Successful Smart Grid Architecture

The smart grid is progressing well on several fronts. Groups such as the Grid Wise Alliance, events such as Grid Week, and national policy citations such as the American Recovery and Reinvestment Act in the U.S., for example, have all brought more positive attention to this opportunity. The boom in distributed renewable energy and its demands for a bidirectional grid are driving the need forward, as are sentiments for improving consumer control and awareness, giving customers the ability to engage in real-time energy conservation.

On the technology front, advances in wireless and other data communications make wide-area sensor networks more feasible. Distributed computation is certainly more powerful – just consider your iPod! Even architectural issues such as interoperability are now being addressed in their own forums such as Grid Inter-Op. It seems that the recipe for a smart grid is coming together in a way that many who envisioned it would be proud. But to avoid making a gooey mess in the oven, an overall architecture that carefully considers seven key ingredients for success must first exist.

Sources of Data

Utilities have eons of operational data: both real time and archival, both static (such as nodal diagrams within distribution management systems) and dynamic (such as switching orders). There is a wealth of information generated by field crews, and from root-cause analyses of past system failures. Advanced metering infrastructure (AMI) implementations become a fine-grained distribution sensor network feeding communication aggregation systems such as Silver Springs Network’s Utility IQ or Trilliant’s Secure Mesh Network.

These data sources need to be architected to be available to enhance, support and provide context for real-time data coming in from new intelligent electronic devices (IEDs) and other smart grid devices. In an era of renewable energy sources, grid connection controllers become yet another data source. With renewables, micro-scale weather forecasting such as IBM Research’s Deep Thunder can provide valuable context for grid operation.

Data Models

Once data is obtained, in order to preserve its value in a standard format, one can think in terms of an extensible markup language (XML)-oriented database. Modern implementations of these databases have improved performance characteristics, and the International Engineering Consortium (IEC) common information/ generic interface definition (CIM/GID) model, though oriented more to assets than operations, is a front-running candidate for consideration.

Newer entries, such as device language message specification – coincidence-ordered subsets expectation maximization (DLMS-COSEM) for AMI, are also coming into practice. Sometimes, more important than the technical implementation of the data, however, is the model that is employed. A well-designed data model not only makes exchange of data and legacy program adjustments easier, but it can also help the applicability of security and performance requirements. The existence of data models is often a good indicator of an intact governance process, for it facilitates use of the data by multiple applications.

Communications

Customer workshops and blueprinting sessions have shown that one of the most common issues needing to be addressed is the design of the wide-area communication system. Data communications architecture affects data rate performance, the cost of distributed intelligence and the identification of security susceptibilities.

There is no single communications technology that is suitable for all utilities, or even for all operational areas across any individual utility. Rural areas may be served by broadband over powerline (BPL), while urban areas benefit from multi-protocol label switching (MPLS) and purpose- designed mesh networks, enhanced by their proximity to fiber.

In the future, there could be entirely new choices in communications. So, the smart grid architect needs to focus on security, standardized interfaces to accept new technology, enablement of remote configuration of devices to minimize any touching of smart grid devices once installed, and future-proofing the protocols.

The architecture should also be traceable to the business case. This needs to include probable use cases that may not be in the PUC filing, such as AMI now, but smart grid later. Few utilities will be pleased with the idea of a communication network rebuild within five years of deploying an AMI-only network.

Communications architecture must also consider power outages, so battery backup, solar recharging, or other equipment may be required. Even arcane details such as “Will the antenna on a wireless device be the first thing to blow off in a hurricane?” need to be considered.

Security

Certainly, the smart grid’s purpose is to enhance network reliability, not lower its security. But with the advent of North American Reliability Corp. Critical Infrastructure Protection (NERC-CIP), security has risen to become a prime consideration, usually addressed in phase one of the smart grid architecture.

Unlike the data center, field-deployed security has many new situations and challenges. There is security at the substation – for example, who can access what networks, and when, within the control center. At the other end, security of the meter data in a proprietary AMI system needs to be addressed so that only authorized applications and personnel can access the data.

Service oriented architecture (SOA) appliances are network devices to enable integration and help provide security at the Web services message level. These typically include an integration device, which streamlines SOA infrastructures; an XML accelerator, which offloads XML processing; and an XML security gateway, which helps provide message-level, Web-services security. A security gateway helps to ensure that only authorized applications are allowed to access the data, whether an IP meter or an IED. SOA appliance security features complement the SOA security management capabilities of software.

Proper architectures could address dynamic, trusted virtual security domains, and be combined not only with intrusion protection systems, but anomaly detection systems. If hackers can introduce viruses in data (such as malformed video images that leverage faults in media players), then similar concerns should be under discussion with smart grid data. Is messing with 300 MegaWatts (MW) of demand response much different than cyber attacking a 300 MW generator?

Analytics

A smart grid cynic might say, “Who is going to look at all of this new data?” That is where analytics supports the processing, interpretation and correlation of the flood of new grid observations. One part of the analytics would be performed by existing applications. This is where data models and integration play a key role. Another part of the analytics dimension is with new applications and the ability of engineers to use a workbench to create their customized analytics dashboard in a self-service model.

Many utilities have power system engineers in a back office using spreadsheets; part of the smart grid concept is that all data is available to the community to use modern tools to analyze and predict grid operation. Analytics may need a dedicated data bus, separate from an enterprise service bus (ESB) or enterprise SOA bus, to meet the timeliness and quality of service to support operational analytics.

A two-tier or three-tier (if one considers the substations) bus is an architectural approach to segregate data by speed and still maintain interconnections that support a holistic view of the operation. Connections to standard industry tools such as ABB’s NEPLAN® or Siemens Power Technologies International PSS®E, or general tools such as MatLab, should be considered at design time, rather than as an additional expense commitment after smart grid commissioning.

Integration

Once data is sensed, securely communicated, modeled and analyzed, the results need to be applied for business optimization. This means new smart grid data gets integrated with existing applications, and metadata locked in legacy systems is made available to provide meaningful context.

This is typically accomplished by enabling systems as services per the classic SOA model. However, issues of common data formats, data integrity and name services must be considered. Data integrity includes verification and cross-correlation of information for validity, and designation of authoritative sources and specific personnel who own the data.

Name services addresses the common issue of an asset – whether transformer or truck – having multiple names in multiple systems. An example might be a substation that has a location name, such as Walden; a geographic information system (GIS) identifier such as latitude and longitude; a map name such as nearest cross streets; a capital asset number in the financial system; a logical name in the distribution system topology; an abbreviated logical name to fit in the distribution management system graphical user interface (DMS GUI); and an IP address for the main network router in the substation.

Different applications may know new data by association with one of those names, and that name may need translation to be used in a query with another application. While rewriting the applications to a common model may seem appealing, it may very well send a CIO into shock. While the smart grid should help propagate intelligence throughout the utility, this doesn’t necessarily mean to replace everything, but it should “information-enable” everything.

Interoperability is essential at both a service level and at the application level. Some vendors focus more at the service, but consider, for example, making a cell phone call from the U.S. to France – your voice data may well be code division multiple access (CDMA) in the U.S., travel by microwave and fiber along its path, and emerge in France in a global system for mobile (GSM) environment, yet your speech, the “application level data,” is retained transparently (though technology does not yet address accents!).

Hardware

The world of computerized solutions does not speak to software alone. For instance, AMI storage consolidation addresses the concern that the volume of data coming into the utility will be increasing exponentially. As more meter data can be read in an on-demand fashion, data analytics will be employed to properly understand it all, requiring a sound hardware architecture to manage, back-up and feed the data into the analytics engines. In particular, storage is needed in the head-end systems and the meter-data management systems (MDMS).

Head-end systems pull data from the meters to provide management functionality while the MDMS collects data from head-end systems and validates it. Then the data can be used by billing and other business applications. Data in both the head-end systems and the master copy of the MDMS is replicated into multiple copies for full back up and disaster recovery. For MDMS, the master database that stores all the aggregated data is replicated for other business applications, such as customer portal or data analytics, so that the master copy of the data is not tampered with.

Since smart grid is essentially performing in real time, and the electricity business is non-stop, one must think of hardware and software solutions as needing to be fail-safe with automated redundancy. The AMI data especially needs to be reliable. The key factors then become: operating system stability; hardware true memory access speed and range; server and power supply reliability; file system redundancy such as a JFS; and techniques such as FlashCopy to provide a point-in-time copy of a logical drive.

Flash Copy can be useful in speeding up database hot backups and restore. VolumeCopy can extend the replication functionality by providing the ability to copy contents of one volume to another. Enhanced remote mirroring (Global Mirror, Global Copy and Metro Mirror) can provide the ability to mirror data from one storage system to another, over extended distances.

Conclusion

Those are seven key ingredients for designing or evaluating a recipe for success with regard to implementing the smart grid at your utility. Addressing these dimensions will help achieve a solid foundation for a comprehensive smart grid computing system architecture.

Thinking Smart

For more than 30 years, Newton- Evans Research Company has been studying the initial development and the embryonic and emergent stages of what the world now collectively terms the smart, or intelligent, grid. In so doing, our team has examined the technology behind the smart grid, the adoption and utilization rates of this technology bundle and the related market segments for more than a dozen or so major components of today’s – and tomorrow’s – intelligent grid.

This white paper contains information on eight of these key components of the smart grid: control systems, smart grid applications, substation automation programs, substation IEDs and devices, advanced metering infrastructure (AMI) and automated meter-reading devices (AMR), protection and control, distribution network automation and telecommunications infrastructure.

Keep in mind that there is a lot more to the smart grid equation than simply installing advanced metering devices and systems. A large AMI program may not even be the correct starting point for hundreds of the world’s utilities. Perhaps it should be a near-term upgrade to control center operations or to electronic device integration of the key substations, or an initial effort to deploy feeder automation or even a complete production and control (P&C) migration to digital relaying technology.

There simply is not a straightforward roadmap to show utilities how to develop a smart grid that is truly in that utility’s unique best interests. Rather, each utility must endeavor to take a step back and evaluate, analyze and plan for its smart grid future based on its (and its various stakeholders’) mission, its role, its financial and human resource limitations and its current investment in modern grid infrastructure and automation systems and equipment.

There are multiple aspects of smart grid development, some of which involve administrative as well as operational components of an electric power utility, and include IT involvement as well as operations and engineering; administrative management of customer information systems (CIS) and geographic information systems (GIS) as well as control center and dispatching operation of distribution and outage management systems (DMS and OMS); substation automation as well as true field automation; third-party services as well as in-house commitment; and of course, smart metering at all levels.

Space Station

I have often compared the evolution of the smart grid to the iterative process of building the international space station: a long-term strategy, a flexible planning environment, responsive changes incorporated into the plan as technology develops and matures, properly phased. What function we might need is really that of a skilled smart grid architect to oversee the increasingly complex duties of an effective systems planning organization within the utility organization.

All of these soon-to-be-interrelated activities need to be viewed in light of the value they add to operational effectiveness and operating efficiencies as well as the effect of their involvement with one another. If the utility has not yet done so, it must strive to adopt a systems-wide approach to problem solving for any one grid-related investment strategy. Decisions made for one aspect of control and automation will have an impact on other components, based on the accumulated 40 years of utility operational insights gained in the digital age.

No utility can today afford to play whack-a-mole with its approach to the intelligent grid and related investments, isolating and solving one problem while inadvertently creating another larger or more costly problem elsewhere because of limited visibility and “quick fix” decision making.

As these smart grid building blocks are put into service, as they become integrated and are made accessible remotely, the overall smart grid necessarily becomes more complex, more communications-centric and more reliant on sensor-based field developments.

In some sense, it reminds one of building the space station. It takes time. The process is iterative. One component follows another, with planning on a system-wide basis. There are no quick solutions. Everything must be very systematically approached from the outset.

Buckets of Spending

We often tackle questions about the buckets of spending for smart grid implementations. This is the trigger for the supply side of the smart grid equation. Suppliers are capable of developing, and will make the required R&D investment in, any aspect of transmission and distribution network product development – if favorable market conditions exist or if market outlooks can be supported with field research. Hundreds of major electric power utilities from around the world have already contributed substantially to our ongoing studies of smart grid components.

In looking at the operational/engineering components of smart grid developments, centering on the physical grid itself (whether a transmission grid, a distribution grid or both), one must include what today comprises P&C, feeder and switch automation, control center-based systems, substation measurement and automation systems, and other significant distribution automation activities.

On the IT and administrative side of smart grid development, one has to include the upgrades that will definitely be required in the near- or mid-term, including CIS, GIS, OMS and wide area communications infrastructure required as the foundation for automatic metering. Based on our internal estimates and those of others, spending for grid automation is pegged for 2008 at or slightly above $1 billion nationwide and will approach $3.5 billion globally. When (if) we add in annual spending for CIS, GIS, meter data management and communications infrastructure developments, several additional billions of dollars become part of the overall smart grid pie.

In a new question included in the 2008 Newton-Evans survey of control center managers, these officials were asked to check the two most important components of near-term (2008-2010) work on the intelligent grid. A total of 136 North American utilities and nearly 100 international utilities provided their comments by indicating their two most important efforts during the planning horizon.

On a summary basis, AMI led in mentions from 48 percent of the group. EMS/ SCADA investments in upgrades, new applications, interfaces et al was next, mentioned by 42 percent of the group. Distribution automation was cited by 35 percent as well.

Spending Outlook

The financial environment and economic outlook do not bode well for many segments of the national and global economies. One question we have continuously been asked well into this year is whether the electric power industry will suffer the fate of other industries and significantly scale back planned spending on T&D automation because of possible revenue erosion given the slowdown and fallout from this year’s difficult industrial and commercial environments.

Let’s first take a summary look at each of the five major components of T&D automation because these all are part and parcel of the operations/engineering view of the smart grid of the future.

Control Systems Outlook: Driven by SCADA-like systems and including energy management systems and distribution management software, this segment of the market is hovering around the $500 million mark on a global scale – excluding the values of turn-key control center projects (engineering, procurement and construction (EPC) of new control center facilities and communications infrastructure). We see neither growth nor erosion in this market for the near-term, with some up-tick in spending for new applications software and better visualization tools to compensate for the “aging” of installed systems. While not a control center-based system, outage management is a closely aligned technology development, and will continue to take hold in the global market. Sales of OMS software and platforms are already approaching the $100 million mark led by the likes of Oracle Utilities, Intergraph and MilSoft.

Substation Automation and Integration Programs: The market for substation IEDs, for new communications implementations and for integration efforts has grown to nearly $500 million. Multiyear programs aimed at upgrading, integrating and automating the existing global base of about a quarter million or so transmission and primary distribution substations have been underway for some time. Some programs have been launched in 2008 that will continue into 2011. We see a continuation of the growth in spending for critical substation A&I programs, albeit 2009 will likely see the slowest rate of growth in several years (less than 3 percent) if the current economic malaise holds up through the year. Continuing emphasis will be on HV transmission substations as the first priority for upgrades and addition of more intelligent electronic devices.

AMI/AMR: This is the lynchpin for the smart grid in the eyes of many industry observers, utility officials and perhaps most importantly, regulators at the state and federal levels of the U.S., Canada, Australia and throughout Western Europe. With nearly 1.5 billion electricity meters installed around the world, and about 93 percent being electro-mechanical, interest in smart metering can also be found in dozens of other countries, including Indonesia, Russia, Honduras, Malaysia, Australia, and Thailand. Another form of smart meters, the prepayment meter, is taking hold in some of the developing nations of the world. The combined resources of Itron, coupled with its Actaris acquisition, make this U.S. firm the global share leader in sales and installations of AMI and AMR systems and meters.

Protection and Control: The global market for protective relays, the foundation for P&C has climbed well above $1.5 billion. Will 2009 see a drop in spending for protective relays? Not likely, as these devices continue to expand in capabilities, and undertake additional functions (sequence of event recording, fault recording and analysis, and even acting as a remote terminal unit). To the surprise of many, there is still a substantial amount (perhaps as much as $125 million) being spent annually for electro-mechanical relays nearly 20 years into the digital relay era. The North American leader in protective relay sales to utilities is SEL, while GE Multilin continues to hold a leading share in industrial markets.

Distribution Automation: Today, when we discuss distribution automation, the topic can encompass any and all aspects of a distribution network automation scheme, from the control center-based SCADA and distribution management system on out to the substation, where RTUs, PLCs, power meters, digital relays, bay controllers and a myriad of communicating devices now help operate, monitor and control power flow and measurement in the medium voltage ranges.

Nonetheless, it is beyond the substation fence, reaching further down into the primary and secondary network, where we find reclosers, capacitors, pole top RTUs, automated overhead switches, automated feeders, line reclosers and associated smart controls. These are the new smart devices that comprise the basic building blocks for distribution automation. The objective will be achieved with the ability to detect and isolate faults at the feeder level, and enable ever faster service restoration. With spending approaching $1 billion worldwide, DA implementations will continue to expand over the coming decade, nearing $2.6 billion in annual spending by 2018.

Summary

The T&D automation market and the smart grid market will not go away this year, nor will it shrink. When telecommunications infrastructure developments are included, about $5 billion will have been spent in 2008 for global T&D automation programs. When AMI programs are adding into the mix, the total exceeds $7 billion. T&D automation spending growth will likely be subdued, perhaps into 2010. However, the overall market for T&D automation is likely to be propped up to remain at or near current levels of spending for 2009 and into 2010, benefiting from the continued regulatory-driven momentum for AMI/ AMR, renewable portfolio standards and demand response initiatives. By 2011, we should once again see healthier capital expenditure budgets, prompting overall T&D automation spending to reach about $6 billion annually. Over the 2008-2018 periods, we anticipate more than $75 billion in cumulative smart grid expenditures.

Expenditure Outlook

Newton-Evans staff has examined the current outlook for smart grid-related expenditures and has made a serious attempt to avoid double counting potential revenues from all of the components of information systems spending and the emerging smart grid sector of utility investment.

While the enterprise-wide IT portions (blue and red segments) of Figure 1 include all major components of IT (hardware, software, services and staffing), the “pure” smart grid components tend to be primarily in hardware, in our view. Significant overlap with both administrative and operational IT supporting infrastructure is a vital component for all smart grid programs underway at this time.

Between “traditional IT” and the evolving smart grid components, nearly $25 billion will likely be spent this year by the world’s electric utilities. Nearly one-third of all 2009 information technology investments will be “smart grid” related.

By 2013, the total value of the various pie segments is expected to increase substantially, with “smart grid” spending possibly exceeding $12 billion. While this amount is generally understood to be conservative, and somewhat lower than smart grid spending totals forecasted by other firms, we will stand by our forecasts, based on 31 years of research history with electric power industry automation and IT topics.

Some industry sources may include the total value of T&D capital spending in their smart grid outlook.

But that portion of the market is already approaching $100 billion globally, and will likely top $120 billion by 2013. Much of that market would go on whether or not a smart grid is involved. Clearly, all new procurements of infrastructure equipment will be made with an eye to including as much smart content as is available from the manufacturers and integrators.

What we are limiting our definition to is edge investment, the components of the 21st century digital transport and delivery systems being added on or incorporated into the building blocks (power transformers lines, switchgear, etc.) of electric power transmission and delivery.

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Our mobile workforce management solution helps utilities empower mobile workers with accurate, real-time information for optimum service and quick on-site decision making. From proactive customer demand forecasting and capacity planning to real-time decision making, incorporating scheduling, mobility and location- based services, ClickSoftware helps service organizations get the most out of their resources.

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ClickSoftware provides a flexible, scalable and proven solution that has been deployed at many utility companies around the world. Highlights include the ability to:

  • Automatically update the schedule based on real-time information from the field;
  • Manage crews (parts and people);
  • Cover a wide variety of job types within one product: from short jobs requiring one person to multi-stage jobs requiring a multi-person team over several days or weeks;
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At Your Service

Today’s utility companies are being driven to upgrade their aging transmission and distribution networks in the face of escalating energy generation costs, serious environmental challenges and rising demand for cleaner, distributed generation from both developing and digital economies worldwide.

The current utilities environment requires companies to drive down costs while increasing their ability to monitor and control utility assets. Yet, due to aging infrastructure, many utilities operate without the benefit of real-time usage and distribution loads – while also contending with limited resources for repair and improvement. Even consumers, with climate change on their minds, are demanding that utilities find more innovative ways to help them reduce energy consumption and costs.

One of the key challenges facing the industry is how to take advantage of new technologies to better manage customer service delivery today and into the future. While introducing this new technology, utilities must keep data and networks secure to be in compliance with critical infrastructure protection regulations. The concept of “service management” for the smart grid provides an approach for getting started.

A Smart Grid

A smart grid is created with new solutions that enable new business models. It brings together processes, technology and business partners, empowering utilities with an IP-enabled, continuous sensing network that overlays and connects a utility’s equipment, devices, systems, customers, partners and employees. A smart grid also enables on-demand access to data and information, which is used to better manage, automate and optimize operations and processes throughout the utility.

A utility relies on numerous systems, which reside both within and outside their physical boundaries. Common internal systems include: energy trading systems (ETS), customer information systems (CIS), supervisory control and data acquisition systems (SCADA), outage management systems (OMS), enterprise asset management (EAM); mobile workforce management systems (MWFM), geospatial information systems (GIS) and enterprise resource planning systems (ERP).

These systems are purchased from multiple vendors and often use a variety of protocols to communicate. In addition, utilities must interface with external systems – and often integrate all of them using a point-to-point model and establish connectivity on an as-needed basis. The point-to-point approach can result in numerous complex connections that need to be maintained.

Service Management

The key concept behind service management is the idea of managing assets, networks and systems to provide a “service,” as opposed to simply operating the assets. For example, Rolls Royce Civil Aerospace division uses this concept to sell “pounds of thrust” as a service. Critical to a utility’s operation is the ability to manage all facets of the services being delivered. Also critical to the operation of the smart grid are new solutions in advanced meter management (AMM), network automation and analytics, and EAM, including meter asset management.

A service management platform provides a way for utility companies to manage the services they deliver with their enterprise and information technology assets. It provides a foundation for managing the assets, their configuration, and the interrelationships key to delivering services. It also provides a means of defining workflow for the instantiation and management of the services being delivered. Underlying this platform is a range of tools that can assist in management of the services.

Gathering and analyzing data from advanced meters, network components, distribution devices, and legacy SCADA systems provides a solid foundation for automating service management. When combined with the information available in their asset management systems, utility companies can streamline operations and make more efficient use of valuable resources.

Advanced Reading

AMM centers on a more global view of the informational infrastructure, examining how automatic meter reading (AMR) and advanced metering infrastructure (AMI) integrate with other information systems to provide value-added benefits. It is important to note that for many utilities, AMM is considered to be a “green” initiative since it has the ability to influence customer usage patterns and, therefore, lower peak demand.

The potential for true business transformation exists through AMM, and adopting this solution is the first stage in a utility’s transformation to a more information-powered business model. New smart meters are network addressable, and along with AMM, are core components of the grid. Smart meters and AMM provide the capability to automatically collect usage data in near real time and to transport meter reads at regular intervals or on demand.

AMR/AMIs that aggregate their data in collection servers or concentrators, and expose it through an interface, can be augmented with event management products to monitor the meter’s health and operational status. Many organizations already deploy these solutions for event management within a network’s operations center environments, and for consolidated operations management as a top-level “manager of managers.”

A smart grid includes many devices other than meters, so event management can also be used to monitor the health of the rest of the network and IT equipment in the utility infrastructure. Integrating meter data with operations events gives network operations center operators a much broader view of a utility’s distribution system.

These solutions enable end-to-end data integration, from the meter collection server in a substation to the back-end helpdesk and billing applications. This approach can lead to improved speed and accuracy of data, while leveraging existing equipment and applications.

Network Automation and Analytics

Most utility companies use SCADA systems to collect data from sensors on the energy grid and send events to applications with SCADA interfaces. These systems collect data from substations, power plants and other control centers. They then process the data and allow for control actions to be sent back out. Energy management and distribution management systems typically provide additional features on top of SCADA, targeting either the transmission or distribution grids.

SCADA systems are often distributed on several servers (anywhere from two to 100) connected via a redundant local area network. The SCADA system, in turn, communicates with remote terminal units (RTUs), other devices, and other computer networks. RTUs reside in a substation or power plant, and are hardwired to other devices to bring back meaningful information such as current megawatts, amps, volts, pressure, open/closed or tripped. Distribution business units within a utility company also utilize SCADA systems to track low voltage applications, such as meters and pole drops, compared to the transmission business units’ larger assets, including towers, circuits and switchgear.

To facilitate network automation, IT solutions can help utilities to monitor and analyze data from SCADA systems in real time, monitor the computer network systems used to deploy SCADA systems, and better secure the SCADA network and applications using authentication software. An important element of service management is the use of automation to perform a wide range of actions to improve workfl ow efficiency. Another key ingredient is the use of service level agreements (SLAs) to give a business context for IT, enabling greater accountability to business user needs, and improving a utility’s ability to prioritize and optimize.

A smart grid includes a large number of devices and meters – millions in a large utility – and these are critical to a utility’s operations. A combination of IT solutions can be deployed to manage events from SCADA devices, as well as the IT equipment they rely on.

EAM For Utilities

Historically, many utility companies have managed their assets in silos. However, the emergence of the smart grid and smart meters, challenges of an aging workforce, an ever-demanding regulatory environment, and the availability of common IT architecture standards, are making it critical to standardize on one asset management platform as new requirements to integrate physical assets and IT assets arise (see Figure 1).

Today, utility companies are using EAM to manage work in gas and electric distribution operations, including construction, inspections, leak management, vehicles and facilities. In transmission and substation, EAM software is used for preventative and corrective maintenance and inspections.

EAM also helps track financial assets such as purchasing, depreciation, asset valuation and replacement costs. This solution helps integrate this data with ERP systems, and stores the history of asset testing and maintenance management. It integrates with GIS or other mapping tools to create geographic and spatial views of all distribution and smart grid assets.

Meter asset management is another area of increasing interest, as meters have an asset lifecycle similar to most other assets in a utility. Meter asset management involves tracking the meter from receipt to storeroom, to truck, to final location – as compared to managing the data the meter produces.

Now there is an IT asset management solution with the ability to manage meters as part of the IT network. This solution can be used to provision the meter, track configurations and provide service desk functionality. IT asset management solutions also have the ability to update meter firmware, and easily move and track the location and status of the assets over time in conjunction with a configuration database.

Reducing the number of truck rolls is another key focus area for utility companies. Using a combination of solutions, companies can:

  • Better manage the lifecycles of physical assets such as meters, meter cell relays, and broadband over powerline (BPL) devices to improve preventive maintenance;
  • Reconcile deployed asset information with information collected by meter data management systems;
  • Correlate the knowledge of physical assets with problems experienced with the IT infrastructure to better analyze a problem for root cause; and
  • Establish more efficient business process workflows and strengthen governance across a company.

Utilities are facing many challenges today and taking advantage of new technologies that will help better manage the delivery of service to customers tomorrow. The deployment of the smart grid and related solutions is a significant initiative that will be driving utilities for the next 10 years or more.

The concept of “service management” for the smart grid provides an approach for getting started. But these do not need to be tackled all at once. Utilities should develop a roadmap for the smart grid; each one will depend on specific priorities. But utilities don’t have to go it alone. The smart grid maturity model (SGMM) can enable a utility to develop a roadmap of activities, investments and best practices to ensure success and progress with available resources.

Empowering the Smart Grid

Trilliant is the leader in delivering intelligent networks that power the smart grid. Trilliant provides hardware, software and service solutions that deliver on the promise of Advanced Metering and Smart Grid to utilities and their customers, including improved energy efficiency, grid reliability, lower operating cost, and integration of renewable energy resources.

Since its founding in 1985, the company has been a leading innovator in the delivery and implementation of advanced metering infrastructure (AMI), demand response and grid management solutions, in addition to installation, program management and meter revenue cycle services. Trilliant is focused on enabling choice for utility companies, ranging from meter, network and IT infrastructures to full or hybrid outsource models.

Solutions

Trilliant provides fully automated, two-way wireless network solutions and software for smart grid applications. The company’s smart grid communications solutions enable utilities to create a more efficient and robust operational infrastructure to:

  • Read meters on demand with five minute or less intervals;
  • Improve cash flow;
  • Improve customer service;
  • Decrease issue resolution time;
  • Verify outages and restoration in real time;
  • Monitor substation equipment;
  • Perform on/off cycle reads;
  • Conduct remote connect/disconnect;
  • Significantly reduce/eliminate energy theft through tamper detection; and
  • Realize accounting/billing improvements.

Trilliant solutions also enable the introduction of services and programs such as:

  • Dynamic demand response; and
  • Time-of-use (TOU), critical peak pricing (CPP) and other special tariffs and related metering.

Solid Customer Base

Trilliant has secured contracts for more than three million meters to be supported by its network solutions and services, encompassing both C&I and residential applications. The company has delivered products and services to more than 200 utility customers, including Duke Energy, E.ON US (Louisville Gas & Electric), Hydro One, Hydro Quebec, Jamaica Public Service Company Ltd., Milton Hydro, Northeast Utilities, PowerStream, Public Service Gas & Electric, San Diego Gas & Electric, Toronto Hydro Electric System Ltd., and Union Gas, among others.

Shaping a New Era in Energy

In the last few years, the world has seen the energy & utilities business accelerate into a significant period of transformation as a result of the smart grid and related technologies. Today, with some early proponents leading the way, the industry is on the verge of a step-change improvement that some might even classify as a full-scale revolution. Utilities are viewed not only as being a critical link in solving the challenges we face related to climate change and the care of our planet’s energy resources, but they’re becoming enablers of growth and innovation – and even new products, services and jobs. Clearly the decisions the industry is making today around the world’s electricity networks will impact our lives for decades to come.

If the current economic environment has muted any enthusiasm for this transformation, it hasn’t been much. With the exception, perhaps, of plummeting oil prices temporarily providing some sense of calm in the sector, there are probably few people left who don’t believe the world needs to urgently address its clean, smart energy future. As of this writing, fledgling signs of an economic recovery are emerging, and along with it, increases in fossil fuel prices. As such, enthusiasm is growing over the debate about how countries will utilize billions in stimulus funding to enable the industry to achieve a new level of greatness.

There is a confluence of events helping us along this path of dramatic and beneficial change. IBM’s recent industry consumer survey (selected findings of which are featured in this publication in "Lighting the Way" by John Juliano) signals a future that is being shaped in part by a younger generation of digitally savvy people who care about – and are willing to participate in – our collective energy future. They willingly engage in more open communication with utility providers and tend to be better at understanding and controlling energy utilization.

As utilities instrument virtually all elements of the energy value chain from the power plant to the plug, they will improve service quality to these customers while reducing cost and improving reliability to a degree never before achievable. Customers engage because they see themselves as part of a larger movement to forestall the effects of climate change, or to battle price instability. This fully connected, instrumented energy ecosystem takes advantage of the data it collects, applying advanced analytics to enable real-time decisions on energy consumption. Some smart grid projects are already helping consumers save 10% of their bills, and reduce peak demand by 15%. Imagine the potential total savings when this is scaled to include companies, governments and educational institutions.

While positive new developments abound, they also are creating a highly complex environment, raising many difficult questions. For example, are families and businesses truly prepared to go on a "carbon diet" and will they stay on it? How will governments, with their increased stake in auto manufacturers, effectively and efficiently manage the transition toward PHEVs? Will industry players collaborate with one another to deal with stealth attacks on smart grids that are no longer the stuff of spy novels, but current realities we must face 24/7? How do we responsibly support the resurgence of nuclear-based power generation?

Matters of investment are also complex. Will there be sufficient public/private partnership to effectively stimulate investment in new businesses and models to profitably progress safe alternative energy forms such as solar, tidal, wind, geothermal and others? Will we have the "smarts" – and the financial commitment – to build more smarts into the reconstruction of ailing infrastructures?

Leading the Way

IBM has been a leading innovator in smart grid technology, significantly investing in energy and environmental programs designed to promote the use of intelligent energy worldwide. We created the Global Intelligent Utility Network Coalition, a strategic relationship with a small group of select utilities from around the world to shape, accelerate and share in the development of the smart grid. With the goal to lead industry organizations to smart grid transformation, we actively lead and participate in a host of global organizations including the GridWise® Alliance, Gridwise Architecture Council, EPRI’s Intelligrid program, and the World Energy Council, among others. By coming together around a shared vision of a smarter grid, we have an unprecedented opportunity to reshape the energy industry and our economic future.

The IBM experts who engage in these groups – along with the thousands of other IBMers working in the industry – have contributed significant thinking to the industry’s progress, not the least of which is the creation of the Smart Grid Maturity Model (SGMM) which has been handed over to the Carnegie Mellon Software Engineering Institute (SEI) for ongoing governance, growth and evolution of the model. Furthermore, the World Energy Council (WEC) has become a channel for the global dissemination of the model among its worldwide network of member committees.

IBM’s own Intelligent Utility Network (IUN) solution enables a utility to instrument everything from the meter in the home to miles of power lines to the network itself. In fact, the IUN looks a lot more like the Internet than a traditional grid. It can be interconnected to thousands of power sources – including climate-friendly ones – and its instrumentation generates new data for analysis, insight and intelligence that can be applied for the benefit of businesses and consumers alike.

Our deep integration skills, leading-edge technology, partner ecosystem and business and regulatory expertise have earned us roles in more than 50 smart grid projects around the globe with showcase projects in the U.S. Pacific Northwest, Texas, Denmark and Malta (See "The Smart Grid in Malta" by Carlo Drago in this publication) to name just a few. IBM also has a role in seven out of the world’s 10 largest advanced meter management projects.

The IBM Solution Architecture for Energy (SAFE), is a specialized industry framework focused on the management, maintenance, and integration of a utility’s assets and information, inclusive of generation, transmission and distribution, and customer operations. This is complemented by a world-class solution portfolio based on the most comprehensive breadth of hardware, software, consulting services, and open standards-based IT infrastructure that can be customized to meet the needs of today’s energy and utilities enterprises around the globe.

These activities are augmented by the renowned IBM Research organization that engages in both industry-specific and cross-industry research that influences our clients’ progress. This includes new computing models to handle the proliferation of end-user devices, sensor and actuators, connecting them with powerful back-end systems. How powerful? In the past year IBM’s Roadrunner supercomputer broke the "petaflop" barrier – one thousand trillion calculations per second using standard chip sets. Combined with advanced analytics and new computing models like "clouds" we’re turning mountains of data into intelligence, making systems like the smart grid more efficient, reliable and adaptive – in a word, smarter.

IBM Research also conducts First-of-a-Kind research – or FOAKs – in partnership with our clients, turning promising research into market-ready products and services. And our Industry Solution Labs around the world give IBM clients the chance to discover how leading-edge technologies and innovative solutions can be assembled and proven to help solve real business problems. For example, we’re exploring how to turn millions of future electric vehicles into a distributed storage system, and we maintain a Center of Excellence for Nuclear Power to improve design, safety analysis, operation, and nuclear modeling / simulation processes.

IBM is excited to be at the forefront of this changing industry – and our changing world. And we’re honored to be working closely with our clients and business partners in helping to evolve a smarter planet.

The Smart Grid Maturity Model

The software industry has been using maturity models to define and measure software development capabilities for decades. These models have helped the industry create a shared vision for these capabilities. They also have driven individual software development organizations to set and pursue aggressive capabilities goals while allowing these groups to measure progress in reaching those objectives along the way.

As the utility industry embarks on the complex and ambitious transformation of the outdated power grid to the new smart grid, it has struggled to develop a shared vision for the smart grid end-state and the path to its development and deployment. Now, the smart grid maturity model (SGMM) is helping the industry overcome these challenges by presenting a consensus vision of the smart grid, the benefits it can bring and the various levels of smart grid development and deployment maturity. SGMM is helping numerous utilities worldwide develop targets for their smart grid strategy, and build roadmaps of the activities, investments and best practices that will lead them to their future smart grid state.

IBM worked closely with members of the Intelligent Utility Network Coalition (IUNC) to develop, discuss and revise several drafts of the SGMM. This team was assisted by APQC, a member-based nonprofit organization that provides benchmarking and best-practice research for approximately 500 organizations worldwide. The goal in the development process was to ensure the SGMM reflects a consensus industry vision for the smart grid, and brings together a wide range of industry experts to define the technical, organizational and process details supporting that vision.

APQC has a long history of benchmarking, performance measurement and maturity definition, and was therefore able to provide critical experience to drive development of a clear, measureable maturity model. IBM has worked on smart grid initiatives with numerous utilities around the world, and provided guidance and some initial structure to help start the development process. But the most important contributors to the SGMM were utilities themselves, as they brought a wealth of deep technical and strategic knowledge to build a shared vision of the smart grid and the various stages of maturity that could be achieved.

Because of this consensus development process, the SGMM reflects a broad industry vision for the smart grid, and it now gives utilities a tool for both strategic and tactical use to guide, measure and assess a utility’s smart grid transformation:

Strategic uses of the SGMM:

  • Establish a shared vision for the smart grid journey;
  • Communicate the smart grid vision, both internally and externally;
  • Use as a strategic framework for evaluating smart grid business and investment objectives;
  • Plan for technological, regulatory, and organizational readiness; and
  • Benchmark and learn from others

Tactical uses of the SGMM:

  • Guide development of a specific smart grid roadmap or blueprint;
  • Assess and prioritize current smart grid opportunities and projects;
  • Use as a decision-making framework for smart grid investments;
  • Assess resource needs to move from one smart grid level to another; and
  • Measure smart grid progress using key performance indicators (KPIs).

The SGMM structure is based on three fundamental concepts:

Domains: eight logical groupings of functional components of a smart grid transformation implementation;

Maturity Levels: five sets of defined characteristics and outcomes; and

Characteristics: descriptions of over 200 capabilities that are expected at each stage of the smart grid journey.

As Figure 1 shows, the domains span eight areas covering people, technology, and process, and comprise all of the fundamental components of smart grid capabilities.

Maturity levels range from an entry level of 1, up to a top level of 5, and can be summarized as follows:

Level 1 – Exploring and Initiating: contemplating smart grid transformation; may have a vision, but no strategy yet; exploring options; evaluating business cases and technologies; may have some smart grid elements already deployed.

Level 2 – Functional Investing: making decisions, at least at a functional level; business cases in place and investments being made; one or more functional deployments under way with value being realized; strategy in place.

Level 3 – Integrating Cross Functional: smart grid spreading; operational linkages established between two or more functional areas; management ensuring decisions span functional interests, resulting in cross-functional benefits.

Level 4 – Optimizing Enterprise-Wide: smart grid functionality and benefits realized; management and operational systems rely on and take full advantage of observability and integrated control, both across and between enterprise functions.

Level 5 – Innovating Next Wave of Improvements: new business, operational, environmental, and societal opportunities present themselves, and the capability exists to take advantage of them.

It is important to note that a utility may not choose to target maturity level 5 in every domain – in fact, it may not target level 5 for any domain. Instead, each utility using the SGMM must consider its own strategic direction and performance goals, and then decide on the levels of smart grid maturity that will support those goals to determine the target maturity in each domain. For example, a utility that is strategically focused on the retail side of the business may want to achieve relatively high maturity in the customer management and experience domain, but have a much lower target for maturity in the grid operations domain.

The key point is that the SGMM is not a report card with those utilities reaching the highest maturity levels "winning the game." Instead, each utility uses the SGMM to understand how the smart grid can help optimize its planning and investment to achieve its aspirations.

With over 200 characteristics describing the capabilities for each domain and maturity level, it is not possible to describe them here, but an example of a typical characteristic shown in Figure 2 provides a good sense of the level of detail in each characteristic of the SGMM.

Taken together, the domains, maturity levels, and characteristics form a detailed matrix that describes smart grid maturity across all critical areas.

Evaluating Smart Grid Maturity

A utility uses two surveys in conjunction with the SGMM structure described above to: assess its smart grid maturity; and track its progress and the resulting benefits during deployment. The first survey is the maturity assessment, which asks a series of about 40 questions that cover the current state of the utility’s smart grid strategy and spending, and the current penetration of smart grid capabilities into various areas of the business. The assessment yields a detailed report, providing the results for each domain, as well as higher-level reports that show the broader view of the utility’s current state and aspirations for the smart grid.

In this example, the utility’s current smart grid maturity is shown by the green circles, while its maturity aspirations are shown by the yellow circles. This highlevel view can be very useful as support for detailed plans on how to get from current state to aspirational state. It is also helpful for conveying maturity concepts and results to various stakeholders – both inside and outside the utility.

The second survey is the opportunity and results survey, which focuses on KPIs that track progress in smart grid deployment, as well as realization of the resulting benefits. For example, many questions in the survey cover grid operations, with the focus on cost, reliability and penetration of smart grid capabilities into the "daily life" of grid operations. The survey is expected to be completed annually, allowing each utility using the SGMM to track its deployment progress and benefits realization.

Using SGMM Results

The results from the SGMM can be applied in many ways to gauge a utility’s smart grid progress. From a practical management standpoint, the following important indicators can be derived directly from the SGMM process:

  • How the utility compares to other survey participants overall;
  • Where the utility has deficiencies in one domain that may adversely affect other domains;
  • Effects of being potentially projectoriented rather than program-driven, resulting in a jagged, "peaks and valleys" maturity profile with uneven advancement;
  • Indications that some domains are too far ahead of others, resulting in the risk of putting the "cart before the horse;" and
  • Confirmation of progress in domains that have been given particular focus by the utility, and indications of domains that may require increased focus.

More broadly, completion of the SGMM surveys provide a utility with the information needed to establish a shared smart grid vision with both internal and external stakeholders, mesh that vision with the utility’s overall business strategy to set maturity targets, and then build a detailed roadmap for closing the gaps between the current and target maturity levels.

Transition of SGMM Stewardship

IBM has been pleased to work with APQC and members of the IUNC to support definition and early roll-out of the SGMM. But as an important and evolving industry tool, IBM believes that the SGMM should be supported and maintained by a broader group. Therefore, we are planning to transition to a stewardship model with three organizations each playing a critical role:

  • Governance, Management, and Growth: the Carnegie Mellon Software Engineering Institute will govern the SGMM, working in conjunction with Carnegie Mellon University and the Carnegie Mellon Electricity Industry Center. The institute and its 500 employees will leverage its 20 years of experience as stewards of the Capability Maturity Model for software development.
  • Global Stakeholder Representation and Advocacy: the World Energy Council will provide representation for stakeholders around the globe. The council was established in 1923, represents 95 member countries and regularly hosts the World Energy Congress. Its mission is to promote the sustainable supply and use of energy for the greatest benefit of all people. This mission fits well with the development of the smart grid and the expanding use of the SGMM.
  • Data Collection and Reporting: APQC will provide further support for the SGMM survey process. With over 30 years of quality and process improvement research, APQC will continue the work it has done to date to assist utilities in assessing their smart grid maturity and tracking their progress during deployment.

Summary

All utilities should consider using the SGMM as they develop their vision for the smart grid and begin to plan and execute the projects that will take them on the journey. The SGMM represents the best strategic and technical thinking of a broad cross-section of the utility industry. We believe that the SGMM will continue to represent a thoughtful and consensus view as the smart grid – and the technology that supports it – evolves over the next few years.

Business Process Improvement

In the past, the utility industry could consider itself exempt from market drivers like those listed above. However, today’s utilities are immersed in a sea of change. Customers demand reliable power in unlimited supply, generated in environmentally friendly ways without increased cost. All the while regulators are telling consumers to “change the way they are using energy or be ready to pay more,” and the Department of Energy is calling for utilities to make significant reductions in usage by 2020 [1].

“The consumer’s concept of quality will no longer be measured by only the physical attributes of the product – it will extend to the process of how the product is made, including product safety, environmental compliance and social responsibility compliance.”

– Victor Fang, chairman of Li and Fang,
in the 2008 IBM CEO Study

If these issues are not enough, couple them with a loss of knowledge and skill due to an aging workforce, an ever-increasing amount of automation and technology being introduced into our infrastructure with few standards, tightening bond markets and economic declines requiring us to do more with less. Now more than ever the industry needs to redefine our core competencies, identify key customers and their requirements, and define processes that meet or exceed their expectations. Business process improvement is essential to ensure future success for utilities.

There is no need to reinvent the wheel and develop a model for utilities to address business process improvement. One already exists that offers the most holistic approach to process improvement today. It is not new, but like any successful management method, it has been modified and refined to meet continuously changing business needs.

It is agnostic in the way it addresses methods used for analysis and process improvement such as Lean, Six Sigma and other tools; but serves as a framework for achieving results in any industry. It is the Baldrige Criteria for Performance Excellence (see Figure 1).

The Criteria for Performance Excellence is designed to assist organizations to focus on strategy-driven performance while addressing key decisions driving both short-term and long-term organizational sustainability in a dynamic environment. Is it possible that this framework was designed for times such as these in the utility industry?

The criteria are essentially simple in design. They are broken into seven categories as shown in figure 2; leadership, strategic planning, customer focus, measurement, analysis and knowledge management, workforce focus, process management and results.

In this model, measurement, analysis and knowledge management establish the foundation. There are two triads. On the left hand side, leadership, strategic planning and customer focus make up the leadership triad. On the right hand side of the model, workforce focus, process management and results make up the results triad. The alignment and integration of these essential elements of business create a framework for continuous improvement. This model should appear familiar in concept to industry leaders; there is not a single utility in the industry that does not identify with these categories in some form.

The criteria are built to elicit a response through the use of how and what questions that ask about key processes and their deployment throughout the organization. On face value, these questions appear to be simple. However, as you respond to them, you will realize their linkage and begin to identify opportunities for improvement that are essential to future success. Leaders wishing to begin this effort should not be surprised by the depth of the questions and the relatively few members within your organization who will be able to provide complete answers.

In assessment of the model’s ability to meet utility industry needs, let’s discuss each category in greater detail, provide relevance to the utility industry and include key questions for you to consider as you begin to assess your own organization’s performance.

Leadership: Who could argue that the current demand for leadership in utilities is more critical today than ever before in our history? Changes in energy markets are bringing with them increased levels of accountability, a greater focus on regulatory, legal and ethical requirements, a need for long-term viability and sustainability, and increased expectations of community support. Today’s leaders are expected to achieve ever increasing levels of operational performance while operating on less margin than ever before.

“The leadership category examines how senior leaders’ personal actions guide and sustain the organization. Also examined are the organization’s governance system and how it fulfills legal, ethical and societal responsibilities as well as how it selects and supports key communities [2].”

Strategic Planning: Does your utility have a strategic plan? Not a dust-laden document sitting on a bookshelf or a financial budget; but a plan that identifies strategic objectives and action plans to address short and long-term goals. Our current business environment demands that we identify our core competencies (and more importantly what are not our core competencies), identify strategic challenges to organizational success, recognize strategic advantages and develop plans that ensure our efforts are focused on objectives that will ensure achievement of our mission and vision.

What elements of our business should we outsource? Do our objectives utilize our competitive advantages and core competencies to diminish organizational challenges? We all know the challenges that are both here today and await us just beyond the horizon. Many of them are common to all utilities; an aging workforce, decreased access to capital, technological change and regulatory change. How are we addressing them today and is our approach systematic and proactive or are we simply reacting to the challenges as they arise?

“The strategic planning category examines how your organization develops strategic objectives and action plans. Also examined are how your chosen strategic objectives and action plans are deployed and changed if circumstances require, and how progress is measured [2].”

Customer Focus: The success of the utility industry has been due in part to a long-term positive relationship with its customers. Most utilities have made a conscientious effort to identify and address the needs of the customer; however a new breed of customer is emerging with greater expectations, a higher degree of sensitivity to environmental issues, a diminished sense of loyalty to business organizations and overall suspicion of ethical and legal compliance.

Their preferred means of communication are quite different than the generations of loyal customers you have enjoyed in the past. They judge your performance against similar customer experiences received from organizations far beyond the traditional competitor.

You now compete against Wal-Mart’s supply chain process, Amazon.com’s payment processes and their favorite hotel chain’s loyalty rewards process. You are being weighed in the balances and in many cases found to be lacking. Worse yet, you may not have even recognized them as an emerging customer segment.

“The Customer Focus category examines how your organization engages its customers for long-term marketplace success and builds a customer-focused culture. Also examined is how your organization listens to the voice of its customers and uses this information to improve and identify opportunities for innovation [2].”

Measurement, Analysis, and Knowledge Management: The data created and maintained by GIS, CIS, AMI, SCADA and other systems create a wealth of information that can be analyzed to obtain knowledge sufficient to make rapid business decisions. However, many of these systems are incapable of or at the very least difficult to integrate with one another, leaving leaders with a lot of data but no meaningful measures of key performance. Even worse, a lack of standards related to system performance leaves many utilities that develop performance measures with a limited number of inconsistently measured comparatives from their peers.

If utilities are going to overcome the challenges of the future, it is essential that they integrate all data systems for improved accessibility and develop standards that would facilitate meaningful comparative measures. This is not to say that comparative measures do not exist, they do. However, increasing the number of utilities participating would increase our understanding of best practices and enable us to determine best-in-class performance.

“The measurement, analysis and knowledge management category examines how the organization selects, gathers, analyzes, manages and improves its data, information and knowledge assets and how it manages its information technology. The category also examines how your organization reviews and uses reviews to improve its performance [2].”

Workforce Focus: We have already addressed the aging workforce and its impact on the future of utilities. Companion challenges related to the utility workforce include the heavy benefits burdens that many utilities currently bear. Also, the industry faces a diminished interest in labor positions and the need to establish new training methods to engage a variety of generations within our workforce and ensure knowledge acquisition and retention.

The new workforce brings with it new requirements for satisfaction and engagement. The new employee has proven to be less loyal to the organization and studies show they will have many more employers before they retire than that of their predecessors. It is essential that we develop ways to identify these requirements and take action to retain these individuals or we risk increased training cost and operational issues as they seek new employment opportunities.

“The workforce focus category examines how your organization engages, manages and develops the workforce to utilize its full potential in alignment with organizational mission, strategy and action plans. The category examines the ability to assess workforce capability and capacity needs and to build a workforce environment conducive to high performance [2].”

Process Management: It is not unusual for utilities to implement new software with dramatically increased capabilities and ask the integrator to make it align with their current processes or continue to use their current processes without regard for the system’s new capabilities. Identifying and mapping key work processes can enable incredible opportunities for streamlining your organization and facilitate increased utilization of technology.

What are your utilities’ key work processes and how do you determine them and their relationship to creating customer value? These are difficult for leaders to articulate; but yet, without a clear understanding of key work processes and their alignment to core competencies and strategic advantages as well as challenges, it may be that your organization is misapplying efforts related to core competencies and either outsourcing something best maintained internally or performing effort that is better delivered by outsource providers.

“The process management category examines how your organization designs its work systems and how it designs, manages and improves its key processes for implementing these work systems to deliver customer value and achieve organizational success and sustainability. Also examined is your readiness for emergencies [2].”

Results: Results are the fruit of your efforts, the gift that the Baldrige Criteria enables you to receive from your applied efforts. All of us want positive results. Many utilities cite positive performance in measures that are easy to acquire: financial performance, safety performance, customer satisfaction. But which of these measures are key to our success and sustainability as an organization? As you answer the questions and align measures that are integral to obtaining your organization’s mission and vision, it will become abundantly clear which measures you’ll need to maintain and develop competitive comparisons and benchmarks.

“The results category examines the organization’s performance and improvement in all key areas – product outcomes, customer-focused outcomes, financial and market outcomes, workforce-focused outcomes, process-effectiveness outcomes and leadership outcomes. Performance levels are examined relative to those of competitors and other organizations with similar product offerings [2].”

A Challenge

The adoption of the Baldrige criteria is often described as a journey. Few utilities have embraced this model. However, it appears to offer a comprehensive solution to the challenges we face today. Utilities have a rich history and play a positive role in our nation. A period of rapid change is upon us. We need to shift from reacting to leading as we solve the problems that face our industry. By applying this model for effective process improvement, we can once again create a world where utilities lead the future.

References

  1. Quote from U.S. Treasury Secretary Tim Geithner as communicated in SmartGrid Newsletter
  2. Malcolm Baldrige National Quality Award, “Path to Excellence and Some path Building Tools.” www.nist.gov/baldrige.

Future of Learning

The nuclear power industry is facing significant employee turnover, which may be exacerbated by the need to staff new nuclear units. To maintain a highly skilled workforce to safely operate U.S. nuclear plants, the industry must find ways to expedite training and qualification, enhance knowledge transfer to the next generation of workers, and develop leadership talent to achieve excellent organizational effectiveness.

Faced with these challenges, the Institute of Nuclear Power Operations (INPO), the organization charged with promoting safety and reliability across the 65 nuclear electric generation plants operating in the U.S., created a “Future of Learning” initiative. It identified ways the industry can maintain the same high standard of excellence and record of nuclear safety, while accelerating training development, individual competencies and plant training operations.

The nuclear power industry is facing the perfect storm. Like much of the industrialized world, it must address issues associated with an aging workforce since many of its skilled workers and nuclear engineering professionals are hitting retirement age, moving out of the industry and beginning other pursuits.

Second, as baby boomers transition out of the workforce, they will be replaced by an influx of Generation Y workers. Many workers in this “millenials” generation are not aware of the heritage driving the single-minded focus on safety. They are asking for new learning models, utilizing the technologies which are so much a part of their lives.

Third, even as this big crew change takes place, there is increasing demand for electricity. Many are turning to cleaner technologies – solar, wind, and nuclear – to close the gap. And there is resurgence in requests for building new nuclear plants, or adding new reactors at existing plants. This nuclear renaissance also requires training and preparation to take on the task of safely and reliably operating our nuclear power plants.

It is estimated there will be an influx of 25,000 new workers in the industry over the next five years, with an additional 7,000 new workers needed if just a third of the new plants are built. Given that incoming workers are more comfortable using technology for learning, and that delivery models that include a blend of classroom-based, instructor-led, and Web-based methods can be more effective and efficient, the industry is exploring new models and a new mix of training.

INPO was created by the nuclear industry in 1979 following the Three Mile Island accident. It has 350 full-time and loaned employees. As a nonprofit organization, it is chartered to promote the highest levels of safety and reliability – in essence, to promote excellence – in the operation of nuclear electric generating plants. All U.S. nuclear operating companies are members.

INPO’s responsibilities include evaluating member nuclear site operations, accrediting each site’s nuclear training programs and providing assistance and information exchange. It has established the National Academy for Nuclear Training, and an independent National Nuclear Accrediting Board. INPO sends teams to sites to evaluate their respective training activities, and each station is reviewed at least every four years by the accrediting board.

INPO has developed guidelines for 12 specifically accredited programs (six operations and six maintenance/technical), including accreditation objectives and criteria. It also offers courses and seminars on leadership, where more than 1,500 individuals participate annually, from supervisors to board members. Lastly, it operates NANTeL (National Academy for Nuclear Training e-Learning system) with 200 courses for general employee training for nuclear access. More than 80,000 nuclear workers and sub-contractors have completed training over the Web.

The Future of Learning

In 2008, to systematically address workforce and training challenges, the INPO Future of Learning team partnered with IBM Workforce and Learning Solutions to conduct more than 65 one-on-one interviews, with chief executive officers, chief nuclear officers, senior vice presidents, plant managers, plant training managers and other leaders in the extended industry community. The team also completed 46 interviews with plant staff during a series of visits to three nuclear power plants. Lastly, the team developed and distributed a survey that was sent to training managers at the 65 nuclear plants, achieving a 62 percent response rate.

These are statements the team heard:

  • “Need to standardize a lot of the training, deliver it remotely, preferably to a desktop, minimize the ‘You train in our classroom in our timeframe’ and have it delivered more autonomously so it’s likely more compatible with their lifestyles.”
  • “We’re extremely inefficient today in how we design/develop and administer training. We don’t want to carry inefficiencies that we have today into the future.”
  • “Right now, in all training programs, it’s a one-size-fits-all model that’s not customized to an individual’s background. Distance learning would enable this by allowing people to demonstrate knowledge and let some people move at a faster pace.”
  • “We need to have ‘real’ e-learning. We’ve been exposed to less than adequate, older models of e-learning. We need to move away from ‘page turners’ and onto quality content.”

Several recommendations were generated as a result of the study. The first focused on ways to improve INPO’s current training offerings by adding leadership development courses, ratcheting up the interactivity of the Web-based and e-learning offerings in NANTeL and developing a “nuclear citizenship” course for new workers in the industry.

Second, there were recommendations about better utilizing training resources across the industry by centralizing common training, beginning with instructor training and certification and generic fundamentals courses. It was estimated that 50 percent of the accredited training materials are common across the industry. To accomplish this objective, INPO is exploring an industry infrastructure that would enable centralized training material development, maintenance and delivery.

The last set of recommendations focused on methods for better coordination and efficiency of training, including developing processes for certifying vendor training programs, and providing a jump-start to common community college and university curriculum.

In 2009, INPO is piloting a series of Future of Learning initiatives which will help determine the feasibility, cost-effectiveness, readiness and acceptance of this first set of recommendations. It is starting to look more broadly at ways it can utilize learning technology to drive economies of scale, accelerative and prescriptive learning, and deliver value to the nuclear electric generation industry.

Where Do We Go From Here ?

Beyond the initial perfect storm is another set of factors driving the future of learning.

First, consider the need for speed. It has been said that “If you are not learning at the speed of change, you are falling behind.”

In his “25 Lessons from Jack Welch,” the former CEO of General Electric said, “The desire, and the ability, of an organization to continuously learn from any source, anywhere – and to rapidly convert this learning into action – is its ultimate competitive advantage.” Giving individuals, teams and organizations the tools and technologies to accelerate and broaden their learning is an important part of the future of learning.

Second, consider the information explosion – the sheer volume of information available, the convenience of information access (due, in large part, to continuing developments in technology) and the diversity of information available. When there is too much information to digest, a person is unable to locate and make use of the information that one needs. When one is unable to process the sheer volume of information, overload occurs. The future of learning should enable the learner to sort through information and find knowledge.

Third, consider new developments in technology. Generations X and Y are considered “digital natives.” They expect that the most current technologies are available to them – including social networking, blogging, wikis, immersive learning and gaming – and to not have them is unthinkable.

Impact of New Technology

Philosophy of training has morphed from “just-in-case” (teach them everything and hope they will remember when they need it), to “just-in-time” (provide access to training just before the point of need), to “just-for-me.” With respect to the latter, learning is presented in a preferred media, with a learning path customized to reflect the student’s preferred learning style, and personalized to address the current and desired level of expertise within any given time constraint.

Imagine a scenario in which a maintenance technician at a nuclear plant has to replace a specialized valve – something she either hasn’t done for awhile, or hasn’t replaced before. In a Web 2.0 world, she should be able to run a query on her iPhone or similar handheld device and pull up the maintenance of that particular valve, access the maintenance records, view a video of the approved replacement procedure, or access an expert who could coach her through the process.

Learning Devices

What needs to be in place to enable this vision of the future of learning? First, workers will need a device that can access the information by connecting over a secure wireless network inside the plant. Second, the learning has to be available in small chunks – learning nuggets or learning assets. Third, the learning needs to be assembled along the dimensions of learning style, desired and target level of expertise, time available and media type, among other factors. Finally, experts need to be identified, tagged to particular tasks and activities, and made accessible.

Fortunately, some of the same learning technology tools that will enable centralized maintenance and accelerated development will also facilitate personalized learning. When training is organized at a more granular level – the learning asset level – not only can it be leveraged over a variety of courses and courseware, it can also be re-assembled and ported to a variety of outputs such as lesson books, e-learning and m-learning (mobile-learning).

The example above pointed out another shift in our thinking about learning. Traditionally, our paradigm has been that learning occurs in a classroom, and when it occurs, it has taken the form of a course. In the example above, the learning takes place anywhere and anytime, moving from the formal classroom environment to an informal environment. Of course, just because learning is “informal” does not mean it is accidental, or that it occurs without preparation.

Some estimates claim 10 percent of our learning is achieved through formal channels, 20 percent from coaching, and 70 percent through informal means. Peter Henschel, former director of the Institute for Research on Learning, raised an important question: If nearly three-quarters of learning in corporations is informal, can we afford to leave it to chance?

There are still several open issues regarding informal learning:

  • How do we evaluate the impact/effectiveness of informal learning? (Informal learning, but formal demonstration of competency/proficiency);
  • How do we record one’s participation and skill-level progression in informal learning? (Information learning, but formal recording of learning completion);
  • Who will create and maintain informal learning assets? (Informal learning, but formal maintenance and quality assurance of the learning content); and
  • When does informal learning need a formal owner (in a full- or part-time role)? (Informal learning, but will need formal policies to help drive and manage).
    • In the nuclear industry, accurate and up-to-date documentation is a necessity. As the nuclear industry moves toward more effective use of informal channels of learning, it will need to address these issues.

      Immersive Learning (Or Virtual Worlds)

      The final frontier for the future of learning is expansion into virtual worlds, also known as immersive learning. Although Second Life (SL) is the best known virtual world, there are also emerging competitors, including Active Worlds, Forterra (OLIVE), Qwag and Unisfair.

      Created in 2003 by Linden Lab of San Francisco, SL is a three-dimensional, virtual world that allows users to buy “property,” create objects and buildings and interact with other users. Unlike a game with rules and goals, SL offers an open-ended platform where users can shape their own environment. In this world, avatars do many of the same things real people do: work, shop, go to school, socialize with friends and attend rock concerts.

      From a pragmatic perspective, working in an immersive learning environment such as a virtual world provides several benefits that make it an effective alternative to real life:

      • Movement in 3-D space. A virtual world could be useful in any learning situation involving movement, danger, tactics, or quick physical decisions, such as emergency response.
      • Engendering Empathy. Participants experience scenarios from another person’s perspective. For example, the Future of Learning team is exploring ways to re-create the control room experience during the Three-Mile Island incident, to provide a cathartic experience for the next generation workforce so they can better appreciate the importance of safety and human performance factors.
      • Rapid Prototyping and Co-Design. A virtual world is an inexpensive environment for quickly mocking up prototypes of tools or equipment.
      • Role Playing. By conducting role plays in realistic settings, instructors and learners can take on various avatars and play those characters.
      • Alternate Means of Online Interaction. Although users would likely not choose a virtual world as their primary online communication tool, it provides an alternative means of indicating presence and allowing interaction. Users can have conversations, share note cards, and give presentations. In some cases, SL might be ideal as a remote classroom or meeting place to engage across geographies and utility boundaries.

      Robert Amme, a physicist at the University of Denver, has another laboratory in SL. Funded by a grant from the Nuclear Regulatory Commission, his team is building a virtual nuclear reactor to help train the next generation of environmental engineers on how to deal with nuclear waste (see Figure 1). The INPO Future of Learning team is exploring ways to leverage this type of learning asset as part of the nuclear citizenship initiative.

      There is no doubt that nuclear power generation is once again on an upswing, but critical to its revival and longevity will be the manner in which we prepare the current and next generation of workers to become outstanding stewards of a safe, effective, clean-energy future.

Modeling Distribution Demand Reduction

In the past, distribution demand reduction was a technique used only in emergency situations a few times a year – if that. It was an all-or-nothing capability that you turned on, and hoped for the best until the emergency was over. Few utilities could measure the effectiveness, let alone the potential of any solutions that were devised.

Now, demand reduction is evolving to better support the distribution network during typical peaking events, rather than just emergencies. However, in this mode, it is important not only to understand the solution’s effectiveness, but to be able to treat it like any other dispatchable load-shaping resource. Advanced modeling techniques and capabilities are allowing utilities to do just that. This paper outlines various methods and tools that allow utilities to model distribution demand reduction capabilities within set time periods, or even in near real time.

Electricity demand continues to outpace the ability to build new generation and apply the necessary infrastructure needed to meet the ever-growing, demand-side increases dictated by population growth and smart residences across the globe. In most parts of the world, electrical energy is one of the most important characteristics of a modern civilization. It helps produce our food, keeps us comfortable, and provides lighting, security, information and entertainment. In short, it is a part of almost every facet of life, and without electrical energy, the modern interconnected world as we know it would cease to exist.

Every country has one or more initiatives underway, or in planning, to deal with some aspect of generation and storage, delivery or consumption issues. Additionally, greenhouse gases (GHG) and carbon emissions need to be tightly controlled and monitored. This must be carefully balanced with expectations from financial markets that utilities deliver balanced and secure investment portfolios by demonstrating fiduciary responsibility to sustain revenue projections and measured growth.

The architects of today’s electric grid probably never envisioned the day when electric utility organizations would purposefully take measures to reduce the load on the network, deal with highly variable localized generation and reverse power flows, or anticipate a regulatory climate that impacts the decisions for these measures. They designed the electric transmission and distribution systems to be robust, flexible and resilient.

When first conceived, the electric grid was far from stable and resilient. It took growth, prudence and planning to continue the expansion of the electric distribution system. This grid was made up of a limited number of real power and reactive power devices that responded to occasional changes in power flow and demand. However, it was also designed in a world with far fewer people, with a virtually unlimited source of power, and without much concern or knowledge of the environmental effects that energy production and consumption entail.

To effectively mitigate these complex issues, a new type of electric utility business model must be considered. It must rapidly adapt to ever-changing demands in terms of generation, consumption, environmental and societal benefits. A grid made up of many intelligent and active devices that can manage consumption from both the consumer and utility side of the meter must be developed. This new business model will utilize demand management as a key element to the operation of the utility, while at the same time driving the consumer spending behavior.

To that end, a holistic model is needed that understands all aspects of the energy value chain across generation, delivery and consumption, and can optimize the solution in real time. While a unifying model may still be a number of years away, a lot can be gained today from modeling and visualizing the distribution network to gauge the effect that demand reduction can – and does – play in near real time. To that end, the following solutions are surely well considered.

Advanced Feeder Modeling

First, a utility needs to understand in more detail how its distribution network behaves. When distribution networks were conceived, they were designed primarily with sources (the head of the feeder and substation) and sinks (the consumers or load) spread out along the distribution network. Power flows were assumed to be one direction only, and the feeders were modeled for the largest peak level.

Voltage and volt-ampere reactive power (VAR) management were generally considered for loss optimization and not load reduction. There was never any thought given to limiting power to segments of the network or distributed storage or generation, all of which could dramatically affect the flow of the network, even causing reverse flows at times. Sensors to measure voltage and current were applied at the head of the feeder and at a few critical points (mostly in historical problem areas.)

Planning feeders at most utilities is an exercise performed when large changes are anticipated (i.e., a new subdivision or major customer) or on a periodic basis, usually every three to five years. Loads were traditionally well understood with predictable variability, so this type of approach worked reasonably well. The utility also was in control of all generation sources on the network (i.e., peakers), and when there was a need for demand reduction, it was controlled by the utility, usually only during critical periods.

Today’s feeders are much more complex, and are being significantly influenced by both generation and demand from entities outside the control of the utility. Even within the utility, various seemingly disparate groups will, at times, attempt to alter power flows along the network. The simple model of worst-case peaking on a feeder is not sufficient to understand the modern distribution network.

The following factors must be considered in the planning model:

  • Various demand-reduction techniques, when and where they are applied and the potential load they may affect;
  • Use of voltage reduction as a load-shedding technique, and where it will most likely yield significant results (i.e., resistive load);
  • Location, size and capacity of storage;
  • Location, size and type of renewable generation systems;
  • Use and location of plug-in electrical vehicles;
  • Standby generation that can be fed into the network;
  • Various social ecosystems and their characteristics to influence load; and
  • Location and types of sensors available.

Generally, feeders are modeled as a single unit with their power characteristic derived from the maximum peaking load and connected kilovolt-amperage (KVA) of downstream transformers. A more advanced model treats the feeder as a series of connected segments. The segment definitions can be arbitrary, but are generally chosen where the utility will want to understand and potentially control these segments differently than others. This may be influenced by voltage regulation, load curtailment, stability issues, distributed generation sources, storage, or other unique characteristics that differ from one segment to the next.

The following serves as an advanced means to model the electrical distribution feeder networks. It provides for segmentation and sensor placement in the absence of a complete network and historical usage model. The modeling combines traditional electrical engineering and power-flow modeling with tools such as CYME and non-traditional approaches using geospatial and statistical analysis.

The model builds upon information such as usage data, network diagrams, device characteristics and existing sensors. It then adds elements that could present a discrepancy with the known model such as social behavior, demand-side programs, and future grid operations based on both spatio-temporal and statistical modeling. Finally, suggestions can be made about sensors’ placement and characteristics to the network to support system monitoring once in place.

Generally, a utility would take a more simplistic view of the problem. It would start by directly applying statistical analysis and stochastic modeling across the grid to develop a generic methodology for selecting the number of sensors, and where to place them based on sensor accuracy, cost and risk-of-error introduction from basic modeling assumptions (load allocation, timing of peak demand, and other influences on error.) However, doing so would limit the utility, dealing only with the data it has in an environment that will be changing dramatically.

The recommended and preferred approach performs some analysis to determine what the potential error sources are, which source is material to the sensor question, and which could influence the system’s power flows. Next, an attempt can be made to geographically characterize where on the grid these influences are most significant. Then, a statistical approach can be applied to develop a model for setting the number, type and location of additional sensors. Lastly sensor density and placement can be addressed.

Feeder Modeling Technique

Feeder conditioning is important to minimize the losses, especially when the utility wants to moderate voltage levels as a load modification method. Without proper feeder conditioning and sufficient sensors to monitor the network, the utility is at risk of either violating regulatory voltage levels, or potentially limiting its ability to reduce the optimal load amount from the system during voltage reduction operations.

Traditionally, feeder modeling is a planning activity that is done at periodic (for example, yearly) intervals or during an expected change in usage. Tools such as CYME – CYMDIST provide feeder analysis using:

  • Balanced and unbalanced voltage drop analysis (radial, looped or meshed);
  • Optimal capacitor placement and sizing to minimize losses and/or improve voltage profile;
  • Load balancing to minimize losses;
  • Load allocation/estimation using customer consumption data (kWh), distribution transformer size (connected kVA), real consumption (kVA or kW) or the REA method. The algorithm treats multiple metering units as fixed demands; and large metered customers as fixed load;
  • Flexible load models for uniformly distributed loads and spot loads featuring independent load mix for each section of circuit;
  • Load growth studies for multiple years; and
  • Distributed generation.

However, in many cases, much of the information required to run an accurate model is not available. This is either because the data does not exist, the feeder usage paradigm may be changing, the sampling period does not represent a true usage of the network, the network usage may undergo significant changes, or other non-electrical characteristics.

This represents a bit of a chicken-or-egg problem. A utility needs to condition its feeders to change the operational paradigm, but it also needs operational information to make decisions on where and how to change the network. The solution is a combination of using existing known usage and network data, and combining it with other forms of modeling and approximation to build the best future network model possible.

Therefore, this exercise refines traditional modeling with three additional techniques: geospatial analysis; statistical modeling; and sensor selection and placement for accuracy.

If a distribution management system (DMS) will be deployed, or is being considered, its modeling capability may be used as an additional basis and refinement employing simulated and derived data from the above techniques. Lastly, if high accuracy is required and time allows, a limited number of feeder segments can be deployed and monitored to validate the various modeling theories prior to full deployment.

The overall goals for using this type of technique are:

  • Limit customer over or under voltage;
  • Maximize returned megawatts in the system in load reduction modes;
  • Optimize the effectiveness of the DMS and its models;
  • Minimize cost of additional sensors to only areas that will return the most value;
  • Develop automated operational scenarios, test and validation prior to system-wide implementation; and
  • Provide a foundation for additional network automation capabilities.

The first step starts by setting up a short period of time to thoroughly vet possible influences on the number, spacing and value offered by additional sensors on the distribution grid. This involves understanding and obtaining information that will most influence the model, and therefore, the use of sensors. Information could include historical load data, distribution network characteristics, transformer name plate loading, customer survey data, weather data and other related information.

The second step is the application of geospatial analysis to identify areas of the grid most likely to have influences driving a need for additional sensors. It is important to recognize that within this step is a need to correlate those influential geospatial parameters with load profiles of various residential and commercial customer types. This step represents an improvement over simply applying the same statistical analysis generically over the entirety of the grid, allowing for two or more “grades” of feeder segment characteristics for which different sensor standards would be developed.

The third step is the statistical analysis and stochastic modeling to develop recommended standards and methodology for determining sensor placement based on the characteristic segments developed from the geospatial assessment. Items set aside as not material for sensor placement serve as a necessary input to the coming “predictive model” exercise.

Lastly, a traditional electrical and accuracy- based analysis is used to model the exact number and placement of additional sensors to support the derived models and planned usage of the system for all scenarios depicted in the model – not just summertime peaking.

Conclusion

The modern distribution network built for the smart grid will need to undergo significantly more detailed planning and modeling than a traditional network. No one tool is suited to the task, and it will take multiple disciplines and techniques to derive the most benefit from the modeling exercise. However, if a utility embraces the techniques described within this paper, it will not only have a better understanding of how its networks perform in various smart grid scenarios, but it will be better positioned to fully optimize its networks for load and loss optimization.