Utility Mergers and Acquisitions: Beating the Odds

Merger and acquisition activity in the U.S. electric utility industry has increased following the 2005 repeal of the Public Utility Holding Company Act (PUHCA). A key question for the industry is not whether M&A will continue, but whether utility executives are prepared to manage effectively the complex regulatory challenges that have evolved.

M&A activity is (and always has been) the most potent, visible and (often) irreversible option available to utility CEOs who wish to reshape their portfolios and meet their shareholders’ expectations for returns. However, M&A has too often been applied reflexively – much like the hammer that sees everything as a nail.

The American utility industry is likely to undergo significant consolidation over the next five years. There are several compelling rationales for consolidation. First, M&A has the potential to offer real economic value. Second, capital-market and competitive pressures favor larger companies. Third, the changing regulatory landscape favors larger entities with the balance sheet depth to weather the uncertainties on the horizon.

LEARNING FROM THE PAST

Historically, however, acquirers have found it difficult to derive value from merged utilities. With the exception of some vertically integrated deals, most M&A deals have been value-neutral or value-diluting. This track record can be explained by a combination of factors: steep acquisition premiums, harsh regulatory givebacks, anemic cost reduction targets and (in more than half of the deals) a failure to achieve targets quickly enough to make a difference. In fact, over an eight-year period, less than half the utility mergers actually met or exceeded the announced cost reduction levels resulting from the synergies of the merged utilities (Figure 1).

The lessons learned from these transactions can be summarized as follows: Don’t overpay; negotiate a good regulatory deal; aim high on synergies; and deliver on them.

In trying to deliver value-creating deals, CEOs often bump up against the following realities:

  • The need to win approval from the target’s shareholders drives up acquisition premiums.
  • The need to receive regulatory approval for the deal and to alleviate organizational uncertainty leads to compromises.
  • Conservative estimates of the cost reductions resulting from synergies are made to reduce the risk of giving away too much in regulatory negotiations.
  • Delivering on synergies proves tougher than anticipated because of restrictions agreed to in regulatory deals or because of the organizational inertia that builds up during the 12- to 18-month approval process.

LOOKING AT PERFORMANCE

Total shareholder return (TSR) is significantly affected by two external deal negotiation levers – acquisition premiums and regulatory givebacks – and two internal levers – synergies estimated and synergies delivered. Between 1997 and 2004, mergers in all U.S. industries created an average TSR of 2 to 3 percent relative to the market index two years after closing. In contrast, utilities mergers typically underperformed the utility index by about 2 to 3 percent three years after the transaction announcement. T&D mergers underperformed the index by about 4 percent, whereas mergers of vertically integrated utilities beat the index by about 1 percent three years after the announcement (Figure 2).

For 10 recent mergers, the lower the share of the merger savings retained by the utilities and the higher the premium paid for the acquisition, the greater the likelihood that the deal destroyed shareholder value, resulting in negative TSR.

Although these appear to be obvious pitfalls that a seasoned management team should be able to recognize and overcome, translating this knowledge into tangible actions and results has been difficult.

So how can utility boards and executives avoid being trapped in a cycle of doing the same thing again and again while expecting different results (Einstein’s definition of insanity)? We suggest that a disciplined end-to-end M&A approach will (if well-executed) tilt the balance in the acquirer’s favor and generate long-term shareholder value. That approach should include the four following broad objectives:

  • Establishment of compelling strategic logic and rationale for the deal;
  • A carefully managed regulatory approval process;
  • Integration that takes place early and aggressively; and
  • A top-down approach for designing realistic but ambitious economic targets.

GETTING IT RIGHT: FOUR BROAD OBJECTIVES THAT ENHANCE M&A VALUE CREATION

To complete successful M&As, utilities must develop a more disciplined approach that incorporates the lessons learned from both utilities and other industrial sectors. At the highest level, adopting a framework with four broad objectives will enhance value creation before the announcement of the deal and through post-merger integration. To do this, utilities must:

  1. Establish a compelling strategic logic and rationale for the deal. A critical first step is asking the question, why do the merger? To answer this question, deal participants must:
    • Determine the strategic logic for long-term value creation with and without M&A. Too often, executives are optimistic about the opportunity to improve other utilities, but they overlook the performance potential in their current portfolio. For example, without M&A, a utility might be able to invest and grow its rate base, reduce the cost of operations and maintenance, optimize power generation and assets, explore more aggressive rate increases and changes to the regulatory framework, and develop the potential for growth in an unregulated environment. Regardless of whether a utility is an acquirer or a target, a quick (yet comprehensive) assessment will provide a clear perspective on potential shareholder returns (and risks) with and without M&A.
    • Conduct a value-oriented assessment of the target. Utility executives typically have an intuitive feel for the status of potential M&A targets adjacent to their service territories and in the broader subregion. However, when considering M&A, they should go beyond the obvious criteria (size and geography) and candidates (contiguous regional players) to consider specific elements that expose the target’s value potential for the acquirer. Such value drivers could include an enhanced power generation and asset mix, improvements in plant availability and performance, better cost structures, an ability to respond to the regulatory environment, and a positive organizational and cultural fit. Also critical to the assessment are the noneconomic aspects of the deal, such as headquarters sharing, potential loss of key personnel and potential paralysis of the company (for example, when a merger or acquisition freezes a company’s ability to pursue M&A and other large initiatives for two years).
    • Assess internal appetites and capabilities for M&A. Successful M&A requires a broad commitment from the executive team, enough capable people for diligence and integration, and an appetite for making the tough decisions essential to achieving aggressive targets. Acquirers should hold pragmatic executive-level discussions with potential targets to investigate such aspects as cultural fit and congruence of vision. Utility executives should conduct an honest assessment of their own management teams’ M&A capabilities and depth of talent and commitment. Among historic M&A deals, those that involved fewer than three states and those in which the acquirer was twice as big as the target were easier to complete and realized more value.
  2. Carefully manage the regulatory approval process. State regulatory approvals present the largest uncertainty and risk in utility M&A, clearly affecting the economics of any deal. However, too often, these discussions start and end with rate reductions so that the utility can secure approvals. The regulatory approval process should be similar to the rigorous due diligence that’s performed before the deal’s announcement. This means that when considering M&A, utilities should:
    • Consider regulatory benefits beyond the typical rate reductions. The regulatory approval process can be used to create many benefits that share rewards and risks, and to provide advantages tailored to the specific merger’s conditions. Such benefits include a stronger combined balance sheet and a potential equity infusion into the target’s subsidiaries; an ability to better manage and hedge a larger combined fuel portfolio; the capacity to improve customer satisfaction; a commitment to specific rate-based investment levels; and a dedication to relieving customer liability on pending litigation. For example, to respond to regulatory policies that mandate reduced emissions, merged companies can benefit not only from larger balance sheets but also from equity infusions to invest in new technology or proven technologies. Merged entities are also afforded the opportunity to leverage combined emissions reduction portfolios.
    • Systematically price out a full range of regulatory benefits. The range should include the timing of “gives” (that is, the sharing of synergy gains with customers in the form of lower rates) as a key value lever; dedicated valuations of potential plans and sensitivities from all stakeholders’ perspectives; and a determination of the features most valued by regulators so that they can be included in a strategy for getting M&A approvals. Executives should be wary of settlements tied to performance metrics that are vaguely defined or inadequately tracked. They should also avoid deals that require new state-level legislation, because too much time will be required to negotiate and close these complex deals. Finally, executives should be wary of plans that put shareholder benefits at the end of the process, because current PUC decisions may not bind future ones.
    • Be prepared to walk away if the settlement conditions imposed by the regulators dilute the economics of the deal. This contingency plan requires that participating executives agree on the economic and timing triggers that could lead to an unattractive deal.
  3. Integrate early and aggressively. Historically, utility transactions have taken an average of 15 months from announcement to closing, given the required regulatory approvals. With such a lengthy time lag, it’s been easy for executives to fall into the trap of putting off important decisions related to the integration and post-merger organization. This delay often leads to organizational inertia as employees in the companies dig in their heels on key issues and decisions rather than begin to work together. To avoid such inertia, early momentum in the integration effort, embodied in the steps outlined below, is critical.
    • Announce the executive team’s organization early on. Optimally, announcements should be made within the first 90 days, and three or four well-structured senior-management workshops with the two CEOs and key executives should occur within the first two months. The decisions announced should be based on such considerations as the specific business unit and organizational options, available leadership talent and alignment with synergy targets by area.
    • Make top-down decisions about integration approach according to business and function. Many utility mergers appear to adopt a “template” approach to integration that leads to a false sense of comfort regarding the process. Instead, managers should segment decision making for each business unit and function. For example, when the acquirer has a best-practice model for fossil operations, the target’s plants and organization should simply be absorbed into the acquirer’s model. When both companies have strong practices, a more careful integration will be required. And when both companies need to transform a particular function, the integration approach should be tailored to achieve a change in collective performance.
    • Set clear guidelines and expectations for the integration. A critical part of jump-starting the integration process is appointing an integration officer with true decision-making authority, and articulating the guidelines that will serve as a road map for the integration teams. These guidelines should clearly describe the roles of the corporation and individual operating teams, as well as provide specific directions about control and organizational layers and review and approval mechanisms for major decisions.
    • >Systematically address legal and organizational bottlenecks. The integration’s progress can be impeded by legal or organizational constraints on the sharing of sensitive information. In such situations, significant progress can be achieved by using clean teams – neutral people who haven’t worked in the area before – to ensure data is exchanged and sanitized analytical results are shared. Improved information sharing can aid executive-level decision making when it comes to commercially sensitive areas such as commercial marketing-and-trading portfolios, performance improvements, and other unregulated business-planning and organizational decisions.
  4. Use a top-down approach to design realistic but ambitious economic targets. Synergies from utility mergers have short shelf lives. With limits on a post-merger rate freeze or rate-case filing, the time to achieve the targets is short. To achieve their economic targets, merged utilities should:
    • Construct the top five to 10 synergy initiatives to capture value and translate them into road maps with milestones and accountabilities. Identifying and promoting clear targets early in the integration effort lead to a focus on the merger’s synergy goals.
    • Identify the links between synergy outcomes and organizational decisions early on, and manage those decisions from the top. Such top-down decisions should specify which business units or functional areas are to be consolidated. Integration teams often become gridlocked over such decisions because of conflicts of interest and a lack of objectivity.
    • Control the human resources policies related to the merger. Important top-down decisions include retention and severance packages and the appointment process. Alternative severance, retirement and retention plans should be priced explicitly to ensure a tight yet fair balance between the plans’ costs and benefits.
    • Exploit the merger to create opportunities for significant reductions in the acquirer’s cost base. Typical merger processes tend to focus on reductions in the target’s cost base. However, in many cases the acquirer’s cost base can also be reduced. Such reductions can be a significant source of value, making the difference between success and failure. They also communicate to the target’s employees that the playing field is level.
    • Avoid the tendency to declare victory too soon. Most synergies are related to standardization and rationalization of practices, consolidation of line functions and optimization of processes and systems. These initiatives require discipline in tracking progress against key milestones and cost targets. They also require a tough-minded assessment of red flags and cost increases over a sustained time frame – often two to three years after the closing.

RECOMMENDATIONS: A DISCIPLINED PROCESS IS KEY

Despite the inherent difficulties, M&A should remain a strategic option for most utilities. If they can avoid the pitfalls of previous rounds of mergers, executives have an opportunity to create shareholder value, but a disciplined and comprehensive approach to both the M&A process and the subsequent integration is essential.

Such an approach begins with executives who insist on a clear rationale for value creation with and without M&A. Their teams must make pragmatic assessments of a deal’s economics relative to its potential for improving base business. If they determine the deal has a strong rationale, they must then orchestrate a regulatory process that considers broad options beyond rate reductions. Having the discipline to walk away if the settlement conditions dilute the deal’s economics is a key part of this process. A disciplined approach also requires that an aggressive integration effort begin as soon as the deal has been announced – an effort that entails a modular approach with clear, fast, top-down decisions on critical issues. Finally, a disciplined process requires relentless follow-through by executives if the deal is to achieve ambitious yet realistic synergy targets.

Achieving Decentralized Coordination In the Electric Power Industry

For the past century, the dominant business and regulatory paradigms in the electric power industry have been centralized economic and physical control. The ideas presented here and in my forthcoming book, Deregulation, Innovation, and Market Liberalization: Electricity Restructuring in a Constantly Evolving Environment (Routledge, 2008), comprise a different paradigm – decentralized economic and physical coordination – which will be achieved through contracts, transactions, price signals and integrated intertemporal wholesale and retail markets. Digital communication technologies – which are becoming ever more pervasive and affordable – are what make this decentralized coordination possible. In contrast to the “distributed control” concept often invoked by power systems engineers (in which distributed technology is used to enhance centralized control of a system), “decentralized coordination” represents a paradigm in which distributed agents themselves control part of the system, and in aggregate, their actions produce order: emergent order. [1]

Dynamic retail pricing, retail product differentiation and complementary end-use technologies provide the foundation for achieving decentralized coordination in the electric power industry. They bring timely information to consumers and enable them to participate in retail market processes; they also enable retailers to discover and satisfy the heterogeneous preferences of consumers, all of whom have private knowledge that’s unavailable to firms and regulators in the absence of such market processes. Institutions that facilitate this discovery through dynamic pricing and technology are crucial for achieving decentralized coordination. Thus, retail restructuring that allows dynamic pricing and product differentiation, doesn’t stifle the adoption of digital technology and reduces retail entry barriers is necessary if this value-creating decentralized coordination is to happen.

This paper presents a case study – the “GridWise Olympic Peninsula Testbed Demonstration Project” – that illustrates how digital end-use technology and dynamic pricing combine to provide value to residential customers while increasing network reliability and reducing required infrastructure investments through decentralized coordination. The availability (and increasing cost-effectiveness) of digital technologies enabling consumers to monitor and control their energy use and to see transparent price signals has made existing retail rate regulation obsolete. Instead, the policy recommendation that this analysis implies is that regulators should reduce entry barriers in retail markets and allow for dynamic pricing and product differentiation, which are the keys to achieving decentralized coordination.

THE KEYS: DYNAMIC PRICING, DIGITAL TECHNOLOGY

Dynamic pricing provides price signals that reflect variations in the actual costs and benefits of providing electricity at different times of the day. Some of the more sophisticated forms of dynamic pricing harness the dramatic improvements in information technology of the past 20 years to communicate these price signals to consumers. These same technological developments also give consumers a tool for managing their energy use, in either manual or automated form. Currently, with almost all U.S. consumers (even industrial and commercial ones) paying average prices, there’s little incentive for consumers to manage their consumption and shift it away from peak hours. This inelastic demand leads to more capital investment in power plants and transmission and distribution facilities than would occur if consumers could make choices based on their preferences and in the face of dynamic pricing.

Retail price regulation stifles the economic processes that lead to both static and dynamic efficiency. Keeping retail prices fixed truncates the information flow between wholesale and retail markets, and leads to inefficiency, price spikes and price volatility. Fixed retail rates for electric power service mean that the prices individual consumers pay bear little or no relation to the marginal cost of providing power in any given hour. Moreover, because retail prices don’t fluctuate, consumers are given no incentive to change their consumption as the marginal cost of producing electricity changes. This severing of incentives leads to inefficient energy consumption in the short run and also causes inappropriate investment in generation, transmission and distribution capacity in the long run. It has also stifled the implementation of technologies that enable customers to make active consumption decisions, even though communication technologies have become ubiquitous, affordable and user-friendly.

Dynamic pricing can include time-of-use (TOU) rates, which are different prices in blocks over a day (based on expected wholesale prices), or real-time pricing (RTP) in which actual market prices are transmitted to consumers, generally in increments of an hour or less. A TOU rate typically applies predetermined prices to specific time periods by day and by season. RTP differs from TOU mainly because RTP exposes consumers to unexpected variations (positive and negative) due to demand conditions, weather and other factors. In a sense, fixed retail rates and RTP are the end points of a continuum of how much price variability the consumer sees, and different types of TOU systems are points on that continuum. Thus, RTP is but one example of dynamic pricing. Both RTP and TOU provide better price signals to customers than current regulated average prices do. They also enable companies to sell, and customers to purchase, electric power service as a differentiated product.

TECHNOLOGY’S ROLE IN RETAIL CHOICE

Digital technologies are becoming increasingly available to reduce the cost of sending prices to people and their devices. The 2007 Galvin Electricity Initiative report “The Path to Perfect Power: New Technologies Advance Consumer Control” catalogs a variety of end-user technologies (from price-responsive appliances to wireless home automation systems) that can communicate electricity price signals to consumers, retain data on their consumption and be programmed to respond automatically to trigger prices that the consumer chooses based on his or her preferences. [2] Moreover, the two-way communication advanced metering infrastructure (AMI) that enables a retailer and consumer to have that data transparency is also proliferating (albeit slowly) and declining in price.

Dynamic pricing and the digital technology that enables communication of price information are symbiotic. Dynamic pricing in the absence of enabling technology is meaningless. Likewise, technology without economic signals to respond to is extremely limited in its ability to coordinate buyers and sellers in a way that optimizes network quality and resource use. [3] The combination of dynamic pricing and enabling technology changes the value proposition for the consumer from “I flip the switch, and the light comes on” to a more diverse and consumer-focused set of value-added services.

These diverse value-added services empower consumers and enable them to control their electricity choices with more granularity and precision than the environment in which they think solely of the total amount of electricity they consume. Digital metering and end-user devices also decrease transaction costs between buyers and sellers, lowering barriers to exchange and to the formation of particular markets and products.

Whether they take the form of building control systems that enable the consumer to see the amount of power used by each function performed in a building or appliances that can be programmed to behave differently based on changes in the retail price of electricity, these products and services provide customers with an opportunity to make better choices with more precision than ever before. In aggregate, these choices lead to better capacity utilization and better fuel resource utilization, and provide incentives for innovation to meet customers’ needs and capture their imaginations. In this sense, technological innovation and dynamic retail electricity pricing are at the heart of decentralized coordination in the electric power network.

EVIDENCE

Led by the Pacific Northwest National Laboratory (PNNL), the Olympic Peninsula GridWise Testbed Project served as a demonstration project to test a residential network with highly distributed intelligence and market-based dynamic pricing. [4] Washington’s Olympic Peninsula is an area of great scenic beauty, with population centers concentrated on the northern edge. The peninsula’s electricity distribution network is connected to the rest of the network through a single distribution substation. While the peninsula is experiencing economic growth and associated growth in electricity demand, the natural beauty of the area and other environmental concerns served as an impetus for area residents to explore options beyond simply building generation capacity on the peninsula or adding transmission capacity.

Thus, this project tested how the combination of enabling technologies and market-based dynamic pricing affected utilization of existing capacity, deferral of capital investment and the ability of distributed demand-side and supply-side resources to create system reliability. Two questions were of primary interest:

1) What dynamic pricing contracts do consumers find attractive, and how does enabling technology affect that choice?

2) To what extent will consumers choose to automate energy use decisions?

The project – which ran from April 2006 through March 2007 – included 130 broadband-enabled households with electric heating. Each household received a programmable communicating thermostat (PCT) with a visual user interface that allowed the consumer to program the thermostat for the home – specifically to respond to price signals, if desired. Households also received water heaters equipped with a GridFriendly appliance (GFA) controller chip developed at PNNL that enables the water heater to receive price signals and be programmed to respond automatically to those price signals. Consumers could control the sensitivity of the water heater through the PCT settings.

These households also participated in a market field experiment involving dynamic pricing. While they continued to purchase energy from their local utility at a fixed, discounted price, they also received a cash account with a predetermined balance, which was replenished quarterly. The energy use decisions they made would determine their overall bill, which was deducted from their cash account, and they were able to keep any difference as profit. The worst a household could do was a zero balance, so they were no worse off than if they had not participated in the experiment. At any time customers could log in to a secure website to see their current balances and determine the effectiveness of their energy use strategies.

On signing up for the project, the households received extensive information and education about the technologies available to them and the kinds of energy use strategies facilitated by these technologies. They were then asked to choose a retail pricing contract from three options: a fixed price contract (with an embedded price risk premium), a TOU contract with a variable critical peak price (CPP) component that could be called in periods of tight capacity or an RTP contract that would reflect a wholesale market-clearing price in five-minute intervals. The RTP was determined using a uniform price double auction in which buyers (households and commercial) submit bids and sellers submit offers simultaneously. This project represented the first instance in which a double auction retail market design was tested in electric power.

The households ranked the contracts and were then divided fairly evenly among the three types, along with a control group that received the enabling technologies and had their energy use monitored but did not participate in the dynamic pricing market experiment. All households received either their first or second choice; interestingly, more than two-thirds of the households ranked RTP as their first choice. This result counters the received wisdom that residential customers want only reliable service at low, stable prices.

According to the 2007 report on the project by D.J. Hammerstrom (and others), on average participants saved 10 percent on their electricity bills. [5] That report also includes the following findings about the project:

Result 1. For the RTP group, peak consumption decreased by 15 to 17 percent relative to what the peak would have been in the absence of the dynamic pricing – even though their overall energy consumption increased by approximately 4 percent. This flattening of the load duration curve indicates shifting some peak demand to nonpeak hours. Such shifting increases the system’s load factor, improving capacity utilization and reducing the need to invest in additional capacity, for a given level of demand. A 15 to 17 percent reduction is substantial and is similar in magnitude to the reductions seen in other dynamic pricing pilots.

After controlling for price response, weather effects and weekend days, the RTP group’s overall energy consumption was 4 percent higher than that of the fixed price group. This result, in combination with the load duration effect noted above, indicates that the overall effect of RTP dynamic pricing is to smooth consumption over time, not decrease it.

Result 2. The TOU group achieved both a large price elasticity of demand (-0.17), based on hourly data, and an overall energy reduction of approximately 20 percent relative to the fixed price group.

After controlling for price response, weather effects and weekend days, the TOU group’s overall energy consumption was 20 percent lower than that of the fixed price group. This result indicates that the TOU (with occasional critical peaks) pricing induced overall conservation – a result consistent with the results of the California SPP project. The estimated price elasticity of demand in the TOU group was -0.17, which is high relative to that observed in other projects. This elasticity suggests that the pricing coupled with the enabling end-use technology amplifies the price responsiveness of even small residential consumers.

Despite these results, dynamic pricing and enabling technologies are proliferating slowly in the electricity industry. Proliferation requires a combination of formal and informal institutional change to overcome a variety of barriers. And while formal institutional change (primarily in the form of federal legislation) is reducing some of these barriers, it remains an incremental process. The traditional rate structure, fixed by state regulation and slow to change, presents a substantial barrier. Predetermined load profiles inhibit market-based pricing by ignoring individual customer variation and the information that customers can communicate through choices in response to price signals. Furthermore, the persistence of standard offer service at a discounted rate (that is, a rate that does not reflect the financial cost of insurance against price risk) stifles any incentive customers might have to pursue other pricing options.

The most significant – yet also most intangible and difficult-to-overcome – obstacle to dynamic pricing and enabling technologies is inertia. All of the primary stakeholders in the industry – utilities, regulators and customers – harbor status quo bias. Incumbent utilities face incentives to maintain the regulated status quo as much as possible (given the economic, technological and demographic changes surrounding them) – and thus far, they’ve been successful in using the political process to achieve this objective.

Customer inertia also runs deep because consumers have not had to think about their consumption of electricity or the price they pay for it – a bias consumer advocates generally reinforce by arguing that low, stable prices for highly reliable power are an entitlement. Regulators and customers value the stability and predictability that have arisen from this vertically integrated, historically supply-oriented and reliability-focused environment; however, what is unseen and unaccounted for is the opportunity cost of such predictability – the foregone value creation in innovative services, empowerment of customers to manage their own energy use and use of double-sided markets to enhance market efficiency and network reliability. Compare this unseen potential with the value creation in telecommunications, where even young adults can understand and adapt to cell phone-pricing plans and benefit from the stream of innovations in the industry.

CONCLUSION

The potential for a highly distributed, decentralized network of devices automated to respond to price signals creates new policy and research questions. Do individuals automate sending prices to devices? If so, do they adjust settings, and how? Does the combination of price effects and innovation increase total surplus, including consumer surplus? In aggregate, do these distributed actions create emergent order in the form of system reliability?

Answering these questions requires thinking about the diffuse and private nature of the knowledge embedded in the network, and the extent to which such a network becomes a complex adaptive system. Technology helps determine whether decentralized coordination and emergent order are possible; the dramatic transformation of digital technology in the past few decades has decreased transaction costs and increased the extent of feasible decentralized coordination in this industry. Institutions – which structure and shape the contexts in which such processes occur – provide a means for creating this coordination. And finally, regulatory institutions affect whether or not this coordination can occur.

For this reason, effective regulation should focus not on allocation but rather on decentralized coordination and how to bring it about. This in turn means a focus on market processes, which are adaptive institutions that evolve along with technological change. Regulatory institutions should also be adaptive, and policymakers should view regulatory policy as work in progress so that the institutions can adapt to unknown and changing conditions and enable decentralized coordination.

ENDNOTES

1. Order can take many forms in a complex system like electricity – for example, keeping the lights on (short-term reliability), achieving economic efficiency, optimizing transmission congestion, longer-term resource adequacy and so on.

2. Roger W. Gale, Jean-Louis Poirier, Lynne Kiesling and David Bodde, “The Path to Perfect Power: New Technologies Advance Consumer Control,” Galvin Electricity Initiative report (2007). www.galvinpower.org/resources/galvin.php?id=88

3. The exception to this claim is the TOU contract, where the rate structure is known in advance. However, even on such a simple dynamic pricing contract, devices that allow customers to see their consumption and expenditure in real time instead of waiting for their bill can change behavior.

4. D.J. Hammerstrom et. al, “Pacific Northwest GridWise Testbed Demonstration Projects, volume I: The Olympic Peninsula Project” (2007). http://gridwise.pnl.gov/docs/op_project_final_report_pnnl17167.pdf

5. Ibid.

The Technology Demonstration Center

When a utility undergoes a major transformation – such as adopting new technologies like advanced metering – the costs and time involved require that the changes are accepted and adopted by each of the three major stakeholder groups: regulators, customers and the utility’s own employees. A technology demonstration center serves as an important tool for promoting acceptance and adoption of new technologies by displaying tangible examples and demonstrating the future customer experience. IBM has developed the technology center development framework as a methodology to efficiently define the strategy and tactics required to develop a technology center that will elicit the desired responses from those key stakeholders.

KEY STAKEHOLDER BUY-IN

To successfully implement major technology change, utilities need to consider the needs of the three major stakeholders: regulators, customers and employees.

Regulators. Utility regulators are naturally wary of any transformation that affects their constituents on a grand scale, and thus their concerns must be addressed to encourage regulatory approval. The technology center serves two purposes in this regard: educating the regulators and showing them that the utility is committed to educating its customers on how to receive the maximum benefits from these technologies.

Given the size of a transformation project, it’s critical that regulators support the increased spending required and any consequent increase in rates. Many regulators, even those who favor new technologies, believe that the utility will benefit the most and should thus cover the cost. If utilities expect cost recovery, the regulators need to understand the complexity of new technologies and the costs of the interrelated systems required to manage these technologies. An exhibit in the technology center can go “behind the curtain,” giving regulators a clearer view of these systems, their complexity and the overall cost of delivering them.

Finally, each stage in the deployment of new technologies requires a new approval process and provides opportunities for resistance from regulators. For the utility, staying engaged with regulators throughout the process is imperative, and the technology center provides an ideal way to continue the conversation.

Customers. Once regulators give their approval, the utility must still make its case to the public. The success of a new technology project rests on customers’ adoption of the technology. For example, if customers continue using appliances as they always did, at a regular pace throughout the day and not adjusting for off-peak pricing, the utility will fail to achieve the major planned cost advantage: a reduction in production facilities. Wide-scale customer adoption is therefore key. Indeed, general estimates indicate that customer adoption rates of roughly 20 percent are needed to break even in a critical peak-pricing model. [1]

Given the complexity of these technologies, it’s quite possible that customers will fail to see the value of the program – particularly in the context of the changes in energy use they will need to undertake. A well-designed campaign that demonstrates the benefits of tiered pricing will go a long way toward encouraging adoption. By showcasing the future customer experience, the technology center can provide a tangible example that serves to create buzz, get customers excited and educate them about benefits.

Employees. Obtaining employee buy-in on new programs is as important as winning over the other two stakeholder groups. For transformation to be successful, an understanding of the process must be moved out of the boardroom and communicated to the entire company. Employees whose responsibilities will change need to know how they will change, how their interactions with the customer will change and what benefits are in it for them. At the same time, utility employees are also customers. They talk to friends and spread the message. They can be the utility’s best advocates or its greatest detractors. Proper internal communication is essential for a smooth transition from the old ways to the new, and the technology center can and should be used to educate employees on the transformation.

OTHER GOALS FOR THE TECHNOLOGY DEMONSTRATION CENTER

The objectives discussed above represent one possible set of goals for a technology center. Utilities may well have other reasons for erecting the technology center, and these should be addressed as well. As an example, the utility may want to present a tangible display of its plans for the future to its investors, letting them know what’s in store for the company. Likewise, the utility may want to be a leader in its industry or region, and the technology center provides a way to demonstrate that to its peer companies. The utility may also want to be recognized as a trendsetter in environmental progress, and a technology center can help people understand the changes the company is making.

The technology center needs to be designed with the utility’s particular environment in mind. The technology center development framework is, in essence, a road map created to aid the utility in prioritizing the technology center’s key strategic priorities and components to maximize its impact on the intended audience.

DEVELOPING THE TECHNOLOGY CENTER

Unlike other aspects of a traditional utility, the technology center needs to appeal to customers visually, as well as explain the significance and impact of new technologies. The technology center development framework presented here was developed by leveraging trends and experiences in retail, including “experiential” retail environments such as the Apple Stores in malls across the United States. These new retail environments offer a much richer and more interactive experience than traditional retail outlets, which may employ some basic merchandising and simply offer products for sale.

Experiential environments have arisen partly as a response to competition from online retailers and the increased complexity of products. The Technology Center Development Framework uses the same state-of-the-art design strategies that we see adopted by high-end retailers, inspiring the executives and leadership of the utility to create a compelling experience that will enable the utility to elicit the desired response and buy-in from the stakeholders described above.

Phase 1: Technology Center Strategy

During this phase, a utility typically spends four to eight weeks developing an optimal strategy for the technology center. To accomplish this, planners identify and delineate in detail three major elements:

  • The technology center’s goals;
  • Its target audience; and
  • Content required to achieve those goals.

As shown in Figure 1, these pieces are not mutually exclusive; in fact, they’re more likely to be iterative: The technology center’s goals set the stage for determining the audience and content, and those two elements influence each other. The outcome of this phase is a complete strategy road map that defines the direction the technology center will take.

To understand the Phase 1 objectives properly, it’s necessary to examine the logic behind them. The methodology focuses on the three elements mentioned previously – goals, audience and content – because these are easily overlooked and misaligned by organizations.

Utility companies inevitably face multiple and competing goals. Thus, it’s critical to identify the goals specifically associated with the technology center and to distinguish them from other corporate goals or goals associated with implementing a new technology. Taking this step forces the organization to define which goals can be met by the technology center with the greatest efficiency, and establishes a clear plan that can be used as a guide in resolving the inevitable future conflicts.

Similarly, the stakeholders served by the utility represent distinct audiences. Based on the goals of the center and the organization, as well as the internal expectations set by managers, the target audience needs to be well defined. Many important facets of the technology center, such as content and location, will be partly determined by the target audience. Finally, the right content is critical to success. A regulator may want to see different information than customers.

In addition, the audience’s specific needs dictate different content options. Do the utility’s customers care about the environment? Do they care more about advances in technology? Are they concerned about how their lives will change in the future? These questions need to be answered early in the process.

The key to successfully completing Phase 1 is constant engagement with the utility’s decision makers, since their expectations for the technology center will vary greatly depending on their responsibilities. Throughout this phase, the technology center’s planners need to meet with these decision makers on a regular basis, gather and respect their opinions, and come to the optimal mix for the utility on the whole. This can be done through interviews or a series of workshops, whichever is better suited for the utility. We have found that by employing this process, an organization can develop a framework of goals, audience and content mix that everyone will agree on – despite differing expectations.

Phase 2: Design Characteristics

The second phase of the development framework focuses on the high-level physical layout of the technology center. These “design characteristics” will affect the overall layout and presentation of the technology center.

We have identified six key characteristics that need to be determined. Each is developed as a trade-off between two extremes; this helps utilities understand the issues involved and debate the solutions. Again, there are no right answers to these issues – the optimal solution depends on the utility’s environment and expectations:

  • Small versus large. The technology center can be small, like a cell phone store, or large, like a Best Buy.
  • Guided versus self-guided. The center can be designed to allow visitors to guide themselves, or staff can be retained to guide visitors through the facility.
  • Single versus multiple. There may be a single site, or multiple sites. As with the first issue (small versus large), one site may be a large flagship facility, while the others represent smaller satellite sites.
  • Independent versus linked. Depending on the nature of the exhibits, technology center sites may operate independently of each other or include exhibits that are remotely linked in order to display certain advanced technologies.
  • Fixed versus mobile. The technology center can be in a fixed physical location, but it can also be mounted on a truck bed to bring the center to audiences around the region.
  • Static versus dynamic. The exhibits in the technology center may become outdated. How easy will it be to change or swap them out?

Figure 2 illustrates a sample set of design characteristics for one technology center, using a sample design characteristic map. This map shows each of the characteristics laid out around the hexagon, with the preference ranges represented at each vertex. By mapping out the utility’s options with regard to the design characteristics, it’s possible to visualize the trade-offs inherent in these decisions, and thus identify the optimal design for a given environment. In addition, this type of map facilitates reporting on the project to higher-level executives, who may benefit from a visual executive summary of the technology center’s plan.

The tasks in Phase 2 require the utility’s staff to be just as engaged as in the strategy phase. A workshop or interviews with staff members who understand the various needs of the utility’s region and customer base should be conducted to work out an optimal plan.

Phase 3: Execution Variables

Phases 1 and 2 provide a strategy and design for the technology center, and allow the utility’s leadership to formulate a clear vision of the project and come to agreement on the ultimate purpose of the technology center. Phase 3 involves engaging the technology developers to identify which aspects of the new technology – for example, smart appliances, demand-side management, outage management and advanced metering – will be displayed at the technology center.

During this phase, utilities should create a complete catalog of the technologies that will be demonstrated, and match them up against the strategic content mix developed in Phase 1. A ranking is then assigned to each potential new technology based on several considerations, such as how well it matches the strategy, how feasible it is to demonstrate the given technology at the center, and what costs and resources would be required. Only the most efficient and well-matched technologies and exhibits will be displayed.

During Phase 3, outside vendors are also engaged, including architects, designers, mobile operators (if necessary) and real estate agents, among others. With the first two phases providing a guide, the utility can now open discussions with these vendors and present a clear picture of what it wants. The technical requirements for each exhibit will be cataloged and recorded to ensure that any design will take all requirements into account. Finally, the budget and work plan are written and finalized.

CONCLUSION

With the planning framework completed, the team can now build the center. The framework serves as the blueprint for the center, and all relevant benchmarks must be transparent and open for everyone to see. Disagreements during the buildout phase can be referred back to the framework, and issues that don’t fit the framework are discarded. In this way, the utility can ensure that the technology center will meet its goals and serve as a valuable tool in the process of transformation.

Thank you to Ian Simpson, IBM Global Business Services, for his contributions to this paper.

ENDNOTE

  1. Critical peak pricing refers to the model whereby utilities use peak pricing only on days when demand for electricity is at its peak, such as extremely hot days in the summer.

How Intelligent Is Your Grid?

Many people in the utility industry see the intelligent grid — an electric transmission and distribution network that uses information technology to predict and adjust to network changes — as a long-term goal that utilities are still far from achieving. Energy Insights research, however, indicates that today’s grid is more intelligent than people think. In fact, utilities can begin having the network of the future today by better leveraging their existing resources and focusing on the intelligent-grid backbone.

DRIVERS FOR THE INTELLIGENT GRID

Before discussing the intelligent grid backbone, it’s important to understand the drivers directing the intelligent grid’s progress. While many groups — such as government, utilities and technology companies — may be pushing the intelligent grid forward, they are also slowing it down. Here’s how:

  • Government. With the 2005 U.S. Energy Policy Act and the more recent 2007 Energy Independence and Security Act, the federal government has acknowledged the intelligent grid’s importance and is supporting investment in the area. Furthermore, public utility commissions (PUCs) have begun supporting intelligent grid investments like smart metering. At the same time, however, PUCs have a duty to maintain reasonable prices. Since utilities have not extensively tested the benefits of some intelligent grid technologies, such as distribution line sensors, many regulators hesitate to support utilities investing in intelligent grid technologies beyond smart metering.
  • Utilities. Energy Insights research indicates that information technology, in general, enables utilities to increase operational efficiency and reduce costs. For this reason, utilities are open to information technology; however, they’re often looking for quick cost recovery and benefits. Many intelligent grid technologies provide longer-term benefits, making them difficult to cost-justify over the short term. Since utilities are risk-aware, this can make intelligent grid investments look riskier than traditional information technology investments.
  • Technology. Although advanced enough to function on the grid today, many intelligent grid technologies could become quickly outdated thanks to the rapidly developing marketplace. What’s more, the life span of many intelligent grid technologies is not as long as those of traditional grid assets. For example, a smart meter’s typical life span is about 10 to 15 years, compared with 20 to 30 years for an electro-mechanical meter.

With strong drivers and competing pressures like these, it’s not a question of whether the intelligent grid will happen but when utilities will implement new technologies. Given the challenges facing the intelligent grid, the transition will likely be more of an evolution than a revolution. As a result, utilities are making their grids more intelligent today by focusing on the basics, or the intelligent grid backbone.

THE INTELLIGENT GRID BACKBONE

What comprises this backbone? Answering this question requires a closer look at how intelligence changes the grid. Typically, a utility has good visibility into the operation of its generation and transmission infrastructure but poor visibility into its distribution network. As a result, the utility must respond to a changing distribution network based on very limited information. Furthermore, if a grid event requires attention — such as in the case of a transformer failure — people must review information, decide to act and then manually dispatch field crews. This type of approach translates to slower, less informed reactions to grid events.

The intelligent grid changes these reactions through a backbone of technologies — sensors, communication networks and advanced analytics — especially developed for distribution networks. To better understand these changes, we can imagine a scenario where a utility has an outage on its distribution network. As shown in Figure 1, additional grid sensors collect more information, making it easier to detect problems. Communications networks then allow sensors to convey the problem to the utility. Advanced analytics can efficiently process this information and determine more precisely where the fault is located, as well as automatically respond to the problem and dispatch field crews. These components not only enable faster, better-informed reactions to grid problems, they can also do real-time pricing, improve demand response and better handle distributed and renewable energy sources.

A CLOSER LOOK AT BACKBONE COMPONENTS

A deeper dive into each of these intelligent grid backbone technologies reveals how utilities are gaining more intelligence about their grid today.

Network sensors are important not only for real-time operations — such as locating faults and connecting distributed energy sources to the grid — but also for providing a rich historical data source to improve asset maintenance and load research and forecasting. Today, more utilities are using sensors to better monitor their distribution networks; however, they’re focused primarily on smart meters. The reason for this is that smart meters have immediate operational benefits that make them attractive for many utilities today, including reducing meter reader costs, offering accurate billing information, providing theft control and satisfying regulatory requirements. Yet this focus on smart meters has created a monitoring gap between the transmission network and the smart meter.

A slew of sensors are available from companies such as General Electric, ABB, PowerSense, GridSense and Serveron to fill this monitoring gap. Tracking everything from load balancing and transformer status to circuit breakers and tap changers, energized downed lines, high-impedance faults and stray voltage, and more, these sensors are able to fill the monitoring gap, yet utilities hesitate to invest in them because they lack the immediate operational benefits of smart meters.

By monitoring this gap, however, utilities will sustain longer-term grid benefits such as reduced generation capacity building. Utilities have found they can begin monitoring this gap by:

  • Prioritizing sensor investments. Customer complaints and regulatory pressure have pushed some utilities to take action for particular parts of their service territory. For example, one utility Energy Insights studied received numerous customer complaints about a particular feeder’s reliability, so the utility invested in line sensors for that area. Another utility began considering sensor investments in troubled areas of its distribution network when regulators demanded that the utility raise its System Average Interruption Frequency Index (SAIFI) and System Average Interruption Duration Index (SAIDI) ratings from the bottom 50 percent to the top 25 percent of benchmarked utilities. By focusing on such areas, utilities can achieve “quick wins” with sensors and build utility confidence by using additional sensors on their distribution grid.
  • Realizing it’s all about compromise. Even in high-priority areas, it may not make financial sense for a utility to deploy the full range of sensors for every possible asset. In some situations, utilities may target a particular area of the service territory with a higher density of sensors. For example, a large U.S. investor-owned utility with a medium voltage-sensing program placed a high density of sensors along a specific section of its service territory. On the other hand, utilities might cover a broader area of service territory with fewer sensors, similar to the approach taken by a large investor-owned utility Energy Insights looked at that monitored only transformers across its service territory.
  • Rolling in sensors with other intelligent grid initiatives. Some utilities find ways to combine their smart metering projects with other distribution network sensors or to leverage existing investments that could support additional sensors. One utility that Energy Insights looked at installed transformer sensors along with a smart meter initiative and leveraged the communications networks it used for smart metering.

While sensors provide an important means of capturing information about the grid, communication networks are critical to moving that information throughout the intelligent grid — whether between sensors or field crews. Typically, to enable intelligent grid communications, utilities must either build new communications networks to bring intelligence to the existing grid or incorporate communication networks into new construction. Yet utilities today are also leveraging existing or recently installed communications networks to facilitate more sophisticated intelligent grid initiatives such as the following:

  • Smart metering and automated meter-reading (AMR) initiatives. With the current drive to install smart meters, many utilities are covering their distribution networks with communications infrastructure. Furthermore, existing AMR deployments may include communications networks that can bring data back to the utility. Some utilities are taking advantage of these networks to begin plugging other sensors into their distribution networks.
  • Mobile workforce. The deployment of mobile technologies for field crews is another hot area for utilities right now. Utilities are deploying cellular networks for field crew communications for voice and data. Although utilities have typically been hesitant to work with third-party communications providers, they’ve become more comfortable with outside providers after using them for their mobile technologies. Since most of the cellular networks can provide data coverage as well, some utilities are beginning to use these providers to transmit sensor information across their distribution networks.

Since smart metering and mobile communications networks are already in place, the incremental cost of installing sensors on these networks is relatively low. The key is making sure that different sensors and components can plug into these networks easily (for example, using a standard communications protocol).

The last key piece of the intelligent grid backbone is advanced analytics. Utilities are required to make quick decisions every day if they’re to maintain a safe and reliable grid, and the key to making such decisions is being well informed. Intelligent grid analytics can help utilities quickly process large amounts of data from sensors so that they can make those informed decisions. However, how quickly a decision needs to be made depends on the situation. Intelligent grid analytics assist with two types of decisions: very quick decisions (veQuids) and quick decisions (Quids). veQuids are made in milliseconds by computers and intelligent devices analyzing complex, real-time data – an intelligent grid vision that’s still a future development for most utilities.

Fortunately, many proactive decisions about the grid don’t have to be made in milliseconds. Many utilities today can make Quids — often manual decisions — to predict and adjust to network changes within a time frame of minutes, days or even months.

no matter how quick the decision, however, all predictive efforts are based on access to good-quality data. In putting their Quid capabilities to use today — in particular for predictive maintenance and smart metering — utilities are building not only intelligence about their grids but also a foundation for providing more advanced veQuids analytics in the future through the following:

  • The information foundation. Smart metering and predictive maintenance require utilities to collect not only more data but also more real-time data. Smart metering also helps break down barriers between retail and operational data sources, which in turn creates better visibility across many data sources to provide a better understanding of a complex grid.
  • The automation transition. To make the leap between Quids and veQuids requires more than just better access to more information — it also requires automation. While fully automated decision-making is still a thing of the future, many utilities are taking steps to compile and display data automatically as well as do some basic analysis, using dashboards from providers such as OSIsoft and Obvient Strategies to display high-level information customized for individual users. The user then further analyzes the data, and makes decisions and takes action based on that analysis. Many utilities today use the dashboard model to monitor critical assets based on both real-time and historical data.

ENSURING A MORE INTELLIGENT GRID TODAY AND TOMORROW

As these backbone components show, utilities already have some intelligence on their grids. now, they’re building on that intelligence by leveraging existing infrastructure and resources — whether it’s voice communications providers for data transmission or Quid resources to build a foundation for the veQuids of tomorrow. In particular, utilities need to look at:

  • Scalability. Utilities need to make sure that whatever technologies they put on the grid today can grow to accommodate larger portions of the grid in future.
  • Flexibility. Given rapid technology changes in the marketplace, utilities need to make sure their technology is flexible and adaptable. For example, utilities should consider smart meters that have the ability to change out communications cards to allow for new technologies.
  • Integration. due to the evolutionary nature of the grid, and with so many intelligent grid components that must work together (intelligent sensors at substations, transformers and power lines; smart meters; and distributed and renewable energy sources), utilities need to make sure these disparate components can work with one another. Utilities need to consider how to introduce more flexibility into their intelligent grids to accommodate the increasingly complex network of devices.

As today’s utilities employ targeted efforts to build intelligence about the grid, they must keep in mind that whatever action they take today – no matter how small – must ultimately help them meet the demands of tomorrow.

Business Intelligence: The ‘Better Light Bulb’ for Improved Decision Making

Although some utilities have improved organizational agility by providing high-level executives with real-time visibility into operations, if they’re to be truly effective, these businesses must do more than simply implement CEO-level dashboards. They must provide this kind of visibility to every employee who needs it. To achieve this, utilities need to be able to collect data from many disparate sources and present it in a way that allows people company-wide to access the right information at the right time in the form of easy-to-use and actionable business intelligence (BI).

The following statement from the Gartner EXP CIO report “Creating Enterprise Leverage: The 2007 CIO Agenda,” led by Mark McDonald and Tina Nunno (February 2007).

Success in 2007 requires making the enterprise different to attract and retain customers. In response, many CIOs are looking for new sources of enterprise leverage, including technical excellence, agility, information and innovation.

This statement holds true. But converting data into useful information for employees in different levels and roles creates a new challenge. Technological advances that produce exponentially increasing volumes of data, coupled with historical data silos, have made it extremely difficult for utilities professionals to access, process and analyze data in a way that allows them to make effective decisions. What’s needed: BI technology tools that are not only available to the C-level executive or the accounting department, but to everyone – civil and electrical engineers, technicians, planners, customer service representatives, safety officers and others.

BI solutions also need to handle data in a way that mirrors the way people work. Such solutions should be capable of supporting the full spectrum of use – from individuals’ personal content to information created by team members for use by the team and formal IT-created structured and controlled content for use enterprise-wide.

The good news is that BI has become more accessible, easier to use and more affordable so that people throughout the enterprise – not just accountants or senior executives – can gain insight into the business and make better informed decisions.

RIGHT-TIME PERFORMANCE MANAGEMENT

“The Gartner Magic Quadrant for Business Intelligence Platforms, 2008,” by James Richardson, Kurt Schlegel, Bill Hostmann and Neil McMurchy (February 2008), has this to say about the value of BI:

CIOs are coming under increasing pressure to invest in technologies that drive business transformation and strategic change. BI can deliver on this promise if deployed successfully, because it could improve decision making and operational efficiency, which in turn drive the top line and the bottom line.

Greg Todd, Accenture Information Management Services global lead for resources at Accenture, advises that monthly, or even weekly, reports just aren’t enough for utilities to remain agile. Says Todd, “The utilities industry is dynamic. Everything from plant status and market demand to generation capacity and asset condition needs near real-time performance management to provide the insight for people enterprise-wide to make the right decisions in a timely fashion – not days or weeks after the event.”

By having access to near real-time performance monitoring across the enterprise, utilities executives, managers, engineers and front-line operations personnel can rapidly analyze information and make decisions to improve performance. This in turn allows them more agility to respond to today’s regulatory, competitive and economic imperatives.

For example, Edipower, one of Italy’s leading energy providers, has implemented an infrastructure that will grow as its business grows and support the BI technology it needs to guarantee power plant availability as market conditions and regulations dictate. According to Massimo Pernigotti, CIO of Edison, consolidating the family of companies’ technology platforms and centralizing its data network allowed the utility to fully integrate its financial and production data analyses. Says Pernigotti, “Using the new application, staff can prepare scorecards and business intelligence summaries that plant managers can then access from portable devices, ensuring near real-time performance management.”

To achieve this level of performance management, utilities professionals need easy access to both structured and unstructured data from multiple sources, as illustrated in Figure 1. This data can be “owned” by many different departments and span multiple locations. It can come from operational control systems, meter data systems, customer information systems, financial systems and human resources and enterprise resource planning (ERP) systems, to name a few sources. New and more widely available BI tools allow engineers and others to quickly view near real-time information and use it to create key performance indicators (KPIs) that can be used to monitor and manage the operational health of an organization.

KPIs commonly include things like effective forced outage factors (EFOFs), average customer downtime, average customer call resolution time, fuel cost per megawatt hour (MWh), heat rates, capacity utilization, profit margin, total sales and many other critical indicators. Traditionally, this data would be reported in dozens of documents that took days or weeks to compile while problems continued to progress. Using BI, however, these KPIs can be calculated in minutes.

With context-sensitive BI, safety professionals have the visibility to monitor safety incidents and environmental impacts. In addition, engineers can analyze an asset’s performance and energy consumption – and solve problems before they become critical.

One of the largest U.S.-based electric power companies recently completed a corporate acquisition and divestiture. As part of its reorganization, the company sought a way to reduce capital expenditures for producing power as well as an effective way to capture and transfer knowledge in light of an aging workforce. By adopting a new BI platform and monitoring a comprehensive set of custom KPIs in near real time, the company was able to give employees access to its generation performance metrics, which in turn led to improved generation demand-and-surplus forecasts. As a result, the company was able to better utilize its existing power plants and reduce capital expenditures for building new ones.

BI tools are also merging with collaboration tools to provide right-time information about business performance that employees at every organizational level can access and which can be shared across corporate boundaries and continents. This will truly change the way people work. Indeed, the right solution combines BI and collaboration, which not only improves business insight, but also enables staff to work together in real time to make sound decisions more quickly and easily and to proactively solve problems.

With these collaboration capabilities increasingly built into today’s BI solutions, firms can create virtual teams that interact using audio and video over large geographical distances. When coupled with real-time monitoring and alerting, this virtual collaboration enables employees – and companies – to make more informed decisions and subsequently become more agile.

Andre Blumberg, group information technology manager for Hong Kong’s CLP Group, believes that user friendliness and user empowerment are key success factors for BI adoption. Says Blumberg, “Enabling users to create reports and perform slice-and-dice analysis in a familiar Windows user interface is important to successfully leveraging BI capabilities.”

As more utilities implement KPI dashboards and scorecards as performance management tools, they open the door for next-generation technologies that feature dynamic mashups and equipment animations, and create a 24×7 collaborative environment to help managers, engineers and operations personnel detect and analyze problems faster and more effectively in a familiar and secure environment. The environment will be common across roles and cost much less than other solutions with similar capabilities. All this allows utilities operations personnel to “see the needle in the haystack” and make quicker and better decisions that drive operational efficiency and improve the bottom line. Collaboration enables personnel to engage in key issues in a timely fashion via this new desktop environment. In addition, utilities can gain preemptive knowledge of operational problems and act before the problems become critical.

BETTER DECISIONS IMPROVE BUSINESS INSIGHT

Everyone in the organization can benefit from understanding what drives a utility, the key metrics for success and how the company is performing against those metrics (see Figure 2). By definition, BI encompasses everyone, so logically everyone should be able to use it.

According to Rick Nicholson, vice president of research for Energy Insights, an IDC company, the nature of BI recently changed dramatically. For many years, BI was a reporting solution and capability used primarily by a small number of business analysts. “Today, BI solutions have become more accessible, easier to use and more affordable, and they’re being deployed to managers, supervisors, line-of-business staff and external stakeholders,” says Nicholson. “We expect the use of business intelligence in the utility industry to continue to increase due to factors such as new report and compliance requirements, changes in trading markets, new customer programs such as energy efficiency and demand response, and intelligent grid initiatives.”

Accenture’s Todd believes that traditional BI focuses on analyzing the past, whereas real-time BI today can provide an immediate chance to affect the future. Says Todd, “Smart users of BI today take the growing volume of corporate operational data and the constant fl ow of raw information and turn it into usable and business-relevant insight – in near real time – and even seek to manage future events using analytics.” (See Figure 2.)

Most importantly, today’s BI gives utility information workers a way of understanding what’s going on in the business that’s both practical and actionable. Dr. J. Patrick Kennedy, the founder and CEO of performance management vendor OSIsoft, says that the transaction-level detail provided from enterprise software often offers a good long-term history, but it does not answer many of the important operations questions. Further, this type of software typically represents a “pull” rather than a “push” technology.

Says Kennedy, “People think in terms of context, trends, interactions, risk and reward – to answer these questions effectively requires actionable information to help them make the right decisions. Integrating systems enables these decisions by providing users with a dynamic BI application within a familiar platform.”

WHAT GOOD BI SYSTEMS LOOK LIKE

Here are some critical characteristics to look for in an enterprise-class BI solution:

  • The BI solution should integrate with the existing IT infrastructure and not require major infrastructure changes or replacement of legacy software applications.
  • The technology should mirror day-today business processes already in place (rather than expect users to adapt to it).
  • The application should be easy to use without extensive IT support.
  • The BI solution should connect seamlessly to multiple data sources rather than require workers to toggle in and out of a broad range of proprietary applications.
  • An effective BI solution will provide the ability to forecast, plan, budget and create scorecards and consolidated financial reports in a single, integrated product.
  • The BI solution should support navigation directly from each KPI to the underlying data supporting that KPI.
  • Analysis and reporting capabilities should be flexible and allow for everything from collecting complex data from unique sources to heavy-duty analytics and enterprise-wide production reporting.
  • The BI solution should support security by role, location and more. If access to certain data needs to be restricted, access management should be automated.

The true measure of BI success is that users actually use it. For this to happen, BI must be easy to learn and use. It should provide the right information in the right amount of detail to the right people. And it must present this information in easily customized scorecards, dashboards and wikis, and be available to anyone. If utilities can achieve this, they’ll be able to make better decisions much more quickly.

SEEING THE LIGHT

BI is about empowering people to make decisions based on relevant and current information so that they can focus on the right problems and pay attention to the right customers. By using BI to monitor performance and analyze both financial and operational data, organizations can perform real-time collaboration and make truly transformational decisions. Given the dynamic nature of the utilities industry, BI is a critical tool for making organizations more flexible and agile – and for enabling them to easily anticipate and manage change.

The Virtual Generator

Electric utility companies today constantly struggle to find a balance between generating sufficient power to satisfy their customers’ dynamic load requirements and minimizing their capital and operating costs. They spend a great deal of time and effort attempting to optimize every element of their generation, transmission and distribution systems to achieve both their physical and economic goals.

In many cases, “real” generators waste valuable resources – waste that if not managed efficiently can go directly to the bottom line. Energy companies therefore find the concept of a “virtual generator,” or a virtual source of energy that can be turned on when needed, very attractive. Although generally only representing a small percentage of utilities’ overall generation capacity, virtual generators are quick to deploy, affordable, cost-effective and represent a form of “green energy” that can help utilities meet carbon emission standards.

Virtual generators use forms of dynamic voltage and capacitance (Volt/ VAr) adjustments that are controlled through sensing, analytics and automation. The overall process involves first flattening or tightening the voltage profiles by adding additional voltage regulators to the distribution system. Then, by moving the voltage profile up or down within the operational voltage bounds, utilities can achieve significant benefits (Figure 1). It’s important to understand, however, that because voltage adjustments will influence VArs, utilities must also adjust both the placement and control of capacitors (Figure 2).

Various business drivers will influence the use of Volt/VAr. A utility could, for example, use Volt/VAr to:

  • Respond to an external system-wide request for emergency load reduction;
  • Assist in reducing a utility’s internal load – both regional and throughout the entire system;
  • Target specific feeder load reduction through the distribution system;
  • Respond as a peak load relief (a virtual peaker);
  • Optimize Volt/VAr for better reliability and more resiliency;
  • Maximize the efficiency of the system and subsequently reduce energy generation or purchasing needs;
  • Achieve economic benefits, such as generating revenue by selling power on the spot market; and
  • Supply VArs to supplement off-network deficiencies.

Each of the above potential benefits falls into one of four domains: peaking relief, energy conservation, VAr management or reliability enhancement. The peaking relief and energy conservation domains deal with load reduction; VAr management, logically enough, involves management of VArs; and reliability enhancement actually increases load. In this latter domain, the utility will use increased voltage to enable greater voltage tolerances in self-healing grid scenarios or to improve the performance of non-constant power devices to remove them from the system as soon as possible and therefore improve diversity.

Volt/VAr optimization can be applied to all of these scenarios. It is intended to either optimize a utility’s distribution network’s power factor toward unity, or to purposefully make the power factor leading in anticipation of a change in load characteristics.

Each of these potential benefits comes from solving a different business problem. Because of this, at times they can even be at odds with each other. Utilities must therefore create fairly complex business rules supported by automation to resolve any conflicts that arise.

Although the concept of load reduction using Volt/VAr techniques is not new, the ability to automate the capabilities in real time and drive the solutions with various business requirements is a relatively recent phenomenon. Energy produced with a virtual generator is neither free nor unlimited. However, it is real in the sense that it allows the system to use energy more efficiently.

A number of things are driving utilities’ current interest in virtual generators, including the fact that sensors, analytics, simulation, geospatial information, business process logic and other forms of information technology are increasingly affordable and robust. In addition, lower-cost intelligent electrical devices (IEDs) make virtual generators possible and bring them within reach of most electric utility companies.

The ability to innovate an entirely new solution to support the above business scenarios is now within the realm of possibility for the electric utility company. As an added benefit, much of the base IT infrastructure required for virtual generators is the same as that required for other forms of “smart grid” solutions, such as advanced meter infrastructure (AMI), demand side management (DSM), distributed generation (DG) and enhanced fault management. Utilities that implement a well-designed virtual generator solution will ultimately be able to align it with these other power management solutions, thus optimizing all customer offerings that will help reduce load.

HOW THE SOLUTION WORKS

All utilities are required, for regulatory or reliability reasons, to stay within certain high- and low-voltage parameters for all of their customers. In the United States the American Society for Testing and Materials (ATSM) guidelines specify that the nominal voltage for a residential single-phase service should be 120 volts with a plus or minus 6-volt variance (that is, 114 to 126 volts). Other countries around the world have similar guidelines. Whatever the actual values are, all utilities are required to operate within these high- and low-voltage “envelopes.” In some cases, additional requirements may be imposed as to the amount of variance – the number of volts changed or the percent change in the voltage – that can take place over a period of minutes or hours.

Commercial customers may have different high/low values, but the principle remains the same. In fact, it is the mixture of residential, commercial and industrial customers on the same feeder that makes the virtual generation solution almost a requirement if a utility wants to optimize its voltage regulation.

Although it would be ideal for a utility to deliver 120-volt power consistently to all customers, the physical properties of the distribution system as well as dynamic customer loading factors make this difficult. Most utilities are already trying to accomplish this through planning, network and equipment adjustments, and in many cases use of automated voltage control devices. Despite these efforts, however, in most networks utilities are required to run the feeder circuit at higher-than-nominal levels at the head of the circuit in order to provide sufficient voltage for downstream users, especially those at the tails or end points of the circuit.

In a few cases, electric utilities have added manual or automatic voltage regulators to step up voltage at one or more points in a feeder circuit because of nonuniform loading and/or varied circuit impedance characteristics throughout the circuit profile. This stepped-up slope, or curve, allows the utility company to comply with the voltage level requirements for all customers on the circuit. In addition, utilities can satisfy the VAr requirements for operational efficiency of inductive loads using switched capacitor banks, but they must coordinate those capacitor banks with voltage adjustments as well as power demand. Refining voltage profiles through virtual generation usually implies a tight corresponding control of capacitance as well.

The theory behind a robust Volt/ VAr regulated feeder circuit is based on the same principles but applied in an innovative manner. Rather than just using voltage regulators to keep the voltage profile within the regulatory envelope, utilities try to “flatten” the voltage curve or slope. In reality, the overall effect is a stepped/slope profile due to economic limitations on the number of voltage regulators applied per circuit. This flattening has the effect of allowing an overall reduction, or decrease, in nominal voltage. In turn the operator may choose to move the voltage curve up or down within the regulatory voltage envelope. Utilities can derive extra benefit from this solution because all customers within a given section of a feeder circuit could be provided with the same voltage level, which should result in less “problem” customers who may not be in the ideal place on the circuit. It could also minimize the possible power wastage of overdriving the voltage at the head of the feeder in order to satisfy customers at the tails.

THE ROLE OF AUTOMATION IN DELIVERING THE VIRTUAL GENERATOR

Although theoretically simple in concept, executing and maintaining a virtual generator solution is a complex task that requires real-time coordination of many assets and business rules. Electrical distribution networks are dynamic systems with constantly changing demands, parameters and influencers. Without automation, utilities would find it impossible to deliver and support virtual generators, because it’s infeasible to expect a human – or even a number of humans – to operate such systems affordably and reliably. Therefore, utilities must leverage automation to put humans in monitoring rather than controlling roles.

There are many “inputs” to an automated solution that supports a virtual generator. These include both dynamic and static information sources. For example, real-time sensor data monitoring the condition of the networks must be merged with geospatial information, weather data, spot energy pricing and historical data in a moment-by-moment, repeating cycle to optimize the business benefits of the virtual generator. Complicating this, in many cases the team managing the virtual generator will not “own” all of the inputs required to feed the automated system. Frequently, they must share this data with other applications and organizational stakeholders. It’s therefore critical that utilities put into place an open, collaborative and integrated technology infrastructure that supports multiple applications from different parts of the business.

One of the most critical aspects of automating a virtual generator is having the right analytical capabilities to decide where and how the virtual generator solution should be applied to support the organizations’ overall business objectives. For example, utilities should use load predictors and state estimators to determine future states of the network based on load projections given the various Volt/VAr scenarios they’re considering. Additionally, they should use advanced analytic analyses to determine the resiliency of the network or the probability of internal or external events influencing the virtual generator’s application requirements. Still other types of analyses can provide utilities with a current view of the state of the virtual generator and how much energy it’s returning to the system.

While it is important that all these techniques be used in developing a comprehensive load-management strategy, they must be unified into an actionable, business-driven solution. The business solution must incorporate the values achieved by the virtual generator solutions, their availability, and the ability to coordinate all of them at all times. A voltage management solution that is already being used to support customer load requirements throughout the peak day will be of little use to the utility for load management. It becomes imperative that the utility understand the effect of all the voltage management solutions when they are needed to support the energy demands on the system.