PHEVs Are on a Roll

The electric vehicle first made its appearance about a century ago, but it is only in recent years – months, to be more precise – that it has achieved breakthrough status as, quite possibly, the single-most important technological development having a positive impact on society today.

Climate change, over-dependence on fossil fuels, and the current economic crisis have combined to impact the automobile sector to a degree unforeseen, forcing technological innovation to direct its urgent attention toward the development of electric vehicles as an alternative means of transport, and a substitute for internal combustion engines. Many countries are supporting the approach in their political, energy and industrial planning directed toward the introduction of this type of vehicle. For example, the U.S. has a target of 1 million Plug-in Hybrid Electric Vehicles (PHEV) in operation by 2015. Spain expects to achieve the same number by 2014.

It is certainly true that there exist pressures capable of driving the introduction of the PHEV forward, but technological advances are the factors that underpin and give coherence to its development. There are several progressive improvements being made in technology, materials, and power generation and supply, which will support the deployment and use of electric vehicles in the coming years. They include: advances in battery manufacture and electronics (particularly in terms of power); the development of new communication protocols; ever more efficient and flexible information technologies; the growth of renewable energy sources in the electrical energy generation mix; and the concept of smart grids focused on more efficient electricity distribution. All of these improvements are underscored by a much greater degree of passion and personal involvement by the end-user.

Stakeholders and Utilities

With technology as the underlying catalyst, the scenario for electric vehicle use will include the impact and involvement of various stakeholders. This consists of: society itself, government and municipal entities, regulators, universities and research institutions, vehicle manufacturers, the ancillary automobile industry and its technological partners, battery manufacturers, the manufacturers of components, electrical and electronics systems, infrastructure suppliers, companies dedicated to mediation, billing and payment methods, ICT (Information and Communication Technology) companies, and of course, utilities.

If the electric vehicle is to become a genuinely alternative means of transportation, then this will depend on the involvement of, and interrelationship between, the above groups. One example of this is the formalizing of various agreements between certain stakeholders at both the national and international level (for example, Saab, Volvo, Wattenfall and ETC Battery in Sweden; Renault, PSA Peugeot Citro├źn, Toyota and EDF in France; and Iberdrola and General Motors at a global level) and the establishment of consortiums such as EDISON (Electric Vehicles in a Distributed and Integrated Market using Sustainable Energy and Open Networks) in Denmark.

If there is one dimension, however, which will be impacted most throughout the whole of the value chain, it is the electrical one. From power generation to retail, the introduction of this vehicle will require changes in current business models, and foreseeably, in utilities operational models. The short-term aim is to provide electrical energy for use in these vehicles in a more reliable and efficient way.

Battery Charging Impact

Given that charging could be the action having the greatest impact on the electrical sector, there are various alternatives for affecting this. These include:

  • Substitution. This involves a rapid exchange of vehicles and/or batteries, and the subsequent charging of both in an offline mode. It would require sharing of cars (vehicle usage and substitution) and battery charging stations for quick and automated battery exchange.
  • Direct Charging. This includes regular charging points situated in car parks, shopping centers and residences, and providing battery recharge while the vehicle is parked. There also need to be fast-charging points that could quickly charge a battery in 10 to 15 minutes.

To examine the advantages and disadvantages of the above methods, it helps to note the various pilot projects and research programs underway at both the conceptual and demonstration stages. These indicate the possibility of a coexistence scenario. Offline charging could be the least invasive method given the current system of fuel distribution. A network of “electricity stations” (as opposed to petrol stations) could provide a dedicated system of energy generation in a given location. As for direct charging, given the itinerant nature of user demand and his or her expected freedom to choose a particular charging method or location, this introduces an element of greater uncertainty, and impact on the electricity grid, requiring a system that better adapts to the lifestyle of the user.

Direct Charging and Its Impact on the Electricity Grid

Direct charging depends on various factors – notably battery characteristics (directly related to vehicle performance) and the range of time spans chosen to carry out the recharge. Associated with these are other variables: charging voltage, mode (DC, single-phase AC, and three-phase AC) and the characteristics of the charging systems employed: technology, components and their location, connectors, insulation, and the power and control electronics. All of these variables will influence the charging times, and will vary according to the power input (more power, less time) as shown in Figure 1. Therefore, depending on the kind of recharging, there will be an impact not only on the characteristics of the individual charging points but also on the supporting system.

Using extended range electrical vehicles (EREV) such as the Chevrolet Volt or Opel/Vauxhall Ampera as an example, it is estimated that annual home energy consumption from vehicle charging could be around 20 percent of the total, although some studies suggest this amount may be twice as much, based on the customer profile.

Based on the charging power input – and this is, of course, related to the methodology employed – it would be possible to fully recharge an EREV battery in about three hours. A fully charged battery would enable operation solely on electrical power for approximately 40 miles, a distance representing about 80 percent of daily car journeys based on the current averages. For a scenario like this it would be possible to use a charging method of about 4 kilowatt/220 volts.

If we analyze the impact in terms of energy supply and power capacity, there appears to be no medium-term problems in supporting these chargings, according to the data above. This is, however, a matter which depends on each individual country and also on the power transmission interconnections between them. In terms of the instantaneous power available, the charging method will have a greater or lesser impact, particularly on the distribution assets, depending on how it is carried out. Figure 2 shows how the power varies according to the charging method and the time of day when it is in use, taking into account the daily energy demand curve. We can, therefore, identify different scenarios from the most favourable (slow charging at off-peak times) to the most unfavourable (fast charging at peak times). With the latter we may find ourselves with distribution assets (e.g., transformers) incapable of supporting the heavy load of instant energy consumption.

It is necessary to link electric vehicle charging to the daily energy demand curve and instantaneous power availability in such a way that charging impacts the system as little as possible and maximizes the available energy resources. Ideally, there would be a move toward slow charging during off-peak periods. Furthermore, this kind of charging would not impact users as 90 percent of vehicles are not used between 11 a.m. and 6 p.m. Operating under such conditions would also permit the use of excess wind-generated power during off-peak times, enabling a clean locomotion device such as the PHEV to also use renewable (clean) energy as its primary source.

This all sounds reasonable, but the itinerant nature of roaming vehicle demand, together with relatively limited battery life, means that other variables such as home charging versus remote charging with the ability to measure consumption and set tariffs must be taken into account. What will be the charging price? How will charging be carried out when the vehicle is not parked at home, nor at its usual charging centre? What method will be used for making payments? Who will be involved in developing all this infrastructure and how will it all interrelate?

Smart Charging

One system providing answers to these questions is smart charging. Based on the concept, purpose and architecture of the smart grid, such technology can optimise charging in the most favorable way by considering several parameters. These may include: the current state of the electrical system; the battery charging level; tariff modes and associated demand-response models which may be applied (such as time of use, or TOU, tariffs); and the ability to use energy distributed and stored locally through an energy management system.

Smart charging would be capable of deciding when to charge in relation to different variables (for example, price and energy availability), and which energy sources to use (in-home energy storage, local and decoupled energy supply, plug-in to the distribution grid, etc.) Supporting the vehicle-to-grid (V2G) paradigm would enable managing and deciding not only when and how to best charge the vehicle, but also when to store energy in the vehicle battery that can later be returned to the grid for use in a local mode as a distributed energy source.

For all of this to be effective, a power and control electronics system (in both local and global mode), supported by information systems to manage those issues, is required. This will enable the optimal charging process (avoiding peak times, and doing fast charging only when necessary) and an intelligent measuring and tariff system. The latter may be either managed by utilities through advanced meter management (AMM), or virtually through energy tariffs and physical economic transactions. Such systems should allow for the interaction of various agents: end users, utilities, energy service companies (ESCO), infrastructure providers, banks and other method-of-payment companies.

Conclusion

Although there are still many unresolved issues around the introduction of electric vehicles (for example, incentives, carbon caps, tax collection, readiness of systems and business processes), the challenge associated with this means of locomotion and its effect on current business systems and models is a fascinating one. From an electrical viewpoint, there would not appear to be any significant impact on energy management in the medium term, but perhaps more so in terms of power requirements. As an example, some regions have adjusted to the massive introduction of air conditioning systems over recent years. While we are reassured as to the viability of electric vehicles, we are also alert to the possible significant impact of widespread vehicle charging, above all when considering a fast charging scenario.

The special characteristics of battery charging and its itinerant nature, the predicted volumes of power outlet and energy, the current state of tariff systems, the available technology, and the vision and state of deployment of smart grids and AMM, all add up to suggest a smart charging type of system would be the best option – though certainly complex to implement. Given the prominent role that information and communication technologies will play in such a system, it will be necessary to achieve consensus among various stakeholders over methodologies to be used, standards development, and in establishing a regulatory framework capable of supporting all the mechanisms and systems to be introduced.

We have already made good progress, and the electric vehicle could become an example that drives change in other business and technology models. It may well stimulate more rapid development of smart grids, encourage the creation of more efficient energy services and technologies, and lead to greater development and use of renewable energy sources, including a generation and distribution scenario based on the V2G paradigm.

It also may open the door to new businesses and stakeholders as well (such as the ESCOs) to introduce more dynamic, interactive demand response programs and broaden the function of battery storage as a provider of spinning reserves and ancillary services. These are all aspects for which it is now necessary to establish a basis for implementation and a short-term viability plan that will allow for the use of this technology with the aim of reaping its recognized benefits. Are we ready to step up to the challenge?

Meeting Future Utility Operating Challenges With a Smart Grid

The classical school of utility operations prescribes four priorities, ranked in the following descending order: safety, reliability, customer service and profit. Although it’s not hard to engage any number of industry insiders in an argument over whether profit in the classical model has recently switched places with customer service (and/or whether it should), most people accept that safety and reliability still reign supreme when it comes to operating a utility. This is true whether one takes a policy-, economic-, utility- or customer-oriented perspective.

Over many decades the utility industry has established a remarkably consistent pattern of power delivery based on the above-described priorities. Large, centralized generation facilities produce electricity from various sources interconnected via a networked transmission system feeding a predominantly radial distribution system. This classical power distribution system supports a predictable demand pattern that utilities can typically manage by using analytics such as similar day load forecasting. Moreover, future demand is also predictable, since average loads have been growing consistently by just a few percentage points annually, year in and year out.

To support this power delivery model, utilities also employ remarkably consistent system design and operational processes. Although any given utility might employ slightly different processes and procedures at varying degrees of efficiency and effectiveness – or deploy operating assets with slightly different design specifications – the underlying elements are generally consistent from one utility to another. They are engineered to either fail safe (safety) and/or not to fail at all (reliability) based on long-term operating patterns.

So why implement a smart grid? After all, the classical method of managing supply and demand has worked reasonably well over the decades. The system is safe and reliable, and most utilities are very profitable even in economic downtimes. However, a smart grid has three interrelated attributes – transparency, conditionality and kinematics – that together radically improve the “situational awareness” of the real-time state of the grid for both utilities and customers.

With this situational awareness comes the high system-state observability (transparency) that drives conditional management (conditionality) of the grid. All of this will ultimately support future power delivery patterns, which will be much more complex and difficult to predict and manage because demand and supply will fluctuate much more radically than at present (kinematics).

TRANSPARENCY

Price transparency is the foundation on which deregulated and competitive markets are built. However, until now price transparency has been limited primarily to wholesale transmission and generation domains. Indeed, the lack of price transparency at the point of distribution (that is, at retail) is a key reason deregulation has stalled in the United States.

Price transparency is of course only one aspect of the issue. Utilities must also synchronize usage transparency with price transparency based on time. That is, the value of knowing real-time pricing is diminished if a customer cannot also see their real-time usage and make energy usage behavior changes in relation to the real-time price signals.

From the utility’s perspective, usage transparency is limited. That’s because the distribution elements of most utility operations are largely opaque to operators. Once beyond the substation, usage disruptions are primarily identified by induction from fault conditions and usage patterns recorded a month after the disruption occurred via meter readings. For example, a distribution circuit may be substantially overloaded, but in most cases the utility won’t know until it fails. And when a failure does occur, utilities still depend on manual processes to determine the precise location and cause of the fault. The customer loads or network conditions that precipitated the failure can only be analyzed well after the event.

A smart grid significantly improves the level of visibility into the distribution grid. Smart meters, line sensors and the embedded processing that takes place within system assets such as switches and reclosers all provide a stream of real-time and near real-time data to the utility about the current operational state of the grid. The result: a dramatic improvement in utilities’ awareness of the state of the distribution grid.

CONDITIONALITY

As is the case with transparency, the consumer’s perspective of conditionality is more mature than the utility’s perspective. For example, the idea of the smart building is all about implementing a mini premise-side smart grid within the customer location and installing simple devices such as motion detectors that turn lights on or off in a room. Commercial energy management systems use even more sophisticated ways of optimizing the lighting, heating and other environmental parameters of a work or living space.

From the utility’s perspective, however, conditionality is much less advanced. In today’s operating world, most maintenance or repair activities take place either too late or too soon. When utilities wait until something in the infrastructure fails, it’s too late. If the grid is inspected based on some set time schedule irrespective of its condition, it’s too soon. Utilities thus fall into a pattern of either fault- or usage-based maintenance.

The alternative – condition-based maintenance – is already being used in many industries. The difference in the utilities industry is that outside of energy generation and transmission activities, there’s little data on the ongoing real-time condition of most of the assets a utility utilizes to provide its customers with service.

The chief benefit of conditionality is that it allows utilities to optimize asset utilization in both over- and under-use situations (Figure 1).

Conditionality also opens up opportunities for utilities to fully automate their utility distribution operations. Not only will this enable them to provide more reliable service to customers, it reduces the need for human intervention and thus dramatically cuts labor costs. In addition, automation can be used to mitigate the utilities industry’s looming problem of an aging workforce. For these and other reasons, conditionality is one of the most important contributions the smart grid will make to the industry.

KINEMATICS

In classical physics, kinematics studies how the position of an object changes with time. In today’s utility operations, neither load nor supply is particularly kinematical because changes to either take a long time and occur slowly (in normal operating conditions) and both can be reliably predicted.

Many industry observers, however, believe that this scenario is about to change dramatically. One thing that’s expected to drive this change is “distributed generation.” Under this scenario, instead of relying on large centralized generation, the industry will see significant growth in distribution-side generation technologies. Unlike today, much of this supply will not be centrally dispatched or under direct central control. The resulting energy supply will be much more complex to predict and manage. To the futurist this may seem like an exciting prospect, but to a grid operator or a utility, this represents a control and management nightmare, because it directly challenges the operational priorities of safety and reliability.

Hybrid and electric automobiles will also substantially alter the pattern of supply and load on the current grid. According to some predictions, electric automobiles will account for upwards of 20 percent of the automobile fleet in the United States in the coming decades. This means that millions of automobiles charging each night could increase customer load profiles over time by upwards of 30 to 50 percent. When coupled with even more futuristic ideas such as “vehicle to grid,” you end up with energy consumption scenarios that no one imagined when the grid was built.

CONCLUSION

The three attributes of the smart grid – transparency, conditionality and kinematics – are interrelated. Transparency provides situational awareness, which enables conditionality. And conditionality likewise is a requirement for managing the kinematic supply and load patterns of the future. But more importantly, the smart grid is the only way the classical operating priorities of the system can be sustained – or enhanced – given the upcoming expected changes to the industry.

Plugging in the Consumer

Thanks to new technologies and the spirit of independence and empowerment fostered by the digital age, consumers are taking on broader and more active roles in an increasing number of industries. Not only are consumers increasingly vocal and decisive about what they will or will not buy, they are in many cases becoming designers, producers, marketers and distributors of the products they once simply purchased.

As an example, consider the evolution of television and other video-based entertainment. Consumers in the early television era were passive participants, watching whatever programs the networks were broadcasting on one of the few available channels at any given time. Decisions regarding content sat firmly in the hands of broadcasters.

But in recent decades the media and entertainment business has changed dramatically. Cable and satellite made early inroads by providing viewers with hundreds of additional channel choices and niche programming. More recently, options such as digital video recorders, video on demand, video programming on mobile devices and online content libraries have emerged, giving consumers much greater control over what, where and when they watch. Moreover, pockets of media enthusiasts are taking on even more participatory roles, producing and marketing their own content.

Could something similar happen in the energy industry? One way to look at this question is to consider parallels between the way that media and entertainment have developed, and some of the realistic future business models for the energy industry. While the two industries are very different, there’s a strong possibility that consumer involvement in the energy business could evolve along similar lines, as illustrated in Figure 1.

Consumers have become more and more accustomed to choice, selectivity and multiple pricing schemes in services used every day. High tech products like mobile phones and Internet service usually spring to mind first, but personalization of services and products is occurring even in centuries-old institutions like medicine, education and food distribution. Fifty years ago, who would have envisioned customers accepting limitations on what doctors they could see in exchange for lower health care costs, pursuing degrees without attending classes or paying a premium for foods that met specific conditions on their production? Yet today health maintenance and preferred provider organizations, online degrees and organic foods are all commonplace concepts.

The more consumers enjoy the benefits of options and active decision making, the stronger the pressure will be on the energy industry to adapt. This means that utilities must revisit long-held beliefs about how best to serve customers and prepare to make fundamental changes in their strategies and operations in order to prosper in a more participatory market.

CONSUMER INVOLVEMENT

Many utility executives are skeptical about whether consumers really want to have different energy service options, and whether they will act on those desires. After all, electricity, natural gas and heating oil are essentially commodities. But so is broccoli, and millions of consumers are unwilling to settle for conventionally grown produce. Instead, they’re willing to pay more for food certified as grown without pesticides and artificial fertilizers, and under conditions that emphasize the use of renewable resources and the conservation of soil and water. [1] Given this perspective, can the energy industry afford not to prepare for rising consumer demand for multiple service programs and different pricing tiers?

To help address some of these questions, IBM conducted a survey of 1,900 energy consumers from six countries in North America, Western Europe and parts of the Asia-Pacific region. The survey focused on consumers’ current views and, perhaps more importantly, their expectations of the utilities that serve them. Their responses underscore four trends in energy consumer behavior, each indicating that customers value the same type of control they exercise in other parts of their lives: consumers are leveraging provider choice options, managing usage more actively, moving toward self-generation of power and making their opinions heard through multiple channels (not just public regulators).

Controlling Their Purchases

In some regions with competitive markets, consumers are already exercising their right to select energy providers. In the United Kingdom’s market of 48 million electricity consumers, for instance, more than 15 percent are switching per year.

In addition, the IBM survey demonstrates that a basic lack of awareness may still be holding consumers back. Across the worldwide respondent sample, one out of every five consumers did not know whether they could choose an alternative electricity provider.

Nevertheless, consumers were clear about wanting a choice. Among those who could not change providers or were not aware of their ability to choose, 84 percent wanted the option, as shown in Figure 2.

While price will always be a factor in consumer behavior, competition is also fostering a host of decision-making criteria that consumers might not have even considered before. According to the results of the IBM survey, consumers now consider a utility’s ethical reputation, alignment with community values and environmental actions as important as traditional “buyer values” like customer service and reliability.

Many consumers now have more choices about the type of energy they buy as well. More than 60 percent of the respondents to the IBM survey said they would be willing to pay a premium for green energy, and a significant minority (one in five consumers) indicated a willingness to pay at least 20 percent more for an environmentally friendly product.

Controlling the Switch

Only 30 percent of the consumers IBM surveyed expected their electricity use to increase over the next five years – yet 60 percent expected higher electricity bills. In times of rising energy costs, there is high motivation for conservation. But with many consumers also assuming a share of the responsibility for protecting the environment, finding new ways to better manage consumption has become a top-of-mind issue.

Although consumers have always been able to reduce usage through “brute force” measures – adjusting thermostats, switching off lights and the like – they are just now gaining the ability to truly manage consumption through greater awareness and better tools.

As smart meter deployment allows more consumers to obtain real-time usage data at the device and appliance level, households and small businesses will know which conservation actions really make an impact. This will enable better decisions and more permanent behavior changes.

Controlling Supply

When providers are unwilling or unable to satisfy their needs, consumers have an increasingly viable alternative: the technology to generate their own electricity.

As consumers weigh the self-generation option, cost is clearly a significant driver but not the only one, as illustrated in Figure 3.

If self-generation could reduce energy costs by 50 percent, well over half of the consumers we surveyed would be motivated to install, maintain and operate their own power generation systems. Yet among those same respondents, reliability and environmental impact seemed to matter more than a small (10 percent) cost reduction.

Interestingly, getting paid for surplus power received the most favorable reaction from survey respondents. Besides offering a financial payback that helps offset upfront investment and operational expense, we suspect this response also reflects an underlying desire to assert more control over a purchase for which conditions have historically been dictated to them.

Many of the industry executives we interviewed agree that widespread adoption of self-generation is not that far off. More than half believe that the value from a low-cost, low-emission generating technology could move a significant percentage of residential and small commercial customers to self-generation within the next decade.

It’s important to note that although the “competition” for traditional utility companies has traditionally been viewed as emerging alternative providers employing the existing distribution system, focusing only on this particular threat results in an incomplete picture. If technologies such as small-scale solar and combined heat and power generation were to rapidly drop in cost, customer migration to these options would serve as another competitive pressure for which utility executives would have to develop a defensive strategy.

Controlling Their Own Destinies

It’s easy to understand why consumers might become skeptical about the utility industry given power blackouts that affect millions of people, price hikes driven by factors that are little understood and the pursuit of mergers and acquisitions without benefits that are clear to customers. Events like these contribute to growing consumer concern – not only about utilities and their motives, but also about the regulatory process currently in place to protect the public. Consumers are increasingly unwilling to wait for regulators to act “in their best interests.” Instead, they’re going directly to lawmakers, the press and special-interest groups to try and enforce change.

For example, in January 2007, a 1997 Illinois deregulation bill expired, ending a 10-year rate freeze. As the shock of a sudden and dramatic rate increase set in, public pressure caused legislators to intervene – ultimately driving the state’s primary distribution utilities to provide a multi-year, billion-dollar rate relief package to help reduce the financial burden on ratepayers. [3]

Other Drivers and Enablers of Customers’ Desire for Control

Climate change is the one driver for which the goals and needs of both utilities and consumers converge. Consumers are clearly interested in the environmental practices of the companies with which they do business. Indeed, 70 percent of those surveyed reported that environmental considerations were already an important factor in choosing products other than energy, and that these concerns would ultimately also influence the energy products they purchased as well.

Of consumers who are aware of renewable power options available to them, almost 40 percent purchased some or all of their power under such a plan. Among the rest, more than 60 percent expressed interest in doing so. Utilities, for their part, are making major investments and operational changes to respond to climate change concerns and policies. In fact, the percentage of utilities spending at least 10 percent of their capital expenditures on environmental compliance over the next five years is expected to double.

To make the improvements needed to address the concerns discussed above, utilities will likely receive strong support for deployment of advanced energy technologies. Many of these have been available in some form for years, but their business cases have been rather lackluster. However, during the last three to five years, the technologies have continued to advance; their benefits have strengthened dramatically; and the costs of deployment have decreased. In the near term, smart meters, network automation and analytics, and distributed generation will likely drive the most industry change.

The emergence of these two trends, combined with growing consumer involvement, will have far-reaching consequences for the utility industry. Collectively, these drivers are overturning traditional assumptions about energy consumers and the fundamental value proposition of the industry itself. Companies will be forced to look at their residential and small commercial customer population in discrete segments, instead of as a largely uniform block of ratepayers. Ultimately, as the degree of control shifts from the utility to consumers, network and generation technologies will move away from the traditional centralized, one-way model to a more dynamic and distributed one. New industry structures will emerge, creating new opportunities and challenging existing models.

CONSUMERS: NO LONGER JUST PASSIVE RATEPAYERS

Our detailed analysis of the consumer survey responses showed that two primary characteristics define different types of consumer behavior. First, personal initiative, or the willingness to make decisions and take action based on specific goals – such as cost control, reliability, convenience and climate change impacts – will drive consumer behavior.

Second, disposable income – or the financial wherewithal to support energy-related goals in early adoption phases – will have a substantial impact on consumer actions, since only those with sufficient resources will be able to implement new technologies and buy more expensive products. Different combinations of these two characteristics lead to four distinct consumer profiles, as shown in Figure 4.

Each consumer segment has specific needs and wants, and utilities will need to adopt different strategies, and likely develop different offerings, for each. However, before utilities can begin tailoring their approaches to particular segments, most will need to invest in tools and capabilities that help them collect and analyze consumer data, particularly as huge quantities of real-time data and new information streams are generated by deployment of advanced sensing, metering and communications technologies.

THE IMPACT OF INCREASED CONSUMERS CONTROL

Recent trials have demonstrated that both customers and local utilities derive benefits from consumers taking a more active role in their energy decisions. For example, in a yearlong program in the Pacific Northwest giving consumers the ability to customize their energy use to save money or maximize comfort, participants saved approximately 10 percent on their electricity bills and reduced peak power use by 15 percent. Throughout the region, the information, communications and control technologies and algorithms provided by Pacific Northwest National Laboratory, IBM and Invensys Controls helped consumers in the study become an integral part of power grid operations on a daily basis – especially in times of extreme stress on the electrical distribution system. A combination of demand response and distributed generation reduced peak distribution loads by as much as 50 percent. (For more information about this program, see “Case Study: The GridWise Olympic Peninsula Project” elsewhere in this book.)

Another pilot, run by Canada’s Ontario Energy Board tested consumers’ inclination to shift and reduce demand when provided with smart meters and time-of-use pricing. On average, three-quarters of the participants shifted enough of their consumption away from peak times to save 3 percent per month on their energy bills. During four peak summer events, when penalties and rebates applied, shifts in consumption led to even greater savings – as much as 25 percent, depending on the specific plan the customer was using. As a result of their awareness of energy usage and behavioral changes, participants also reduced total consumption. This “conservation effect” amounted to a 6 percent reduction in overall usage. When combined with the effects of shifting, this allowed 90 percent of the participants to pay less than they would have paid on their prior plans – results that are particularly remarkable given that consumers were relying on monthly usage statements; if consumers had a near real-time view of their energy
usage, these reductions might have been even more dramatic. (For more on this trial, see “Case Study: Smarter Prices for Smarter Consumers in Ontario” elsewhere in this book.)

INDUSTRY MODELS: TOWARD A PARTICIPATORY NETWORK

We believe that studies like those outlined above demonstrate the strong benefits of both technology evolution and shifts in the balance of control between utilities and consumers. The nature of the benefits will depend on the path chosen to move from current “passive” business models to more active ones. Specific types of technology and customer behavior evolution will give rise to four industry models, as shown in Figure 5.

Although each model is distinct and requires different capabilities, the industry as a whole – at least in the near term – will represent an amalgam of all four models. In fact, many utilities will find themselves operating in more than one model, particularly if a company operates in different geographies. In addition, moves across boundaries will tend to be evolutionary and depend on local conditions.

Where consumers aren’t as eager to assume control of decision-making – or regulators don’t allow them the freedom to do so – companies will be most likely move from traditional models through a state of operations transformation before fully enabling participatory networks. Where this path dominates, utilities will need to build business cases around cost savings and environmental benefits to deploy new technologies. In a high-cost, carbon-constrained environment, however, this should be an easier sell to regulators and investors than in the past.

In markets where consumer demand for control grows faster than new technologies can be deployed, particularly in heavily regulated rate-of-return environments, constrained choice will dominate in the near term. Utilities will be pressured to meet demand for control in creative – and sometimes untested – ways. And regulators may need to be more flexible in viewing these investments than they might be with traditional utility capital investments. For both parties, early assessment of needs and review of available options will be critical.

Whichever path is adopted, we anticipate a steady progression toward a participatory network – a technology ecosystem comprising a wide variety of intelligent network-connected devices, distributed generation and consumer energy management tools.

Although the precise time frame for reaching this end-state is unknown, our research suggests a few major milestones. Within five years, the percentage of the world’s electric utilities generating at least 10 percent of their power from renewable sources should double. In that same time frame, we believe that sufficient supplier choice will allow meaningful consumer switching to emerge in most major competitive markets. Also, based on both consumer and utility responses, we expect utility demand management initiatives to expand dramatically and electric power generation by consumers to increase dramatically within a decade.

IMPLICATIONS: CUSTOMER FOCUS AS A COMPETITIVE ADVANTAGE

By leveraging the new technology ecosystem, utilities will be able to meet key objectives in coming years. Specifically, they’ll be able to:

  • Prepare for an environment in which customers are more active participants;
  • Capitalize on new sources of real-time consumer and operational information, and decide which role(s) to play in the industry’s evolving value chain; and
  • Better understand and serve an increasingly heterogeneous customer base.

The utility industry is fast approaching a tipping point beyond which consumers can (and increasingly will) demand equal footing with their providers. Those utilities that are prepared to share responsibility with their residential and small commercial customers, and help them meet their specific energy goals, can expect to enjoy significant competitive advantage.

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 Customer-Focused Utility

THE CHANGING DYNAMICS OF CUSTOMER RELATIONSHIPS

The utilities industry is in transition. External factors – including shifts in governmental policies, a globally felt sense of urgency about conserving energy, advances in power generation techniques and new technologies – are driving massive changes throughout the industry. Utilities are also under internal pressure to prevent profit margins from eroding. But most significantly, utilities must evolve to compete in a marketplace where consumers increasingly expect high-quality customer service and believe that no company deserves their unconditional loyalty if it cannot perform to expectations. These pressures are putting many utility providers into seriously competitive, market-driven situations where the customer experience becomes a primary differentiator.

In the past, utility companies had very limited interactions with customers. Apart from opening new accounts and billing for services, the relationship was remote, with customers giving no more thought to their power provider than they would to finding a post office. Consumers were indifferent to greenhouse gas (GHG) emissions and essentially took a passive view of all utility functions, only contacting the utility if their lights temporarily went out.

In contrast, the utility of the future can expect a much more intense level of customer involvement. If utilities embrace programs to change customers’ behaviors – for example, by implementing time-of-use rates – customers will need more information on a timelier basis in order to make educated decisions. In addition, customers will expect higher levels of service to keep up with changes in the rest of the commercial world. As consumers get used to checking their bank account and credit card balances via mobile devices, they’ll soon expect the same from all similar services, including their utility company. As younger consumers (Generation Y and now Generation Z) begin their relationships with utilities, they bring expectations of a digital, mobile and collaborative customer service experience. Taking a broader perspective, most age segments – even baby boomers – will begin demanding these new multichannel experiences at times that are convenient for them.

The most significant industry shifts will alter the level of interaction between the utility grid and the home. In the past, this was a one-way street; in the future, however, more households will be adopting “participatory generation” due to their increased use of renewable energy. This will require a more sophisticated home/ grid relationship, in order to track the give and take of power between consumers as both users and generators. This shift will likely change the margin equation for most utility companies.

Customer Demands Drive Technology Change; Technology Change Drives Customer Demand

Utilities are addressing these and other challenges by implementing new business models that are supported by new technologies. The most visible – and arguably the most important – of the new technologies are advanced metering infrastructure (AMI) and the technical components of the smart grid, which integrates AMI with distribution automation and other technologies to connect a utility’s equipment, devices, systems, customers, partners and employees. The integration of these technologies with customer information systems (CIS) and other customer relationship management (CRM) tools will increase consumer control of energy expenditures. Most companies in the industry will need to shift away from the “ratepayer” approach they currently use to serve residential and small business customers, and adapt to changing consumer behavior and emerging business models enabled by new network and generation technologies.

Impacts on the Customer Experience

There are multiple paths to smart grid deployment, all of which utility firms have employed to leverage new sources of data on power demand. If we consider a gradual transformation from today’s centralized, one-way view to a network that is both distributed and dynamic, we can begin to project how technological shifts will impact the utility-consumer relationship, as illustrated in Figure 1.

The future industry value chain for grid-connected customers will have the same physical elements and flow as the current one but be able to provide many more information-oriented elements. Consequently, the shift to a customer-focused view will have serious implications for data management. These include a proliferation of data as well as new mandates for securely tracking, updating, accessing, analyzing and ensuring quality.

In addition, utilities must develop customer experience capabilities in parallel with extending their energy information management capabilities. Taking the smart grid path requires customers to be more involved, as decision-making responsibility shifts more toward the consumer, as depicted in Figure 2.

It’s also important to consider some of the new interactions that consumers will have with their utility company. Some of these will be viewed as “features” of the new technology, whereas others may significantly change how consumers view their relationship with their energy provider. Still others will have a profound impact on how data is captured and deployed within the organization. These interactions may include:

  • Highly detailed, timely and accurate individuated customer information;
  • Interaction between the utility and smart devices – including the meter – in the home (possibly based on customers’ preferences);
  • Seamless, bidirectional, individual communication permitting an extended dialogue across multiple channels such as short message service, integrated voice response, portals and customer care;
  • Rapid (real-time) analysis of prior usage, current usage and prediction of future usage under multiple usage and tariff models;
  • Information presented in a customer-friendly manner;
  • Analytical tools that enable customers to model their consumption behavior and understand the impact of changes on energy cost and carbon footprint;
  • Ability to access and integrate a wide range of external information sources, and present pertinent selections to a customer;
  • Integration of information flow from field operations to the customer call center infrastructure; and
  • Highly skilled, knowledgeable contact center agents who can not only provide accurate information but can advise and recommend products, services, rate plans or changes in consumption profiles.

Do We Need to Begin Thinking About Customers Differently?

Two primary factors will determine the nature of the interface between utilities and consumers in the future. The first is the degree to which consumers will take the initiative in making decisions about the energy supply and their own energy consumption. Second, the amount and percentage of consumers’ disposable income that they allocate to energy will directly influence their consumption and conservation choices, as shown in Figure 3.

How Do Utilities Influence Customers’ Behavior?

One of the major benefits of involving energy customers in generation and consumption decisions is that it can serve to decrease base load. Traditionally, utilities have taken two basic approaches to accomplishing this: coercion and enticement. Coercion is a penalty-based approach for inducing a desired behavior. For example, utilities may charge higher rates for peak period usage, forcing customers to change the hours when they consume power or pay more for peak period usage. The risks of this approach include increased customer dissatisfaction and negative public and regulatory opinion.

Enticement, on the other hand, is an incentive-based approach for driving a desired behavior. For example, utilities could offer cost savings to customers who shift power consumption to off-peak times. The risks associated with this approach include low customer involvement, because incentives may not be enough to overcome the inconvenience to customers.

Both of these approaches have produced results in the past, but neither will necessarily work in the new, more interactive environment. A number of other strategies may prove more effective in the future. For example, customer goal achievement may be one way to generate positive behavior. This model offers benefits to customers by making it easier for them to achieve their own energy consumption or conservation goals. It also gives customers the feeling that they have choices – which promotes a more positive relationship between the customer and the utility. Ease of use represents another factor that influences customer behavior. Companies can accomplish this by creating programs and interfaces that make it simple for the customer to analyze information and make decisions.

There is no “silver bullet” approach to successfully influencing all customers in all utility environments. Often, each customer segment must be treated differently, and each utility company will need to develop a unique customer experience strategy and plan that fits the needs of its unique business situation. The variables will include macro factors such as geography, customer econo-graphics and energy usage patterns; however, they’ll also involve more nuanced attributes such as customer service experiences, customer advocacy attitudes and their individual emotional dispositions.

CONCLUSION

Most utilities considering implementing advanced metering or broader smart grid efforts focus almost exclusively on deploying new technologies. However, they also need to consider customer behavior. Utilities must adopt a new approach that expands the scope of their strategic road map by integrating the “voice of the customer” into the technology planning and deployment process.

By carefully examining a utility customer’s expectations and anticipating the customer impacts brought on by innovative technologies, smart utility companies can get ahead of the customer experience curve, drive more value to the bottom line and ultimately become truly customer focused.

Developing a Customer Value Transformation Road Map

Historically, utility customers have had limited interactions with their electric or gas utilities, except to start or stop service, report outages, and pay bills or resolve billing questions. This situation is changing as the result of factors that include rising energy prices, increasing concerns about the environment and trends toward more customer interaction and control among other service providers – such as cell phone companies. Over the next five to 10 years, we expect utility customers to continue seeking improvements in three key areas:

  • Increased communication with their utility company, through a greater variety of media;
  • Improved understanding of and control over their own energy use; and
  • More accurate and timely information on outage events and service restoration.

Moreover, as the generations that have grown up with cell phones, the Internet, MP3 players and other digital devices move into adulthood, they will expect utilities to keep pace with their own technological sophistication. These new customers will assume that they can customize the nature of their communications with both friends and businesses. Utilities that can provide these capabilities will unlock new sources of revenue and be better able to retain customers when faced with competition.

The intelligent utility network (IUN) will be a key enabler of these new customer capabilities and services. But not all customers will want all of the new capabilities, so utilities need to understand and carefully analyze the value of each among various customer segments. This will require utilities to prepare sound business cases and prioritize their plans for meeting future customer needs.

One of the first initiatives that utilities launching an IUN program should undertake is the development of a “customer value transformation road map.” The road map approach allows utilities to establish the types of capabilities and services that customers will want, to identify and define the gaps in current processes and systems that must be overcome to meet these needs, and to develop plans to close those gaps.

TRANSFORMATION ROAD MAP DEVELOPMENT APPROACH

Our approach for developing the customer value transformation road map includes four tasks, as depicted in Figure 1.

Task 1: Customer Requirements

The primary challenge facing utilities in defining customer requirements is the need to anticipate their desires and preferences at least five to 10 years into the future. Developing this predictive vision can be difficult for managers because they’re often “locked into” their current views of customers, and their expectations are based largely on historical experience. To overcome this, utilities can learn from other industries that are already traveling this path.

The telecommunications providers, as one example, have made substantial progress in meeting evolving customer needs over the last decade. While more changes lie ahead for telecommunications, the industry has significantly enhanced the customer experience, created differentiated capabilities for various customer segments and succeeded in developing many of these capabilities into profit-generating services. This progress can serve as both an inspiration and a guide as utilities start down a similar path.

The first step in defining future customer requirements is to segment the customer base into the various customer groups that are likely to have different needs. Although these segments will likely vary for each utility, we believe that the following seven major customer segments serve as a useful starting point for this work:

  • Residential – tech savvy. These are customers who want many different electronic communication pathways but don’t necessarily want to develop a detailed understanding of the trends and patterns in their energy usage.
  • Residential – low tech. These customers prefer traditional, less high tech ways of communicating, but may want to perform analysis of their usage.
  • Residential – low income. These are customers who want to understand what’s driving their energy expenditures and how to reduce their bills; many of these customers are also tech savvy.
  • Special needs. These customers, often elderly, may live on fixed incomes and are accustomed to careful planning, and want no surprises in their interactions with providers of utility services. They frequently need help from others to manage their daily activities.
  • Small business. These commercial customers are typically very cost-conscious and highly adaptable and seek creative but relatively simple solutions to their energy management challenges.
  • Large commercial. These are customers who are cost-conscious and capable of investing substantial time and money in order to analyze and reduce their energy use in sophisticated ways.
  • Industrial. These very large customers are sophisticated, cost-conscious and increasingly focused on environmental issues.

The next step in defining future customer requirements is to understand the points in the utility value chain at which customers will interact with their utility. Based on recent trends for both utilities and other industries, the following “touch point” areas are a good starting point:

  • Reliability and restoration;
  • Billing;
  • Customer service;
  • Energy information and control; and
  • Environment.

Not all of these requirements will be important to all customer segments. It is essential to establish the most important requirements for each segment and each touch point. Figure 2 provides one example of a preliminary assessment of the relative importance of selected customer requirements for the reliability and restoration category, across the seven specified customer segments. Each customer need is assigned a high (H), medium (M) or low (L) rank.

Once this preliminary assessment is completed, utilities should consider conducting several workshops with participants from various functional departments. The goal of these workshops is to obtain feedback, to evaluate even more thoroughly the importance of each potential requirement and to begin to secure internal acceptance of the customer requirements that are determined to be worth pursuing. Departments that should participate in such workshops include those focused on regulatory requirements, billing, corporate communications, demand-side management, customer operations, complaint resolution and outage management.

One way of making the workshop process more “real” and therefore more effective is to develop customer use scenarios that incorporate each potential requirement. For example, the following billing scenarios could be used to illustrate potential customer requirements and to facilitate more effective evaluation of what will be needed for billing:

  • Billing Scenario 1. I want my gas and electric bills to be unified so that I don’t have to spend extra time making multiple payments. Also, I want the choice of paying my bill electronically, by mail or in person, based on what’s convenient for me, not what’s convenient for my utility.
  • Billing Scenario 2. My parents, who are now retired, receive fixed pension checks, and I want their utility to set up a payment plan for them that results in equal payments over the year, rather than high payments in the summer and low payments in the winter. My parents also want the ability to see a summarized version of their bill in large print, so that they can easily read and understand their energy use and costs.
  • Billing Scenario 3. My kids are on their computer nearly all of the time, and the remainder of the time they seem to be playing their video games. Also, they rarely turn off lights, and all of these things are increasing my energy bills. I want my utility to help me set up a balance limit so that if our energy usage reaches a set level, I’m automatically notified and I have the option of taking some corrective actions. I also expect my meter readings to be accurate rather than simply rough estimates, because I want to understand exactly how much energy I am consuming and what it’s costing me.

In addition to assessing the value of each requirement to customers, it is also important to rank these requirements based on other factors, such as their impacts on the utility. Financial costs and benefits, for example, clearly need to be estimated and considered when evaluating a requirement, regardless of how important the requirement will be to customers. To draw all of these assessments together, it is useful to assign weights to each assessment area – for example, a weight of 35 percent for customer importance, 30 percent for utility costs/benefits and 35 percent for the value that regulators will perceive. Once an appropriate weighting scheme is applied, the utility can rank the requirements and develop a list of those with the highest priority.

Task 2: Gaps

To assess gaps in current capabilities that could prevent a utility from meeting important and valuable customer requirements, the utility should next identify the business processes, organizations and technologies that will “deliver” those requirements. This requires a careful analysis of current and planned process, organizational and technology capabilities, which can be challenging because other initiatives will be affecting these areas even as customer requirements evolve. Moreover, many utilities do not have accurate, detailed documentation of current processes and systems. Therefore, a series of workshops and interviews with functional and technology leaders and staff is necessary. The results of these workshops should be supplemented by analysis of planned systems and process transformations, in order to assess current gaps and to determine whether those gaps will be closed – based on plans that are already in place. If such gaps remain, new projects and capital investments may be required to close
them and to meet expected customer requirements.

During the gap assessment process, it’s critical that the customer value team work closely with other IUN teams to ensure that the customer value gap analysis is coordinated with the broader gap analysis for the IUN program. Important areas to coordinate include automated meter information, demand-side management, outage management and asset management.

Task 3: Business Case Support

While conducting the first two tasks, the assessment team should be able to develop a deep understanding of the costs required to meet the important customer requirements as well as the financial benefits. Because it’s typical to develop consolidated business cases for the IUN, the customer value team should work with the overall IUN business case team to support business case development by bringing this information into the process.

Task 4: Transformation Road Map

This final task builds on an understanding of both the customer requirements and the gaps in current operations to create the customer value transformation road map. The initiatives in the road map will typically be defined across the following primary areas:

  • Process;
  • Technology;
  • Performance metrics;
  • Organization and training; and
  • Project management.

For each of these areas, the road map will establish the timing and sequence of initiatives to close the gaps, based on:

  • The utility’s strategic priorities and capacity for change;
  • Linkages to the utility’s overall IUN transformation plans; and
  • Technology dependencies and links to other work areas.
  • Figure 3 provides a summary of the initiatives from a typical customer value transformation road map. The detail behind this summary provides a path to transforming the customer-related operations to meet expected customer requirements over the next five to 10 years.

    CONCLUSION

    Our “customer value transformation road map” approach provides utilities with a structured process for identifying, assessing and prioritizing future customer requirements. Utilities that are successful in developing such a road map will be better prepared to build customer needs into their overall IUN transformation plans. These companies will in turn increase the likelihood that their IUN transformation will improve customer satisfaction, reduce customer care costs and lead to new sources of revenue.

The GridWise Olympic Peninsula Project

The Olympic Peninsula Project consisted of a field demonstration and test of advanced price signal-based control of distributed energy resources (DERs). Sponsored by the U.S. Department of Energy (DOE) and led by the Pacific Northwest National Laboratory, the project was part of the Pacific Northwest Grid- Wise Testbed Demonstration.

Other participating organizations included the Bonneville Power Administration, Public Utility District (PUD) #1 of Clallam County, the City of Port Angeles, Portland General Electric, IBM’s T.J. Watson Research Center, Whirlpool and Invensys Controls. The main objective of the project was to convert normally passive loads and idle distributed generation into actively participating resources optimally coordinated in near real time to reduce stress on the local distribution system.

Planning began in late 2004, and the bulk of the development work took place in 2005. By late 2005, equipment installations had begun, and by spring 2006, the experiment was fully operational, remaining so for one full year.

The motivating theme of the project was based on the GridWise concept that inserting intelligence into electric grid components at every point in the supply chain – from generation through end-use – will significantly improve both the electrical and economic efficiency of the power system. In this case, information technology and communications were used to create a real-time energy market system that could control demand response automation and distributed generation dispatch. Optimal use of the DER assets was achieved through the market, which was designed to manage the flow of power through a constrained distribution feeder circuit.

The project also illustrated the value of interoperability in several ways, as defined by the DOE’s GridWise Architecture Council (GWAC). First, a highly heterogeneous set of energy assets, associated automation controls and business processes was composed into a single solution integrating a purely economic or business function (the market-clearing system) with purely physical or operational functions (thermostatic control of space heating and water heating). This demonstrated interoperability at the technical and informational levels of the GWAC Interoperability Framework (www.gridwiseac.org/about/publications.aspx), providing an ideal example of a cyber-physical-business system. In addition, it represents an important class of solutions that will emerge as part of the transition to smart grids.

Second, the objectives of the various asset owners participating in the market were continuously balanced to maintain the optimal solution at any point in time. This included the residential demand response customers; the commercial and municipal entities with both demand response and distributed generation; and the utilities, which demonstrated interoperability at the organizational level of the framework.

PROJECT RESOURCES

The following energy assets were configured to respond to market price signals:

  • Residential demand response for electric space and water heating in 112 single-family homes using gateways connected by DSL or cable modem to provide two-way communication. The residential demand response system allowed the current market price of electricity to be presented to customers. Consumers could also configure their demand response automation preferences. The residential consumers were evenly divided among three contract types (fixed, time of use and real time) and a fourth control group. All electricity consumption was metered, but only the loads in price-responsive homes were controlled by the project (approximately 75 KW).
  • Two distributed generation units (175 KW and 600 KW) at a commercial site served the facility’s load when the feeder supply was not sufficient. These units were not connected in parallel to the grid, so they were bid into the market as a demand response asset equal to the total load of the facility (approximately 170 KW). When the bid was satisfied, the facility disconnected from the grid and shifted its load to the distributed generation units.
  • One distributed microturbine (30 KW) that was connected in parallel to the grid. This unit was bid into the market as a generation asset based on the actual fixed and variable expenses of running the unit.
  • Five 40-horsepower (HP) water pumps distributed between two municipal water-pumping stations (approximately 150 KW of total nameplate load). The demand response load from these pumps was incrementally bid into the market based on the water level in the pumped storage reservoir, effectively converting the top few feet of the reservoir capacity into a demand response asset on the electrical grid.

Monitoring was performed for all of these resources, and in cases of price-responsive contracts, automated control of demand response was also provided. All consumers who employed automated control were able to temporarily disable or override project control of their loads or generation units. In the residential realtime price demand response homes, consumers were given a simple configuration choice for their space heating and water heating that involved selecting an ideal set point and a degree of trade-off between comfort and price responsiveness.

For real-time price contracts, the space heater demand response involved automated bidding into the market by the space heating system. Since the programmable thermostats deployed in the project didn’t support real-time market bidding, IBM Research implemented virtual thermostats in software using an event-based distributed programming prototype called Internet- Scale Control Systems (iCS). The iCS prototype is designed to support distributed control applications that span virtually any underlying device or business process through the definition of software sensor, actuator and control objects connected by an asynchronous event programming model that can be deployed on a wide range of underlying communication and runtime environments. For this project, virtual thermostats were defined that conceptually wrapped the real thermostats and incorporated all of their functionality while at the same time providing the additional functionality needed to implement the real-time bidding. These virtual thermostats received
the actual temperature of the house as well as information about the real-time market average price and price distribution and the consumer’s preferences for set point and comfort/economy trade-off setting. This allowed the virtual thermostats to calculate the appropriate bid every five minutes based on the changing temperature and market price of energy.

The real-time market in the project was implemented as a shadow market – that is, rather than change the actual utility billing structure, the project implemented a parallel billing system and a real-time market. Consumers still received their normal utility bill each month, but in addition they received an online bill from the shadow market. This additional bill was paid from a debit account that used funds seeded by the project based on historical energy consumption information for the consumer.

The objective was to provide an economic incentive to consumers to be more price responsive. This was accomplished by allowing the consumers to keep the remaining balance in the debit account at the end of each quarter. Those consumers who were most responsive were estimated to receive about $150 at the end of the quarter.

The market in the project cleared every five minutes, having received demand response bids, distributed generation bids and a base supply bid based on the supply capacity and wholesale price of energy in the Mid-Columbia system operated by Bonneville Power Administration. (This was accomplished through a Dow Jones feed of the Mid-Columbia price and other information sources for capacity.) The market operation required project assets to submit bids every five minutes into the market, and then respond to the cleared price at the end of the five-minute market cycle. In the case of residential space heating in real-time price contract homes, the virtual thermostats adjusted the temperature set point every five minutes; however, in most cases the adjustment was negligible (for example, one-tenth of a degree) if the price was stable.

KEY FINDINGS

Distribution constraint management. As one of the primary objectives of the experiment, distribution constraint management was successfully accomplished. The distribution feeder-imported capacity was managed through demand response automation to a cap of 750 KW for all but one five-minute market cycle during the project year. In addition, distributed generation was dispatched as needed during the project, up to a peak of about 350 KW.

During one period of about 40 hours that took place from Oct. 30, 2006, to Nov. 1, 2006, the system successfully constrained the feeder import capacity at its limit and dispatched distributed generation several times, as shown in Figure 1. In this figure, actual demand under real-time price control is shown in red, while the blue line depicts what demand would have been without real-time price control. It should be noted that the red demand line steps up and down above the feeder capacity line several times during the event – this is the result of distributed generation units being dispatched and removed as their bid prices are met or not.

Market-based control demonstrated. The project controlled both heating and cooling loads, which showed a surprisingly significant shift in energy consumption. Space conditioning loads in real-time price contract homes demonstrated a significant shift to early morning hours – a shift that occurred during both constrained and unconstrained feeder conditions but was more pronounced during constrained periods. This is similar to what one would expect in preheating or precooling systems, but neither the real nor the virtual thermostats in the project had any explicit prediction capability. The analysis showed that the diurnal shape of the price curve itself caused the effect.

Peak load reduced. The project’s realtime price control system both deferred and shifted peak load very effectively. Unlike the time-of-use system, the realtime price control system operated at a fine level of precision, responding only when constraints were present and resulting in a precise and proportionally appropriate level of response. The time-of-use system, on the other hand, was much coarser in its response and responded regardless of conditions on the grid, since it was only responding to preconfiured time schedules or manually initiated critical peak price signals.

Internet-based control demonstrated. Bids and control of the distributed energy resources in the project were implemented over Internet connections. As an example, the residential thermostats modified their operation through a combination of local and central control communicated as asynchronous events over the Internet. Even in situations of intermittent communication failure, resources typically performed well in default mode until communications could be re-established. This example of the resilience of a well-designed, loosely coupled distributed control application schema is an important aspect of what the project demonstrated.

Distributed generation served as a valuable resource. The project was highly effective in using the distributed generation units, dispatching them many times over the duration of the experiment. Since the diesel generators were restricted by environmental licensing regulations to operate no more than 100 hours per year, the bid calculation factored in a sliding scale price premium such that bids would become higher as the cumulative runtime for the generators increased toward 100 hours.

CONCLUSION

The Olympic Peninsula Project was unique in many ways. It clearly demonstrated the value of the GridWise concepts of leveraging information technology and incorporating market constructs to manage distributed energy resources. Local marginal price signals as implemented through the market clearing process, and the overall event-based software integration framework successfully managed the bidding and dispatch of loads and balanced the issues of wholesale costs, distribution congestion and customer needs in a very natural fashion.

The final report (as well as background material) on the project is available at www.gridwise.pnl.gov. The report expands on the remarks in this article and provides detailed coverage of a number of important assertions supported by the project, including:

  • Market-based control was shown to be a viable and effective tool for managing price-based responses from single-family premises.
  • Peak load reduction was successfully accomplished.
  • Automation was extremely important in obtaining consistent responses from both supply and demand resources.
  • The project demonstrated that demand response programs could be designed by establishing debit account incentives without changing the actual energy prices offered by energy providers.

Although technological challenges were identified and noted, the project found no fundamental obstacles to implementing similar systems at a much larger scale. Thus, it’s hoped that an opportunity to do so will present itself at some point in the near future.

Ontario Pilot

Smart metering technologies are making it possible to provide residential utility customers with the sophisticated “smart pricing” options once available only to larger commercial and industrial customers. When integrated with appropriate data manipulation and billing systems, smart metering systems can enable a number of innovative pricing and service regimes that shift or reduce energy consumption.

In addition, by giving customers ready access to up-to-date information about their energy demand and usage through a more informative bill, an in-home display monitor or an enhanced website, utilities can supplement smart pricing options and promote further energy conservation.

SMART PRICES

Examples of smart pricing options include:

  • Time-of-use (TOU) is a tiered system where price varies consistently by day or time of day, typically with two or three price levels.
  • Critical peak pricing (CPP) imposes dramatically higher prices during specific days or hours in the year to reflect the actual or deemed price of electricity at that time.
  • Critical peak rebate (CPR) programs enable customers to receive rebates for using less power during specific periods.
  • Hourly pricing allows energy prices to change on an hourly basis in conformance with market prices.
  • Price adjustments reflect customer participation in load control, distributed generation or other programs.

SMART INFORMATION

Although time-sensitive pricing is designed primarily to reduce peak demand, these programs also typically result in a small reduction in overall energy consumption. This reduction is caused by factors independent of the primary objective of TOU pricing. These factors include the following:

  • Higher peak pricing causes consumers to eliminate, rather than merely delay, activities or habits that consume energy. Some of the load reductions that higher peak or critical peak prices produce are merely shifted to other time periods. For example, consumers do not stop doing laundry; they simply switch to doing it at non-peak times. In these cases the usage is “recovered.” Other load reductions, such as those resulting from consumers turning off lights or lowering heat, are not recovered, thus reducing the household’s total electricity consumption.
  • Dynamic pricing programs give participants a more detailed awareness of how they use electricity, which in turn results in lower consumption.
  • These programs usually increase the amount of usage information or feedback received by the customer, which also encourages lower consumption.

The key challenge for utilities and policy makers comes in deciding which pricing and communications structures will most actively engage their customers and drive the desired conservation behaviors. Studies show that good customer feedback on energy usage can reduce total consumption by 5 to 10 percent. Smart meters let customers readily access more up-to-date information about their hourly, daily and monthly energy usage via in-home displays, websites and even monthly bill inserts.

The smart metering program undertaken by the province of Ontario, Canada, presents one approach and serves as a useful example for utility companies contemplating similar deployments.

ONTARIO’S PROGRAM

In 2004, anticipating a serious energy generation shortfall in coming years, the government of Ontario announced plans to have smart electricity meters installed in 800,000 homes and small businesses by the end of 2007, and throughout Ontario by 2010. The initiative will affect approximately 4.5 million customers.

As the regulator of Ontario’s electricity industry, the Ontario Energy Board (OEB) was responsible for designing the smart prices that would go with these smart meters. The plan was to introduce flexible, time-of-use electricity pricing to encourage conservation and peak demand shifting. In June 2006, the OEB commissioned IBM to manage a pilot program that would help determine the best structure for prices and the best ways to communicate these prices.

By Aug. 1, 2006, 375 residential customers in the Ottawa area of Ontario had been recruited into a seven-month pilot program. Customers were promised $50 as an incentive for remaining on the pilot for the full period and $25 for completing the pilot survey.

Pilot participants continued to receive and pay their “normal” bimonthly utility bills. Separately, participants received monthly electricity usage statements that showed their electricity supply charges on their respective pilot price plan, as illustrated in Figure 1. Customers were not provided with any other new channels for information, such as a website or in-home display.

A control group that continued being billed at standard rates was also included in the study. Three pricing structures were tested in the pilot, with 125 customers in each group:

  • Time-of-use (TOU). Ontario’s TOU pricing includes off-peak, mid-peak and peak prices that changed by winter and summer season.
  • TOU with CPP. Customers were notified a day in advance that the price of the electricity commodity (not delivery) for three or four hours the next day would increase to 30 cents per kilowatt hour (kWh) – nearly six times the average TOU price. Seven critical peak events were declared during the pilot period – four in summer and three in winter. Figure 2 shows the different pricing levels.
  • TOU with CPR. During the same critical peak hours as CPP, participants were provided a rebate for reductions below their “baseline” usage. The base was calculated as the average usage for the same hours of the five previous nonevent, non-holiday weekdays, multiplied by 125 percent.

The results from the Ontario pilot clearly demonstrate that customers want to be engaged and involved in their energy service and use. Consider the following:

  • Within the first week, and before enrollment was suspended, more than 450 customers responded to the invitation letter and submitted requests to be part of the pilot – a remarkable 25 percent response rate. In subsequent focus groups, participants emphasized a desire to better monitor their own electricity usage and give the OEB feedback on the design of the pricing. These were in fact the primary reasons cited for enrolling in the pilot.
  • In comparison to the control group, total load shifting during the four summertime critical peak periods ranged from 5.7 percent for TOU-only participants to 25.4 percent for CPP participants.
  • By comparing the usage of the treatment and control groups before and during the pilot, a substantial average conservation effect of 6 percent was recorded across all customers.
  • Over the course of the entire pilot period, on average, participants shifted consumption and paid 3 percent, or $1.44, less on monthly bills with the TOU pilot prices, compared with what they would have paid using the regular electricity prices charged by their utility. Of all participants, 75 percent saved money on TOU prices. Figure 3 illustrates the distribution of savings.
  • When this shift in consumption was combined with the reduction in customers’ overall consumption, a total average monthly savings of more than $4 resulted. From this perspective, 93 percent of customers would pay less on the TOU prices over the course of the pilot program than they would have with the regular electricity prices charged by their utility.
  • Citing greater control of their energy costs and benefits to the environment, 7 percent of participants surveyed said they would recommend TOU pricing to their friends.

There were also some unexpected results. For instance, there was no pattern of customers shifting demand away from the dinnertime peak period in winter. In addition, TOU-only pricing alone did not result in a statistically significant shifting of power away from peak periods.

CONCLUSION

In summary, participants in the Ontario Energy Board’s pilot program approved of these smarter pricing structures, used less energy overall, shifted consumption from peak periods in the summertime and, as a result, most paid less on their utility bills.

Over the next decade, as the utility industry evolves to the intelligent utility network and smart metering technologies are deployed to all customers, utilities will have many opportunities to implement new electricity pricing structures. This transition will represent a considerable technical challenge, testing the limits of the latest communications, data management, engineering, metering and security technologies.

But the greater challenge may come from customers. Much of the benefit from smart metering is directly tied to real, measurable and predictable changes in how customers use energy and interact with their utility provider. Capturing this benefit requires successful manipulation of the complex interactions of economic incentives, consumer behavior and societal change. Studies such as the OEB Smart Pricing Pilot provide another step in penetrating this complexity, helping the utility industry better understand how customers react and interact with these new approaches.

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.

SmartGridNet Architecture for Utilities

With the accelerating movement toward distributed generation and the rapid shift in energy consumption patterns, today’s power utilities are facing growing requirements for improved management, capacity planning, control, security and administration of their infrastructure and services.

UTILITY NETWORK BUSINESS DRIVERS

These requirements are driving a need for greater automation and control throughout the power infrastructure, from generation through the customer site. In addition, utilities are interested in providing end-customers with new applications, such as advanced metering infrastructure (AMI), online usage reports and outage status. In addition to meeting these requirements, utilities are under pressure to reduce costs and automate operations, as well as protect their infrastructures from service disruption in compliance with homeland security requirements.

To succeed, utilities must seamlessly support these demands with an embedded infrastructure of traditional devices and technologies. This will allow them to provide a smooth evolution to next-generation capabilities, manage life cycle issues for aging equipment and devices, maintain service continuity, minimize capital investment, and ensure scalability and future-proofing for new applications, such as smart metering.

By adopting an evolutionary approach to an intelligent communications network (SmartGridNet), utilities can maximize their ability to leverage the existing asset base and minimize capital and operations expenses.

THE NEED FOR AN INTELLIGENT UTILITY NETWORK

As a first step toward implementing a SmartGridNet, utilities must implement intelligent electronic devices (IEDs) throughout the infrastructure – from generation and transmission through distribution directly to customer premises – if they are to effectively monitor and manage facilities, load and usage. A sophisticated operational communications network then interconnects such devices through control centers, providing support for supervisory control and data acquisition (SCADA), teleprotection, remote meter reading, and operational voice and video. This network also enables new applications such as field personnel management and dispatch, safety and localization. In addition, the utility’s corporate communications network increases employee productivity and improves customer service by providing multimedia; voice, video, and data communications; worker mobility; and contact center capabilities.

These two network types – operational and corporate – and the applications they support may leverage common network facilities; however, they have very different requirements for availability, service assurance, bandwidth, security and performance.

SMARTGRIDNET REQUIREMENTS

Network technology is critical to the evolution of the next-generation utility. The SmartGridNet must support the following key requirements:

  • Virtualization. Enables operation of multiple virtual networks over common infrastructure and facilities while maintaining mutual isolation and distinct levels of service.
  • Quality of service (QoS). Allows priority treatment of critical traffic on a “per-network, per-service, per-user basis.”
  • High availability. Ensures constant availability of critical communications, transparent restoration and “always on” service – even when the public switched telephony network (PSTN) or local power supply suffers outages.
  • Multipoint-to-multipoint communications. Provides integrated control and data collection across multiple sensors and regulators via synchronized, redundant control centers for disaster recovery.
  • Two-way communications. Supports increasingly sophisticated interactions between control centers and end-customers or field forces to enable new capabilities, such as customer sellback, return or credit allocation for locally stored power; improved field service dispatch; information sharing; and reporting.
  • Mobile services. Improves employee efficiency, both within company facilities and in the field.
  • Security. Protects the infrastructure from malicious and inadvertent compromise from both internal and external sources, ensures service reliability and continuity, and complies with critical security regulations such as North American Electric Reliability Corp. (NERC).
  • Legacy service integration. Accommodates the continued presence of legacy remote terminal units (RTUs), meters, sensors and regulators, supporting circuit, X.25, frame relay (FR), and asynchronous transfer mode (ATM) interfaces and communications.
  • Future-proofing. Capability and scalability to meet not just today’s applications, but tomorrow’s, as driven by regulatory requirements (such as smart metering) and new revenue opportunities, such as utility delivery of business and residential telecommunications (U-Telco) services.

SMARTGRIDNET EVOLUTION

A number of network technologies – both wire-line and wireless – work together to achieve these requirements in a SmartGridNet. Utilities must leverage a range of network integration disciplines to engineer a smooth transformation of their existing infrastructure to a SmartGridNet.

The remainder of this paper describes an evolutionary scenario, in which:

  • Next-generation synchronous optical network (SONET)-based multiservice provisioning platforms (MSPPs), with native QoS-enabled Ethernet capabilities are seamlessly introduced at the transport layer to switch traffic from both embedded sensors and next-generation IEDs.
  • Cost-effective wave division multiplexing (WDM) is used to increase communications network capacity for new traffic while leveraging embedded fiber assets.
  • Multiprotocol label switching (MPLS)/ IP routing infrastructure is introduced as an overlay on the transport layer only for traffic requiring higher-layer services that cannot be addressed more efficiently by the transport layer MSPPs.
  • Circuit emulation over IP virtual private networks (VPNs) is supported as a means for carrying sensor traffic over shared or leased network facilities.
  • A variety of communications applications are delivered over this integrated infrastructure to enhance operational efficiency, reliability, employee productivity and customer satisfaction.
  • A toolbox of access technologies is appropriately applied, per specific area characteristics and requirements, to extend power service monitoring and management all the way to the end-customer’s premises.
  • A smart home network offers new capabilities to the end-customer, such as Advanced Metering Infrastructure (AMI), appliance control and flexible billing models.
  • Managed and assured availability, security, performance and regulatory compliance of the communications network.

THE SMARTGRIDNET ARCHITECTURE

Figure 1 provides an architectural framework that we may use to illustrate and map the relevant communications technologies and protocols.

The backbone network in Figure 1 interconnects corporate sites and data centers, control centers, generation facilities, transmission and distribution substations, and other core facilities. It can isolate the distinct operational and corporate communications networks and subnetworks while enforcing the critical network requirements outlined in the section above.

The underlying transport network for this intelligent backbone is made up of both fiber and wireless (for example, microwave) technologies. The backbone also employs ring and mesh architectures to provide high availability and rapid restoration.

INTELLIGENT CORE TRANSPORT

As alluring as pure packet networks may be, synchronous SONET remains a key technology for operational backbones. Only SONET can support the range of new and legacy traffic types while meeting the stringent absolute delay, differential delay and 50-millisecond restoration requirements of real-time traffic.

SONET transport for legacy traffic may be provided in MSPPs, which interoperate with embedded SONET elements to implement ring and mesh protection over fiber facilities and time division multiplexing (TDM)-based microwave. Full-featured Ethernet switch modules in these MSPPs enable next-generation traffic via Ethernet over SONET (EOS) and/or packet over SONET (POS). Appropriate, cost-effective wave division multiplexing (WDM) solutions – for example, coarse, passive and dense WDM – may also be applied to guarantee sufficient capacity while leveraging existing fiber assets.

BACKBONE SWITCHING/ROUTING

From a switching and routing perspective, a significant amount of traffic in the backbone may be managed at the transport layer – for example, via QoS-enabled Ethernet switching capabilities embedded in the SONET-based MSPPs. This is a key capability for supporting expedited delivery of critical traffic types, enabling utilities to migrate to more generic object-oriented substation event (GOOSE)-based inter-substation communications for SCADA and teleprotection in the future in accordance with standards such as IEC 61850.

Where higher-layer services – for example, IP VPN, multicast, ATM and FR – are required, however, utilities can introduce a multi-service switching/routing infrastructure incrementally on top of the transport infrastructure. The switching infrastructure is based on multi-protocol label switching (MPLS), implementing Layer 2 transport encapsulation and/or IP VPNs, per the relevant Internet engineering task force (IETF) requests for comments (RFCs).

This type of unified infrastructure reduces operations costs by sharing switching and restoration capabilities across multiple services. Current IP/MPLS switching technology is consistent with the network requirements summarized above for service traffic requiring higher-layer services, and may be combined with additional advanced services such as Layer 3 VPNs and unified threat management (UTM) devices/firewalls for further protection and isolation of traffic.

CORE COMMUNICATIONS APPLICATIONS

Operational services such as tele-protection and SCADA represent key categories of applications driving the requirements for a robust, secure, cost-effective network as described. Beyond these, there are a number of communications applications enabling improved operational efficiency for the utility, as well as mechanisms to enhance employee productivity and customer service. These include, but are not limited to:

  • Active network controls. Improves capacity and utilization of the electricity network.
  • Voice over IP (VoIP). Leverages common network infrastructure to reduce the cost of operational and corporate voice communications – for example, eliminating costly channel banks for individual lines required at remote substations.
  • Closed circuit TV (CCTV)/Video Over IP. Improves surveillance of remote assets and secure automated facilities.
  • Multimedia collaboration. Combines voice, video and data traffic in a rich application suite to enhance communication and worker productivity, giving employees direct access to centralized expertise and online resources (for example, standards and diagrams).
  • IED interconnection. Better measures and manages the electricity networks.
  • Mobility. Leverages in-plant and field worker mobility – via cellular, land mobile radio (LMR) and WiFi – to improve efficiency of key work processes.
  • Contact center. Employs next-generation communications and best-in-class customer service business processes to improve customer satisfaction.

DISTRIBUTION AND ACCESS NETWORKS

The intelligent utility distribution and access networks are subtending networks from the backbone, accommodating traffic between backbone switches/applications and devices in the distribution infrastructure all the way to the customer premises. IEDs on customer premises include automated meters and device regulators to detect and manage customer power usage.

These new devices are primarily packet-based. They may, therefore, be best supported by packet-based access network technologies. However, for select rings, TDM may also be chosen, as warranted. The packet-based access network technology chosen depends on the specifics of the sites to be connected and the economics associated with that area (for example, right of way, customer densities and embedded infrastructure).

Regardless of the access and last-mile network designs, traffic ultimately arrives at the network via an IP/MPLS edge switch/router with connectivity to the backbone IP/MPLS infrastructure. This switching/routing infrastructure ensures connectivity among the intelligent edge devices, core capabilities and control applications.

THE SMART HOME NETWORK

A futuristic home can support many remotely controlled and managed appliances centered on lifestyle improvements of security, entertainment, health and comfort (see Figure 2). In such a home, applications like smart meters and appliance control could be provided by application service providers (ASPs) (such as smart meter operators or utilities), using a home service manager and appropriate service gateways. This architecture differentiates between the access provider – that is, the utility/U-Telco or other public carrier – and the multiple ASPs who may provide applications to a home via the access provider.

FLEXIBLE CHARGING

By employing smart meters and developing the ability to retrieve electricity usage data at regular intervals – potentially several readings per hour – retailers could make billing a significant competitive differentiator. detailed usage information has already enabled value-added billing in the telecommunications world, and AMI can do likewise for billing electricity services. In time, electricity users will come to expect the same degree of flexible charging with their electricity bill that they already experience with their telephone bills, including, for example, prepaid and post-paid options, tariff in function of time, automated billing for house rental (vacation), family or group tariffs, budget tariffs and messaging.

MANAGING THE COMMUNICATIONS NETWORK

For utilities to leverage the communications network described above to meet business key requirements, they must intelligently manage that network’s facilities and services. This includes:

  • Configuration management. Provisioning services to ensure that underlying switching/routing and transport requirements are met.
  • Fault and performance management. Monitoring, correlating and isolating fault and performance data so that proactive, preventative and reactive corrective actions can be initiated.
  • Maintenance management. Planning of maintenance activities, including material management and logistics, and geographic information management.
  • Restoration management. Creating trouble tickets, dispatching and managing the workforce, and carrying out associated tracking and reporting.
  • Security management. Assuring the security of the infrastructure, managing access to authorized users, responding to security events, and identifying and remediating vulnerabilities per key security requirements such as NERC.

Utilities can integrate these capabilities into their existing network management infrastructures, or they can fully or partially outsource them to managed network service providers.

Figure 3 shows how key technologies are mapped to the architectural framework described previously. Being able to evolve into an intelligent utilities network in a cost-effective manner requires trusted support throughout planning, design, deployment, operations and maintenance.

CONCLUSION

Utilities can evolve their existing infrastructures to meet key SmartGridnet requirements by effectively leveraging a range of technologies and approaches. Through careful planning, designing, engineering and application of this technology, such firms may achieve the business objectives of SmartGridnet while protecting their current investments in infrastructure. Ultimately, by taking an evolutionary approach to SmartGridnet, utilities can maximize their ability to leverage the existing asset base as well as minimize capital and operations expenses.