MISO Energy Storage Study Phase 1 Report

Transcription

1 MISO Energy Storage Study Phase 1 Report Product ID # Final Report, November 2011 EPRI Project Manager Dan Rastler ELECTRIC POWER RESEARCH INSTITUTE 3420 Hillview Avenue, Palo Alto, California PO Box 10412, Palo Alto, California USA askepri@epri.com

2 DISCLAIMER OF WARRANTIES AND LIMITATION OF LIABILITIES THIS DOCUMENT WAS PREPARED BY THE ORGANIZATION(S) NAMED BELOW AS AN ACCOUNT OF WORK SPONSORED OR COSPONSORED BY THE ELECTRIC POWER RESEARCH INSTITUTE, INC. (EPRI). NEITHER EPRI, ANY MEMBER OF EPRI, ANY COSPONSOR, THE ORGANIZATION(S) BELOW, NOR ANY PERSON ACTING ON BEHALF OF ANY OF THEM: (A) MAKES ANY WARRANTY OR REPRESENTATION WHATSOEVER, EXPRESS OR IMPLIED, (I) WITH RESPECT TO THE USE OF ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS DOCUMENT, INCLUDING MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, OR (II) THAT SUCH USE DOES NOT INFRINGE ON OR INTERFERE WITH PRIVATELY OWNED RIGHTS, INCLUDING ANY PARTY'S INTELLECTUAL PROPERTY, OR (III) THAT THIS DOCUMENT IS SUITABLE TO ANY PARTICULAR USER'S CIRCUMSTANCE; OR (B) ASSUMES RESPONSIBILITY FOR ANY DAMAGES OR OTHER LIABILITY WHATSOEVER (INCLUDING ANY CONSEQUENTIAL DAMAGES, EVEN IF EPRI OR ANY EPRI REPRESENTATIVE HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES) RESULTING FROM YOUR SELECTION OR USE OF THIS DOCUMENT OR ANY INFORMATION, APPARATUS, METHOD, PROCESS, OR SIMILAR ITEM DISCLOSED IN THIS DOCUMENT. THE FOLLOWING ORGANIZATION(S), UNDER CONTRACT TO EPRI, PREPARED THIS REPORT: Electric Power Research Institute (EPRI) NOTE For further information about EPRI, call the EPRI Customer Assistance Center at or askepri@epri.com. Electric Power Research Institute, EPRI, and TOGETHER SHAPING THE FUTURE OF ELECTRICITY are registered service marks of the Electric Power Research Institute, Inc. Copyright 2011 Electric Power Research Institute, Inc. All rights reserved.

3 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 EPRI 3412 Hillview Avenue Palo Alto, CA 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

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5 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 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 MISO State of the Markets Market Monitor Report June 2011, Potomac Economics v

6 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

7 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

8 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.

9 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

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

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

13 8 STUDY CONCLUSIONS A TECHNICAL REVIEW GROUP WORKSHOP AGENDAS AND TAKEAWAYS...A-1 B ACRONYMS...B-5 C STAKEHOLDER FEEDBACK...C-1 xiii

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15 LIST OF FIGURES Figure 1-1: RPS Mandates and Goals Within the MISO Footprint (Source MISO) Figure 2-1: Average Hourly Wind Generation and Prices in MISO (Source Iowa Stored Energy Park Analysis) Figure 2-2: Operational Planning Timeframes in ISO Balancing Markets (Source EPRI) Figure 2-3: MISO Wind Curtailment (Source MISO State of Market Monitor) Figure 3-1: Representative Positioning of Energy Storage Technologies (Source EPRI) Figure 3-2: FERC Registered Pumped Storage Projects, July Figure 3-3: Pumped Storage Capacity Worldwide (GW) Source MISO Figure 3-4: Fast Response Capabilities for Adjustable Speed PHS (Source MISO) Figure 3-5: Advanced CAES Plant Schematic (Source:EPRI) Figure 4-1: MISO RT SCED Dispatch Algorithm Figure 5-1: EGEAS and PLEXOS Model Interaction 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) Figure 5-3: 2011 Installed Capacities by Fuel Category Assumption for MISO Energy Study Figure 5-4: PLEXOS CAES Storage Model Output (Source Energy Exemplar) 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 Figure 6-3: Simple Load Duration Curve Illustration Showing Wind Impact on Storage Charging and Generation Figure 7-1: Detailed vs Aggregated Transmission Areas for PLEXOS Simulation Figure 7-2: Three Stage Process to Decompose Long-Term Storage Constraints into the Real Time Market with PLEXOS 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 xv

16 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 LIST OF TABLES Table 3-1: Definition of Energy Storage Applications (Source EPRI ) Table 3-2: ISEPA Net Benefit CAES Plant Economic Comparison (Source ISEPA) Table 4-1 : Stored Energy Resource Operating Parameter Data Summary (Source MISO BPM-002) Table 5-1: EGEAS Energy Storage Study Plant Assumptions Table 6-1: EGEAS Model Storage Selection Cases Table 7-1: PLEXOS Phase 1 Analysis Lessons Learned

17 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 1-1

18 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 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 MISO State of the Markets Market Monitor Report June 2011, Potomac Economics 1-2

19 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. ER and ER

20 Introduction 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. 1-4

21 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

22 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 (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

23 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

24 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.

25 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

26 THE MISO PLANNING ENVIRONMENT 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: years from portfolio in-service date Discount rate: (capital borrowing cost) 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 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 2-6

27 THE MISO PLANNING ENVIRONMENT Figure 2-3: MISO Wind Curtailment (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

28

29 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 , 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

30 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 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 Electricity Energy Storage Technology Options, December 2010 EPRI 3-2

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

32 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.

33 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

34 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

35 ENERGY STORAGE TECHNOLOGIES Figure 3-5: Advanced CAES Plant Schematic (Source: EPRI) 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

36 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 due in service during 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 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 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). 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

37 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 EPRI DOE Handbook of Energy Storage for Transmission & Distribution Systems Dec

38 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

39 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 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. ER and ER March

40 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 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

41 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 Midwest ISO FERC Electric Tariff, Fourth Revised Volume No. 1, Section 1.628, Second Revised Sheet No

42 STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF 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) 4-4

43 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

44 STORED ENERGY RESOURCE TREATMENT IN THE MISO TARIFF The key proposed model features include: 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. ER , 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. ER and ER Informational Report of the Midwest Independent Transmission System Operator, Inc., Docket Nos. ER and ER March

45 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 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 AD Frequency Regulation Compensation in the Organized Wholesale Power Markets Docket Nos. RM , AD Oct 20,

46 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

47 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

48 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 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 5-2

49 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

50 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

51 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

52 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: Natural gas $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) 5-6

53 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 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: 0Draft%20for%20PAC%20Review.pdf 23 MISO MTEP10 Final Report, p

54 ENERGY STORAGE MODELS AND ASSUMPTIONS Unit Max. Capacity Heat Rate btu/k Wh Forced Outage Rate Fixed O&M $/kw/ yr Overnight Construction Cost ($/kw) Low/Mid/ Efficiency Variable O&M $/MWh Mainten ance Weeks per Year Constru ction Lead Time (years) High PSH 2400 MW CAES 2160 MW -- 1% $5 1500/2250/ % $ % $15 833/1250/ % $ BATTERY 200 MW -- 1% $ /2500/ % $ 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 (49%) Gas (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

55 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,

56 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

57 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

58 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 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

59 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

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61 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

62 EGEAS ANALYSIS RESULTS 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). 6-2

63 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

64 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 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 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 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

65 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

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67 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 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

68 INITIAL PLEXOS ANALYSIS 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. 7-2

69 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

70 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

71 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

72 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