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Redefining Smart Grid Architectural Thinking Using Stream Computing

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Using stream computing, power utilities can capture and analyze data generated by smart meters to achieve new thresholds of performance, while building better consumer relationships.

Using stream computing, power utilities can capture and analyze data generated by smart meters to achieve new thresholds of performance, while building better consumer relationships.

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  • 1. • Cognizant 20-20 InsightsRedefining Smart Grid ArchitecturalThinking Using Stream Computing Executive Summary tecture will help utilities transform their power grids into adaptive and intelligent infrastructures After an extended pilot phase, smart meters that inform continuous improvements in opera- have moved into the mainstream for measuring tional efficiency and business effectiveness. the performance of a multiplicity of business functions across the power utilities industry. This white paper explores the challenges and Moving forward, the next objective is to create new benefits of Smart Grid creation and offers concrete ways of handling large data sets for constructing thinking on new architectural approaches built actionable responses to smart-meter-generated on emerging software standards that more data, particularly when it comes to processes effectively leverage established forms of stream such as validation estimation and evaluation, computing.1 It examines new thinking on ways to demand response and load management. capture and analyze data generated by smart meters that can help power utilities achieve new As smart meters proliferate across power grids, thresholds of performance over the near- and energy utilities are dealing with huge packets of long-term, while building better relationships with data coursing through their IT networks. More and consumers. We examine how stream data2 aids more granular data holds the promise of enabling usage forecasts (predicted by converting historic faster and more informed decision making that data coupled with real-time events into opera- drives operational improvements and, perhaps, tional KPIs) and identifies anomalies and patterns enables consumers to better manage their own in an ever-changing and high-transaction environ- power consumption. To get there, however, ment. In our view, when operational data is trans- utilities must first conquer growing network ported on a pervasive communication infrastruc- latency challenges caused not only by the huge ture (and coupled with two-way communication profusion of smart-meter-generated data but between utilities and consumers) data integration also by processing inefficiencies created by their challenges can be overcome, setting the stage for dependence on more centralized models. a brighter and more energy-efficient future. Forward-thinking utilities need more distributed and virtual complex event processing models that Using Cloud Platforms for Smart Meter transform real-time operational data into applied Infrastructure insights. Creating real-time operational knowledge One way to unlock the data treasure trove can drive better demand response management, enabled by smart meters is by tapping virtual improve quality of service and preempt fraud and data processing infrastructure delivered via service outages before they inflict reputational cloud computing. Clouds offer the advantages of damage. Rethinking their basic information archi- scalable and elastic resources to build software cognizant 20-20 insights | june 2011
  • 2. Consumers and Smart Meters: Interactions on a Cloud Stream Active feedback of pricing Load curtailment signals Pow er co nsum ption Residential data Consumption strea m Hourly Consumption Prediction Pattern Recognition d ata Weather ata Commercial ond Consumption ucti prod Historian Power w er Generation PoFigure 1infrastructure that support such dynamic, shows, Smart Grid applications that span smartalways-on applications. But the unique needs of meters (distributed at the consumer level),energy informatics applications also highlight the cloud platforms (for data integration by servicechallenges of using cloud platforms, such as the providers) and clusters (at energy utilities)need to support efficient and reliable streaming, introduce systems heterogeneity, which efficientlow-latency scheduling and scale-out, as well as streaming is positioned to rationalize.effective data sharing. The need to perform complex processing withCloud platforms are an intrinsic component in minimal latency over large volumes of data hascreating a software architecture to drive more led to the evolution of various data processingeffective use of Smart Grid applications. The paradigms. Industry majors such as IBM, Oracle,primary reason: Cloud data centers can accom- Microsoft and SAP have developed event-orientedmodate the large-scale data interactions that application development approaches to decreasetake place on Smart Grids and are better archi- the latency in processing large volumes of data.tected than centralized systems to process the These efforts reveal the following:huge, persistent flows of data generated acrossthe utility value chain. Figure 1 shows how this • Since smart meters generate interval datamight work within a power utilities company. that is time-series in nature, companies need efficient ways of processing queries incremen-The computational demand for demand-response tally and via in-memory technologies. Theyapplications will be a function of the energy then need a way to apply the results to theirdeficit between supply and demand. This typically emerging dynamic business process models.oscillates based on the time of the day andpossible weather conditions. This translates into a • Since this buffered data is also stored offline for static analysis, mining, tracing and back-need for compute- intensive, low-latency response testing, companies need a means of managingat midday and limited analysis in off-peak evening and accessing these stores efficiently.hours. The elastic nature of cloud resources makesit possible for utilities to avoid costly capital As Smart Grids proliferate, businesses requireinvestment for their peak computation needs. greater data availability rates. Companies can no longer afford to collect all time-series data, load itThis results in information sharing on real-time into a database and then build database indexesenergy usage and power pricing. As Figure 1 for query efficiency. Instead, businesses need cognizant 20-20 insights 2
  • 3. these queries to be continuously and incremen- Ease of Managementtally computed and updated as new relevant data To effectively deploy smart meters and the dataarrives from smart meter sources. This will avoid they generate, a number of factors need to bethe need to re-process existing data. Incremental addressed, including query composability andcomputation is necessary to create a low-latency ease of deployment over a variety of environ-response to continuously flowing time-series data. ments, such as single servers and clusters. QueryComplex event processing (CEP) is a widely used composability requires the ability to “publish”technique in smart meter data processing, where query results, as well as the ability for Continuousdata is continuously monitored, verified and acted Query (CQ) to consume results of existing CQsupon, given ongoing and changing conditions. and streams.With this approach, data, including the event Typical meter streaming queries entail rules suchstreams from multiple sources, is processed based as:on a declarative query language. Importantly, allof this is accomplished with near-zero latency. • Present the top three values every 10 minutes.Event-Driven Data Processing • Compute a running average of each sensor value over the last 20 seconds.ChallengesThe key attributes of complex event processing • Filter out sensor readings when the device was in a maintenance period.include:• Express fundamental query logic: Incorpo- • Illustrate when event “A” was followed by event “B” within three minutes. rate windowed processing and time progress as a core component for query logic. OSIsoft’s PI System provides power utilities• Handle error or delayed data: Delayed with the leading operation data management processing until guaranteed, with no late-arriv- infrastructure for Smart Grid components that ing events. This increases latency; otherwise, encompass power generation, transmission and aggressively process event and produce distribution. This software provides capabilities tuples.3 for energy management, condition-based mainte-• Extensibility: Given the complexity of meter nance, operational performance monitoring, cur- data and event operations, there is a need tailment programs, renewable energy monitoring to support custom-built streaming logic as and phasor monitoring of transmission lines, libraries. among other functionalities. `• Universal specification: Semantics of query OSIsoft MDUS integrates a utility’s meter system logic need to be independent of when and how and SAP’s AMI Integration for Utilities to perform programmers physically read and understand tasks such as billing. It also provides the ability to events. Applications time, rather than system integrate meter data with other operational data. time, is used to enable a universal time zone. It serves as a real-time data collector, which is head-end system vendor-independent.These attributes ensure that with complex eventprocessing, query logic is kept generic regarding Integration of meter data into business systemshow events are read and how their output is inter- such as billing requires data validation and otherpreted. Tuples should follow universal time, which forms of aggregations. OSIsoft has chosen tocan be read and processed on any system. leverage CEP to accomplish this task. CEP provides the scalability required by SAP AMI and utilizes aPerformance Implications SQL-based language for defining the validationIn-stream processing doesn’t allow data to be rules. OSIsoft uses Microsoft’s StreamInsightwritten back to the disk for processing later from CEP engine, which enables utilities to define andinternal state in main memory. With smart meter implement required meter data validation. Thisdata, an event queue is filled to capacity once puts this important facet of regulatory compliancethe arrival rate is greater than the processing requirements into the hands of the utility’s IT andcapability of the system. The metrics used to business specialists.manage the data stream are latency, throughput,correctness and memory usage. cognizant 20-20 insights 3
  • 4. PI Interface Node Foreign Device System PI Server Input Adapter(s) Output Adapter(s) Data Source Queries Stream Insight Engine (vs .NET- LINQ) There are two ways streaming can be adopted in Complex Event Processing Engine and deployed on the Eucalyptus4 private cloud,5 current meter data systems: shows 50% bandwidth savings, resulting from adaptive stream rate control. • Server-side streaming: The stream is pro- cessed on the (OSIsoft) PI snapshot and Low-latency stream processing is a key com- streamed with the processed results back to ponent of the software architecture required the PI server (see Figure 2). to support demand-response applications. The stream processing system ingests smart meter data arriving from consumers and acts as a first PI Server responder to detect local and global power usage skews and to alert the utility operator. At 1KB per Input Adapter(s) Output Adapter(s) event generated each minute, 2TB of data will Queries Stream Insight Engine (vs .NET-LINQ) stream each day. Processing such large-scale streams can be compute- and data-intensive; public or private cloud platforms provide a scal- Figure 2 able and flexible infrastructure for building such Smart Grid applications. • Edge processing: In this model, the CQs are applied at the data source (and at the PI However, computational and bandwidth con- interface level), where the results are only straints at the consumer and utility levels mean stored as processed data (see Figure 3). that power usage data streamed at static rates from smart meters to the utility can either be at too high a latency to detect usage skews in a PI Interface Node timely manner or at too high a rate to computa- Foreign Device tionally overwhelm the system. Smart meters System connect to the utility using heterogeneous PI Server Input Adapter(s) Output Adapter(s) Data Queries networks and range from low bandwidth power Source Stream Insight Engine (vs .NET- LINQ) line carriers at ~20Kbps, to 3G cellular networks Complex Event Processing Engine at ~2Mbps, as well as ZigBee at ~250Kbps. Network bandwidth can thus be a scare resource Figure 3 at the consumer end. In the case of smart meters, traffic can be bursty, since data is sent indepen- dently, causing instantaneous bandwidth needs Cloud and Adaptive Rate Control to spike. The growing importance for utilities to process and analyze thousands of meter data streams In the case of high power demand, meters emit PI Server suggests that they should a large volume of information, which requires a The growing consider the adoption of Input Adapter(s) Output Adapter(s) throttle controller to respond to these events and control latency.importance for utilities cloud platforms.NET-LINQ) Stream Insight scalable, to achieve Queries Engine latency-sensitive (vsto process and analyze stream processing. One Applying InfoSphere Streams thousands of meter approach to consider is IBM InfoSphere Streams is a stream processingdata streams suggests adaptive rate control, which system that continuously analyzes massive is the process of restrict- volumes of streaming data for business activity that they should ing the stream rate to meet monitoring and active diagnostics. It consists consider the adoption accuracy requirements for of a runtime environment that contains stream of cloud platforms Smart Grid applications. instances running on one or more hosts. Within This approach consumes InfoSphere is a Stream Processing Application to achieve scalable, less bandwidth and com- Declarative Engine (known as SPADE), which is latency-sensitive putational overhead within a stream programming model (executed by the stream processing. the cloud for stream runtime environment) that supports stream processing. The experi- data sources that continuously generate tuples mentation of the Smart Grid stream processing containing typed attributes. pipeline, modeled using IBM InfoSphere Streams cognizant 20-20 insights 4
  • 5. Tracking Energy Consumption A stream processing pipeline is used to continuously monitor energy usage. Processing elements in dotted lines show the addition of throttle logic. Notify Notify DB/File 1 max if(u 1 >U ) if(u 1 >.136*u avg) 1 1 Update u1sum Update u1avg (m1,t1,u11) Store Running AMI’s 15-min Condition Condition average daily sum R1++ Increase AMI rate (mn,t1,un 1) if(c1-u1avg < accept) Condition Utility’s 15-min Condition average Network Update u avg 1 Decrease Superscript = Meter ID AMI rate Subscript = Time R1Figure 4Figure 5 shows the smart meters present on the performed for each smart meter stream (shadedpublic Internet that generate power usage data in brown in Figure 4.streams accessible over TCP sockets. Here, the Next, the pipeline aggregates smart meter tuplesInfoSphere streams run on a cluster that doesn’t across all streams using a tumbling window tosupport out-of-box deployment on a cloud plat- calculate the cumulative consumer energy usageform. To instantiate a stream processing environ- within a 15-minute time window. This streamment on a Eucalyptus private cloud, a customized operator (shaded blue in Figure 4) calculates theVM image must be built that supports InfoSphere total load on the utility. It can be used to alert thestreams. Communication to the stream instance utility manager in case, say, the total consumptionis activated when the VM instances are online. reaches 80%, 90% and >100% of available powerThis communication, however, is initiated exter- capacity at the utility. Operators shown in dottednally by a SPADE application started on a stream lines (Figure 4) are not part of the applicationinstance and configured with a list of named logic and form the adaptive throttling introducedstream instances on specific hosts. next. This core model could be used in demandEach smart meter is a stream source whose response management.tuples have the identity of the smart meter,power used within a time duration, as well as the SAP Event Insighttimestamps of the measurement interval. Addi- The emergence of smarter grids powered bytional meta data about the smart meter and con- stream computing has made clear the need forsumer is part of the payload but will be ignored more robust processing at the enterprise systemsfor the purposes of this discussion. Each tuple level. These systems typically struggle to keepis about 1KB in size. The pipeline first checks if pace with high data volume and a large numbereach individual power usage tuple reports usage of heterogeneous and widely dispersed datathat exceeds a certain constant threshold, Umax sources and changing data requirements. This ism defined by the utility. being resolved by enterprise software systems such as mySAP ERP, which have begun to adaptCrossing this threshold will trigger a critical in-memory processing algorithms for this newnotification to a utility manager. Next, a relative architectural proposition. The result is that SAPcondition will check to see if the user’s consump- can now deliver an event insight application thattion increases by more than 25% since his/her understands the impact of operational eventsprevious consumption. This will trigger a less in real time. In-memory processing not onlycritical notification. The pipeline then archives brings just-in-time rhyme and reason to real-timethe tuple into a sink file and proceeds to compute business events, but it can also do so with signifi-a running sum of the daily usage by the consumer. cantly less effort, a reduction in reporting, oper-Subsequently, the running average over a ational and opportunity costs, which can powertumbling window is updated. These operations are competitive advantage. cognizant 20-20 insights 5
  • 6. Architecture of Stream Processing and the Throttle Controller Control Feedbacks Throttle Controller InfoSphere Streams Input Streams Streams Processing Response TCP/IP Electric AMI Gas Industrial/Commercial Data Files Electric AMI Gas Data Files Residential BuildingFigure 5Looking Down the Road network optimization and intelligent processing, saving money by automating their demandWe see stream computing as a key element of the response program and load managementfuture of work that could be applied broadly by processes. Standardizing these processes savesthe power utilities industry. Our view is that its IT maintenance expense, freeing capital to bedeployment would minimize network latency and invested in other core business activities.function as a key component for demand responsemanagement. Moreover, we are planning to inves- In a business context, this approach will helptigate stream computing on the cloud platform. utilities with energy efficiency programs andOur research will appraise the throughput of grid management. It does this by providing aa stream processing system and its latency in mechanism to convert dollars saved by eliminat-processing each tuple as the stream rates adapt. ing inefficient energy generation and distribution toward more effective asset management.This approach will help utilities that are adoptingSmart Grids in their mainstream business withFootnotes1 Stream 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.2 Stream data is a sequence of digitally encoded coherent signals (packets of data or data packets) used to transmit or receive information.3 Tuple is an ordered pair of energy data to be processed and is an effective way of representing in-stream computing.4 Eucalyptus Cloud is a software platform for the implementation of private cloud computing on computer clusters. cognizant 20-20 insights 6
  • 7. 5 Private clouds are internal clouds that, according to some vendors, emulate cloud computing on private networks. These (typically virtualization automation) products offer the ability to host applications or virtual machines in a company’s own set of hosts. They provide the benefits of utility computing, such as shared hardware costs, the ability to recover from failure and the ability to scale up or down depending upon demand.References“IBM Infosphere Streams Version 1.2.1: Programming Model and Language Reference,” IBM, Oct. 4,2010, http://publib.boulder.ibm.com/infocenter/streams/v1r2/topic/com.ibm.swg.im.infosphere.streams.product.doc/doc/IBMInfoSphereStreams-LangRef.pdf.D. J. Abadi, Y. Ahmad, M. Balazinska, U. Cetintemel, M. Cherniack, J. H. Hwang, W. Lindner, A. Maskey,A. Rasin, E. Ryvkina, N. Tatbul, Y. Xing and S. B. Zdonik, “The Design of the Borealis Stream ProcessingEngine,” Proceedings of the Second Biennial Conference on Innovative Data Systems Research, pp277-289, January 2005.D. J. Abadi, D. Carney, U. Cetintemel, M. Cherniack, C. Convey, S. Lee, M. Stonebraker, N. Tatbul and S.Zdonik. “Aurora: A New Model and Architecture for Data Stream Management,” The VLDB Journal, Vol12, Issue 2, August 2003.A. Arasu, S. Babu and J. Widom. “The CQL Continuous Query Language: Semantic Foundations andQuery Execution.” The VLDB Journal, Vol 15, Issue 2, June 2006.A. M. Ayad, J. F. Naughton. “Static Optimization of Conjunctive Queries with Sliding Windows Over InfiniteStreams,” Proceedings of the International Conference on Management of Data, SIGMOD 2004, ACM.C. Ballard, D. M. Farrell, M. Lee, P. D. Stone, S. Thibault and S. Tucker, “IBM InfoSphere Streams HarnessingData in Motion,” IBM, September 2010.A. Biem, E. Bouillet, H. Feng, A. Ranganathan, A. Riabov, O. Verscheure, H. Koutsopoulos and C. Moran,“IBM InfoSphere Streams for Scalable, Real-Time Intelligent Transportation Services,” Proceedings ofthe International Conference on Management of Data, SIGMOD 2010, pp 1,093-1,104, ACM.S. Chandrasekaran, O. Cooper, A. Deshpande, M. J. Franklin, J. M. Hellerstein, W. Hong, S. Krishnamurthy,S. Madden, V. Raman, F. Reiss and M. A. Shah, “TelegraphCQ: Continuous Dataflow Processing for anUncertain World,” SIGMOD 2003, ACM.StreamBase, http://www.streambase.com/D. Abadi et al., “The Design of the Borealis Stream Processing Engine.”“Why IP is the Right Foundation for the Smart Grid,” Cisco Systems, Inc., January 2010.“The Role of the Internet Protocol (IP) in AMI Networks for Smart Grid,” National Institute of Standardsand Technology, NIST PAP 01, Oct. 24, 2009.D. Zinn, Q. Hart, B. Ludaescher and Y. Simmhann, “Streaming Satellite Data to Cloud Workflows forOn-Demand Computing of Environmental Products,” Workshop on Workflows in Support of Large-ScaleScience (WORKS), 2010.Arvind Arasu, Shivnath Babu, Jennifer Widom, ”CQL: A Language for Continuous Queries over Streamsand Relations,” Database Programming Languages, 9th International Workshop, DBPL 2003, Potsdam,Germany, Sept. 6-8, 2003.“Pattern Detection with StreamInsight” Microsoft StreamInsight blog, Sept. 2, 2010, http://tinyurl.com/2afzbhd“InfoSphere Streams,” IBM, http://www.ibm.com/software/data/infosphere/streams cognizant 20-20 insights 7
  • 8. About the AuthorAjoy Kumar is a Senior Architect within Cognizant’s Manufacturing and Logistics Practice, where heis working on the Smart Grid program that focuses on Smart Grid architecture, design performance,demand response, enterprise integration and meter data management. Before joining Cognizant, heworked with OSIsoft, Inc. where he led numerous initiatives, including one in which he spearheadedthe development of a meter data unification system integrating OSIsoft and SAP AG. Ajoy has alsoworked extensively in the energy, pharma, chemical and mining and steel industries and has spent over17 years focused on information technology. Ajoy holds a Master’s Degree in Computer Science. He canbe reached at ajoykumar.arumugam@cognizant.com.About CognizantCognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process out-sourcing services, dedicated to helping the world’s leading companies build stronger businesses. Headquartered inTeaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industryand business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50delivery centers worldwide and approximately 111,000 employees as of March 31, 2011, Cognizant is a member of theNASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing andfastest growing companies in the world. Visit us online at www.cognizant.com or follow us on Twitter: Cognizant. World Headquarters European Headquarters India Operations Headquarters 500 Frank W. Burr Blvd. Haymarket House #5/535, Old Mahabalipuram Road Teaneck, NJ 07666 USA 28-29 Haymarket Okkiyam Pettai, Thoraipakkam Phone: +1 201 801 0233 London SW1Y 4SP UK Chennai, 600 096 India Fax: +1 201 801 0243 Phone: +44 (0) 20 7321 4888 Phone: +91 (0) 44 4209 6000 Toll Free: +1 888 937 3277 Fax: +44 (0) 20 7321 4890 Fax: +91 (0) 44 4209 6060 Email: inquiry@cognizant.com Email: infouk@cognizant.com Email: inquiryindia@cognizant.com© Copyright 2011, Cognizant. All rights reserved. No part of this document may be reproduced, stored in a retrieval system, transmitted in any form or by anymeans, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein issubject to change without notice. All other trademarks mentioned herein are the property of their respective owners.

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