## April 15, 2010

DOE/NETL-2010/1406



# Backup Generators (BUGS): The Next Smart Grid Peak R 

# esourceBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 ii 

# D ISCLAIMER

This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of an y information, apparatus, product, or process disclosed, or represents that its use would not infr inge privately owned rights. Reference therein to any specific commercial product, process, or se rvice by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. The views and opinions of authors expressed therein do not necessarily state or reflect those of the United States Government or any agency thereof.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 iii 

# BUGS: The Next Smart Grid Peak Resource

DOE/NETL-2010/1406

April 15, 2010

NETL Contact: Keith Dodrill Integrated Electric Po wer Systems Division Office of Systems, Analyses and Planning

National Energy Technology Laboratory www.netl.doe.govBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 iv This page is intentionally left blankBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 v 

# T ABLE OF 

# C ONTENTS

Disclaimer .................................................................................................................... ................................. ii

Table of Contents ............................................................................................................. ............................ v

Acronyms ...................................................................................................................... ............................. viii

BUGS: The Next Smart Grid Peak Resource ....................................................................................... ........ 1
Executive Summary ............................................................................................................. ..................... 1

Introduction .................................................................................................................. ............................ 2 Challenges to the Traditiona l Model of Ge neration ........................................................................... 2 Growing Interest in Di stributed Generation..................................................................................... ... 2
Size and Topology of To day’s Generation Fleet ................................................................................. .... 2 Conventional Generation ....................................................................................................... .............. 2 Distributed Generation ........................................................................................................ ................ 4 Distributed Generation Technologies ........................................................................................... ...... 5 Distributed Generation Applications ........................................................................................... ........ 7
Critiquing the Current Peak Load Paradigm ..................................................................................... ...... 9 DG Can Address the Peak Better than Centralized Peakers ............................................................. 9 A Smart Grid Creates New Opportunities ........................................................................................ 10
Breaking the Paradigm: Up-Converting BUGS to Integrated DG Resources..................................... 11 Greater Use through Greater Resp onsibility and Intelligence ........................................................ 11
Potential Uses for Integrated BUGS ............................................................................................ ......... 12
Dealing with Barriers to Grea ter Utilization of BUGS .......................................................................... 13

Economics ..................................................................................................................... ........................ 14

Environmental Metrics ......................................................................................................... ................. 15

Conclusions ................................................................................................................... ........................ 15

Bibliography .................................................................................................................. ............................. 16Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 vi 

## Prepared by: Booz Allen Hamilton (BAH)

Steve Pullins and Alex Zheng Horizon Energy Group

Joe Miller Horizon Energy Group

DOE Contract number: DE-FE000400Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 vii 

# Acknowledgements

This report was prepared by Booz Allen Ha milton, Inc. (BAH) for the United States Department of Energy’s National Energy Technology Laboratory. This work was completed under DOE NETL Contract Number DE-FE 000400, and performed under BAH Task 430.04. The authors wish to acknowledge the excellent guidance, contributions, and cooperation of the NETL staff, particularly:
Steven Bossart, Integrated Electric Power Systems Division Director, NETL Keith Dodrill, Integrated Electric Power Systems Division, NETLBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 viii 

# A CRONYMS

BUGS Backup Generators
CHP Combined Heat and Power
DG Distributed Generation EIA Energy Information Administration GW Gigawatts GWh Gigawatt Hours IEEE Institute of Electrical and Electronic Engineers
kW Kilowatt MW Megawatt
PGE Portland General Electric RTOs Regional Transmission organizations SAIC Science Applications International Corporation
TWh Terawatt HoursBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 1 

# BUGS: T HE 

# N EXT 

# S MART 

# G RID 

# P EAK 

# R ESOURCE

**E**

**XECUTIVE** 

## S **UMMARY** Tomorrow’s smart grid will accommodate and enab le a wide variety of generation and storage options. Today, there is a large untapped resource on the consumer side of the meter at many commercial and industrial customer facilities. Consumer backup generators (BUGS), which are distributed generation (DG) units for either emergency or standby applications, are plentiful and well distributed. BUGS can play a significant role in flattening the utility load profile to the economic and environmental benefit of utilities, consumers, and society. Properly integrated, BUGS can have an environmental as well as economic benefit because they are well distributed, quick to startup and shutdown, close to the point of consumption, and more responsive than large, traditional power
sources. The low asset utilization of standby and emergenc y applications represents 170 GW of untapped capacity suggesting they could potentially serve larger loads, certainly at peak. This 170 GW of BUGS capacity represents about 22 percent of the peak and 36 percent of the average load in 2009 and could be available to address the peak demand. This could be a significant benefit to consumers and utilities. A Portland General Elec tric (PGE) demonstration showed a significantly lower conversion cost per kW as well as a 30% or more reduction cost per KW over simple cycle gas turbine costs. The key to increasing the capacity factor of BUGS is interconnecting them to the grid and using them to serve larger loads. The generators within the emergency power are predominately diesel fueled reciprocal engines. At first glance the environmental benefit of this resource may not be apparent. However, preliminary calculations indicate there is a reduction in CO 2, NO X, and SO X compared to gas turbine peaking units. The Science Applications International Corporation (SAIC) Pollution Reduction Estimator (v1.0) shows that a national program to engage half of the BUGS for peak reduction during 200 hours a year results in emission reduction compared to natural gas peaking units. Specifically,

 More than 935,000 tons a year reduction in CO 2 emissions.

 More than 54,000 tons a year reduction in NO X emissions.

 More than 33,000 tons a year reduction in SO X emissions. The Smart Grid as described in the Modern Grid Strategy Seven Principal Characteristics envisions the accommodation of all generation resources and optimizes the efficiency of the available assets. Enabling BUGS as a peak resource is one option that builds on the enablement provided by the Smart Grid. This report addresses the following broad issues that will shape the role of BUGS in the smart grid:

 Size and topology of today’s generation fleet.

 Critiquing the current peak load paradigm.

 Breaking the paradigm: enabling BUGS as a DG resources.

 Potential uses for integrated BUGS.

 Dealing with barriers to gr eater BUGS utilization.

 Economics.

 Environmental Metrics.

 Reliability.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 2 

## I **NTRODUCTION** 

### Challenges to the Traditional Model of Generation With construction costs high, lengthy regulatory lag times, and public opposition to most types of central power plants (Not-In-My-Back-Yard concerns), significant barriers exist for new generation and transmission today. Yet with high growth pr ojected in electricity demand, public concern about greenhouse gases and global climate change, and a digital economy requiring higher power quality, the need for some new generation seems unavoidable. For the past 70 years, the utility system has dealt with growing demand for electricity by building more of everything. The philosophy was to provide whatever power the customer wanted whenever it was wanted. Much of this generation, transmission, and distribution capacity was added to compensate for a lack of built-in intelligent controls within the power system and a lack of cost awareness at the point of consumption. The national paradigm of overbuilding to meet the peak has resulted in a low capacity factor of only 47 percent (see Figure 1). The traditional model for building generation is not economically sustainable. However, with tools like the smart gr id, backup generators (BUGS), and energy storage, the peak can be addressed more efficiently, allowing new central-station generation to be deferred or cancelled. 

### Growing Interest in Distributed Generation Distributed generation (DG) has begun to play a larger role in generation investment over the past few decades. The International Energy Association lists five major factors that have contributed to an increase in DG around the world (International Energy Agency, 2002).

 Developments in DG technology.

 Constraints on the construction of new transmission lines.

 Increased customer demand for highly reliable electricity.

 The liberalization of electricity markets.

 Concerns about climate change. These factors have driven private power consumers and utilities alike to purchase greater levels of distributed generation at a pace of about $5 billion per year (International Energy Agency, 2002). Generators purchased by private consumers are generally chartered as backup power for a specific load, to provide a higher level of reliability for a sp ecific operation than the grid can offer. A handful of these generators have achieved a greater level of integration; however, the vast majority of these generators are not connected to the grid and operate infrequently in a backup mode only.


## S **IZE AND** 

## T **OPOLOGY OF** 

## T **ODAY** 

## ’ **S** 

## G **ENERATION** 

## F **LEET**



### Conventional Generation In 2009, the U.S. peak demand was 764 gigawatts (GW), and the corresponding system generation capacity was about 1,013 GW. (Energy Information Administration, 2009). This generation base is primarily composed of large, centralized, fossil-fuel-based generators. Figure 1 shows the relative electric generation mix of the 1,013 GW in capacity delivering energy totaling 4.16 million gigawatt hours (GWh) in 2007. However, from an energy perspective, the 4.16 million GWh divided by the total hours in a year (8,760 hours), reveals that we only used the ca pacity equivalent of 475 GW of central-station generation. Hence, the percent utilization of fleet nameplate capacity was only 47 percent.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 3 Annual plant maintenance is required, unexpected outages occur, and reserves are needed, so a 100 percent capacity factor for the fleet is virtually impossible. In addition, some technologies and sources lend themselves to higher capacity factors than others due to the nature of their operating principles. However, the national average of 47 percent remains a useful benchmark when comparing new technologies to the status quo. Fi gure 1 shows the capacity factor of various kinds of generators based on their primary fuel.

Figure 1 : National Average Capacity Factor of U.S. Gene ration Mix (Energy Information Administration, 2009)

Since the late 1990s, the nation has focused on building generation and transmission capacity to serve the peak. Figure 2 shows how the grid-co nnected generation capacity (blue) has increased significantly (greater than 200 GW) since 1998 while the average delivered (red curve) has not increased as much (about 50 GW equivalent). Source: Energy Information Admin., Form EIA-860, “Annual Electric Generator Report,” EIA-906, & Annual Energy Outlook 2008 

### National Average Capacity: 47%

50% 19% 20%* 9% <2%

% Portfolio Mix 20%* <1% *sharedBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 4 Figure 2 : U.S. Power Capacity versus Actual Averag e Power (Energy Information Administration, 2009) Figure 2 shows that the total generation capacity ov er the past few years has increased significantly, as indicated by the upward sloping blue line. Inst ead of increasing capacity, however, an alternative is to make smarter use of existing energy resources by increasing the capacity factor (on the graph this would reduce the gap between the red and blue lines). The gap between the lines represents the amount of capacity that is, on average, overbuilt. The traditional model of generation building of ch asing the peak is no longer the best economic choice for the U.S. consumer because of its high cost and low efficiency. Better options are becoming available through technology advances and the smart grid. The U.S. economy needs new peak load solutions like demand resp onse, storage, and DG—including BUGS.


### Distributed Generation In addition to centralized generation, a significant amount of DG exists throughout the United States. Estimates of the amount of DG in the United States vary, depending on how it is defined. A common definition of DG is the generation of electricity by generators of less than 60 MW for consumption near the DG unit. Using this definition, the U.S. DG fleet as of 2003 was composed of about 12.3 million units, had 222 GW of generation capacity, and produced about 232,000 GWh of energy annually ( DG Monitor , 2005). The U.S. DG fleet has been growing slowly throughout recent decades. As of 2004, about 68 percent of the total U.S. DG capacity was installed between 1990 and 2003 ( DG Monitor , 2005). Carnegie Mellon’s Electricity Industry Center reports that there are 12 million DG units in the United States with over 200 GW of generating capacity, gr owing at about 5 GW a year (Gilmore, 2007). Figure 3 shows the increase in DG installations ov er the past few years, delineated by technology type and seems to be an extension of the DG trend observed in 2000–2003. It is also clear that internal combustion engines remain the dominant te chnology in DG, and that many of the recent DG 0 100 200 300 400 500 600 700 800 900 1,000

Capacity (GW) Average Power (GW)GWBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 5 deployments use this technology. Other technologies appear to be holding steady, with the exception of wind and other nonconventional source s, which have been growing rapidly in the past few years.

Figure 3 : Capacity of Dispersed and Dist ributed Generators Installed per Year by Technology Type (Energy Information Administration, 2009). 

### Distributed Generation Technologies The current fleet of DG can be classified based on their technology and their application type. The technology types are presented in Figure 4 below:

Figure 4 : Distributed Generation Technologies (DG Monitor, 2005) 0 2,000 4,000 6,000 8,000 10,000 12,000 14,000

2004 2005 2006 2007 

## Capacity of Dispersed and Distributed Generators Installed per Year by Technology Type (MW)

Internal Combustion
Combustion Turbine
Steam Turbine Hydropower
Wind and OtherMWBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 6 The six technologies primarily found in DG include the following ( DG Monitor, 2005):

 Reciprocating Engines

o 192 GW capacity and 113 terawatt hours (TWh) generated in 2003.

o Low capital cost.

o Fast startup (20 seconds).

o High reliability and efficiency.

o Combust gasoline or fuel gases.

o Uses the same operating principle as conventional vehicles.

o $300–$600 per kilowatt (kW) capital costs and $0.09–$0.20 per kWh operating costs.

 Combustion Turbines

o 14.8 GW capacity and 4.9 TWh generated in 2003.

o Air is compressed in order to turn a series of blades that turn a generator (like a jet engine).

o Generally these use a gaseous fuel like natural gas, or a liquid fuel.

o Low emissions.

o $800–$1200 per kW capital cost.

 Steam Turbines

o 12.7 GW capacity per 53.6 TWh generated in 2003.

o Use high pressure steam to turn a se ries of blades to generate power.

o Generally only suitable for la rge, industrial applications.

 Hydropower

o 1.27 GW capacity per 6.55 TWh generated in 2003.

o Can store power by pumping water back up for use later.

o High efficiency.

o Low emissions.

 Fuel Cells

o 0.07 GW capacity per 0.55 TWh generated in 2003.

o Use a chemical reaction to generate electricity.

o Use a variety of gases to operate.

o Generates considerable waste heat.

 Combined Cycle

o 9.37 GW capacity per 4.99 GWh generated in 2003.

o A combination of a combustion turbine and a steam turbine.

o Exhaust heat from the combustion turbine is used to generate steam for the steam turbine. Wind, photovoltaics, and plug-in electric vehicles could also be included in the DG list. However, they still comprise a relatively small portion of DG and are generally not useful for addressing peak load. The following data is taken from the DG Monitor ’s 2005 edition of the report “The Installed Base of U.S. Distributed Generation,” published by Resource Dynamics Corporation ( DG Monitor , 2005). The U.S. DG landscape is dominated in terms of quan tity by reciprocating engines, primarily running on gasoline or fuel oil. Most of the U.S. DG fleet (97.9 percent) is composed of reciprocating engines smaller than 100 kW in size. Figure 5 also shows a significant growth in DG that took place during 2000–2003, concurrent with the growth of the central-station natural gas peaker fleet.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 7 Figure 5 : Capacity Factor by DG Technology Comparing Figure 5 to Figure 1, the small combined cycle, combustion turbine and hydropower have higher capacity factors than their larger counterp arts. This is because the smaller “distributed generators” are tailored to specific needs or matche d with specific loads. This is a good lesson for the future; tailoring DG applications to yield more effective use of capital. 

### Distributed Generation Applications DG technologies can be used in a variety of applicat ions. The five primary applications are shown in Figure 6.

Figure 6 : Distributed Generation Applications (DG Monitor, 2005)Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 8 These are the five primary applications of DG ( DG Monitor , 2005):

 Baseload Electric Power-Only (10.4 GW capacity, 73.9 TWh generated in 2003)—Baseload power is where DG is operated almost continuous ly, primarily to generate electric power for a specific load, which is generally industrial.

 Combined Heat and Power (CHP) (34.7 GW capacity, 136 TWh generated in 2003)—CHP is operated almost continuously to generate both electricity and heat. CHP operations must have a reliable heat load nearby and are mostly used for industrial applications.

 Peaking Power (7.11 GW capacity, 18.6 TWh generated in 2003)—Peaking power applications are not operated continuously but rather are used to reduce overall electricity costs through reductions in peak power purchases and by offsetting electricity use during high-price periods. Utilities and a number of industrial consumers use these.

 Interconnected Standby (1.29 GW capacity, 1.17 TWh generated in 2003)—Interconnected standby generators include spinning reserves, which are grid-connected, but generate little energy throughout the year except to offer regulation services for utilities. Interconnected standby also includes DG operated by private companies, but these are connected to the grid to occasionally offer peaking power support. These units spend most of their time idle.

 Emergency Power (169 GW capacity, 2.38 TWh generated in 2003)—Emergency power systems are grid-independent systems that provid e power to a limited load for a limited time frame if the normal source of power fails.

 BUGS —Can be considered a combination of DG used in emergency power and interconnected standby applications. These ge nerators may or may not be grid-connected, but they share a common thread in that they are considered secondary sources of power (they only operate to replace or supplement the grid) and have very low capacity factors. Figures 7 and 8 summarize the U.S. DG fleet’s topology by application. The relative contribution of each application to the total fleet is viewed through a variety of lenses.

Baseload

CHP
Peaking

Standby

Emergency 

# Capacity by Application (MW)Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 9 Figure 7 : DG Capacity by Application Figure 7 reveals that among the various applications , CHP and baseload applications tend to involve larger generation units. Because of the sheer size of the fleet, emergency backup generators still comprise the majority of the capacity, but they have a low capacity factor. BUGS have the greatest potential as a significant resour ce for addressing peak load.

Figure 8 : Energy Generated by DG Application Figures 7 and 8 show that with the capacity factor taken into account, CHP and baseload applications have a more significant energy role in the DG fleet than peaking, standby, and emergency, as expected. Emergency and stan dby units have low capacity factors and are underutilized compared to DG peaking power units, wh ich are similar in technology characteristics.


## C **RITIQUING THE** 

## C **URRENT** 

## P **EAK** 

## L **OAD** 

## P **ARADIGM**



### DG Can Address the Peak Better than Centralized Peakers The current paradigm in peak load management of overbuilding to meet the peak has increased generation capacity on both sides of the meter but without much coordination between the utility and the private consumer. This has resulted in low asset utilization levels both in utility-owned and privately-owned generators. According to the Ener gy Information Administra tion (EIA)’s “Electric Power Annual 2007,” natural gas peaking plants have a capacity factor of about 11 percent (Energy Information Administration, 2009). Sector-wide su rveys of DG show similarly low capacity factors (less than 10 percent), with large variations by application ( DG Monitor , 2005). These low-usage rates represent a huge opportunity to defer additional new generation builds, if only these assets could be used more intelligently. Baseload

CHP Peaking Standby Emergency 

# Generation by Application (GWh)Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 10 Integrated DG resources, which are paired with lo ads and are activated during the peak, can address the peak more efficiently than centralized peak ers. For example, DG units used in a peaking application have on average a capacity factor of about 30 percent compared to their central-station fleet counterpart (natural gas peakers) that oper ate at about an 11 percen t capacity factor. One likely explanation is that DG units are closer to the load, thus are better able to address local peaks and focus on optimizing economics and reliability for the end-customer rather than dealing with the aggregate peak and aggregate reliability like the util ity. This suggests that up-converting a BUG into a peaking application is more cost effective than constructing a new central-station peaker to meet the same peak since the DG unit has a more appropriate size and location for meeting the local needs of the load. Assuming the DG fleet is perfectly distributed at the load, the efficiency level is also higher with a DG peaking application.

Figure 9 : Capacity Factor by DG Application Figure 9 shows there is significan t potential for increasing the capacity factors of emergency and standby DG units or BUGS by changing the applic ations. The low asset utilization of standby and emergency applications coupled with the significant capacity they represent (170 GW) suggests that they could potentially serve larger loads, certai nly at peak, displacing the otherwise necessary capacity additions. This 170 GW of BUGS capacity represents about 22 percent of the peak and 36 percent of the average load; it could be availabl e to address the peak without adding generating capacity in the nation. This could be a significant benefit to consumers and utilities. The key to increasing the capacity factor of BUGS is the in terconnection to the grid and thus serving larger loads. 

### A Smart Grid Creates New Opportunities The growing smart grid will enable alternative ways of managing the electric power system. Instead of building more generation a hundred miles aw ay, utilities can tap into seldom-used BUGS to provide distributed support for a locally-strained gr id. With smarter protecti on and control systems, sensors, and components, consumers and utilities ca n enable BUGS to do more than just provide 0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90

Baseload CHP Peaking Standby Emergency 

# Capacity Factor by DG ApplicationBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 11 emergency power: they can become a dispatchable utility resource. By properly connecting BUGS to the new smart grid, BUGS can save consumers from periods of high power cost and peak power charges, improve reliability, and provide valuable an cillary services for utilit ies and grid operators.


## B **REAKING THE** 

## P **ARADIGM** 

## : **U P**  

## -C **ONVERTING** 

## BUGS **TO** 

## I **NTEGRATED** 

## DG **R ESOURCES**  Within the DG universe, the critical point of leverage is clearly BUGS composed of DG units in emergency and standby applications. DG units applie d to BUG applications make up 99.7 percent of all DG units in the United States. At the same time , backup generation has the lowest capacity factor of any DG application, 0.2 percent. This means that more than 170 GW of capacity (already paid for) could be made available to address peak loads at or near the load. Consider the lessons from the Portland General Electric (PGE) Dispatchable Standby Generation Program (discussed in the Economics section of this report), which yielded improvements in economic s and responsiveness. However, emergency backup units cannot always be readily shifted to other applications. Many emergency backup units are connected only to emergency circuits, such as emergency lighting, not to the main electrical circuit of a building. Circuit modifications and power electronics would be required to take advantage of these resources. In order to achieve a higher level of asset utilization, BUGS must be tied into more active loads. This would allow BUGS to serve these loads during prol onged emergencies and duri ng peak load hours. Doing so could help the owner of the backup genera tor recoup costs more quickly by avoiding high power costs during peak hours and by reducing peak power charges. 

### Greater Use through Greater Re sponsibility and Intelligence While adding a grid interconnection and pairing ge neration to local load are necessary steps to tapping BUGS in a more useful way, they are not sufficient for achieving the larger paradigm shift away from overbuilding capacity for peaks. A shif t in thinking is required, plus integrating embedded intelligence at each load center. Consider the difference between these three operating paradigms for meeting an imaginary Load A: Old Operating Paradigm (BUGS)

 During normal operation, Load A is supplied by the grid.

 The grid controls the cost of energy for Load A and is primarily responsible for supplying it with power.

 During extreme grid events or emergencies, Load A uses its backup generator to remain partially operational.

 The DG operates “whenever the grid cannot.” Integrated Operating Paradigm (Baseload and CHP)

 During normal operation, Load A is supplied in large part by its DG resources, which are sized to closely match its load characteristics.

 Load A’s DG resources control the cost of ener gy and are primarily responsible for supplying it with power.

 Load A uses the grid to supplement its DG resources, especially during emergency—or extreme—events.

 The DG operates “whenever it can.” New Operating Paradigm (Microgrid):

 During normal and abnormal operation, Load A’s microgrid-controller is responsible for determining the optimal (in terms of cost and reliability) mix of its power suppliers.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 12  Load A’s microgrid-controller exerts intelligent co ntrol of its cost of energy and is primarily responsible for finding an appropriate energy supply for it.

 The DG operates “whenever it is economically or technically appropriate to do so based on current conditions.” The old operating paradigm gives the grid primar y responsibility for managing the load. This relegates the BUGS to the role of a contingency for the larger grid, acting only when the grid fails. This is inefficient and leads to overbuilding, as each side must build to meet the extremes of the other side. That is, the grid gets no help from BU GS when it is needed the most (during peak load), and the BUGS get no help from the grid when help is needed the most (during a blackout). At the other end of the spectrum, certain situations lend themselves to closely integrating DG with load, for example with CHP or base load applicat ions. These situations result in higher asset utilization for the DG, and generally higher in e fficiency, but depend entirely on a good loadgeneration pairing. However, these cases are limited and not common in the United States, where distances between generation and load tend to be long. In addition, this kind of pairing cannot always be accomplished at a cost lower than grid-supplied power. The ability to embed decision-inte lligent devices on the customer side through smart grid technology has led to a third option. This third option allows greater control at the customer side by optimizing among whatever energy resources are available. This can include a variety of DG resources, energy storage, and the grid. Additional upfront cost is required in order to create the microgrid controller and enable various operating modes. However, on an ongoing basis this option will do a better job of meeting customer needs in terms of econ omics, reliability, and other objectives. In general, the more autonomous and intelligent a DG system, the more it can understand conditions such as grid strain and respond in a way that opti mizes customer objectives and helps the system as a whole. In addition, more autonomous and intell igent DG systems will have asset utilization levelsthat are more economical and efficient. Achieving these goals can be accomplished in part by upconverting BUGS into higher value DG applications th an they have now. By converting BUGS around the country into more intelligent and autonomous DG applications, the United States can meet the peak more efficiently and economical ly than is possible today.


## P **OTENTIAL** 

## U **SES FOR** 

## I **NTEGRATED** 

## BUGS The current utility paradigm views most of the nationwide BUGS as independent, or outside the scope of the electric power system. According to the EIA, about 75 percent of commercial businesses have backup generators, of an average si ze of 18 kW (U.S. Department of Energy, 2008). This resource is perfectly distributed and matched with appropriate loads, but utilities have held these resources at arm’s length until recently. Utilities can operate the electric power system more effectively by bringing these resources into the system and by using them in a coordinated, intelligent way. DG, especially BUGS, incorporated intelligently with in a smart grid, can provide a number of benefits to the utility:

 Improved capacity, especially during peak hours —By interconnecting greater amounts of BUGS, and adding standby power or peaking power roles to their emergency backup power resources, utilities will have greater system capacity and flexibility.

 Increased system efficiency through greater supply-side price elasticity — By incorporating a larger number of power supply options, such as multiple-fuel DG and CHP, the generation fleet as a whole will have a wider set of option s for responding to price changes. This will result in greater demand-side responsiveness to price signals and a more efficient electricity market. This can result in a lower overall cost of operating the system.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 13  Enhanced network security and resilience to disruptions —Because BUG systems are geographically distributed, use a variety of fuel s, and have a variety of operating principles, they are more resilient to disruptions such as price spikes, supply shortages, natural disasters, and sabotage attempts. Having a di verse fleet of generators reduces the systemic risk in the electric power system.

 Deferral of major capital investments —Through the use of BUGS and energy storage, utilities can defer major capital investments by temporaril y increasing capacity in critical areas as needed. A number of utilities have used this approach to delay major replacements at substations and in the transmission system. This deferral of major capital investments keeps downward pressure on pr ices charged to consumers.

 Higher power quality and reliability —By making use of generation located closer to the load, utilities have a finer set of tools for provid ing better power quality and reliability for customers when these are used in conjunction with a smart grid.


## D **EALING WITH** 

## B **ARRIERS TO** 

## G **REATER** 

## U **TILIZATION OF** 

## BUGS A number of steps need to be taken to deal with barriers to interconnecting greater numbers of BUGS to the grid. Many of these are regulatory barriers, but others are financial, programmatic, or technical. For example, utilities can take a numb er of measures to ensure system reliability and safety when connecting to higher levels of BUG power (Zheng, 2008), such as the following:

 Maintaining adequate voltage regulation through the use of voltage regulators is an important first step. Voltage support must be sufficient to maintain adequate voltage with DG disconnected. A recent paper from the Institute of Electrical and Electronic Engineers (IEEE) estimates that adding a fast responding voltage regulator to a feeder can increase the acceptable backup generator contribution from 5 percent to 10 percent of feeder capacity (Dugan, 2001).

 Installing appropriately rated fuses and reclosers to detect faults will prevent unnecessary fuse burnouts. Fuses and reclosers must be rated correctly and coordinated in order to resolve issues that could arise from the addition of DG units.

 Limiting and filtering harmonics introduced by backup generation units are important for maintaining power quality. Grid-connected DG units can introduce harmonics into the system, potentially increasing harmonic distorti on above acceptable levels. As more backup generation is connected using power electronics, this problem will continue to affect the power quality of loads on the line. Efforts mu st be taken by utilities and/or customers to ensure that these harmonics do not exceed acceptable levels (Dugan, 2001).

 Avoiding unexpected islanding is an important safety factor. Maintaining fuses and installing important safety equipment protects both utility work crews and the public from danger. Likewise, under intentional islanding schemes, indications and safety procedures must be incorporated to protect workers and the public. Revisions to protective relay schemes are also required to safely accommodate two-way power flow on the distribution network that can come from using BUGS to address peak loads.

 Providing appropriate visual ization and decision support both to the utility and to customers must be accomplished. As more backup genera tion is incorporated into the grid, effective dispatch and management will become more comm onplace. Due to its inherent complexity, modern (21 st century) visualization and decision-support software will be required to increase transparency and improve coordination throughout the grid.

 Adding adequate energy storage will be important if backup generators are expected to play a larger role in managing grid strain. Specif ically, replacing spinning reserves with quickstarting DG will require signific ant investments in energy storag e to help manage transientsBackup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 14 and carry the load while the DG is being ramped up. In addition, energy storage provides an important buffer against unreliable response rates from privately-owned DG equipment.

 Enabling two-way power flow is a critical pre-requisite to higher utilization of BUGS, since this allows BUGS to serve loads larger than the loca lly connected load. This involves a number of steps at the utility side but can now be accomplished more easily, thanks to smart grid technology.

To overcome these barriers, policymakers can take a number of measures to align the interests of owners of BUGS with those of utilities:

 Provide incentives for more efficient forms of DG —The development of more efficient and cost-effective DG technologies has been one of the major drivers behind the increase of DG use. Providing incentives or prizes for the further refinement of these technologies may result in even more innovations.

 Encourage DG owners to pursue applic ations with higher asset utilization —Historically, the asset utilization of base load and CHP applicat ions has been higher than those of peaking, standby, and emergency backup applications. Some states have already begun providing incentives to pursue more integrated DG a pplications, such as CHP. Creating these incentives and removing barriers to interconnection will result in the necessary shift from BUGS to higher value DG applications.

 Implement real-time pricing schemes —By using real-time pricing, time-of-use pricing, criticalpeak pricing, or other similar methods of pricin g electricity and ancillary services, regulators and regional transmission organizations (RTOs) create a natural incentive for DG owners to operate their resources in a way that is more in tune with the needs of the grid.

 Reduce regulatory red tape —Creating standard classes for DG interconnection—or using other methods to reduce the lead time for interconnecting DG—can greatly increase the number of BUGS that are converted to interconnected DG applications and decrease the burden on government agencies as well.


## E **CONOMICS** A few years ago, Portland General Electric (PGE) initiated a program to take advantage of the installed DG base in Portland, Oregon, to offset peaking power purchases from outside their territory. This program includes DG from 100 kW to a few MW in size with a total dispatchable capacity of more than 50 MW, which is about 7 percent of PGE’s peak load. PGE gives a good business model, with a cost to integrate the technology and insta ll emissions controls at roughly $175 per kW and an operating cost of about $0.17 per kWh for peak lo ad offset. This capital conversion cost is significantly below the cost per kW for a simple cycle gas turbine and its associated transmission infrastructure and losses. From an operating perspective, while $0.17 per kWh is expensive, it is less than the wholesale peak price (primarily supplied by natural gas turbine units), which is often $0.25 per kWh to $0.50 per kWh, depending on the region. While the industry needs more examples of such programs in operation to prove the economic case, the few existing programs (PGE, and PowerSecure of North Carolina) do bear out the economic savings. With more such programs deployed across the nation, the industry could learn about regional differences, tune the economics, develop addition al business models, and increase the emissions benefits.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 15 

## E

**NVIRONMENTAL** 

## M **ETRICS** Environmental questions arise with the use of diesel engines that comprise the majority of BUGS. This may appear to be a major issue at first gl ance; however, it becomes less of a concern when considering the actual annual energy delivered in a real peaking application. Due to the considerable ramp-up and ramp-down times for cent ral-station peakers, all of which occur at suboptimal heat rates (higher emissions per MWh), the actual total emissions associated with addressing the peak can actually be less by applying BUGS to peaking applications. This is made possible because the quick startup times of DG units allows them to remain off until they are needed, avoiding emissions associated with idling. PowerSecure’s Interactive DG system claims a 50 percent reduction in carbon emissions through optimized run-times and offsetting utility-spinning reserves. The PGE program shows that emissions controls on the diesel greatly reduces emissions and still operates at a lower cost than a traditional peaker. The Science Applications International Corporation (SAIC) Pollution Reduction Estimator (v1.0) shows that a national program to engage half of the BUGS for peak reduction during 200 hours a year roughly yields (compared to natural gas peaking units):

 More than 935,000 tons a year reduction in CO 2 emissions.

 More than 54,000 tons a year reduction in NO X emissions.

 More than 33,000 tons a year reduction in SO X emissions. Again, more such programs operating across th e nation will help the industry learn about environmental pros and cons of using BUGS to address peak loads.


## C **ONCLUSIONS** BUGS could become an important and cost-effe ctive addition to the demand response and distributed energy resource strategy at a national and state level. A flatter load profile, achieved through an efficient use of BUGSs, will enable a more efficient (economically and environmentally) use of the entire generation fleet.Greater utilization of the U.S. BUGS fleet to address needs in the electric system through upconverting them to more autonomous and intelligent DG applications has the potential to offer significant advantages for the nation: 1) A more efficient and economical way to address the peak. 2) A more emission-friendly approach. 3) Enabled by the Smart Grid, the existing installed generation base that can offset new generation, transmission, and distribution construction. Taking full advantage of this underutilized resour ce will require a smart grid-enabled distribution network.Backup Generators (BUGS): The Next Smart Grid Peak Resource v1.0 16 

# B IBLIOGRAPHY

DG Monitor . (2005). “The Installed Base ofU.S. Distributed Generation .” Resource Dynamics Corporation. Dugan, R. (2001). “Interconnecting Single-Phase Backup Generation to the Utility Distribution System .” Oak Ridge National Laboratory. IEEE Tran smission and Distribution Conference and Exposition. Energy Information Administration. (2009). Electric Power Annual 2007. U.S. Department of Energy. Gilmore, L. L. (2007). “Increasing Backup Generation Capacity and System Reliability by Selling Electricity During Periods of Peak Demand .” Carnegie Mellon Elec tricity Industry Center. International Energy Agency. (2002). “Distributed Generation in Liberalised Electricity Markets”

. Oak Ridge National Laboratory. (2008). “Combined He at and Power: : Effect ive Energy Solutions for a Sustainable Future.” Pullins, S. (2006). “The Electric Financial System .” NETL Modern Grid Strategy. Science Applications International Corporation. (SAIC). Pollution Reduction Estimator (v1.0). U.S. Department of Energy. (2008). Electricity, U.S. Data. Retrieved from Energy Information Administration: http://www.eia.doe.gov/fuelelectric.html Zheng, A. (2008). “The Untapped Value of Backup Generation.” Smart Grid News

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