DATA MANAGEMENT AND ANALYTICS FOR UTILITIES. In-depth Briefing

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1 DATA MANAGEMENT AND ANALYTICS FOR UTILITIES In-depth Briefing

2 DATA MANAGEMENT FOR UTILITIES DATA MANAGEMENT AND ANALYTICS FOR UTILITIES In-depth Briefing About Smart Grid Update Smart Grid Update is a research-driven news, market analysis, online networking portal and conference producer. Our work focuses on three core areas of the smart energy technology sector: Providing business intelligence in all areas of smart energy. Building smart energy communities for all key players to enable the exchange and sharing of ideas. Producing conferences that assemble senior management, decision makers, and innovators to produce results for the smart energy initiative. Disclaimer The information and opinions in this document were prepared by Smart Grid Update (FC Business Intelligence) and its partners. FC Business Intelligence has no obligation to tell you when opinions or information in this document change. Smart Grid Update makes every effort to use reliable, comprehensive information, but we make no representation that it is accurate or complete. In no event shall Smart Grid Update (FC Business Intelligence) and its partners be liable for any damages, losses, expenses, loss of data, loss of opportunity or profit caused by the use of the material or contents of this document. No part of this document may be distributed, resold, copied or adapted without Smart Grid Update's prior written permission. Author Jason Deign Jason Deign Associates Carlos Márquez Salazar, Research manager FC Business Intelligence Ltd 2013 DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 2

3 WELCOME Welcome Dear Colleague If knowledge really is power then utilities in 2013 have more power at their disposal than ever before. Or at least, they could. But the truth is that while the amount of data being collected by utility companies has increased vastly with the advent of smart grids, not all that information is being converted to meaningful intelligence. Having invested in the means to gather masses of data from their grids and networks, utilities are now grappling with the issue of how best to use it. There is no clear-cut answer to this question, but a growing body of experience is at least helping to map out areas where it would be useful to focus attention. This briefing aims to provide a snapshot of the latest thinking on this issue. We hope it will help inform your strategies for data management and ultimately add more power to your business. Smart Grid Update Ashley Daugherty Vice President Smart Grid Update Join the conversation! SmartGrid Update LinkedIn DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 3

4 ACKNOWLEDGMENTS Acknowledgments Smart Grid Update wishes to thank the following people and organizations for their help in compiling this briefing: Ben Bixby, Chief Executive Officer, MyEnergy Co-founded MyEnergy in 2007 and is responsible for its overall vision and strategy. Helps guide the development of MyEnergy's core technology and platform. Giri Iyer, Product Line Leader for Grid IQ Insight, GE Digital Energy Has a business team developing and commercializing a world class, real-time analytical system that combines a Big Data platform and 4D visualization technologies. Brian Rich, Vice President of Business Technology, PG&E Accountable for providing the strategic direction and oversight for technology planning, project delivery and critical system operations for customer care, smart meter and demand-side management programs. Gib Sorebo, Vice President and Chief Cyber-Security Technologist, Science Applications International Corporation Assists government and private sector organizations in complying with legal and regulatory requirements and address ongoing risks to their infrastructure. Usman Syed, Senior Policy Adviser, Ontario Ministry of Energy Leading policy development on smart meter implementation, time-of-use rollout and various smart grid files including the original design and launch of Ontario s USD$50 million smart grid fund. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 4

5 CONTENTS Contents About Smart Grid Update 2 Welcome 3 Acknowledgments 4 Index of tables 5 Introduction 6 1. Utility data management: market overview Why are utilities turning to Big Data solutions? Options for data management Data management vendors and offerings Market size and evolution Barriers and drivers Drivers Barriers Case study: PG&E Concluding remarks 19 Industry ecosystem 19 Abbreviations 22 References 23 Figures 1 Data analytics options based on IT resources and investment 11 2 Key challenges in data management for utilities 14 Tables 1 Sources of Big Data in utilities 7 2 Data center components and vendors 8 3 Utility data analytics options 9 4 IT vendors and products for the utility sector 10 5 Pros and cons of different data analytics options 11 6 Security considerations 16 7 Utility data analytics industry ecosystem 20 DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 5

6 INTRODUCTION Introduction Data has not traditionally been a major concern for utilities. Until recently the most data-intensive elements within the utility business tended to be in customer-related fields such as billing. And in these, the level of complexity was average-tolow compared to that seen in other sectors, such as financial services or telecommunications. This made it possible to handle the requirements of the business with modest doses of information technology (IT) investment and skill. The process of generating and distributing electricity, gas and water was essentially dumb, with the utility only collecting very rudimentary data relating to the levels of consumption on customer premises. Smart grids have changed things. The deployment of advanced metering infrastructure (AMI) and intelligent supervisory control and data acquisition (SCADA) systems is essentially all about improving the amount and quality of data that utilities have on supply and distribution. Now this equipment is to a growing extent in place and starting to deliver data back to the utility, the question is: what should be done with this data to meet the objectives of the smart grid deployments and, presumably, generate a return on the investment? To help answer this question, this briefing will seek to show why utilities are increasingly relying on data management, which companies can provide the necessary solutions, how the market is evolving, and what drivers and barriers it has. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 6

7 MARKET OVERVIEW 1. Utility data management: market overview 1.1 Why are utilities turning to Big Data solutions? Before smart grids, the data that a utility would collect from its customers frequently amounted to little more than a monthly meter reading: one data point a month per customer. The advent of AMI has increased the level of data collection dramatically. The Columbia Water and Light Department, for example, has a pilot where meters provide data on around half a dozen household circuits every minute. That is more than 43,000 data points per customer per month. Even if, as is often the case, readings are taken at more infrequent intervals, say every five, 10 or 15 minutes, the increase is very significant. And this is just one element of what is currently termed Big Data, data sets that are terabytes to exabytes in size and come from a range of sources (Nie, 2011), which puts their management and analysis beyond the scope of traditional IT tools (see table 1). Table 1: Sources of Big Data in utilities Data type Technology involved Notes AMI Distribution automation Third-party Asset management Smart meters Grid equipment Off-grid data sets Firmware for all smart devices and associated operating systems Increased sampling frequency leads to 1,000 to 10,000- fold increase in data levels Real-time monitoring and control requires much more granular readings than those taken by smart meters. The GridSim simulation package described by Anderson et al (2011), for example, uses a default sample rate of 30 samples per second, per sensor Utilities are increasingly also having to integrate and handle highly granular data from other sources, such as pricing details for demand response or forecasting information for renewable energy Maintaining smart grid technology once it has been rolled out requires frequent firmware upgrades and the like, which equates to a considerable amount of asset management data DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 7

8 MARKET OVERVIEW Table 2: Data center components and vendors Component Function/notes Main vendors Data center facilities Dedicated, custom-built housing incorporating redundant power and cooling systems, high-speed network connections and physical security to guard against unauthorized access Usually customer-owned or leased from telecommunications or IT services provider Storage arrays Provide data storage functionality EMC Corporation, NetApp and IBM Server platforms Storage area network equipment Database systems Virtualization systems Computing hardware needed for data handling Connects server, storage and external network resources Software systems for data management and analysis Allow more efficient use of discrete storage and computing resources HP, IBM and Dell Brocade, Cisco and QLogic Oracle, IBM and Microsoft VMware, Citrix and Microsoft 1.2 Options for data management Big Data poses a major problem for utilities: namely, where do they put the data and what do they do with it? The answer to the first part of the question is increasingly in the creation of dedicated data centers, the key components of which are summarized in table 2. When considering data center deployment, significant consideration needs to be given to the following factors: Because of the value of the information, data centers typically have some form of contingency for disaster recovery. This increases the level of capital expenditure, which may be problematic for utilities carrying out smart grid rollouts. Virtualization allows all available resources to be pooled and reallocated, which can improve efficiency and return on investment (ROI), but requires additional technology and complexity. Cloud computing provides access to virtual resources located anywhere, which can further improve efficiency and ROI but can lead to concerns over data security and integrity (since data could end up in foreign jurisdictions). Cloud-based delivery of IT infrastructure, platforms and software on an as a service basis means that instead of having to invest upfront in a data center, a utility could instead just hire the capacity it needs from a service provider. The economics of cloud computing and as-a-service delivery are hotly disputed (Cohen, 2012) and in the long run it may be more economical for a utility to build its own data center facilities, particularly for very large operations. However, as-a-service delivery is nevertheless of value in that it can help overcome capital expenditure constraints and thus potentially give utilities a way of gaining value from data more quickly and easily. Similarly, it could provide an inspiration for smaller, geographically linked utilities to pool resources via a shared private cloud, and thus benefit from economies of scale that no single player could get on their own. One remaining concern is whether the resources available on a commercial as-a-service basis can be optimized for utility applications. On this point, utilities will need to seek assurances and service-level agreements from the vendors concerned. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 8

9 MARKET OVERVIEW Table 3: Utility data analytics options Data analytics option Develop systems in house Rely on OT systems Rely on IT systems Point products and pure-play providers Third-party data analytics service providers Notes Allows utility to address the specific challenges and requirements it faces, and thus end up with a platform that is fully made to measure The benefit of relying on OT vendors for data analytics is that these companies are well versed in the issues facing utilities and can therefore provide products that are highly relevant to the industry Traditionally within utilities IT companies have centered on delivering enterprise applications and specific systems such as customer relationship management (CRM) databases. Such systems are frequently part of the mix required for full smart grid data analytics and, as a consequence, mainstream IT vendors are well placed to provide the remaining pieces of the puzzle Different components of the smart grid infrastructure usually come with their own management systems and dashboards and these may on occasion be sufficient for the requirements of the utility Third-party data analytics service providers, best exemplified by MyEnergy, take data feeds from utilities and analyze them in order to provide value-added services to energy customers Vendors/providers Very few utilities are likely to have the development skills and resources needed to create a fully-fledged data analytics system from scratch. A more realistic scenario is that a utility with a large in-house IT capability might be able to carry out a certain amount of development and integration of existing systems, thus reducing the need to buy in skills and products from outside Existing in-house systems for functions such as distribution, energy, transmission, outage, or demand response management are classically the domain of vendors like GE Energy, which is beginning to tackle data integration through products such as Grid IQ. Another OT vendor active in this area is ABB, which provides data analytics systems through its subsidiary Ventyx and last year invested in a water utility software-as-a-service startup called TaKaDu Mainstream IT vendors such as IBM, Microsoft and Oracle, or system integrators such as Accenture, Capgemini or Infosys, have deep and long-standing relationships with most enterprise players, including of course those in the energy sector. Most large IT providers now have significant energy industry practices, offering complex solutions for the sector (see below) Meter makers such as emeter, Itron and Telvent offer sophisticated management tools for their products and for the utility market in general. Itron, to take one example, has separate software tools for smart grid analytics, conservation and demand response, customer care and billing, distribution design and asset management, revenue assurance, plus energy forecasting and load research It is possible to envisage an extension of the MyEnergy concept that covers other areas of utility data management, from asset automation to asset tracking. While the MyEnergy concept works well in the US, which is unique in having around 1,200 different power transmission and distribution companies, it remains to be seen whether it could be viable in other markets with much fewer players DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 9

10 MARKET OVERVIEW Table 4: IT vendors and products for the utility sector Vendor Product Notes Capgemini EMC IBM Oracle SAP Teradata Smart analytics platform Smart Grid Analytics Netezza data warehouse appliance Utilities Meter Data Analytics Smart Meter Analytics Data Warehouse Appliance Helps utility companies extract insight from the complex data generated by smart grid and advanced metering infrastructure deployment Built to improve grid reliability, reduce operational expense of grid management and customer experience, and ingest and process data at petabyte scale with in-database analytics Delivers high-performance analytical capabilities from smart grid and smart meter deployments Offers prebuilt dashboards that help utilities track metering performance, protect revenue and improve billing efficiency Allows utilities to make crucial decisions faster thanks to instant, in-depth customer analysis, advanced segmentation based on energy consumption patterns and energy efficiency benchmarking Being used by Oklahoma Gas & Electric is using for its smart grid rollout (Teradata, 2010) 1.3 Data management vendors and offerings Broadly speaking there are five options for dealing with utility data analytics: Develop the systems in house. Siemens (Accenture, 2012), which could presumably indicate a higher level of IT-OT system integration than might be the case with IT-only vendor solutions. To complete this discussion, the benefits and disadvantages of the different approaches discussed above are summarized in the table below. Buy systems from an established operational technology (OT) vendor. Buy systems from an established IT vendor. Buy and integrate point products from specialist vendors. Outsource data analytics to a third party. It should be noted that these options are not mutually exclusive and in practice it may be beneficial for a utility to use a mix within its data management strategy. Details of each option are summarized in table 3 above. Given the large number of IT vendors operating in the utility sector, it is useful to take a closer look at some of the main names and products on offer, as summarized above in table 4. In addition to the above, Accenture deserves special mention for offering a solution in partnership with DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 10

11 MARKET OVERVIEW Table 5: Pros and cons of different data analytics options Data analytics option Benefits Disadvantages Develop systems in house Rely on OT vendor system Rely on IT vendor system Rely on point product or pure-play vendor system Rely on third-party service provider system Highly tailored to the utility s requirements Close alignment with operational processes and requirements; good integration with OT Good integration with IT systems; robust design and support Close alignment with operational processes and good integration with OT Reduced cost, improved analytics Lack of development skills and resources Analytics and IT integration capabilities might not be as good as for IT vendors May include unnecessary features that increase cost; lack of alignment with operational processes Potential lack of cross-product and IT system integration; lifetime support concerns Evolving concept with limited track record; data sharing may require culture shift A further consideration of these strengths and weaknesses enables a rough analysis of which options might be of greatest interest to utilities depending on a) the IT resources and capabilities at their disposal and b) their investment appetite and capability. This analysis is summarized in figure 1. Figure 1: Data analytics options based on IT resources and investment DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 11

12 MARKET OVERVIEW 1.4 Market size and evolution Supplying electricity is a near-universal business and utilities therefore represent a major industrial sector. In the US, which represents the world s biggest utility market, there are roughly 1,200 companies dedicated to power transmission and distribution, with total annual revenues of some USD$465 billion (First Research, 2012). Recent estimates of the size of the global market for utility data analytics range from USD$3.8 billion by 2020 (Leeds, 2012) to $4.2 billion by 2015 (Pike, 2010). While a detailed analysis of the size and evolution of the market is beyond the scope of this document, progress towards the lower range of current estimates seems likely given that the US market accounts for a significant proportion of global spend and there: Stimulus funding is coming to an end. Pricing for Big Data infrastructures and services is likely to be affected by ongoing competition between vendors. New consumption models such as cloud computing and as-a-service offerings could potentially reduce infrastructure costs. It should be noted that the second two of these factors could create something of a dilemma for utilities. Those that delay implementing Big Data projects may miss out on value but could find the costs of implementation are lower further down the line. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 12

13 BARRIERS AND DRIVERS 2. Barriers and drivers 2.1 Drivers The key driver for the adoption of data management strategies is clearly the need to handle and analyze the large amounts of information utilities are now faced with. Usman Syed, senior policy adviser at the Ontario Ministry of Energy, states: All of a sudden there was this data avalanche, with so much more data than there ever was before". The data from the meters is one thing, but there is also data from substations, transformers and other elements on the distribution system that can help distributors monitor and manage things like device asset life, for example. Other processes that utilities hope to enhance through better analysis of data include: Business operation efficiency. Data analytics will allow utilities to see where consumers may be stealing electricity and will allow for better asset management as well as better system planning. Implementing efficiency measures. Data allows for time-of-use rates which in turn allow customers to monitor their consumption and save money by shifting use away from times when rates are higher, while also reducing the need for more expensive forms of peak generation to be built. management systems to help retailers and customers manage their energy use. Better data will also allow utilities to identify and rectify problems with the grid more quickly, in some cases even before the customer has a chance to report the issue or outage. Engaging customers. Having more information about customers and their usage patterns is generally considered important in helping deliver a better, more tailored service. This is perhaps most evident in the case of MyEnergy, which pools data from many utilities. MyEnergy can draw on a much greater data set, and therefore perform much more accurate analyses, than any utility could on its own; the company uses this to help US utility customers manage their energy consumption. We are the only ones that pull in as much data from utilities, in the world, states co-founder Ben Bixby. 2.2 Barriers Smart Grid Update carried out in-depth research interviews among 40-plus utility professionals, ranging from chief executives to engineers, to find out what they felt were the three most pressing challenges they faced regarding data management (see figure 2). Developing new business models. By adding intelligence to the grid operators can offer new services, such as energy management, engineering, high-speed Internet services, cable TV and private network links. Improving grid resilience and load management. Distribution companies must reduce power demand to prevent outages, for example installing energy load DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 13

14 BARRIERS AND DRIVERS Figure 2: Key challenges in data management for utilities What data is relevant? Integration of different systems Organizational silos Budget constraints Regulatory constraints Data acquisition Aging workforce and finding talent Lack of strategic direction Standards and interoperability Consumer engagement Deploying data management analytics Deploying predictive analytics Lack of hardware capacity Legacy system challenges IT security issues Renewable energy integration Cultural inertia/reluctance to change Concerns over vendors Asset management Impact on operations By far the most important topic is actually a set of concerns around the data itself, rather than allied issues such as device interoperability, budgets and regulation. These concerns include: How can data be used for decision making? What data will be needed in future? What data should be collected (and analyzed) now? While utilities recognize the need for data analysis, there is still significant doubt over the best way to go about this. As Giri Iyer of GE says: In the US, many utilities are taking a stab at Big Data. There are several isolated use cases, but we see few utilities yet with an Enterprise Big Data strategy and commitment and they are looking for early evidence of approach and beneficial business outcomes. A particularly pressing problem for many utilities appears to be whether the data they are now collecting should mainly be used for reporting or real-time decision-making. One expert interviewed by Smart Grid Update summed up as follows: Many organizations just want reporting of data as a first step, and for reporting what they want to do is build a computer database and build business objects that produce reports." My position has been that you don t know what you want to report on until you have experts taking data and figuring out what s meaningful. Other significant barriers to faster and wider data management adoption, highlighted in interviews, are discussed below. Budget. Unsurprisingly, availability of cash is an important barrier for many utilities, with interview respondents citing a lack of clear ROI models as a challenge for getting budget signoffs from the board. We have a limited budget and this is a large investment, so we want to feel comfortable going down that road, said one respondent. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 14

15 BARRIERS AND DRIVERS Interoperability. Faced with a bewildering array of new hardware and software requirements, many utilities are justly concerned about the ability of technologies to communicate effectively with each other. Interoperability is a wide-ranging challenge that covers areas as diverse as software discrepancies to standard formats for exchanging data between systems. Regulation. Regulatory environments can have an impact on data analysis by determining the potential use and value that utilities can derive from data in the first place. A common theme in interviews was whether deregulation was proceeding quickly enough, and in the right direction. Data acquisition. Notwithstanding the large amounts of data flowing in from AMI, data acquisition continues to be a barrier for some utilities, particularly in relation to hardware such as reclosers. Data acquisition needs to be refined, and will be software related, believed one interviewee. Human resources. Utilities appear to face three challenges in human resource terms. The first is that finding talented IT experts is hard at a time when most bright technology innovators would rather work in Silicon Valley, and those that can be attracted to a utility are at a premium and may not stay long. The second problem is that the people with the best operational experience are of an older generation so there is some urgency about the need to carry out knowledge transfer. The third is that people who may have the skills to help deal with the new data becoming available are generally already tied up with other tasks, and may thus face a conflict of priorities. Management buy-in. One of the consequences of old-school management is that many long-time energy sector professionals may lack an insight into the true value that proper data management can provide, and thus require some convincing. This is all the more difficult to do in the absence of clear ROI models as described above. Silo-based teams. Experts who have spoken with Smart Grid Update have frequently mentioned organizational silos as a reason why it is difficult to get data management strategies off the ground. For example, says one consulting engineer with a large US utility: We store data in multiple formats in multiple databases. Retrieving it out of the database is a challenge. It is not as user-friendly as it could be. Silo-based operations. This silo effect is exacerbated by the fact that operational teams tend to work in silos, too. Iyer says: It can be challenging for different groups to communicate with one another and share data. You ve got an operations team that has outage data. Why aren t they communicating with the vegetation management team that has information about tree trimming operations? If you could corroborate that data you could, for example, have more efficient vegetation management processes. Reluctance to change. Regulatory processes and monochronistic business models make utilities culturally resistant to engage in the kinds of process changes that could help break down silos. Iyer explains: Utilities often have well-established operational processes that were set up years ago. Typically, with good reason, these processes can be difficult to modify. The rewards must exceed the risks inherent in business process change for utility-wide Big Data management approaches. Meter analytics and outage analytics seem to be the first areas where we are seeing evidence of this change. Market structure. In markets where energy distribution and retail are decoupled there likely to be much less incentive for utilities to invest heavily in data management technologies that, after all, might not be entirely relevant to their business. Access to funding. If the rates electricity distributors can charge are regulated by state public utility commissions then winning rate increases to reflect higher costs can be difficult, which in turn can restrict the funding available for Big Data. Planning uncertainty. In many markets legislation to tackle issues such as carbon emissions are still evolving. This leads to uncertainty that can curtail a utility s appetite for major investments in Big Data infrastructure. Technological complexity. Utilities implementing smart grids have to get used to a whole new set of technologies. They may understandably want to get to grips with this innovation before adding Big Data infrastructure and applications on top. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 15

16 BARRIERS AND DRIVERS Finally, although it is currently not recognized as a major issue it is likely that IT security will emerge as a significant challenge (if not a barrier) for utilities implementing data management strategies, for reasons discussed in table 6. Table 6: Security considerations No discussion of data management would be complete without at least touching on the subject of IT security. To date, utilities have largely escaped the attention of cyber criminals, but there are signs this is changing: in 2012 one of the star attractions at the Def Con Kids cyber-security meeting for young hackers was a talk on how to hack the grid (Deign, 2012). Recent cyber attacks on power-related companies such as Aramco in Saudi Arabia and Ras Gas in Qatar further highlight the increasing vulnerability of the sector (Hall and Blas, 2012). IT security has never been much of an issue for utilities because the infrastructure concerned was hard to access and the potential rewards for hackers were low. While there still may be few incentives to hack into a power system, however, the fact that most western homes will at some point in the near future be directly connected to intelligent smart grids means it is much easier for hackers to access the power network. Gib Sorebo, vice president and chief cyber-security technologist at Science Applications International Corporation (SAIC), says: Even small changes in the data could affect the stability of the grid and even jeopardize human safety." Additionally, utilities will be collecting extensive data on consumer energy usage that could be exploited to identify when people are not at home, whether they own an electric vehicle, or in some cases find out the brand of appliance someone is using. At the very least, he adds, privacy laws around the world dictate that utilities do all they can to protect this data. To do so, however, requires multiple technology components. Companies like McAfee, Sorebo says, are investing millions in developing instrumented solutions for industrial control systems and other embedded environments, to improve situational awareness and reduce threat. He cites EMC Corporation and Oracle as long-standing leaders in data management and security. In addition, utilities would need to rely on specialist companies for features such as radio frequency security, encryption technology, vulnerability scanning, data leak protection, and monitoring. Finally, companies such as SAIC are needed to integrate these capabilities and deliver a comprehensive security system. Privacy rules are currently the main driver for improved IT security in many jurisdictions, but it is clear that the sector increasingly realizes this is not the only reason to guard data. Cyber threat actors now have the capability and the intent to destroy hardware and data and the potential to disrupt operations, Sorebo says. While the potential impacts of these threats are not always fully understood, utilities recognize they need to act. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 16

17 CASE STUDY: PG&E 3. Case study: PG&E Pacific Gas and Electric Company, incorporated in California in 1905, is one of the largest combination natural gas and electric utilities in the United States. Based in San Francisco, the company is a subsidiary of PG&E Corporation. There are approximately 20,000 employees who carry out Pacific Gas and Electric Company's primary business the generation, transmission and delivery of energy. The company provides natural gas and electric service to approximately 15 million people throughout a 70,000-square-mile service area in northern and central California. In February 2012 PG&E was named one of America s most intelligent utilities in the third annual "UtiliQ" ranking by Intelligent Utility magazine and IDC Energy Insights. But to achieve such praise the company has had to immerse itself in the field of process analytics. The utility is being inundated with phenomenal amounts of data, says Brian Rich, Vice President of Business Technology. Process analytics means that for the first time the company is breaking down traditional silos that haven t had the information required to integrate their processes. The key challenge the company has faced with the rollout of AMI is that discrete events on the demand side of the business are now directly affecting events on the supply and distribution side. This means decisions on either side can no longer be taken in isolation. Instead, both sides have to share and jointly analyze information. PG&E is facing up to this with a number of projects and pilot initiatives. Rich says the company is encountering issues that broadly fall into three categories: Dealing with unstructured data is a challenge for most available analytics systems, particularly at the levels found within PG&E. We have a lot of it, says Rich, from the maintenance of our asset records in our field systems to customer service data to all other sources across the board, unstructured data creates a great challenge in analytics. While there are many IT systems that purport to deal with generic Big Data issues, Rich says there is a dearth of products tailored to the utility industry. We see a lot of solutions but not many yet which demonstrate an understanding of the utility data model and have productized integration into key utility operational systems, such as SCADA and AMI, he says. Data volumes are a problem. PG&E has 70 terabytes of AMI interval data alone and that volume is increasing by 3 terabytes a month. Because data is arriving at such a rate, PG&E has found that any attempt to perform deep analyses, for example for demand response settlements or evaluation measurement and verification (EM&V) for energy efficiency programs, risk holding up the flow and affecting operations. Its solution is to replicate the data coming in from operations. The original data set is subjected to core operational functions such as billing and online presentation to customers. The replica, meanwhile, is reserved for more in-depth data mining and analytics. When dealing with the influx of operational data from AMI and beyond, warns Rich: Most utilities will find this the biggest challenge in optimizing analytics: keeping up with the sheer volume of data while also delivering on the promise of the business value associated with it. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 17

18 CASE STUDY: PG&E Despite the challenges, however, Rich is convinced PG&E has benefited significantly from process analytics. One of the results has been a much better knowledge of consumption patterns, allowing the utility to help customers drive down their costs. Before we were flying blind, says Rich. Now we can have much more meaningful conversations with our customers when we want to engage them about our portfolio of demand-side management programs. Another area where the company is benefiting is in outage restoration. Previously PG&E was dependent on customer phone calls to assess the size of an outage, but now outage notifications and progress on restoration can be tracked via smart meters. Allied to this, says Rich: We are able to do a lot of overthe-air remediation. If we have a meter that is not giving us readings we would previously have had to roll a truck, but now we can often fix it by pushing a firmware upgrade to it over the network. Finally, for other utilities considering process analytics, Rich has this advice: Really prioritize your use cases and then walk back the data sources that are required for those use cases." Getting the utility to track firstly what you are trying to achieve, and then drilling down to the data, is important. A lot of these deployments are really complex, and without this it could all be an exercise in futility. DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 18

19 CONCLUDING REMARKS 4. Concluding remarks With smart grids, utilities asked for more information. More information to give customers detailed pricing signals. More information to detect faults and nontechnical losses. More information to stabilize networks laden with renewable power. That wish for more information has been granted. And now utilities are realizing that it is one thing to collect data and quite another to manage and analyze it in a way which can deliver the greatest possible value to the business. As a result, few utilities can currently claim to be getting the full benefit of their smart grid deployments. In fact it is not unusual for power companies to still be managing data at a level of granularity comparable to that of pre-smart grid days, despite now having technology that can deliver much more detailed information. Why is this so? One reasonable concern is that the amount of data coming out of AMI and other deployments is simply too great to handle, that utilities will be swamped in a data deluge and their IT systems will not be able to cope. on investment. On this point there is no one-size-fits-all solution, since the return will to a large degree be dependent on the nature of a company s business model and operating environment. Nevertheless there is a good general awareness of the benefits that can be derived from better data management, and growing number of real-life examples that demonstrate how these benefits can be achieved. In conclusion, although utilities are still only setting out on the road to better data management, they are increasingly clear about the routes they need to take, and are rapidly picking up speed. By and large, however, this is not the case. While the challenges associated with managing the masses of data associated with smart grids are certainly significant, they are not overwhelming. Indeed, utility information management is simply one of several Big Data areas which a growing number of IT vendors are rushing to solve. The tools to deal with AMI data may yet need some refining, but they certainly exist. Instead, the challenges are more to do with cultural shift and particularly the progression from silo-based operations to a more connected and interconnected way of working. At the same time, there is a perhaps understandable hesitation in prioritizing data management projects because at this stage of the game utilities are uncertain about what activities might provide the greatest return DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 19

20 CONCLUDING REMARKS Industry ecosystem A list of the main players in utility data management (excepting utilities) is provided in table 7. Note this list is not exhaustive but contains the names of organizations referenced most frequently in interviews carried out by Smart Grid Update. Table 7: Utility data analytics industry ecosystem Name Type Notes ABB OT vendor Provides data analytics through Ventyx and networking through Tropos Accenture IT vendor Partners with Siemens for AMI solutions Aclara Alstom Power American Energy Solutions American Public Power Association Booz Allen Hamilton Brocade Canadian Electricity Association Specialist utility data systems provider OT vendor Consultancy Industry body Consultancy SAN vendor Industry body Capgemini IT vendor Delivers smart analytics platform Cisco SAN vendor Also active in smart grid infrastructure Citrix Consert Virtualization vendor Specialist utility data systems provider Cooper Power Systems Dell OT vendor Server vendor Now owned by Eaton; recently acquired Eka Systems Edison Electric Institute Industry body Represents US utilities Electric Power Supply Association Electricity Distributors Association Industry body Industry body EMC IT/storage vendor Offers Smart Grid Analytics emeter AMI vendor GE Energy OT vendor Markets Grid IQ analytics system HP IBM Server vendor IT vendor Also provides storage, servers and databases, plus Netazza warehouse appliance DATA MANAGEMENT AND ANALYTICS FOR UTILITIES 20

DATA MANAGEMENT & ANALYTICS FOR UTILITIES 2014

DATA MANAGEMENT & ANALYTICS FOR UTILITIES 2014 DATA MANAGEMENT & ANALYTICS FOR UTILITIES 2014 In-depth briefing Author Stephen Witt DATA MANAGEMENT FOR UTILITIES DATA MANAGEMENT & ANALYTICS FOR UTILITIES 2014 In-depth briefing About Smart Grid Update

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