Redefining Smart Grid Architectural Thinking Using Stream Computing

As smart meters move to the mainstream for performance measurement across the power utilities industry, the next objective is to create new ways of handling large data sets for processes such as validation estimation and evaluation, demand response and load management.

As smart meters proliferate across power grids, energy utilities they generate huge packets of data that are coursing through their IT networks. More and more granular data holds the promise of enabling faster and more informed decision making that drives operational improvements and, perhaps, enables consumers to better manage their own power consumption. To get there, however, utilities must first conquer growing network latency challenges caused not only by the huge profusion of smart–meter–generated data but also by processing inefficiencies created by their dependence on more centralized computing models.

In our view, when operational data is transported on a pervasive communications infrastructure (and coupled with two–way communication between utilities and consumers) data integration challenges can be overcome, setting the stage for a more energy–efficient future

Using Cloud Platforms for Smart Meter Infrastructure

One way to unlock the data treasure trove enabled by smart meters is by tapping virtual data processing infrastructure delivered via cloud computing. Clouds offer the advantages of scalable and elastic resources to build software infrastructure that support such dynamic, always–on applications. But there are challenges of using cloud platforms, such as the need to support efficient and reliable streaming, low–latency scheduling and scale–out, as well as effective data sharing.

Cloud data centers can accommodate the large–scale data interactions that take place on Smart Grids and are better architected than centralized systems to process the huge, persistent flows of data generated across the utility value chain. The figure below shows how this might work within a power utilities company.

The computational demand for demand–response applications will be a function of the imbalance between energy supply and demand. This typically oscillates based on the time of the day and possible weather conditions. This translates into a need for compute–intensive, low–latency response at midday and limited analysis in off–peak evening hours. The elastic nature of cloud resources makes it possible for utilities to avoid costly capital investment for their peak computation needs.

As a result, information sharing reflects real–time energy usage and power pricing. As the figure shows, Smart Grid applications that span smart meters (distributed at the consumer level), cloud platforms (for data integration by service providers) and clusters (at energy utilities) introduce systems heterogeneity, which efficient streaming is positioned to rationalize.

The need to perform complex processing with minimal latency over large volumes of data has led to the evolution of various data processing paradigms. Industry majors such as IBM, Oracle, Microsoft and SAP have developed event–oriented application development approaches to decrease the latency in processing large volumes of data.

As Smart Grids proliferate, businesses require greater data availability rates. Companies can no longer afford to collect all time–series data, load it into a database and then build database indexes for query efficiency. Instead, businesses need these queries to be continuously and incrementally computed and updated as new relevant data arrives from smart meter sources.

Complex Event Processing (CEP)

Complex event processing (CEP) is a widely used technique in smart meter data processing, where data is continuously monitored, verified and acted upon, given ongoing and changing conditions. Its key attributes include:

• Express fundamental query logic.
• Handle error or delayed data.
• Extensibility.
• Universal specification.

Ease of Management

OSIsoft's PI System provides power utilities with the leading operation data management infrastructure for Smart Grid components that encompass power generation, transmission and distribution. This software provides capabilities for energy management, condition–based maintenance, operational performance monitoring, curtailment programs, renewable energy monitoring and phasor monitoring of transmission lines, among other functionalities.

OSIsoft MDUS integrates a utility's meter system and SAP's AMI Integration for Utilities to perform tasks such as billing. It also provides the ability to integrate meter data with other operational data. It serves as a real–time data collector, which is head–end system vendor–independent.

Integration of meter data into business systems such as billing requires data validation and other forms of aggregations. OSIsoft has chosen to leverage CEP to accomplish this task. CEP provides the scalability required by SAP AMI and utilizes a SQL–based language for defining the validation rules. OSIsoft uses Microsoft's StreamInsight CEP engine, which enables utilities to define and implement required meter data validation. This puts this important facet of regulatory compliance requirements into the hands of the utility's IT and business specialists.

Cloud and Adaptive Rate Control

The growing importance for utilities to process and analyze thousands of meter data streams suggests that they should consider the adoption of cloud platforms to achieve scalable, latency–sensitive stream processing.¹ One approach to consider is adaptive rate control, which is the process of restricting the stream rate to meet accuracy requirements for Smart Grid applications. This approach consumes less bandwidth and computational overhead within the cloud for stream processing.

Applying InfoSphere Streams

IBM InfoSphere Streams is a stream processing system that continuously analyzes massive volumes of streaming data for business activity monitoring and active diagnostics. It consists of a runtime environment that contains stream instances running on one or more hosts. Within InfoSphere is a Stream Processing Application Declarative Engine (known as SPADE), which is a stream programming model that supports stream data² sources that continuously generate tuples³containing typed attributes.

SAP Event Insight

The emergence of smarter grids powered by stream computing has made clear the need for more robust processing at the enterprise systems level. These systems typically struggle to keep pace with high data volume and a large number of heterogeneous and widely dispersed data sources and changing data requirements. This is being resolved by enterprise software systems such as mySAP ERP, which can accommodate in–memory processing algorithms for this new architectural proposition.

Looking Down the Road

Stream computing could be applied broadly by the power utilities industry to minimize network latency and function as a key component for demand response management.

This approach will help utilities that are adopting Smart Grids in their mainstream business with network optimization and intelligent processing, saving money by automating their demand response program and load management processes. Standardizing these processes saves IT maintenance expense, freeing capital to be invested in other core business activities.

To learn more, read the white paper Redefining Smart Grid Architectural Thinking Using Stream Computing (PDF), or learn more about Cognizant's energy and utilities practice

Footnotes

¹Stream computing is a high–performance computer system that analyzes multiple data streams from many sources, live. Stream computing uses software algorithms to analyze data in real time, which increases speed and accuracy when dealing with data handling and analysis.

²Stream data is a sequence of digitally encoded coherent signals (packets of data or data packets) used to transmit or receive information.

³Tuple is an ordered pair of energy data to be processed and is an effective way of representing in–stream computing.