Smart Data: Making business sense out of AMI Data
Almost 2 years in to the Smart Grid journey that started with the first pillar of Smart Grids i.e. AMI now has reached a stage where Utilities now start to see ultra high volume transactional data flowing in from Smart Meters. Next challenge is what to do with this data. Of course traditional use of the meter data has been (and will still remain to be) revenue collection and billing purposes. But is that enough to justify 100s of millions of dollars of investment?
Of course there are many answers to this question and very valid one, but I think when I wear the shoe of a Utility Operations or Business Manager I will think and ask:
- How make business sense out of this huge valume of data?
- Where do I start with this data other than processing it in MDMS (Meter Data Management System)?
- Is it enough to flush this data in to the Data warehouse?
- What are the business benefits?
In my opinion the strength of this data is not in the shear volumn and capability to read get more frequent meter reads, but the inherent intelligence in this data. This inherent intelligence in the Smart Data (oops!! Data from Smart Meters) is due to the communication capabilities clubbed with GIS systems which is changing the way Utilities look at their network connctivity model because now their connectivity model includes each and every customer (with Smart Meter) in the distribution grid. And that is what I call the true intelligence in the Smart Data.
The information and actionable intelligence that can be extracted out of Smart Data and the way it can help Utilities in Planning and Network Operations is unprecedented. These smart meters are not only capable of providing meter reads over multiple channels but also can record alarms and events related to service and secondary side voltage, currents and harmonics. This way we can think of various use cases of this Smart Data (AMI Data provided by Smart Meters) that can significantly benefit the distribution network operations and planning.
We have identified following use cases of Smart Data in the area of Distribution Operaitons and Planning, and are builiding point solutions around these use cases:
Enterprise, Operational & System Planning
- Energy Theft Detection
- Distribution Grid Load Assessment
- Tariff & Financial Planning
Engineering & Operations
- Power Quality Monitoring & Analytics
- Distribution Transformer Load Assessment
- Voltage Monitoring
- Load Profiling
- Bus Load Analysis
- Demand Side Management
- Price-sensitive Demand Response
- Aggregate Demand Response
- Peak Loss Evaluation
I would like to discuss two most important usage of Smart Data:
Power Quality & Voltage Monitoring
The results from the power quality & voltage monitoring at customer premise (provided by smart meters) can be aggregated at the distribution transformer level using the customer linked network data model and can be fed back to the DMS/SCADA applications and hence can serve as additional SCADA points, eliminating the need to install additional sensors in the field. The voltage monitoring can help in following areas of distribution operation:
- Loss Analysis
- Input to load forecasting models
- Voltage and VAR Control
- Transformer voltage regulation
- Automatic feeder and capacitor bank switching
- Power Quality Monitoring and Reporting
The benefits of power quality & voltage monitoring if used in conjunction with voltage control are as follows:
- Reduction in losses
- Improvement in operational efficiency
Another important use case is:
Distribution Transformer Loading Assessment
The objective is to perform the Load analysis and management.
AMI data together with connectivity model can give information related to transformer loading. Peak load analysis, what if analysis, etc can be performed if we have the connectivity model and then roll up the values to get transformer data.Winding losses and core losses for DT can be calculated using this method( core loss-Provided we have voltage information)
Distribution System Loss evaluations are very much dependent on the available data. Historically, data has been limited but now with AMI/DMS/SCADA we can Estimate peak demand losses with a basic engineering model. Apart from these this use case can help plan distribution circuits with high peneration of PHEVs because this use case will help utility to monitor the load right up to the distribution transformer level hence better load planning can be performed.
In the interest of not making this blog too big, I would like to provoke a thought here to my fellow readers that what is the real use of AMI data and how it can be used to create the business sense and yield the maximum business benefits.
Sooner or later these questions will be asked and will have to be answered.
I would appreciate your comments and feedback in this important topic.