A take on AMI Big Data analysis and its leverages for Utilities

Introduction –

There are currently several studies on Big Data and Data Analytics applications for AMI data. However, it is evident that fractional developments do not incorporate a global integration of architectural components of the data life cycle or consider a complete methodology. On the one hand, some Big Data and Data Analytic methodologies have already been defined. On the other hand, there are architectures to develop Smart Grids in which the AMI systems are framed.

What is AMI? –

AMI (Advanced Metering Infrastructure) is a two-way communication system to collect detailed metering information throughout a utility's service industry. AMIs consist of four basic components including the head-end system (HES), the collector (MDMS), Smart Meters, and the communication channels (GPRS, RF Mesh, PLC etc.). These components work together in order to provide AMIs with their functionality AMI is typically automated and allows real-time, on-demand interrogations with metering endpoints.

What is Big Data? –

AMI typically provides a substantial payload of information. Usage information, tamper indication, and interval data is available for electric, water and gas meters, along with specific advanced capabilities for electric metering endpoints.

Here is a list of detailed information that can be supplied via AMI systems:

• Cumulative kWh usage
• Daily kWh usage
• Peak kW demand
• Last interval demand
• Load profile
• Voltage
• Voltage profile
• Logs of voltage sag and swell events
• Voltage event flags
• Phase information
• Outage counts.
• Outage logs
• Tamper notification
• Power factor
• Time-of-Use kWh and Peak kW readings

MI systems have a variety of uses:

• Verify power outages and service restoration.
• Perform remote service disconnects and reconnects.
• Allow automated net metering.
• Transmit demand response and load management messages.
• Interrogate and control distribution automation equipment.
• Facilitate pre-paid metering.

The aim is to present a Data Analytics/Big Data framework for AMI data using human expertise and skills as a binding element. The human expertise incorporates architectures (Where?) and methods (How?) to transform and give value to the AMI data. Such transformation brings profits to the Smart Grid, the company, and its customers, in addition to the evidenced requirements, which are not only technological but also related to human training and skills.

Benefits of Big Data Analysis for Utility –

·      The current benefits realization levels for Home Energy Networks, Digital Access and Direct Load Control are a fraction of the levels to be realized in the future. And much of the upside will require enabling an AMI-driven ecosystem of marketing strategies relying upon greater individualization of offerings based on actual, rather than hypothetical, customer needs.

·      Utilities moving to a fuller realization of AMI’s insights and cost-saving innovations are also optimizing their data policies and their segmentation-based market outreach.

·      Rules for bundling customer data and making it anonymous have been enacted in a number of states and provide a foundation for more robust markets based on greater uptake and benefits realization from real-time pricing.

• Reducing unplanned outages by predicting failures and replacing overloaded transformers before the onset of the peak-loading season.
• Informed and sound decision-making to determine the load growth for any given transformer to decide whether to move a given customer from one transformer to another.
• Improving customer service and avoiding costly emergency/overtime work by replacing a large percentage of unplanned outage work effort with planned maintenance work activity.
• Accurately optimizing capacitor bank size and location based on actual Volt/VAR readings.
• Discovering suspect transformers proactively before they lead to customer voltage complaints.
• Better asset utilization by relocating transformers or rebalancing the customer load on under-utilized and overloaded transformers.
• Choosing the appropriate transformer size when replacing failed transformers.
• Troubleshooting voltage problems by knowing the voltage at each customer access point on the feeder.

Scheduling maintenance and adjusting load based on accurate indications of conductor, sectionalized and regulator overload rather than estimates and periodic scheduling.