electric power enterprises.

The operation data of the whole power system can be collected by using intelligent terminal devices such as smart meters, and then the collected power big data can be systematically processed and analyzed, so as to realize real-time monitoring of the power grid. It further combines big data analysis and power system model to diagnose, optimize and predict power grid operation, so as to guarantee the safe, reliable, economical and efficient operation of power grid.

I. Power grid monitoring and maintenance

1. Timely response of operation and maintenance monitoring system

Enphase Energy(Enphase Energy, Inc.)

Enphase Energy collects about 2.5 terabytes of data a day from 250,000 systems in 80 different countries. This data can be used to monitor generation and facilitate remote maintenance and repair to ensure seamless system operation. In addition, Enphase Energy uses data collected from the generation system to monitor, control or adjust the generation and load status of the network, responding to the grid and when errors or upgrades are needed.

2. Special analysis of equipment maintenance and operation

Power enterprises can carry out in-depth analysis of various business fields based on the one-stop big data analysis platform.

Such as in the field of power grid maintenance operations, through to the electric power equipment asset management, equipment management, equipment, technology management, technical management of overhaul, etc., from the aspects of safety, efficiency, cost three key indicators selection, analysis in the management of inspection "security", "efficiency" and "cost" mutual influence between the comprehensive optimal coordination of three factors, At the same time, the real-time online monitoring of maintenance indicators of power grid enterprises can be realized to provide guidance and services for the company's maintenance strategy formulation.

3. Prevent power failure caused by basic equipment failure

American Electric Power Co., Inc. (AEP)(American Electric Power, Inc.)

At AEP's Asset Health Center, data analysts combine device-derived runtime information with intelligent information applications. By adopting big data algorithms and analytics software, they can closely monitor the performance of the transmission infrastructure.

Today, AEP uses smart meters, communication networks, and data management systems to generate robust routine information. Smart grid technology allows customers to use electricity more efficiently and manage electricity costs properly. The data collected also helps the company customize power management programs and personalized services for customers.

2. Improve operational efficiency and customer experience

Big data analytics can help power companies improve operational efficiency and customer experience. Operational benefits include revenue assurance, network and product management, demand forecasting, asset management, and support function optimization. Similarly, analytics can help improve the customer experience through customer relationship optimization, proactive marketing, and customized offers and services.

1.Gulf Power, a Florida division of Southern Electric

Gulf Power used big data analysis to confirm that customer satisfaction is highest when power is restored 10 minutes earlier than expected in the event of a power outage.

Interestingly, it found that restoring power more than two hours before the expected time had a negative impact on customer satisfaction. Understanding metrics like these can help power companies solve their biggest customer experience challenges. An executive at a German power company confirmed that improving customer satisfaction increases customer retention.

"Analytics allows you to communicate well with customers with personalized offers on existing contracts," he explains. It's a great way to increase retention."

In fact, utilities like EDF Energy are already using big data analytics to reduce customer churn, saving up to $30 million a year.

2.Lakeland Electric(Lakeland, Florida)

Load research is a process used to analyze customer consumption patterns for various customer groups (residential, commercial, and industrial). It helps assess the cost of serving each specific group. The researchers believe that using AMI(Advanced Metering Architecture) and data capture capabilities, every metering point and smart grid-enabled device could be helpful for this study.

Lakeland recently completed a cost review of electricity services using these new technologies. In addition to addressing the need for additional revenue, they are able to design alternative rates for customers to choose from, reducing peak electricity demand on the one hand and customers saving money in the process on the other. It not only effectively reduces power failures during peak periods, but also improves user experience, improves user retention, and enables the company to have better word of mouth and visibility.

3. Effectively improve the marketing service level of the power industry through data analysis

Power users can provide more detailed data to business departments based on Yonghong one-stop big data analysis platform, and business departments can self-serve the data application.

Through the analysis of customer service and customer relationship, electricity charge management, electricity metering and information collection, market and orderly electricity consumption, new business, comprehensive management and other aspects, grasp the development of key marketing work. To achieve effective monitoring of customer service, electricity management, smart meters, orderly implementation of electricity consumption and efficiency management, new business and marketing audit quality indicators.

Three, reduce losses, reduce costs

1. Reduce electricity theft Reduce losses

According to the Northeast Group, LLC. report Smart Grid for Energy Markets: 2015 Outlook Research, $89.3 billion is lost globally each year due to electricity theft. And smart grid technology could help power companies combat the theft of millions of dollars worth of electricity each year.

Enel, based in Italy, is one of the world's largest power companies, operating 670m meters in 40 countries. In Italy, Enel consolidates more than 50 billion lines of data from 11 legacy systems and has identified 93 percent of possible causes of theft or other non-technical losses, making it the world's largest smart-grid analytics system. In Italy alone, the economic benefits of its revenue protection and predictive asset maintenance analysis are estimated at more than 350 million euros per year.

2. Use analysis to reduce transformer replacement cost

PSE&G(Public Service Electric and Gas Company) is one of the largest integrated electric and gas companies in the United States, serving 1.8 million gas customers and 2.2 million electric customers. It has assets worth about $17 billion and revenues of nearly $8 billion.

PSE&G operates a Computerized Maintenance Management System, or CMMS, to assist in repair, replacement and maintenance decisions for assets including transformers and other equipment.

According to the humidity, dielectric strength, combustible gas change rate and cooling performance and other factors, to analyze the transformer, generate equipment condition score. Based ON THE ASSET REPLACEMENT (forecasting) ALGORITHM, they analyze the equipment condition score and other factors (age, spare parts availability) to determine the appropriate time for transformer replacement.

PSE&G also uses advanced analytics on real-time sensors to track various operational metrics. The use of analytics has helped the company identify and remedy problems before they occur, saving millions of dollars in equipment failure avoidance. The company also decided to proactively replace some transformers by using analytical models rather than replacing them after problems, which helped it save more than $100 million over 25 years.

conclusion

New information technologies such as big data analysis will definitely activate the value contained in power big data and release the market potential of power big data. According to GTM Research, the global power big data management system market will reach $3.8 billion by 2020, and the power big data collection, management, analysis and service industry will usher in unprecedented growth opportunities.

Under such opportunities, more power enterprises will choose to make active exploration in the big data analysis technology, for enterprise system operation and maintenance monitoring, improve customer satisfaction, reduce losses and costs and many other aspects. More and more power enterprises choose to cooperate with third-party big data analysis platform manufacturers to bring professional technology and service support for enterprise data operation, compared with building third-party platform with more reasonable cost and more stable and efficient.