Elecric evolution by Erb Institute

ELECTRIC EVOLUTION Issues Posed and Opportunities Presented by the Emergence of the Smart Grid A Discussion Paper for Electricity Utilities, Legislators, Regulators and Consumers

David Fribush Scudder Parker Shawn Enterline VEIC Consulting Division January 2010

VEIC is a national energy efficiency and renewable energy organization with headquarters in Burlington, Vermont, and additional offices in New Jersey and Massachusetts. It was founded in 1986 as a mission-driven non-profit organization. Today, it has a staff of 170 energy efficiency and renewable energy professionals and an annual budget of approximately $40 million. We have served a wide variety of public- and private-sector clients in more than 25 states and 6 Canadian provinces, in China, and in several other countries. VEIC is nationally acclaimed for its highly successful role in creating and running Efficiency Vermont, the nation’s first energy efficiency utility. Operated as a contract under the Vermont Public Service Board, Efficiency Vermont has met or exceeded nearly every goal set by the state’s regulators since its inception in 2000. In 2008 alone, Efficiency Vermont achieved incremental annual savings equal of 2.5% of Vermont’s sales – more than any other state in the country. The depth of these savings is considered a factor in turning Vermont’s electric load growth negative -- an accomplishment in both 2007 and 2008. VEIC is also a national leader in the delivery of customer-sited renewable energy programs. VEIC established the Northeast’s first “Million Solar Roofs Partnership” with the U.S. Department of Energy in 1998. This initiative helped in VEIC’s successfully developing and operating the Renewable Energy Resource Center in Vermont, which has provided consumer education, information, outreach, and customer rebate program design and administration services for the last decade. VEIC has also worked for more than a decade with regulators and utilities in New Jersey on the design, development, and delivery of New Jersey’s Clean Energy Programs. New Jersey is now one of the leading markets for solar electric photovoltaics in the United States. VEIC staffs a four-person office in New Jersey and provides the front-line, day-to-day, program implementation and administration services for the statewide renewable energy incentive program. VEIC is widely respected for its expert energy efficiency and renewable energy analysis services. We have also helped numerous clients design programs and policies to promote efficiency and renewable energy. VEIC is an outstanding leader in creatively developing new program and policy frontiers, and its technical expertise and credibility have brought in clients ranging from utilities to environmental groups, from consumer advocates to regulators, to other government agencies.

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Document Summary In many ways, the U.S. electric grid is a product of evolution. Originating in the laboratories of Nikola Tesla and Thomas Edison, it has grown and extended its reach into virtually every space that we inhabit. Despite its tremendous complexity, it faithfully supplies energy 99.97% of the time. Yet the daily per-capita cost to reliably power our homes’ refrigerators, televisions, air conditioners, computers, and all other electricity-using devices is a mere $1.22. There is perhaps no greater testament to its evolutionary success than the degree to which we take the grid for granted. Evolution is, however, a continual process, and many forces are now converging on the grid to make the evolutionary leap to become “smart.” The Smart Grid has the potential to significantly improve upon its progenitor by delivering energy from cleaner sources, with greater efficiency and reliability, while also providing more equity and transparency for both electricity consumers and producers. That implementation actually delivers fully upon this promise, however, will require an unprecedented level of leadership, cooperation, understanding, and foresight from the major players creating the Smart Grid. In particular, federal and state legislators and regulators will significantly influence both the form that the Smart Grid evolution takes and the speed at which it evolves. This document presents an overview of the driving forces in this process and some important implications they have for decision makers. It also seeks to articulate some underlying policy and program opportunities that this continuing evolution could make possible. Our goal is not to present an exhaustive list of considerations for decision makers, as relevant considerations will vary considerably by region. Rather, we provide a big-picture understanding of the Smart Grid and a contextual foundation from which relevant questions about technology and policy can be explored. Our broader goal is that the Smart Grid evolve to provide the greatest efficiency and reliability in the generation, delivery, and use of electricity, while minimizing long-term consumer and environmental costs. The document is divided into two parts. Part 1 gives a background on the current electric grid, why it needs to be “smarter,” and what the Smart Grid actually is. Part 2 explores relevant policy questions for the effective deployment of Smart Grid technologies and programs.

Calculated from 2007 EIA summary data.

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Table of Contents Part 1: Smart Grid Overview 1..........The Grid: An Appreciation of Impact 5..........What is Driving the Gridâ&#x20AC;&#x2122;s Evolution? 11........What is the Smart Grid? 16........The Smart Gridâ&#x20AC;&#x201D;A Daily Snapshot

Part 2: Policy considerations 19........Dynamic pricing and Advanced Metering Infrastructure (AMI) 24........Demand Response (DR) 31........Potential Stumbling Blocks in Dynamic Pricing, AMI, and DR Implementation 37........Distributed Generation (DG) 39........Plug-in (Hybrid) Electric Vehicles (PHEVs) 44........Conclusion

Appendices 43........Appendix A: Resources For Further Reading 45........Appendix B: Acronym Key 46........Appendix C: Technology Standardization 47........Appendix D: AMI Minimum Functionality (as Determined by California PUC) 48........Appendix E: AMI Deployments in the United States 50........Appendix F: NERC Generation Regions and Associated Fuel Sources Mix 51........Appendix G: Net Metering and RPS Programs By State 53........Appendix H: Department of energy Programs Working on Smart Grid Development 54........Appendix I: Other Federal Agencies Working on Smart Grid Development 55........Appendix J: Non-Government Organizations Working on Smart Grid Development

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part 1 Smart Grid Overview

Grid Overview

The Grid: An Appreciation of Impact It is difficult to put the almost 4 trillion kilowatt-hours of energy that the U.S. electric grid delivered in 2007 into a context that allows one to appreciate the magnitude of that figure. Perhaps it is more instructive to look at what it took to make that much energy: 1 billion tons of coal, 110 million barrels of oil, and 6.7 trillion cubic feet of natural gas, along with energy from nuclear, hydroelectric, and other renewable sources. This massive consumption of fossil fuels results in the grid being the largest emitter of carbon dioxide (CO2) of any sector of the economy. In 2007, its 2.5 trillion metric tons (gigatons) of CO2 production comprised 40% of total U.S. emissions. This is equivalent to the yearly CO2 emissions of 482 million cars, or taking all the gasoline used in the United States last year and burning it -- twice. Electricity generation also creates the vast majority of U.S. sulfur dioxide (SO2) pollution (primarily from burning coal) and is the second-largest emitter of nitrogen oxides (NOx) after vehicles. The cost to consumers of producing this energy was $365 billion in 2008, of which $157 billion was spent by residential electricity ratepayers. While a seemingly large number, it represents only 1.5% of total personal consumption, and is significantly less than the $206 billion consumers spent in the same year on alcohol and cigarettes. It is not unreasonable to say that the grid has been so successful that it has helped make electricity cheap enough for us to waste. All the current talk about moving to the Smart Grid implies that the current grid lacks “smarts,” which, of course, simply is not true. There is a tremendous amount of intelligence embedded in the planning and operation of the grid. Indeed, electrification is considered by many to be the most significant engineering achievement of the twentieth century.

How the Grid Works The grid is made up of more than 9,000 generating plants connected to more than 300,000 miles of transmission lines that carry electricity at high voltage to utility substations, where it is stepped down to lower voltages and distributed via an estimated 5.5 million miles of power lines to homes and buildings. Since electricity cannot at present be stored in large quantities, the supply of electricity must always be managed such that it matches demand. This has resulted in the grid being designed largely around electricity consumption patterns.

2007 U.S. Energy Information Administration (EIA) data Calculated from EIA data, 2007. Based on 11,500 pounds of CO2 per year per average car. http://www.epa.gov/oms/consumer/f00013.htm Based on CO2 emissions from EIA, Table 10, http://www.eia.doe.gov/oiaf/1605/ggrpt/carbon.html#emissions. Other emissions include 9 million metric tons of sulfur dioxide (SO2), and 3.7 million tons of nitrogen oxides (NOx). EPA: http://www.epa.gov/air/emissions/index.htm Calculated from EIA consumption and BEA expenditure data. Cigarette and alcohol source: Euromonitor International as quoted in http://www.nytimes.com/interactive/2008/09/04/business/20080907-metrics-graphic.html?th&emc=th. Verified with BusinessMonitor online data. “The Smart Grid: An Introduction,” Litos Strategic Communication (Prepared for U.S. DOE), http://www.oe.energy.gov/1165.htm Ibid. “Grid Modernization 101,” Dundee Capital Markets, May 1, 2009.

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Grid Overview The typical daily consumption pattern in New England, for example, is illustrated in below: Typical Summer Load Profile

Source: ISO-NE 2008 Data: http://www.iso-ne.com/markets/hstdata/znl_info/hourly/index.html

One can see from these consumption patterns that electricity demand is a function of both time of day and time of year, with time of day determining the general shape of the curve, and time of year determining the magnitude of consumption, primarily during daytime hours.

Generation To serve this pattern of consumption, power generators must be able to readily supply both energy, measured in megawatt-hours (MWh), and the capacity to produce energy, measured in megawatts (MW). This response to consumption patterns has resulted in a tiered structure of generation. •

Baseload plants generate electricity 24 hours a day at a more or less constant level of output to serve the constant portion of daily electricity demand.

•

Intermediate plants supply extra electricity when energy demand rises above what can be supplied by baseload generation. Thus these plants generally do not run all the time. However, they are often used as “spinning reserves” where they are run at low capacity so as to be able to respond rapidly to increased electricity demand.

•

Peaking plants deliver power for the short periods of time when electricity demand is particularly high, such as in the middle of the day.

The ratio of electricity demand at any given point in the day to the available generating capacity at that point is termed the grid’s load factor or capacity factor. The higher the load factor is, the greater the use of available capacity, and vice versa.

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Grid Overview

Although it varies by utility district, the average load factor in the United States is 55%,10 which means that, on average, 55% of the generating capacity of the grid is being used to create electricity and 45% of generating capacity is idle.

Transmission and Distribution Once generated, electricity is transmitted via high-voltage lines to substations and stepped down to lower voltages, continuing through distribution and feeder lines into homes and businesses.

Bundled Rates The electricity bill that virtually all residential, and most commercial, customers receive is based on a flat rate per kilowatt-hour (kWh), and is generally composed of the following main elements (different utilities use different terms):

10 11

•

Transmission charges: Fee per kWh for use of interstate transmission lines. This fee is set by the Federal Energy Regulatory Commission (FERC).

•

Distribution charge: Fee per kWh, representing a share of the amortized costs of investments in distribution equipment and other costs incurred by the utility. This is set by the state public utility commission (PUC) or municipal utility (Muni).

•

Capacity (or demand) charge: Fee, independent of kWh consumed, representing a share of the amortized costs of investments in power plant capacity. In regulated markets it is set by the state. In deregulated markets it is set by the market, and typically makes up 33% to 50% of the total bill.11

•

Energy charge: Fee per kWh consumed, representing the variable costs (primarily fuel costs) of producing the electricity consumed. This is set by the state or market.

•

Customer charge or General and Administrative charge: Flat fee for overhead charges incurred by utility. Set by PUC or Muni.

•

Other fees: Miscellaneous charges set by PUC and / or state, such as taxes, system benefits charges, reliability charges, environmental surcharges, nuclear decommissioning, etc. K. Schneider et al., “Impact Assessment of Plug-In Hybrid Vehicles on Pacific Northwest Distribution Systems,” IEEE, 2008. “The Smart Grid: An Introduction”

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Grid Overview

What is Driving the Grid’s Evolution? Despite the grid’s success at delivering electricity cheaply and reliably, various forces are converging that will force it to adapt to evolving economic and environmental realities. A brief description of these forces and their implications follows.

Business and Economic Forces Utilities are interested in new grid technologies for achieving operational savings. An example of this is seen in “smart” meter deployment, which allows utilities to reduce the cost of reading customer meters through automation. California’s three top utilities are currently installing 11 million smart meters,12 with costs largely justified to the public utility commission by operational savings. Among private companies and investors, there is a keen interest in being a part of upgrading the nearly $1 trillion of grid infrastructure and the $350 billion spent by consumers on electricity each year.

Capacity Constraints — Generation The increase in the total demand for electricity averaged 1.1% per year between 2000 and 2007.13 This is equal to over 40 million MWh of additional demand each year. Where will this new supply come from? Coal (48% of generation): Building coal plants is a rapidly waning option. Source: U.S. EIA Table 8.1 Electricity Overview, 1949-2007. Awareness of climate change and other pollution-related impacts of burning coal has increased both public opposition and economic uncertainty (in the form of looming carbon legislation). This has led many utilities to scale back on or abandon new coal-fired generation plants. In the first quarter of 2009, for example, 2,000 MW of additional coal-fired generation was proposed, whereas over 8,000 MW were canceled.14 Nuclear (19% of generation): No nuclear plant has begun construction in the United States since 1977, and it takes on average more than nine years to bring a nuclear plant online (not including the permitting process).15 This timeline speaks to the enormous challenges nuclear energy faces with financing, spent-fuel issues, and “not in my back yard” concerns. Hydroelectric (6% of generation): Most of the better locations for generating hydroelectric power in the U.S. have already been tapped. Furthermore, hydroelectric power declined by over 14% 12 Camille Ricketts, “California grids are about to get a whole lot smarter,” VentureBeat, May 14, 2009. http://venturebeat. com/2009/05/14/californian-grids-are-about-to-get-a-whole-lot-smarter/ 13 EIA, “Annual Energy Outlook 2009 with Projections to 2030,” March 2009. http://www.eia.doe.gov/oiaf/aeo/electricity.html 14 Eric Shuster, “Tracking New Coal-Fired Power Plants,” Presentation, Office of Systems Analysis and Planning, NETL, http:// www.netl.doe.gov/coal/refshelf/ncp.pdf 15 EIA Data, http://www.eia.doe.gov/cneaf/nuclear/page/nuc_reactors/reactsum.html.

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Grid Overview

in 2007 due to droughts, which have been attributed to impacts from climate change.16 Without any major hydroelectric projects under construction and decreases in existing hydroelectric production, hydroelectric power appears more likely to be a declining source of capacity. Additional capacity requirements have thus been met by increasing natural gas-fired generation (up 10% in 2007), nuclear generation (up 2.5% in 2007 due to capacity utilization improvements), and renewable generation (up 9% in 2007, primarily from wood and wind).17 There is also a time lag element to capacity development. It takes three to six years to bring a new baseload generating plant online.18 Thus the cancellation of baseload projects today has significant future implications on the grid’s ability to meet predicted demand. For example, American Electric Power (AEP), a utility serving 52 million customers in several states in the South, forecasts that the possibility exists for customer summer peak-hour demand to exceed the capacity of all of its current supply within the next four to seven years.19

Source: U.S. EIA Table 1.1. 1996 through 2007

Whereas carbon capture and sequestration technology has been touted as an option for continuing to use coal as a fuel without emitting CO2 into the atmosphere, this technology has not yet been proven on any meaningful scale and would involve tremendous infrastructure investments to support it. Natural gas generation, although cleaner than coal, has two important drawbacks: It still emits carbon dioxide, and it has much higher price volatility than other major electricity sources. Capacity constraints, therefore, require us to use existing capacity more efficiently and to bring more carbon-neutral generation online, necessitating the adoption of Smart Grid technologies.

Capacity Constraints —Transmission Although the capacity to produce power has kept up with demand, the capacity to transmit it has not. Electricity demand has increased by about 25% since 1990, but the construction of transmission facilities has decreased about 30%. 20 Seventy percent of transmission lines are 30 years old or older, 21 and investments in transmission and distribution have significantly lagged other grid-related investments. The resulting congestion—pushing more and more current through existing wires and distribution components—poses both a reliability hazard, through increased risk of overloads and outages, and an efficiency cost, through increased transmission and distribution (T&D) losses. T&D losses reached 9.5% in 2001, versus 5% in 1970. This 4.5% 16 17 18 19 20 21

EIA, Electric Power Annual – Electric Industry 2007 Year in Review, http://www.eia.doe.gov/cneaf/electricity/epa/epa_sum.html Ibid. Darren Kelsey, “AEP’s gridSMART Initiative,” Presentation: EEI National Accounts Workshop, March 17, 2009 Ibid. “Overview of the Electric Grid,” U.S. DOE GridWorks, http://sites.energetics.com/gridworks/grid.html “Grid Modernization 101”

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Grid Overview

increase in congestion-related losses results in carbon emissions that are equivalent to those of 22 million cars per year. 22 There are important financial implications as well. One study found that transmission congestion currently costs electricity ratepayers in the eastern United States $16.5 billion per year in the form of higher electricity prices. 23 Smart Grid technologies can reduce demands on existing transmission infrastructure, and will need to be integrated into new transmission infrastructure.

Distributed and Renewable Energy Integration The current grid was designed for central power plants to deliver power one way to customers across large areas. Incorporating relatively tiny amounts of renewable power generation (which are often intermittent in the case of wind and solar) is difficult, given the needs of grid operators to reliably and instantaneously match energy demand with supply. Complicating this situation further is the fact that some of the best sources of solar and wind energy are located far from existing transmission lines. A U.S. Department of Energy (DOE) study determined that the United States could supply 20% of its electricity demand in 2030 from wind power alone. 24 However, two key obstacles to achieving this are: (1) how to get these distributed renewable energy resources to the grid, and (2) how to manage their intermittent flow of electricity to the grid once they are connected. Non-renewable distributed generation faces integration problems as well. Combined Heat and Power (CHP) plants, which are usually small, on-site generation facilities that use normally wasted combustion heat to drive a secondary turbine and provide building heating. They can achieve far greater efficiencies than conventional plants (65% to 80% efficiencies in energy extraction versus 35%). 25 However, due to utility and regulatory requirements such as strict interconnection requirements and high backup rates, economic forces often prevent the installation of these more efficient distributed generation resources. The need to incorporate renewable and distributed generation into the grid necessitates the adoption of Smart Grid technologies.

Inefficiency There are three elements of inefficiency related to the grid: inefficiency in generation of energy, inefficiency in consumption of energy, and inefficiency in use of capital.

Inefficiency in Generation Most power plants are only about 33% efficient at converting source fuel to electricity. 26 Of the energy that is converted, 5% is used for running the plant, and approximately 10% is lost in transmission and distribution, as noted above. Thus, for every 100 units of fuel burned, only 28 units’ worth of energy reach the customer. 22 23 24 2009. 25 26

Calculated from EIA electric emissions and T&D loss data, Table 8.1, and EPA vehicle emissions data. IESO Smart Grid Presentation, “Ontario Smart Grid Forum,” Capgemini, 2008. Bracken Hendricks, “Wired For Progress: Building a National Clean Energy Smart Grid,” Center for American Progress, Feb. EPA: http://www.epa.gov/CHP/basic/efficiency.html “Overview of the Electric Grid”

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Grid Overview

Inefficiency in Consumption The combination of electricity’s low cost and the fact that the vast majority of consumers receive feedback on their usage only once a month, along with numerous other barriers to adopting more efficient technologies and strategies, contribute to inefficient use of energy by consumers.

Inefficiency in Use of Capital The low average load factor of 55% for the national grid means that billions of dollars are invested in power plants and associated infrastructure that are idle most of the time. 27 For example, in the ISO New England jurisdiction, 50% of the generation capacity is needed only 16% of the year. 28 This is the equivalent of electric ratepayers’ investing in an airline that flies with planes half-empty all year except for July and August. Smart Grid technologies could improve efficiency in all three areas of inefficiency.

Reliability and Security Although still rare, grid outages affecting more than 50,000 customers more than doubled between the early 1990s and 2000s. The estimates of the economic costs of outages and power quality issues vary widely, with various reports citing sums between $25 and $150 billion per year. 29

Electric Outages in U.S.

Reliability is complicated by the fact that grid operators frequently do not have real-time visibility into what is happening in different parts of the grid. Phone calls from customers, as opposed to automated sensing systems, are still what many utilities rely on for information about power quality problems, downed lines, and smallscale outages. The Northeast blackout in 2003 highlights many of the reliability and security vulnerabilities in the current grid. A software bug in an Ohio generation plant, combined with operator error, caused the plant to shut down during a period of high electricity demand. Increased demand on other generating resources led to high congestion in two transmission lines that physically began to sag due to the heat effects of line congestion, came into contact with trees, and failed. This caused a cascading series of system failures that resulted in a blackout affecting 50 million people in eight states and one Canadian province.30 The economic costs were estimated at $5 billion, with lifestyle disruptions and some 27 “Grid Modernization 101” 28 Calculated from ISO-NE data. 29 “Grid Modernization 101”, “The Smart Grid: An Introduction,” “Overview of the Electric Grid,” “Building the smart grid,” The Economist Technology Quarterly, June 6, 2009. 30 Wikipedia citing “Major power outage hits New York, other large cities”. CNN. http://www.cnn.com/2003/US/08/14/power.outage/. Retrieved on 2008-09-16

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Grid Overview attributed fatalities underscoring the degree to which we have come to depend on the grid.31

The event highlighted not just reliability issues with the grid, but security issues as well. If “overgrown trees”—which were ultimately cited as the primary culprit in the blackout—could cause such a widespread failure in the grid, it is not hard to imagine the potential effects of intentional sabotage.32 Smart Grid technologies have the potential to improve both grid reliability and security.

Political Forces The primary political drivers pushing for a grid upgrade are increased emphasis on reducing carbon dioxide emissions, an interest in “green-jobs” creation, and concerns about energy and grid security. These drivers have led to several legislative outcomes: •

The Energy Independence and Security Act of 2007, which provides federal grants for up to 20% of the cost of Smart Grid technologies and directs states to consider authorizing utilities to recover costs of advanced meter deployment through the rate base.33

•

The American Recovery and Reinvestment Act of 2009 (ARRA), which provides over $11 billion for grid modernization (with individual grants for Smart Grid projects up to $200 million) and an increase in federal matching funds, from 20% to 50%, for Smart Grid investments.34

•

State Renewable Portfolio Standards (RPS), which mandate a certain percentage of energy come from renewable energy, and effectively force utilities to develop the capabilities to integrate renewable generation into the grid.

Summary While the current grid has served us well to this point, it has many shortcomings that the Smart Grid could improve upon. The main questions we address next are: •

What exactly is the Smart Grid, and what does it look like?

•

How will it address the grid’s current weaknesses?

•

What might be the results for our energy bills and carbon footprint?

31 “The Smart Grid: An Introduction” 32 See Wikipedia note above 33 Stephen S. George, Josh Bode, and Michael Wiebe “Benefit-Cost Analysis for Advanced Metering and Time-Based Pricing,” Prepared for VT Department of Public Service by Freeman, Sullivan & Co. and MWConsulting, March 26, 2008. 34 “Grid Modernization 101”

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What is the Smart Grid?

What is the Smart Grid? The Federal Energy Regulatory Commission (FERC) defines the Smart Grid as “a power system architecture that permits two-way communication between the grid and essentially all devices that connect to it, ultimately all the way down to consumer appliances.”35 The National Association of Regulatory Utility Commissioners (NARUC) defines it as: making the existing electricity delivery system “smart” by linking and applying seamless communications systems that can: • gather and store data and convert the data to intelligence; • communicate intelligence omni-directionally among components; and • allow automated control that is responsive to that intelligence.36 Both of these definitions, while doing a good job describing the “what” of the Smart Grid, fail to address the “why”. Particularly given the size of the investment that ratepayers will be asked to make, it seems important to attach a purpose to the definition. Therefore we propose to define the Smart Grid as taking either the FERC or NARUC definitions and adding the following: …so as to enable the greatest efficiency and reliability in the generation, delivery, and use of electricity while minimizing long-term consumer and environmental costs. We suggest that it is against this definition of purpose that regulators, legislators, and ratepayers should consider what constitutes a valid Smart Grid investment. Table 1 provides a synopsis of the following discussion.

Evolution of the Grid —Adding Nerves This is the addition of improved sensory devices at the consumer and grid level to provide the data from which smart choices, across the entire system, can be made.

Advanced Metering Infrastructure Advanced Metering Infrastructure (AMI), or “smart meters,” are the consumer-level nerve system of the Smart Grid and are its fundamental enabling technology. FERC defines AMI as “a metering system that records customer consumption (and possibly other parameters) hourly or more frequently and provides for daily or more frequent transmittal of measurements over a communication network to a central collection point.” 37 This central collection is primarily the utility, but could also include transmission to home and building area networks and authorized third parties. The primary functions of AMI devices are to: •

provide automated meter reading for utilities and enable more granular grid monitoring;

35 FERC, “Smart Grid Policy”, Docket No. PL09-4-000, issued March 19, 2009. 36 Miles Keogh, “The Smart Grid: Frequently Asked Questions for State Commissions,” NARUC, May 2009. 37 “Assessment of Demand Response and Advanced Metering,” Staff Report, Federal Energy Regulatory Commission, December 2008.

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What is the Smart Grid? •

enable dynamic pricing, in which consumers buy or sell electricity at rates more reflective of the true cost of electricity at the time it is provided; and

•

give customers greater ability to understand and manage their electricity usage via enhanced feedback on energy consumption.

How frequently AMI devices record and transmit data, defined as the device’s latency, is a fundamental question.

AMI vs AMR

AMI (Advanced Metering Infrastructure) devices allow for more extensive two-way communication between the utility and customer, enabling Smart Grid functionality that can increase efficiency and reliability. AMR (Automated Meter Reading) devices allow utilities to read meters remotely, eliminating the need to send a worker to read each meter individually. While they do represent a certain amount of two-way communication, this functionality is limited and does not increase the efficiency or reliability of the grid. As such, we do not consider AMR devices as Smart Grid investments.

Advanced Visualization Technologies At the distribution and transmission level, advanced visualization technology could be deployed to give grid operators more real-time, wide-area awareness of grid status. This capability would allow for enhanced optimization of power generation, transmission, and distribution, as well as more rapid response to problems. Currently a delay of 2 seconds or more before a grid operator sees an event is not uncommon, and such an interval might prevent the operator from being able to take action to control system instability, leading to a blackout.38 An example of advanced visualization technologies being deployed are synchrophasors, which can give voltage and current readings in transmission lines faster than 30 times per second (compared to every four seconds with typically deployed technology). A report by the Pacific Northwest National Laboratory stated that the broad deployment of synchrophasors “could be used as an early warning system to help halt or prevent power surges before they develop into massive blackouts.” 39 Although a larger description of system-level technologies is beyond the scope of this paper, it is important to note that deployment of Smart Grid “nerve” technologies is an extensive effort beyond AMI, and that state officials overseeing deployment should expect to address visualization technology issues at both the consumer and grid levels.

Evolution of the Grid —Adding Brains This refers to taking the sensory information provided by Smart Grid “nerves” and intelligently using it. At the consumer level, the primary form that this will take is Demand Response.

Demand Response Demand Response (DR) is a change in customer energy consumption in response to a signal from a utility. 38 39

“Smart Grid Issues Summary,” NIST, March 10, 2009, www.naspi.org/draft_nist_smart_grid_issues_summary_20090310.pdf “Building the Smart Grid”

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What is the Smart Grid?

DR is not a new concept, and it does not require AMI. FERC estimates that 8% of customers are in some type of DR program.40 DR is currently accomplished primarily via informal or negotiated agreements between utilities and high-use customers to reduce power consumption during times of critical peak energy demand. This mechanism for DR has typically been a phone call from the utility to a customer asking for power reduction when needed. More recently, thirdparty companies have emerged that contract with utilities for a specified amount of DR, and then aggregate multiple commercial customers to reduce demand during periods of critical peak use, often installing their own Smart Meters in the process. The information provided by AMI systems, however, presents the opportunity to expand DR to all utility customers. There could be various mechanisms for this, but the primary and most powerful one is dynamic pricing. Since the rate consumers currently pay is fixed and does not necessarily reflect the true cost of providing electricity at the time it is supplied, consumers have no incentive to consume energy during off-peak periods when electricity is more economically produced. However, with AMIenabled real-time pricing, price signals provided via AMI devices could motivate consumers to shift their energy consumption from high-price peak periods to lower price off-peak periods. This would smooth out the grid’s load curve (reducing the need for power generators to run highcost, high-emission peaking plants), reduce transmission and distribution line congestion, and improve the grid’s capital and energy efficiency. Real-time pricing could potentially also make consumer installation of solar generation more financially viable because electricity sold to the grid during periods of peak demand, when the sun is at its strongest, would receive higher prices than such power does under current net metering plans. The management of DR would occur via Home Area Networks (HANs) on the residential level, or Building Automation and Control Networks (BACnets) for large multi-unit residential and commercial buildings. The network would be connected to the Smart Meter and would be accessed via a Web-based (or similar) interface that would allow customers to set parameters for controlling their electricity-using appliances in response to electricity prices. “Smart Appliances” would be designed to communicate with energy monitoring devices and operate under their control. According to the National Institute of Standards and Technology (NIST), “Demand Response is a priority area because of its important role in maintaining grid stability as the grid is operated closer to capacity and as more renewables are brought online with their less stable generation characteristics. DR is key, at least in the short term, to changing load shape and replacing peaking generation plants.”41

Meter Data Management Systems At the utility level, a Meter Data Management System (MDMS) will be the central location for the collection and management of meter data received from customers. The more frequently AMI systems transmit data and the more closely electricity is priced to real-time, the more robust

40 41

“Assessment of Demand Response and Advanced Metering.” “Smart Grid Issues Summary”

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What is the Smart Grid?

and capable the MDMS system must be. The MDMS system will be responsible for a significant amount of the utility-side functionality of the Smart Grid.

End-Use Energy Efficiency End-use efficiency can be understood as the application of intelligence and feedback to reduce energy usage at the consumer level. Smart Grid capabilities could provide a range of benefits to energy efficiency efforts already in place. These include: 1) much more detailed electric consumption feedback for customers to enable greater understanding and control of energy use; 2) more detailed and reliable information for energy efficiency program managers to better target and evaluate efficiency programs; and 3) significant improvement for utilities in the ability to track and forecast load changes both grid-wide and for specific portions of the grid.

Evolution of the Grid —Adding Muscle Adding muscle refers to the integration of distributed energy from renewable sources, highefficiency combined heat and power (CHP) plants, and energy storage devices, thus making energy delivery more robust and secure.

Distributed Generation Distributed Generation (DG) refers to the generation of electricity from various sources dispersed throughout the grid (as opposed to solely from centralized generating facilities). The two types of DG most commonly mentioned in connection with the Smart Grid are renewable generation (primarily wind and solar) and high-efficiency fossil fuel or biomass generation from CHP plants. It should be noted, however, that DG is not synonymous with cleaner generation. A highly polluting diesel generator, for example, also represents distributed generation. For the purposes of this paper, however, we will focus on renewables and CHP. Renewable and CHP DG are important components of the Smart Grid because of their ability to supply new capacity with reduced or zero carbon emissions. In 2007, for example, wind represented the second largest portion of capacity additions to the grid after hydroelectricity.42 Managing the intermittency of renewable sources, however, poses a particular challenge for the grid, which must instantaneously match electricity demand and supply. Smart Grid “nerve” technologies, such as advanced grid visualization components, will play an important role in renewable DG integration.

Energy Storage Technologies that enable large-scale storage of energy have the potential to significantly increase the efficiency of the grid by allowing for higher load factors. An example of a current technology for storing energy is pumped water storage, in which surplus grid capacity is used to pump water to a higher elevation in off-peak periods, and then the water flows downhill to spin a generator in high-peak periods. Current energy storage technologies being developed include high-capacity batteries, supercapacitors, compressed air, high-capacity flywheels, ice storage, and others. 42 5,186 MW were added from wind, 4,582 MW from natural gas. “Electric Power Industry Overview 2007,” U.S. EIA, http://www. eia.doe.gov/cneaf/electricity/page/prim2/toc2.html

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13.

What is the Smart Grid?

An often-mentioned energy storage possibility is via Plug-in Hybrid Electric Vehicles (PHEV) combined with Vehicle to Grid (V2G) technology. This would allow next-generation PHEVs to serve as a dispersed energy storage network for the grid. Cars charged at night and during offpeak periods can be plugged in at the workplace during the day and used to supply critical peak power and voltage regulation to the grid, as needed.

Evolution of the Grid —Adding Bones Bones here refers to the grid-level hard infrastructure that will need to be upgraded to accommodate the needs of the Smart Grid. This upgrading could take the form of: •

New transmission lines to reach remote sources of renewable generation. These might be High Voltage Direct Current (HVDC) lines, which are more efficient over long distances than traditional AC transmission lines;

•

High-efficiency transformers to reduce energy loss when high voltage is stepped down to lower voltages;

•

Superconducting power cables that greatly reduce energy loss during transmission by using materials that act like superconductors at normal outside temperatures;

•

Integrated “least cost” transmission and distribution planning and implementation. Table 1. The Smart Grid’s Major Components Nerves

- AMI (meters and network) - Advanced grid sensing and visualization technology

Brains

Muscle

- Distributed generation from renewable, CHP, and other sources - Energy storage technologies (including PHEVs)

Bones

- New transmission lines (HVDC, superconducting) - New transformers and substation equipment

Demand Response (through dynamic pricing) Building energy management systems Meter Data Management Systems (MDMS) End-use energy efficiency

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14.

What is the Smart Grid?

The Smart Grid—A Daily Snapshot It is useful to sketch out a sample day to show how the pieces of technology might interact. Midnight – 7 a.m.: The grid runs its most efficient baseload generating plants at optimal capacity, storing excess energy via several different distributed storage technologies. Among these storage locations are the batteries of residential ratepayers’ plug-in hybrid electric cars. Additional energy from overnight wind generation in remote energy is transmitted to populated areas over superconducting HVDC lines and used or stored as needed. 7 a.m. – 9 a.m: Residents drive to work on all-electric power using the lowest-cost energy stored from the grid overnight. 9 a.m. – 3 p.m.: As electricity use increases, along with its price, on-site DR systems regulate appliance use by turning off appliances that are not in use and adjusting the levels of those that are, like lighting and air-conditioning. Buildings that have their own energy storage technologies make use of this capacity now with energy that was bought overnight at the lowest rates. The grid makes use of solar generation to supply needed capacity, taking advantage of solar’s increasing capacity as the day gets brighter and hotter. In buildings that have PHEV charging infrastructure installed, the grid is able to buy electricity from workers’ cars at a high price. 3:30 p.m.: Construction workers building a new office building accidentally sever a distribution line. The grid’s automated sensing technology immediately detects the outage, shutting down the line and routing power via alternate routes, preventing a cascading system failure. 4 p.m. – 8 p.m.: After a day of work, workers drive home on the remaining electric power in their PHEVs or, in the case of having sold most battery capacity to the grid, on power supplied from their cars’ internal combustion engine. With most energy storage systems tapped out, electricity prices reach their highest levels, encouraging further DR measures from smart appliances. The wind begins to pick up, resulting in increase wind capacity that the grid can immediately put to use. 8 p.m. – midnight: As electricity use and prices fall off, washing machines, dishwashers, and other deferred appliances begin to run. The combination of DR measures and the integration of renewable capacity have enabled the utility to avoid running low-efficiency peaking plants, with the savings being passed directly to ratepayers through real-time prices and in the cases of homes with solar or wind generation, the purchase of that electricity at high rates. Taken together, the Smart Grid’s features and technologies create a set of integrated effects, all of which are presented in Table 2.

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What is the Smart Grid? Table 2. The Smart Gridâ&#x20AC;&#x201D;Features, Technologies, and Effects Primary Effects

Improves efficient use of capital

Improves efficiency of generation and / or delivery

Improves efficiency of consumption

Improves reliability of grid

Reduces CO2

AMI + DR, Energy Storage

Yes

Yes a

Increased use of renewable and CHP generation

DG, AMI, Grid Visualization

Yes

Reduced T&D losses

New T&D infrastructure, AMI + DR

Yes

Faster detection of and response to system disturbances and outages

Grid visualization, new T&D infrastructure, AMI

Yes

Increased consumer awareness of and response to energy use

AMI + DR

Yes

Features of Smart Grid

Primary Smart Grid Technologies

Increased load factors and flatter load profile

Although usually true, this will depend on the resulting mix of generation fuel sources.

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part 2 Policy considerations

Policy Considerations: Dynamic Pricing and AMI

17.

Dynamic pricing and Advanced Metering Infrastructure At its most basic, the Smart Grid is an opportunity for policy makers to transform the electricity market so that prices reflect the true costs (including externalities) of electricity use. With the proper market framework that sends true price signals, innovation and private enterprise will be motivated to deliver products and services that enable the greatest efficiency and reliability in the generation, delivery, and use of electricity with the least amount of carbon dioxide emission. The appropriate place for decision makers to begin a discussion of the Smart Grid is with a consideration of rate design and dynamic pricing, since their effects on demand (and supply) will generate the most benefits from the Smart Grid.

Considerations for Rate Design Broad guidelines for rate design from the principles of James Bonbright are that rate programs provide for:43 Economic Efficiency •

Energy efficiency: Conveys dynamic cost of providing electricity and provides incentives for energy conservation

•

Demand Response: Provides reliable and significant reduction in peak demand with short notification

Simplicity •

Clear communication: Conveys information that is easy to understand (cause-andeffect relationship) and enables action

•

Ease in implementation for both utilities and customers

Equity •

A balance between eliminating subsidies (that is, those implicit in current flat-rate pricing) and providing predictable, affordable bills

Choice •

Empowerment of customers to make trade-offs, such as cost vs. convenience and cost vs. service level

“Rethinking Rate Design.”

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Policy Considerations: Dynamic Pricing and AMI

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Dynamic Pricing Various pricing frameworks can deliver more accurate price information to consumers. Some of those are:44 1. Time of Use (TOU): The same timevarying prices on all weekdays—not really a dynamic rate 2. Peak Time Rebate (PTR): Incentives to reduce energy use during peak periods on high-demand days. 3. Pure Critical Peak Pricing (CPP): Time varying prices on high-demand days only. Represent only 1% to 2% of year. Price for power can be 5 to 10 times higher than other periods.45 4. Critical Peak Pricing/Time of Use (CPP / TOU): Time-varying prices on both high demand and other weekdays, with the highest prices occurring on high-demand days 5. Real-Time Pricing (RTP): Prices change hourly or more frequently in response to market conditions The closer a utility can price electricity to the actual costs incurred, the more dynamic the rate. So while FERC defines AMI as a method in which a meter records data hourly or more frequently, there may be significant benefit to a meter that records data more frequently. However, there will come a point at which the benefits of varying prices more frequently will be outweighed by the difficulty consumers would have in responding to those price changes. Once the requirements of the dynamic pricing program have been chosen, decision makers can then turn their attention to the technologies needed to bring that program into play. Appendix A provides an overview of technological standardization needed for the successful deployment of hardware and software in Smart Grid applications. That is, the first consideration will be setting standards for the AMI components that can support the chosen type of dynamic pricing.

Advanced Metering Infrastructure & Meter Data Management Systems Technological Considerations Although the meter and network are separate technology components, it is important to consider them as a system when determining technological specifications.46 NARUC laid out several important technology considerations in a recent paper. 47 These include:

44 Stephen S. George et al. 45 Nancy Brockway. 46 There are generally two pathways for meter deployment. The first is a retrofit of the existing electromechanical meter with technology that can read its measurements and communicate with the utility, but does not involve replacing the meter itself. While a less expensive alternative, this technology provides less functionality and accuracy than the more common pathway of replacing the entire meter with a new model. Our discussion here will be limited therefore to the replacement option. 47 Miles Keogh, “The Smart Grid: Frequently Asked Questions for State Commissions.”

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Policy Considerations: Dynamic Pricing and AMI •

Latency: How often electric consumption data can be recorded and transmitted. Per FERC’s definition, the maximum latency permitted for AMI is one hour, although desired functionality and pricing programs might require a shorter latency;

•

Upgradeability: How easily and inexpensively firmware (embedded software) in the meter and network can be upgraded to accommodate new standards and services;

•

Bandwidth: How much information the meter and network can accommodate at any given time. Per NARUC, “bandwidth should accommodate the expected application that has the highest bandwidth needs to prevent the need for future upgrades”;

•

Communication: How information will be communicated. Current technologies for wired communication include Power Line Carrier (PLC)—injecting information into current or voltage,48 broadband over power line—generation of a high-frequency radio wave that is fed into medium voltage lines, or using existing public broadband networks. Wireless networks include Fixed Radio Frequency (RF), WiFi, cellular, and satellite. An ideal network configuration might include a combination of wireless and wired technologies, addressing the following concerns:.

19.

- Cybersecurity: Firmware must “be protected—for example, with encryption, certification and authentication; and software must be deployed in such a way that even if an attack is successful, it will be unproductive, unappealing, unprofitable, and traceable”;49 and - Compatibility: Meters, networks, “smart” appliances, and other devices must be able to effectively interact with each other.

Meter Data Management System Considerations The minimum functionality of a MDMS is to collect and store data generated by the AMI system. The shorter the latency of AMI devices, the more robust the MDMS needs to be. For example, an AMI system with 5-minute latency generates more than 10 times as many data as a system with 1-hour latency.50 The requirements for validating, editing, and estimating raw usage data and parsing it into billing segments greatly increases in complexity as well. Thus the type of dynamic pricing put in place is highly interconnected with the MDMS that utilities must have in place to serve that pricing scheme. Furthermore, the MDMS must be integrated with many other utility business applications, “including the customer information system, outage management system, mobile workforce management, geographic information system, transformer load management and others….If multiple AMI technologies are used to produce the least cost network, the MDMS must be able communicate with each meter and communication system type.”51

Cost-Benefit Analysis Considerations Primary cost components of an AMI rollout are: - Purchasing and installing meters that can record and store usage data on an hourly or more frequent basis

48 Stephen S. George, Josh Bode, and Michael Wiebe “Benefit-Cost Analysis for Advanced Metering and Time-Based Pricing,” Prepared for Vermont Department of Public Service by Freeman, Sullivan & Co. and MWConsulting, March 26, 2008. 49 Miles Keogh. 50 Based on a 30-day month. 51 Stephen S. George et al.

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Policy Considerations: Dynamic Pricing and AMI

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- Purchasing hardware for and installing a two-way communications network between smart meters and utility - Purchasing hardware for and installing a meter data management system (MDMS) to manage data from consumers’ meters - Operations and management of AMI network and MDMS system Direct benefits include: + Avoided meter-reading costs + Reduced outage management and restoration costs + Fewer unnecessary service trips + Remote connect/disconnect of service Indirect benefits cited include: + Enabling of secondary technologies and services such as dynamic pricing, Demand Response, home and building energy management systems, real-time priced renewable energy sales, and others that can provide financial benefit to consumers + Improved service, such as faster detection of outages, increased meter accuracy, detailed billing, etc. + Improved T&D planning, transformer sizing and other distribution planning benefits52 + Grid voltage and phase monitoring, to improve grid stability and distribution reliability53 + Reduced energy theft

Direct Cost Estimates Estimates range between $110 and $525 per smart meter, depending on the functionality enabled. See Appendix E for a list of existing AMI programs and costs. Utilities currently deploying AMI include Pacific Gas and Electric (PG&E), which expects to spend approximately $340 per meter, and Southern California Edison, which expects to spend $370 per meter. This includes the cost of billing changes, DR functionality, and utility-side software.54 Some experts note that AMI could be deployed more cheaply in the absence of this additional functionality. An expert testifying on a cost recovery case in Maine cited three utilities that had implemented AMI at between $125 and $150 per meter. Municipal AMI investments have apparently been less costly as well.55 A study of AMI deployment in Vermont determined an average cost per meter of $115, with approximately 75% of the cost incurred from meter hardware and installation.56 A National Regulatory Research Institute (NRRI) report noted that total meter costs can run as much as $7 per month, depending on the AMI configuration and the extent of back-office software revisions.57 52 Ibid. 53 Nancy Brockway, “Advanced Metering infrastructure: What Regulators Need to Know About Its Value to Residential Customers,” National Regulatory Research Institute, February 13, 2008. 54 Ibid. 55 Ibid. 56 Stephen S. George et al. 57 Nancy Brockway.

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21.

Policy Considerations: Dynamic Pricing and AMI

Direct Benefit Estimates Much of the direct benefit of AMI comes from the savings accruing to utility operations from the avoided costs of manual meter reading. A report by the NRRI found that for utilities that switched from manual meter reading to AMI systems, over 50% of the operational cost savings were attributable to the AMI.58 A breakdown of direct benefits is presented in Table 3. The two utilities are PG&E and an unnamed utility on the East Coast. Specific attribution was omitted due to confidentiality agreements. Table 3. Operational savings from AMI deployment in two utilities.

Major Categories of Operational Savings % of Total Operational Savings

Benefit Category

Utility A

Utility B

Eliminate manual meter-reading costs

53%

60%

Remote Turn-On / Shut-Off

25%

Improved billing accuracy/timing/ reduce theft

11%

Electric Transmission and Distribution

10%

Other Employee-Related Costs

11%

n/a

Meter Operations

n/a

Reduced Customer Contact Costs

The Vermont study found that 88% of direct benefits came from avoided meter reading, with the remaining 12% spread relatively equally among savings from reduced call center, outage restoration, and avoided service call activity.

Indirect Benefits Estimates Indirect benefit estimates are, as noted, difficult to quantify. What makes a cost-benefit analysis for AMI particularly challenging is that the services it enables, rather than the AMI technology itself, accounts for the majority of its benefits. By far the most significant of these with regard to AMI is the enabling of dynamic pricing and DR.

58â&#x20AC;&#x192;

Ibid.

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Policy Considerations: Dynamic Pricing and AMI

Demand Response Once dynamic pricing is established with the appropriate AMI and MDMS to support it, DR will occur via price signals communicated by utilities to consumers.

Cost and Benefits of Demand Response Primary costs of DR: - Utility side software and hardware for processing meter data and pricing and billing energy usage. This will likely be a component of MDMS system. - Creation and funding of agencies and / or organizations to develop and administer DR programs - Marketing, communication, and education of consumers Primary benefits of DR: + Demand Reduction Induced Price Effect + Deferral of or avoided construction of new generating capacity + Reduced variable costs from avoided use of peaking plants + Reduced congestion-related T&D losses + Energy cost savings for customers able to shift consumption to less expensive periods + Greater customer awareness of and control over energy usage and bills + Potentially lower emissions The majority of the costs in deploying DR programs are embedded in the costs of deploying AMI/MDMS infrastructure. As such, we focus here on the primary benefits of DR.

Impacts of DR on Wholesale Electricity Prices Wholesale electricity is priced using the bid-stack method. Power producers submit their bids to supply electricity at a given price, and the bids are then stacked in order of increasing price. The price of the last unit of electricity used determines the price for all the electricity purchased during that period. Reducing demand, therefore, lowers the price of all electricity purchased in that period, a phenomenon called the Demand Reduction Induced Price Effect (DRIPE). For example, in the adjacent illustration, all electricity at 4:05 p.m. is purchased at $35 per MWh, while all electricity purchased at 4:25 p.m. costs $38 per MWh, due to the need to move to higher priced generation. During low-use and intermediate periods, the DRIPE is likely to be marginal because bids are quite competitive. As demand reaches critical peak levels, the price of WWW.VEIC.ORG

Illustration of Bid-Stack Pricing

(throughout a half-hour trading period)

Source: Sean Kelly, Presentation to Energy Consumers Council, Government of South Australia Department for Transport, Energy and Infrastructure, Feb. 2009.

Policy Considerations: Demand Response

23.

electricity spikes enormously. For example, in one region in Australia in January 2009, the price ramped from $400 per MWh to $10,000 per MWh during one 7-hour period.59 Thus DR can have a very significant impact on electricity costs when it reduces demand during critical peak events.

Impacts of DR on Generation and T&D Efficiency The primary effect of DR on energy efficiency in generation would be to flatten the grid load profile and reduce the use of inefficient peaking plants. This effect will vary, depending on the utility district and types of peaking plants used. On average, a peaking plant uses more than 25% more energy per kWh produced than a baseload plant, so a shift away from peaking plant usage represents a potential economic and environmental benefit.60 Energy efficiency from T&D improvements would result from reduced line congestion, improved grid awareness and monitoring, and other electricity management and routing improvements. It is unclear what portion of the 300 million MWh lost in T&D in 2007 could be saved due to DR programs,61 though an Electric Advisory Committee cites a report that claims that “up to a 30% reduction in distribution losses is possible from optimal power factor performance and system balancing” potentially enabled by the Smart Grid.62 The largest efficiency gains, however, would likely be economic efficiency in the form of avoided power plant construction. Integrated T&D planning, facilitated by the Smart Grid, could also help avoid or defer capital investments in T&D capacity. A simulation model created by the Brattle Group projects that dynamic pricing programs enabled though AMI have the potential to reduce peak demand by 5% and save $35 billion across a 20year horizon. This potential performance is attributable to avoided generation capacity costs, avoided energy costs, and avoided T&D capacity costs. The estimate assumes that 43% of customers in each sector (residential, and commercial and industrial) implement a cost-effective combination of demand response enabling technologies.63 Utility Benefits Under CPP Rate (Based on Model)

Source: Ahmad Faruqui and Lisa Wood, The Brattle Group

59 Source: Sean Kelly, “Wholesale Pricing in the National Electricity Market,” Presentation to Energy Consumers Council, Government of South Australia Department for Transport, Energy and Infrastructure, Feb. 2009. http://www.dtei.sa.gov.au/ECC/media/documents/meeting_85/ECC_pres_040209_Wholesale_Pricing_in_the_NEM.pdf 60 Calculated from slide 20, “Thermal Energy Storage: A System for the Green Capitalist,” CALMAC Presentation to Efficiency Vermont, June 18, 2009. 61 Calculated from EIA data for 2007, Table 8.1 Electricity Overview, 1949-2007. 62 “Smart Grid: Enabler of the New Economy,” Electric Advisory Committee, December 2008. http://www.oe.energy.gov/DocumentsandMedia/final-smart-grid-report.pdf 63 “The Green Grid,” EPRI Technical Update.

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Policy Considerations: Demand Response

Impacts of DR on Energy Efficiency and Conservation Energy efficiency is using less energy to provide the same level of a given energy service. Conservation is accepting a reduced level of service for the purpose of saving energy. For example, changing an incandescent bulb for a CFL that provides the same lumens is an example of efficiency, whereas setting the air-conditioner a few degrees warmer on a hot day is an example of conservation. For end-use consumption, DR does not necessarily lead to efficiency in energy use, since it primarily shifts loads from one point in the day to another. The washing machine still runs, just at a different time. However, demand responses for nondeferrable loads, such as lighting dimmed during a peak period, would be a permanent reduction for that day and result in a conservation effect.

The Difference Between Demand Response and Energy Efficiency Demand Response targets a reduction in energy load (MW), whereas Energy Efficiency targets a reduction in energy consumption (MWh). Put another way, the primary effect of DR is to shift electricity use from one point in time to another, whereas the primary effect of EE is to reduce the amount of electricity used.

A meta-analysis of more than 100 DR programs over 25 years found that they have a conservation effect, cutting consumption up to 20% annually (with a few showing energy use increasing), and that dynamic pricing programs averaged 4% savings and customer feedback programs averaged 11%.64 There are also efficiency and conservation effects from providing feedback on energy consumption (and costs). Feedback on end use consumption appears to generate both conservation and efficiency effects, although reliable estimates on these feedback effects are difficult to obtain. A review of the literature on feedback, across 30 years, found that most studies saw an average savings from feedback of 4% to 15%.65 A report by the Electric Power Research Institute (EPRI) determined that the average kWh reduction for indirect feedback (that is, via detailed billing) was 8.4% and for direct feedback (via in-home displays) was 11.5%.66 The data, according to EPRI, suggest that the energy reduction “is a function of electricity rate levels, rate design structure, regional attitudes towards energy conservation, information delivery mechanism (online and / or mailed delivery), and data presentation (graphical representation, normative and historical benchmark comparisons, choice of highlighted metrics, etc.).” 67 The frequency with which information can be provided to consumers appears to be a vital determinant of its effects on efficiency and conservation. A researcher examining energy data and consumer behavior change at Stanford’s Precourt Energy Efficiency Center explained that human brains, which have evolved to respond to quick feedback, “create a stronger emotional connection between a behavior (like turning on an appliance) and an outcome (a visual display of a spike in energy use and money spent) when the delay between the behavior and the outcome is very short (less than 2 seconds). Longer delays could be less motivating.” 68

64 David Nemtzow. 65 See note 78. 66 B. Neenan, “Characterizing and Quantifying the Societal Benefits Attributable to Smart Metering Investments” EPRI, July 2008 67 “The Green Grid,” EPRI Technical Update, June 2008. 68 Katie Fehrenbacher, “Why the Smart Grid Won’t Have the Innovations of the Internet Any Time Soon,” Earth2Tech blog, http:// earth2tech.com/2009/06/05/why-the-smart-grid-wont-have-the-innovations-of-the-internet-any-time-soon

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Policy Considerations: Demand Response

Impacts of DR on Emissions Although economic efficiency is served by a flattening of the grid’s load profile, emissions under DR programs may not be reduced -- and may even increase -- depending on the configuration of power generation in a given utility region. Emissions per source of power generation are presented in Table 4: Table 4. Emissions per fuel source in power generation. Fuel Source

Average Emissions (lbs/MWh) CO2

SOx

NOx

Coal

2,249

Oil

1,672

Natural gas

1,135

0.1

1.7

Nuclear

Hydro

Wind

Solar

Source: EPA data: http://www.epa.gov/cleanenergy/energy-and-you/affect/index.html

The emissions impact of DR is a function of the type of generation plant in use at the time of demand deferral and the time of eventual energy consumption. Displacing loads from a peaking natural gas plant to a coal baseload generation plant, while having a positive economic effect, could have a negative emissions effect since typical coal plants emit almost twice as much carbon per unit of electricity as does a natural gas plant.69 As regulators evaluate the overall emissions impacts of AMI and DR programs, it is vital they consider specific emissions profiles of their local generating plants whose use will change as a result of these devices and programs.

Impacts of DR on Customer Electricity Usage and Costs in Pilot Studies A Pacific Northwest National Laboratory study from March 2006 to March 2007 looked at how consumers responded to dynamic energy pricing. It gave a choice of contracts, from fixed prices to real-time prices that varied in five-minute intervals. Smart appliances including thermostats, water heaters, and clothes dryers were installed in 112 residences. Its results were a reduction in peak load of approximately 15% over the year, and that customers saved approximately 10% on their electricity bills. It further estimated that if all customers engaged in peak reductions at this level, the resulting savings in capacity infrastructure would be $70 billion.70 An Ontario Smart Price Pilot (SPP) found that customers on a Critical Peak Pricing (CPP) plan reduced their loads by 25% on summertime critical peak days, and by 2.4% to 11.9% over the entire summertime peak period.71 There is evidence from other pilots in Idaho and Missouri that dynamic pricing will lower critical peak loads by more than 10% for the average household.72 69 B. Neenan, “Characterizing and Quantifying the Societal Benefits Attributable to Smart Metering Investments” EPRI, July 2008 70 “GridWise Demonstration Project Fast Facts,” Pacific Northwest National Laboratory, December 2007. http://gridwise.pnl. gov/docs/pnnl_gridwiseoverview.pdf 71 Nancy Brockway. 72 “Rethinking Rate Design,” Presentation for California PUC Dynamic Pricing Issues Workshop, The Brattle Group, September 7, 2007.

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Policy Considerations: Demand Response

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A California Statewide Pricing Pilot (SPP) from 2003 to 2004 involving approximately 2,500 customers, residential CPP rates reduced peak period demand by more than 14%, the impacts were persistent across two years of the experiment, and participants associated dynamic rates with saving money and conservation and, as a result, made behavioral changes in how they used energy.73 A smaller part of the study tested an Automated Demand Response System, involving 145 homes that were outfitted with sophisticated monitoring and control technologies.74 That study found that 88% of customers realized bill savings during the summer, with an average savings of $132.75 Other findings from the California SPP study relevant to state policy makers were that most respondents do not understand how electricity use is currently measured or priced, but could easily understand the logic of time-differentiated prices (on-peak/offpeak), and almost 90% reported that they felt the time-varying rates were fair.76 In pilot programs by PG&E and Ottawa Hydro, more than 75% of customers reported being satisfied with the program and 80% said they would recommend the program to a friend or relative.77 Although these results are encouraging as to the effectiveness of AMI combined with dynamic pricing, an NRRI study, which evaluated several pilots, contained some cautionary notes. For example, average load reduction was the result of large reductions by a small number of participants, and that not all participants were able to reduce their loads.78 Furthermore, customers with direct load control devices (that is, Programmable Communicating Thermostats (PCTs) responded at dramatically higher rates (up to 41% on CPP days) than those without automated devices (between 10 and 15 %).79 Evidence also suggests that demand response among small commercial customers is almost non-existent without enabling technology.” 80 NRRI also notes that these pilots excluded AMI costs from their evaluation of benefits, and raised the possibility that the customers volunteering to be part of pilot programs may not be representative of all electric customers. This is far from a conclusive list of pilot program results, and decision makers will likely want to consider results of pilots with demographics and constituencies similar to their own and / or conduct their own pilots to gauge local impacts of AMI/DR programs more accurately. 73 “California Statewide Pricing Pilot (SPP) Overview and Results 2003-2004,” sites.energetics.com/madri/toolbox/pdfs/pricing/ pricing_pilot.pdf 74 Including a smart meter, thermostat, and smart appliances and a Web-enabled user interface and data management software, with technology programmed to automatically respond to electricity prices. 75 Computed from data on slide 47, “GridWise Demonstration Project Fast Facts” 76 “California Statewide Pricing Pilot (SPP) Overview and Results 2003-2004,” sites.energetics.com/madri/toolbox/pdfs/pricing/ pricing_pilot.pdf. Fairness statistic: Stephen S. George et al. 77 See note 71, George citation. 78 Nancy Brockway. 79 Nancy Brockway. 80 Stephen S. George et al.

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Policy Considerations: Demand Response

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A Return to Costs and Benefits The disparity of results among different Smart Grid studies, pilots, and simulations all point to the fact that the benefits of Smart Grid implementation are heavily dependent on the specifics of the programs and services enabled by it. Policy, intention, and planning are vital. As Bernard Neenan, a technical executive at EPRI writes: The installation of Smart Metering technology by itself does not produce societal benefits. Rather, Smart Metering serves an enabling role when combined with other initiatives, such as the implementation of demand response programs, the revision of outage restoration practices, and the adoption of devices that communicate consumption and price/event information to consumers. Additional benefits are attributable to the energy and demand changes that result from change in consumption behavior, including lower environmental impacts and improvements in employment and wages in the local economy. Quantifying societal benefits requires sorting these streams of benefits in a way that characterizes them by the source so that proper value transformation function can be applied.81 Utility operational savings are responsible for the majority of direct benefits, and in cases where utilities have not recently deployed AMR systems, these savings are responsible for the bulk of positive net present value analyses supporting AMI deployment. Yet a failure to identify and attach significance to the societal benefits of AMI could lead to underinvestment in the technologies, programs, and services that best take advantage—from a societal benefits perspective—of its capabilities. A good effort at summarizing the primary indirect benefits of AMI is seen below:

Source: B. Neenan, “Characterizing and Quantifying the Societal Benefits Attributable to Smart Metering Investments” EPRI, July 2008

References to studies that more fully attempt to quantify societal benefits from DR are provided in the reading references at the end of this document. Rather, here we would like to elucidate some questions that legislators and regulators should consider when commissioning and / or evaluating cost benefit studies in their jurisdictions.

B. Neenan.

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28.

Policy Considerations: Potential Stumbling Blocks

Potential Stumbling Blocks in Dynamic Pricing, AMI, and DR Implementation As regulators and legislators begin to explore different dynamic pricing schemes and AMI deployment, we raise the following concerns:

Investing in the Wrong Technologies Although it is easy to make the evaluation in hindsight, most analysts would probably look back on the decision to deploy AMR technology (for the utilities that did deploy it) as an example of an investment in the wrong technology. It involved many of the same costs as an AMI deployment, but delivers a fraction of the benefits. Yet because automatic meter reading represents a substantial portion of the savings attributable to an AMI deployment, the utilities that deployed AMR have a much more difficult benefit case to make to regulators with AMI. It is of course impossible to know with any certainty when a new technology is mature enough to merit deployment. The challenge for regulators, then, is to keep a clear perspective on bigpicture goals and ensure that investment decisions are not rushed simply because the money is there now. Finding the right balance between imprudence and caution will be a continuing challenge for regulators. Key Questions: •

Are software and firmware easily upgradable remotely?

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What is the optimal latency of meter reading, one that finds a proper balance between creating the greatest SG functionality while still being economical to deploy and use?

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Since upgradeable equipment often has higher first costs than non-upgradeable equipment, how can decision makers ensure that the they do not under invest in technology due to lowest first-costs planning requirements only to have higher operational or upgrade-related costs in the future?

Moving to Dynamic Pricing Without Sufficient Technological and Programmatic Support The successful demonstration of demand response in pilots is as much a function of the technology to take advantage of dynamic pricing (such as programmable communicating thermostats (PCTs) and smart appliances) as it is the rates themselves. A Vermont AMI/DR study stated that “enabling technology, such as PCTs, can boost demand response by 50 percent or more compared with residential customers who face time-varying rates but do not have technology that helps automate demand response. One might extend the logic further to imagine that customers without enabling technology such as PCTs and smart appliances could be worse off under dynamic pricing. This is because the burden of both knowing current electricity prices and having the ability to make consumption adjustments based on that information is then placed on the customer, who is not accustomed to WWW.VEIC.ORG

“Smart meters and dumb prices represent bad public policy…smart metering enables a stream of new benefits, but by itself does not ensure that they will be forthcoming.” -Vermont AMI/DR

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Policy Considerations: Potential Stumbling Blocks this paradigm. Key Questions: •

What minimum technologies must be installed in consumers’ homes and businesses to enable an effective response to dynamic prices?

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What is the relationship between latency and how much technology is needed to respond to that latency? For example, does 5-minute latency require significantly more technological capabilities on the part of the consumer than does 60-minute latency?

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What public communication programs will be put in place to ensure adequate education of consumers to adapt to dynamic pricing?

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What entities will be responsible for managing and evaluating DR programs? How will these programs be integrated with existing energy efficiency programs?

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How will the utilities and regulatory bodies that are involved in resolving customer difficulties handle a potentially increased flow of complaints as customers transition to new pricing programs and technologies?

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How will programs be rolled out? All at once? Pilot programs leading to wider and wider roll-outs?

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What body will be responsible for conducting evaluation studies and how will they be funded?

Determining Mandatory vs. Opt-in Dynamic Pricing Some of the Smart Grid literature claims that in order for the indirect benefits of AMI/DR to be realized, Smart Grid technologies must be deployed widely. This would indicate that some type of mandatory deployment would be warranted. However, the argument for widespread deployment seems to be at least partially contradicted by pilot studies that showed that the average decrease in peak consumption was driven by a small number of “star” performers who reduced consumption dramatically.82 This finding might indicate that moving only “star” performers to AMI/DR could result in sufficient decreases in average peak electric consumption, but at a lower deployment cost. A challenge to both voluntary and mandatory program implementation will be effective communication to consumers of the new pricing structures. Although the California studies found that customers were able to understand tiered pricing, these customers had self-selected into the pilots and also had the technology to take advantage of dynamic pricing properly. How a broader range of customers will react is still not known. Key Questions:

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How does partial vs. full deployment of dynamic pricing affect the cost-benefit analysis? Do the majority of benefits require full deployment?

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How could “star” performers be identified? customers?

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Does offering AMI/DR as an option to customers cause “star” performers to selfselect into the program?

Is it feasible to target only these

Nancy Brockway.

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How difficult and costly would it be to maintain AMI/DR and non-AMI/DR customers?

Addressing Vulnerability of Low-Income Customers to Dynamic Pricing There is some concern that low-income consumers may be made worse off by a move to certain types of dynamic pricing. This is because: •

The ability to make appropriate use of dynamic pricing appears to be closely linked to the availability of in-home or in-building technology that automatically adjusts electricity consumption based on current prices. Low-income consumers would presumably have less access to this technology.

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Not all electricity consumption can be deferred to off-peak periods, such as refrigeration and lighting. Low-income customers, for whom non-discretionary appliances make up a larger proportion of their electricity use, could be less able to decrease electric consumption during peak periods.83 Furthermore, they might lack the funds to replace inefficient non-discretionary appliances with more efficient ones, or to make their homes more efficient overall.

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Dynamic pricing introduces volatility into electricity costs, and low-income customers, particularly those with fixed incomes, might be less able to cope with unexpectedly high bills.

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AMI’s capability for remote disconnection could put customers who are unable to understand or pay their bills at risk for remote disconnection, before they can interact directly with utility personnel.84

All of these concerns appear to be logical; however, the data from pilot studies so far do not indicate that low-income customers have been disadvantaged. One analyst looking at the California SPP study found that low-income consumers saved proportionately more money than high-use customers.85 A pilot study of 1,400 households in Chicago found low-income participants were as demand responsive as higher-income participants.86 Still, with sparse data it would be unwise to draw firm conclusions from relatively small pilots. As the NRRI states, “One cannot simply look at the levels and percentages of demand response by customer group, and infer that bill impacts will correspond. The entire design of a tariff, and the usage patterns of different customer groups, have as much if not more to do with bill impacts as the customers’ different responses to critical peak events.” 87 Key Questions:

83 84 85 86 87

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What measures or programs have to be put in place to ensure that low income customers will be adequately protected in a dynamic pricing structure?

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What safeguards are in place to protect those (such as non-English speakers or the mentally ill) who have difficulty understanding or adapting to dynamic pricing?

Nancy Brockway. Nancy Brockway. Karen Herter, “Residential implementation of critical-peak pricing of electricity,” Energy Policy, Vol 35, Issue 4, April 4, 2007. David Nemtzow. Nancy Brockway.

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What safeguards are in place to protect consumers using health-related energy consumption that cannot be deferred (for example, air conditioning for an elderly person during a hot summer afternoon)?

Ensuring Consistent Demand Response Because system operators typically contract for their capacity requirements years in advance, an inconsistent level of demand response might make it difficult to incorporate DR reductions into capacity planning. As NRRI states, the market value of DR (from a capacity avoidance point of view) is essentially zero if the response is not persistent enough for ISOs to plan for it.88 Key Question: •

How can a minimum level of demand response be guaranteed so as to assure incorporation into ISO forward capacity commitments?

Ensuring New Pricing and DR Programs Do Not Inadvertently Increase Consumption Off-peak prices are lower than standard flat rates, so customers who can shift consumption to off-peak periods stand to see lower bills. It is possible that consumers could increase their consumption in low-cost off-peak periods more than they reduce it in on-peak periods, leading to a net increase in consumption. Presumably the net economic effect for these customers would be break-even or better, but the net environmental effect could be negative. Key Question: •

What incentives (or penalties) are in place to align the economic and reducedemissions goals of Smart Grid investments?

Focusing on Smart Grid Programs at the Expense of Existing Energy Efficiency Programs Energy efficiency is still the least-cost investment for reducing consumers’ energy bills and electricity-related emissions. Over the past two decades programs to meet energy needs by systematic investment in energy efficiency technologies and strategies have become a powerful part of the operation of the Grid. In some jurisdictions load reductions approaching 2% of annual sales are being attained and in others they being planned for. Energy efficiency programs have developed significant capabilities to understand and market to customers; this wealth of experience should be drawn upon as AMI technologies and new pricing strategies are introduced. In fact, much of the delivery of customer-sited AMI capability may best take place as an integrated part of existing or re-designed energy efficiency programs. Key Questions:

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How will effective energy efficiency programs continue to be funded, supported, and expanded as investment in AMI ramps up?

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How can efficiency and DR programs be best integrated to generate results that are greater than the sum of their parts? See note 97.

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Protecting Privacy of Customer Consumption Data Utility bills are not public information, and assurances might be necessary to prevent identifiable energy usage data from being shared beyond the utility and other authorized parties. This is particularly pertinent when multiple utilities share an MDMS system. Key Questions: •

What privacy rights do consumers have with regard to their energy consumption data?

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How will the AMI network and MDMS system protect the privacy of customer data?

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What is the proper balance between consumers’ privacy concerns and the societal benefits of data collection on energy usage? Are technological solutions available (i.e., making data anonymous) that can address the needs of both constituents?

managing Legacy Equipment/Stranded Assets Certain existing equipment will not meet Smart Grid standards and will need to be replaced before fully depreciated. The stranded assets referenced most often are existing meters, 89 particularly when a utility has invested in AMR devices that do not offer Smart Grid capabilities. Another example is the arrival of a promising technology that merits deployment before the technology it replaces has been fully depreciated. The amended Public Utility Regulatory Policies Act (PURPA) directs each state to consider authorizing electric utilities to recover the cost of AMI systems through the rate base and to continue recovering the remaining book-value costs of any equipment rendered obsolete by the deployment of Smart Grid systems.90 Such a process could mitigate the risk to utilities of stranded assets, but potentially at the expense of the ratepayer. Key Questions: •

How should the costs of stranded assets be distributed?

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What should the decision metric be for determining when the benefits of a new technology outweigh the costs of not-fully-depreciated assets it replaces?

Understanding Limitations of Net Present Value Analysis The highly unpredictable nature of future energy generation and management technologies makes any cost-benefit analysis suspect. The financial model in a cost-benefit analysis is only as predictive as its assumptions are correct. One of the implicit assumptions in net present value (NPV) models, particularly for those covering extended time periods such as in utility-related NPV analyses, is that some underlying conditions change little, if at all. A prime example is in the choice of a discount rate. A slight change in the discount rate over a 20-year time horizon can have a profound effect on the result of an NPV analysis. Yet as the recent financial crisis highlighted, discount rates can change radically in a very short period of time. Disruptive events can and do have drastic impacts on financial realities, yet we continue to rely on relatively static NPV analyses to do financial modeling. For example, imagine how different the NPV analysis for a mobile phone application developer looked before the iPhone was invented. The iPhone created an ancillary industry in applications 89 “Trilliant helps answer burning question of stranded assets”, SmartGridToday.com, July 1, 2009. http://www.smartgridtoday. com/members/457.cfm 90 Stephen S. George et al.

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that generates tens of millions of dollars in revenues, provides users with a far more functional device than they had imagined at time of purchase, and provides creators of those applications with a market for their products and the potential for significant financial gain. Traditional NPV analyses do not account for the benefits of unforeseeable technologies and services that will likely be created with a robust Smart Grid. An alternative approach to valuing the necessary investments, therefore, is to use “real options analyses.” While still relying on a large number of assumptions, options analyses do incorporate dynamic probabilities into their calculations. The result can be that a project that looked inadvisable with a traditional NPV analysis could be advisable in a real options analysis. Key Question: •

Should decision makers to utilize a more dynamic form of financial modeling than traditional NPV-based cost-benefit analyses?

Summary AMI cannot be considered in a vacuum, and analyses of its implementation should include demand response as a primary benefit. Effective demand response necessitates a transition from flat-rate pricing to dynamic pricing, which more accurately reflects the true costs of electricity delivery at different times of the day. The shift in consumption patterns resulting from dynamic pricing will lead to a more capital-efficient and energy-efficient grid. It will also likely reduce overall greenhouse gas emissions as well. The main challenges for decision makers are: •

Determining which type of dynamic pricing to implement.

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Establishing minimum technology standards for AMI technologies.

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Ensuring that lowest-first-cost requirements do not result in an underinvestment in sufficiently upgradeable technology.

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Providing effective communication and education efforts to help smooth customers’ transition to dynamic pricing.

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Ensuring consumers have the appropriate technologies that enable them to effectively respond to dynamic prices.

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Providing for protection of customers who are unable to respond effectively to dynamic prices.

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Finding the right balance between compensating utilities for legitimately unforeseeable stranded asset cases while holding utilities responsible for making short-sighted investments.

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Pacing investments in Smart Grid in such a way that the cumulative benefits correspond with the timing of needs (consumer and / or utility). For example, a utility will need to launch its demand response program several years ahead of when it needs the additional capacity.91

91 John McDonald , “Leader or Follower? The Four Essentials of a Safe-and-Sane Smart Grid Plan,” SmartGridNews.com, June 8, 2009. http://www.smartgridnews.com/artman/publish/commentary/Leader_or_Follower_The_Four_Essentials_of_a_Safe-andSane_Smart_Grid_Plan-598.html

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Policy Considerations: Distributed Generation

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Policy considerations: Distributed Generation New Infrastructure and Programs There is considerable consensus that the Smart Grid will better enable the integration of energy from small-scale widely distributed sources, particularly renewable generation and customersited co-generation or CHP. One clear requirement is the construction of new transmission lines to geographic regions with excellent sun and wind U.S. Wind and Solar Resourtces and resources. In most cases these will be interstate lines New Transmission Lines Needed and require regulators in affected regions to engage in the associated permitting process. Within the current T&D system, the mechanisms by which Smart Grid components further enable distributed generation are less straightforward. At the distribution scale, there will be a need for improved real-time sensing and switching capabilities to enable utilities to understand and make use of available distributed capacity, as well as to manage the intermittency and variances in voltages and frequencies of these sources.” 92 At the consumer level, most states already have netmetering regulations in place that allow consumers to sell excess energy produced on site (via wind, solar, etc.) to the grid, for which they receive retail rates. (See Appendix D for a list of the 42 states that currently allow net metering). The primary benefit the Smart Grid would bring to these customers would occur indirectly via dynamic pricing. Since electricity prices during the day and peak periods would likely be higher than existing flat rates, energy produced during these times would have a higher value. This could improve the financial case for distributed generation sources, particularly those like solar, which generate the most power during the periods of highest demand.93 Key Questions: •

Do existing net metering programs support dynamic pricing for DG energy sales to the grid? If not, what changes need to be made to ensure that they do?

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What impacts will DG have on utility capacity planning? What particular challenges do DG resources that utilities do not control present for utilities?

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How can DG be used strategically to lower T&D new capacity costs?

92 “Smart Grid: Enabler of the New Economy” 93 It has been noted this might also devalue wind resources, since they do not have a peak output that is coincident with the grid’s peak demand.

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Stranded Utility Assets Utility rates include a per-KW capacity charge to pay for investments in generation, transmission, and distribution. When existing utility customers install on-site generation, they reduce their purchases of electricity from the utility and the utility receives less money to pay for its capacity investments. The extra costs are eventually passed along to remaining customers. To illustrate the implications of this, let’s suppose a utility builds a 100 MW power plant and related distribution infrastructure to serve the expected demand of a group of consumers, among which is a large commercial customer. The utility passes the costs of construction and distribution along to consumers in the form of an extra capacity charge. The commercial customer later decides to build an on-site 10 MW CHP plant to supply the majority of its power needs. The utility now finds itself receiving 10% less income to reimburse its investment in capacity. Taken to an extreme, if half the customers install their own generation, the remaining customers would have to shoulder twice the capacity charges to repay the utility investment. To protect utilities and non-DG customers from this type of scenario, some states have enacted rate design elements such as:94 •

Exit fees (or stranded asset fees): Fees assessed on the departing load to offset lost capacity charge revenue

•

Standby rates: Extra fees assessed on DG customers to cover costs of grid-supplied capacity in case of a DG failure or shortfall. (Grid supplied back-up power is usually a regulatory requirement)

These fees can negatively affect the financial projections for some DG investments enough to render many unprofitable, effectively creating a barrier. Key Questions: •

What is the best way to protect the utilities and ratepayers from shouldering stranded asset costs without creating a barrier to investment in high efficiency and / or renewable distributed generation?

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Does empirical evidence support stranded asset rate elements? That is, does capacity actually go unused? Are existing standby capacity requirements appropriate?

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Are the indirect benefits of high efficiency and renewable DG reflected in rate design?

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How would performance-based rates affect utility position regarding DG?

Summary The major challenges for regulators with distributed generation will be to find the right balance between fairly compensating utilities for capacity investments without penalizing distributed generation investments, and to ensure that consumers investing in DG receive the full benefits of dynamic pricing.

U.S. EPA Utility Rates Fact Sheet: http://www.epa.gov/CHP/state-policy/utility_fs.html.

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Policy Considerations: PHEVs

Policy considerations: Plug-in (Hybrid) Electric Vehicles A discussion of the Smart Grid almost inevitably leads to a discussion of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs). PHEVs combine a battery and an electric motor with a small internal combustion engine. The battery provides for all-electric or electric-assisted driving over a certain range, after which the internal combustion engine can be used to power the car directly or charge the battery. PEVs consist of only a high-capacity battery and an electric motor. For the purposes of our discussion, we will use PHEV to denote both types of vehicles. Large automakers such as GM, Toyota, and Ford are planning to introduce PHEVs into the market in the next few years. Although their success depends on still-maturing high-capacity battery technology, current indications are that it is the price of the batteries and not the technology that is the main barrier to the vehicles’ mass-marketability. It is therefore reasonable to assume that like most new technologies, there will be a pattern of high-cost, limited initial adoption to wider adoption as the price of the technology decreases. There are two main grid-related issues with PHEVs, and the issues are often conflated: 1) The need to provide for adequate generation capacity for charging; and 2) The potential for vehicle-to-grid (V2G) applications.95

PHEVS and Grid Capacity On a national level, there is plenty of available grid capacity to charge PHEVs. A study by the Pacific Northwest National Laboratory found that 73% of the country’s light-duty vehicles could be supported using existing grid infrastructure.96 However, as with a consideration of emissions impacts, a consideration of PHEV impacts needs to occur at regional levels. A later study by the Institute of Electrical and Electronics Engineers (IEEE), for example, found that the maximum potential for the Pacific Northwest to supply PHEVs, given the region’s capacity mix, is 18% of its light-duty vehicles.97 This number also assumes that the vehicles are charged at night and in other off-peak periods when existing generation capacity is idle. A good summary of the capacity implications for PHEVs is given in a report by Oak Ridge National Laboratory: …there has generally been the expectation that the grid will not be greatly affected by the use of the vehicles, because the recharging would only occur during off-peak hours, or the number of vehicles will grow slowly enough that capacity planning will respond adequately. But this expectation does not incorporate that end-users will have control of the time of recharging and the inclination for people will be to plug in when convenient for them, rather than when utilities would prefer... 98 95 The technical distinction is that a PHEV may have bi-directional grid communication; only a V2G vehicle has bi-directional power flow (i.e., an AC-AC inverter/power electronics). 96 Michael Kintner-Meyer, Kevin Schneider, et al., “Impacts Assessment of Plug-In Hybrid Vehicles on Electric Utilities and Regional U.S. Power Grids, Pacific Northwest National Laboratory, November 2007. 97 K. Schneider et al. 98 Stanton Hadley, “Impact of Plug-in Hybrid Vehicles on the Electric Grid,” Oak Ridge National Laboratory, October, 2006.

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Policy Considerations: PHEVs

... Depending on when and where the vehicles are plugged in, they could cause local or regional constraints on the grid. They could require both the addition of new electric capacity along with an increase in the utilization of existing capacity. Local distribution grids will see a change in their utilization pattern, and some lines or substations may become overloaded sooner than expected. Furthermore, the type of generation used to recharge the vehicles will be different depending on the region of the country and timing when the PHEVs recharge.99

Estimating Capacity Requirements The fact that there are so many variables to consider makes estimating capacity requirements for PHEVs no easy task. Assumptions need to be made about the technical specifications of the vehicle, market penetration, average number of miles driven per car on electric power, and the vehicle’s management of battery discharge. Since available capacity is a function of the time of day, further assumptions must be made the timing and duration of PHEV charging. Studies by EPRI and the DOE predict PHEVs will account for 25% of all vehicle sales by 2020.100 These numbers might be optimistic given the nascent stage of battery technology and the expected high costs of first-generation models. Making assumptions about vehicle performance is particularly difficult because the vehicles are still in development and reliable data are lacking. There are several studies on PHEVs that model future scenarios with different vehicle specifications and fuel mixes. The results are so varied that it is hard to draw any firm conclusions other than that the PHEV market is likely to grow considerably, and how this impacts the grid is largely a function of whether or not the vehicles are charged during off-peak periods.

Emissions Impacts Automotive fuels account for 70% of U.S. oil consumption and 34% of CO2 emissions so a shift to vehicle electrification has important ramifications for both CO2 emissions as well as energy independence and security.101 As mentioned, the emissions impact of using the grid to charge PHEVs is a function of the generation fuel mix and the time of day when the batteries are charged. In February, 2009 Argonne National Laboratory released a model-based greenhouse gas emissions analysis of PHEV impacts in several different NERC regions. It assumed 25% market penetration by 2020, and simulated different scenarios using PHEVs powered by different fuel mixes and with different all-electric ranges (AER). Its results for 20-mile AER PHEVs powered by gasoline compared to a standard internal combustion engine vehicle are summarized in the following chart:

99 Stanton Hadley. 100 Bernie Woodall, “Plug-in autos charged overnight OK for grid,” Reuters.com, Jun 29, 2009. http://www.reuters.com/article/ GCA-GreenBusiness/idUSTRE55S63G20090629 101 A. Elgowainy et al., “Well-to-Wheels Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Electric Vehicles,” Argonne National Laboratory, February 2009. Carbon dioxide emissions calculated from 2007 EIA data.

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Policy Considerations: PHEVs Comparison of energy use and emissions of a conventional gasoline internal combustion engine (ICEV) vs. a PHEV-20 (20 miles all-electric range, supplemented by gasoline engine). Figures are for (left to right) • Fossil fuel energy use • Petroleum energy use • GHG emissions Results are averages for entire electric grid. Region-specific results varied slightly. “WTW” = Well-to-Wheels

Source: A. Elgowainy et al., “Well-to-Wheels Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Elctric Vehicles,” Argonne National Laboratory, February 2009.

A joint report by EPRI and the Natural Resources Defense Council (NRDC) found that PHEV adoption would result in significant decreases in greenhouse gas emissions across all of its nine scenarios, which included different PHEV adoption and electric generation CO2 intensity.102 A study published in Energy Policy found that even if PHEVs were charged by 100% coalfired generation, emissions would still be lower than emissions from conventional vehicles— ranging from 6 to 16% lower.103 It is noteworthy that both studies reveal that PHEVs would not deliver significantly lower CO2 emissions (and in some cases would increase CO2 emissions) over existing non-plug-in hybrid cars. However, as regional fuel mixes become cleaner, the emissions advantages move toward favoring the use of PHEVs. Key Questions: •

How many PHEVs can a region’s specific capacity mix support?

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What are region-specific emissions impacts of PHEVs?

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What pricing programs or mandates (or combination thereof) should be used to encourage off-peak charging? What technologies need to be in place to monitor and verify compliance?

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Does a regulatory decision need to be made regarding 120V vs. 240V charging? The charging of a 240V battery is many times faster than that for a 120V battery charge.104 However, 240V charging will result in shorter and spikier load draws from PHEVs, which might make it more difficult for the grid to supply and manage the loads.

•

What are the rate recovery considerations for upgrades on substations, transformers, and feeder lines to accommodate PHEV charging?

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Do PHEVs merit special AMI requirements, such as requiring that the PHEV communicate to the grid when it is charging, so as to allow grid planners to aggregate data on charging patterns for use in capacity planning?

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Do specific net metering policies need to be established for PHEVs?

102 “Environmental Assessment of Plug-in Hybrid Electric Vehicles. Volume 1: Nationwide Greenhouse Gas Emissions,” EPRI with NRDC, Final Report July 2007. 103 Paulina Jaramillo et al, “Greenhouse gas implications of using coal for transportation: Life-cycle assessment of coal-to-liquids, plug-in hybrids, and hydrogen pathways,” Energy Policy, Vol 37, Issue 7, July 2009. 104 A pilot by CenterPoint energy found that at 120V charging, it took 5 and 7 hours to charge a PHEV Toyota Prius and Ford Escape respectively. At 240V it took one hour for both cars.

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Policy Considerations: PHEVs •

Does the value of offsetting fossil fuel use through electricity, and the prospect of maximizing the efficiency of the grid, increase the value of investments in energy efficiency and distributed generation, and thus warrant increased investment in these resources?

Since regulators only have jurisdiction over the utilities and the grid, not over transportation infrastructure or the vehicles themselves, cooperation among a variety of independent regulating agencies will be vital in bringing the potential benefits of PHEVs to reality.

Vehicle-to-Grid (V2G) V2G is an extension of the logic of PHEVs, seeing the vehicles as a mobile energy storage source for the grid.105 Vehicles charged at night during low-use periods would be driven to work, plugged into the workplace building’s electrical infrastructure, and used to provide electricity to the grid during peak demand periods. The vehicles could then be driven home on the remaining charge or the internal combustion engine. The economic logic behind this view of the future is that utilities need stored energy only for a small portion of the day, and it would be more economically efficient for utilities to share the cost of the batteries (which are expensive) with consumers, who could use them the rest of the day for transportation. However, this view also requires that: •

Adequate charging infrastructure be installed at enough workplaces to provide meaningful vehicle battery capacity;

•

Enough PHEVs are in use to provide meaningful vehicle battery capacity;

•

The availability of aggregate PHEV battery capacity will be predictable enough to be useful to utilities in planning their capacity requirements.

As such, one must question whether the practical realities of V2G are at odds with the theoretical efficiency value of using PHEVs as grid storage devices. At the very least, it does not seem to be requisite for regulators and legislators to make decisions regarding V2G capabilities at this point. As one PHEV analyst put it, “There will be a decade in which to collect data before the utilities would ever put themselves in a situation where they would rely on PHEVs for basic supply and figure out how best to do it.”106 In the nearer term, it seems more realistic to imagine that PHEVs plugged in at home and charged off-peak could be used to sell electricity to the grid during times of high peak demand. The advantage to the homeowner is his or her ability to take advantage of energy arbitrage (buying off-peak and selling on-peak). This would conceivably be no different from net metering on a solar panel, and would not appear to necessitate any additional technologies other than a meter capable of reading and communicating electricity consumption or supply. Key Questions: •

105 106

Who would bear the cost of installing necessary PHEV charging/discharging infrastructure to support V2G functionality? Should non-PHEV owners share in the cost?

The batteries could also be used to provide voltage and frequency modulation for the grid. Correspondence with Laura Schewel, former analyst specializing in PHEVs at Rocky Mountain Institute

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Policy Considerations: PHEVs •

Should the costs of V2G infrastructure be borne primarily by those who have the ability to make use of that infrastructure? Or can V2G be seen as a public good that benefits all ratepayers?

•

What method of infrastructure roll-out would be most appropriate? That is, how best to match installation of PHEV charging/discharging stations with available PHEVs?

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Do building codes need to be changed to mandate that new commercial construction be made V2G capable?

Summary The major near-term area of decision-making for regulators with regard to PHEVs will be the assurance of predictable off-peak charging. Since the majority of plug-in vehicles will be hybrids that can run on an internal combustion engine when battery capacity is depleted, regulators will not diminish the utility of these vehicles by putting safeguards in place to ensure that the grid is not overloaded by PHEV battery charging.

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Conclusion One way or another, the grid will evolve and Smart Grid technologies will be a major part of this evolution. The question for legislators, regulators, and consumer is: Will this evolution enable the greatest efficiency and reliability in the generation, delivery, and use of electricity in a manner that minimizes long-term consumer and environmental costs? Whether it delivers fully upon this promise is contingent upon thoughtful policy leadership, an understanding of the interactions among issues and technologies, and upon an unprecedented level of cooperation, understanding, and foresight from the major players creating the Smart Grid. The disparity of results among different Smart Grid studies, pilots, and simulations all point to the fact that the benefits of Smart Grid implementation are heavily dependent on the specifics of the programs and services enabled by it. In particular, state legislators and regulators will significantly influence both the form that the Smart Grid evolution takes and the speed at which it occurs. At a very broad level, the Smart Grid represents the genesis of a new electricity marketplace in which energy is priced to reflect its true costs. This also represents a transition in roles for regulators and legislators, from setting rates to creating this new marketplace and ensuring it is fair for all constituents. This is no easy task, and while the literature is fairly extensive on Smart Grid technologies, the discussion of implementation is just beginning. This paper is far from an exhaustive discussion of what decision makers will need to consider as the Smart Grid is deployed, and the issues will vary significantly from state to state. The goal of this discussion has been to provide a big-picture understanding of the Smart Grid and a foundation and context from which relevant questions about policy and technology and their interaction can begin to be explored. Suggested resources for further reading and other information are provided in the appendices.

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appendices

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Appendices

Appendix a resources For Further Reading Overview of Electric Generation “Electric Power Industry Overview 2007,” U.S. EIA, http://www.eia.doe.gov/cneaf/electricity/page/prim2/toc2. html “Overview of the Electric Grid,” U.S. DOE GridWorks, http://sites.energetics.com/gridworks/grid.html EIA, Electric Power Annual – Electric Industry 2007 Year in Review, http://www.eia.doe.gov/cneaf/electricity/ epa/epa_sum.html

Overview of Smart Grid “Building the Smart Grid,” The Economist Technology Quarterly, June 6, 2009. “Grid Modernization 101,” Dundee Capital Markets analyst report, May 1, 2009. “Overview of the Smart Grid – Policies, Initiatives, and Needs,” ISO New England, Feb. 17, 2009. “Smart Grid: Enabler of the New Economy,” Electric Advisory Committee, December 2008. http://www. oe.energy.gov/DocumentsandMedia/final-smart-grid-report.pdf “The Smart Grid: An Introduction,” Litos Strategic Communication (Prepared for U.S. DOE), http://www. oe.energy.gov/1165.htm Bracken Hendricks, “Wired For Progress: Building a National Clean Energy Smart Grid,” Center for American Progress, Feb. 2009. IESO Smart Grid Presentation, “Ontario Smart Grid Forum,” Capgemini, 2008.

Smart Grid Policy Miles Keogh, “The Smart Grid: Frequently Asked Questions for State Commissions,” NARUC, May 2009. “Demand Response and Smart Metering Policy Actions Since the Energy Policy Act of 2005: A Summary for State Officials,” National Council on Electricity Policy, Fall 2008. John McDonald , “Leader or Follower? The Four Essentials of a Safe-and-Sane Smart Grid Plan,” SmartGridNews.com, June 8, 2009, http://www.smartgridnews.com/artman/publish/commentary/ Leader_or_Follower_The_Four_Essentials_of_a_Safe-and-Sane_Smart_Grid_Plan-598.html “Top Federal Energy Policy Goals,” Rocky Mountain Institute, January 2009 http://www.rmi.org/images/ PDFs/Energy/RMI-Top-Federal-Energy-Policies.pdf FERC, “Smart Grid Policy”, Docket No. PL09-4-000, issued March 19, 2009. “The Green Grid,” EPRI Technical Update, June 2008. “Smart Grid Issues Summary,” NIST, March 10, 2009, http://www.naspi.org/draft_nist_smart_grid_issues_ summary_20090310.pdf

Advanced Metering Infrastructure and Demand Response “Ahmad Faruqui and Lisa Wood, “Quantifying the Benefits of Dynamic Pricing in the Mass Market,” Prepared for Edison Electric Institute by The Brattle Group, January, 2008. “Assessment of Demand Response and Advanced Metering,” Staff Report, Federal Energy Regulatory Commission, December 2008. “California Statewide Pricing Pilot (SPP) Overview and Results 2003-2004,” sites.energetics.com/madri/ toolbox/pdfs/pricing/pricing_pilot.pdf “GridWise Demonstration Project Fast Facts,” Pacific Northwest National Laboratory, December 2007. http://gridwise.pnl.gov/docs/pnnl_gridwiseoverview.pdf “Rethinking Rate Design,” Presentation for California PUC Dynamic Pricing Issues Workshop, The Brattle Group, September 7, 2007. B. Neenan, “Characterizing and Quantifying the Societal Benefits Attributable to Smart Metering Investments” EPRI, July 2008 Darren Kelsey, “AEP’s gridSMART Initiative,” Presentation: EEI National Accounts Workshop, March 17, 2009 WWW.VEIC.ORG

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Appendices

David Nemtzow, “The Green Effect,” Public Utilities Fortnightly, March 2007. Karen Herter, “Residential implementation of critical-peak pricing of electricity,” Energy Policy, Vol 35, Issue 4, April 4, 2007. Nancy Brockway, “Advanced Metering infrastructure: What Regulators need to Know About Its Value to Residential Customers,” National Regulatory Research Institute, February 13, 2008. Source: Sean Kelly, “Wholesale Pricing in the National Electricity Market,” Presentation to Energy Consumers Council, Government of South Australia Department for Transport, Energy and Infrastructure, Feb. 2009. http://www.dtei.sa.gov.au/ECC/media/documents/meeting_85/ECC_pres_ 040209_Wholesale_Pricing_in_the_NEM.pdf Stephen S. George, Josh Bode, and Michael Wiebe “Benefit-Cost Analysis for Advanced Metering and Time-Based Pricing,” Prepared for VT Department of Public Service by Freeman, Sullivan & Co. and MWConsulting, March 26, 2008.

Distributed Generation “Net Metering Policies,” U.S. DOE Energy Efficiency and Renewable Energy, http://apps3.eere.energy.gov/ greenpower/markets/netmetering.shtml “PV Systems and Net Metering” http://www1.eere.energy.gov/solar/net_metering.html “States with Renewable Portfolio Standards,” U.S. DOE Energy Efficiency and Renewable Energy, http:// apps1.eere.energy.gov/states/maps/renewable_portfolio_states.cfm U.S. EPA Renewable Portfolio Standards Fact Sheet, http://www.epa.gov/chp/state-policy/renewable_fs.html Lynne Kiesling, “Smart grid and renewables interconnection,” KnowledgeProblem.com, March 6, 2009, http://knowledgeproblem.com/2009/03/06/smart-grid-and-renewables-interconnection-part-4-of-5/

Plug-in Hybrid Electric Vehicles “Environmental Assessment of Plug-in Hybrid Electric Vehicles. Volume 1: Nationwide Greenhouse Gas Emissions,” EPRI with NRDC, Final Report July 2007. A. Elgowainy et al., “Well-to-Wheels Energy Use and Greenhouse Gas Emissions Analysis of Plug-in Hybrid Electric Vehicles,” Argonne National Laboratory, February 2009. K. Schneider et al., “Impact Assessment of Plug-In Hybrid Vehicles on Pacific Northwest Distribution Systems,” IEEE, 2008. Michael Kintner-Meyer, Kevin Schneider, et al, “Impacts Assessment of Plug-In Hybrid Vehicles on Electric Utilities and Regional U.S. Power Grids, Pacific Northwest National Laboratory, November 2007. Paulina Jaramillo et al, “Greenhouse gas implications of using coal for transportation: Life-cycle assessment of coal-to-liquids, plug-in hybrids, and hydrogen pathways,” Energy Policy, Vol 37, Issue 7, July 2009. Plug-in Hybrid Electric Vehicle R&D Plan – Working Draft, U.S. DOE, June 2007. Stanton Hadley, “Impact of Plug-in Hybrid Vehicles on the Electric Grid,” Oak Ridge National Laboratory, October, 2006

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Appendices

appendix b Acronym Key AMI – Advanced Metering Infrastructure AMR – Automated Meter Reading ARRA – American Recovery and Reinvestment Act CHP – Combined Heat and Power CPP – Critical Peak Pricing DER – Distributed Energy Resources DG – Distributed Generation DR – Demand Response DRIPE - Demand Reduction Induced Price Effect EE – Energy Efficiency FERC – Federal Energy Regulatory Commission HAN – Home Area Network HVDC – High Voltage Direct Current MDMS – Meter Data Management System NARUC – National Association of Regulatory Utility Commissioners PCT – Programmable Communicating Thermostat PHEV – Plug-in Hybrid Electric Vehicle PEV – Plug-in Electric Vehicle PTR – Peak Time Rebate PUC – Public Utility Commission PURPA – Public Utility Regulatory Policies Act RPS – Renewable Portfolio Standard RTO – Regional Transmission Organizations RTP – Real-Time Pricing TOU – Time of Use V2G – Vehicle-to-Grid

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Appendices

Appendix C Technology Standardization Uniform technology standards are vital to the development of the communications backbone of the Smart Grid. These standards will allow disparate devices from numerous different manufacturers to communicate with each other. Governmental and non-governmental organizations are currently working on technology standardization. The primary considerations in the development of uniform technology standards are to provide for adequate interoperability and security. Interoperability refers to the ability of different devices in the grid to communicate and function together “without special effort.” Federal agencies, particularly DOE and the National Institute of Standards and Technology, are engaged in standards development; however, there is currently no federal authority to demand compliance with any standard. As NIST writes, “In most areas, the existing U.S. power grid is operated with proprietary systems that are fragmented and isolated. Attempts to create interoperable systems often fail because there is no third-party certification of conformance.” This has compelled some states, such as California, via its Public Utility Commission, to provide financial incentives for utilities to comply with interoperability requirements. In the absence of federal legislation demanding compliance, state utility commissions may find themselves having to provide incentives for or mandate compliance with uniform technology standards. California’s PUC essentially has done this; the standards they set can be seen below. Security The interconnectedness of Smart Grid systems poses increased cybersecurity risks. Current cybersecurity standards (CIP Cyber Security standards) exist for the bulk power system and are set by the North American Electric Reliability Corporation. The NERC CIP Cyber Security standards only apply to the Bulk Power System and do not address the cyber security requirements of other domains such as home and building area networks. An Advanced Metering Infrastructure Security Task Force (AMI-SEC) is defining common requirements and standardized specifications for securing AMI system elements. The implication for states is that they regulate the utilities that are bound to comply with NERC standards. States could participate in the NERC processes or add NERC compliance to the utility performance incentives. A current list of NIST technology standards can be found at: http://edocket.access.gpo.gov/2009/E9-13514.htm

NIST definition. Alex Tcherkassky, “Smart Grid Benefits May Require Strings, Industry Reps Say,” Broadband Census, May 13, 2009, http://broadbandcensus.com/2009/05/smart-grid-benefits-may-require-strings-industry-reps-say/ “Smart Grid Issues Summary” Camille Ricketts. See note 121.

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Appendices

APPENDIX d AMI Minimum Functionality (as Determined by California public Utility Commission) 1. Supports implementation of time-varying tariffs for: i. Residential and small commercial customers (under 200 kW): 1. Time-of-Use (TOU) rates; 2. Critical Peak Pricing with fixed notification (CPP-F) and CPP with variable or hourly notification (CPP-V); 3. Flat/inverted tier rates. ii. Large customers (200 kW to 1 MW) on an opt-out basis: 1. Critical Peak Pricing with fixed or variable notification; 2. Time-of-Use rates; 3. Two part hourly Real-Time Pricing. iii. Very large customers (over 1 MW) on an opt-out basis: 1. Two-part hourly Real-Time Pricing; 2. Critical Peak Pricing with fixed or variable notification; 3. Time-of-Use Pricing. 2. Allows collection of usage data at a level of detail that supports customer understanding of hourly usage patterns and how those usage patterns relate to energy costs. 3. Provides customer access to personal energy usage data with sufficient flexibility to ensure that changes in customer preference of access frequency do not result in additional AMI system hardware costs. 4. Compatible with applications that (1) use collected data to provide customer education, energy management information and customized billing; and (2) support improved complaint resolution. 5. Compatible with utility system applications that promote and enhance system operating efficiency and improve service reliability, such as remote meter reading, outage management, reduction of theft and diversion, improved forecasting, workforce management, etc. 6. Capable of interfacing with load control communication technology.

Nancy Brockway.

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Appendices

Appendix e AMI Deployments in the United States Park Associates released figures in July, 2009 for smart-meter installations in the U.S., claiming that there are 8 million units installed, or about 6 percent of all meters. Announced Large AMI Deployments in the US Utility

Commodity

AMI type

Number

Year Started

Electric

Fixed RF

450,000

1994

Electric & Gas

Fixed RF

1,400,000

1995

Electric

Fixed RF

550,000

1995

Electric & Gas

Fixed RF

1,900,000

1996

Electric

Fixed RF

415,000

1997

Electric & Gas

Fixed RF

1,325,000

1997

Electric

Fixed RF

450,000

1997

Electric & Gas

Fixed RF

2,100,000

1999

United Illuminating (CT)

Electric

Fixed RF

320,000

1999

Wisconsin Public Service (WI)

Electric

PLC

650,000

1999

Wisconsin Public Service (WI)

Gas

Fixed RF

200,000

2000

JEA (FL)

Electric

Fixed RF

450,000

2001

PPL (PA)

Electric

PLC

1,300,000

2002

Electric & Gas

Fixed RF

1,000,000

2002

Electric

PLC

125,000

2004

Electric & Gas

Various

5,000,000

2004

Electric

Fixed RF

400,000

2005

Kansas City Power & Light (MO) Ameren (MO) Duquesne Light (PA) Xcel Energy (MN) Indianapolis Power & Light (IN) Puget Sound Energy (WA) Virginia Power Exelon (PA)

WE Energies (WI) Bangor Hydro Hundreds of Small Utilities Colorado Springs Laclede

Gas

Fixed RF

650,000

2005

Electric

BPL

2,000,000

2005

Ameren (IL)

Electric & Gas

Fixed RF

1,000,000

2006

PG&E (CA)

Electric

PLC

5,100,000

2006

PG&E (CA)

Gas

Fixed RF

4,100,000

2006

TXU

Total

30,885,000

LaMonica, M. (July 17, 2009). “Smart meters cracking into U.S. homes.” from http://news.cnet.com/8301-11128_310289495-54.html. Stephen S. George et al.

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Appendices Pending or In-Process San Diego Gas & Electric

Approval to deploy roughly 1.4 million electric meters and 900,000 gas meters

Southern California Edison

Application pending before the California Public Utilities Commission (CPUC) to deploy roughly 5 million advanced electric meters

All New York utilities

Have analyzed the benefits and costs of AMI and two distribution companies of Energy East (Rochester Gas & Electric and New York State Electric & Gas) are seeking approval to move forward with AMI for roughly 1 million electricity consumers. Consolidated Edison and Central Hudson recently received permission to conduct pilots in anticipation of full scale implementation

Central Maine Power

Has requested regulatory approval to deploy AMI to roughly 550,000 electricity customers

Southern Company

Planning to deploy advanced meters to more than 4.3 million electricity consumers, with the initial application focused on AMR but with the option to upgrade to full AMI functionality

Ontario, Canada

Mandated by the provincial government to deploy AMI meters to all 4.5 million electricity customers by no later than 2010. Hydro One has already begun its deployment of 1.5 million meters

http://newsroom.parksassociates.com/article_display.cfm?article_id=5168

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Appendices

Appendix f NERC Generation Regions and Associated Fuel Sources Mix

A. Elgowainy et al.

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Appendices

Appendix g Net Metering and RPS Programs By State

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Appendices

Summary of State Renewable Portfolio Standards 10 The following table gives a rough summary of state renewable portfolio standards and links to organizations that are administering these standards or explain the details involved. Percentages refer to a portion of electricity sales and megawatts (MW) to absolute capacity requirements. Most of these standards phase in over years, and the date refers to when the full requirement takes effect. State Amount Arizona 15% California 33% Colorado 20% Connecticut 23% District of 20% Columbia Delaware 20% Hawaii 20% Iowa 105 MW Illinois 25% Massachusetts 15% Maryland 20% Maine 40% Michigan 10% Minnesota 25% Missouri 15% Montana 15% New Hampshire 23.8% New Jersey 22.5% New Mexico 20% Nevada 20% New York 24% North Carolina 12.5% North Dakota* 10% Oregon 25% Pennsylvania 8% Rhode Island 16% South Dakota* 10% Texas 5,880 MW Utah* 20% Vermont* 10% Virginia* 12%

2019 2020 2025 2020 2022 2017 2015 2025 2021 2015 2025 2021 2020 2015 2013 2021 2015 2025 2020 2019 2015 2015 2025 2013 2022

Washington Wisconsin

2020 2015

15% 10%

Year 2025 2030 2020 2020 2020

Organization Administering RPS

Arizona Corporation Commission California Energy Commission Colorado Public Utilities Commission Department of Public Utility Control DC Public Service Commission

Delaware Energy Office Hawaii Strategic Industries Division Iowa Utilities Board Illinois Department of Commerce Massachusetts Division of Energy Resources Maryland Public Service Commission Maine Public Utilities Commission Michigan Public Service Commission Minnesota Department of Commerce Missouri Public Service Commission Montana Public Service Commission New Hampshire Office of Energy and Planning New Jersey Board of Public Utilities New Mexico Public Regulation Commission Public Utilities Commission of Nevada New York Public Service Commission North Carolina Utilities Commission North Dakota Public Service Commission Oregon Energy Office Pennsylvania Public Utility Commission Rhode Island Public Utilities Commission South Dakota Public Utility Commission Public Utility Commission of Texas Utah Department of Environmental Quality Vermont Department of Public Service Virginia Department of Mines, Minterals, and Energy Washington Secretary of State Public Service Commission of Wisconsin

10 “States with Renewable Portfolio Standards,” U.S. DOE Energy Efficiency and Renewable Energy, http://apps1.eere. energy.gov/states/maps/renewable_portfolio_states.cfm

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Appendices

Appendix H Department of energy Programs working on Smart Grid Development Department of Energy

Department of Energy Reliability and Electric Security Vision of a wide array of public and private stakeholders for an electric power system that connects everyone to abundant, affordable, clean, efficient, and reliable electric power anytime, anywhere and provides the best and most secure electric services available in the worldâ&#x20AC;?.

GridWise Alliance

GridWorks

Wide array of technology stakeholders and utilities working with NIST on common standards and interoperability of entire electricity delivery supply chain

Improve reliability of electric power system through modernization of key grid components, such as cables and conductors, substations and protective systems, and power electronics

Smart Grid Task Force

Electric Advisory Committee

Responsible for coordinating standards development, guiding research and development projects, and reconciling the agendas of a wide range of stakeholders

Provide advice to the U.S. Department of Energy in implementing the Energy Policy Act of 2005, executing the Energy Independence and Security Act of 2007, and modernizing the nationâ&#x20AC;&#x2122;s electricity delivery infrastructure

GridApps (Advanced Grid Applications Consortium)

Smart Grid Clearinghouse

Applies best utility technologies and practices to modernize electric transmission and distribution

Consolidate public technical, legislative, and other information on Smart Grid development and practices, and direct website users to additional information sources both in the United States and internationally.Â

NETL (National Energy Technology Laboratory) Modern Grid Strategy (MGS) program

Modern Grid Initiative

Seeks to accelerate modernization of grid by fostering development of a common, national vision among grid stakeholders

Enable and accelerate grid modernization, including providing analytic support to DOE-supported demonstration programs

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Appendices

appendix I Other Federal agencies and Programs Working on Smart Grid Development

Federal Energy Regulatory Commission(FERC)

National Institute of Standards and Technologies (NIST)

Independent agency that regulates the interstate transmission of natural gas, oil, and electricity. FERC also regulates natural gas and hydropower projects

Establishing “interoperability framework” “to describe standards and protocols needed by “smart devices” to enable the S&R and communication capabilities requires for automated real-time control of electricity supply and demand”

North American Electric Reliability Corporation (NERC)

National Association of Regulatory Utility Commissions (NARUC)

Mission is to ensure the reliability of the bulk power system in North America. To achieve that, we develop and enforce reliability standards; assess reliability annually via 10-year and seasonal forecasts; monitor the bulk power system; and educate, train, and certify industry personnel

Providing forum for state and local regulators to discuss issues and make recommendations for state and federal policies to support the Smart Grid

North American Energy Standards Board (NAESB) Developing demand-response standards

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Appendices

appendix j Non-Government Organizations Working on Smart Grid Development

Electric Power Research Institute (EPRI)

Open AMI Task Force

Conducts research and development relating to the generation, delivery and use of electricity for the benefit of the public. An independent, nonprofit organization.

Funded by EPRI to enhance functionality, reduce costs, and foster rapid market adoption of advanced metering and demand-response solutions through the development of an open-standards-based reference design and data model.

Intelligrid Consortium

ZigBee Alliance

Mission to enable the development, integration, and application of technologies to facilitate the transformation of the electric infrastructure to cost effectively provide secure, high-quality, reliable electricity products and services.

An association of companies working together to enable reliable, cost-effective, low-power, wirelessly networked, monitoring and control products based on an open global standard.

Galvin Electricity Initiative

Edison Electric Institute (EEI)

Applies principles of total quality management to the electric power industry with goal of developing â&#x20AC;&#x153;perfectâ&#x20AC;? power delivery system to meet the needs of the rapidly evolving digital economy and society.

The association of U.S. Shareholder-Owned Electric Companies. EEI provides public policy leadership, critical industry data, market opportunities, strategic business intelligence, one-of-a-kind conferences and forums, and top-notch products and services.

Institute of Electrical and Electronics Engineers (IEEE)

Utility Communication Architecture International Users Group OpenSG Subcommittee

Assists in coordinating information exchange among all the groups working on Smart Grid projects and is developing standards to address interconnection, operations, control, and monitoring capabilities for implementing distributed generation.

A not-for-profit corporation focused on assisting users and vendors in the deployment of standards for real-time applications for several industries with related requirements. The Users Group does not write standards, however works closely with those bodies that have primary responsibility for the completion of standards. Launched task force to develop framework for deploying Smart Grid technologies.

American National Standards Institute (ANSI) Oversees the creation, promulgation and use of thousands of norms and guidelines that directly impact businesses in nearly every sector: from acoustical devices to construction equipment, from dairy and livestock production to energy distribution, and many more.

American Public Power Association (APPA) Launched task force to develop framework for deploying Smart Grid technologies

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Appendices

about the authors David Fribush is a graduate student at the University of Michigan’s Erb Institute for Global Sustainable Enterprise pursuing joint MBA and Environmental Science degrees with the Ross School of Business and School of Natural Resources and Environment This document was completed as part of a graduate summer internship with the Vermont Energy Investment Corporation. He has previously worked for Johnson Controls’ Global Energy and Sustainability Team and was a Fellow at Rocky Mountain Institute. His earlier experience was in film and video production, with several awards to his credit. He received a B.A. in Economics from Wesleyan University and completed post-graduate science coursework at Columbia University. He can be contacted via e-mail at: dfri@umich.edu Scudder Parker is a Managing Consultant in the Planning and Evaluation Department at VEIC. He manages a variety of complex projects focusing on approaches to achieving aggressive efficiency and renewable energy targets. This has included development of energy policy recommendations for several jurisdictions; analysis of the role efficiency can play in deferring the need for new power plants and other supply side investments; development of plans for structuring and launching new and / or improved efficiency operations; leading negotiations with utilities and other stakeholders regarding efficiency goals, efficiency program designs, integration of efficiency and renewable energy efforts; and the development of regulatory testimony in both the U.S. and Canada. He is regarded by many as the “father” of the Vermont efficiency utility, having shaped the concept during his time as Director of the Energy Efficiency Division of the state’s Department of Public Service. He also has led consulting projects focusing on the development of the infrastructure necessary to achieve aggressive efficiency savings and renewable energy generation targets. Shawn Enterline is a Consultant in the Planning and Evaluation Department at VEIC. He has over 10 years of experience working for municipal utilities, investor owned utilities, and interstate pipelines. His experience includes power and gas marketing, pipeline and power plant development, utility rate making, and integrated resource planning. He authored the Burlington Electric Department’s (BED) 2008 Integrated Resource Plan and led efforts in implementing a successful demand response program solicitation for the City of Burlington in which BED acquired over 5 MW of peak shaving demand side resources. Shawn holds a BS and MS in Mineral Economics from The Pennsylvania State University