Energy Storage Technoecon Final Report_Revised March 2014

Comparison of battery, compressed air and power to gas energy storage technologies in the Alberta context

Puneet Mannana, Greg Badenb, Leonard Oleinb, Caitlin Brandona, Brent Scorfielda, Nahid Nainib, Jake Chengb

a Alberta Innovates – Technology Futures, b BECL and Associates Ltd

Techno-economics of

Energy Storage

Contact: Puneet Mannan Alberta Innovates – Technology Futures Phone: (780) 450-5380 Email: [email protected] November 19, 2013, revised March 24, 2014

mailto:[email protected]

Final Report Version 1.0 Oct 17th, 2011

Disclaimer

This Report was prepared as an accounting of work conducted by Alberta Innovates – Technology Futures (AITF). All reasonable efforts were made to ensure that the work conforms to accepted scientific, engineering and environmental practices, but AITF makes no representation and gives no other warranty with respect to the reliability, accuracy, validity or fitness of the information, analysis and conclusions contained in this Report. Any and all implied or statutory warranties of merchantability or fitness for any purpose are expressly excluded. The reader acknowledges that any use or interpretation of the information, analysis or conclusions contained in this Report is at his/her own risk. Reference herein to any specified commercial product, process or service by trade name, trademark, manufacturer or otherwise does not constitute or imply and endorsement or recommendation by AITF.

This report is intended to add to the understanding of the technical and economic aspects of energy storage. This report does not represent Government of Alberta policy, nor does it anticipate or imply any future policy direction of the Government of Alberta.

Any authorised copy of this report distributed to a third party shall include an acknowledgement that the report was prepared by AITF and shall give appropriate credit to AITF and the authors of the report.

AITF confirms that the Alberta Department of Energy (ADOE) is entitled to make such additional copies of this Report as ADOE may require, but all such copies shall be copies of the entire Report. ADOE shall not make copies of any extracts of this Report without the prior written consent of AITF.

Copyright AITF 2013. All rights reserved.

ALBERTA INNOVATES – TECHNOLOGY FUTURES

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ACKNOWLEDGEMENTS

This study was funded by the Alberta Department of Energy (ADOE) and the project team gratefully acknowledges ADOE’s support for advancing the understanding of energy storage in Alberta. The team is thankful to Christopher Holly, Susan Carlisle and their colleagues from the ADOE for reviewing the report and providing valuable feedback.

Thanks also to Dave Teichroeb (Enbridge), Lorry Wilson (Rocky Mountain Power), Jan van Egteren (Rocky Mountain Power) and Rob Harvey (Hydrogenics) for their technical guidance throughout the project.


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EXECUTIVE SUMMARY

This Alberta Department of Energy funded study provides a techno-economic comparison of three energy storage technologies – sodium sulphur batteries, compressed air storage and power to gas – operating in conjunction with two wind power generating facilities under two operating strategies in the Alberta electricity market. These energy storage technologies were selected for their maturity over a broad range of applications from transmission and distribution grid support, to load shifting and bulk power management, and well documented technical and operating parameters. The combination of two operating strategies, Behind-the-Fence and Merchant, along with each technology and wind power generating facility resulted in sixteen different scenarios or cases for modelling. The results of each case were compared to a Base Case, the wind farm operating without energy storage, to determine the revenue changes resulting from the modelled operation of the energy storage technology. In addition a number of sensitivity cases were developed to further explore aspects of the results from sixteen modelled cases.

The study used actual hourly wind production data from the Wintering Hills and the Castle River wind power generating facilities. These wind farms were selected because they represent regions with different wind characteristics. Wintering Hills is an 88-megawatt (MW) wind power generating facility located in south-central Alberta. In 2012, Wintering Hills produced about 292 gigawatt hours (GWh) of electricity resulting in a capacity factor of about 38 per cent. In addition to achieving one of the highest capacity factors of all the wind power facilities in the province, Wintering Hills was also one of the most consistent producing wind facilities in Alberta. Castle River is a 44 MW generating facility that in 2012 produced about 110 GWh of electricity, yielding a capacity factor of about 29 per cent. The Castle River wind facility energy production was highly variable with a coefficient of variation of 1.1 versus Wintering Hills with a coefficient of 0.9.

Hindcast mathematical models were prepared to analyse the economic benefit to a wind farm with energy storage and a merchant energy storage operator. The model used actual market data for 2012 and inserted the energy storage facilities into the historical setting, and adjusted the historical electricity prices to account for that insertion using a supply merit order curve for the historic electricity price. The hindcast approach allowed for the retention of unique characteristics of the Alberta market price volatility and the underlying correlation between wind generation and market prices. However, the hindcast approach did introduce some distortion in the electricity market price (a price depression effect which increases as more stored energy is withdrawn), but that distortion was kept to a small level by limiting the energy storage facilities to 30 MW of charging and discharging capacity and by adjusting the hourly market price for the effects of charging and discharging the energy storage capacity.

To model the dynamic effects of charging and discharging of an energy storage facility on the hourly market price, a representative merit order curve was developed based on a sampling of 2012 merit order curves. The merit order curve was used to calculate an adjustment to the hourly market price resulting from the energy storage operation. The effect of withdrawing a quantity of electricity from storage thereby increasing the hourly supply of electricity, reduced the hourly market price, and the effect of injecting energy into storage was to increase hourly demand for electricity resulting in an increase in the hourly market price.

The storage operations strategy was determined using a switch price – the price at which the preference to charge switches to a preference to discharge and vice versa. The switch price was calculated each


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hour of the modelled year by an algorithm that used as inputs, the expected inventory level, current average cost of inventory, and variable operating costs. The effect of the algorithm was as the inventory level declined, the switch price increased up to a maximum price of $80 per MWh. Conversely, as inventory levels rose the switch price declined, but never below the sum of the inventory cost and variable cost. If the hourly price for electricity was less than the switch price, the model injected electricity into storage; and, if the hourly price for electricity was greater than the sum of switch price and the variable operating cost, the model discharged electricity from storage.

Behind-the-Fence operations strategy assumes that (1) the storage facility was controlled by the wind farm operator; (2) the operator did not purchase any electricity from the grid; and (3) the combination of storage discharge and wind output was constrained by the contracted transmission capacity at 50 MW. Merchant operations strategy assumes that (1) the storage facility was controlled by the operator of a co-located 50 MW wind power generating facility; (2) the operator was free to buy or sell electricity from or to the grid or from the co-located wind power facility; and (3) the combination of storage discharge and wind output was constrained by the contracted transmission capacity of 50 MW. To simplify the analysis, transmission charges were dealt with separately as a sensitivity case.

All the modelled cases shared these parameters: (1) the storage facility was co-located with 50 MW wind power facility and shared 50 MW of transmission system access capacity with the wind power generating facility; (2) 30 MW of charging and discharging capacity; and (3) 210 MWh of storage capacity or seven hours of storage when charging or discharging at full capacity. For the storage modelling exercise, the hourly output from each of the wind power generating facilities were normalised to reflect an installed generating capacity of 50 MW. The process of normalising the generating capacity for each wind power generating facility resulted in two hourly data sets with Wintering Hills effectively producing about 168 GWh at an average price of $46.59/MWh and Castle River producing about 143 GWh at an average price of $36.43/MWh.

The study has shown that co-locating an energy storage facility at a wind power generation facility results in an increase in total revenues for the wind operator. Under the Behind-the-Fence operating strategy, the selling prices achieved from storing electricity during low priced hours and withdrawing and selling the stored electricity during higher priced hours were at a minimum 28 per cent higher to a maximum of 50 percent higher than the average base cases selling prices for the modelled wind power generating facilities. The higher selling prices were partially offset by losses and auxiliary energy requirements related to the operation of each of the energy storage technologies reviewed, resulting in net revenue changes of between 2 per cent and 45 per cent.

Wintering Hills realised the overall highest revenues in all cases using the Behind-the-Fence operating strategy and in all but one case, achieved the largest percentage increase in revenues. The Castle River case using the Behind-the-Fence operating strategy and a CAES energy storage system achieved a slightly higher revenue increase (45.2%) on a percentage basis than the comparable case for Wintering Hills (43.0%). The reasons for the slightly better percentage increase in revenue for Castle River are likely related to variability of the Castle River output and the characteristic of a CAES energy storage facility, which produces more energy, through the use of natural gas, than it stores. The modelled CAES energy storage facility at Wintering Hills was likely constrained a few more hours due to the 50 MW transmission capacity limit than the modelled CAES facility at Castle River was.

Similarly, under the Merchant operating strategy selling prices were between 30 per cent and 93 per cent higher than the average selling prices in the base cases and resulted, after losses and auxiliary energy requirements, in net revenue increases of between 9 per cent and 105 per cent compared to base case revenues. In all of the cases modelled using the Merchant operating strategy the Wintering


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Hills cases achieved the highest overall revenues compared to the Castle River cases. Somewhat unanticipated, the more variable wind power generation facility, Castle River, realised the largest percentage revenue improvement from following the Merchant operating strategy for each of the energy storage technologies.

The application of supply transmission service (STS) and demand transmission service (DTS) charges will reduce the incremental net revenues associated with operation of an energy storage facility as modelled by the study. Especially for the merchant storage facilities all electricity purchased from the grid and stored will be subject to the DTS charges and when the same energy is withdrawn and sold, the energy will be subject to STS charges. The tariffs charged in this case will result in double charging or what is sometimes referred to as “rate pancaking”. However, in a CAES facility using natural gas, some incremental quantity of electricity is generated over what was originally stored which would attract the application of STS charges.

Two sensitivity cases were developed to examine the potential revenue improvements that could be gained from participation in the Alberta operating reserve (OR) markets. The first scenario was based on the Wintering Hills Merchant Battery case and participation in the active regulating reserve market for the AM Super Peak block. The second scenario was based on the same Wintering Hills case and participation in the standby spinning reserve market for the On Peak block. Overall, the opportunity to participate in the OR markets was found to be attractive to energy storage operators, even though some opportunities in the hourly energy market are forgone. The Wintering Hills Merchant Battery case was chosen for modelling participation in both the active regulating reserve and standby spinning reserve market, despite the fact that the current rules for spinning reserve limit participation only to generators, to avoid introducing any uncertainty in results by using two different storage technologies. There is no reason to believe the results for CAES or Power-to-Gas would be materially different from those observed for batteries.

The introduction of the dynamic pricing (adjusting the hourly market price to account for the effects of charging and discharging energy storage capacity) reduced the value of storage for the modelled sensitivity cases. On a per unit basis, dynamic pricing had an impact on the value of storage of $5.59 per MWh compared to static or unadjusted pricing. Dynamic pricing also reduced the average pool price by $2.04 per MWh.

Increasing the storage capacity of the modelled cases does result in increased revenues, up to a point. This study indicates that electricity market price volatility and shape of the supply merit curve appear to be the key drivers for storage technology selection, sizing of energy storage capacity and charging and discharging capacity.

Price volatility is a measure of how quickly prices change in a market that affects the value of storage capacity and the value of injection and discharge capacity. As an example, a market with relatively low price volatility, and characterised by higher winter and summer prices and lower prices in the interim months would favour the bulk storage technologies – CAES and Power-to-Gas – with lower unit costs for storage capacity. In the same market, storage capacity and charging and discharging capacity would likely be sized to allow as much as a month of continuous discharging at the peak discharge rate.

Conversely, markets characterised by high price volatility, like Alberta, favour storage technologies that can switch quickly from charging to discharging and that have lower charging and discharging costs. The optimum storage capacity in Alberta for the current market size and characteristics appears to be about three days at the peak discharge rate. Increasing the storage capacity beyond a few days results in higher costs and the stored energy does not get sold because the higher market prices do not persist


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long enough to allow the stored energy to be withdrawn. Increasing the discharge capacity also does not appear to help as was found in one of the sensitivity case analysis.

Increasing the discharge capacity increases the potential available supply of electricity in any hour. The larger the discharge capacity, the larger the dampening effect on market prices. The analysis of effects of dynamic pricing showed that for Castle River dynamic pricing reduced the value of storage by over $5.00/MWh. The discharge capacity of Castle River cases analysed was 30 MW so it is reasonable to expect that the effect of increasing the discharge capacity from 30 MW to 300 MW would likely be greater than $5.00/MWh.

The study concludes that:

1. Wind generation facilities whose electricity output varies considerably day-to-day may benefit from installing energy storage capacity behind-the-fence of the wind facility.

2. Merchant energy storage may be the most attractive option for developing energy storage capacity in Alberta.

3. The optimal storage capacity for a merchant energy storage facility appears to be about seventy hours of capacity at the peak discharge rate.

4. Based on the simplified present value of revenue cash flows, publicly available capital cost for the considered technologies and selling price of natural gas during the analysis period, CAES has the most financially attractive business case for energy storage in Alberta.

5. The operating reserve markets are attractive markets for energy storage operators.

This study did not explore many of the other important aspects of energy storage, some of which could be of special interest for Alberta as well as candidates for future work1. For example, certain energy storage configurations (e.g., adiabatic CAES and power-to-gas) could be candidates for lowering the carbon intensity of energy production in Alberta. Diesel power generation with energy storage could be explored for remote applications. Power-to-gas provides opportunities for interplay between electricity, gas and heat markets, and how energy storage could optimally play in those markets is yet to be understood. Power-to-gas generates an energy vector, hydrogen, which could be channelled into different value propositions (transportation and heating fuel, and chemicals production) and those value propositions could be explored within the Alberta context.

1 Impacts related to electricity market operation and rules and transmission and distribution infrastructure are

being considered by the Alberta Electric System Operator.


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TABLE OF CONTENTS

Acknowledgements .......................................................................................................................... i

Executive Summary ..........................................................................................................................ii

TABLE OF CONTENTS...................................................................................................................... vii

LIST OF TABLES ................................................................................................................................ ix

LIST OF FIGURES ............................................................................................................................... x

1. Introduction ............................................................................................................................ 1 1.1 Current study objectives and scope ............................................................................................ 1

2 Benefits of energy storage ..................................................................................................... 4

3 The Alberta electricity market ............................................................................................... 5 3.1 Update ......................................................................................................................................... 5 3.2 Current market rules ................................................................................................................... 6

4 Storage technologies under evaluation ................................................................................ 7 4.1 Rationale for selection ................................................................................................................. 7 4.2 Sodium-Sulphur Batteries ............................................................................................................ 8

4.2.1 Description .............................................................................................................................. 8 4.2.2 Cost .......................................................................................................................................... 8

4.3 Compressed Air Energy Storage .................................................................................................. 9 4.3.1 Description .............................................................................................................................. 9 4.3.2 Costs ...................................................................................................................................... 11

4.4 Power to gas .............................................................................................................................. 11 4.4.1 Description ............................................................................................................................ 11 4.4.2 Cost ........................................................................................................................................ 14

5 Model Description ................................................................................................................ 15 5.1 Methodology ............................................................................................................................. 15

5.1.1 Bid and Offer Strategy ........................................................................................................... 15 5.1.2 Prices ..................................................................................................................................... 16 5.1.3 Effects on Hourly Clearing Price ............................................................................................ 17 5.1.4 Wind Power Facility Selection ............................................................................................... 19 5.1.5 Storage operation ................................................................................................................. 20

5.2 Modelling Parameters ............................................................................................................... 20 5.2.1 Description of model cases ................................................................................................... 20 5.2.2 NaS Battery ............................................................................................................................ 21 5.2.3 CAES ...................................................................................................................................... 23 5.2.4 Power to Gas 1 ...................................................................................................................... 24 5.2.5 Power to Gas 2 ...................................................................................................................... 25 5.2.6 Sensitivity Cases .................................................................................................................... 26

6 Results ................................................................................................................................... 30 6.1 Modelled Cases.......................................................................................................................... 30


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6.1.1 NaS Battery Cases.................................................................................................................. 30 6.1.2 Compressed Air Energy Storage ............................................................................................ 33 6.1.3 Power-to-Gas 1...................................................................................................................... 36 6.1.4 Power-to-Gas 2...................................................................................................................... 37

6.2 Sensitivity Cases......................................................................................................................... 39 6.2.1 Transmission Demand and Supply Charges .......................................................................... 39 6.2.2 Operating Reserve Market .................................................................................................... 41 6.2.3 Increased Storage .................................................................................................................. 42

6.3 Comparison to the 2011 Study Results ..................................................................................... 45 6.4 Simple Cashflow Analysis .......................................................................................................... 46

7 Discussions............................................................................................................................ 47 7.1 Overall ....................................................................................................................................... 47

7.1.1 Effects of the Behind-the Fence and Merchant Operating Strategies .................................. 47 7.2 NaS Battery Energy Storage ....................................................................................................... 47 7.3 CAES ........................................................................................................................................... 48 7.4 Power-to-Gas 1 .......................................................................................................................... 48 7.5 Power-to-Gas 2 .......................................................................................................................... 49 7.6 Transmission Demand and Supply Charges ............................................................................... 49 7.7 Increased Storage ...................................................................................................................... 50 7.8 Capital Costs .............................................................................................................................. 51

8 Conclusions ........................................................................................................................... 52

9 Recommendations ............................................................................................................... 53

10 References ............................................................................................................................ 54

11 Appendices ............................................................................................................................. 1

A. Alberta’s electricity market..................................................................................................... 1 A.1. Alberta Electric System Overview................................................................................................ 1 A.2. Market Structures ........................................................................................................................ 1 A.3. Demand ....................................................................................................................................... 2 A.4. Supply .......................................................................................................................................... 3 A.5. Wholesale Electricity Market....................................................................................................... 4 A.6. Market Operation ........................................................................................................................ 5 A.7. Pool Prices ................................................................................................................................... 6 A.8. Potential Value of Wind plus Energy Storage in the Energy Market ........................................... 6 A.9. Ancillary Services Markets ........................................................................................................... 8 A.9.1. Operating Reserve Products ................................................................................................... 8 A.9.2. Operating Reserve Market .................................................................................................... 10

B. CCEMC Backgrounder ............................................................................................................. 1

C. TransCanada Gas Quality Specifications ................................................................................. 1

D. Power to Gas Announcement ................................................................................................. 1


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LIST OF TABLES

TABLE 1: SUMMARY OF MODELLED CASES ................................................................................................................. 21 TABLE 2: NAS BATTERY CASES - OPERATIONAL RESULTS ............................................................................................ 30 TABLE 3: NAS BATTERY CASES - FINANCIAL RESULTS .................................................................................................. 30 TABLE 4: NAS BATTERY CASES - EFFICIENCY RESULTS ................................................................................................. 32 TABLE 5: CAES CASES - OPERATIONAL RESULTS .......................................................................................................... 33 TABLE 6: CAES CASES - FINANCIAL RESULTS ................................................................................................................ 33 TABLE 7: COMPARISON OF REVENUES AND PRODUCTION – CASTLE RIVER CAES ...................................................... 35 TABLE 8: COMPARISON OF REVENUES AND PRODUCTION – WINTERING HILLS CAES ............................................... 35 TABLE 9: CAES CASES – EFFICIENCY RESULTS .............................................................................................................. 36 TABLE 10: POWER-TO-GAS 1 - OPERATIONAL RESULTS .............................................................................................. 36 TABLE 11: POWER-TO-GAS 1 - FINANCIAL RESULTS .................................................................................................... 36 TABLE 12: POWER-TO-GAS 1 - EFFICIENCY RESULTS ................................................................................................... 37 TABLE 13: POWER-TO-GAS 2 - OPERATIONAL RESULTS .............................................................................................. 37 TABLE 14: POWER-TO-GAS 2 - FINANCIAL RESULTS .................................................................................................... 38 TABLE 15: POWER-TO-GAS 2 - EFFICIENCY RESULTS ................................................................................................... 38 TABLE 16: WINTERING HILLS BATTERY BEHIND-THE-FENCE CASE WITH STS .............................................................. 39 TABLE 17: CASTLE RIVER CAES BEHIND-THE-FENCE CASE WITH STS ........................................................................... 40 TABLE 18: WINTERING HILLS BATTERY MERCHANT CASE ........................................................................................... 40 TABLE 19: CASTLE RIVER CAES MERCHANT CASE ........................................................................................................ 41 TABLE 20: OPERATING RESERVE MARKET SENSITIVITY RESULTS – WINTERING HILLS BATTERY MERCHANT CASE .... 41 TABLE 21: INCREASED STORAGE CAPACITY SENSITIVITY RESULTS – WINTERING HILLS CAES MERCHANT CASE ........ 42 TABLE 22: INCREASED STORAGE CAPACITY SENSITIVITY RESULTS – WINTERING HILLS POWER-TO-GAS 1 MERCHANT

CASE ................................................................................................................................................................... 43 TABLE 23: COMPARISON OF BATTERY RESULTS FOR CASTLE RIVER ........................................................................... 45 TABLE 24: SIMPLE CASHFLOW ANALYSIS ..................................................................................................................... 46


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LIST OF FIGURES

1 FIGURE 1: LOCATION OF WIND FACILITIES .................................................................................................................... 1 FIGURE 2: SYSTEM BOUNDARY FOR THE MODEL .......................................................................................................... 2 FIGURE 3: ENERGY STORAGE OPERATING CASES MODELLED ...................................................................................... 3 FIGURE 4: E.ON POWER-TO-GAS FACILITY ............................................................. 13 FIGURE 5: DISTRIBUTION OF HOURLY ELECTRICITY PRICE - 2012 ............................................................................... 16 FIGURE 6: DAILY NATURAL GAS PRICES - 2012 ............................................................................................................ 17 FIGURE 7: TYPICAL ALBERTA SUPPLY MERIT ORDER CURVE ....................................................................................... 18 FIGURE 8: DETERMINING THE ADJUSTED MARKET PRICE ........................................................................................... 19 FIGURE 9: NAS BATTERY ENERGY BALANCE ................................................................................................................ 22 FIGURE 10: AUXILIARY ENERGY REQUIREMENT .......................................................................................................... 22 FIGURE 11: CAES ENERGY BALANCE ............................................................................................................................ 23 FIGURE 12: POWER-TO-GAS ENERGY BALANCE .......................................................................................................... 25 FIGURE 13: POWER-TO-GAS 2 ENERGY BALANCE ....................................................................................................... 26 FIGURE 14: CASTLE RIVER BATTERY CASES OCTOBER 22 - 24 .................................................................................... 31 FIGURE 15: WINTERING HILLS BATTERY CASES OCTOBER 22 – 24 .............................................................................. 32 FIGURE 16: CASTLE RIVER CAES CASES OCTOBER 22 - 24............................................................................................ 34 FIGURE 17: WINTERING HILLS CAES CASES OCTOBER 22 - 24 ..................................................................................... 34 FIGURE 18: PTG 1 SENSITIVITY CASES – UTILIZATION OF INCREASED ENERGY STORAGE CAPACITY .......................... 44 FIGURE 19: PTG 1 SENSITIVITY CASES – UTILIZATION OF INCREASED ENERGY STORAGE CAPACITY .......................... 44


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1. INTRODUCTION

Energy storage technologies convert electricity into other forms of energy that can be stored and retrieved on demand. Energy can be stored, as chemical energy in the case of batteries; as potential energy in the case of pumped hydro; as kinetic energy in the case of flywheels; as compressible potential energy in the case of compressed air; and as chemical energy and compressible potential energy in the case of power-to-gas (PtG). This study presents the results of a modelling exercise using three energy storage technologies – power-to-gas, sodium sulphur batteries and compressed air, co-located at two existing wind generation facilities under two operating strategies within the Alberta electricity market.

PtG requires a special note at the very outset. It is a novel energy storage technology where excess electricity is used to produce hydrogen through electrolysis of water. Hydrogen gas can be stored by injection into either the natural gas pipeline system or geological structures, and converted back into electricity or it can be delivered to consumers as low-carbon heat or low-carbon transportation fuel. The potential also exists to use PtG to link the growing hydrogen demand, for oil refining/upgrading. Section 5.0 provides a summary of the modelled technologies and their energy storage operating cases.

1.1 CURRENT STUDY OBJECTIVES AND SCOPE

This study expanded on the scope of the 2011 study by AITF – Energy Storage: Making Intermittent Power Dispatchable (Andy Reynolds, et al.), (hereinafter referred to as the 2011 study) – which looked at the relative maturity of various energy storage technologies, reviewed Alberta’s energy and ancillary services markets, and conducted financial analysis for determining effective storage operating rules and cost-benefits for pursuing the opportunities identified for a wind farm. The objective of the current study is to advance the techno-economic understanding of selected energy storage technologies in the Alberta context.

Key differences between the 2011 study and the current study are described in the following paragraphs.

The current study uses actual hourly wind production data from the Wintering Hills and the Castle River wind generation facilities (Figure 1) and hourly market prices from 2012. The previous study used data from the Castle River and Chin Chute wind power generating facilities and hourly Figure 1: Location of Wind Facilities


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market prices from 2007 to 2010. Chin Chute was replaced with Wintering Hills to capture the effects of the revenue of wind profile for a location other than the area where the majority of the operating wind generation facilities in Alberta are located, i.e., the Pincher Creek – Medicine Hat region in southern Alberta.

The current study considers both Behind-the-Fence and Merchant operations of three different storage technologies – the sodium-sulphur battery, compressed air energy storage and power to gas – whereas, the previous study considered only behind-the-fence operation of two storage technologies (batteries and compressed air). Comparison of both Merchant and Behind-the-Fence energy storage allows for a more complete exploration of the value of storage within the Alberta electricity market.

The Behind-the-Fence operation assumes that the energy storage operation is co-located with a wind power generating facility and buys electricity only from that wind power generating facility for storing. Whereas, the Merchant operation assumes the energy storage facility, even though co-located at the wind power generating facility, is controlled by an independent entity that buys and sells electricity to capture price arbitrage or other electricity market opportunities. The Merchant operator buys electricity off the grid or under contract with a wind or other renewable energy facility. The modelling of the Merchant operation provides insight into the potential revenues and costs of an independent energy storage operator, an entity that does not exist in the Alberta electricity market currently. The Base Case models the wind power generating facility without energy storage.

This study aims to define and quantify the value of PtG, battery and compressed air energy storage technologies in the Alberta electricity market. Mathematical modelling is used to determine the potential value of each energy storage option. Figure 2 shows the boundary for the mathematical model.

Figure 2: System Boundary for the Model

Electricity

(Fossil fuel,

Hydro, etc.)

Electricity

(Wind

generated)

Merchant

Operation

Behind the

Fence

Operation

Electric

Grid

Conversion

Technology Energy Storage

(Limited

capacity)

Electricity

Generation

Other

Applications

Performance

Indicators Financial

GHG

Benefits

Other

Indicators

Modelling

Boundary


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For each wind facility eight energy storage operating cases are considered as shown in Figure 3 below. Additional cases are included as the sensitivity cases of some of the operating cases.

Figure 3: Energy Storage Operating Cases Modelled

Two versions of PtG are examined – one version models hydrogen being stored in an underground storage cavern and being used in a fuel cell to generate electricity and the other version models hydrogen being transported and stored in a natural gas storage facility and burned in a conventional natural gas-fired combined cycle generation facility. Specific details on each modelled case are presented in Section 5.

The study and modelling parameters adhered to the rules and processes of the Alberta electricity market and performance limits of each of the storage technologies. Furthermore, offers to sell or bids to purchase electricity were based on the information that would have normally been available to a storage operator at the time the operator would have submitted an offer or bid. In fact, the switch price mechanism, described in Section 5, used the current hour valuation of the inventory and inventory level to calculate hourly offers and bids and not a forecast of the future hourly price. If the actual market price in any hour was less than the switch price the operator was deemed to have purchased electricity and if the market price was higher than the switch price the operator was deemed to have sold electricity.

The presented cases are not optimised in the sense of what a generation developer would normally do to build a business case for an energy storage project that achieves a maximum return at an acceptable level of risk. Instead, the case results provide an indication of the potential value (in terms of revenue) of energy storage in the Alberta electricity market when combined with intermittent generating resources such as wind power.

The sensitivity cases explore the potential incremental returns from participation in the operating reserve markets and increasing the size of the storage capacity. Two examples of the potential of incremental revenues available to energy storage operators from participation in


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the operating reserve market are modelled. While the exercise is not a complete analysis, the two examples provide an indication of the potential revenues available from participation in the regulating reserve and standby spinning reserve markets. The effects of expanding the energy storage capacity are examined in four examples, two of which are based on compressed air storage and two based on power-to-gas.

The current study models the effects of selling stored electricity on the hourly market price, whereas, the previous study did not consider the dynamic effects of storage on electricity price. The effects of selling stored electricity from a single energy storage facility (such as the ones modelled in this study) are not, in an overall sense, found to be that significant on the hourly market price. A greater penetration of energy storage capacity in the supply mix will likely dampen the hourly price volatility and reduced the frequency of extreme high and low hourly prices. However, the effort to model the price effect does represent an improvement over the previous study.

In short, this study is intended to provide insights to developers, renewable generation owners and operators and policy makers of the benefits and costs of the application of energy storage in the Alberta electricity market.

2 BENEFITS OF ENERGY STORAGE

What differentiates energy storage technologies from typical generation or load and makes them valuable is the ability to quickly switch from behaving like a generator to behaving like a load in response to market price signals. The 2011 AITF study identified benefits to wind power generators from the use of behind-the-fence energy storage to allow generators to “time-shift” energy sales from low priced hours to higher priced hours. Various studies (e.g. Eyer, J. and Corey, G., 2010) have identified benefits from energy storage applicable to virtually all segments of the electric supply chain. Beyond time shifting, energy storage facilities are able to supply virtually all forms of ancillary services from active regulation to stand-by load shedding and black start. Energy storage can also be strategically located to reduce transmission congestion and defer investment in new transmission or distribution capacity. All that said, so far no new unique ancillary services have been developed based on energy storage technologies. Energy storage will, no doubt bring new competitors and operating strategies to the ancillary services markets.

Energy storage is also widely recognised as the enabling technology for integrating the electricity generated by intermittent renewables with the electric grid. It was the ability of energy storage technologies to balance the intermittency of renewable generation that was initially recognised. What this study shows is that energy storage technologies can also improve the economic returns of intermittent renewable generation. The combination of renewably generated electricity and energy storage could be one of the options for reducing the greenhouse gas emission intensity of power generation in Alberta and elsewhere.


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3 THE ALBERTA ELECTRICITY MARKET

3.1 UPDATE

Since the previous dispatchability study was completed in 2011, a number of changes have occurred in the Alberta’s electricity market. Some significant changes that require mention within the context of the current study are:

Installed wind power generating capacity increased from just under 800 MW to almost 1,100 MW, an increase of 40 per cent over two years. At the same time, total installed generating capacity increased by about 1,300 MW or 10 per cent.

The Alberta Electric System Operator (AESO) initiated a review of market rules and standards applicable to energy storage facilities with intent of identifying changes that may be required to ensure energy storage facilities have fair and equal access to the Alberta electricity market. Subsequently in June 2013, the AESO issued a paper detailing issues identified during its initial evaluation of energy storage integration. Following up on the issues paper, the AESO seeking industry input, set up a working group to provide input on the issues and ideas for changes that will form the basis of a discussion paper to be issued in 2014.

From a technology demonstration perspective, Suncor Energy and Teck were selected by the Climate Change and Emissions Management Corporation (CCEMC) to receive about $9 million in funding for a proposed three megawatt / six point nine megawatt-hour battery energy storage facility at the companies’ Wintering Hills Wind Power Project. The proposed project will test the feasibility of shifting power from off-peak periods to on-peak periods and participation in the ancillary service markets. A copy of the CCEMC announcement can be found in Appendix C.

Enbridge is actively pursuing PtG projects in Alberta.

System Access Service Requests (SASR) have been filed with the Alberta Electric System Operator (AESO) for three energy storage projects:

the previously mentioned Wintering Hills Battery Project;

AltaLink Investment Limited Partnership’s battery energy storage for wind integration (8.5 MWh of storage capable of supplying up to 20 MW (+/- 10 MW) of regulating reserve and 12 MW of spinning reserve).

Rocky Mountain Power’s proposed Alberta Saskatchewan Intertie Storage (ASISt) project, which will include 150 MW of compressed air energy storage capacity, to be located in the Lloydminster area along the Alberta Saskatchewan border.


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3.2 CURRENT MARKET RULES

The Alberta electricity market rules, technical standards and tariffs do not recognise the unique attributes of energy storage technologies. Other than for a few more recent changes, the rules and technical standards predate the latest advances in energy storage technologies. The AESO has recognised by way of the issues paper and the energy storage working group that some of the rules, technical standards and tariff may need to be changed to ensure it abides by its duties to operate a fair, efficient and openly competitive market with respect to energy storage developments.

The current AESO tariff would require a transmission grid-connected Merchant energy storage facility operating in Alberta to be treated as both a generator and a load, and hence subject to the demand transmission service tariff (DTS) and supply transmission service tariff (STS). For a transmission grid connected Behind-the-Fence energy storage facility located within the fence of an operating wind power generating project, the wind power generating facility will pay the STS tariff for electricity delivered directly to the grid and the energy storage facility will pay the STS for electricity that is stored and delivered at a later time to the grid. Since, a Behind-the-Fence energy storage facility will not purchase electricity from the grid it will not pay a DTS charge. The effects of the tariff charges on both Merchant and Behind-the-Fence energy storage facilities were modelled and are presented in Section 6.

Given that at this time there are three energy storage projects under development in Alberta, there is some urgency for the AESO to deal with any barriers that might unfairly reduce or restrict participation by these projects in the energy and operating reserve markets.


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4 STORAGE TECHNOLOGIES UNDER EVALUATION

This section briefly describes the selected storage technologies and their technical parameters.

4.1 RATIONALE FOR SELECTION

The technologies chosen for this study were:

1. Sodium sulphur battery (NaS)

2. Compressed air energy storage (CAES)

3. Power to gas (PtG)

The rationale for selecting these technologies remains essentially the same as the 2011 study: selecting technologies that are reasonably mature for grid scale implementation, and for which the technical and operating constraints are well documented. Additionally, the selected technologies represent a broad range of application areas from transmission and distribution grid support, to load shifting and bulk power management.

NaS is a relatively small-scale storage technology that has been deployed in a number of projects worldwide. NaS batteries exhibit asymmetry in parasitic thermal loads that results in lower overall efficiencies compared to other newer battery technologies such as lithium ion. CAES on the other hand is a well understood, large-scale storage system technology. The CAES system components (e.g. compressors, turbines etc.) are generally mature technologies. One aspect that is unique about conventional CAES operations is the exposure to natural gas price risk. NaS and CAES are by far the two storage technologies of greatest planned future deployment (Bloomberg, 2011; quoted in Reynolds A., et al, 2011).

PtG is a newer energy storage concept. The individual technical components of the PtG route, which uses electrolysis to produce hydrogen and then converts the produced hydrogen, after blending with natural gas, back to electricity, are technically mature. Continuous improvements are underway for more efficient electrolysers and turbines that could use hydrogen directly. The technologies for using hydrogen for generating electricity directly (i.e. fuel cells, or reversible solid oxide fuel cells) are at various stages of technical maturity. PtG was selected because it is the only technology that could have multiple value propositions:

injecting hydrogen into natural gas system and using it for its heating characteristics as a blend with natural gas;

using the produced hydrogen for industrial applications (for bitumen upgrading and as a precursor chemical etc.) to lower the emissions;

injecting hydrogen into natural gas storage facility and later withdrawing the hydrogen mixed with natural gas to fuel a combined cycle generator; and

storing the hydrogen and withdrawing it later to use in a fuel cell to generate electricity.


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The potential for multiple value propositions make this technology somewhat more complex to model and quantify.

4.2 SODIUM-SULPHUR BATTERIES

4.2.1 Description

The NaS battery is the most mature battery technology and represents the majority of existing and planned grid-scale battery installations. For this reason, there is a large body of publicly available information about NaS battery operation and performance to draw on for modelling purposes. While advances have been made in alternative battery chemistries, there is currently much less publicly available information on the operation and performance of those battery chemistries.

The normal operating temperature range of a NaS battery is between 300 and 340 degrees Celsius. One of the operational challenges with NaS batteries is that the charging reaction is endothermic and the discharging reaction is exothermic, necessitating charging and discharging limits to help maintain temperatures within the operating temperature range and an external heat source to maintain battery temperatures as required.

NGK of Japan remains the only manufacturer of grid-scale sodium-sulphur batteries, which were commercialised as the NaS battery in 2002. The NaS battery cells are packaged into modules with specified AC power capacity of approximately 400 kW. Each module is thermally insulated, and equipped with resistance heaters for temperature control. NGK reports a module standby heating requirement of 3.4 kW for a power storage module.

Currently, the largest individual installation of NaS battery technology is 70 MW, with 490 MWh planned for Italy in 2013. Estimates for AC-AC round trip efficiency of the NAS battery is around 80 per cent (EPRI, 2010).

4.2.2 Cost

Capital costs are in the range of $3,100-3,300/kW or $520-550/kWh (EPRI, 2010). Regular maintenance suggested by NGK includes continuous remote monitoring, physical inspections every 3 years, and adjustment of the module enclosure vacuum every 1,000 cycles to control standby heat loss. Based on existing installations, NGK estimates labour of 3 hours per 400kW module based on installations of 20 modules or greater.

The NaS operating life is affected by the depth of discharge: NGK states that 2,500 cycles are possible with 100 per cent depth of discharge (DOD), 4,500 cycles for 90 per cent DOD, and 6,500 cycles for 65 per cent DOD. End of life costs are expected to be low. NGK estimates that 98 per cent of the NaS battery materials can be recycled.


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4.3 COMPRESSED AIR ENERGY STORAGE

4.3.1 Description

In a CAES system, energy is stored as compressed air, which is later expanded through a turbine or a series of turbines to generate electricity. A CAES system, in the simplest terms, is comprised of a compressor, an air storage chamber and a gas turbine generator.

Currently, there are two grid-scale CAES systems in operation: one in Huntorf, Germany (since 1986) and one in McIntosh, Alabama (since 1991). Both store air in caverns excavated in underground salt formations. The Huntorf CAES system is capable of providing 290 MW for up to two hours. Comparatively, the McIntosh CAES system provides 110 MW with a 26-hour discharge time and a ramp up time of only 14 minutes.

CAES is the only storage technology, other than pumped hydro storage, that has been demonstrated on a large scale (+100 MW). A number of new CAES projects are being developed:

Apex Bethel Energy Center, Texas 317 MW CAES project that is expected to initiate construction in early 2014. Apex recently awarded Dresser Rand a contract for the manufacture of the compression and expansion trains.

In Larne, Northern Ireland, Gaelelectric is investigating the feasibility of developing a CAES project.

ADELE an adiabatic compressed air energy storage demonstration project is under development by RWE in Germany. Construction is expected to start in 2016 with commissioning planned for 2020.

Some of the advantages of CAES are:

compression and generation capacity can be developed in modules and easily expanded by adding more modules;

energy storage capacity, which is limited by the volume and pressure of the reservoir, can be increased relatively economically; and

the operational flexibility allows a CAES facility to compete in both ancillary service and energy markets.

Conventional CAES systems are diabatic where some of the heat energy generated during compression is lost. Energy lost during compression is compensated through the use of natural gas in the expansion phase, making CAES sensitive to the price of natural gas. Storage efficiencies of the currently operating conventional CAES systems are reported as 42 per cent (Huntorf) and 54 per cent (McIntosh).

Alternative compressed air techniques are being explored to minimize heat loss and improve efficiency. The German ADELE CAES is attempting to achieve 70 per cent efficiency with an adiabatic compression process where heat loss during compression will be stored and used during expansion. The ADELE plant is not expected to enter production prior to 2020.


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This study used conventional CAES technology in the modelling with an estimated overall efficiency of about 50 per cent.

4.3.1.1 Storage

Salt cavern storage of liquids (oils, naphtha, kerosene, gasoline) and liquefied hydrocarbons (LPG) are well established and operate with “brine compensation” to manage pressure. In this case, brine is injected into the bottom of the cavern and an equivalent amount of stored liquid is withdrawn. For storage of gaseous hydrogen, the hydraulically compensated system would provide pressure regulation through control of the hydraulic head. The disadvantage of brine compensation is the requirement to store large quantities of brine on the surface. Pressure regulation in the cavern could also be provided using ‘cushion gas,’ which is the volume of the gas that permanently resides in the cavern as inventory for providing adequate pressure and deliverability rates during the withdrawal of gas from the reservoir. For CAES, the US Department of Energy (USDOE) is researching the use of supercritical carbon dioxide as the cushion gas2 for its carbon sequestration benefit. The cost of the cushion gas inventory, the difference between the density of hydrogen and cushion gas (tendency to mix), their tendency to react and the need for a gas separation unit on the surface are some of the factors that would determine if the use of cushion gas is a better alternative than hydraulic compensation.

It is however understood that there may either be no salt deposits or unsuitable salt deposits at the wind farms selected for this study. Cavern storage has been assumed for those sites to understand how energy storage economics will unfold in the Alberta context if that indeed was the case.

Thinner and deeper salt deposits compared to those used in the existing CAES operations exist in the eastern half of the province, and that reduces their functionality for cavern development. The salt beds shallow towards the north-east. East of 111 degrees longitude, salt deposits exist above 1 km depth; this is approaching the depths of caverns for existing CAES operations.

As well, the salt deposits in Alberta are all bedded salts. Compared to the domal salts used for the caverns at both the Huntorf and McIntosh plants, bedded salts are thinner, and generally less pure. Since total energy output of a CAES plant is dependent on the reservoir volume, for a given plant design, smaller diameter caverns can be constructed in thicker salts; caverns mined from salt domes can be tall and narrow with minimal roof spans as is the case at both the Huntorf and McIntosh CAES facilities. Multiple caverns, or caverns with large aspect ratios are required in thinner salt beds. Multiple caverns will increase construction cost. Large aspect ratios exacerbate structural problems associated with material creep, which is of concern in salt cavern stability (Bachu and Rothenburg, 2003; DeVries, 2005). The depth of salts in Alberta (1000 - 2000 m) increases the in-situ stress. The caverns must also be really large since the salt is thin in comparison to those used at the existing CAES plants. These two factors mean that

2 See http://techportal.eere.energy.gov/technology.do/techID=115


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maintenance of the stability of the salt cavern may be more difficult in any Albertan CAES projects than at the existing sites.

The presence of impurities in the salt beds also complicates cavern development. Durable impurities, such as clay lenses or anhydrite beds in the salt might further compromise the structural integrity of the cavern by introducing inhomogeneities in the material properties of the material hosting the cavern. They will also remain behind during solution mining of the cavern, filling the cavern bottom with a rubble layer and reducing its effective volume. Further complexities are caused by the presence of soluble impurities in the salt beds that may dissolve preferentially during the solution mining (and in the pressure compensating brines, if these are used), and lead to difficulties in controlling cavern development. The Lower and Upper Lotsberg salts are very pure, but anhydrite layers and sylvite (potassium chloride) are common impurities in the Prairie Evaporite (Grobe, 2000). Although the Prairie Evaporite is the most extensive salt deposit in the province, the presence of these impurities may greatly increase the cost of cavern development in those salts.

Based on the above considerations, any perspective CAES operations in Alberta utilising salt reservoirs should strive to keep cavern volumes small, which means operating using a compensated cavern design. Optimal cavern sizing requires a good understanding of the cycling frequency of the power generation phases prior to construction; such an understanding must be established early in any planning phase.

4.3.2 Costs

Typical overnight capital costs reported by the referenced sources for a CAES plant range from $1,100 to $1,300 per kW of installed generating capacity. These figures are in U.S. dollars and vary with the size and design of the plant and do not include the cost of the storage reservoir.

Storage costs vary substantially between surface and sub-surface storage with subsurface costs reported in the range of $11 to $17 USD per kWh and surface costs in the range of $115 to $180 USD per kWh. Obviously, the cost of subsurface storage is greatly dependent on the subsurface geology of the site selected for the CAES facility.

4.4 POWER TO GAS

4.4.1 Description

Power to gas refers to the generation of hydrogen from electrolysis of water using electricity, followed by the storage of the hydrogen gas and ultimately the conversion of hydrogen back to electricity.

4.4.1.1 Electrolysis

In the past few years advances in the alkaline electrolyser technology has led to improvements in efficiency and operating current density while reducing capital cost for a specified hydrogen output rate. Hydrogen production volumes of 500 – 760 Nm3/h are possible, corresponding to


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electric power consumption of approximately 2,150 – 3,534 kW. The operating temperature range is controlled at generally between 5 – 100 degrees Celsius.

To prevent conditions that could lead to the formation of flammable gas mixtures, production rates are typically limited to 25 – 100 per cent of the nominal range. Above the minimum operating rate, the electrolyser operation can rapidly follow the input power and DC current.

The purity of hydrogen and oxygen produced can reach 99.9 and 99.7 volume per cent, respectively. In order to operate safely and protect electrodes from damage, the purity of water input to the electrolyser must be high with an electrical conductivity below 5µS/cm.

In a typical installation, several electrolyser units are connected together with additional pressure chambers, cooling systems and control electronics. Control electronics can selectively turn off individual electrolysers to maintain minimum operation rates on remaining “on” units. The electrode lifetime is not strongly affected by cycling. With control electronics, the electrolyser stacks are generally robust to fluctuating power sources and the efficiency of operation is fairly constant over the operating range.

4.4.1.2 Storage

For this project, hydrogen storage is being considered in both salt caverns and natural gas systems. Salt cavern storage is used in conjunction with a solid oxide fuel cell for generating electricity and storage in the natural gas system is used with a conventional combined cycle generator for electricity generation. For storage in the natural gas system, the energy content of the hydrogen injected into the natural gas system would be accounted for, and the hydrogen would be blended with the natural gas. When the hydrogen is in effect withdrawn from storage for conversion to electricity, an amount of natural gas that would be the energy equivalent of the amount of hydrogen that was withdrawn is used instead.

4.4.1.2.1 Salt caverns

In the UK, there have been several examples of hydrogen gas storage, including three brine compensated salt caverns at Teeside. The caverns were at a depth of 366 metres, and stored hydrogen at 5,000 kPa pressure for industrial chemical applications. Technical issues for hydrogen gas storage in geological structures have been researched for over 25 years (Phillips, 1985). Geological storage of hydrogen gas is now common in fuel processing industries where there is little requirement for gas purity.

For high purity hydrogen gas storage, Praxair has recently developed salt caverns with capacity of 2.5 billion standard cubic feet. A process of injection into the salt cavern for storage and re-uptake with filtration to maintain purity has been patented (Morrow J.M., Corrao M., 2006). The hydrogen gas storage cavern is connected to Praxair’s existing 750 million standard cubic feet pipeline for the US Gulf Coast petrochemical industry.


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4.4.1.2.2 Gas pipeline storage

Storage of hydrogen in the natural gas pipeline has been proposed and researched, but only recently has been reported in operation. In June 2013, the German power and gas company E.ON injected hydrogen into the natural gas pipeline for the first time as a full system test; plant operations commenced in August 2013 (see press release in Appendix D). The company stated that regulations allow up to five per cent hydrogen in the natural gas pipeline.

Figure 4: E.ON Power-to-Gas Facility

In Alberta, the TransCanada Pipeline (TCPL) natural gas quality specifications do not directly limit the amount of hydrogen that can be injected into TCPL pipeline; however, the lower limit on the heating value limits the quantity of hydrogen that can be blended into a TCPL pipeline at any point. For this study it is assumed that hydrogen blended up to a concentration of five per cent with pipeline quality natural gas, which typically has a higher heating value of at least 37 MJ/m3, to meet the TCPL quality specification of a minimum heat rate of 36 MJ/m3. On an operational level achieving the five per cent concentration level requires that hydrogen be injected into a pipeline of sufficient size and flow rate to achieve the necessary dilution of the hydrogen.

Storing hydrogen in a natural gas storage facility up to the five per cent concentration limit is not expected to create any concerns for a storage operator.

4.4.1.2.3 Conversion of hydrogen gas to electricity

To convert hydrogen back to electricity, two methods are considered:

contracted use of a gas-fired electricity generation plant

use of solid oxide fuel cell

The solid oxide fuel cell (SOFC) can take pure hydrogen gas – dry or humidified. While the sulphur tolerance level of the SOFC is higher than other fuel cell technologies; hydrogen sulphide levels of approximately 80 ppm can cause contamination of the cell. The SOFC is capable of handling input gases other than pure hydrogen; and, generally the cells can run on conventional fuels such as propane, butane, methane and gasified biomass.


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The efficiency of the SOFC is generally higher than other fuel cells. The company Ceramic Fuel Cells out of Australia3 has reported 60 per cent efficiency in their BluGen commercial cell.

The output voltage of the SOFC is sensitive to many parameters, including temperature and pressure of the inlet gases. For connection to the grid, the SOFC requires a power conditioning unit (PCU) to control inlet gases, regulate cell DC output voltage and provide DC-AC conversion (Hajimolana, 2009 and Sedghisigarchi, 2004).

It is recognised that the modelled operating strategy for PtG (electricity-hydrogen-electricity) may not be the optimal strategy from a PtG operator’s perspective. There could be more lucrative operating options such as storing hydrogen for capturing the seasonal variability in the demand of natural gas, or using hydrogen as a clean combustion fuel for its heating value. These operating strategies were not modelled because of maintaining consistency in comparison with the NaS and CAES operating strategies.

4.4.2 Cost

Given that at the time of this study, there was only one Power-to-Gas facility operating in the world and that facility only started operating a few months ago, there is no publicly available data on the installed capital and operating costs of a complete power-to-gas system. The referenced sources only provided capital estimates for the power-to-gas components such as the electrolyser, reported to cost about $1,000 per kW of capacity. For the second power to gas case, which uses a solid oxide fuel cell, the referenced sources show capital costs ranging from $3,000 USD per kW to as high as $8,000 USD per kW.

3 "Ceramic Fuel Cells:: BlueGen - Ceramic Fuel Cells Limited." 2010. 20 Sep. 2013

<http://www.cfcl.com.au/bluegen>

http://www.cfcl.com.au/bluegen


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5 MODEL DESCRIPTION

5.1 METHODOLOGY

The models were designed and built to analyse the economic benefits, to a wind power generating facility in the case of a Behind-the-Fence storage operator, and to a Merchant energy storage operator. As opposed to forecast models, the study models were hindcast in that each model used actual market data and in effect inserted the energy storage facilities into that historical setting. There are positive and negative effects from this approach. On the positive side unique characteristics of the Alberta market price volatility are retained, along with the underlying correlation between wind generation and market prices. On the negative side, a certain amount of distortion is introduced, but by limiting the size of the energy storage facilities to 30 MW of charging and discharging capacity the error is limited. Overall, the positive effects are felt to outweigh the negative effects.

Although energy storage is recognised as providing a number of benefits to the electrical grid, not all of the benefits were modelled in the current study. The benefits accrued from participation in the hourly energy market and two operating reserve markets were modelled. Rather than modelling all of the sixteen cases, participation in the operating reserve markets was modelled by two sensitivity cases using the Wintering Hills wind power generating facility and NaS battery energy storage facility under a Merchant operating strategy. Similarly the effects of the transmission tariffs and increased storage capacity were modelled as sensitivity cases using only two of the study cases. The intent of the sensitivity case was to provide an indication of the benefits or effects of varying some of the study key parameters.

5.1.1 Bid and Offer Strategy

The key element of the storage operations strategy was the switch price, or the price at which the preference to charge switches to a preference to discharge and vice versa. The model effectively set a bid and offer4 price for each hour dependent on the switch price. The switch price was calculated each hour of the modelled year by an algorithm that used as inputs, the expected inventory level, current average cost of inventory, and variable operating costs. The effect of the algorithm was as the inventory level declined, the switch price increased up to a maximum price of $80 per MWh. Conversely, as inventory levels rose the switch price declined, but never below the sum of the inventory cost and variable costs. If the hourly price for electricity was less than the switch price, the model injected electricity into storage; and, if the hourly price for electricity was greater than the sum of switch price and the variable operating cost, the model discharged electricity from storage.

4 The definition of a bid price is, what a buyer is willing to pay to acquire, in the case of the study, electricity and

the offer price is what a seller is asking for in order to sell electricity.


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Offer and bid volumes took into account forecast wind output and desired storage activity. The real time hourly market price determined the actual volume to be sold or purchased; and, the storage operator dispatched the storage to meet the sold or purchased volume as closely as possible.

5.1.2 Prices

Actual 2012 Alberta hourly prices for electricity and operating reserves were used in the study. Figure 5 shows the hourly electricity prices for 2012. Over the year, electricity prices averaged $64.32/MWh and for half of the hours settled below $25/MWh. For the remaining 4,392 hours the average price was over $110/MWh with sixteen hours settling between $990/MWh and $1,000/MWh, the market price cap.

Figure 5: Distribution of Hourly Electricity Price - 2012

Similarly, as required, the actual 2012 daily prices for natural gas shown in Figure 6 were used. Since, the “Gas Day” for scheduling receipts and deliveries of natural gas is defined as a 24-hour period starting 08:00 Mountain Time, for modelling the natural gas price applicable in any hour was changed at 08:00 each day.


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Figure 6: Daily Natural Gas Prices - 2012

5.1.3 Effects on Hourly Clearing Price

To model the effects of storage behaviour, a representative merit order curve was developed based on a sampling of 2012 merit order curves. The representative merit order curve shown in Figure 7 displays all of the typical characteristics of the Alberta merit order, namely:

zero dollar offers of 6,000 MW or more;

a section of slowly rising offers up to an inflection point at about $90 per MWh which occurs around the 8,000 to 8,500 MW cumulative offer point;

beyond the inflection point at about $90 per MWh a steeply sloping section with offers reaching $900 per MWH; and

above $900 per MWh a tail section where the rate of increase of the offer price begins to slow down and caps at $1,000 per MWh.


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Figure 7: Typical Alberta Supply Merit Order Curve

The merit order curve was used to calculate an adjustment to the hourly market price resulting from the energy storage operation. The effect of withdrawing a quantity of electricity from storage thereby increasing the hourly supply of electricity was to reduce the hourly market price, and the effect of injecting energy into storage was to increase hourly demand for electricity resulting in an increase in the hourly market price.

The following explains how the Supply Merit Order Curve was used during an hour in which electricity was injected into storage to determine the adjusted hourly market price:

1. Each hour the model would determine the deemed offer volume by using the actual hourly market price and the corresponding offer volume, which is shown on Figure 8 as the path defined from A to B to C.

2. The quantity of energy would be added to the deemed offer volume, shown as line C to D

3. The new market price was determined by selecting the corresponding market price for the combined deemed offer volume and injected quantity, shown as line D to E.

A similar procedure was used to determine the adjusted market price in the hour in which energy was withdrawn from storage. The only difference being instead of adding the quantity of energy withdrawn from storage to the deemed offer volume, the withdrawn quantity is subtracted from the deemed offer volume. The path defined as F to G to H to D to E in Figure 8, displays the process.


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Figure 8: Determining the Adjusted Market Price

5.1.4 Wind Power Facility Selection

Wintering Hills is an 88-megawatt (MW) wind power generating facility located in south-central Alberta. In 2012, Wintering Hills produced about 292 gigawatt hours (GWh) of electricity resulting in a capacity factor of about 38 per cent. In addition to achieving one of the highest capacity factors of all the wind power facilities in the province, Wintering Hills was also one of the most consistent producing wind facilities in Alberta. Castle River is a 44 MW generating facility that in 2012 produced about 110 GWh of electricity, yielding a capacity factor of about 29 per cent. The Castle River wind facility energy production was highly variable with a coefficient of variation5 of 1.1 versus Wintering Hills with a coefficient of 0.9.

For the storage modelling exercise, the hourly output from each of the wind power generating facility was normalised to reflect an installed generating capacity of 50 MW. The process of normalising the generating capacity for each wind power generating facility resulted in two hourly data sets with Wintering Hills effectively producing about 168 GWh at an average price of $46.59/MWh and Castle River producing about 143 GWh at an average price of $36.43/MWh.

5 Coefficient of variation is a measure of the dispersion of a frequency distribution and is calculated as the ratio of

the standard deviation of a distribution to the mean of the distribution. The higher the value of the coefficient, the greater is the dispersion of the wind farm output.


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5.1.5 Storage operation

Behind-the-Fence operations strategy assumes the:

o storage facility is controlled by the wind farm operator;

o operator does not purchase any electricity from the grid;

o combination of storage discharge and wind output is constrained by the contracted transmission capacity at 50 MW; and

o operator only pays the STS tariff according to the existing AESO rules.

Merchant operations strategy assumes the:

o storage facility is controlled by the operator of a co-located 50 MW wind power generating facility;

o operator is free to buy or sell electricity from or to the grid or from the co-located wind power facility;

o combination of storage discharge and wind output is constrained by the contracted transmission capacity of 50 MW; and

o operator pays both the STS and DTS tariffs according to the existing AESO rules.

5.2 MODELLING PARAMETERS

5.2.1 Description of model cases

All the modelled cases shared the following parameters:

the storage facility is co-located with 50 MW wind power facility and shares 50 MW of transmission system access capacity with the wind power facility;

30 MW of charging and discharging capacity; and

210 MWh of storage capacity or seven hours of storage when charging or discharging at full capacity.

Table 1, overleaf, provides a summary of each model case for comparison.


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Table 1: Summary of Modelled Cases

Scenario Energy Storage

Technology Case

Behind-the-Fence NaS Battery Wintering Hills - Behind-the-Fence - Battery

Castle River - Behind-the-Fence - Battery

CAES Wintering Hills - Behind-the-Fence - CAES

Castle River - Behind-the-Fence - CAES

Power-to-Gas 1 Wintering Hills - Behind-the-Fence - P2G1

Castle River - Behind-the-Fence - P2G1

Power-to-Gas 2 Wintering Hills - Behind-the-Fence - P2G2

Castle River - Behind-the-Fence - P2G2

Merchant NaS Battery Wintering Hills - Merchant – Battery

Castle River - Merchant – Battery

CAES Wintering Hills - Merchant – CAES

Castle River - Merchant – CAES

Power-to-Gas 1 Wintering Hills - Merchant - P2G1

Castle River - Merchant - P2G1

Power-to-Gas 2 Wintering Hills - Merchant - P2G2

Castle River - Merchant - P2G2

5.2.2 NaS Battery

In addition to the common storage charging, discharging and total capacities, the NaS battery cases were also based on the following parameters:

The depth of discharge (DOD) was limited to not more than 90 per cent of the total energy storage capacity; or, in other words, the operator did not discharge the batteries down to a point where there was less than 21 MWh in storage;

The co-located wind power generating facility consistent with the transmission grid requirements produces an AC signal that had to be converted to DC for charging the batteries; and similarly with discharging, the battery energy had to be converted from DC to AC;

Battery efficiency was assumed to be 85 per cent;

Inverter efficiency was assumed to be 95 per cent for AC to DC and for DC to AC conversion; and

Overall cycle efficiency was estimated to be about 77 per cent.

The limit on discharging was consistent with the manufacturer’s direction and will extend the expected battery life to 4,500 charging and discharging cycles.

Figure 9 below is a diagram of the energy flows for the modelled battery system based on the parameters described above. The system shown in Figure 9 is very simple, as are all battery systems, consisting of an inverter to convert the incoming electricity from AC to DC current and


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an outgoing inverter to convert energy withdrawn from the batteries from DC to AC current. The AC-to-AC efficiency of the NaS battery operations described in the modelled cases is about 77 per cent, excluding auxiliary energy. The overall efficiency did vary from case to case as the auxiliary energy load varied with the frequency and depth of the charging cycle.

Figure 9: NaS Battery Energy Balance

The auxiliary energy requirements for heating the battery to maintain battery temperatures within the recommended operating range were modelled on an hourly basis using the equation (of best fit) shown in the Figure 10 below. The graph was used in the 2011 study and is based on a number of sources including “Sodium Sulfur Energy Storage and Its Potential to Enable Further Integration of Wind (Wind-to-Battery Project) Xcel Energy Renewable Development Fund Contract #RD3-12” (Himelic, J., Novachek F. 2010).

Figure 10: Auxiliary Energy Requirement


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For modelling the auxiliary energy load was treated as a cost and not a parasitic load and therefore the auxiliary energy requirements were priced using the adjusted hourly market price and shown as a cost in the model results. The auxiliary energy loads were not deducted from the energy delivered or received from the transmission grid.

5.2.3 CAES

Figure 11: CAES Energy Balance

Figure 11 above shows a similar (to NaS) energy balance for a CAES system. The modelled CAES system is obviously more complex than a battery system. The following paragraphs provide a simple description of the system that the CAES models were based upon.

The air compressor compresses air in several stages from atmospheric pressure to the pressure required for injection of the air into the storage cavern. Since compressing air causes the temperature of the gas to increase, there is a small requirement for cooling to keep the air temperature within the operating range of the compressor.

As required, the compressed air is withdrawn from the storage cavern to generate electricity. The model shown in Figure 11 generates electricity by expanding the air in two stages. During the first expansion stage the air pressure is reduced to a level suitable for a gas turbine, while at the same time recovering energy from the expanding air through the use of a turbo expander-generator. The temperature of expanding air will drop and to prevent the possibility of any water vapour contained in the air from freezing, the air is heated. Fortunately the gas turbine used in the second expansion stage produces a significant quantity of waste heat. The model, in effect, uses the hot exhaust from the gas turbine to heat the expanding air.

In the second expansion phase, the compressed air is mixed with natural gas; and, the mixture is ignited in a gas turbine that drives a generator. The gas turbine used in a CAES system is different than all other gas turbines in that the inlet compressor section that is normally used to compress air is not needed and for modelling purposes was removed. The compressor section of a standard gas turbine consumes one-half to two-thirds of a gas turbine’s mechanical output.


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Without the inlet compressor, the CAES gas turbine heat rate6 is about 35 per cent lower than a high efficiency natural gas-fired combined cycle generating plant.

The use of natural gas results in a CAES system generating more electricity than what is actually stored. The modelled CAES system yielded about 1.3 MWh for every MWh consumed compressing air. On average, the round-trip efficiency of the modelled CAES system is about 49 per cent.

The following parameters were used in the CAES models:

30 MW air compression capacity;

brine compensated salt cavern storage at a depth of 1,300 metres;

cavern operating pressure of 13 MPa;

injection/withdrawal air flow of 172,000 kg/hour;

injection surface pressure of 11.5 MPa;

discharge surface pressure of about 10 MPa;

an initial expansion-generation stage to reduce the air pressure from 10 MPa to 0.23 MPa;

a natural gas requirement of about 170 GJ/hour during hours when the gas turbine is operating;

actual daily natural gas prices for each gas day; and

gas turbine heat rate 4.5 GJ/MWh HHV

5.2.4 Power to Gas 1

Figure 12 below, shows the energy balance for the Power-to-Gas 1 system. The Power-to-Gas 1 system starts with an electrolyser that splits water into hydrogen and oxygen. The oxygen is vented and the hydrogen is captured and compressed to the normal operating pressure of the TransCanada Alberta system. As already described in the preceding section on CAES, compressing a gas causes the temperature of the gas to increase; and similar to compressing air, there is a small requirement for cooling hydrogen to keep the hydrogen temperature within the operating range of the compressor. Once in the pipeline the hydrogen is, in effect, delivered to a natural gas storage facility. In reality, the hydrogen once injected into the pipeline likely never reaches the natural gas storage facility. The operator of the Power-to-Gas facility is instead credited with a quantity of energy entitling the operator to withdraw that quantity from the natural gas storage facility. The model assumes that in responses to hourly electricity

6 Heat rate is the ratio of the natural gas consumed by the gas turbine to the output of the generator coupled to

the gas turbine. A gas turbine –generator set that consumes 750 GJ of natural gas per hour and produces 100 MWh of electricity has a heat rate of 7.5 GJ/MWh.


PAGE 25


market prices the storage operator withdraws a quantity of natural gas from the natural gas storage reservoir for delivery at a combined cycle natural gas-fired generating facility for conversion to electricity.

Figure 12: Power-to-Gas Energy Balance

Based on the operation of the Power-to-Gas facility described above, the overall efficiency of the Power-to-Gas system is about 36 per cent.

The Power-to-Gas 1 system was modelled on the following parameters:

30 MW of electrolyser capacity producing 545 kg/hour of hydrogen with a conversion efficiency of 72 per cent HHV;

200 kW of hydrogen compression capacity to boost the hydrogen pressure from 3,000 kPa at the outlet of electrolyser to 6,000 kPa in order to inject hydrogen into a natural gas pipeline;

storage of hydrogen in a natural gas storage facility;

storage demand costs of $0.50 per GJ of stored energy and injection and withdrawal fees of $0.02 per GJ

withdrawal of an equivalent quantity of energy for delivery to a natural gas-fired combined cycle power plant; and

a natural gas-fired combined cycle generating facility efficiency of 50 per cent, equivalent to a heat rate of 7.2 GJ/MWh HHV.

5.2.5 Power to Gas 2

Figure 13 shows the energy balance for the Power-to-Gas 2 system. The Power-to-Gas 2 system, similar to the Power-to-Gas 1 system, starts with an electrolyser. Hydrogen gas produced by the electrolyser is compressed for injection into a storage cavern. As required, hydrogen is withdrawn from the storage cavern and expanded through a turbo expander – generator, to recover the energy available from expansion. Next, the hydrogen is heated in an exchanger along with air to about 800 degrees Celsius; both the hydrogen and air are then fed into a solid oxide fuel cell. The solid oxide fuel cell converts the energy released from the reaction of hydrogen and oxygen in the cell to form water, into electricity. At the same time,


PAGE 26


the fuel cell generates heat that the model uses to heat the incoming hydrogen and air. The Power-to-Gas 2 system as described has an efficiency of about 30 per cent.

Figure 13: Power-to-Gas 2 Energy Balance

The Power-to-Gas 2 system was modelled on the following parameters:

30 MW of electrolyser capacity producing 545 kg/hour of hydrogen with a conversion efficiency of 72 per cent HHV;

500 kW of hydrogen compression capacity to boost the hydrogen pressure from 3,000 kPa at the outlet of electrolyser to about 13,000 kPa in order to inject hydrogen into a storage cavern;

brine compensated salt cavern storage at a depth of 1,300 metres;

cavern operating pressure of 13 MPa;

injection/withdrawal air flow of 545 kg/hour;

injection surface pressure of 13 MPa;

discharge surface pressure of about 12.6 MPa;

three stages of expansion-generation stage to reduce the hydrogen pressure from about 12.6 MPa to 0.56 MPa; and

a solid oxide fuel cell generator operating at 1,073 degrees Kelvin (about 800 degrees Celsius) with an efficiency of 60 per cent.

5.2.6 Sensitivity Cases

5.2.6.1 Transmission Demand and Supply Charges

As previously mentioned in Section 3.2, an energy storage facility operating behind-the-fence of a wind power generating facility would have been charged for supply transmission services (STS) during the hours that the energy storage facility delivered electricity to the Alberta transmission grid. If the wind power generating facility was operating at the same time as the energy storage facility was delivering energy to the grid, the STS charges would be for the total


PAGE 27


quantity of electricity delivered. STS charges are calculated as, the sum over the hours in a month of the product of the hourly market price, the delivered energy in the hour and the loss factor, plus applicable rate riders. In Alberta, a portion of the cost of transmission system losses is allocated to each generator connected to the transmission system through loss factors are calculated annually for each generation facility.

A merchant energy storage facility would pay the STS charges for all energy delivered to the transmission grid and the delivery transmission service (DTS) for all energy withdrawn from the grid. DTS charges are calculated based on contracted demand, the metered energy and the coincident peak factor plus a number of rate riders. The coincident peak factor is the ratio of the metered demand coincident with the system peak demand in any month divided by the contract demand. Since the objective of a storage operator is to buy and store energy at low prices and low prices normally occur when system demand is lower, the coincident peak factor which is about 75 per cent for a typical load, was set, conservatively, at 50 per cent for the storage facility. In total the DTS charges are significantly higher than the STS charges on a per MWh basis.

Four sensitivity cases were developed to assess the potential transmission charges related to both the Merchant and Behind-the-Fence operations strategy cases. Two cases are based on the Wintering Hills wind power generating facility with battery storage and two cases are based on the Castle River wind power generating facility with CAES. For the Behind-the-Fence cases the STS charges were calculated on an hourly basis assuming a contracted capacity of 50 MW. For the Merchant cases the DTS charges were calculated monthly based on a contract capacity of 30 MW, or 31.6 MW for the battery cases only to account for inverter losses, and assuming that the substation was shared by the energy storage facility and the wind power generating facility. The applicable 2012 loss factors and rate rider values were used in the sensitivity cases. The results of the sensitivity cases are shown in Section 6.2.

5.2.6.2 Operating Reserve Market

Two sensitivity cases were developed to examine the potential incremental revenues available to a NaS energy storage system from participation in the Alberta operating reserve (OR) market. The first sensitivity case modelled participation in the active regulating reserve market and the second case modelled participation in the standby spinning reserve market. More details on the Operating Reserve markets are available in Appendix A.

The NaS battery energy storage system is chosen for both sensitivity cases to allow comparison of the results of both sensitivity cases without having to adjust the results to account for the effects of the storage technology. Currently batteries are not eligible to supply spinning reserve in the Western Electricity Coordinating Council region, which includes Alberta. There is no fundamental technical reason why a battery energy storage facility could not supply spinning reserve, prohibition on eligibility is likely more to do with what is familiar practice and experience.


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Regulating Reserve

The first sensitivity case modelled the effects of NaS battery system operator offering 15 MW7 of regulating reserves into the OR market at the switch price8 for the AM Super Peak9 time block. Prior to submitting the offer, the model confirmed there was sufficient energy in storage and at least 15 MW of available transmission capacity for the three AM Super Peak hours. If the Dispatch Price was higher than the switch price for each of the three hours of the AM Super Peak block, the model assumed that the offer has been accepted.

When a regulating reserve offer has been accepted, the model reduces the inventory level by 15 MWh and the maximum quantity of energy that can be delivered on the transmission grid was set at 35 MW, the difference of the 50 MW contracted transmission capacity and the 15 MW regulating reserve offer. The facility revenue was increased by the product of the Dispatch Price times 15 MWh.

If the regulating reserve offer was accepted, the storage facility was also eligible for a directive payment, if the AESO directed the facility to provide energy during the AM Super Peak hours. Since there was no certainty whether the facility was going to be directed to provide energy, the model results shown in Section 6.2 show the revenue associated with the payment of the Dispatch Price and directive payment separately. To calculate the directive payment the model assumed the storage facility was directed for each of the three AM Super Peak hours. The least amount the storage facility might receive by offering regulating reserves for AM Super Peak hours is the sum of the Dispatch Price payments; and the largest amount the energy storage facility may receive is the sum of the Dispatch Price payment plus the directive payments.

It is important to note that the result of the offer strategy was that during some AM Super Peak hours when the regulating reserve offer is deemed to be accepted the storage facility misses an opportunity to sell electricity into the hourly market at a price higher than what was deemed to have been received selling regulating reserve services. These lost opportunities were accounted for by comparing the model results for the regulating reserve case to the case for the NaS battery system, which assumes the system is only participating in the hourly energy market. The results and comparison are shown in Section 6.2.

Standby Spinning Reserve

The second OR sensitivity case models the NaS battery system operator offering 10 MW into the standby spinning reserve market. Similar to the first sensitivity case, the offer of standby spinning reserve was made at the switch price for the sixteen-hour on-peak block. If a standby offer was accepted, the NaS battery system operator was paid an availability payment based on

7 15 MW is the minimum offer for regulating reserve with additional offers in blocks of 5 MW each. See Appendix A

for a summary on the Alberta Ancillary Services market.

8 See section 5.1.1 for an explanation of the switch price.

9 AM Super Peak time block extends from hour ending 06:00 to hour ending 08:00 each day.


PAGE 29


the availability price offered by the marginal on-peak standby-spinning supplier. Once accepted to provide standby spinning, the NaS battery system may be activated for some or all of the on-peak hours and paid the offered activation price for all hours the NaS battery system was activated. Once activated, the NaS battery system was dispatched for one hour out of the sixteen on peak hours; and, during that hour the operator was paid the hourly market price for all electricity delivered to the grid. Potentially, a market participant offering standby spinning could receive three payments: an availability payment, an activation payment and a dispatch payment.

When a standby spinning reserve offer was deemed to have been accepted and subsequently activated by the AESO, the model reserves 10 MW of transmission capacity for the sixteen hours of the on-peak block. For those hours when the spinning capacity is deemed to be dispatched, the model reduces the energy inventory by the dispatched energy. Similar to the regulating reserve sensitivity case, participation in the standby spinning reserve market resulted in lost opportunities to sell electricity into the hourly market at higher prices than those offered by the OR market. Again, the results shown in Section 6.2 compare the standby spinning reserve case to the hourly electricity market case.

5.2.6.3 Storage Capacity

In total seven sensitivity cases were developed to determine the effects on revenues from increasing the energy storage capacity. The first two examined the effects of increasing the energy storage capacity for a CAES system and five cases examined the effects of increasing the energy storage capacity on the revenues produced by a Power-to-Gas 1 system.


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6 RESULTS

6.1 MODELLED CASES

6.1.1 NaS Battery Cases

Operationally, the use of battery storage resulted in less energy being delivered to the grid compared to the Base Case, due to energy losses related to the operation of the battery energy storage system. Over the twelve-month period analysed, the total energy losses, shown in Table 2 in the column labelled Change %, varied between 2.2 per cent and 3.3 per cent of wind farm output with the Merchant operations strategy cases having the highest overall losses.

The Castle River Merchant operations strategy case had the highest energy losses and also was the case that purchased the largest quantity of electricity from the grid. The higher level of charging and discharging activity that resulted from the merchant electricity purchases resulted in higher energy losses. At the same time, comparing the energy losses to the Auxiliary Energy requirements for heating the battery modules shows that the higher level of charging and discharging activity reduced the need for energy to heat the batteries by 25 per cent.

Table 2: NaS Battery Cases - Operational Results

Wind Power Project

Operations Strategy

Wind Production

(MWh)

Purchased from the Grid

(MWh)

Sold to Grid (MWh)

Net Energy Deliveries

(MWh)

Power Output Change (MWh)

Change (%)

Auxiliary Energy (MWh)

Castle River Behind the Fence 142,985

135,238 135,238 -7,747 -5.4% 8,737

Wintering Hills Behind the Fence 167,726

158,419 158,419 -9,307 -5.5% 7,807

Castle River Merchant 142,985 27,326 158,918 131,592 -11,393 -8.0% 6,614

Wintering Hills Merchant 167,726 21,548 178,046 156,498 -11,228 -6.7% 6,701

Despite the losses, in all cases the use of NaS batteries resulted in higher revenues than the Base Case revenues, the revenues generated by wind facility without any energy storage. The Merchant cases showed the largest improvement. The revenue change values shown in Table 3 were net of the cost of auxiliary energy. Wintering Hills revenues were greater than the corresponding Castle River revenues for both operations strategies due to the higher level of electricity production from the Wintering Hills over the twelve-month period.

Table 3: NaS Battery Cases - Financial Results

Wind Power Project

Operations Strategy

Base Case Net Revenue

($ Million)

Modelled Net Revenue

($ Million)

Revenue Change

($ Million)

Revenue Change

(%)

Average Selling Price

($/MWh)

Cost of Auxiliary Power

($ Million)

Castle River Behind the Fence $5.2 $6.1 $0.9 17% $49.03 $0.5

Wintering Hills Behind the Fence $7.8 $9.5 $1.7 22% $62.39 $0.4

Castle River Merchant $5.2 $8.8 $3.6 70% $62.13 $0.2

Wintering Hills Merchant $7.8 $10.8 $3.0 38% $65.38 $0.2


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The revenue change from the Base Case revenues for the Behind-the-Fence operations strategy cases were, on a percentage basis, lower for Castle River when compared to Wintering Hills. Whereas, for the Merchant operations strategy cases, Castle River revenues increased from the Base Case revenues by about 77 per cent and the revenues for Wintering Hills increased by about 43 per cent.

The switch in the relative performance between the operations strategies was due to the lower capacity factor of Castle River. The lower capacity factor provided more opportunity to purchase low cost electricity from the market, resulting in a greater revenue uplift for Castle River compared to Wintering Hills in the Merchant operations strategy cases. This is illustrated below in Figures 14 and 15. The Figures compare the model results for the Castle River and Wintering Hills battery cases over a 48-hour period starting 08:00 on October 22 and ending 07:00 on October 24. During most of the 48-hour period the Castle River wind power generating facility production was zero or close to zero resulting in almost no stored energy in the Behind-the-Fence operations strategy case and very little revenue, despite the market price spikes.

In comparison, under the Merchant operations strategy case Castle River, in effect, took advantage of the lower priced hours between the price spikes to fill the storage capacity and thereby had energy available to sell during the higher priced hours, which resulted in substantially more revenue. The storing of energy in any hour is shown in the Figures 14 and 15 by the negative values for electricity delivered to the grid for the Merchant case.

Figure 14: Castle River Battery Cases October 22 - 24

For Wintering Hills over the same 48-hour period, as shown in Figure 15, wind production starts out at zero or close to zero and then ramps up to over 40 MW for nearly twenty hours allowing the battery storage capacity to fill under both the Behind-the-Fence and Merchant strategies. The higher levels of electricity production from the Wintering Hills wind power generating


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facility significantly reduced the opportunity to purchase electricity during low priced periods and as a result the revenues over the 48 hour period for the Merchant operations strategy case are almost the same as for Behind-the-Fence case. While Figures 14 and 15 show only a 48-hour period it is expected that with higher level of energy production from Wintering Hills that similar situation occurred a number of times over the twelve-month period of the analysis.

Figure 15: Wintering Hills Battery Cases October 22 – 24

The higher efficiencies in the Merchant cases (Table 4) were a result of the increased cycling of the batteries that occurs in those cases. As the batteries are cycled more, the time span between charging and discharging is reduced. Since charging cools the batteries and discharging heats batteries, a shorter time span between charging and discharging means the batteries do not consume as much auxiliary energy for heating between charging and discharging events. The result is that as the rate of cycling increased, discharge energy increased, auxiliary energy decreased, and efficiency increased.

Table 4: NaS Battery Cases - Efficiency Results

Wind Power Project

Operations Strategy

Discharge Energy Grid Side (MWh)

Charge Energy Grid Side (MWh)

Auxiliary Energy (MWh)

Overall Efficiency

Average DOD

Castle River Behind the Fence 26,285 34,032 8,737 61.5% 40%

Wintering Hills Behind the Fence 31,777 41,084 7,807 65.0% 48%

Castle River Merchant 38,000 50,193 6,614 68.3% 56%

Wintering Hills Merchant 38,298 49,526 6,701 68.1% 56%


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6.1.2 Compressed Air Energy Storage

Unlike the Battery storage systems, the CAES system generated more energy than is stored due to the use of natural gas in the generating stage. The use of natural gas more than offset the losses in a CAES system. The Charging Electricity Ratio (CER), a performance index used for comparing CAES systems that is calculated as the ratio of generator output to compressor motor input was 1.27 for the modelled system. The typical CER range for CAES system is 1.2 to 1.8. (Succar S., Williams R., 2008) As previously stated, the objective of this study is not to develop optimised energy storage systems, but rather to provide an indication of the potential value of a CAES system. A CER at low end of the typical range does indicate that there may be additional value to be gained through optimisation of the CAES system. In Table 5 below, the Power Output Change was calculated as the Net Energy Deliveries minus the Wind Production.

Table 5: CAES Cases - Operational Results

Wind Power Project

Operations Strategy

Wind Production

(MWh)


(MWh)

Sold to Grid

(MWh)


(MWh)


Change (%)

Natural Gas Requirement

(GJ*)

Castle River Behind-the-Fence 142,985

149,914 149,914 6,929 4.8% 149,829

Wintering Hills Behind-the-Fence 167,726

176,115 176,115 8,388 5.0% 180,736

Castle River Merchant 142,985 21,331 174,424 153,093 10,107 7.1% 217,997

Wintering Hills Merchant 167,726 15,742 193,420 177,678 9,952 5.9% 215,328

* Higher Heating Value

As with the Battery cases, the revenues for each of the CAES cases were higher than the corresponding Base Case revenues. The cost of natural gas and associated transportation charges were calculated using the actual 2012 daily natural gas prices for Alberta and prevailing pipeline tariff. The Revenue Change values, shown in Table 6, were net of the all-in cost of natural gas, any applicable transmission costs related to delivery or supply of electricity to the Alberta grid and all capital and operating costs.

Table 6: CAES Cases - Financial Results

Wind Power Project

Operations Strategy


($ Million)


($ Million)

Revenue Change ($ Million)

Revenue Change

(%)


($/MWh)

Cost of Natural Gas

($ Million)

Castle River Behind-the-Fence $5.2 $7.6 $2.4 45% $54.65 $0.4

Wintering Hills Behind-the-Fence $7.8 $11.2 $3.4 43% $67.71 $0.5

Castle River Merchant $5.2 $10.7 $5.5 105% $70.26 $0.6

Wintering Hills Merchant $7.8 $12.6 $4.8 62% $72.14 $0.5

Similar to the Battery cases, the increase in revenues between the Merchant and Behind-the-Fence operations strategies was greatest for Castle River, even though Wintering Hills generated the largest revenues under each of the strategies. Figures 16 and 17, which are similar to Figures 14 and 15, compare the modelling results for Castle River CAES cases for the same 48-hour period from 08:00 on October 22nd to 07:00 on October 24th. Again, Castle River under the Merchant operations strategy was able to take advantage of the price volatility to significantly increase revenues compared to the Behind-the-Fence operations strategy.


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Figure 16: Castle River CAES Cases October 22 - 24

Figure 17: Wintering Hills CAES Cases October 22 - 24

The relationship between capacity factor and the incremental revenues achieved from the use of energy storage was explored further. Tables 7 and 8 on the next page, show a distribution of energy generated by Castle River and Wintering Hills during the twelve-month analysis period, grouped according to the corresponding daily average output in 10 MW tranches, along with the revenues calculated for each grouping and operations strategy.


PAGE 35


Table 7: Comparison of Revenues and Production – Castle River CAES

Daily Avg. Wind Production

(MW)

Energy Generated

(MWh) Days

Base Case Revenue

($)

Behind-the-Fence Revenue

($)

Merchant Revenue

($)

40 - 50 29,706 29 $871,717 $850,963 $861,740

30 -40 46,659 56 $1,237,332 $1,206,358 $1,216,075

20 - 30 35,615 60 $1,205,167 $1,426,518 $1,520,832

10 - 20 19,654 56 $1,058,356 $1,473,636 $1,725,967

0 - 10 11,352 150 $836,289 $2,596,995 $4,618,227

0 - 15 $- $10,896 $737,038

Totals 142,985 366 $5,208,861 $7,565,365 $10,679,880

For Castle River (Table 7), in both the Behind-the-Fence and Merchant operations strategy cases, a majority of the revenue increases occurred during the hours when the wind power generating facility output was between zero and 10 MW. Furthermore, for the Merchant operations strategy case, about 14 per cent of the total revenue increase was realised during the hours when the output from wind power generating facility was zero. Compared to those for the Base Case, revenues actually declined under both operations strategies when the wind power generating facility daily average output was greater than 30 MW.

Overall, Castle River realised, a 45 per cent increase in revenues with the Behind-the-Fence operations strategy, and over a 100 per cent increase in revenues with the Merchant operations strategy.

Table 8: Comparison of Revenues and Production – Wintering Hills CAES

Daily Avg. Wind Production

(MW)

Energy Generated

(MWh) Days

Base Case Revenue

($)

Behind-the-Fence Revenue

($)

Merchant Revenue

($)

40 – 50 24,893 24 $1,176,605 $1,197,634 $1,204,815

30 -40 41,943 50 $1,929,546 $2,301,728 $2,305,948

20 – 30 44,514 75 $2,207,169 $3,042,885 $3,040,234

10 – 20 44,189 122 $1,889,574 $3,008,556 $3,435,447

0 – 10 12,187 93 $611,098 $1,622,570 $2,655,347

0 - 2 $- $1,156 -$1,546

167,726 366 $7,813,992 $ 11,174,531 $12,640,244

The results for Wintering Hills are a little different. In both the Behind-the-Fence and Merchant operations strategy cases, a majority of the revenue increases occurred over a larger range of daily average outputs from zero to 30 MW. On the days when the wind power generating facility output was zero the revenue increase was very small for Behind-the-Fence operation and negative for the Merchant operation. Unlike the Castle River cases the revenues for both operating strategies did not decline when the daily average output was greater than 30 MW.

The Behind-the-Fence strategy generates considerable value above zero MW and below 30 MW. On those days, Wintering Hills generated 100,890 MWh, or over 50 per cent more energy compared to 66,621 MWh for Castle River. Since the Behind-the-Fence strategy depends on


PAGE 36


wind generation to inject into storage, storage at Wintering Hills was able to create more value than storage at Castle River under this strategy.

At Wintering Hills, the Merchant strategy generates the most value on days with less than 10 MW of average generation. However, Wintering Hills had only 95 such days, while Castle River had 165 of such days. The result is that the Merchant strategy had a bigger impact on storage at Castle River than storage at Wintering Hills.

Table 9: CAES Cases – Efficiency Results

Wind Power Project

Operations Strategy



Natural Gas Requirement

(GJ*)

Overall Efficiency

Castle River Behind-the-Fence 33,295 26,283 149,829 48.8%

Wintering Hills Behind-the-Fence 40,163 31,714 180,736 48.8%

Castle River Merchant 48,444 38,253 217,997 48.8%

Wintering Hills Merchant 47,628 37,609 214,328 48.8%

* Higher Heating Value

CAES systems are not as efficient as battery systems under both the Behind-the-Fence and Merchant strategies (Table 9). However, CAES does produce more energy per unit of electricity stored due to the use of natural gas. Additionally, there is almost no variability in the Overall Efficiency because, unlike batteries, the energy requirements do not vary with the cycling dynamics.

6.1.3 Power-to-Gas 1

The Power-to-Gas 1 systems experience the largest losses of the four energy systems examined by the study (Tables 10 and 11).

Table 10: Power-to-Gas 1 - Operational Results

Wind Power Project

Operations Strategy

Wind Production

(MWh)


(MWh)

Sold to Grid

(MWh)


(MWh)


Change %

Compression Energy MWh

Castle River Behind the Fence 142,985

114,680 114,680 -28,305 -19.8% 212

Wintering Hills Behind the Fence 167,726

134,286 134,286 -33,440 -19.9% 251

Castle River Merchant 142,985 35,051 136,917 101,866 -41,119 -28.8% 308

Wintering Hills Merchant 167,726 31,030 157,051 126,021 -41,705 -24.9% 313

Table 11: Power-to-Gas 1 - Financial Results

Wind Power Project

Operations Strategy


($ Million)


($ Million)

Revenue Change

($ Million)

Revenue Change

(%)


($/MWh)

Cost of Compression Energy

($ Million)

Castle River Behind the Fence $5.21 $5.34 $0.14 2.6% $46.66 $0.01

Wintering Hills Behind the Fence $7.81 $8.12 $0.30 3.9% $60.05 $0.01

Castle River Merchant $5.21 $6.46 $1.25 24.0% $55.19 $0.01

Wintering Hills Merchant $7.81 $8.57 $0.75 9.6% $58.53 $0.01


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As a result of the increased losses, the Modelled Net Revenue was almost the same as the Base Case Net Revenue for the Behind-the-Fence cases. For the Merchant cases the Revenue Change was far below that seen for NaS battery and CAES technologies. As with the other technologies, the Merchant case resulted in significantly more revenue than the Behind-the-Fence cases and particularly for Castle River. Also similar to the NaS battery and CAES results, Wintering Hills total revenues were larger than those for all modelled cases for Castle River. The revenue values shown do not include any deductions for applicable transmission costs related to delivery or supply of electricity to the Alberta grid, or capital and operating costs.

Table 12: Power-to-Gas 1 - Efficiency Results

Wind Power Project

Operations Strategy Discharge Energy

Grid Side (MWh)


Compression Energy (MWh)

Overall Efficiency

Castle River Behind the Fence 15,862 44,167 212 35.7%

Wintering Hills Behind the Fence 18,796 52,236 251 35.8%

Castle River Merchant 23,058 64,178 308 35.8%

Wintering Hills Merchant 23,405 65,110 313 35.8%

The overall efficiency for the Power-to-Gas technologies was about 36 per cent. The low efficiency results in lower operational and financial outcomes compared to the other energy storage systems modelled.

6.1.4 Power-to-Gas 2

The operational results for the Power-to-Gas 2 cases were not as good as results for the Power-to-Gas 1 cases; and, this is primarily due to the lower overall efficiency of the Power-to Gas 2 process compared to that of the Power-to-Gas 1 process.

Table 13: Power-to-Gas 2 - Operational Results

Wind Power Project

Operations Strategy

Wind Production

(MWh)


(MWh)

Sold to Grid

(MWh)


(MWh)


Change (%)


Castle River Behind-the-Fence 142,985

112,530 112,530 -30,456 -21.3% 4,933

Wintering Hills Behind-the-Fence 167,726

131,970 131,970 -35,756 -21.3% 5,809

Castle River Merchant 142,985 41,331 138,914 97,582 -45,403 -31.8% 7,364

Wintering Hills Merchant 167,726 36,791 158,736 121,944 -45,782 -27.3% 7,357

The compression requirements for cavern storage of hydrogen resulted in higher losses and lower revenues for all of the Power-to-Gas 2 cases compared to corresponding Power-to-Gas 1 cases (Tables 14 and 15, next page). Similar to the previous cases, the revenue values shown do not include any deductions for applicable transmission costs related to delivery or supply of electricity to the Alberta grid, or capital and operating costs.


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Table 14: Power-to-Gas 2 - Financial Results

Wind Power Project

Operations Strategy


($ Million)


($ Million)

Revenue Change ($

Million)

Revenue Change

(%)


($/MWh)

Cost of Compression Energy

($ Million)

Castle River Behind-the-Fence $5.21 $5.03 -$0.18 -3.5% $48.99 $0.49

Wintering Hills Behind-the-Fence $7.81 $7.69 -$0.12 -1.5% $63.05 $0.63

Castle River Merchant $5.21 $5.80 $0.59 11.3% $58.53 $1.02

Wintering Hills Merchant $7.81 $7.96 $0.15 1.9% $62.99 $0.92

As indicated earlier, the overall efficiency of the Power-to Gas 2 system, as shown in Table 15, was lower than the efficiency of the Power-to-Gas 1 system.

Table 15: Power-to-Gas 2 - Efficiency Results

Wind Power Project

Operations Strategy




Overall Efficiency

Castle River Behind-the-Fence 17,324 47,780 4,933 32.9%

Wintering Hills Behind-the-Fence 20,402 56,159 5,809 32.9%

Castle River Merchant 25,863 71,266 7,364 32.9%

Wintering Hills Merchant 26,097 71,879 7,357 32.9%


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6.2 SENSITIVITY CASES

6.2.1 Transmission Demand and Supply Charges

Tables 16 shows the results of a calculation of the STS charges applicable to a battery energy storage facility at Wintering Hills operated under the Behind-the-Fence operations strategy. In total, the STS charges increased by about 30 per cent over the Base Case STS charges, even though a smaller quantity of electricity was delivered to the grid. Since, STS charges are linked to the hourly market price at the time of delivery, and given that the objective of the energy storage facility was to shift the energy produced by the wind power generating facility from lower priced hours to higher priced hours, it should not be surprising that the STS charges increased, despite the reduced quantity of electricity that was delivered to the grid.

Table 16: Wintering Hills Battery Behind-the-Fence Case with STS

Month Hours Average

Pool Price ($)

Contract Capacity

(MW)

Peak Demand

(MW)

Base Case Wind Power Output

(MW)

Delivered to the Grid

(MW)

Loss Factor

(%)

Base Case STS Charge

($)

Behind-the-Fence Case STS Charge

($)

Jan 744 $84.54 50.0 50.0 18,441 17,747 4.84% $47,211 $55,723

Feb 696 $43.67 50.0 50.0 14,650 13,859 4.84% $33,120 $42,624

Mar 744 $51.08 50.0 50.0 18,645 17,714 4.84% $59,119 $70,977

Apr 720 $41.69 50.0 50.0 15,915 15,229 4.84% $23,095 $27,063

May 744 $29.46 50.0 50.0 12,880 12,163 4.84% $10,875 $14,742

Jun 720 $49.30 50.0 50.0 13,481 12,555 4.84% $19,242 $32,162

Jul 744 $68.39 50.0 50.0 11,080 10,366 4.84% $12,798 $21,475

Aug 744 $56.54 50.0 50.0 12,028 11,265 4.84% $20,245 $29,974

Sep 720 $110.39 50.0 50.0 12,588 11,722 4.84% $27,412 $40,812

Oct 744 $91.36 50.0 50.0 13,947 13,338 4.84% $46,157 $55,285

Nov 720 $87.41 50.0 50.0 12,164 11,404 4.84% $37,623 $49,390

Dec 744 $57.62 50.0 50.0 11,908 11,058 4.84% $22,034 $27,953

Total

167,726 158,419

$358,931 $468,180

Similar to the Wintering Hills Battery Behind-the-Fence case the STS charges in the second sensitivity case based on the Castle River CAES Behind-the-Fence case, shown in Table 17, also increased. In this second case, due to the additional electricity generated from the use of natural gas, the increase in STS charges was about 50 per cent over the Base Case STS charges.


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Table 17: Castle River CAES Behind-the-Fence Case with STS

Month Hours Average

Pool Price ($)

Contract Capacity

(MW)

Peak Demand

(MW)

Base Case Wind Power Output

(MW)

Delivered to the Grid

(MW)

Loss Factor

(%)

Base Case STS Charge

($)

Behind-the-Fence Case STS Charge

($)

Jan-2012

744 $84.54 50.0 50.0 21,539 22,049 2.23% $19,814 $22,493

Feb-2012

696 $43.67 50.0 50.0 12,709 13,422 2.23% $13,984 $22,370

Mar-2012

744 $51.08 50.0 50.0 14,822 15,613 2.23% $16,942 $22,211

Apr-2012

720 $41.69 50.0 50.0 10,231 10,571 2.23% $5,600 $7,458

May-2012

744 $29.46 50.0 50.0 11,690 12,265 2.23% $8,464 $12,312

Jun-2012

720 $49.30 50.0 50.0 12,243 12,976 2.23% $8,343 $19,298

Jul-2012 744 $68.39 50.0 49.8 5,806 6,100 2.23% $3,878 $7,920

Aug-2012

744 $56.54 50.0 50.0 4,767 5,190 1.69% $3,979 $6,779

Sep-2012

720 $110.39 50.0 50.0 8,300 8,879 1.69% $4,268 $8,984

Oct-2012

744 $91.36 50.0 50.0 10,590 11,374 1.69% $6,608 $10,278

Nov-2012

720 $87.41 50.0 50.0 14,370 15,097 1.69% $8,880 $15,125

Dec-2012

744 $57.62 50.0 50.0 15,919 16,376 1.69% $9,210 $12,677

Total

142,985 149,914

$109,970 $167,906

The next two sensitivity cases differ from the previous two sensitivity cases in using the Merchant operations strategy instead of the Behind-the-Fence strategy. As previously discussed in Section 5, the purchases of grid electricity that occur with the Merchant strategy are subject to DTS charges. Tables 18 and 19 show the estimated monthly DTS charges applicable to the Wintering Hills Battery Merchant case and the Castle River CAES Merchant case, respectively.

The revenue improvement for Wintering Hills Battery Merchant, shown in Table 3, would be reduced by almost 57 per cent from the application of the DTS tariff. Similarly, the revenue improvement for the Castle River CAES Merchant case, shown in Table 6, would be reduced by about 30 per cent.

Table 18: Wintering Hills Battery Merchant Case

Month Hours Average

Pool Price ($)

Contract Capacity

(MW)

Peak Demand

(MW)

Demand Ratchet (MW)

Billing Demand

(MW)

Load Factor (%)

DTS Charge ($)

Jan 744 $84.54 31.6 31.6 28.44 31.6 5.18% $137,547

Feb 696 $43.67 31.6 31.5 28.44 31.5 6.66% $137,674

Mar 744 $51.08 31.6 31.5 28.44 31.5 5.28% $137,273

Apr 720 $41.69 31.6 31.6 28.44 31.6 4.75% $137,794

May 744 $29.46 31.6 27.0 28.44 28.4 5.13% $129,501

Jun 720 $49.30 31.6 31.6 28.44 31.6 8.07% $139,203

Jul 744 $68.39 31.6 31.6 28.44 31.6 5.93% $138,662

Aug 744 $56.54 31.6 31.6 28.44 31.6 6.36% $138,874

Sep 720 $110.39 31.6 31.6 28.44 31.6 8.09% $139,618

Oct 744 $91.36 31.6 31.6 28.44 31.6 10.36% $141,428

Nov 720 $87.41 31.6 31.6 28.44 31.6 8.82% $140,418

Dec 744 $57.62 31.6 31.6 28.44 31.6 9.92% $141,187

Total

$1,659,178


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Table 19: Castle River CAES Merchant Case

Month Hours Average

Pool Price ($)

Contract Capacity

(MW)

Peak Demand

(MW)

Demand Ratchet (MW)

Billing Demand

(MW)

Load Factor (%)

DTS Charge ($)

Jan 744 $84.54 31.6 31.5 28.44 31.5 5.67% $137,395

Feb 696 $43.67 31.6 31.5 28.44 31.5 7.41% $137,842

Mar 744 $51.08 31.6 31.5 28.44 31.5 5.07% $137,185

Apr 720 $41.69 31.6 31.5 28.44 31.5 6.06% $138,110

May 744 $29.46 31.6 31.5 28.44 31.5 3.66% $137,093

Jun 720 $49.30 31.6 31.3 28.44 31.3 5.18% $137,343

Jul 744 $68.39 31.6 31.4 28.44 31.4 5.60% $137,973

Aug 744 $56.54 31.6 31.5 28.44 31.5 4.72% $137,779

Sep 720 $110.39 31.6 31.5 28.44 31.5 11.52% $141,023

Oct 744 $91.36 31.6 31.5 28.44 31.5 12.84% $142,537

Nov 720 $87.41 31.6 31.5 28.44 31.5 11.04% $141,339

Dec 744 $57.62 31.6 31.5 28.44 31.5 8.36% $140,049

Total

$1,665,668

6.2.2 Operating Reserve Market

Two sensitivity cases were developed to examine the potential revenue improvements that can be gained from participation in the Alberta operating reserve (OR) markets. The first scenario is based on the Wintering Hills Merchant Battery case and participation in the active regulating reserve market for the AM Super Peak block. The second scenario is based on the same Wintering Hills case and participation in the standby spinning reserve market for the On Peak block. The switch price for each case is used to determine which hours the Wintering Hills facility is accepted to provide either regulated reserves or standby spinning reserves. More details on how the switch price was used are available in Section 5.

Table 20: Operating Reserve Market Sensitivity Results – Wintering Hills Battery Merchant Case

Ancillary Service

Modelled Energy

Revenue ($ Million)

Standby Revenue

($ Million)

Activation Revenue

($ Million)

Directive Revenue

($ Million)

Total Revenue

($ Million)

Change from Wind Only ($ Million)

Value of Ancillary Services

($ Million)

None $10.77

$10.79 $2.96

Active RR AM Super-Peak $10.75

$0.56 $0.12 $11.43 $3.62 $0.66

Standby SR On-peak $10.76 $0.18 $0.21 $0.01 $11.16 $3.35 $0.39

The results presented in Table 20, are broken down to show the individual payments an operator is potentially to receive from participation in the OR markets. For the case involving participation in the active regulating reserve, the operator will be paid activation payment when its offer is deemed to have been accepted; and if actually directed to provide energy, the operator is paid the applicable hourly market price for all energy delivered. The directive revenue shown in Table 20 assumes the operator is directed to provide energy in each hour of the three hours of AM Super Peak block.


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For the standby spinning reserve the model assumes that when the operator’s standby offer is deemed to have been accepted, the operator is paid the standby fee for all the hours of the On-Peak block and the activation fee for all hours in which activation energy is called for by the AESO. In addition, once activated the model assumes that the facility has been directed to provide energy for only one hour; and hence the reason the directive revenue is so much smaller than the standby and activation revenue.

Overall, the opportunity to participate in the OR markets should be attractive to energy storage operators, even though some opportunities in the hourly energy are forgone. One point of clarification, the Wintering Hills Merchant Battery case was chosen for modelling participation in both the active regulating reserve and standby spinning reserve market, despite the fact that the current rules for spinning reserve limit participation only to generators, to avoid introducing any uncertainty in results by using two different storage technologies. There is no reason to believe the results for CAES or Power-to-Gas would be materially different from those observed for batteries.

6.2.3 Increased Storage

CAES and Power-to-Gas 1 were used to evaluate the effect of increasing the energy storage capacity. The results of the evaluation are shown in Tables 21 and 22 below.

For CAES, two sensitivity cases were modelled based on the Wintering Hills wind facility and the Merchant operations strategy. The first sensitivity case analysed the benefits of doubling the available storage capacity from 210 MWh to 420 MWh and the second sensitivity case analysed the effect of increasing the energy storage capacity by a factor of ten. The results show that the increased storage capacity would increase revenues. Doubling the energy storage capacity increased revenues by almost 20 per cent and increasing by a factor of ten, increased revenue by an incremental 13 per cent over the doubling case (Table 21). This sensitivity analysis does not consider the incremental cost of expanding the storage capacity.

Table 21: Increased Storage Capacity Sensitivity Results – Wintering Hills CAES Merchant Case Storage Capacity (MWh)

Discharge Capacity

(MW)

Continuous Discharge Capability

(Hours)

Modelled Energy Revenue

($ million)

Change from Wind Only

($ million)

Revenue Change from the 210 MWh Storage Case

(%)

210 30 7 $12.64 $4.83

420 30 14 $15.08 $7.27 19.3

2,100 30 70 $16.83 $9.02 33.1


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For Power-to-Gas 1, five sensitivity cases were modelled based on the Wintering Hills wind facility and the Merchant operations strategy. The first two cases were the same as the two cases evaluated for CAES, which is, doubling the energy storage capacity and increasing the energy storage capacity by a factor of ten. However, since for Power-to-Gas 1 energy was stored in a natural gas storage reservoir that compared to the other energy storage technologies had significantly larger storage capacity, three additional sensitivity cases were modelled. The third sensitivity case evaluated increasing the energy storage by a factor of 100 or almost a month of storage capacity at the maximum discharge rate of 30 MW. The fourth and fifth sensitivity cases evaluated increasing the energy storage by a factor of 600 to provide almost six months of energy storage capacity at the maximum discharge rate. The fifth case differed from the fourth case by increasing the discharge capacity from 30 MW to 300 MW.

Table 22: Increased Storage Capacity Sensitivity Results – Wintering Hills Power-to-Gas 1 Merchant Case

Storage Capacity (MWh)

Discharge Capacity

(MW)

Continuous Discharge Capability

(Hours)

Modelled Energy Revenue

($ million)

Change from Wind Only

($ million)

Revenue Change from the 210 MWh Storage Case

(%)

210 30 7 $8.57 $0.76

420 30 14 $9.75 $1.94 13.8%

2,100 30 70 $10.49 $2.68 22.4%

21,000 30 700 $10.47 $2.66 22.2%

126,000 30 4200 $9.21 $1.40 7.5%

126,000 300 420 $9.76 $1.95 13.9%

The results of the increased energy storage evaluation for Power-to-Gas 1, shown in Table 22, show increasing revenue improvements in up to the cases with 2,100 MWh and 21,000 MWh. The last two sensitivity cases with 126,000 MWh10 of storage capacity resulted in lower revenues. Figures 18 and 19 show modelled performance of the energy storage cases. The cases show that as energy storage capacity increases the utilisation of the capacity declines. Even increasing the discharge capacity by a factor of ten as with the last case – 126,000 MWh / 300 MW- does not improve the utilisation significantly.

10

126,000 MWh of energy storage capacity is the equivalent of 12 hours of continuous discharge at the peak demand recorded in 2012 of 10,609 MW.


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Figure 18: PtG 1 Sensitivity Cases – Utilization of Increased Energy Storage Capacity

Figure 19: PtG 1 Sensitivity Cases – Utilization of Increased Energy Storage Capacity

The performance of these cases shows how price volatility in the Alberta electricity market affects the utilisation of the energy storage capacity. Up to a point increasing energy storage capacity resulted in improved revenues but not beyond 2,100 MWh or 70 hours of capacity.


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6.3 COMPARISON TO THE 2011 STUDY RESULTS

Table 23 compares the results for battery storage at Castle River from the previous study with 2012 data under both static and dynamic power price assumptions. In the 2011 study, power prices were held constant despite storage behaviour. In the results presented in the preceding section, real time power prices were adjusted depending on injections or withdrawals from storage. In the table below, the storage utilizes the Behind-the-Fence strategy and does not purchase power from the grid11. The results for 2007-2010 have been annualised for comparison purposes.

Table 23: Comparison of Battery Results for Castle River

Years Effect of

Storage on Power Price

Annual Average Wind

Production (MWh)

Annual Energy Change (MWh)

Energy Change

(%)

Annual Base Case Net Revenue

($ Million)

Revenue Change

($ Million)

Revenue Change

(%)

Average Pool Price ($/MWh)

2007 - 2010 Static 136,966 -4,699 -3.4% $7.3 $1.1 15% $63.91

2012 Static 142,985 -4,817 -3.4% $5.2 $1.8 34% $64.32

2012 Dynamic 142,985 -3,192 -2.2% $5.2 $1.1 21% $62.28

In comparison, the 2007- 2010 time period and 2012 were very similar: wind production was only four per cent higher and the pool price was only one per cent higher. However the timing of wind production resulted in 30 per cent less revenue for the Castle River wind facility. Lower base case revenue resulted in a much higher storage value when assumption of static prices was maintained; $1.8 million in 2012 vs. $1.1 million in the earlier period.

The introduction of the dynamic pricing reduced the value of storage to $1.1 million, or 21 per cent of base case revenue. On a per unit basis, dynamic pricing had an impact on the value of storage of $5.59 per MWh compared to static pricing.

Dynamic pricing also reduced the average pool price by $2.04. The difference between how the dynamic pricing affected the value of storage and the average pool price was due to the shape of the supply merit order curve shown in Figure 7. Withdrawals from storage, which reduced the electricity market price by effectively adding supply to the market, occur at higher prices where the slope of merit order curve is steeper. Meanwhile injections into storage, which increase the power price, occurred at lower prices where the slope of the merit order curve is almost flat.

11

This was referred to as the “Firming” strategy in the 2011 paper.


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6.4 SIMPLE CASHFLOW ANALYSIS

The financial analysis in Table 24 is similar to a cash flow analysis developed for the 2011 study and based on the assumptions and installed costs presented in the table illustrate the:

current value of a future stream of revenues equal to the annual revenue change;

current overnight installed cost of the storage facility; and

current value of excess revenues (current value of future revenues less the installed cost of a storage facility.

This analysis does give an indication of which technology and operating strategy is close to being financially viable in the Alberta market. A CAES system operating as Merchant facility appears to have a better chance of succeeding in Alberta.

Table 24: Simple Cashflow Analysis Assumptions

Installed Costs

Cost of Debt 7.0%

High Case Low Case

Cost of Equity 15.0%

NaS Batteries $/kW $4,000 $2,000

Debt Ratio 60%

CAES $/kW $3,000 $1,000

Equity Ratio 40%

Discount Rate 10.2%

Term 20

Storage Capacity (MW) 30

Annual

Revenue Change

Current Value of Future

Revenue

Storage Facility Installed Cost

Current Value of Future Excess Revenues

High Case Low Case High Case Low Case

Battery, Castle River, Behind-the-Fence $1,099,298 $10,174,315 $120,000,000 $60,000,000 $0 $0

Battery, Castle River, Merchant $4,003,824 $37,056,534 $120,000,000 $60,000,000 $0 $0

Battery, Wintering Hills, Behind-the-Fence $1,986,962 $18,389,904 $120,000,000 $60,000,000 $0 $0

Battery, Wintering Hills, Merchant $3,372,246 $31,211,094 $120,000,000 $60,000,000 $0 $0

CAES, Castle River, Behind-the-Fence $2,356,504 $21,810,117 $90,000,000 $30,000,000 $0 $0

CAES, Castle River, Merchant $5,471,019 $50,635,835 $90,000,000 $30,000,000 $0 $20,635,835

CAES, Wintering Hills, Behind-the-Fence $3,360,539 $31,102,744 $90,000,000 $30,000,000 $0 $01,102,744

CAES, Wintering Hills, Merchant $4,826,710 $44,672,578 $90,000,000 $30,000,000 $0 $014,672,578


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7 DISCUSSIONS

7.1 OVERALL

The comments in this section provide additional details, clarifications and discussion on aspects of the modelling process and results where it was deemed that additional information would be helpful to the reader.

7.1.1 Effects of the Behind-the Fence and Merchant Operating Strategies

The study has shown that co-locating an energy storage facility at a wind power generation facility results in an increase in total revenues. Under the Behind-the-Fence operating strategy, the selling prices achieved from storing electricity during low priced hours and withdrawing and selling the stored electricity during higher priced hours were at a minimum 28 per cent higher to a maximum of 50 percent higher than the average base case selling prices for the modelled wind power generating facilities. The higher selling prices were partially offset by losses and auxiliary energy requirements related to the operation of each of the energy storage technologies reviewed, resulting in net revenue changes (see Tables 3, 6, 11 and 14) of between 2 per cent and 45 per cent.

Wintering Hills realised the overall highest revenues in all cases using the Behind-the-Fence operating strategy and in all but one case, achieved the largest percentage increase in revenues. The Castle River case using the Behind-the-Fence operating strategy and a CAES energy storage system achieved a slightly higher revenue increase (45.2%) on a percentage basis than the comparable case for Wintering Hills (43.0%). The reasons for the slightly better percentage increase in revenue for Castle River are likely related to variability of the Castle River output and the characteristic of a CAES energy storage facility, which produces more energy, through the use of natural gas, than it stores. The modelled CAES energy storage facility at Wintering Hills was likely constrained due to the 50 MW transmission capacity limit for a few more hours than the modelled CAES facility at Castle River was.

Similarly, under the Merchant operating strategy selling prices were between 30 per cent and 93 per cent higher than the average selling prices in the base cases and resulted, after losses and auxiliary energy requirements, in net revenue increases (see Tables 3, 6, 11 and 14) of between 9 per cent and 105 per cent compared to base case revenues. In all of the cases modelled using the Merchant operating strategy the Wintering Hills cases achieved the highest overall revenues compared the Castle River cases. Somewhat unexpectedly and as discussed previously in Section 6.0, the more variable wind power generation facility, Castle River, realised the largest percentage revenue improvement from following the Merchant operating strategy for each of the energy storage technologies.

7.2 NAS BATTERY ENERGY STORAGE

Batteries offer a number of advantages over the other energy storage technologies studied:

Highest overall efficiency – 77 per cent;

Can be located anywhere unlike CAES and Power to Gas 2;


PAGE 48


The fastest response to changes in market conditions to switch from charging to discharging or vice-a-versa; and

Combining battery storage with a renewable energy source results in zero GHG emissions.

The significant disadvantage with batteries is the installed cost, which over time is expected to decline, in light of the significant global research effort into battery technologies.

7.3 CAES

The advantages of CAES are:

The only technology, other than pumped hydro and power-to-gas, capable of providing large scale bulk energy storage;

Using natural gas results in more energy being produced than stored;

Capable of switching quickly back and forth from charging to discharging;

Lowest unit overnight installed cost; and

Lower GHG emissions than conventional fossil fuel generating technologies.

The significant disadvantages of CAES are:

The need to locate in areas where underground storage can be economically developed; and

The exposure to volatile natural gas prices.

Current research into adiabatic and other advanced (hybrid CAES, i.e., CAES with solar thermal etc.) CAES systems may lead to CAES systems that have lower GHG emissions and are not dependent on natural gas.

7.4 POWER-TO-GAS 1

The advantages of Power-to-Gas 1 are:

Uses existing natural gas storage reservoirs, so there is no need to develop energy storage capacity;

Electrolysers can switch on and off quickly to adjust to market conditions; and

Combined with a renewable energy source Power-to-Gas 1 will not produce any GHG emissions.

The disadvantages of Power-to Gas 1 are:

Lower efficiency; and

Higher overnight installed cost in some configurations.

Electrolyser efficiencies are expected to increase with further development and research.


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7.5 POWER-TO-GAS 2

The advantages of Power-to-Gas 2 are:

Electrolysers can switch on and off quickly to adjust to market conditions; and

Low or no GHG emissions.

The disadvantages of Power-to-Gas 2 are:

High cost of solid oxide fuel cell and electrolysers;

The need to locate in areas where underground storage can be economically developed; and

The need to keep the fuel cell at 1,073 degrees Kelvin (about 800 degrees Celsius) reduces the overall efficiency and increases the cost of operation.

7.6 TRANSMISSION DEMAND AND SUPPLY CHARGES

The sensitivity analysis presented in Section 6.2.1 shows that the application of STS and DTS charges will reduce the incremental net revenues associated with operation of an energy storage facility as modelled by the study. While the transmission sensitivity analysis was not undertaken for all of the modelled cases, it is possible from the analysis that was done to draw a number of inferences on the effects of transmission charges on energy storage facilities, based on the current Alberta transmission tariff.

1. For behind-the-fence energy storage facilities and the associated wind power generating facilities, STS tariff costs will increase as a result of the higher electricity prices realised through the operation of the energy storage capacity.

2. Similarly, for merchant energy storage facility co-located with a wind power generating facility a portion of the increased STS charges will be due to higher prices realised on electricity generated by the wind facility that was stored and later sold.

3. Additionally, for merchant energy storage facilities all electricity purchased from the grid and stored will be subject to the DTS charges and when the same energy is withdrawn and sold, the energy will be subject to STS charges.

Increases in STS resulting from the storage of electricity generated “behind-the-fence” in case of 1 and 2 above should be expected by the operator of the energy storage facility and the wind facility operator. The tariff charges applied in the circumstances described in 3 will result in double charging or what is sometimes referred to as “rate pancaking”. Following the path of a quantity of electricity purchased by a merchant energy storage operator:

first, the initial generator of the electricity would have paid an STS charge on the quantity of electricity purchased by the energy storage operator;

next, the energy storage operator would pay a DTS charge when the electricity is withdrawn from the grid and stored;


PAGE 50


later, the energy storage operator would pay an STS charge when the electricity is withdrawn from storage and delivered to the grid; and

finally, the ultimate consumer of the electricity would pay a DTS charge when the electricity is delivered.

Without a doubt, this is a simplistic description as it does not account for losses and the effects of price changes between injection into storage and withdrawal from storage, but these are relatively small effects. In addition, a CAES facility using natural gas does generate incremental quantities of electricity. The situation is complex, but ultimately, a quantity of electricity following the path described would attract two STS and two DTS charges.

7.7 INCREASED STORAGE

Increasing the storage capacity of the modelled cases does result in increased revenues, up to a point, as previously discussed in Section 6.2.3. Building on that previous discussion, the key drivers for storage technology selection, sizing of energy storage capacity and charging and discharging capacity appear to be electricity market price volatility and shape of the supply merit curve.

Price volatility is a measure of how quickly prices change in a market that affects the value of storage capacity and the value of injection and discharge capacity. As an example, a market with relatively low price volatility, and characterised by higher winter and summer prices and lower prices in the interim months would favour the bulk storage technologies - CAES and Power-to-Gas - with lower unit costs for storage capacity. In the same market, storage capacity and charging and discharging capacity would likely be sized to allow as much as a month of continuous discharging at the peak discharge rate.

Conversely, markets characterised by high price volatility, like Alberta, favour storage technologies that can switch quickly from charging to discharging and that have lower charging and discharging costs. The optimum storage capacity in Alberta for the current market size and characteristics appears to be about three days at the peak discharge rate. Increasing the storage capacity beyond a few days results in higher costs and the stored energy does not get sold because the higher market prices do not persist long enough to allow the stored energy to be withdrawn, as shown in Figures 18 and 19. Increasing the discharge capacity also does not appear to help as shown in the last sensitivity case.

Increasing the discharge capacity increases the potentially available supply of electricity in any hour. As discussed in Section 6.3 with respect to the supply merit order curve, Figure 7, withdrawals from storage dampen market prices. The larger the discharge capacity, the larger the dampening effect on market prices. Again referring to Section 6.3, the analysis of effects of dynamic pricing showed that for Castle River, dynamic pricing reduced the value of storage by over $5.00/MWh. The discharge capacity of Castle River cases analysed was 30 MW so it is reasonable to expect that the effect of increasing the discharge capacity from 30 MW to 300 MW would likely be greater than $5.00/MWh.


PAGE 51


7.8 CAPITAL COSTS

Developing capital cost estimates for any of the modelled cases was outside the scope of the study. The Simplistic Cashflow sensitivity analysis shown in Section 6.4 does use installed unit cost estimates in order to develop a simple indication of the potential financial viability of battery energy storage and CAES. The cashflow sensitivity cases did not include either of the power-to-gas energy storage processes due to the fact that until very recently, there were no operating examples of either process and information on installed costs was not available.

Based on available information and some expert advice it is possible to provide a high level comparison of the installed costs of the energy storage technologies. Batteries have the highest reported installed costs, $2,000 to $4,000 per kW of charging capacity. The installed costs of CAES with salt cavern storage are in the range of 50 to 70 per cent of the installed costs of batteries, or $1,000 to $3,000 per kW of charging or discharging capacity. The installed cost of a Power-to-Gas 1 system would likely range from as low as $1,500 per kW of installed capacity for a case where the Power-to-Gas operator contracts with a third party owner of a natural gas fired generation facility for conversion of stored energy to electricity; to as high as $3,000 per kW for a case where the Power-to-Gas operator also develops the generation facility. One advantage the Power-to-Gas 1 process has is that it avoids the costs of developing storage capacity by using existing natural gas storage reservoirs. The installed cost of a Power-to-Gas 2 process is likely in the range of $3,000 to $4,000 given the current costs of solid oxide fuel cell and the requirement to develop a storage cavern or an alternative.

These installed cost estimates are provided for comparison purposes only. Ultimately, installed cost is only one of many factors that should be considered in analysing an energy storage opportunity.


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8 CONCLUSIONS

1. Wind generation facilities whose electricity output varies considerably day-to-day may benefit from installing energy storage capacity behind-the-fence of the wind facility.

2. Merchant energy storage may be the most attractive option for developing energy storage capacity in Alberta.

3. Electricity market price volatility and shape of the supply merit curve appear to be the key drivers for storage technology selection, sizing of energy storage capacity and charging and discharging capacity.

4. Markets with relatively low price volatility, and characterised by higher winter and summer prices and lower prices in the interim months would favour the bulk storage technologies – CAES and Power-to-Gas – with lower unit costs for storage capacity. Conversely, markets characterised by high price volatility, like Alberta, favour storage technologies that can switch quickly from charging to discharging and that have lower charging and discharging costs.

5. The optimal storage capacity for a merchant energy storage facility appears to about seventy hours of capacity at the peak discharge rate.

6. Based on the simplified present value of revenue cash flows, publicly available capital cost for the considered technologies and selling price of natural gas during the analysis period, CAES has the most financially attractive business case for energy storage in Alberta.

7. The operating reserve markets are attractive markets for energy storage operators.


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9 RECOMMENDATIONS

This study did not explore many of the other important aspects of energy storage, some of which would be of special interest for Alberta as well as candidates for future work12. For example, certain energy storage configurations (e.g., adiabatic CAES and power-to-gas) could be candidates for lowering the carbon intensity of energy production in Alberta. Diesel power generation with energy storage could be explored for remote applications. Power-to-gas provides opportunities for interplay between electricity, gas and heat markets, and how energy storage could optimally play in those markets is yet to be understood. Power-to-gas generates an energy vector, hydrogen, which could be channelled into different value propositions (transportation and heating fuel, and chemicals production) and those value propositions could be explored in Alberta.

12 Impacts related to electricity market operation and rules, transmission and distribution infrastructure are being

already considered by the Alberta Electric System Operator.


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10 REFERENCES

Akhil, A, G Huff, A B Currier, B C Kaun, and D M Rastler. DOE/EPRI 2013 Electricity Storage Handbook in Collaboration with NRECA. SAND2013-5131. Albuquerque, New Mexico and Livermore, California: Sandia National Laboratory, United States Department of Energy, 2013

Bachu S. and Rothenburg L., 2003, Carbon Dioxide Sequestration in Salt Caverns: Capacity and Long Term Fate. Presented at 2nd Annual Conference on Carbon Sequestration. Available November 15, 2013 at http://www.netl.doe.gov/publications/proceedings/03/carbon-seq/PDFs/011.pdf.

Bito, A. Overview of the Sodium-Sulfur Battery for the IEEE Stationary Battery Committee. Audio, N.p.: Transactions of the IRE Professional Group, 2005.

Carlson, C. Wind and Energy Storage: Potential Market Opportunities in Alberta. Alberta: Alberta Innovates Technology Futures, 2012.

Chambers, A. Electric Energy Storage Technology. Alberta: Alberta Innovates Technology Futures, 2010.

Crotogino, F, S Donadei, U Bünger, H Landinger, D Stolten, and T Grube. “Large-Scale Hydrogen Underground Storage for Securing Future Energy Supplies”. Germany: 18th World Hydrogen Energy Conference, 2010.

Cutter, E, L Alagappan, and S Price. Impact of Market Rules on Energy Storage Economics. N.p.: E3 Economics, 2009.

Darmeveil, H, G van Dijk, D Last, G Pieters, M Rotink, J Overdiep, and B Slim. “Should We Add Hydrogen to the Natural Gas Grid to Reduce Co2 Emissions? (Consequences for Gas Utilization Equipment)”. Amsterdam: 23rd World Gas Conference, 2006.

Denholm, P, E Ela, B Kirby, and M Milligan. The Role of Energy Storage with Renewable Electricity Generation NREL/TP-6A2-47187. Golden, Colorado: National Renewable Energy Laboratory, 2010.

DeVries, K. L.,, K. D. Mellegard, G. D. Callahan, and W. M. Goodman, Cavern Roof Stability For Natural Gas Storage In Bedded Salt, United States Department of Energy National Energy Technology Laboratory Topical Report

RSI-1829, DE-FG26-02NT41651, June 2005Eckroad, S. 1MW/7.3 MWh NaS Battery Demonstration and Case Study. N.p.: Electric Power Research Institute, 2008.

Energy Information Administration. Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants. Washington, D.C.: United States Energy Information Administration, 2013.

EPRI, December 2010, Electricity Energy Storage Technology Options: a White Paper Primer on Applications, Costs and Benefits. Report 1020676, Palo Alto, CA.


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Eyer, J, and G Corey. Energy Storage for the Electricity Grid: Benefits and Market Potential Assessment Guide Sandia National Laboratories Report, SAND2010-0815, Albuquerque, New Mexico: Sandia National Laboratory, United States Department of Energy, 2010.

Figueiredo, F, P Flynn, and E Cabral. “The Economics of Energy Storage in 14 Deregulated Power Markets”. N.p.: Energy Studies Review 14 (2): 131–152, 2006.

Gardiner, M, and A Burke. “Comparison of Hydrogen Storage Technologies: a Focus on Energy Required for Hydrogen Input”. Cal. Vol. 1. Institute of Transportation Studies, University of California, Davis. 2002.

Goodson, J. History of First U.S. Compressed Air Energy Storage (CAES) Plant (110-MW-26 H). N.p.: Electric Power Research Institute, 1992.

Gotschall, H. 2008 Update to the EPRI-DOE Handbook Supplement of Energy Storage for Grid Connected Wind Generation Applications. N.p.: Technology Insights, 2008.

Grobe, M., 2000, Distribution and thickness of salt within the Devonian Elk Point Group, Western Canada Sedimentary Basin; Alberta Energy and Utilities Board, EUB/AGS Earth Sciences Report 2000-02, 12 p., 13 maps.

Gyuk, I. 2010. “Electrical Energy Storage for Grid Applications.” N.p.: United States Department of Energy, 2011.

Hajimolana, S Ahmad, and Masoud Soroush. 2009. “Dynamic Behavior and Control of a Tubular Solid-Oxide Fuel Cell System.” Pp. 2660–2665 in 2009 American Control Conference. St. Louis, MO.

Harrison, K. Analysis of Hydrogen and Competing Technologies for Utility-Scale Energy Storage. Golden, Colorado: National Renewable Energy Laboratory, United States Department of Energy, 2012.

Hauptmeier, I. Adiabatic Compressed Air Energy Storage in Context of the Markets. Brussels: RWE, 2013.

Himelic, J., Novachek F. Sodium Sulfur Energy Storage and Its Potential to Enable Further Integration of Wind (Wind-to-Battery Project) Xcel Energy Renewable Development Fund Contract #RD3-12. Xcel Energy, 2010.

Hussein, Z, L Cheung, M Siam, and A Ismail. “Modeling of Sodium Sulfur Battery for Power System Applications.” Elektrika 9 (2): 66–72, 2007.

Kintner-Meyer, M, PJ Balducci, C Jin, T Nguyen, M Elizondo, M Viswanathan, X Guo, and F Tuffner. Energy Storage for Power Systems Applications: a Regional Assessment for the Northwest Power Pool (NWPP) PNNL 19300. Richland, Washington: Pacific Northwest National Laboratory United States Department of Energy, 2010.

Lamont, A. Assessing the Economic Value and Optimal Structure of Large-Scale Electricity Storage. N.p.: Lawrence Livermore National Laboratory, U.S. Department of Energy, 2012.


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Lord, A, P Kobos, and D Borns. A Life Cycle Cost Analysis Framework for Geologic Storage of Hydrogen: a Scenario Analysis. SAND2010-6938. Albuquerque, New Mexico and Livermore, California: Sandia National Laboratory, United States Department of Energy, 2010.

Marean, J. Compressed Air Energy Storage Engineering and Economic Study. Albany, New York: New York State Energy Research and Development Authority (NYSERDA), 2009.

McGrail, B, J Cabe, C Davidson, F Knudsen, D Bacon, M Bearden, M Charmness, et al. Techno-economic Performance Evaluation of Compressed Air Energy Storage in the Pacific Northwest. Richland, Washington: Pacific Northwest National Laboratory, U.S. Department of Energy, 2013.

Meiwes, H. “Technical and Economic Assessment of Storage Technologies for Power-Supply Grids.” Acta Polytechnia 49 (No. 2-3/2009) (September 2): 1–6, Czech Technical University Publishing House, 2009.

Melaina, M, O Antonia, and M Penev. Blending Hydrogen Into Natural Gas Pipeline Networks: a Review of Key Issues. NREL/TP-5600-51995. Golden, Colorado: National Renewable Energy Laboratory, United States Department of Energy, 2013.

Minh, N. Reversible Solid Oxide Fuel Cells for Power Generation and Hydrogen/Chemical Production. Fuel Cell Seminar 2011, Orlando Florida, November 1-3, 2011, 2011.

Moutoux, R, and F Barnes. Wind Integrated Compressed Air Energy Storage in Colorado. Boulder , Colorado: University of Colorado at Boulder. 2007.

Narang, A. PG&E Compressed Air Energy Storage in California Report. San Francisco: Pacific Gas & Electric Company, 2012.

Penev, M. Hydrogen Energy Storage. Golden, Colorado: National Renewable Energy Laboratory, United States Department of Energy, 2013.

Rastler, D, Electricity Energy Storage Technology Options. Palo Alto, California: Electric Power Research Institute. 2010.

Redissi, Y, H Er-rbib, and C Bouallou. Storage and Restoring the Electricity of Renewable Energies by Coupling with Natural Gas Grid. Paris, France: MINES ParisTech, Centre Energetique et Procedes (CEP), 2013.

Reynolds A., et al., Energy Storage-Making Intermittent Power Dispatchable, Alberta Innovates-Technology Futures, 2011.

Ridge Energy Storage & Grid Services L.P. The Economic Impact of CAES on Wind in Texas, Oklahoma and New Mexico. N.p.: Texas State Energy Conservation Office and the U.S. Department of Energy, 2005.

Sanchis, P. and L.M.G. and A.U., 2012. Hydrogen Production from Water Electrolysis: Current Status and Future Trends. , 100(2).

Saran, P, J Goentzel, and C Siegert. Economic Analysis of Wind Plant and Battery Storage Operation Using Supply Chain Management Techniques. Power and Energy Society General Meeting, 2010. IEEE, 2010.


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Sedghisigarchi, Kourosh, Student Member, Ali Feliachi, and Senior Member. 2004. “Dynamic and Transient Analysis of Power Distribution Systems With Fuel Cells — Part I: Fuel-Cell Dynamic Model.” IEEE Transactions on Energy Conversion 19(2):423–428.

Sedghisigarchi, Kourosh, Student Member, Ali Feliachi, Senior Member, and A Active Power Output. 2004. “Dynamic and Transient Analysis of Power Distribution Systems With Fuel Cells — Part II: Control and Stability Enhancement.” IEEE Transactions on Energy Conversion 19(2):429–434.

Schainker, R. Compressed Air Energy Storage(CAES) Technology: Lessons Learned. Palo Alto, California: Electric Power Research Institute, 2011.

Schoenung, S. Economic Analysis of Large-Scale Hydrogen Storage for Renewable Utility Applications SAND2011-4845. Albuquerque, New Mexico and Livermore, California: Sandia National Laboratory, United States Department of Energy, 2011.

Schulte, R, K Holst, N Critelli, and G Huff. Lessons From Iowa: Development of a 270 Megawatt Compressed Air Energy Storage Project in Midwest Independent System Operator SAND2012-0388. Albuquerque, New Mexico and Livermore, California: Sandia National Laboratory, United States Department of Energy, 2012.

Shufelt, J. SMARTCAES Equipment Makes the Grade - Insights Winter 2010. Dresser –Rand Group Inc. 2010.

Specht, M., Baumgart, F., & Feigl, B. (2009). Storing bioenergy and renewable electricity in the natural gas grid, 69–78.

Succar S., Williams R., Compressed Air Energy Storage: Theory, Resources, And Applications for Wind Power. Princeton Environmental Institute. Princeton University. 2008

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Updated Capital Cost Estimates for Utility Scale Electricity Generating Plants. Washington, D.C.: U.S. Energy Information Administration. U.S. Department of Energy, 2013


PAGE A-1


11 APPENDICES

A. ALBERTA’S ELECTRICITY MARKET

This section reviews Alberta’s energy and ancillary markets. For the most part, the material below reproduces that was described in the 2011 study, but some sections have been revised to reflect changes that have occurred since 2011

A.1. ALBERTA ELECTRIC SYSTEM OVERVIEW

Alberta was the first jurisdiction in North America to open its wholesale electricity market to competition. The 1995 Electric Utilities Act removed the need for regulatory approval to build new generation, making investment in new generation capacity a business decision and risk to be borne by shareholders. It also created a mandatory power pool for the wholesale market through which all electricity would be exchanged. The 1998 Electric Utilities Amendment Act added significant reforms, creating independent organizations to oversee the operation of the markets and transmission system, setting the stage for addressing wholesale market power issues, and allowing competition in the retail market.

In 2005, the Alberta Department of Energy laid out a comprehensive policy framework for improving both the wholesale and retail markets (ADOE, 2005). One of the many areas identified as needing work was better integration of wind generation in the market and on the system.

A.2. MARKET STRUCTURES

Alberta has an energy-only wholesale market, operated by the Alberta Electric System Operator (AESO), which sets an hourly real-time price for electricity. The AESO is Alberta’s non-profit independent system operator. It is responsible for the efficient design and operation of the wholesale market, and for ensuring the reliability of the system as a whole. Reliability in the short run requires that the demand and supply of power be precisely balanced on a moment-to-moment basis, and this requires being able to compensate for normal fluctuations in demand throughout the day as well as unexpected events, such as a generating unit suddenly becoming unavailable or wind generation ramping up or down unexpectedly.

The AESO procures a variety of arrangements called ancillary services to ensure system reliability. Some of these services are common across all electricity markets; some address elements unique to the Alberta system.

There are also both forward physical and financial markets for electricity in Alberta. Forward financial contracts that allow market participants to hedge the hourly pool price are transacted through the Natural Gas Exchange (NGX) or over the counter (OTC) brokers13. Alberta’s Market

13

Some bilateral contracts are negotiated privately, particularly outside OTC operating hours.


PAGE A-2


Surveillance Administrator (MSA) believes that most participants hedge their exposure to the hourly pool price through the financial forward markets (MSA, 2010).14 Forward physical transactions are much less common.

Figure A1 - Alberta's Electricity Markets

The retail market is a mix of competitive supply and on-going regulation. Residential customers can enter into contracts with varying terms provided by competitive retailers or they can choose a regulated rate option that is approved by the Alberta Utilities Commission. All large industrial and commercial customers purchase power from competitive retailers or are self-retailers (AUC, n.d.).

A.3. DEMAND

Demand in Alberta follows fairly consistent daily, weekly, and seasonal patterns. Peak demand is highest during the winter, followed by summer, while spring and fall are the lowest. Demand is higher during the day (on peak hours) than during the night (off peak hours), and weekdays are higher than weekends.

Industrial customers account for an unusually high percentage of the energy demand in Alberta compared to other jurisdictions. Industrial consumption (including oilsands) accounted for approximately 60 per cent of the energy consumed in 2010 (AESO, 2012a)15. This means that changes in economic conditions are key drivers of energy usage and peak demand in Alberta.

So it is no surprise that there has been relatively little growth in demand over the last few years. From 2008 through 2012, peak demand grew by about two per cent and energy use by only 1per cent. However, growth in the oil and gas industry is expected to be high over the next five years. The AESO is forecasting annual peak load growth of four to five per cent through 2016, slowing

14

The MSA is responsible for monitoring the operation of natural gas and electricity markets in Alberta to ensure that the markets are working efficiently. 15

Long-term Transmission Plan (filed June 2012) (AESO, 2012a)

Power PoolAncillary Services

Forward Markets

Retail Market


PAGE A-3


to annual increases of two to three per cent following that (AESO, 2012b)16. This implies a peak load forecast of around 19,000MW in 2029, almost double the peak demand in 2010.

Figure A2 shows historic and forecast peak demands for Alberta Internal Load (AIL), where AIL includes sales through the Pool, transmission and distribution losses, and Behind-the-Fence load.17

Figure A2: Alberta Internal Load Peak Demand

A.4. SUPPLY

Alberta’s generating capacity is fueled predominantly by coal and gas, together accounting for 85 per cent of capacity in 2011. Hydro has historically been the next most important resource, but there is now as much wind power (six per cent) as hydro (seven per cent) on the system. Prior to industry restructuring, the only wind farm in Alberta was a 0.6MW project in Castle River (CanWEA, n.d.). Wind started to take off in 2001 as it become competitive with conventional generation. Concerns about system stability and the need for transmission reinforcements led the AESO to impose a 900MW cap on wind in 2006. The cap was lifted the following year after the AESO launched its wind integration initiatives.

The AESO believes that there will be significantly more investment in new gas-fired generation than coal over the next twenty years, in large part because of new federal standards on emissions from coal-fired plants and the cost of meeting those standards (AESO, 2012a). That,

16

Long-term Transmission Plan (filed June 2012) – Appendices (AESO, 2012b)

17 Historic peak demand is from ADOE (2011); forecast peak demands are from AESO (2012b).

0

5000

10000

15000

20000

2005 2010 2015 2020 2025

MW

Historic Forecast


PAGE A-4


combined with the expected retirement of existing coal-fired plants, leads them to forecast that coal-fired capacity will drop to 29per cent and gas-fired to rise to half of overall capacity by 2020.

Wind generation is expected to grow rapidly, outpacing both coal and cogeneration additions. The AESO anticipates that over 1,800 MW of wind will be added to the system through 2020.18 This means wind’s share of overall capacity relative to today will rise from its current six per cent to 11 per cent of generating capacity.

Table A1 – AESO Forecast of Generation Additions, by Type (AESO, 2012b)

Figure A3 – Current and 2020 Installed Capacity by Fuel Type (AESO, 2012b)

A.5. WHOLESALE ELECTRICITY MARKET

The wholesale electricity market is an energy-only market that sets a single hourly spot price for the entire province. Unlike some other jurisdictions, Alberta does not operate a capacity market, nor does it have a binding day-ahead energy market.

18

This is the AESO’s Baseline Scenario 1, which assumes that no nuclear generation is added to Alberta’s system.

Fuel Type to 2020 2021 to 2029 6

Coal 834 970

Gas 2,714 3,333

Cogeneration 1,687 865

Hydro 100 1,500

Wind 1,864 2,000

Other 290 700

New Capacity Additions


PAGE A-5


The wholesale market is the largest of the electricity services markets in Alberta, with about $5 billion of transactions in 2009 (MSA, 2010). Active market participants include internal generators, importers, exporters, retailers, and some large industrial customers.

A.6. MARKET OPERATION

All energy (except Behind-the-Fence production and consumption) must be transacted through the Power Pool. Power sources with 5 MW or more of generating capacity – except for wind – must make offers to sell power through the pool. Power sinks are allowed to place bids to buy through the pool, although they are not required to do so.19 In general terms, offers and bids for each hour of the day are submitted to the ISO by noon the day before. These are used to set a merit order determining which units will be dispatched for each hour of the next day. Offers may be restated, subject to restrictions discussed below, up until 2 hours before the hour in question. A single hourly pool price is determined based on an average of the highest priced supply offers necessary to clear the market in that hour.

Each generating asset in the system with 5 MW or more of capacity has an identified Maximum Capability (MC) for that asset, where MC is defined as “…the maximum quantity (MW) that the generating asset is physically capable of providing under optimal operating conditions for that asset while complying with all applicable ISO rules” (AESO, 2009, p. 6). Generators submit up to seven hourly price/quantity blocks by noon for each hour of the following next seven days. The total quantity offered must be equal to their MC unless the generator can provide an operationally acceptable reason for offering less. Offer prices can range from $0 to $999.99. This rule, called “must offer, must comply (MOMC),” was instituted by the Pool in 2007 (AESO, 2009).

Voluntary restatements of price and quantity blocks in an hourly offer are allowed up until two hours prior to the hour in question. Reductions in the total capacity offered are allowed only if the generator can provide proof that they are constrained operationally to provide less. Otherwise, generators are free to redefine the quantity and price of each individual block.

Wind generators are currently exempt from MOMC. However, they could be subject to it in the future, since applying MOMC to wind generators is one of the proposed options in the AESO’s long run wind integration plan.

Load rarely bids into the wholesale market (MSA, 2010). However, there are large industrial customers who participate in ancillary reserve markets, and who will self-curtail up to 300MW total if the pool price is (or is forecast to be) high.20

19

Which raises an interesting question if a large ES facility was installed in Alberta that could be charged from the grid. Should a decision to charge be a required bid into the market?

20 AESO anonymous reviewer, personal communication, October 6, 2011.


PAGE A-6


A.7. POOL PRICES

Pool prices are determined by a combination of the demand on the system, available supply, and generators’ offers.

The AESO’s Energy Trading System ranks offers from highest to lowest to create a merit order for each hour of the following day. Generators are responsible for making their offers consistent with any operating constraints they face. Generating units are dispatched on and off in real time to meet system demand according to the hourly merit order, subject to reliability constraints. A System Marginal Price (SMP) is defined every minute by the highest price offer block dispatched. The pool price is then set equal to the average of the SMPs during that hour. A small uplift is paid by load to cover the cost of compensating any generator dispatched during the hour if the average of the SMPs is less than the generator’s offer.21

Due to historical events, neither imports nor exports are allowed to set the pool price. This prohibition is implemented by requiring importers to offer at $0 and exporters to bid $999.99, which is the maximum wholesale pool price allowed under ISO rules.

A.8. POTENTIAL VALUE OF WIND PLUS ENERGY STORAGE IN THE ENERGY MARKET

Wind as an intermittent source and non-storable energy is the prime consideration for integration with energy storage technologies. Using energy storage to firm output from wind can have benefits to both the system and to the individual wind generator. Benefits to the system will vary across jurisdictions, since they depend on the system-specific variables: the physical characteristics of the grid, the amount of wind penetration, and other available options for managing intermittency (Denholm, et al., 2010).

Benefits to individual wind generators will also vary, depending on the rules and structure of the market system in which they operate. For example, some systems in the U.S. have forward capacity markets in addition to wholesale energy markets. Wind generators are allowed to participate in capacity markets operated by PJM, the New York ISO, ISO New England, and other systems in the U.S., although each has a unique method of determining the capacity a wind generator is deemed to contribute (Milligan and Porter, 2008). Energy storage would presumably increase the capacity credits allocated to wind generators in those markets, but the incremental value would depend on the capacity credit currently awarded.

Under Alberta’s current system, wind generators do not make offers and so never set the SMP in the energy wholesale market. Being able to make a firm offer and influence price could be a source of value. If wind generators in Alberta were to become subject to MOMC, then the benefit of firming would also include the avoided cost of satisfying that obligation by other means, which in turn would depend on other factors: how exactly MOMC is applied, the

21

See AESO (2011d) for details on payments made to suppliers on the margin and the allocation of those costs to load (Rules 6(1) through 6(3) and Rule 11). The AESO estimated that average uplift costs were around $0.05/MWh in the first six months of 2008 (AESO 2009c).


PAGE A-7


accuracy of wind forecasts, and so on. System and site-specific studies are clearly needed to estimate the value of energy storage in individual cases.

Another potential source of value in the energy market is energy arbitrage, where energy is stored if wholesale prices are low and the wind is blowing, and then sold when wholesale prices are higher. In its simplest form, this is posed as an “on-peak, off-peak” strategy. Across any given day, the variation in hourly pool price is correlated with the load on the system (see Figure A2). Low demand during off-peak hours (HE24 through HE07) generally results in lower prices than during on-peak hours (HE08 through HE23). This difference between on and off peak prices is one possible source of value for a wind generator with energy storage, since it could store energy at night when it would otherwise have to sell at the lower off-peak prices, and then sell the stored energy during the day at higher peak prices.

Most studies of the value of storage for energy arbitrage have used the “on-peak, off-peak” charge and discharge strategy to estimate incremental revenues. Estimates of the annual value in U.S. studies surveyed by Denholm, et al. (2010) range from $37/kW to $240/kW, depending on the market, location within the market and assumptions about the efficiency and size of the energy storage technology.

Figure A4 – Alberta Hourly Load and Pool Price, January 15, 2010

However, a recent MIT study by Saran, Goentzel, and Siegert (2010) indicates that revenues from energy arbitrage could be significantly improved with a more sophisticated strategy. They modelled a 120MW wind plant located in ISO New England with a capacity factor of 33per cent and battery storage with 75per cent round trip efficiency. They then compared a simple on-peak, off-peak charge and discharge strategy to an optimized dynamic strategy. The dynamic

0

25

50

75

100

HE01 HE06 HE12 HE18 HE245000

6250

7500

8750

10000

Hourl

y Pool Pri

ce (

$/M

Wh)

Hourl

y Load

(M

W)

Pool Price Load


PAGE A-8


strategy determined charge or discharge decisions on an hourly basis, taking into account the hourly wholesale prices, wind plant output, and the technical constraints of the battery.

The price dynamic model added $2,775/day to the incremental gross profits of wind coupled with energy storage. Adding the ability to charge from the grid if wholesale prices were low was even more impressive, increasing incremental gross profits by $6,300/day.22 The improved results of their optimization came from three main factors:

Storage may not always be adequately charged in time for peak hours;

Storage may have excess energy at the end of peak hours, forcing the sale of stored energy at low off peak hours; and

Hourly price variations throughout the day can be more important than average on peak, off peak differences.

A.9. ANCILLARY SERVICES MARKETS

Ancillary Services are those acquired by the Alberta Electric System Operator (AESO) to ensure that electricity can be transmitted reliably, efficiently and securely across Alberta’s interconnected transmission system. The AESO acquires four types of ancillary services: operating reserve, transmission must run, black start and load shed scheme services based on standards set by the Western Electricity Coordinating Council (WECC)23. Operating Reserves are purchased each day through WattEx, an on-line exchange, and the other three ancillary services are acquired through contracts with generators and loads. Energy storage facilities may qualify to provide all of these types of services. The following sections provide more detailed descriptions of each type of ancillary service.

A.9.1. OPERATING RESERVE PRODUCTS

At any moment, the electricity supplied by generators and imports must equal the electricity consumed by loads and exports. To maintain the balance between supply and load the AESO acquires:

22

Saran, Goentzel, and Siegert do not report baseline revenues for the wind facility: they only report the incremental profits associated with different storage strategies.

23 WECC is the regional entity responsible for coordinating and promoting bulk electric system reliability in the

Western Interconnection. WECC is the largest of eight similar regional electricity entities responsible for electric system reliability and extend across the western half of North America from British Columbia and Alberta to the northern portion of Baja California, Mexico and from California east to Colorado.


PAGE A-9


regulating reserves;

spinning reserves; and

supplemental reserves.

The three are collectively referred to as operating reserves. Regulating reserves provide short term instantaneous increases or decreases in the electricity supply required to balance the system following a change in load while the system operator dispatches up or down generating assets to restore the system balance. Spinning and supplemental reserves, also known as contingency reserves, are normally employed when an unexpected system event occurs, such as a sudden unplanned shut down of a generating facility. Spinning reserves are the faster responding contingency reserve and are supplied by generators that are already operating and synchronized to the grid. Supplemental are slower responding reserves that are not required to be synchronized. Recent changes to the supplement reserve technical requirements have permitted loads, for the first time, to supply supplement reserves.

Figure A5: Operating Reserve Products

The AESO procures Operating Reserves primarily through the Watt-Ex a third party online exchange that provides clearing services. Any generator or load that meets the AESO’s requirements for supplying operating reserves can register on Watt-Ex and begin offering operating reserves. The AESO is the sole buyer of operating reserves. The requirements for supplying operating reserves are described in Table A2.


PAGE A-10


Table A2: Operating Reserve Requirements

Regulating Resource (RR) Spinning

Resource (SR) Supplemental-Resource Supplemental-Generation

Minimum Capacity

15 MW of Regulating Range

10 MW 5 MW 5MW

Minimum Ramp Rate

1/10 of the Maximum Regulating Range per

minute

Minimum Continuous Operation

60 minutes at any point in the Regulating Range

A regulating resource has to be able to produce

any Real Power with the Regulating Range without manual

intervention by the resource operator.

60 minutes -following an AS Directive within

a dead band equal to the

greater of +/- 5 per cent of the

AS Directive or 1 MW

60 minutes 60 minutes - following the first 10 minutes of an AS

Directive, must be able to maintain Real Power output

for the duration of the AS Directive at the greater of 95 per cent of the AS Directive or within 1 MW of the AS

Directive volume

AS Dispatch Response Time

15 minutes 15 minutes 15 minutes 15 minutes

Control Signal/ Directive Response Time

40 seconds to “short ramp” control signals 28 seconds for Real

Power

10 minutes 10 minutes - the volume of Real Power

shall be reduced to; at minimum 100 per cent,

and at maximum 110 per cent of the AS Directive

volume

10 minutes - the volume of Real Power

shall be changed to; at minimum 100 per cent, and at maximum 110 per cent of

the AS Directive volume

Qualified generators and loads can offer regulating reserves, spinning reserves and supplemental reserves as active or standby volumes for either on-peak or off-peak hours on Watt-Ex. Regulating reserves can also be offered for two additional time periods:

morning, or AM super peak; and

evening, or PM super peak.

Active operating reserves are purchased by the AESO to meet the operating reserve requirements of the electric system under normal operating conditions and are dispatched first by the system controller. The AESO acquires standby reserves to ensure there are sufficient reserves available in the event of an outage or loss of active reserves, and thereby avoiding conscription of reserves and other means available to the AESO for procurement of operating reserves.

A.9.2. OPERATING RESERVE MARKET

Each business day between 09:00 and 10:10, the AESO procures operating reserve products from market participants through Watt-EX, for the following business day, or for a weekend on Friday for Saturday, Sunday and Monday, or for a holiday on the last business day before the


PAGE A-11


holiday. A description of the Active and Standby Reserve Market are shown below along with some recent Operating Reserve market prices in Table A3.

Active Reserve

Market participants submit price and volume offers for each active reserve product (e.g. on-peak regulating reserve) for one or more of the time periods with prices expressed as a discount or premium to what the hourly price will be for the delivery hour. The marginal offer – the last offer necessary to satisfy the total reserve volume required – will set the price; and, all offers that are better than the marginal offer will be accepted. When dispatched, the market participants will be paid the hourly energy price plus the equilibrium price for all reserve volumes provided. The equilibrium price is the average of the AESO bid price and the marginal offer price. In cases where the sum of the hourly energy price and equilibrium price results in a negative value, market participants are not required to pay the AESO. In addition to price paid for providing reserve volumes, market participants will also be paid the current hourly energy price for all energy provided.

Standby Reserves

The standby reserve market operated by Watt-Ex uses a different pricing process than the active reserve market. Buyers and sellers are able to complete a transaction by accepting the current offer or bid shown on Watt-Ex for a standby reserve product. A two-part option price mechanism is used; the first price is referred to as the Premium Price and the second as the Activation Price. By accepting a bid posted on Watt-Ex, or having its offer accepted, a seller is providing the AESO system controller the option to call on the reserve volume if required. For the option the seller will be paid the Premium Price. If the system controller dispatches the reserve the seller will be paid the Activation Price. If the system controller directs the seller a dispatched reserve to provide energy the seller will be paid the prevailing hourly price for the energy provided. In total, a seller of a standby reserve product will be paid at least the accepted Premium Price and possibly the Activation Price and the hourly market price.

Table A3: Annual Average Operating Reserve Prices

Active Standby Premiums Standby Activation Total OR Cost ($ M)

Average hourly pool

price ($/MWh)

RR SR SUP RR SR SUP RR SR SUP

2008 $51 $43 $38 $7 $5 $5 $163 $151 $133 $270 $89.95

2009 $23 $16 $11 $5 $4 $3 $96 $85 $69 $104 $47.81

2010 $27 $21 $16 $7 $4 $4 $141 $115 $91 $137 $50.88

2011 $55 $57 $51 $6 $8 $7 $98 $121 $95 $328 $76.22

2012 $50 $52 $48 $11 $13 $12 $133 $80 $50 $326 $64.32


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B. CCEMC BACKGROUNDER


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C. TRANSCANADA GAS QUALITY SPECIFICATIONS


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D. POWER TO GAS ANNOUNCEMENT

Energy Storage Technoecon Final Report_Revised March 2014

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