05.18.11Subroto Kar, Practice Manager, Infosys Technologies Ltd.

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Utility electric industry is in the midst of major transformation. According to the Guide Book for ARPA (American Recovery and Reinvestment Act) Smart Grid Program Metric and Benefit, Dec 7, 2009, the United States Department of Energy is investing approximately $4 billion to accelerate deployment and demonstration through the Smart Grid Investment Grant and Smart Grid Demonstration programs. The projects under consideration include metering, customer systems, equipment manufacturing, integrated and crosscutting systems. The approved projects receive financial assistance of up-to 50% of the eligible project cost.

As the utilities take this important journey and 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 -- some term it as 'data Tsunami' -- which is likely to pose a formidable IT challenge unless the utilities are well prepared.

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. Consequently, the market for smart grid analytics expected to generate $4.2bn worldwide by 2015, rising from just $356m in 2010, according to Pike Research. This is driving many enterprise software vendors to further enhance analytics capabilities for utility solutions.

"Analytics" to help improving customer engagement

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 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 market place 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, understanding consumer behavior, better customer service will become an important goal that no utility companies can ignore today. As new smart meters being 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.

This brings me to my next point about how Utility companies should adopt analytics as one of the 'must have' core strategic initiatives as part of the IT infrastructure modernization and enterprise solution investments to better prepare for the smart grid transformation journey.

1. Prioritize and address your Key Performance Indicators
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   One size does not fit all -- by the same token each utility faces unique challenge and you need specific insight to address. While each suffers from natural or climatic pressures -- snow storm, flood, earth quake, some have strong union, severe cost cutting pressures, demanding customers, regulatory or even low employee morale. Consider a balanced score card that focuses on key areas such as stake holder, Customer, Regulatory and Employee. For each area, list the KPI (Key Performance Indicators) that you wish to drive improvement. For example, list and prioritize the areas that are most important from customer satisfaction perspective such as consumption profile, time to restore and outage, billing accuracy, asset reliability, minimizing costs during peak hours through dynamic pricing. For employee, it could be workforce competency and capability, performance, employee satisfaction, employee health and safety. Apply a similar approach to develop a comprehensive yet purposeful KPI for your utility enterprise system; this will set the foundation for your analytic framework.

2. Build a Smart Grid Enterprise Information Model that works for you

   The key applications landscape for your Smart Grid utility includes:

   • Meter Data Management: Supports loading, validating, editing and estimation of meter data from AMI / AMR for customer billing, outage management and customer service.

   • Outage Management: Supports utility in restoration of power. Calculates restoration time, predicts outage or failure, manages crews assisting restoration, predicts location of the breaker or the fuse that caused failure.

   • Asset Management: Supports maintenance of property, plants and infrastructure - enables minimizing operation and maintenance costs.

   • Demand Response: Support mechanism that motivates consumers to reduce peak demand for the energy.

   • Mobile Workforce Management: Automates field operations via crew dispatch, scheduling and reduces operating expense.

   • Load Profiling and Settlement: Supports aggregation of consumption and financial data to forecast and settle with retailers and suppliers.

   • Customer Care and Billing: Supports several aspects of customer care that includes service connection, meter data management, billing, payment processing and debt collection.

   
   
   Given overwhelming volume and granular data, it's often a difficult task to decide what data to keep and for how long and what to discard. Utilities must develop an Enterprise Information Model that is optimal and implemented as a foundation for analytical solution.

3. Know your design considerations

   With several products and solutions in the market, designing an optimal analytics solution that works for your organization can't be easy process. In the following section I talk about the some of the key capabilities that utilities must look for while designing a flexible, scalable and standard based analytical solution.

   1. Whole is greater than sum of its parts

      Many utilities back office applications 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.

      Needless to say, integrating multiple disparate applications together is a cumbersome and costly process. While planning for Smart Grid IT and application infrastructure, consider gradually transforming into a suite of enterprise applications that are designed to work together, built on SOA and support a common Enterprise Service Bus, conforms to standards for seamless data, process orchestration.

   2. Consider scalable, flexible and SOA based architecture

      The enterprise application supporting Smart Grid -- be in package or custom must have the capability to unify information from various segment of the Grid network, distributed energy sources, service delivery, customer interaction and consumer energy usage. Interoperability among applications is an important aspect and SOA compliant interface with real time messaging is an important consideration. The key characteristics include following:

      • Insulates process from the physical infrastructure

      • Helps integrate and manage large meter batch files and sensor data. The underlying architecture provides standard based integration with the technology infrastructure

      • Provides real-time messaging for inter-process communication and ability to integrate with non-SOA systems including legacy applications

      • Supports business rules to create and manage business process without having to rewrite application

   3. Plan future-friendly data storage for your enterprise

      Many utilities are preparing to capture data from smart meters every 15 minutes -- that's require something like 200 TB of storage including disaster recovery factored in. When they move into 5 minutes interval, that would become 800 TB -- 1 minute would become 1.5 PB (peta byte)! This does not factor in local sources of alternate energy such as solar or wind -- in which case the volume will further increase 3 -- 5 times or higher. The unprecedented volume of data and the expectation to do real time data analytics presents unique challenge to the storage technology.

      The next generation utility companies planning to transition into Smart Grid must think about how to architect, secure and deploy data centers that will accommodate such huge volume of data. In the recent times, there has been emergence of modern database machines, as a solution for storing, accessing and interpreting real time data -- this combines data ware house and OLTP (online transaction processing). Oracle Exadata version 2 provides such capability. Contrary to the modern database machine, the other option is to have separate brand of OLTP and data warehousing. The vendors include Teradata , Netezza , Greenplum and Sybase, Vertica, Aster Data, Infobright, Kognitio, et al. Decision to select an appropriate storage and data ware house must be an important step and should factor key criteria such as response time, scalability, management / monitoring, ease of deployment, support for multiple databases and total cost of ownership.

4. Make "Analytics" as one of the core solutions in your Smart Grid initiatives

As you begin to start deploy smart meters, consider "analytics" as one of the core solutions for your smart grid journey. Planning ahead and bearing in mind the information model for your Utilities; will allow you to implement strategies for the optimal yet next generation analytics solutions. One of the toughest challenges that utilities face is island of information across multiple applications within the smart grid ecosystem ; consider some of the key strategies described above -- such as gradual transformation of your smart grid applications into a suite of integrated applications, scalable, flexible and SOA based architecture and future friendly data storage.

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