MISO Energy Storage Study Phase 1 Report - Utility Wind ...

MISO Energy Storage Study Phase 1
Report
Product ID # 1024489
Final Report, November 2011
EPRI Project Manager
Dan Rastler
ELECTRIC POWER RESEARCH INSTITUTE
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800.313.3774 650.855.2121 askepri@epri.com www.epri.com

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ACKNOWLEDGMENTS
The following organizations, under contract to the Electric Power Research Institute (EPRI),
prepared this report:
Hoffman Power Consulting
The Electric Power Writing Experts
322 Digital Drive
Morgan Hill, CA 95037
EPRI
3412 Hillview Avenue
Palo Alto, CA 94304
www.epri.com
Principal Investigator
D. Rastler
This report describes research conducted by MISO.
EPRI gratefully acknowledges
The MISO and its staff for conducting this research and the stakeholders in the Technical
Review Group who provided comments.
Dan Rastler
November 2011
iii

EXECUTIVE SUMMARY
Introduction
MISO is a non-profit member based organization regulated by the federal energy regulatory
commission (FERC). As a Regional Transmission Organization (RTO), MISO provides
electricity consumers in 13 states with regional grid management and open access to
transmission facilities through a tariff closely regulated by FERC.
State legislated renewable portfolio standards (RPS) within the MISO footprint in Montana,
Minnesota, Wisconsin, Iowa, Missouri, Illinois, Michigan, Ohio, and Pennsylvania require
varying percentages of electrical energy be met from renewable energy resources starting in
2010. These mandates resulted in initiatives to integrate renewable energy generation, primarily
from wind, into the MISO market. Wind resources now account for 6 percent of installed
capacity (approximately 9.2 GW) and 3.5 percent of generation, producing hourly capacity up to
6.7 GW 1 . Economic electricity generation from wind typically occurs some distance from load
centers, requiring new transmission be constructed to reach consumers. Typical wind patterns
produce higher energy at times when electricity demand is low. Wind generation is also variable
and has to be carefully balanced with conventional resources in order to maintain system
reliability.
As significant variable generation resources are added to the transmission grid the system
complexities associated with balancing generation and demand increase. Greater flexibility is
required to maintain reliable service. In this circumstance, the role that energy storage plays in
systems planning becomes important. Long-term energy storage is attractive because it can be
used to shift electricity generated during low demand periods for use during peak demand. Shortterm
energy storage also has potential value in providing a frequency regulating resource to
maintain system stability. MISO currently accommodates long-term storage resources in its
markets in the form of pumped hydro storage (PHS). Short-term storage is accommodated as a
regulating reserve resource in the MISO ancillary services market (ASM).
To better understand the role of energy storage, the Energy Storage Study was initiated by MISO
to model several hypotheses around battery, compressed air, and pumped hydro energy storage
technologies. The study explores reliability, market, and planning benefits that storage
technologies could potentially provide. The study seeks to determine economic potential for
storage technologies in MISO. It will estimate the price inflection point at which energy storage
may become economically feasible. Finally, the study will suggest potential MISO energy and
operating reserve markets enhancement products.
1 2010 MISO State of the Markets Market Monitor Report June 2011, Potomac Economics
v

Project Objectives
The MISO Energy Storage Study objectives are to:
• Provide stakeholders with recommendations based on analysis from modeling three key
energy storage technologies.
• Identify the economic potential for energy storage technologies with longer-term
capabilities in the MISO footprint.
• Review storage treatment in the existing MISO ancillary services market (ASM) for
adjustments that could be considered to encompass additional short-term storage
technologies (e.g. battery).
• Highlight potential enhancements to existing tariffs that complement storage
technologies.
• Provide MISO transmission planners with a better understanding of storage technology
modeling, in order to recommend future guidelines for the MTEP process. The simulation
studies will also identify key market impacts from storage and future sensitivity to
regulatory and fuel price scenarios that emerge from the analysis.
Three drivers underpin the MISO Energy Storage Study. The first is the State RPS mandates that
require MISO to respond to increased renewable energy integration. The second driver concerns
the way that storage is treated in the MISO tariff. Ongoing discussions and rulings between the
FERC and MISO since the start of the ASM in 2009 have centered on how short and long-term
storage resources should be treated in the tariff. MISO and its stakeholders benefit from the
greater understanding that the Energy Storage Study brings to these discussions.
A third study driver is the need for MISO to improve its capabilities in energy storage modeling.
MISO is a leader among regional transmission organizations in enhancing transmission planning
to meet regional and interregional objectives. The MISO Transmission Expansion Plan (MTEP)
extends traditional bottom up planning to incorporate wind integration and to consider
neighboring regions as well as multiple future scenarios. The Energy Storage Study provides
MISO with better understanding about how energy storage technologies can be modeled as a
component in transmission and generation planning.
Approach
The Energy Storage Study is a targeted study assigned to the MISO Transmission Expansion
Plan 2011 (MTEP11) cycle. MTEP targeted studies begin as efforts to identify particular
problems or explore planning, reliability and/or market enhancements. The study approach is to
model energy storage for the three storage technologies that MISO has experience with. The first
technology is pumped hydro storage (PHS) that stores energy pumped into a water reservoir.
Current registered MISO PHS capacity is 2500 MW. The second technology is compressed air
energy storage (CAES) that stores energy in the form of compressed air in a cavern or above
ground pipe system. There are no CAES plants in MISO but an economic feasibility analysis was
recently conducted for a proposed plant in Iowa. The first two technologies offer long-term
energy storage capability that can be used to store energy during low demand periods and release
energy during high demand, periods – a process known as energy arbitrage. The third
technology is battery storage that typically provides storage for shorter time periods and has

greater potential value in supporting system frequency and regulation. MISO has experience with
battery storage working with the Xcel project that uses solid-state dry cell batteries.
During the Phase 1 study, MISO seeks first to understand whether there is economic potential for
energy storage in their footprint and second to start to understand how that energy storage is best
utilized. Two existing MISO planning models are used to identify these energy storage impacts.
The first model is the electric generation expansion analysis system (EGEAS), which is designed
by EPRI to find the optimum (least cost) integrated resource plan for a given demand level. The
EGEAS model is used to identify circumstances when adding energy storage resources to the
MISO footprint is justified economically. The second model is a production cost model called
PLEXOS that offers co-optimization functionality and models system constrained economic
dispatch in day ahead and real time markets with intra-hourly granularity. PLEXOS is used for
deeper analysis to understand how energy storage resources can best be utilized in the MISO
market. The Phase 1 study concentrated on modeling with EGEAS and did some preliminary
calibration and testing with PLEXOS.
Key Findings
By using the EGEAS model in Phase 1, MISO gained experience with modeling energy storage
technologies and is able to relate this experience directly to existing transmission planning using
EGEAS. The Phase 1 EGEAS model runs allowed sensitivity analysis around several different
future scenarios. These scenarios match the future cases used in MTEP11 planning including fuel
costs (natural gas prices), EPA regulations, a carbon tax and RPS mandate percentages. The
EGEAS model identifies economic benefits from energy arbitrage storage in several cases and
thus fulfills a primary study objective to prove economic benefit is available from energy storage
in MISO.
The study team recognizes that EGEAS has limitations for modeling energy storage
technologies, particularly short-term storage from batteries because the model does not capture
any benefit from the ASM. There are other shortcomings to the EGEAS model with regard to
storage benefits from energy arbitrage because the price data used may not have the granularity
to capture optimal energy arbitrage economics. EGEAS also does not model the congestion
market. The EGEAS model can, however run a large number of scenarios in a short time and
highlights cases where energy storage has the greatest economic benefit. Since PLEXOS only
models energy storage resources that already exist, EGEAS plays a critical role in selecting the
cases that are appropriate for PLEXOS analysis. This allows the study group to choose
appropriate cases for Phase 2 analysis.
The PLEXOS Phase 1 analysis was designed to provide a framework in which a fully functional
model could be developed for use in the Phase 2 PLEXOS analysis. The major findings for
PLEXOS in Phase 1 were insights gained from calibrating the model assumptions and variables.
These insights are invaluable to MISO and an expected learning curve from modeling a new
technology. The limited PLEXOS results obtained in Phase 1 did show economic benefit from
energy storage in all three technologies. The analysis was limited to the day-ahead market in
Phase 1 so that real time benefits from short-term energy resources were not captured. The
PLEXOS cases to be modeled in Phase 2 were defined during the Phase 1 analysis.
The Energy Storage Study provides valuable feedback and lessons learned about the modeling
tools used (EGEAS and PLEXOS) and their suitability for assessing potential MISO benefits
vii

from energy storage. The lessons learned in Phase 1 owe a lot to the complexities that surround
modeling energy storage technologies in the MISO environment.
Conclusions and Recommendations
Phase 1 of the Energy Storage Study has allowed MISO to become familiar with challenges
inherent in modeling energy storage technology in a complex nodal market with an ASM. The
study group has gained a good understanding about storage modeling using EGEAS, which is the
primary MISO tool for transmission resource planning.
The study results demonstrate that there is economic potential for energy storage in the MISO
footprint. Benefits were observed in cases using both EGEAS and PLEXOS. These benefits will
be explored in greater depth during Phase 2.
The Phase 1 results show that EGEAS is not the right tool to properly understand energy storage
potential. The critical role for EGEAS is in identifying cases where storage is beneficial so that
these cases can be analyzed further by PLEXOS in the Phase 2 study.
The PLEXOS experience during Phase 1 allowed for fine-tuning the model parameters and
important lessons were learned regarding storage model setup.
The cases to be modeled in Phase 2 have been selected.
Phase 2 of the Energy Storage Study will provide richer analysis from which to make
conclusions and recommendations. Considerable groundwork has been accomplished in Phase 1.
This report will provide extremely useful reference material for industry transmission planners.
Report Organization
This report covers Phase 1 of the MISO Energy Storage Study. The first chapter provides an
introduction and background to the Energy Storage Study. Chapter 2 introduces the MISO
transmission-planning environment. The MTEP process is described as well as the recent
Regional Generation Outlet Study (RGOS) and subsequent adoption into MTEP of
portfolio/scenario analysis to identify multi-value projects (MVPs). The future scenarios and
sensitivities used by MTEP are reviewed because they form the basis for the modeling scenarios
in the energy storage study.
Chapter 3 reviews the energy storage technology landscape and then provides more detailed
technology descriptions for the three key storage technologies - compressed air energy storage
(CAES), pumped hydro storage (PHS) and battery that MISO is evaluating in the Energy Storage
Study. Chapter 4 describes the current MISO ASM for storage including unresolved differences
between the FERC and the MISO concerning stored energy resource treatment.
Chapter 5 reviews the modeling tools and methodology that MISO used for the Energy Storage
Study. The use cases and parameters for the EGEAS and PLEXOS models are provided, together
with the assumptions for model scenarios.

Chapters 6 and 7 contain the results and analysis from modeling. Chapter 6 covers the EGEAS
model and Chapter 7, PLEXOS. In this first Phase study report, the PLEXOS results are only
preliminary. Chapter 8 presents conclusions from the study results, a preview of Phase 2 analysis
to-date and potential next steps for MISO including recommendations for future adjustments to
the MTEP process.
Keywords
Midwest Independent Transmission System Operator
MISO
Compressed air energy storage
CAES
Electric energy storage
Renewable energy
CO 2 emission reduction
Renewable Portfolio Standards
Ancillary Services
Regulation
Contingency Reserves
Energy Arbitrage
ix

CONTENTS
1 INTRODUCTION .................................................................................................................... 1-1
Background and Objectives .................................................................................................. 1-1
Study Objectives ................................................................................................................... 1-4
2 THE MISO PLANNING ENVIRONMENT ............................................................................... 2-1
Background and Recent Challenges..................................................................................... 2-1
Variable Generation Resource Challenges ........................................................................... 2-1
Ancillary Service Markets...................................................................................................... 2-2
The MISO Transmission Planning Process .......................................................................... 2-4
The Energy Storage Study .................................................................................................... 2-6
3 ENERGY STORAGE TECHNOLOGIES ................................................................................ 3-1
The Energy Storage Landscape ........................................................................................... 3-1
Energy Storage Technology Overviews................................................................................ 3-4
Pumped Hydro Storage .................................................................................................... 3-4
Compressed Air Energy Storage (CAES)......................................................................... 3-6
CAES Technology ....................................................................................................... 3-6
The Iowa Stored Energy Park Case Study .................................................................. 3-8
Battery Storage ................................................................................................................ 3-9
Lead Acid Batteries...................................................................................................... 3-9
NAS (Sodium Sulfur) Batteries .................................................................................... 3-9
Zinc-Bromine and Halogen Flow Batteries ................................................................ 3-10
Vanadium Redox Flow Battery .................................................................................. 3-10
4 STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF................................ 4-1
Current MISO Tariff............................................................................................................... 4-1
Pumped Hydro Storage Tariff ...................................................................................... 4-2
Short Term Energy Storage Resources ........................................................................... 4-3
xi

Real Time (5 minute) Security Constrained Economic Dispatch (SCED) Energy
Dispatch ........................................................................................................................... 4-4
Ramp Capability For Load Following in MISO.................................................................. 4-5
FERC Correspondence Regarding MISO Tariff SER Treatment...................................... 4-6
Phase 2 Opportunities for Tariff Enhancements .............................................................. 4-7
5 ENERGY STORAGE MODELS AND ASSUMPTIONS ......................................................... 5-1
Planning Models.................................................................................................................... 5-1
MISO Energy Storage Study Models .................................................................................... 5-1
Study Models – EGEAS ........................................................................................................ 5-2
EGEAS Model Functionality ........................................................................................ 5-3
EGEAS Benefits .......................................................................................................... 5-5
EGEAS Drawbacks...................................................................................................... 5-5
EGEAS Energy Storage Model Assumptions .............................................................. 5-5
EGEAS Sensitivities .................................................................................................... 5-6
EGEAS Assumptions................................................................................................... 5-6
Study Models – PLEXOS ...................................................................................................... 5-9
PLEXOS Benefits ...................................................................................................... 5-12
PLEXOS Assumptions............................................................................................... 5-12
Phase 2 Recommendations to Improve Storage Modeling ............................................ 5-12
6 EGEAS ANALYSIS RESULTS .............................................................................................. 6-1
Results Summary for EGEAS ............................................................................................... 6-1
Phase 1 EGEAS Results....................................................................................................... 6-1
Energy Arbitrage Analysis Based on EGEAS Results...................................................... 6-4
EGEAS Model Takeaways.................................................................................................... 6-5
7 INITIAL PLEXOS ANALYSIS ................................................................................................ 7-1
PLEXOS Phase 1.................................................................................................................. 7-1
Challenges Uncovered During Phase 1 PLEXOS Analysis .................................................. 7-1
Modeling Challenges Identified Using PLEXOS ................................................................... 7-3
Initial Conclusions from Phase 1 PLEXOS Analysis ............................................................. 7-4
Lessons Learned FROM Phase 1 PLEXOS Analysis ........................................................... 7-7
PLEXOS Next Steps – Pre Phase 2...................................................................................... 7-7
PLEXOS Next Steps – Phase 2 ............................................................................................ 7-8

8 STUDY CONCLUSIONS ........................................................................................................ 8-1
A TECHNICAL REVIEW GROUP WORKSHOP AGENDAS AND TAKEAWAYS...................A-1
B ACRONYMS ..........................................................................................................................B-5
C STAKEHOLDER FEEDBACK ...............................................................................................C-1
xiii

LIST OF FIGURES
Figure 1-1: RPS Mandates and Goals Within the MISO Footprint (Source MISO).................... 1-3
Figure 2-1: Average Hourly Wind Generation and Prices in MISO 2008-2011 (Source
Iowa Stored Energy Park Analysis).................................................................................... 2-2
Figure 2-2: Operational Planning Timeframes in ISO Balancing Markets (Source EPRI) ......... 2-4
Figure 2-3: MISO Wind Curtailment 2008-2010 (Source MISO State of Market Monitor) ......... 2-7
Figure 3-1: Representative Positioning of Energy Storage Technologies (Source EPRI) ......... 3-3
Figure 3-2: FERC Registered Pumped Storage Projects, July 2011 ......................................... 3-4
Figure 3-3: Pumped Storage Capacity Worldwide (GW) Source MISO..................................... 3-5
Figure 3-4: Fast Response Capabilities for Adjustable Speed PHS (Source MISO) ................. 3-5
Figure 3-5: Advanced CAES Plant Schematic (Source:EPRI)................................................... 3-7
Figure 4-1: MISO RT SCED Dispatch Algorithm........................................................................ 4-5
Figure 5-1: EGEAS and PLEXOS Model Interaction ................................................................. 5-2
Figure 5-2: EGEAS Screening Curve with PSH and CAES (PHS and CAES are “mid”
values - $2250/kW and $1250/kW respectively, CO2 tax= 0 in this case, Source
MISO)................................................................................................................................. 5-6
Figure 5-3: 2011 Installed Capacities by Fuel Category Assumption for MISO Energy
Study .................................................................................................................................. 5-9
Figure 5-4: PLEXOS CAES Storage Model Output (Source Energy Exemplar) ...................... 5-11
Figure 6-1: One Branch of the EGEAS Energy Storage Analysis Decision Tree....................... 6-2
Figure 6-2: Results from Phase 1 EGEAS Energy Storage Analysis Showing
Circumstances Where CAES is Selected .......................................................................... 6-2
Figure 6-3: Simple Load Duration Curve Illustration Showing Wind Impact on Storage
Charging and Generation................................................................................................... 6-4
Figure 7-1: Detailed vs Aggregated Transmission Areas for PLEXOS Simulation .................... 7-2
Figure 7-2: Three Stage Process to Decompose Long-Term Storage Constraints into the
Real Time Market with PLEXOS ........................................................................................ 7-4
Figure 7-3: PLEXOS Initial Results – CAES. Higher Variable Costs and Efficiency
Losses Cause CAES to Only Operate for Limited Periods ................................................ 7-5
Figure 7-4: PLEXOS Initial Results – PHS. At a Lower Variable Cost Than CAES, PHS
Operates more Frequently ................................................................................................. 7-5
xv

Figure 7-5: Initial PLEXOS Results - CAES Revenue Components. The Reserve Pricing
Method Used causes reserve Revenue Spikes for This Run............................................. 7-6
Figure 7-6:Initial PLEXOS Results - PHS Revenue Components. The Reserve Pricing
Method Used causes reserve Revenue Spikes in this Analysis ........................................ 7-6
LIST OF TABLES
Table 3-1: Definition of Energy Storage Applications (Source EPRI 1020676) ......................... 3-3
Table 3-2: ISEPA Net Benefit CAES Plant Economic Comparison (Source ISEPA) ................. 3-8
Table 4-1 : Stored Energy Resource Operating Parameter Data Summary (Source MISO
BPM-002)........................................................................................................................... 4-4
Table 5-1: EGEAS Energy Storage Study Plant Assumptions .................................................. 5-8
Table 6-1: EGEAS Model Storage Selection Cases .................................................................. 6-3
Table 7-1: PLEXOS Phase 1 Analysis Lessons Learned .......................................................... 7-7

1
INTRODUCTION
Background and Objectives
The Midwest Independent Transmission System Operator (MISO) is a non-profit member based
organization regulated by the federal energy regulatory commission (FERC). As a Regional
Transmission Organization (RTO), MISO provides electricity consumers in 13 states with
regional grid management and open access to transmission facilities at a tariff closely regulated
by FERC.
The guiding philosophy behind RTO’s is to deliver safe and reliable wholesale energy markets
for utility members. MISO does not own generation capacity or transmission so its planning
activities center on ensuring that transmission capacity delivers generation at the best price for
consumers. MISO is required to engage in comprehensive planning in order to meet reliability
criteria in its region and with its neighbors. In addition, MISO runs the wholesale energy and
ancillary market for electricity. The balancing market price mechanism (locational marginal
pricing, LMP) is designed to attract investment in new generation when congestion raises prices.
The MISO transmission-planning process accommodates new generation interconnections.
When RTO’s were first created in the early 2000’s, the transmission planning emphasis was
purely on reliability and security. During the past ten years there has been a change in emphasis
for MISO and other RTO’s to expand their transmission planning activities beyond a pure focus
on reliability and security in order to meet economic planning goals (FERC Order 890) as well as
broader public policy goals.
MISO currently follows a top-down (regional) and bottoms-up (local) transmission planning
process intended to address reliability, economic, and public policy driven transmission issues 2 .
MISO’s guiding principles in transmission planning are as follows:
• Provide access to the lowest possible delivered electric energy cost
• Reliability
• Support for State and Federal renewable energy objectives
• Provide an appropriate transmission cost allocation mechanism
• Develop a transmission system scenario model and make it available to stakeholders in
general.
2 See http://www.midwestiso.org/Planning/TransmissionExpansionPlanning
1-1

Introduction
MISO follows a cycle known as the MISO transmission expansion plan (MTEP) that results in
annual recommendations to proceed with expansion projects, subject to approval by the
independent MISO board of directors. The planning process is open to stakeholder participation
and its deliberations are disseminated via the MISO website.
State legislated renewable portfolio standards (RPS) within the MISO footprint in Montana,
Minnesota, Wisconsin, Iowa, Missouri, Illinois, Michigan, Ohio, and Pennsylvania require
varying percentages of electrical energy be met from renewable energy resources starting in
2010. These mandates resulted in initiatives to integrate renewable energy generation, primarily
from wind, into the MISO market (see Figure 1-1). Wind resources now account for 6 percent of
installed capacity (approximately 9.2 GW) 3 . Economic electricity generation from wind typically
occurs some distance from load centers, requiring new transmission be constructed to reach
consumers. Wind generation is also variable and has to be carefully balanced with conventional
resources in order to maintain system reliability.
In January 2009, MISO started an ancillary services market (ASM) to introduce competition to
services that ensure system reliability. The ASM market allows generators to bid as operating
and contingency reserves in the real time market. A transparent market for these resources opens
up the possibility that unconventional resources such as demand side resources and various
storage technologies can be encouraged by receiving additional compensation for ancillary
services. The ASM in turn attracts resources that will be needed to balance increased variable
generation from resources such as wind and solar power. The 2010 MTEP10 transmission
planning cycle included a regional generation outlet study (RGOS) that focused on planning
scenarios necessary to integrate increased electricity generated from wind resources.
Also since 2009 MISO has allowed a portion of the intermittent wind resources to be counted
toward resource adequacy requirements. In 2012 the effective capacity portion will allow about
14 percent of the installed wind capacity to be treated as resource capacity; however the actual
realized capacity is less than this because the transmission system is the limiting factor. The
balance between how much transmission should be expanded as the benefits might diminish is an
on-going aspect of transmission planning. In 2011 MISO introduced an ASM product called
Dispatchable Intermittent Resources (DIR), where rather than wind resources simply being
curtailed, those resources can get paid to reduce output and contribute to mitigating congestion.
While curtailment or price signaled DIR manages reliability, the corresponding reduced output
represents a marginal decrease toward achieving renewable energy mandate targets.
As significant variable generation resources are added to the transmission grid, long-term energy
storage becomes a potentially attractive option because it can be used to shift electricity
generated during off-peak periods for use during peak demand. Short-term energy storage is also
valuable for contingency reserves and as a frequency regulating resource (operating reserves).
MISO currently accommodates long-term storage resources in its markets in the form of pumped
hydro storage (PHS). PHS technology involves pumping water up a gradient using low price offpeak
electricity and discharging the water through turbines to produce electricity during peak
consumption periods. These resources have participated in MISO markets since April 2005 and
are treated in a comparable manner to generators and price sensitive loads. There is
3 2010 MISO State of the Markets Market Monitor Report June 2011, Potomac Economics
1-2

Introduction
approximately 2500 MW of pumped storage resource registered in MISO. Short-term energy
storage is currently only accommodated as a regulating resource in the ASM tariff, a provision
that was added to facilitate the use of flywheel energy storage technology. This stored energy
resource provision is the subject of ongoing debate between the FERC and MISO 4 . The FERC is
requesting equitable market treatment for any stored energy resource technology including
longer-term resources.
The Energy Storage Study is a targeted study carried out during the 2011 MTEP cycle. The
Energy Storage Study models several hypotheses around battery, compressed air, and pumped
hydro energy storage technologies. The study explores reliability, market, and planning benefits
that storage technologies provide. Study objectives are to determine the economic potential and
feasibility of storage technologies for MISO and to suggest potential MISO energy and operating
reserve market enhancement products, if appropriate.
Figure 1-1: RPS Mandates and Goals Within the MISO Footprint (Source MISO)
Three drivers underpin the MISO Energy Storage Study as follows:
• The first is the open question on short and long-term storage treatment in the ASM. The
FERC expresses concern that any short-term energy storage technology (not just
flywheel) should be accommodated by the ASM tariff and able to participate in other
4 See FERC Docket Nos. ER07-1372-014 and ER09-1126-000
1-3

Introduction
1-4
markets besides regulation. In addition, longer-term stored energy resources should be
able to participate fully in ASM, day ahead and real time markets.
• Second, increasing renewable portfolio standards (RPS) in MISO footprint states
encourages wind penetration that storage resources may complement. Current state RPS
mandates average out over MISO territory to 13 percent by 2020 (see Figure 1-1).
• Third, MISO needs to improve storage modeling. The 2010 MTEP identifies plans to
consider energy storage as a resource option in planning (section 9.5, MTEP10). MISO
requires a better understanding about how energy storage technologies can be modeled as
a component in transmission and generation planning. Several complexities exist around
storage products with regard to their optimization in ASM markets and how storage
owners are compensated for storage benefits.
Study Objectives
The MISO Energy Storage Study objective is to provide stakeholders with recommendations
based on analysis from modeling three key energy storage technologies. The study is expected to
identify the economic potential for energy storage technologies with longer-term capabilities in
the MISO footprint. Stakeholders also want to identify storage value in the existing MISO
ancillary services market (ASM) including adjustments to encompass additional short-term
storage technologies (e.g. battery). MISO would like to identify enhancements to existing tariffs
to complement storage technologies. Such tariff changes will involve MISO stakeholder inputs
and ultimately new filings with the FERC. An important driver for the Energy Storage Study is
to provide improved understanding around storage treatment in the MISO tariff. MISO has been
engaged in ongoing discussions about storage treatment with the FERC over the past two years.
Another major goal for MISO transmission planners from the study is to better understand
storage technology modeling in order to recommend future guidelines for the MTEP process.
The simulation studies will also identify key market impacts from storage and future sensitivity
to regulatory and fuel price scenarios that emerges from the analysis.

2
THE MISO PLANNING ENVIRONMENT
Background and Recent Challenges
This chapter describes the MISO planning environment from which the Energy Storage Study
requirement emerged. As a regional transmission organization, MISO is required to produce
regular long term transmission expansion plans to provide continued reliable and secure electric
service to its members under its FERC tariff terms. Transmission plans are generally designed to
meet reliability needs, to provide connection to new generators, to meet a particular stakeholder
local need or to improve transmission efficiency. New renewable energy drivers make the
planning process more complex. MISO is responding with additional analysis and modeling to
evaluate more sophisticated scenarios including increased generation from renewable resources.
The Energy Storage Study uses similar scenario analysis to evaluate how and when energy
storage technologies can provide economic value to MISO stakeholders.
In the MISO region, installed generation capacity is approximately 50 percent coal, 30 percent
gas, 10 percent nuclear and 10 percent renewables. However, based on production costs in the
region, the energy being produced is approximately 75 percent from coal, 15 percent from
nuclear, and 10 percent from other sources. State RPS programs mandate increases in energy use
from renewable sources such as wind. Environmental mandates are placing pressure on the life
expectancy of coal plants. These social choices are not based purely on production costs. As a
consequence, MISO planners are being challenged to engage in more complex transmission
studies that take regional and public policy variables into account. The Energy Storage Study
forms one part of this expanding agenda
MISO planning is therefore evolving from an emphasis on reliability and resource adequacy to
respond to new market and regulatory challenges. Key challenges confronting MISO include
implementing new renewable energy policies, reducing grid congestion, and incorporating new
generation and demand side resources—all while still meeting load growth requirements.
Overlying these newer challenges are an aging transmission infrastructure, as well as the need to
keep cost allocation fair.
Variable Generation Resource Challenges
The challenges posed by variable renewable resources (e.g. wind and solar) arise from their
energy characteristics. Unlike a coal-fired power plant, for example, that can be dispatched for
dependable output up to its nameplate capacity, a 200-MW wind farm can only generate up to
the level that can be captured from the wind energy source, and cannot be predictably defined by
capacity. Wind and solar energy vary with the underlying energy sources. This variation results
in daily variability over any 24-hour period due to heating and cooling effects, as well as
seasonal changes. Variability can also result from specific weather system movements,
turbulence, shadow affect, and high-speed cutout. Finally, prevailing wind patterns do not
2-1

THE MISO PLANNING ENVIRONMENT
coincide with peak energy usage hours (i.e. when load and price are high). In fact, wind
generation is often greatest during off-peak hours when prices are low (see Figure 2-1).
2-2
Figure 2-1: Average Hourly Wind Generation and Prices in MISO 2008-2011 (Source Iowa
Stored Energy Park Analysis)
The need to integrate renewable energy presents system planners with additional complexities.
Since wind resources are typically located in remote areas away from population centers,
electricity generated from wind requires that new transmission be built to deliver the power to
market. Traditional planning and analysis required to justify new transmission to meet local
needs does not accommodate building expensive transmission to deliver remotely generated
renewable power. Economically building a local plant using conventional fuel is usually less
expensive, but it does not consider wider political and regulatory issues posed by RPS mandates.
Another complication for MISO planners arises because EPA regulations to limit emissions and
an aging generation fleet lead to coal plants being retired and replaced by gas fired generation.
The coal plants perform an important role in MISO by providing regulation services. Regulation
ensures that the system frequency is kept at or near a constant 60 HZ. Coal plants have more
flexibility to absorb system shocks and maintain regulation. With the coal fleet reduced, the
overall system becomes less stable and more back-up reserve units (ancillary services) are
required. Adding large wind generation quantities to the system increases the instability because
wind is not dispatchable and is variable over time. The net result is an increase in the need for
ancillary services such as regulation and contingency.
Ancillary Service Markets
In January 2009, MISO began operating an ancillary services market (ASM) to introduce
competition to services that ensure system reliability. Any energy market requires ancillary
services, but they are often controlled by the ISO unilaterally directing the necessary system
resources. An ASM market allows generators to bid resources as operating and contingency

THE MISO PLANNING ENVIRONMENT
reserves in the day ahead and real time markets in addition to bidding resources for energy
dispatch. In MISO day ahead and real time energy markets, prices are cleared at the intersection
of supply and demand based on the marginal cost for the last generation unit required. In the
same way, ASM’s clear prices at a zonal or system wide level based on reliability and
contingency requirements in each zone. By making ancillary services subject to market forces,
MISO encourages market participants to provide adequate services to ensure reliability in a cost
effective manner. Operations planning timeframes dictate the resources needed (see Figure 2-2).
The following ancillary services are observed in MISO:
• Regulation: automated second by second system balancing
• Frequency Regulation: maintains the power system frequency within a predetermined
range. This service requires the unit to be very flexible at short notice. Ranges from
inertial response that may be 1-2 seconds following a frequency disturbance, to primary
frequency response (5-10 seconds) and regulation (10 seconds to several minutes).
Mostly implemented by automated generator control (AGC).
• VAR (Volts-Amp-Reactive): maintains the electrical transmission system power factor
at a level close to 1.0, reducing losses.
• Contingency: energy supply available at short notice to meet unexpected system changes
• Spinning reserve: provides contingency generation that can be switched into use
immediately in order to respond to a system outage or sudden power loss. Spinning
reserves are designated synchronous if they are in sync with the transmission system (and
can be called upon more quickly) or non-synchronous. Sufficient reserves are required to
counter an “N-1” contingency meaning the largest expected unit outage from a system
failure. Supplemental spinning reserves are 10-minute start – meaning that they can be
dispatched in 10 minutes.
• Ramping: provides rapid ramping power (up ramp and down ramp) when demand
increases or decreases at a high rate (minutes to hours). Ramping is also referred to as
load following (see Figure 2-2). It is typically used during the shoulder hours either side
of the peak daily load. The MISO tariff does not fully recognize ramping resource value
although a separate MISO study group is analyzing the issue.
Ancillary services are an important component in estimating the benefits that energy storage
provides. Because energy storage can be switched on and off quickly, it is an attractive resource
for contingency and regulation. Where storage technology is less flexible, (e.g. when a pumped
storage system changes from pumping mode to storage mode it may take several minutes to
respond), then ancillary benefits are lower. For storage technologies such as batteries that only
have short-term power, the current cost is hard to justify without including ancillary benefits.
One ancillary service that energy storage could potentially provide in MISO is ramping. A
separate MISO study is reviewing the existing ramp resource Tariff treatment and this initiative
is explained in more detail in Chapter 4. RPS mandated variable generation and coal plant
retirement because of EPA regulation increase the ramping requirements on natural gas fired
generation. The result is that CC and CT units will be cycled more frequently than they were
designed to be. As a result, there is good potential benefit in using energy storage for ramp
services to reduce this cycling.
2-3

THE MISO PLANNING ENVIRONMENT
Because energy storage may provide ancillary service benefits during both the charging cycle
(e.g. by providing load to help smooth ramping down) and the discharge cycle (e.g. by providing
energy during ramping up), the optimization algorithm required to dispatch energy storage
ancillary services is complex. These complexities extend to modeling ancillary services since the
relative benefit to using storage is quite often due to rapid response capability (in the seconds)
where models may only optimize on an hourly basis. In addition, current FERC tariff treatment
places no value on rapid performance in ASM’s and this seems biased against short-term storage
providers. FERC is currently reviewing this treatment (see FERC Docket RM11-7, February
2011).
2-4
Figure 2-2: Operational Planning Timeframes in ISO Balancing Markets (Source EPRI)
The MISO Transmission Planning Process
MISO and its stakeholders engage in continuous transmission expansion planning through the
MISO Transmission Expansion Planning (MTEP) process. The MTEP process objectively
evaluates expansion issues and opportunities, identifies economic savings and operational
efficiencies, and tracks regulatory requirements to ensure compliance. Under the MTEP planning
process, transmission extension plans are accepted under the following five categories:
• Baseline Reliability Projects: required to meet North American Electric Reliability
Corp. (NERC) standards.
• Generator Interconnection Projects: upgrades that ensure system reliability when new
generators interconnect.
• Transmission Service Delivery Projects: required to satisfy a stakeholder transmission
service request. The costs are assigned to the requestor.
• Market Efficiency Projects: meeting Attachment FF (of the MISO Tariff) requirements
for reduction in market congestion.

THE MISO PLANNING ENVIRONMENT
• Multi Value Projects (MVP): meeting Attachment FF requirements to provide regional
public policy economic and/or reliability benefits
MTEP projects are prioritized based on their documentation, justification and approval. Each
annual MTEP plan lists projects in appendices according to these priorities. Projects start in
Appendix C when submitted into the MTEP process, transfer to Appendix B when MISO has
documented the project need and effectiveness, then move to Appendix A after approval by the
MISO board of Directors.
MISO planning efforts expanded in 2010 to meet the challenges associated with integrating
renewable energy. The MVP project category represents new transmission that provides regional
public policy economic and/or reliability benefits. The MVP category requires extensive analysis
and modeling to determine which transmission expansions best meet regional public policy goals
under different future economic and regulatory scenarios. The Regional Generation Outlet Study
(RGOS) and the Regional Economic Criteria and Benefits (RECB) task force were created to
determine the transmission needed to meet MISO stakeholder RPS policy goals as well as how to
fairly allocate costs associated with these projects.
The RGOS analysis includes refining future generation and load scenarios, and modeling
transmission values under a full range of future possibilities. The study produced three reliable
transmission portfolios based on different scenarios. Elements common between these three
portfolios, and common with previous reliability, economic and generation interconnection
analyses were identified to produce a 2011 candidate MVP portfolio.
The 2011 MTEP studies 5 evaluate the candidate MVP portfolio to identify near-term, robust
transmission solutions that fulfill multiple transmissions and reliability needs. This represents a
first step towards a truly regional transmission solution to integrate wind resources into the
MISO footprint.
The future resource and load planning scenario detail used during the MTEP11 MVP analysis is
presented here to explain the model environment that MISO transmission planners currently use
to evaluate wind integration projects. The Energy Storage Study uses similar future scenarios and
assumptions to model the additional impact that using energy storage might have on the MISO
resource and load plan.
The MTEP11 MVP modeling analysis uses the following alternative future scenarios and
parameters:
• Future Policy Scenarios
o Business as usual with continued low demand and energy growth (assumes that
current energy policies will be continued, with continuing, recession-level low
demand and energy growth projections).
o Business as usual with historic demand and energy growth (assumes that current
energy policies will be continued, with demand and energy returning to prerecession
growth rates).
5 The MTEP2011 Planning Cycle is still in progress
2-5

THE MISO PLANNING ENVIRONMENT
2-6
o Carbon constraint (assumes that current energy policies will be continued with the
addition of a carbon cap modeled on the Waxman-Markey bill).
o Combined energy policy (assumes a myriad of energy policies are enacted,
including a 20 percent federal RPS, a carbon cap modeled on the Waxman-
Markey bill, the implementation of a smart grid, and the widespread adoption of
electric vehicles).
• Time horizon: 20 – 40 years from portfolio in-service date
• Discount rate: (capital borrowing cost) 3.00 - 8.2 percent
• Wind Turbine Capital Cost: $2.0 – $2.9 Million / MW
• Operating Reserve Optimization Benefit: $5 - $7 / MWh
• Natural Gas Prices:
o Business as Usual Scenarios: $5 - $8 / MMBtu
• Carbon and Combined Policy Scenarios: $8 - $10 / MMBtu
• A natural gas price of $5 was used for the base business case analysis. Higher natural gas
prices were used as sensitivities.
The Energy Storage Study
The Energy Storage Study is a targeted study assigned to the MISO Transmission Expansion
Plan 2011 (MTEP11) cycle. MTEP targeted studies begin as efforts to identify particular
problems or explore planning, reliability and/or market enhancements. The study objectives
include improved understanding about how to represent energy storage in MISO models. The
future resource and load planning scenarios used to model energy storage technologies are
similar to those used for MTEP 11.
While MISO’s renewable energy production is growing, new generation sources are complicated
to integrate into the existing network. Wind production in MISO as a percentage of total energy
increased from 0.65 percent in 2006 to 3.8 percent in 2010. There were however, 2,117 wind
curtailments in 2010 where potential wind generation was backed down either because it could
not reach a load due to congestion or because the wind generation was surplus to requirements
(see Figure 2-3). Providing additional transmission is one way to alleviate wind curtailment but
although this eliminates congestion, it does not guarantee that wind energy can be consumed.
Wind energy is typically greatest during off-peak hours when demand for electricity is low.
Additional wind generated electricity during off-peak hours therefore often attracts low or
negative prices. Energy storage technologies have the potential to consume wind energy at low
off-peak prices

THE MISO PLANNING ENVIRONMENT
Figure 2-3: MISO Wind Curtailment 2008-2010 (Source MISO State of Market Monitor)
during their charge cycle and deliver electricity to the market during peak periods when prices
are higher. This phenomenon, known as energy arbitrage, alleviates wind curtailment and
potentially reduces the need for new transmission and generation to meet peak demand. The
Energy Storage Study will increase MISO understanding about whether, where and how energy
storage resources can be applied to reduce transmission costs and wind curtailment.
2-7

3
ENERGY STORAGE TECHNOLOGIES
The Energy Storage Landscape
A confluence of industry drivers—including increased renewable generation, higher costs for
serving peak demands, and capital investments in grid infrastructure for reliability, efficiency
and smart grid initiatives—is creating new interest in electric energy storage technologies. The
EPRI report: Electricity Energy Storage Technology Options: A White Paper Primer on Applications,
Costs and Benefits 1020676, Final Report, December 2010 provides a good reference to the current
energy storage landscape and a summary is provided below.
Key benefits to energy storage in the Regional Transmission Organization environment include:
• Energy storage compensates for variable energy sources and congestion by absorbing the
excess energy when generation exceeds demand levels and providing it back to the grid
when generation levels fall short 6.
• By enabling variable renewable penetration from resources such as wind and solar power,
storage helps reduce the electricity sector’s carbon footprint and satisfies regulatory
requirements such as RPS– if the additional renewables that would be enabled offset the
carbon footprint attributed to the storage device carbon footprint
• Storage improves system efficiency and return on investment (ROI) by shifting peak load
to off-peak hours and potentially reducing new investment in transmission infrastructure
– if the storage is properly located with respect to transmission system constraints.
• Providing regulation support at a time when variable generation and coal plant
retirements make balancing supply and demand with conventional plants quite resource
intense.
• Storage provides quick response to system contingencies such as equipment failure or
power plant outages
• Stored energy can be used to help ramp up (during discharge) and ramp down (during
charging) system loads when demand increases or falls rapidly. This smoothing process
avoids cycling thermal plants in a way that they have not been designed to be run.
Understanding the value of these benefits and using modeling tools to quantify these benefits is a
key industry research activity for stakeholders and ISO planners.
6 Energy Storage for Power System Applications: A Regional Assessment for the Northwest Power Pool, DOE,
April 2010
3-1

ENERGY STORAGE TECHNOLOGIES
While many different energy storage technologies are installed and operating worldwide,
pumped hydro systems (127,000 MW) are the most widely deployed. Compressed air energy
storage (CAES) installations are the next largest with 440 MW, followed by sodium-sulfur
batteries with approximately 316 MW installed and 606 MW planned or announced. All
remaining energy storage resources worldwide total less than 85 MW combined, and are
typically one-off installations 7 .
EPRI identifies ten key energy storage applications along the electrical system value chain from
end user to system operation (see Table 3-1). Different energy storage technology characteristics
lend themselves to different applications along the electricity value chain. Comparing system
power ratings and module size to the discharge time at rated power indicates generally where
particular technologies will be valuable (see Figure 3-1).
Despite the large anticipated need for energy storage solutions within the electric enterprise, very
few grid-integrated storage installations are in actual operation in the United States today. This
landscape is expected to change during 2012-2013 as new storage demonstrations supported by
more than $250 million in U.S. stimulus funding emerge. In general, based on present-day
technology, some energy storage systems are not cost effective since their capital costs are too
high. Technology costs and application benefits are very sensitive to configuration and location
with respect to both discharge and energy storage.
7 1020676 Electricity Energy Storage Technology Options, December 2010 EPRI
3-2

ENERGY STORAGE TECHNOLOGIES
Table 3-1: Definition of Energy Storage Applications (Source EPRI 1020676)
Figure 3-1: Representative Positioning of Energy Storage Technologies (Source EPRI)
3-3

ENERGY STORAGE TECHNOLOGIES
Energy Storage Technology Overviews
Pumped Hydro Storage
Pumped storage hydro (PSH) has been a proven energy storage technology for over 40 years.
PSH utilizes large, aboveground reservoirs to store water at different elevations. The facility
draws energy from the grid to pump water from the lower to the higher reservoir, and supplies
energy to the grid when the water that is allowed to run back down to the lower reservoir drives a
water turbine that powers the generator. Current worldwide PSH capacity is around 100 GW (see
Figure 3-3). US capacity is 16 GW at FERC licensed plants (see Figure 3-2) with a further 33
GW in currently permitted proposed projects.
3-4
Figure 3-2: FERC Registered Pumped Storage Projects, July 2011
Pumped hydro systems are customarily used for energy arbitrage opportunities. At low demand
periods (off-peak), low cost electric power is used to pump water from a lower reservoir to a
higher reservoir. At peak demand periods, when the electricity price is high, water is released
through a turbine to generate electricity. Only when the differential between peak and off-peak
prices is sufficiently large to compensate for the energy losses incurred during round-trip
charge/discharge cycle, does it make economic sense to dispatch PSH. Besides the energy
arbitrage potential, energy storage can provide operating reserves (contingency reserves) and
system balancing services to the grid because of its fast response characteristics.

ENERGY STORAGE TECHNOLOGIES
Newer, adjustable speed pumped hydro storage (ASH) units have been in commercial operation
since 1995 and six units are currently going through FERC licensing in the US. ASH units are
more flexible than conventional PSH. The adjustable speed mechanism supports fast response
frequency regulation and load following in both the pumping (charge) and discharging modes,
increasing the revenue potential from ancillary services (see Figure 3-4).
Figure 3-3: Pumped Storage Capacity Worldwide (GW) Source MISO
Figure 3-4: Fast Response Capabilities for Adjustable Speed PHS (Source MISO)
3-5

ENERGY STORAGE TECHNOLOGIES
Compressed Air Energy Storage (CAES)
CAES facilities utilize large underground caverns to store air that is compressed during off-peak
hours. The compressed air is then fed into a natural gas fired expander or combustion turbine
(CT) to provide power back to the grid during peak demand periods. Key benefits of CAES
include relatively lower capital costs (versus other storage technologies), lower carbon emissions
(versus conventional combined-cycle facility) and greater options for siting (versus pumped
hydro).
CAES Technology
CAES plants use off-peak electricity to compress air into an underground reservoir, surface
vessel, or a piping air storage system. In one approach, when electricity is needed, the air is
withdrawn from storage, heated via recuperation, and passed through an expansion turbine to
drive an electric generator. Such plants burn about one-third the premium fuel of a conventional
combustion turbine and produce about one-third the pollutants per kWh generated.
In another approach (called a “chiller option”), no fuel is used to heat the air before it is passed
through the expansion turbine, since the air is heated with stored energy from the waste heat
produced during the off-peak compression process and/or it is heated from the exhaust of a
combustion turbine, which is part of the CAES plant (see Figure 3-5). The compressed air can be
stored in several types of underground media, including porous rock formations, depleted gas/oil
fields, and salt or rock cavern formations. The compressed air can also be stored in above ground
or near surface pressurized air vessels/pipelines, including those used to transport high-pressure
natural gas.
3-6

Figure 3-5: Advanced CAES Plant Schematic (Source: EPRI)
ENERGY STORAGE TECHNOLOGIES
CAES plants can be built in modular fashion by adding capacity in 100 MW increments—such
as 100 MW, 200 MW, or 400 MW sizes with ten hours of storage. Additionally, capital costs are
less than for pumped storage—$1374/kW in 2010 dollars 8 . The standard configuration with ten
hours of storage can be easily enhanced to facilitate twenty or thirty hours of storage if the
operating economics allow. Additional storage can be charged during weekends or holidays
when electricity prices are off-peak. Lack of cavern space is usually not considered a barrier to
expanding the storage hours, since the volume required to store compressed air for a CAES plant
is usually only a small part of the typical geological cavern structure used. Storage volumes on
the order of tens of millions of cubic feet are required for CAES plants. Natural gas storage that
uses similar geological structures usually contains on the order of billions of cubic feet of
capacity.
8 Total Capital Cost estimate from Iowa Stored Energy Project Economics Analysis 2010
www.isepa.com
3-7

ENERGY STORAGE TECHNOLOGIES
The Iowa Stored Energy Park Case Study 9
The Iowa Stored Energy Park Agency (ISEPA) developed a CAES proposal for a 270 MW plant
located in Dallas Center, Iowa, in 2010-11 due in service during 2015. The plant, however did
not proceed owing to problems with the rate that the compressed air could be extracted from the
geological cavern. Before the project ended, however, analysis was carried out to determine plant
economics. This economic analysis informs the Energy Storage Study because ISEPA is located
within the MISO footprint and represents 57 MISO member utilities.
The ISEPA scheme relied initially on harnessing low cost off-peak wind power and reselling that
power during peak hours at higher prices to provide intrinsic value. However, economics
analysis showed that the proposed CAES plant procured 20-30 percent of revenues from
extrinsic value by providing contingency reserves and ramping services. The ISEPA economics
study looked at forecast MISO market prices for energy and reserves, from 2015 to 2034. The
plant design included a very fast 10 minute ramp rate from cold steel to full 270 MW load and a
very low 15 percent minimum output (allowing greater down ramping than other MISO
generation units). The plant capital cost was estimated to be $1547 /KW and higher than an
equivalent size combined cycle ($1205 KW) or a combined cycle gas turbine ($805/KW) but
producing similar benefits (see Table 3-2).
3-8
Table 3-2: ISEPA Net Benefit CAES Plant Economic Comparison (Source ISEPA)
The ISEPA economic analysis indicated plant benefit would reduce by $140 KW if cap and trade
markets were introduced for carbon emissions in the MISO market, because the latter would
increase off-peak prices relative to on peak and thus reduce energy arbitrage. The ISEPA project
“lessons learned” indicate that the plant would benefit from improved treatment of energy
storage in the MISO ASM. The study concluded that current MISO tariffs do not fully recognize
the value of fast ramping resources and generally tend to undervalue ancillary services. In
particular, CAES would benefit from relaxing spinning reserve requirements during shortage
9 www.isepa.com

ENERGY STORAGE TECHNOLOGIES
periods and increasing the limited 5-minute “look ahead” capability to dispatch fast-ramping
resources.
In addition, the ISEPA study suggested MISO consider a tariff that recognizes and rewards fully
dispatchable, fast ramping off-peak loads. This would be similar to a demand side resource tariff,
but for off-peak load rather than on peak load reduction.
Battery Storage
Long-term energy storage using batteries can involve many different types of electro-chemical
batteries, including, but not limited to, advanced lead acid, flow batteries, liquid-metal batteries
(i.e. NaS and NaNiCl2), Ni-Cd and Li-ion batteries. The battery is charged when excess power
generation is available and then discharged as needed when alternative power sources are not
available or constrained (e.g. during peak hours). Batteries have several key benefits including
very few locational constraints, very rapid response times and high power efficiency levels (often
90 percent or higher). Many different battery storage technologies are currently available or
being developed and demonstrated as fully integrated ac power systems. The four relatively
mature technologies that are suitable for use in a grid setting are reviewed briefly below.
Lead Acid Batteries 10
Lead-acid batteries are the prevalent electrical energy storage system in use today. They have a
commercial history of well over a century, and are applied in every area of the industrial
economy, including portable electronics, power tools, transportation, materials handling,
telecommunication, emergency power, and auxiliary power in stationary power plants.
Because of their low cost and ready availability, lead-acid batteries have come to be accepted as
the default choice for energy storage in new applications. Advanced lead acid batteries, which
include technology for improving durability and number of discharge cycles area also beginning
to be deployed and demonstrated.
NAS (Sodium Sulfur) Batteries
The Japanese company NGK is the only vendor of sodium sulfur batteries for utility
applications. The company uses the NAS® trademark, registered in Japan.
NGK markets two NAS battery models. The NAS Peak Shaving (PS) Module is designed for
energy management up to ~20 MW AC. This battery is used for load leveling, broad peak
demand reduction and mitigating power disturbances and outages for up to several hours. The
NAS Power Quality (PQ) Module is designed for pulse power applications up to ~100 MW AC
such as prompt spinning reserve, voltage and frequency support, short duration power quality
protection and short peak demand reduction. NAS battery costs are quite high and a key
challenge is the scale-up of mass production facilities to achieve lower unit costs and prices.
10 1001834 EPRI DOE Handbook of Energy Storage for Transmission & Distribution Systems Dec 2003
3-9

ENERGY STORAGE TECHNOLOGIES
Zinc-Bromine and Halogen Flow Batteries
Rechargeable zinc battery technology is attractive for large-scale energy storage systems,
because it has high energy density and relatively low cost. Flow batteries are a favorable
technology for large systems, because they are eminently scalable and allow flexibility in system
design. The zinc-bromine flow battery combines these two technologies and thus has significant
potential for use in large-scale utility applications. The zinc-bromine battery has undergone
major development and field-testing efforts. Utility scale zinc-bromine systems have very limited
deployment history and are still at a relatively early maturity level. Advanced Zn-halogen
systems are also under development and may be suitable for bulk storage system applications.
Vanadium Redox Flow Battery
Vanadium redox batteries are the most technically mature of all flow battery systems available.
When electricity is needed, the electrolyte flows to a redox cell with electrodes, and current is
generated. The electrochemical reaction can be reversed by applying an over potential, allowing
the system to be repeatedly discharged and recharged. Like other flow batteries, many variations
of power capacity and energy storage are possible depending on the size of the electrolyte tanks.
3-10

4
STORED ENERGY RESOURCE TREATMENT IN THE
MISO TARIFF
This chapter describes how both long term and short-term stored energy resources are treated in
the current MISO tariff. Recent FERC correspondence with MISO and the electricity market
regarding the treatment of stored energy resources is also reviewed. During Phase 2 of the
Energy Storage Study, discussion about potential changes to the MISO tariff to encourage energy
storage will be added.
Current MISO Tariff
The current MISO Tariff treats long-term and short-term energy storage devices separately and
differently. Short-term resources are defined as being able to provide energy for one hour or less.
Long-term resources can provide sustained energy for more than one hour.
Long Term Energy Storage Resources
In response to FERC requests for information regarding the MISO Tariff treatment of long-term
storage resources in December 2009, MISO submitted an informational report in March 2010 11 .
The report describes how MISO treats long-term storage resources in detail as follows:
“The Midwest ISO currently accommodates long-term storage resources in its markets in the
form of pumped storage resources. These resources have participated in the Midwest ISO
markets since implementation of the energy market in April 2005 and are treated in a comparable
manner to generators and price sensitive loads in the markets. To this end, there is approximately
2500 MW of pumped storage resources registered in Midwest ISO.
Such long-term storage resources are able to participate in both the Day-Ahead and Real-Time
markets. A participant with a pumped storage resource has the option of defining a generator
commercial pricing node (CPnode), a Load Zone CPnode, and/or a demand side resource Type II
CPnode, or I for the pumped storage resource. In the Day-Ahead market, the participant can
submit bids or schedule load, to purchase energy from the market at the Load Zone CPnode to
store in hours of its choice, or submit offers or self-schedules to reduce demand to provide
energy or reserves at the demand side resource CPnode. The participant can submit offers or selfschedules
to sell energy or reserves into the market at the generation CPnode in other hours. The
participant takes into account its forecasts of market conditions while using these mechanisms to
11 Informational Report of the Midwest Independent Transmission System Operator, Inc.,
Docket Nos. ER07-1372-014 and ER09-1126-000 March 2010
4-1

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
schedule pumping and generation. Similarly, the participant may adjust its bids, offers and/or
schedules in the Real-Time market. From January 6, 2009, through December 31, 2009, pumped
storage facilities used 2,353,300 MWh of energy to pump, produced 1,647,124 MWh of energy,
and provided 94,836 MWh of regulating reserves, 309,390 MWh of spinning reserves and 7,942
MWh of supplemental reserves in real-time.”
The MISO Business Process Model (BPM) describes the current operating procedure for pumped
storage as follows: 12
“Load at a pumped storage facility when operating in pumping mode should be included in the
load forecast supplied by the load balancing authority for the reliability assessment commitment
(RAC) processes. Inter-control center communications protocol (ICCP) values for the load and
generator should be sent to MISO. The load measurement would be a positive value and the
generator measurement would be “zero” when pumping and vice versa when generating. During
real-time, load served by the pumped storage facility can be handled by either the load balancing
authority and or MISO continuously updating the load forecast to include load from pumping.”
Pumped Hydro Storage Tariff
There are two challenges in operating storage under the existing MISO tariff. The first is that
charging and discharging are treated separately by MISO. This means that when the plant offers
generation and load into the day ahead energy market, the Locational Marginal Price (LMP) for
electricity purchases to charge the storage is not linked to the generation price (LMP for
electricity sales). This allows for the possibility that when generation clears in the day ahead
market and load does not, the storage owner is left committed to provide power at an unknown
cost. It is possible to hedge both load and generation positions using financial virtual bids 13 but
the virtual bids are also not guaranteed to clear in the day ahead market. The second challenge is
that plant turbines are treated as separate units in the market, increasing energy arbitrage
calculation complexity for the plant operator.
If the storage plant could be treated as one unit with both generation and load, then stored energy
could be linked to the generation to optimize revenue. In addition, the unit dispatch model could
incorporate a look-ahead horizon to forecast when stored energy arbitrage is optimal. This is
particularly useful in periods when peak prices are low, since cycling the storage every day may
not be the optimal asset utilization throughout the year.
These operational shortcomings are not, however reflected in the modeling for the Energy
Storage Study. Both the EGEAS and PLEXOS models used in this study optimize over longer
time periods, allowing for the energy arbitrage to be maximized.
12 MISO BPM-002 June 2011
13 Virtual bids allow market participants to hedge day ahead market positions (load or generation) by entering an
opposite financial position to their physical bids to eliminate price risk
4-2

Short Term Energy Storage Resources
STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
Short term energy storage resources, known as stored energy resources (SER’s) are only eligible
to be bid as regulation resources in the ASM day ahead and real time operating reserve markets
as follows 14 :
• Stored Resources are Resources capable of supplying Regulating Reserve, but not Energy
or Contingency Reserve, through the short-term storage and discharge of electrical
Energy in response to Setpoint Instructions 15 .
• “Stored Energy Resource Offers consist of data submitted by MPs for consideration in
commitment and dispatch activities. Such Offer data may be submitted for the Day-
Ahead and Real-Time Energy and Operating Reserve Markets. Regulating reserves in
DA and RT (hourly) and self-scheduled regulation in DA hourly market. In all cases, the
minimum offer submitted per hour is 1MW. A number of storage device parameters are
submitted with the resource offer. All stored energy resources are registered as
Regulation Qualified Resources, and may submit Regulating Reserve Offers in $/MW for
use in the Energy and Operating Reserve Markets.”
The MISO Business Process Manual (BPM) for SER operating parameters provides the
following definitions (see also Table 4-1):
• Hourly Bi-directional Ramp Rate: only applicable for use in real-time and will apply to
all Stored Energy Resources to limit the change in Energy Dispatch Target and/or limit
the total amount of Regulating Reserve that can be cleared on the Resource.
• Hourly Ramp Rate: used in the day ahead market and all RAC processes but not within
the operating hour.
• Hourly Regulation Minimum Limit: the minimum operating level in MW at which the
resource can operate (varying hourly if required)
• Hourly Regulation Maximum Limit: the maximum operating level in MW at which the
resource can operate (varying hourly if required)
• Hourly Maximum Energy Charge Rate: the maximum rate in MWh/minute at which
the energy storage level of a stored energy resource can increase (charge)
• Hourly Maximum Energy Discharge Rate: the maximum rate in MWh/minute at
which the energy storage level of a stored energy resource can decrease (discharge)
• Hourly Maximum Energy Storage Level: the maximum storage level in MWh to which
a stored energy resource can be charged
• Hourly Energy Storage Loss Rate: the rate in MWh/min at which energy must be
consumed to maintain a stored energy resource at its maximum energy storage level
14 MISO BPM-002 Section 4.2.6
15 Midwest ISO FERC Electric Tariff, Fourth Revised Volume No. 1, Section 1.628, Second Revised Sheet No. 282.
4-3

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
4-4
• Hourly Full Charge Energy Withdrawal Rate: the rate in MWh/min at which a stored
energy resource can continue to absorb energy while the storage level is at the resource’s
maximum energy storage level.
Table 4-1 : Stored Energy Resource Operating Parameter Data Summary (Source MISO BPM-
002)
The defaults for these parameters are set through the tariff when the storage asset is registered. If
the parameter is updated in the resource offer, the updated value overrides the defaults.
Real Time (5 minute) Security Constrained Economic Dispatch (SCED) Energy
Dispatch
As stated above, SER’s can only be bid as regulation and the resource offer includes parameter
values describing the resource storage level and ramp rate etc. SER is then dispatched as energy
by the real time SCED dispatch model as follows:
• Dispatched MW is limited to a value that is the mean of MaxLimit and MinLimit based
on the latest telemeter data from the SER.
• The dispatch MW value indicates whether SER requires charging (negative MW) or has
available stored energy to dispatch (positive MW)

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
• A SER energy penalty variable is set at the current cleared regulation demand price. The
penalty screens out charging the SER when the current energy price is above the penalty
or discharging the SER when the current energy price is below the penalty. This
screening variable is designed to minimize uneconomic use of the SER resource.
Figure 4-1: MISO RT SCED Dispatch Algorithm
In this model, SER dispatch is deliberately limited by the MaxLimit and MinLimit parameters
due to concern about the risk to system reliability when a short term SER is not sustainable.
There is however special provision for overriding the SER dispatch limits where the system
operator identifies value for example to relive transmission constraint. In this case a SER Energy
Slack variable is added to the objective function (see Figure 4-1).
Ramp Capability For Load Following in MISO 16
Recent MISO research proposes a ramp capability model that can be applied from the day-ahead
market through real-time dispatch. The proposed ramp capability model manages the available
resources responding to dispatch instructions in a way that better positions them to be able to
respond to variations and uncertainty in the net load forecast. The goal is to increase
responsiveness to maintain system reliability and reduce the frequency of scarcity events.
16 Ramp Capability for Load Following in the MISO Markets Navid, Rosenwald and Chaterjee, MISO July 2011
4-5

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
The key proposed model features include:
4-6
• Ramp capability requirements (system-wide and zonal if required), which are determined
to be large enough to address the desired level of expected variability and uncertainty in
the net load within a defined response time
• Resource contribution to ramp capability including allowance for availability offers and
contributions from offline units if desired
• Ramp capability demand curve to model the costs of not meeting the desired level
variability coverage
• Simultaneous co-optimization of the ramp capability with energy and ancillary services
The ramp capability model reserves rampable capacity in one interval’s dispatch to provide
response capability to expected and/or uncertain changes in the future net load.
The ramp capability model research opens the way for pricing ramp services to be considered
which might permit energy storage to be bid as a ramping resource.
FERC Correspondence Regarding MISO Tariff SER Treatment
In 2007 MISO filed a proposal with FERC for the setting up of an Ancillary Services Market. In
agreeing to the MISO proposal, FERC sought further details from MISO including specifics
regarding the treatment of stored resources 17 . Further correspondence followed between FERC
and MISO on stored resource treatment. On May 12, 2009 in Docket No. ER09-1126-000, the
Midwest ISO filed proposed modifications to provisions in its currently effective Tariff. The
proposed changes characterize Stored Resources as short-term storage devices where Stored
Resources would be limited to offering Regulating Reserves, and not Energy or Contingency
Reserves, in the Midwest ISO markets. This filing also describes the method for dispatching a
Stored Resource, where unlike other Resource types the Energy dispatch on a Stored Resource is
not to be included in the co optimization algorithm, but instead, the Energy dispatch will be
determined in a way that maximizes the Resource's capability to provide Regulating Reserve.
On December 31, 2009 FERC accepted the May 12, 2009 MISO proposed Tariff modifications
regarding SER treatment, to come into effect in January 2010, but requested an informational
report from MISO about the treatment of long term energy storage which was not included in the
MISO ASM provision (SER’s specifically apply to short term storage). The FERC also required
further clarification from MISO regarding the calculation of the reference energy storage loss
rate. In May 2010 the FERC accepted MISO’s revision to clarify the calculation of the reference
energy storage loss rate. In March 2010 MISO submitted an informational report about current
and proposed long term energy storage resource treatment 18 .
The informational report describes the current treatment for pumped hydro (see the Current
MISO Tariff section above). The report further stated that MISO is currently investigating both
internally and through discussions with its stakeholders the potential need for Tariff
17 See FERC Docket Nos. ER07-1372-014 and ER09-1126-000
18 Informational Report of the Midwest Independent Transmission System Operator, Inc., Docket Nos. ER07-1372-
014 and ER09-1126-000 March 2010

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
modifications that might enhance the ability of alternative long-term storage resources to
participate in its markets.
On February 17, 2011, FERC issued a Notice of Proposed Rulemaking 19 that will require each of
the grid operators under its jurisdiction to structure their regulation market tariffs to provide payfor-performance.
Under pay-for-performance tariffs, grid operators would implement a pricing
structure that pays faster-ramping resources a higher price for their service.
The proposal is designed to favor energy resources that are available for rapid frequency
regulation service (such as SER’s). Commercial manufacturers of fast storage devices such as
Beacon Power Corporation have requested this rulemaking. Beacon's flywheel systems react in
seconds to a grid operator's control signal -- a response that is exponentially faster than
conventional fossil fuel-based regulation resources, pay-for-performance tariffs would enable the
company to earn increased revenue from any regulation services it provides in those markets.
Such markets include the New York ISO, where Beacon is already operating a regulation facility
that is expected to reach its full 20 megawatts of capacity in the second quarter of 2011. On
October 20, 2011 FERC issued a ruling on frequency regulation treatment (effective December
2011) as follows:
“Specifically, this Final Rule requires RTOs and ISOs to compensate frequency regulation
resources based on the actual service provided, including a capacity payment that includes the
marginal unit’s opportunity costs and a payment for performance that reflects the quantity of
frequency regulation service provided by a resource when the resource is accurately following
the dispatch signal. 20 ”
This ruling confirms the performance payment concept based on the degree to which frequency
regulation service follows the dispatch signal accurately. The assumption being that fast ramping
regulation will receive a higher performance payment.
Phase 2 Opportunities for Tariff Enhancements
During Phase 2 the MISO Energy Storage Study will investigate opportunities for tariff
enhancement and make recommendations. These will include potentially adding contingency
reserve payments for stored energy resources as well as extending the ASM to compensate longterm
storage resources. Other possible areas where the tariff could be amended include capturing
stored energy resource value in reducing congestion and offsetting new transmission investment
costs. Note, the study scope is to make recommendations, not to change the tariff.
In addition, integrating increased variable generation resources will require additional ramp
management capability. Acquiring ramp capability through a market mechanism so that a price
signal can be sent to the market could alleviate this requirement (see Ramp Capability for Load
Following section above).
19 Frequency Regulation Compensation in the Organized Wholesale Power Markets, Notice of Proposed
Rulemaking, IV FERC Stats. & Regs., Proposed Regs. 32,672 (2011) Docket Nos. RM11-7 and AD10-11
20 
 Frequency
Regulation
Compensation
in
the
Organized
Wholesale
Power
Markets
Docket
Nos.
RM11‐7‐000 ,
 AD10‐11‐000 
 Oct
20,
2010
4-7

STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF
Although it is easy for modeling software to use hindsight to optimize time sensitive
opportunities, these are not so easy to integrate into the tariff. For example, optimizing energy
arbitrage requires looking ahead to forecast the best (most profitable) time to dispatch a stored
resource. Deciding when to charge is equally time sensitive. The current MISO operational
dispatch does not include a look-ahead capability that may improve stored resource optimization.
Ongoing storage tariff review includes changing FERC requirements to handle regulation and
MISO’s interpretation of FERC requests to incorporate specific long-term energy storage
treatment in the ASM.
4-8

5
ENERGY STORAGE MODELS AND ASSUMPTIONS
The Energy Storage Study is using two commercial models to evaluate the potential economic
benefits that stored energy resources can offer MISO. These two models are known by their
acronyms as EGEAS and PLEXOS and will both be discussed in detail in this Chapter. Each
model has different characteristics and capabilities that help increase understanding about the
economic and system benefits that energy storage can provide.
Planning Models
The available model choices for electricity market planners are increasing in range and
sophistication. MISO uses several models in the MTEP process described in Chapter 2. As has
been discussed, providing adequate resources in a large RTO market such as MISO has increased
in complexity. New environmental emission requirements and RPS mandates increase the
variables in models. The traditional “classic” approach to resource planning was to use separate
models to determine capacity requirements, system operating costs and economics. These
models would be run iteratively for different expansion plans to find the least cost solution.
Newer resource planning models use an optimization approach that includes all the possible
inputs and combinations in one large model. Shorter-term economic dispatch models require
significant data at an hourly or sub-hourly level for many thousand nodes on the grid.
MISO Energy Storage Study Models
The models that MISO is using to model energy storage for this study have distinctly different
capabilities. The EGEAS model is used by MISO for long term resource adequacy planning. The
EGEAS model identifies when a particular resource (generation, storage or demand side)
provides economic benefit in a future market scenario being analyzed. The economic benefit is
recognized when a storage resource has the lowest cost to benefit ratio (including long term
construction costs). EGEAS can therefore be used in the Energy Storage Study to identify future
scenarios where energy storage is beneficial. EGEAS only recognizes benefits from energy
arbitrage and ignores ancillary services. PLEXOS on the other hand is a production cost model
that is able to combine analysis of day ahead and real time markets and to model data in higher
granularity (e.g. intra hourly). PLEXOS can therefore identify and co-optimize ancillary service
market benefits as well as energy arbitrage. It is necessary to use EGEAS during Phase 1in order
to identify the future scenario cases where energy storage is beneficial using PLEXOS for more
in-depth analysis because the latter can only analyze a known resource case in detail (see Figure
5-1).
Both model types provide insight into energy storage benefits. Longer-term energy storage
resources such as PHS and CAES units rely primarily on energy arbitrage to justify their
investment. These units are built on a larger scale with a 30 year expected life. Investment
decisions are made based on long term performance assumptions with changing environmental
5-1

ENERGY STORAGE MODELS AND ASSUMPTIONS
scenarios. Long-term resource adequacy models provide the economic perspectives and
scenarios required to justify large-scale storage. For shorter term energy storage resources it is
more important to be able to model sophisticated intraday RTO markets and economic dispatch
over shorter time periods. The short term planning models can mimic sophisticated ASM
scenarios.
Study Models – EGEAS
5-2
Figure 5-1: EGEAS and PLEXOS Model Interaction
The electric generation expansion analysis system (EGEAS) is designed by EPRI as a model to
find the optimum (least cost) integrated resource plan for meeting a given demand level. EGEAS
expands supply-side and demand-side resources to identify the best resource fit, including
storage. EGEAS strengths are rapid analysis for long time scales (30-40 years) and the ability to
identify the least cost resource fit for given input parameters. The EGEAS model weaknesses are
that transmission constraint (congestion) is not considered and that the model data is not more
than daily granularity.
The primary reason for MISO to use the EGEAS model is for resource planning to identify
future capacity needs beyond the typical 5-year project-planning horizon. The model is used to
find the optimized capacity expansion plan to meet demand (load + losses + planning reserve
margin target), by adding supply-side and demand-side resources based on assumptions provided
to the model.
For the Energy Storage Study, the EGEAS model can be used with pre-existing data assembled
for the MTEP11 planning process to compare model results in different scenarios with or without
storage available as a resource. The analysis is expected to indicate where stored energy units
can produce economic value in the MISO resource mix over a twenty-year horizon. EGEAS
provides a capability to rapidly analyze multiple future scenarios over many years to identify the

ENERGY STORAGE MODELS AND ASSUMPTIONS
cases where energy storage proves to be economically beneficial. These cases will then be
analyzed in further detail using the PLEXOS model.
EGEAS Model Functionality
The EGEAS optimization process is based on a dynamic programming method where all
possible resource addition combinations that meet user-specified constraints are enumerated and
evaluated.
The EGEAS objective function minimizes the present value (PV) of revenue requirements. The
revenue requirements include both carrying charges for capital investment and system operating
costs. The EGEAS optimization uses the following tools 21 :
• Generalized Bender’s Decomposition: a non-linear technique based on an iterative
interaction between a linear master problem and a non-linear probabilistic production
costing sub problem
• Dynamic Program: based on the enumeration of all possible resource additions to
identify those units that are superfluous
• Screening Curve Option: produces {cost by capacity factor} results for evaluating many
alternatives
• Prespecified Pathway Option: provides more detailed analysis of a plan than is
computationally feasible within an optimization. Also allows user defined plans to be
analyzed
The following optimization constraints are used:
• Reliability
• Economic
o Reserve margin – maximum or minimum
o Unmet energy – maximum
o Loss-of-load probability – maximum
o Low earnings asset ratio
o Low interest coverage ratios
o Large increase in system average rate
• Tunneling: used to specify the upper and lower limits for the annual and or cumulative
resources available for consideration
• Environmental
21 Resource Planning and EGEAS Overview, NG Planning, October 2010
5-3

ENERGY STORAGE MODELS AND ASSUMPTIONS
5-4
o Optimize to a pollutant cap level
o Incorporate system site or unit limits
• Limited Fuel
The EGEAS model considers supply and demand side resources as follows:
• Supply side alternatives
o Thermal units
o Retirements
o Staged resources
o Life extensions
o Hydro
o Storage
o Non-dispatchable (e.g. wind, solar)
• Demand side alternatives
o Conservation/energy efficiency
o Load management / demand side resource
� Peak shaving
� Load shifting
� Storage
� Rate design
o Strategic marketing or load building
Additional EGEAS capabilities include:
• Purchase and Sale Contracts
• Interconnections With 9 Other Systems
• Avoided Capacity and Operating Costs
• Customer Class Revenue and Sales
• Environmental Tracking and Emissions Dispatch for up to 8User Defined Variables
• Production costing details
o Capacity levels – rated, operating, emergency and reserve – varied by year and
month
o Five loading points or blocks including heat rates, capacities and forced outages
o Automatic and fixed maintenance scheduling

o Spinning reserve designations and options
o Monthly fuel pricing and target limitations
o Operating and maintenance costs
o Dispatch modifier costs
o Monthly limited energy data
• Demand side management (DSM) resource capabilities
EGEAS Benefits
o Customer costs
o Rebound benefits
o Direct customer benefits
o Rate change related benefits
o Transmission and distribution costs/savings
o Price elasticity by customer class
o Customer class rates
ENERGY STORAGE MODELS AND ASSUMPTIONS
A considerable benefit to using EGEAS is that the model commonly runs in one hour or less for
20-year planning studies, with most runs taking less than 15 minutes. Quick run times allow
MISO staff to analyze wide sensitivity ranges and generation options. EGEAS inputs data can
be easily changed to perform various sensitivity analyses using different fuel price, regulatory
regimes and other scenarios. EGEAS quickly narrows down cases suitable for further analysis
using more detailed production costing models such as PLEXOS.
EGEAS Drawbacks
EGEAS does not model transmission constraints, pool-to-pool transactions, etc. EGEAS
dispatches the generation system using a monthly duration curve method, whereas production
cost models dispatch generation intra-hourly (PLEXOS).
EGEAS Energy Storage Model Assumptions
For the Energy Storage Study, EGEAS models a 20-year capacity expansion starting in 2011
with each year broken into 12 segments for generation. Since MISO already uses the EGEAS
model in MTEP studies, the Energy Storage Study is able to piggyback data from existing
analysis. The MTEP studies have always included pumped hydro storage since MISO has these
resources in use today. The MTEP 2011 analysis included CAES as a supply side alternative.
The Energy Storage Study added battery storage to PHS and CAES. The key sensitivities
explored in the study are gas prices, RPS levels, carbon tax, coal retirements and storage unit
construction costs. For the Energy Storage Study, MISO staff used the EGEAS dynamic
programming tool. Screening curves were developed separately of the model for MISO staff to
better understand the way EGEAS “looks” at various alternatives (see Figure 5-2).
5-5

ENERGY STORAGE MODELS AND ASSUMPTIONS
Figure 5-2: EGEAS Screening Curve with PSH and CAES (PHS and CAES are “mid” values -
$2250/kW and $1250/kW respectively, CO2 tax= 0 in this case, Source MISO)
EGEAS Sensitivities
The following sensitivities are evaluated in the MISO Energy Storage Study:
5-6
• Natural gas prices @ $4, $6, $8, $10 and $12 / MMBTU
• Coal plant retirements (known retirements, 3 GW, 12.6 GW)
• RPS (State Mandates – 13 % by 2025, 20 % by 2025, 30 % by 2030) 1
• Carbon tax ($0, $50, $100 per ton)
• Overnight construction costs for storage units
o Low: CAES $833/kW, PHS $1500/kW, Battery $1667/kW
o Mid: CAES $1250/kW, PHS $2250/kW, Battery $2500/kW
o High: CAES $1667/kW, PHS $3000/kW, Battery $3333/kW
1 State RPS percentages are based on total generated energy
EGEAS Assumptions
The economic base case assumption used for the Energy Storage Study is taken from the MTEP
2010 future scenario analysis. The scenario chosen is the planning advisory committee (PAC)

ENERGY STORAGE MODELS AND ASSUMPTIONS
scenario 8 - business as usual with mid-low demand and energy growth rates. This scenario is
carried forward into MTEP 11 planning as the default business as usual scenario 22 . The detailed
description for this scenario is as follows:
“The PAC Business As Usual with Mid-low Demand and Energy Growth Rates future scenario
(S8) is considered a status quo future scenario and continues the impact of the economic
downturn on growth in demand, energy and inflation rates. This future scenario models the
power system as it exists today with reference values and trends, with the exception of demand,
energy and inflation growth rates, which are based on recent historical data and assumes that
existing standards for resource adequacy, renewable mandates, and environmental legislation
remains unchanged. Renewable Portfolio Standard (RPS) requirements vary by state.” 23
The study period for the EGEAS analysis is 20 years from 2011. The EGEAS model also
includes an extension period from 20 to 30 years to counteract any “end effect”. The end effect is
caused because asset-planning horizons exceed 5 years causing retirements, regulations and
construction to taper off during the final study years.
The demand and energy annual growth rate assumption is 1.26 percent. The starting value for
demand is 103,845 MW and for energy 530,575 GW. Inflation is assumed to be 1.9% per annum
and affects fuel prices and economic costs.
Plant revenue assumptions (see Table 5-1) are based on low medium and high overnight
construction costs and are calculated from capital and production costs over the twenty-year
period. Overnight construction costs for CAES are about 55.5 percent of the values for PHS
reflecting the higher infrastructure cost to build pumped hydro. Battery overnight costs are
approximately double CAES in $kW terms. These cost assumptions are extremely important in
the EGEAS energy study analysis since the model chooses new plant investment based on costs.
The equivalent “mid” overnight construction costs assumed for CT and CC units are $665/kW
and $1,003/kW. The CT and CC costs were not varied when the energy storage costs were raised
or lowered (low and high values) because the estimates are more stable – using the latest EIA
construction cost estimates 24 .
The unit capacities input into EGEAS for PSH and CAES are equivalent at 2400 and 2160 MW
respectively. Battery capacity is a much smaller 200 MW. Construction lead-time for PSH is the
longest at 5 years, battery storage is given a 2-year lead-time and CAES is estimated at 3 years.
The CAES heat rate is assumed to be 4000 btu/kWh, which is just over half the btu rate for an
equivalent combined cycle or CT generating plant. This is because the compressed air in the
CAES plant improves generation efficiency during the discharge cycle, although there are
electricity costs incurred during charging.
22 The Draft MTEP11 Plan EGEAS Assumptions are available here:
https://www.midwestiso.org/Library/Repository/Study/MTEP/MTEP11/MTEP11%20Appendix%20E2%2
0Draft%20for%20PAC%20Review.pdf
23 MISO MTEP10 Final Report, p. 142
24 http://205.254.135.24/oiaf/beck_plantcosts/pdf/updatedplantcosts.pdf
5-7

ENERGY STORAGE MODELS AND ASSUMPTIONS
Unit Max.
Capacity
PSH 2400
MW
CAES 2160
MW
5-8
Heat
Rate
btu/k
Wh
Forced
Outage
Rate
Fixed
O&M
$/kW/
yr
Overnight
Construction
Cost
($/kW)
Low/Mid/
High
Efficiency Variable
O&M
$/MWh
Mainten
ance
Weeks
per
Year
-- 1% $5 1500/2250/3000 75% $0.50 4 5
4000 3.25% $15 833/1250/1667 123% $1.70 2 3
BATTERY 200 MW -- 1% $10 1667/2500/3333 90% $1.00 1 2
Table 5-1: EGEAS Energy Storage Study Plant Assumptions
Unit efficiency is highest for CAES at 123 percent (energy output versus energy used to charge)
and lowest for PSH (75 percent) with battery efficiency assumed to be 90 percent. The study
assumes that peak hours are Monday to Friday, 6:00 am to 8:00 pm.
Maintenance is scheduled for low demand periods. Pumped storage average maintenance is
based on historic averages at 4 weeks a year. CAES is assumed to have similar outages to a CC
unit and battery outage is estimated as one week a year. Overhead and maintenance costs for
CAES are highest at $15/kW year, with battery second at $10/kW year and PSH lowest at $5/kW
year. Forced outage rates for CAES are higher at 3.25 percent than for PSH and battery storage,
which are both 1 percent. This is because units operating with fuel are more vulnerable to outage.
EGEAS Study Generation Mix Assumptions
The study uses the following 2011 installed capacity (by fuel category in MW) as the baseline
for resource planning (see Figure 5-3):
• Coal 63757 (49%)
• Gas 37214 (29%)
• Wind 9581 (7%)
• Nuclear 8176 (6%)
• Oil 5696 (4%)
• PHS 2490 (2%)
• Hydro 1248 (1%)
• Biomass 636 (0.49%)
• Other 557 (0.43%)
• Total 129354
Constru
ction
Lead
Time
(years)

ENERGY STORAGE MODELS AND ASSUMPTIONS
Figure 5-3: 2011 Installed Capacities by Fuel Category Assumption for MISO Energy Study
The system planning reserve margin is assumed to be 17.4 percent of capacity.
Study Models – PLEXOS
Energy Exemplar (a software, consulting and information services company) owns the PLEXOS
model. MISO is a licensee of the PLEXOS modeling software. Energy Exemplar has experience
in CAES energy storage analysis for the Sacramento Municipal Utility District (SMUD) and has
been engaged in many wind integration studies. PLEXOS is a mixed integer programming (MIP)
based next-generation energy market simulation and optimization software. Co-optimization
architecture is based on the Ph.D. work of Glenn Drayton. Advanced MIP is the core algorithm
of the simulation and optimization. The model is the foundation for the mathematical
formulation of the New Zealand, Australia, and Singapore energy and spinning reserve
markets 25 .
Utilities, ISO’s, consulting firms and regulatory agencies use PLEXOS for:
• Operations
o Day-ahead generation scheduling (unit commitment and economic dispatch) to
minimize cost or maximize profit
o Portfolio management
• Planning and Risk
25 Information in this overview based on Energy Exemplar presentation to MISO Energy Storage Workshop, June
29, 2011
5-9

ENERGY STORAGE MODELS AND ASSUMPTIONS
5-10
o Resource Expansion and Valuation
o Utility Planning and Energy budgeting
• Market Analysis
o LMP and AS Market Price Forecast
o Energy Market Design and Monitoring
• Transmission (Network) Analysis
o Transmission Expansion
o CRR (or) FTR Valuation
o Bilateral contracts valuation
PLEXOS algorithms co-optimize thermal, hydro, energy, contingency, regulation, and fuel
markets. The model provides physical (primal) as well as financial (dual) output e.g. provides
information on shadow prices. The model contains three algorithms providing long-term security
assessment, mid term and short term simulation. The model contains a storage algorithm with the
following features:
• Simultaneous solution for all resources
o All decision variables determined at same time
o Perfectly arbitrage all available markets
o Co-optimize energy, ancillary services, storage, DC-optimal power flow
o Co-optimization includes limited resources: hydro energy, fuel, emissions, etc.
• Can model 5-minute or greater time step
o Real-time markets
o Sequential Day-ahead and Real-Time market simulation to capture wind / load
variability and uncertainty
� DA simulation produces unit commitment schedules using forecasted
wind generations and loads
� RT simulation reveals the ramp capacity adequacy using “actual” wind
generation and loads
PLEXOS models the following energy storage characteristics:
• Charging or pumping
o Minimum and Maximum charge power (MW)
o Ramp Rate (MW/minute)
• Round trip efficiency percentage
• Generating mode

o Minimum and maximum generation (MW)
o Ramp rate (MW/minute)
o Start-up costs (CAES)
o Associated heat rate (CAES)
• Ancillary services
o In both charging and generating modes
o Defined limits and ramp rates (MW/minute)
• Reservoir or storage device 26
o Minimum and maximum storage (MWh)
o Storage natural inflow and losses modeled
ENERGY STORAGE MODELS AND ASSUMPTIONS
o Daily or weekly cycle or optimization storage targets for large storage
These features allow PLEXOS to provide accurate modeling for sophisticated storage plant
operation such as CAES. PLEXOS can model the pumped storage (air compression) costs as
well as the combustion turbine costs and the generation efficiency for the CAES discharge. The
CAES unit can be dispatched hourly with optimized sales for energy, regulation and contingency
reserve markets (see Figure 5-4).
Figure 5-4: PLEXOS CAES Storage Model Output (Source Energy Exemplar)
26 PLEXOS is able to model ancillary service benefits from an adjustable speed pumped hydro storage system
5-11

ENERGY STORAGE MODELS AND ASSUMPTIONS
PLEXOS Benefits
The PLEXOS model allows sophisticated energy storage unit analysis compared to EGEAS. The
data granularity is as frequent as five-minute interval – mimicking the MISO real time market.
PLEXOS can therefore capture benefits from ancillary services in addition to simple energy
arbitrage. Ancillary service revenues are an important contribution to CAES storage and may
become important for new adjustable speed pumped hydro storage units. Battery storage
economics is likely to rely heavily on ancillary service revenues. The PLEXOS model
accommodates details about transmission, generation characteristics and outages that are not
available to EGEAS. PLEXOS has the ability to integrate long and short-term horizons to arrive
at an optimal solution. One drawback to PLEXOS compared to EGEAS is the model run time.
Analysis over a one-year study period can take a week or more.
In the MISO Energy Storage Study context, PLEXOS is used to complement EGEAS because
EGEAS can run many long-term scenarios quickly to determine areas of interest for detailed
granular analysis with PLEXOS. The goal with PLEXOS is to integrate the day ahead and real
time markets into one simulation. With this capability MISO planners will be better able to
model sudden changes in load, wind, or other system variations and determine the value of
resources that are able to respond to these.
PLEXOS Assumptions
The PLEXOS analysis performed under Phase 1 of the MISO Energy Storage Study is very
limited (see Chapter 7). The assumptions for PLEXOS will be similar to or the same as the
EGEAS MTEP11 base case “business as usual” scenario. EGEAS results will indicate the
sensitivities (e.g. fuel price, carbon tax, coal retirements, RPS levels) where storage benefits the
MISO system. PLEXOS will then be used to analyze these sensitivities further. In addition,
PLEXOS will run an hourly analysis for one complete study period year (April 2012- March
2013). Then more granular 5-minute analysis will be run for summer, winter and shoulder month
periods.
MISO has used EGEAS for MTEP study analysis and therefore already has a defined data set
covering the MISO territory as a base case from which to compare performance after various
storage units are added. In the same way, MISO can also reuse PLEXOS data from MTEP11
transmission planning. PLEXOS has already been used by MISO to model PSH and a typical
CAES unit. The data setup for PLEXOS is considerably more sophisticated than EGEAS since
transmission and congestion are included.
Phase 2 Recommendations to Improve Storage Modeling
The EGEAS model is used in the Energy Storage Study to identify the cases where storage is
economically beneficial. EGEAS cannot identify storage benefits in ancillary service markets.
EGEAS is therefore of little value in assessing the detailed benefit to operating energy storage in
MISO. The value that energy storage resources bring to ancillary services makes using PLEXOS
essential because the latter is able to co-optimize day ahead and real time markets. It is clear,
however, from Phase 1 of the study, that energy storage modeling adds considerable complexity
to the energy market modeling discipline. During Phase 2, the study group expects to increase
5-12

ENERGY STORAGE MODELS AND ASSUMPTIONS
knowledge and experience with PLEXOS in order to make recommendations to MISO for
improvements in modeling energy storage resources.
5-13

6
EGEAS ANALYSIS RESULTS
Results Summary for EGEAS
The Phase 1 EGEAS modeling results for the Energy Storage Study indicate that although there
is overall opportunity for long-term storage resources in certain future scenarios, existing MISO
market and tariff conditions do not justify large-scale investment in storage. As noted in Chapter
5, the EGEAS storage model has limitations that hide potential benefits from storage resources.
In particular, because EGEAS did not model intraday ancillary services, any benefits from these
are ignored. While this constraint is clearly identified upfront in the analysis, it effectively
precludes the model from identifying economic benefits from short-term (e.g. battery) resources.
Where the EGEAS model did identify economic benefit from energy arbitrage, it was restricted
by two significant market factors. The first is that MISO has more than enough existing
generation capacity, including abundant coal generation. A significant proportion of the coal
plant fleet is considered must run and therefore runs during off-peak hours. The need to keep
coal plants running off-peak reduces the impact that “free” wind generation has in bringing down
the off-peak power price since the system operator curtails the wind if the capacity is not needed.
The consequence is that higher off-peak prices reduce energy arbitrage (and that the reduction is
magnified when carbon emission tax costs are added to coal prices). The second market factor
that reduces economic benefit from energy arbitrage is that wind energy is treated as nondispatchable
and, due to the wind profile, some wind penetration is experienced even during
peak hours. The EGEAS model uses wind first whenever it is available before considering
alternative resources such as stored energy. During peak hours the result is that wind generation
is effectively ”netted out” of the load duration curve, which, in situations with higher wind
penetrations, results in coal being the marginal unit. Lower peak prices squeeze energy arbitrage
benefits from stored resources 27 .
Phase 1 EGEAS Results
The EGEAS analysis assumptions are described in Chapter 5 as well as the sensitivities that were
chosen. Using a base case business as usual economic forecast and moderate growth in demand
27 The MISO analysis did not take into account the effects of DIR. Preliminary internal discussions indicated that
the DIR designation would be tough to capture in the EGEAS model because wind has no fuel cost associated and if
the model dispatched wind it would almost certainly be dispatched on peak (at 100 percent, which is not correct for
wind). Making the wind non-dispatchable allows for instructing EGEAS when the most wind is typically available,
based on NREL historical data.
6-1

EGEAS ANALYSIS RESULTS
6-2
Figure 6-1: One Branch of the EGEAS Energy Storage Analysis Decision Tree
Figure 6-2: Results from Phase 1 EGEAS Energy Storage Analysis Showing Circumstances
Where CAES is Selected
over the twenty-year period, the analysis tested model sensitivity to gas price, carbon tax,
construction cost, RPS levels and coal retirements due to EPA rules (see Figure 6-1).

EGEAS ANALYSIS RESULTS
The results indicate the circumstances under which a storage resource could become economical
(see Figure 6-2). In each sensitivity circumstance EGEAS identifies the operating savings that
could accrue to MISO from energy arbitrage. If the operating savings exceed the capital cost of a
storage plant, then EGEAS adds the plant to the optimized resource plan. Because the energy
arbitrage estimates in the model are relatively low (see energy arbitrage analysis below), EGEAS
only selected storage in 18 of the 405 sensitivity cases (see Table 6-1) and, in all 18 cases, only
CAES was chosen due to it having the lowest modeled capacity cost of the three storage types.
Furthermore, CAES was only selected when the lowest capital cost was used ($833 kW).
Table 6-1: EGEAS Model Storage Selection Cases
The CAES unit was only picked in cases where the CO 2 cost is zero, i.e. there is no carbon tax.
As fuel costs (gas prices) went up, storage tended to get picked earlier in the study period
because CAES storage uses less fuel (4000 MMBTU/MWh) than conventional CC or CT plants.
As RPS levels went up, the general need for storage was pushed further out in the study horizon
and the amount of installed storage reduced as RPS increased. This was caused by the negative
impact on energy arbitrage resulting from high wind penetration (see the energy arbitrage
analysis section below for an explanation regarding this counter intuitive result).
6-3

EGEAS ANALYSIS RESULTS
Energy Arbitrage Analysis Based on EGEAS Results
The Phase 1 EGEAS results indicate that for long term resource planning, energy storage can
only be justified in circumstance where energy arbitrage offsets storage plant capital costs. Since
the storage technology with the lowest capital cost (CAES) was the only choice made by the
model, there are clearly important assumptions in the EGEAS model that are reducing energy
arbitrage.
One factor reducing energy arbitrage is that the existing MISO generation mix is highly
leveraged towards coal (approximately 50 percent of installed capacity and 75-80 percent of
energy) and that there is excess capacity available. The 2010 State of the Market Report by the
MISO independent market monitor estimates an actual reserve margin in the range of 28 percent
to 37 percent which exceeds the MISO planning reserve margin requirement of 17.4 percent in
2011.. Coal plants are run as baseload (i.e. 24 X 7) because their costs are low and a significant
proportion (15,000 MW) of coal units are “must run” and are used for off-peak generation
regardless of alternatives. In the EGEAS model, baseload off-peak is therefore using coal quite
frequently (not wind) because the coal has to run. Baseload coal is quite inexpensive to run and,
in fact, wind helps to lower the price even further. The arbitrage is lost when large amounts of
6-4
Figure 6-3: Simple Load Duration Curve Illustration Showing Wind Impact on Storage
Charging and Generation
wind force coal to be on the margin during peak demand. When that happens, the only energy
arbitrage available is the difference between cheap, must-run, efficient coal and more expensive,
less efficient, on peak coal.
A second factor is that increased wind penetration (model increases RPS) is run 100 percent
before any other unit is considered because wind is not dispatchable in the same way as
conventional generation 28 . The wind is therefore generating peak power and bringing down the
marginal price for peak power to the coal price level. This is because EGEAS uses an annual
28 The study did not take into account DIR. See Footnote 27

EGEAS ANALYSIS RESULTS
hourly profile to indicate when the wind is available. Inevitably, some of the wind will be
available on peak and if enough wind is forced in, this on peak wind will drive out the need to
run conventional CT/CC plants that are more expensive. Increased amounts of wind effectively
lower the load duration curve to the point that coal is setting LMP both on and off peak. (see
Figure 6-3).
EGEAS Model Takeaways
The EGEAS model results point to how sensitive stored energy plant economics are to energy
arbitrage. It is likely that in a production system, energy arbitrage will be reduced further since
the model has the advantage of perfect hindsight in selecting arbitrage opportunities that would
be more hit and miss in production.
Two features of the MISO market play an important part in reducing arbitrage opportunities, the
excess of coal generation capacity and the increasing penetration of wind generation over the
next twenty years. If there are more coal plant retirements for environmental and end of life
reasons, arbitrage will increase. If there are transmission constraints that curtail wind generation
during peak hours (a factor that EGEAS does not take into account) then arbitrage will increase.
EGEAS has identified the circumstances when energy arbitrage provides the best economic
benefit for energy storage. PLEXOS analysis can provide more detail of intraday arbitrage
opportunities and identify the as yet undetermined storage benefits from ancillary services.
6-5

7
INITIAL PLEXOS ANALYSIS
PLEXOS Phase 1
The PLEXOS Phase 1 analysis is designed to provide a framework in which a fully functional
model is developed for use in Phase 2. The PLEXOS Energy Storage Study model was jointly
created with MISO’s work on the Manitoba Hydro Wind Synergy Study. To coordinate with
that effort the modeled data used during Phase 1 is April 2012-March 2013.
Three separate storage types are modeled in PLEXOS: CAES, PHS, and Battery. In PLEXOS,
reservoir limits are used to account for non-electrical energy and this is how the model
distinguishes a storage unit from a regular generating plant. The head reservoir is where energy
is stored after charging and the tail reservoir is the location from which the unit is discharged.
The CAES unit is modeled as two separate generators linked together with constraints. Two
separate reservoirs are modeled for CAES representing the air storage cavern. Air is pumped into
the reservoir (charging) using electricity from the grid and then later discharged. When CAES is
discharged, gas is added and the unit supplies power to the grid. The storage unit is placed at a
bus in the model which has exhibited larger than average price spreads and is affected by the
transmission overlay. PHS and batteries are modeled in the same way as CAES without the
second generating unit (which represents the gas fired portion of the CAES unit.) There is one
generator with a head and tail reservoir. The unit charges energy during one time period and
discharges during another with a slight lose in energy due to its efficiency characteristics.
Energy storage resources are allowed to participate in both the ASM and energy markets and are
co-optimized between the two.
In Phase 1 only the day ahead (DA) market (24 steps of 1 hour in each optimization window for
365 days) was run. The link between the DA and real time (RT) market in PLEXOS was not
modeled in Phase 1. In Phase 2 the DA and RT will be linked together to approximate MISO
market simulation. The DA and RT link will pass the DA unit commitment, ASM allocations
and stored energy values into the RT simulation. The RT simulation will be run at 5-minute
intervals to reflect the MISO market.
Challenges Uncovered During Phase 1 PLEXOS Analysis
The on-off peak spread (energy arbitrage) in the shoulder months 29 is smaller than expected.
This means storage devices do not run during these months. Further study will be conducted
before Phase 2 analysis to understand this phenomenon.
29 Shoulder months are before and after the Summer peak (April/May and September)
7-1

INITIAL PLEXOS ANALYSIS
7-2
Figure 7-1: Detailed vs Aggregated Transmission Areas for PLEXOS Simulation
The model runs very slowly. This led to MISO reducing the transmission detail to increase the
execution speed. Companies not in MISO, Manitoba Hydro, MRO-US, and PJM-MISO border
are aggregated (see Figure 7-1). Reserves are modeled for Manitoba Hydro and MISO only.
More work is required to try to align ASM model values to reflect current market prices. In the
MISO ASM market, each generator that is cleared for a reserve product is paid the marketclearing
price (MCP) for reserves plus the opportunity cost to not generate energy. MCP
represents optimized bidding offers from all generators, while opportunity cost is the energyclearing
price.
Each generator submitting a bid to a reserve product can use a different bidding strategy based
on their specific cost to supply that reserve. Generally the costs considered include the
opportunity cost to reserve the energy, generator wear and tear, and generator characteristics.
Some generators (e.g. coal and nuclear) sustain higher maintenance costs for cycling output.
These generators will probably either not participate in the ASM market or submit a high bid to
cover possible maintenance cost. However, this may not be true for all coal plants since different
plant technologies enable easier cycling. The challenge is that apart from the opportunity costs,
other elements of ASM bids are highly unit specific.

Modeling Challenges Identified Using PLEXOS
INITIAL PLEXOS ANALYSIS
The greatest challenges to modeling with PLEXOS are data and run-time issues. To effectively
model a future storage unit in PLEXOS it is necessary to model with and without the storage unit
to identify the delta. To accomplish this accurately requires a model that simulates market prices
for the storage unit to be evaluated against. A storage unit has the potential to reduce its own
energy arbitrage value proposition by raising off-peak prices and lowering on-peak prices. To
determine if this will happen, a simulation needs to be developed that evaluates the whole system
once the unit is in place. This simulation needs to contain enough information to determine the
response to the storage unit from other participants.
The data requirement to meet this modeling challenge is especially large when simulating both
day ahead and real time markets. Co-optimization of the energy and ASM markets increases the
problem size further since a generator is required to decide between energy, regulation, spin
and/or supplemental.
MISO is attempting to use PLEXOS to get a close to actual market simulation engine while still
retaining the flexibility needed for planning. A particular problem associated with this quest for
“reality” is that of simulating companies outside the MISO footprint that affect MISO member
operations. The real MISO market does not have this problem since it operates in normal time,
but the planning group needs to be able to predict how external markets will react to an internal
change in MISO.
The interactions between the day ahead (DA) and real time (RT) markets are complex. MISO has
worked extensively to devise a process to simulate this in planning. During the Phase I Energy
Storage Study the team determined that the method devised in PLEXOS for this linkage was not
adequate. For Phase 2, the team is working with Energy Exemplar to develop PLEXOS in a way
that models the basic interactions between RT and DA as accurately as possible.
Another challenge is including both long-term constraints and long-term opportunities for
storage units in the RT market. In PLEXOS this is accomplished using a three stage process.
Three separate simulations are conducted and key information is passed between them (see
Figure 7-2). First an annual simulation decomposes the reservoir constraints into optimal values
for the unit to run at over different days of the year. This creates end of day targets for the
storage unit to meet so that it is best set up for the next day. The DA simulation is then run to
identify the charge and discharge storage unit commitment status along with the reserve
allocations and the value of the energy in storage. This data is passed to the RT simulation,
where the storage unit follows a pre-defined generation profile unless the energy price deviates a
given amount from the DA simulation in which case the unit will compensate. Using this three
stage simulation process increases the potential value that a storage unit can extract from the RT
market.
7-3

INITIAL PLEXOS ANALYSIS
Figure 7-2: Three Stage Process to Decompose Long-Term Storage Constraints into the Real
Time Market with PLEXOS
Initial Conclusions from Phase 1 PLEXOS Analysis
Energy storage units are economically dispatched in the PLEXOS model and have positive net
revenue throughout the year. Further analysis is needed to merge the PLEXOS results with
EGEAS to yield a total cost/benefit for storage units. Pumped storage units have a greater
revenue stream than CAES units in PLEXOS since they have lower operating costs. This is the
opposite result to the EGEAS analysis because EGEAS takes into account the higher
construction cost of PHS whereas PLEXOS is a marginal cost production model. In the first set
of runs, PLEXOS showed annual operating net revenue of $15.2M for a 1080MW CAES unit
and $24.6M for a 2040MW PS unit. Higher variable costs and efficiency losses caused CAES to
only operate for limited periods during the study (see Figure 7-3). PHS operates more frequently
because the variable costs are lower (see Figure 7-4). The revenue components from both CAES
and PHS during ASM operation in the Phase 1 study could not be accurately assessed because
the reserve pricing method in these model runs caused spikes in revenues (see Figures 7-5 and 7-
6).
7-4

INITIAL PLEXOS ANALYSIS
Figure 7-3: PLEXOS Initial Results – CAES. Higher Variable Costs and Efficiency Losses
Cause CAES to Only Operate for Limited Periods
Figure 7-4: PLEXOS Initial Results – PHS. At a Lower Variable Cost Than CAES, PHS
Operates more Frequently
7-5

INITIAL PLEXOS ANALYSIS
Figure 7-5: Initial PLEXOS Results - CAES Revenue Components. The Reserve Pricing Method
Used causes reserve Revenue Spikes for This Run
Figure 7-6: Initial PLEXOS Results - PHS Revenue Components. The Reserve Pricing Method
Used causes reserve Revenue Spikes in this Analysis
7-6

INITIAL PLEXOS ANALYSIS
Moving into Phase 2 MISO will use the results and lessons learned (see Table 7-1) from Phase 1
and implement them into a more complete PLEXOS analysis.
PLEXOS has turned out to be a useful tool to identify problems and benefits for storage
technology, but it has also shown that it is very complicated to build a robust and complete
model that accurately mimics the real world system. At the conclusion to Phase I the model is
very close to a good approximate representation and Phase 2 will yield more informative results.
Lessons Learned FROM Phase 1 PLEXOS Analysis
Challenges Driver/Root cause Work around/Solution
PLEXOS model run time Run time per week ranges
from 26 minutes to 54 hours
depending on settings used
PLEXOS determination of
ASM prices
Data validation on an
Eastern Interconnection
wide scale
On-peak and Off-Peak price
spread
Realistic representation of
MISO real time market in
PLEXOS
PLEXOS only prices ASM
products at the opportunity
cost of the generators
Large systems have a lot of
moving parts
Spread is narrower than
expected from historic
market activity
We found that the real time
market simulation in
PLEXOS needed a few
tweaks
Table 7-1: PLEXOS Phase 1 Analysis Lessons Learned
PLEXOS Next Steps – Pre Phase 2
Aggregate companies
outside of MISO, MHEB,
MRO and border PJM
companies
Currently using price adders
from the 2010 historical
MISO ASM prices
Verified data with other
models and general market
trends
Still under investigation
The vendor created a more
realistic representation of the
market in the model
• Include and test the new linkage between the Day Ahead and Real Time markets.
• Determine why the price spread between on and off peak is so low in the shoulder
months.
7-7

INITIAL PLEXOS ANALYSIS
7-8
• Refine method used to determine ASM prices.
• Find additional methods to reduce run time without reducing accuracy.
PLEXOS Next Steps – Phase 2
• Model defined cases with and without storage
• Model defined cases with and without transmission
• Model defined cases with and without market improvements
• Merge results of the PLEXOS simulation with the EGEAS simulation to create a
complete life cycle analysis for storage devices

8
STUDY CONCLUSIONS
The Energy Storage Study Phase 1 forms the first part of analysis by MISO’s transmission
planning group to evaluate the impact that additional stored energy resources could have on the
MISO footprint. The three main study drivers were; State RPS mandates for renewable energy,
ongoing discussions and rulings between the FERC and MISO since the start of the ASM in
2009 regarding the MISO energy storage tariff and a desire by MISO transmission planners to
increase their knowledge and understanding about energy storage modeling.
Key Findings
By using the EGEAS model in Phase 1, MISO gained experience with modeling energy storage
technologies and is able to relate this experience directly to existing transmission planning using
EGEAS. The Phase 1 EGEAS model runs allowed sensitivity analysis around several different
future scenarios. These scenarios match the future cases used in MTEP11 planning including fuel
costs (natural gas prices), EPA regulations, a carbon tax and RPS mandate percentages. The
EGEAS model indicates economic benefits from energy arbitrage storage in several cases and
thus confirms a primary study objective by proving that economic benefit exists from energy
storage in MISO.
The study team recognizes that EGEAS has limitations for modeling energy storage
technologies, particularly short-term storage from batteries because the model does not capture
any benefit from the ASM. There are other shortcomings to the EGEAS model with regard to
storage benefits from energy arbitrage because the price data used may not have the granularity
to capture optimal energy arbitrage economics. EGEAS also does not model the congestion
market. The EGEAS model is however useful for running a large number of scenarios in a short
time in the form of reserve capacity plans. These runs reveal when energy storage becomes
economically viable. This helped the study group to choose appropriate cases for a deeper dive
Phase 2 analysis using PLEXOS.
The PLEXOS Phase 1 analysis is designed to provide a framework in which a fully functional
model could be developed for use in the Phase 2 PLEXOS analysis. The major findings for
PLEXOS in Phase 1 were insights gained from calibrating the model assumptions and variables.
In particular, a number of challenges were noted in modeling the ASM markets for day ahead
and real time reserves. The PLEXOS team engaged in a dialogue with the modeling software
licensor to optimize the setup. These insights are invaluable to MISO and an expected learning
curve from modeling a new technology. The limited PLEXOS results obtained in Phase 1 did
show economic benefit from energy storage in all three technologies. The analysis was limited to
the day-ahead market in Phase 1 so that real time benefits from short-term energy resources were
not captured. The PLEXOS cases to be modeled in Phase 2 were defined during the Phase 1
analysis.
8-1

STUDY CONCLUSIONS
The Energy Storage Study provides valuable feedback and lessons learned about the modeling
tools used (EGEAS and PLEXOS) and their suitability for assessing potential MISO benefits
from energy storage. The lessons learned in Phase 1 owe a lot to the complexities that surround
modeling energy storage technologies in the MISO environment.
Conclusions and Recommendations
Phase 1 of the Energy Storage Study has allowed MISO to become familiar with challenges
inherent in modeling energy storage technology in a complex nodal market with an ASM. The
study group has gained a good understanding about storage modeling using EGEAS, which is the
primary MISO tool for transmission resource planning.
The study results demonstrate that there is economic potential for energy storage in the MISO
footprint. Benefits were observed in cases using both EGEAS and PLEXOS. These benefits will
be explored in greater depth during Phase 2.
The Phase 1 results show that EGEAS is not the right tool to properly understand energy storage
potential. This was not unexpected however and the knowledge gained will illuminate the
PLEXOS analysis in Phase 2.
The PLEXOS experience during Phase 1 has allowed for fine-tuning the model parameters and
important lessons were learned regarding storage model setup.
The cases to be modeled in Phase 2 have been selected.
Phase 2 of the Energy Storage Study will provide richer analysis from which to make
conclusions and recommendations. Considerable groundwork has been accomplished in
Phase 1. This report will provide extremely useful reference material for transmission
planners throughout the industry.
8-2

A
TECHNICAL REVIEW GROUP WORKSHOP AGENDAS
AND TAKEAWAYS
I would also like to add in the appendix to your report, our Technical review group agenda's and
key take-aways from each meetings. Very high-level in a bullets fashion, so that we don't loose
track of the stakeholder meetings we have had this year before going to Phase 2
MISO Energy Storage Workshop
St. Paul, MN
June 29, 2011
9:00 am to 4:00 pm CPT
Dial-in and WebEx information available at www.misoenergy.org
Agenda
Keynote Address, Principle Advisor Dale Osborne 9:00
1. Energy Storage PLEXOS Study Overview Rao Konidena 9:15
2. Stored Energy Resources Overview Marc Keyser 9:30
3. Manitoba Hydro Reservoir Storage Overview Manitoba Hydro 10:00
Break 10:30
4. Ludington Pumped Storage Overview Consumers Energy 10:45
Detroit Edison
5. Xcel Energy – EPRI Study Overview Xcel Energy / EPRI 11:15
6. CTW Energy Development & CNA Consulting CTW Energy Dev. &
Engineers Storage Overview CNA Consulting Eng. 11:45
Lunch 12:15
7. CMMPA Storage Overview CMMPA 1:00
8. Electric Thermal Storage TM Systems - Steffes Overview Steffes Corporation 1:30
9. Dynamic Power Sources TM - Xtreme Power Overview Xtreme Power 2:00
10. Flywheel Energy Storage – Beacon Power Overview Beacon Power Corp 2:30
11. PLEXOS Model Overview PLEXOS Solution 3:00
A-1

TECHNICAL REVIEW GROUP WORKSHOP AGENDAS AND TAKEAWAYS
12. Wrap-up and Next Steps MISO 3:30
13. Adjourn 4:00
A-2
MISO Energy Storage Workshop
Carmel, IN
August 3, 2011
9:00 am to 2:00 pm ET
Dial-in and WebEx information available at www.misoenergy.org
Agenda
1. Preliminary Thoughts on EGEAS Analysis for Storage MISO 9:00
2. Discussion of the Draft Scope, Comments Received All 9:30
3. Lunch 12:00
4. MH – Wind Hydro Synergy Study Update MISO 12:30
5. Preliminary Thoughts on PLEXOS Analysis for Storage MISO 12:45
6. Next Steps and Future Meeting All 1:30
7. Adjourn 1:45
Key Takeaways captured from Energy Storage 1 st TRG 8/3/2011
1 How is Capital construction costs of Pumped Hydro modeled?
a. Upload EIA estimates for each year
2 Should we go both directions with benefits to rerun EGEAS
3 Does MISO study capture ramping correctly?
4 When is PLEXOS going to be run, years, etc?
5 Thoughts on how to model storage in EGEAS (profile etc.)
6 How to model EPA coal retirements in this MISO Energy Storage Study?
7 All inputs for storage options – MISO to receive feedback from TRGs
8 MISO should think about two types of pumped storage: traditional and fast response
9 How is the capacity credit for an energy storage unit determined?

TECHNICAL REVIEW GROUP WORKSHOP AGENDAS AND TAKEAWAYS
Energy Storage Study Technical Review Group
St. Paul, MN
August 31, 2011
12:30 to 4:30 pm CT
Dial-in and WebEx information available at www.misoenergy.org
Agenda
1. Lunch 11:30
2. Welcome and Re-cap R. Konidena 12:30
3. MwH Global Perspective on Hydro Power P. Donalek 12:45
4. EGEAS Draft Results D. Van Beek 1:15
5. Break 2:00
6. PLEXOS Simulations – Modeling Details T. Gao 2:15
7. MH – Wind Hydro Synergy Study Z. Zhou 3:15
8. Action Items and Next Steps 3:30
9. Adjourn 3:45
Energy Storage Study Technical Review Group
Carmel, IN
September 22, 2011
9:00 am to 2:00 pm ET
Dial-in and WebEx information available at www.misoenergy.org
Agenda
1. Introduction and Re-cap R. Konidena 9:00
2. Iowa Stored Energy Park – Lessons Learned IESPA Team 9:15
3. Break 10:30
4. Stored Energy Resources – Examples M. Keyser 10:45
5. Lunch 11:30
6. Ramp Management Modeling N. Navid 12:15
7. EGEAS Results – Status D. Van Beek 1:30
8. Recap and Next Steps R. Konidena 1:45
A-3

TECHNICAL REVIEW GROUP WORKSHOP AGENDAS AND TAKEAWAYS
A-4
9. Adjourn 2:00
Energy Storage Study Technical Review Group
St. Paul, MN
October 4, 2011
12:30 – 2:45 pm CT
Dial-in and WebEx information available at www.misoenergy.org
Agenda
1. Introduction R. Konidena 12:30
2. Detailed EGEAS Results D. Van Beek 12:45
3. PLEXOS Model Runs – Plan J. Bakke 1:45
4. Phase 1 Report Outline R. Konidena 2:15
5. Recap and Next Steps R. Konidena 2:30
6. Adjourn 2:45
TRG 
 Date 
 Agenda
1 st – Carmel, IN 
 08/03/11 
 Discussed scope comments, initial thoughts on EGEAS and
PLEXOS Models
2 nd – St Paul, MN 
 08/31/11 
 Will discuss draft EGEAS results including sensitivities
3 rd – Carmel, IN 
 09/22/11 
 Scheduled to discuss complementary efforts at MISO for
storage (e.g. ramp management, Look Ahead
Commitment), Stored Energy Resource
Workshop 
 10/04/11 
 EPRI Hydro Power Workshop
4 th – St Paul, MN 
 10/04/11 
 Finalized EGEAS results, Initial draft of PLEXOS results,
Final draft of report outline
5 th – Carmel, IN 
 Mid-Nov 
 Finalized PLEXOS results, draft report

B
ACRONYMS
$/kW Dollars per kilowatt
$/kWh Dollars per kilowatt hour
ASM Ancillary Services Market
Btu British Thermal Unit, heat needed to raise one pound of water, one degree
Fahrenheit at sea level
CAES Compressed air energy storage
CC Combined cycle
CO2
Carbon dioxide
CT Combustion turbine
DA Day ahead
DOE Department of Energy
EGEAS Electric Generation Expansion Analysis System
EPRI Electric Power Research Institute
FERC Federal Energy Regulatory Authority
GW Gigawatt
ISO Independent System Operator
Kg/sec Kilograms per second
kW Kilowatt
kWh Kilowatthour
LMP Locational Marginal Price
Acronyms
B-5

Acronyms
MCP Marginal Clearing Price
MISO Midwest ISO
MW Megawatt
MWH Megawatthour
MMBtu million Btu
MTEP MISO Transmission Expansion Planning
MVP Multi-Value Project
NOx
B-6
Nitrogen oxides
PHS Pumped Hydro Storage
RGOS Regional Generation Outlet Study
RPS Renewable portfolio standards
RT Real time
SER Stored Energy Resource
TRG Technical Review Group

C
STAKEHOLDER FEEDBACK
Response to
MISO Energy Storage Study Phase 1 Report
by
CTW Energy Development, LLC and CNA Consulting Engineers
Prepared by Max DeLong
The focus of the subject study is reiterated numerous times in the report, with explicit language,
as an effort to study how to model energy storage technologies. Less noticeable is the implicit
notion that energy storage technologies are simply modeled and evaluated as just another
generation source. True, the MISO grid serves and manages the load/demand on the grid by
supplying generation capacity. But it also provides other services to achieve grid stability, grid
reliability, demand-management, and access to electric energy markets with considerable
concern for the economic and environmental aspects involved.
However, MISO needs to focus on the use of storage technologies for more than just energy
production. Storage technologies can provide other services already considered in the portfolio
of MISO offerings, and also they can provide newly defined services for the grid. Storage
technologies can provide:
1. Demand management (i.e., provide load at a time period when generation is available to
avoid stranded renewable energy);
2. Consolidation of variable and intermittent wind generation for re-delivery to periods
when its cost best approaches its value (night to day arbitrage);
3. Co-location platforms with wind production to
a. Enhance transmission reconfiguration for the renewable energy-to-market
pathways;
b. Retain benefits within the jurisdiction and garner sufficient revenue to the wind
production region to cover costs of wind, costs of transmission system operation
and improvements, and avoid outsourcing the pricing function for exported
energy (i.e., energy users outside the RPS jurisdiction boundary buy high value
energy at low off-peak commodity pricing); and
4. A more cost effective way to balance the variable and intermittent wind energy
production compared with conventional fossil power production.
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STAKEHOLDER FEEDBACK
As substantial quantities of renewable energy come on to the grid (from policy mandates), the
report recognizes that the grid does not need additional generation capacity to meet energy
demand. Yet storage technologies like CAES are applied and evaluated in the report primarily as
generation capacity. It is not surprising that storage capability, as basic generation is not selected
in the market place. However, storage technology, strategically applied, could provide
alternatives of keen interest to policy and regulator groups since objectives and quotas will have
optional approaches that should allow economic comparisons.
Stranded wind energy should be used for charging and not simply dumped (potential for
recovery of 15-20% of wind potential based on MISO data). When wind energy is not available
for charging CAES, then the use of additional energy from must-run capacity will be available at
just the incremental variable and fuel costs. The carbon emission impacts of utilizing CAES will
be less than the use of conventional fossil generation for balancing.
Further studies of storage technologies by MISO should incorporate the following features:
A. A more precise representation of the grid load duration curve information ( report, Fig. 6-
3) that include:
a. The amount of must-run capacity for grid reliability, stability and response;
b. The distribution of wind capacity (other than an annual-average) for the duration
depicted that reveals stranded and undervalued wind energy that represent
opportunities for CAES applications for extraction and redelivery of energy;
B. An analysis of transmission system reconfiguration approaches that would benefit from
CAES facility co-location with wind production;
C. An analysis of how CAES facilities enable RPS jurisdictions to achieve energy
penetration goals, (vs. the situation where stranded wind energy would prohibit reaching
those goals);
A full display of the MISO generation platform for the various runs and combinations. Wind
energy is being accommodated in all these runs, and the new generation capacity required, the
old generating capacity being shed, the wind energy import/export amounts, the transmission
systems losses all need to be considered and revealed to enable judgment of CAES and
conventional generation balancing requirements for achieving grid operation objectives and
political and regulatory policy goals.
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Comments and clarifications from Matthew Schuerger
Matthew J. Schuerger, P.E.
Energy Systems Consulting Services, LLC
651-699-4971 (office)
STAKEHOLDER FEEDBACK
Thank you for the opportunity to review and comment on the draft MISO Energy Storage Phase I
Report.
There are several errors in the draft report regarding the technical characterization of wind
generation and how it works technically in the context of the power system.
1. Page v and Page 1-2, last sentence of the second paragraph states: "Wind generation is also
variable and has to be carefully balanced with conventional resources in order to maintain system
reliability."
It is correct to state that wind generation is variable. It is not correct to state that wind generation
"has to be carefully balanced with conventional resources in order to maintain system
reliability." We do not balance any individual resources (or classes of resources) on the system.
In order to maintain system reliability, controllable resources must carefully balance net
variability on the power system. This is often referred to as net load and is the net of variability
due to load, Net Scheduled Interchange, wind generation, and conventional resources (e.g.
sudden outages and/or failure to follow dispatch of conventional resources). Increasing wind
generation on a system will increase the variability of the system but it is the overall net
variability that must be balanced; this is done with both controllable supply resources and with
controllable load resources. Key references which describe this issue include MISO's white
paper Ramp Capability for Load Following in the MISO Markets (July 2011) and the
International Energy Agency's Harnessing Variable Renewables, A Guide to the Balancing
Challenge (June 2011).
This sentence (last sentence of the second paragraph on Page v and on Page 1-2) should be
corrected by replacing it with "Wind generation is variable and, along with load, Net Scheduled
Interchange, and conventional resources, adds to the variability of the power system. The overall
net variability of the system must be carefully balanced in order to maintain system reliability."
Or, "and has to be carefully balance with conventional resources in order to maintain system
reliability" should be deleted.
2. Page 2-2, second to last sentence of the second full paragraph states: "Adding large wind
generation quantities to the system increases the instability because wind is not dispatchable and
is variable over time." The preceding sentence states: "With the coal fleet reduced, the overall
system becomes less stableS". The following sentence states: "The net result is an increase in
the need for ancillary services such as regulation and contingency."
It is not correct to state that "Adding large wind generation quantities to the system increases the
instability because wind is not dispatchable and is variable over time." Wind turbine technology
and capabilities have significantly matured and developed over recent years. The majority of
wind turbines being installed currently and in coming years are Type 3 (doubly fed asynchronous
generator) and Type 4 (full converter interface - asynchronous or synchronous generator).
Modern wind turbines can now contribute to the reliability of the grid by offering the following
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STAKEHOLDER FEEDBACK
capabilities: Voltage and var control and regulation, Fault ride through, Real power control,
ramping, and curtailment, Primary frequency regulation, Inertia response, and Short circuit duty
control. Key references which describe this issue include: IEEE PES's Balancing Act, NERC's
Integration of Variable Generation Task Force (Lauby et al, November/December 2011) and
NREL's Operating Reserves and Variable Generation (August 2011).
This sentence (second to last sentence of the second full paragraph on Page 2-2) should be
corrected by replacing it with "Although wind generation is variable over time, modern wind
turbines can contribute to the reliability of the grid (e.g. by offering Voltage and var control and
regulation, Fault ride through, Real power control, ramping, and curtailment, Primary frequency
regulation, Inertia response, and Short circuit duty control)." Or, the sentence should be deleted.
Also, it is misleading to state that "With the coal fleet reduced, the overall system becomes less
stableS" and "The net result is an increase in the need for ancillary servicesS". Whether or not
the system becomes less stable when the coal fleet is reduced depends on a number of factors
including the characteristics of replacement resources (both supply side and demand side
resources), the robustness of the regional and local transmission systems, and the market rules
which can either facilitate or constrain access to existing system flexibility.
The preceding sentence (third to the last sentence of the second full paragraph on Page 2-2)
should be clarified / corrected by replacing it with "With the coal fleet reduced, the system must
be carefully studied to determine whether there are system stability concerns." Or, the sentence
should be deleted. The following sentence (last sentence of the second full paragraph on Page 2-
2) should be clarified by replacing it with "The net result could be an increase in the need for
ancillary services."
These corrections and clarifications are important because, as we have discussed numerous times
in the TRG meetings, this is a study of the potential benefits of storage to the MISO system.
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