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Role of Data analytics in Smart Grid

Data is the lifeline of any business. We all know that most of the organizations, whether public or private take day to day business decisions or strategic decisions based on data generally stored in large data repositories. The Power distribution utilities are not an exception to this. Smart Grid envisages to deploy several integrated critical applications / systems like advanced metering infrastructure, robust billing engine, various avenues of collection of receivables, geographical information system, asset management systems, field force management systems etc. leading to generation of tons of data. The smart move would be to convert this data into useful information to benefit the power distribution utility. 

In a power distribution utility there are several sources of data. Mentioning some of them for reference purpose. One of the major sources of data is Advanced Metering Infrastructure (AMI), where most of the energy related data comes from. The deployment of smart meters on power transformers, distrbution sub-stations, distribution transformers, High tension feeders and consumer homes enables the distribution utility to collect data related to voltage, current, active & reactive power, energy consumption, demand, power factor etc. This data is not just for instantaneous in nature but also for load profile, cumulative and tamper. Another source of data is the distribution automation system (Control and automation system fitted on various feeders and control equipments). This data would be about the normally on contacts, the normally off contacts, the faulty feeders etc. and the status of the various equipments connected to the power distribution system. One of the other major sources of data is robust billing engine which houses data on individual meter readings, consumption history of consumer, applicable tariff, payment pattern, cheque bounce history, credit history, seasonal variation etc. There could be several other sources of data like supervisory Control and Data Acquisition (SCADA), Geographical information system etc. These systems / applicationed mentioned above are indicative and there are definitely several other applications functional in a power distribution system which also generate data.   

The core challenge faced by the power distribution utilities today is to make the right decision about where to cut spending and where to invest? How to make the consumer satisfaction index go up? How to manage peaks within the given power constraints? How to optimize the operating expenses? and the list goes on.

The million dollar question is what the power distribution utilities are doing in order to make the best possible use of the plethora of data. How is this information being made use of in the day to day decision making? How is this inforation being displayed in form of a dashboard? How are the discrepencies in the system being highlighted or brought to the attention of the higher management? These are some of the questions which need to be answered along with the smart grid implementation.

Some of the benefits that could be derived by the power distribution utilities are listed below.
1. Cost reduction (Reduced operational cost on administrative, maintenance, repairs etc.)
2. Better consumer services (Faster restoration of no-supply complaints, More avenues for paying bills etc.) 
3. Improved consumer satisfaction indices (Happy consumers)
4. Reduction of peak demand (Better pallning and control)
5. Controlled and balanced use of electricity by consumers (Energy consumption)
6. Proactive load forcasting (Better planning)
7. Improved regulatory compliance

In a nutshell, Smart grid data analytics offers valuable information about the performance of the power distribution system and its various assets in order to help achieve several benefits as listed above. It all depends on how the utility makes use of this golden opportunity.

In my next blog I would talk about the various KPI's that could be measured and monitored based on the pool of data that is getting generated.

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