The Infosys Utilities Blog seeks to discuss and answer the industry’s burning Smart Grid questions through the commentary of the industry’s leading Smart Grid and Sustainability experts. This blogging community offers a rich source of fresh new ideas on the planning, design and implementation of solutions for the utility industry of tomorrow.

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February 18, 2011

Why do utilities need EIM?

 

I have talked about Enterprise Information Management (EIM) in one on my prior posts. The need for EIM is increasingly becoming clear to many utilities and they are slowly starting enterprise wide initiatives to get this going. In this post, I will talk about an illustrative use case and the various components of EIM.

 

Outage management is one of the critical component of any utility smart grid program. There are a number of key benefits of outage management to both the utility as well as it's customers. A utility will be able to use smart meter data and locate outages. Once an outage is located a utility can re-route power thereby restoring power to the customer almost immediately. Obviously, this will improve the customer experience and customer satisfaction. Outage detection allows utility to dispatch nearby crew to a specific address where problem is detected helping utility improve operations. Customer Service reps will be able to have up-to-date information on a premise and will be able to provide updated information to any customer that calls in to inquire about the outage.

 

To be able to successfully implement an outage management use case, a utility needs to ensure a number of pieces are taken care of. Such a use case needs to integrate data across systems e.g. MDM, OMS. Customer master data will ensure a consistent definition and view across applications. Having appropriate policies and procedures will provide ability to create and maintain data ensuring data health over the long run. With well-defined data standards, data delivery standards utilities can improve data sharing and interoperability. These various components pose a huge challenge but are critical to a successful implementation of outage management use case. This can be achieved by having a well-defined Enterprise Information Management strategy and implementation plan in place. Each utility will have to look at the components of EIM - Master Data Management, Data Governance, Data Architecture, Quality Management, Metadata Management etc. - and create its own version of EIM structure to ensure long-term success. 

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

Energy Efficiency

  • Demand Side Management
  • Price-sensitive Demand Response
  • Aggregate Demand Response
  • TOU
  • 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.

February 4, 2011

Should "Analytics" be one of the core strategic systems in Utility Smart Grid initiatives?

As the utilities transition into the smart grid, one of the major changes the companies will experience is the availability of vast information with high degree of granularity. Many utilities are preparing to capture data from smart meters every 15 minutes - that requires something like  200 TB of storage, including disaster recovery factored in. When they move into 5 minutes intervals, that would become 800 TB - 1 minute would become 1.5 PB (peta byte)! If you include local sources of alternate energy such as solar or wind, the volume will further increase to 2 -3 times. On the other hand, it would allow the next generation smarter utility companies to do real-time optimization and drive predictive analytics to improve operational efficiencies, customer service, energy efficiency and better asset utilization.


Think about how the entire notion of customer engagement will change as utilities begin to learn more about consumer behavior - much like how mobile or credit card companies are able to slice and dice the customer data. Using predictive analysis, these companies will be able to figure out when the customer might potentially switch to another provider. In a deregulated market such as Texas, Maryland, New Jersey, Pennsylvania, Connecticut and many other states that follow, the utility companies will face unlimited competition within the marketplace where the consumer is free to choose any electric provider - with the notion that more choice and more competition will lead to lower electricity monthly bill. Therefore, with understanding consumer behavior, better customer service will become an important goal that no utility company can ignore today. As the new smart meters are deployed, the meter data can be used to accurately predict - a meter that is likely to malfunction in 30 days, Power outage in next 1 hour or demand spike in 3 hours - things like these will bring in change the way the quality of customer service is provided.


"Analytic solutions" - are no longer a "nice to have" , IT enabled systems that are often done at the end of implementation. Given its significance in smart grid, utilities must think about "analytics" as one of the core strategic initiatives, a part of the IT infrastructure modernization and enterprise solution investments to better prepare for the smart grid transformation journey. Top 3 key implications that utilities need to think about are:

a. Information is in silos - Many utilities back office applications were built over several years and many are isolated from one another. As demand grows, some of these applications suffer from severe scalability and information redundancy issues.
b. Interoperability - challenge in information unification across the Grid network, distributed energy sources, service delivery, customer interaction and consumer energy usage
c. A robust information storage technology - The unprecedented volume of data and the expectation to do real time data analytics presents unique challenges to the storage technology.

February 2, 2011

Live Update Distributech 2011 Day 2

Wow!! another great day, lot's of activity. Many people stopping by and seeing Smart Integrator Demo. Matt Dhillon attended the session on IT for Smart Grids. Very good content.

Generating lot of interest in Infosys capabilities and Smart Grid solutions. So far had 5 demonstrations (since 10:30 AM) and each one of us impressed with the content and depth offered by the solutions.