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Analytics and the smart utility

Making the power grid smart

by Alyssa Farrell, Product Marketing Manager, Energy and Sustainability, SAS

The term "smart grid" represents significant technology projects underway at many utilities today. Utilities are not only implementing new meters at homes and businesses, but also installing new devices along the power lines and transmission networks.

The capabilities that these new technologies enable – such as time-of-use energy pricing, sophisticated outage management, and theft detection – are intended to help meet growing energy demands without building new air-polluting plants. Utilities also hope that the investments extend the life of an antiquated power grid. However, none of these benefits can be realized without the application of analytics.

The landscape has changed 
The quiet industrial giant of the mid- 20th century is no longer. Deregulation, globalization and sustainability have changed the operating landscape for electric utilities. Here are a few facts that help quantify the scale of change:

  • 240 million smart meters are anticipated to be deployed across Europe by 2020.1 
  • India and China's electric power producers will consume three times as much coal in 2035 as they did in 1990.2
  • One utility retailer in a competitive market recently experienced a 17 percent churn rate over a six-month period. Churn was as high as 26 percent in one region. 3
  • 30 percent of the European utility workforce is more than 50 years of age.4

As these examples illustrate, utilities are under pressure to deliver a highly functioning IT environment with new connected devices, meet growth expectations in developing countries, and retain customers in mature deregulated markets. Unfortunately, the business is changing nearly as rapidly as organizational knowledge is retiring.

Opportunity to innovate and optimize
Just as the optimist seeks a silver lining to every dark cloud, leading utilities are taking advantage of the confluence of market forces to modernize the data management and analytical capabilities of their organizations. Without this, they cannot hope to achieve the benefits expected from smart grid projects.

Research from Ovum highlighted that "Smart meters and smarter grids in themselves do not bring actionable insight or competitive differentiation. If new meters make a utility smart, it is through business intelligence (BI) and analytics that a utility becomes intelligent." 5 

Increasingly, with near-real-time data on the smart grid, analytics is being applied to determine the best-case scenario for delivering reliable power to end consumers. For example, utilities are enrolling customers in demand response programs and compensating them each month for the option to tweak their largest energy - consuming device (usually air conditioners) during peak events. Each enrolled customer has unique quantified load reduction potential. The utility must determine which units (customers) to schedule, when, and for how long. To make that determination, utilities require more data and proven models that are available for decision support, returning results quickly and reliably.

Four targets for analytics in the utility business
Data analytics drive down costs and improve service delivery quality for telecommunications providers, and can do the same for utilities managing similarly complex networks.6 We have identified four areas that have significant return on investment.

Planning. Whether it is determining optimal deployment of capital, long-term demand forecasting, or integration of renewables and microgeneration, a utility's ability to deliver profits will increase alongside improvements in data-driven planning.

" If new meters make a utility smart, it is through business intelligence (BI) and analytics that a utility becomes intelligent. "

— Research from Ovum

Customer Insights. Every year, energy companies write off millions in bad debt caused by customers who don't pay their bills. Utilities are facing increasing pressure from shareholders and regulators alike to minimize those losses, while continuing to provide services to consumers who are not likely to pay. An analytic approach can help utilities take into account regulatory reasonable care demands while building risk scores for all customers based on credit ratings, usage patterns and payment history. In competitive markets, a utility can improve customer relationships by matching the right offer– from an increasingly large and diverse set of offers – to the right customer, through the right communication vehicle. To optimize the offer process, utilities are turning to advanced analytics to cluster customers by likes and dislikes and then assess their propensity to switch to a different offer.

Risk. Utilities must manage risk associated with trading activities. The global credit crisis showed many energy and utility companies that their risk models were too limited, restricting their ability to analyze market movements, measure corporate exposures and develop mitigation plans. Advanced analytics provide utilities with the ability to aggregate internal and external data and transform it into useful information quickly for accurate risk exposures.

240 million smart meters are anticipated to be deployed across Europe by 2020.

Operations. The application of analytics on top of maintenance planning or enterprise asset management systems can increase the uptime and lifetime of aging assets by predicting asset failures in advance. The significant capital tied up in transformers and other distribution equipment makes this a worthwhile investment. Distribution transformers can account for as much as 20 percent of total distribution capital spending in a year.7 In addition to transformers, Asia Pacific and North American markets are making significant investments in synchrophasors, also called phasor measurement units (PMU).8 PMU measurements are taken at high speed (typically 30 observations per second – compared to one every four seconds using conventional technology). Each measurement is time-stamped in order to build a comprehensive view of the electricity on the grid. With the application of analytics, synchrophasors enable a better indication of grid stress, and can be used to trigger corrective actions to maintain reliability.

The way forward
Utilities often underestimate the ability to use vast resources of new data to optimize their daily business activities. As a result, valuable information remains trapped in silos and utilities continue to underperform in critical areas such as meter data quality, asset management and customer service. Executive leadership must support the fundamental transformation from separate business units to an organization that is focused on information sharing and data quality. The benefits of this approach should inspire a new generation of energy practitioners as well as the workforce that is nearing retirement.

The business of utilities is under a massive transformation. Analytics is the key to riding the wave of transformation and delivering business value from smart grid investments.

Bio: Alyssa Farrell leads global industry marketing for SAS' business within the energy sector, including utilities, oil and gas. She also has responsibility for SAS' sustainability solutions and works with customers around the world to understand best practices and solutions for managing their business with environmental responsibility in mind.

References:
1. www.thegreenitreview.com/2011/03/ pike-research-forecasts-240-million.html
2.International Energy Outlook 2011. US Energy Information Administration. September 19, 2011.
3. www.switchwise.com.au/blogs/
4. Electricity, gas and steam production and distribution statistics. Eurostat.
5. BI and Analytics: Making the Smart Utility Intelligent. Ovum Research. Stuart Ravens. June 2010.
6. "Data Analytics for Utility Communications Networks." Christine Herzog. IEEE Smart Grid Newsletter. November 2011.
7. DSTAR's Transformer Cost Analysis Software Enhances Utility Decision Process. GE Energy. 2004. http://static.dstar.org.
8. Synchrophasors in Smart Grid – Global Market Analysis and Forecasts to 2015. GlobalData. January 2011.

 

Alyssa Farrell, SAS

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  • Download the first quarter 2012 issue of Intelligence Quarterly to read more articles on energy transformation.
  • Check out SAS solutions for the utilities industry.