Redefining Smart Grid Architectural Thinking Using Stream Computing

Transcription

1 Cognizant Insights Redefining Smart Grid Architectural Thinking Using Stream Computing Executive Summary After an extended pilot phase, smart meters have moved into the mainstream for measuring the performance of a multiplicity of business functions across the power utilities industry. Moving forward, the next objective is to create new ways of handling large data sets for constructing actionable responses to smart-meter-generated data, particularly when it comes to processes such as validation estimation and evaluation, demand response and load management. As smart meters proliferate across power grids, energy utilities are dealing with huge packets of data 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 models. Forward-thinking utilities need more distributed and virtual complex event processing models that transform real-time operational data into applied insights. Creating real-time operational knowledge can drive better demand response management, improve quality of service and preempt fraud and service outages before they inflict reputational damage. Rethinking their basic information architecture will help utilities transform their power grids into adaptive and intelligent infrastructures that inform continuous improvements in operational efficiency and business effectiveness. This white paper explores the challenges and benefits of Smart Grid creation and offers concrete thinking on new architectural approaches built on emerging software standards that more effectively leverage established forms of stream computing. 1 It examines new thinking on ways to capture and analyze data generated by smart meters that can help power utilities achieve new thresholds of performance over the near- and long-term, while building better relationships with consumers. We examine how stream data 2 aids usage forecasts (predicted by converting historic data coupled with real-time events into operational KPIs) and identifies anomalies and patterns in an ever-changing and high-transaction environment. In our view, when operational data is transported on a pervasive communication infrastructure (and coupled with two-way communication between utilities and consumers) data integration challenges can be overcome, setting the stage for a brighter and 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 cognizant 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 data Residential Consumption strea m Pattern Recognition ata Hourly Consumption Prediction d Weather u rod p er ata Commercial Consumption nd o cti Historian w Po Power Generation Figure 1 infrastructure that support such dynamic, always-on applications. But the unique needs of energy informatics applications also highlight the 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 platforms are an intrinsic component in creating a software architecture to drive more effective use of Smart Grid applications. The primary reason: 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. Figure 1 shows how this might work within a power utilities company. The computational demand for demand-response applications will be a function of the energy deficit between 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. This results in information sharing on real-time energy usage and power pricing. As Figure 1 cognizant insights 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. These efforts reveal the following: Since smart meters generate interval data that is time-series in nature, companies need efficient ways of processing queries incrementally and via in-memory technologies. They then need a way to apply the results to their emerging dynamic business process models. Since this buffered data is also stored offline for static analysis, mining, tracing and backtesting, companies need a means of managing and accessing these stores efficiently. 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 2

3 these queries to be continuously and incrementally computed and updated as new relevant data arrives from smart meter sources. This will avoid the need to re-process existing data. Incremental computation is necessary to create a low-latency response to continuously flowing time-series data. 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. With this approach, data, including the event streams from multiple sources, is processed based on a declarative query language. Importantly, all of this is accomplished with near-zero latency. Event-Driven Data Processing Challenges The key attributes of complex event processing include: Express fundamental query logic: Incorporate windowed processing and time progress as a core component for query logic. Handle error or delayed data: Delayed processing until guaranteed, with no late-arriving events. This increases latency; otherwise, aggressively process event and produce tuples. 3 Extensibility: Given the complexity of meter data and event operations, there is a need to support custom-built streaming logic as libraries. Universal specification: Semantics of query logic need to be independent of when and how programmers physically read and understand events. Applications time, rather than system time, is used to enable a universal time zone. These attributes ensure that with complex event processing, query logic is kept generic regarding how events are read and how their output is interpreted. Tuples should follow universal time, which can be read and processed on any system. Performance Implications In-stream processing doesn t allow data to be written back to the disk for processing later from internal state in main memory. With smart meter data, an event queue is filled to capacity once the arrival rate is greater than the processing capability of the system. The metrics used to manage the data stream are latency, throughput, correctness and memory usage. Ease of Management To effectively deploy smart meters and the data they generate, a number of factors need to be addressed, including query composability and ease of deployment over a variety of environments, such as single servers and clusters. Query composability requires the ability to publish query results, as well as the ability for Continuous Query (CQ) to consume results of existing CQs and streams. Typical meter streaming queries entail rules such as: Present the top three values every 10 minutes. Compute a running average of each sensor value over the last 20 seconds. Filter out sensor readings when the device was in a maintenance period. Illustrate when event A was followed by event B within three minutes. 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. cognizant insights 3

4 Foreign Device System Data Source LINQ) There are two Complex ways Event streaming Processing Engine can be adopted in current meter data systems: Server-side streaming: The stream is processed on the (OSIsoft) PI snapshot and streamed with the processed results back to the PI server (see Figure 2). Figure 2 Edge processing: In this model, the CQs are applied at the data source (and at the PI interface level), where the results are only stored as processed data (see Figure 3). Foreign Device System Data Source Figure 3 Input Adapter(s) Input Adapter(s) Cloud and Adaptive Rate Control The growing importance for utilities to process and analyze thousands of meter data streams PI Server suggests that they should The growing consider the adoption of Input Adapter(s) Output Adapter(s) cloud platforms to achieve Queries Stream Insight scalable, Engine latency-sensitive (vs.net- LINQ) stream processing. 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. The experimentation of the Smart Grid stream processing pipeline, modeled using IBM InfoSphere Streams 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. PI Interface Node Stream Insight Engine PI Server Stream Insight Engine Input Adapter(s) Output Adapter(s) Output Adapter(s) PI Interface Node Stream Insight Engine Output Adapter(s) Complex Event Processing Engine Queries (vs.net - Queries (vs.net - LINQ) Queries (vs.net - LINQ) PI Server PI Server and deployed on the Eucalyptus 4 private cloud, 5 shows 50% bandwidth savings, resulting from adaptive stream rate control. Low-latency stream processing is a key component of the software architecture required to support demand-response applications. The stream processing system ingests smart meter data arriving from consumers and acts as a first responder to detect local and global power usage skews and to alert the utility operator. At 1KB per event generated each minute, 2TB of data will stream each day. Processing such large-scale streams can be compute- and data-intensive; public or private cloud platforms provide a scalable and flexible infrastructure for building such Smart Grid applications. However, computational and bandwidth constraints at the consumer and utility levels mean 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 timely manner or at too high a rate to computationally overwhelm the system. Smart meters connect to the utility using heterogeneous networks and range from low bandwidth power line carriers at ~20Kbps, to 3G cellular networks at ~2Mbps, as well as ZigBee at ~250Kbps. Network bandwidth can thus be a scare resource at the consumer end. In the case of smart meters, traffic can be bursty, since data is sent independently, causing instantaneous bandwidth needs to spike. In the case of high power demand, meters emit a large volume of information, which requires a throttle controller to respond to these events and control latency. 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 (executed by the runtime environment) that supports stream data sources that continuously generate tuples containing typed attributes. cognizant 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 (m 1,t 1,u 1 1) (m n,t 1,u n 1 ) if(u 1 1 >U max ) if(u 1 1 >.136*u 1 avg) Update u 1 sum Update u 1 avg Condition Condition Condition Store Running daily sum AMI s 15-min average Utility s 15-min average R 1 ++ Increase AMI rate if(c 1 -u 1 avg < accept) Condition Network Superscript = Meter ID Subscript = Time Update u 1 avg R 1 Decrease AMI rate Figure 4 Figure 5 shows the smart meters present on the public Internet that generate power usage data streams accessible over TCP sockets. Here, the InfoSphere streams run on a cluster that doesn t support out-of-box deployment on a cloud platform. To instantiate a stream processing environment on a Eucalyptus private cloud, a customized VM image must be built that supports InfoSphere streams. Communication to the stream instance is activated when the VM instances are online. This communication, however, is initiated externally by a SPADE application started on a stream instance and configured with a list of named stream instances on specific hosts. Each smart meter is a stream source whose tuples have the identity of the smart meter, power used within a time duration, as well as the timestamps of the measurement interval. Additional meta data about the smart meter and consumer is part of the payload but will be ignored for the purposes of this discussion. Each tuple is about 1KB in size. The pipeline first checks if each individual power usage tuple reports usage that exceeds a certain constant threshold, U max m defined by the utility. Crossing this threshold will trigger a critical notification to a utility manager. Next, a relative condition will check to see if the user s consumption increases by more than 25% since his/her previous consumption. This will trigger a less critical notification. The pipeline then archives the tuple into a sink file and proceeds to compute a running sum of the daily usage by the consumer. Subsequently, the running average over a tumbling window is updated. These operations are performed for each smart meter stream (shaded in brown in Figure 4. Next, the pipeline aggregates smart meter tuples across all streams using a tumbling window to calculate the cumulative consumer energy usage within a 15-minute time window. This stream operator (shaded blue in Figure 4) calculates the total load on the utility. It can be used to alert the utility manager in case, say, the total consumption reaches 80%, 90% and >100% of available power capacity at the utility. Operators shown in dotted lines (Figure 4) are not part of the application logic and form the adaptive throttling introduced next. This core model could be used in demand response management. 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 have begun to adapt in-memory processing algorithms for this new architectural proposition. The result is that SAP can now deliver an event insight application that understands the impact of operational events in real time. In-memory processing not only brings just-in-time rhyme and reason to real-time business events, but it can also do so with significantly less effort, a reduction in reporting, operational and opportunity costs, which can power competitive advantage. cognizant insights 5

6 Architecture of Stream Processing and the Throttle Controller Control Feedbacks InfoSphere Streams Input Streams Electric Industrial/Commercial Response TCP/IP Gas Data Files Electric Residential Building AMI Streams Processing Throttle Controller Gas AMI Data Files Figure 5 Looking Down the Road We see stream computing as a key element of the future of work that could be applied broadly by the power utilities industry. Our view is that its deployment would minimize network latency and function as a key component for demand response management. Moreover, we are planning to investigate stream computing on the cloud platform. Our research will appraise the throughput of a stream processing system and its latency in processing each tuple as the stream rates adapt. 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. In a business context, this approach will help utilities with energy efficiency programs and grid management. It does this by providing a mechanism to convert dollars saved by eliminating inefficient energy generation and distribution toward more effective asset management. Footnotes 1 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 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, 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 Processing Engine, Proceedings of the Second Biennial Conference on Innovative Data Systems Research, pp , January 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, Vol 12, Issue 2, August A. Arasu, S. Babu and J. Widom. The CQL Continuous Query Language: Semantic Foundations and Query Execution. The VLDB Journal, Vol 15, Issue 2, June A. M. Ayad, J. F. Naughton. Static Optimization of Conjunctive Queries with Sliding Windows Over Infinite Streams, 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 Harnessing Data in Motion, IBM, September 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 of the 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 an Uncertain World, SIGMOD 2003, ACM. StreamBase, 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 The Role of the Internet Protocol (IP) in AMI Networks for Smart Grid, National Institute of Standards and Technology, NIST PAP 01, Oct. 24, D. Zinn, Q. Hart, B. Ludaescher and Y. Simmhann, Streaming Satellite Data to Cloud Workflows for On-Demand Computing of Environmental Products, Workshop on Workflows in Support of Large-Scale Science (WORKS), Arvind Arasu, Shivnath Babu, Jennifer Widom, CQL: A Language for Continuous Queries over Streams and Relations, Database Programming Languages, 9th International Workshop, DBPL 2003, Potsdam, Germany, Sept. 6-8, Pattern Detection with StreamInsight Microsoft StreamInsight blog, Sept. 2, 2010, com/2afzbhd InfoSphere Streams, IBM, cognizant insights 7

8 About the Author Ajoy Kumar is a Senior Architect within Cognizant s Manufacturing and Logistics Practice, where he is 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, he worked with OSIsoft, Inc. where he led numerous initiatives, including one in which he spearheaded the development of a meter data unification system integrating OSIsoft and SAP AG. Ajoy has also worked extensively in the energy, pharma, chemical and mining and steel industries and has spent over 17 years focused on information technology. Ajoy holds a Master s Degree in Computer Science. He can be reached at ajoykumar.arumugam@cognizant.com. About Cognizant Cognizant (NASDAQ: CTSH) is a leading provider of information technology, consulting, and business process outsourcing services, dedicated to helping the world s leading companies build stronger businesses. Headquartered in Teaneck, New Jersey (U.S.), Cognizant combines a passion for client satisfaction, technology innovation, deep industry and business process expertise, and a global, collaborative workforce that embodies the future of work. With over 50 delivery centers worldwide and approximately 111,000 employees as of March 31, 2011, Cognizant is a member of the NASDAQ-100, the S&P 500, the Forbes Global 2000, and the Fortune 500 and is ranked among the top performing and fastest growing companies in the world. Visit us online at or follow us on Twitter: Cognizant. World Headquarters 500 Frank W. Burr Blvd. Teaneck, NJ USA Phone: Fax: Toll Free: inquiry@cognizant.com European Headquarters Haymarket House Haymarket London SW1Y 4SP UK Phone: +44 (0) Fax: +44 (0) infouk@cognizant.com India Operations Headquarters #5/535, Old Mahabalipuram Road Okkiyam Pettai, Thoraipakkam Chennai, India Phone: +91 (0) Fax: +91 (0) 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 any means, electronic, mechanical, photocopying, recording, or otherwise, without the express written permission from Cognizant. The information contained herein is subject to change without notice. All other trademarks mentioned herein are the property of their respective owners.