1

Impacts of Transmission Tariff on Price Arbitrage Operation

of Energy Storage System in Alberta Electricity Market

Abiola I. Adebayo1, Payam Zamani-Dehkordi, Hamidreza Zareipour, Andrew M. Knight

Department of Electrical and Computer Engineering, University of Calgary, Alberta, Canada

Abstract

This paper investigates the application of existing tariff structures to understand how they impact the

economic operation of energy storage system (ESS) for arbitrage. The scope of this research covers

impacts on profitability, operating cost, energy-traded volume, and price volatility. Two facilities of

different scales are considered: an ESS unit small enough to have no impact on price, and an ESS unit,

large enough to have a quantifiable impact on pool price. The hourly impact of ESS operations on the

pool price is estimated by modeling the price sensitivity quota curve from actual hourly market data.

Keywords: energy storage system, transmission tariff, electricity market

1 Corresponding Author: Abiola Adebayo;

Email: abiola.adebayo@ucalgary.ca; Phone: +1-587-433-2855; Fax: +1-403-282-6855

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Notations

Index

k step size for Bid blocks

s step size for Offer blocks

t time in hours

Parameters

𝜆𝑡,𝑠 Hourly price factoring impact offer block s

𝜆𝑡,𝑘 Hourly price factoring impact of bid block k

𝛾𝑠 Storage efficiency

𝛾𝑐 Conversion efficiency

𝐵𝑑𝑘 Bids in blocks of 10MW

Lf Loss factor

Nk total number of Bid steps

Ns total number of Offer steps

𝑂𝑓𝑠 Offers in blocks of 10MW

𝑇𝑅𝐷𝑐 Trading charge by the system operator

𝑉𝑂𝑀𝑐 Variable operation and maintenance cost

Variables

𝐵𝑡,𝑘 Bid variables ranging from 0-9MW

𝐷𝑜𝑑 Depth of discharge𝑂𝑡,𝑠 offer variables ranging from 0-9MW

𝑃𝑐ℎ𝑡 Power from the grid to charge the system

𝑃𝑑𝑐ℎ𝑡 Power discharged to the grid

𝑆0 Initial state of charge

𝑆𝑡 State of charge at any time t

𝑈𝑡,𝑘 Binary variable indicating active bid block and charging status at any time t

𝑋𝑡,𝑠 Binary variable indicating active offer block and discharging status at any time t

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1.0 INTRODUCTION

Increasing commercial interest in investment in energy storage systems (ESSs) has generated a need to

investigate factors that can affect the profitability of arbitrage operation in relevant electricity markets.

Common to all ESS facilities, operation and maintenance cost, capital cost, price variation, round-trip

efficiency, energy capacity-to-power ratio and self-discharge loss (Bradbury et al., 2014) all impact

operational profitability of ESS. The capital cost is rapidly changing and has been projected to decrease

significantly by 2020 in a report by Viswanathan et al. (2013) from Pacific Northwest National

Laboratory. Unique to each electricity market, the regulatory policy on storage operation also has

significant impact. Several jurisdictions in North America are reviewing existing policies or formulating

new policies to aid the integration of energy storage into the electricity market.

From a policy perspective, the past three years have been very interesting for energy storage. The U.S.

Federal Energy Regulatory Commission (FERC) implemented a series of related orders (755, 784, and

792) applicable to the electric power markets of Pennsylvania, Jersey, Maryland (PJM), Midcontinent

Independent System Operator (MISO), California Independent System Operator (CAISO), New York

Independent System Operator (NYISO), and Independent System Operator for New England (ISO-NE).

FERC order 755 ensures system operators develop pay for performance tariff for ancillary services

(Masiello et al., 2014); order 784 requires system operator to consider speed and accuracy in

formulating requirement for ancillary services, while order 792 places energy storage on same level

with conventional generators by considering it as a power source (Kintner-Meyer, 2014). Kintner-

Meyer (2014) exhaustively discusses the details of the implementation in each of those jurisdictions.

Even though the Independent System Operator in Alberta; Alberta Electric System Operator (AESO) is

still formulating suitable regulatory policy applicable to ESS, in general, the most important policy to

energy storage proponents in Alberta at the time of writing is the transmission tariff policy.

Transmission tariffs are important because merchant energy storage proponents are very interested in

how tariffs will affect their operating profit. ESS are not currently allowed to participate in the ancillary

service market, only synchronous facilities are allowed based on the current operating reserve technical

requirements (Chen, 2013). The approval of the proposed Western Electricity Coordinating Council

(WECC) Contingency Reserves Standard is expected to allow non-synchronous facilities to participate

in the regulating and spinning reserve market in Alberta (AESO, 2015a), and this is expected to be a

good source of revenue for ESS merchants.

The existing tariff structure in Alberta was not formulated with the consideration of bulk energy storage

facilities. There have been back-and-forth arguments as to how best to classify ESS. Some are of the

opinion that it should be treated as a transmission facility because its operation has the benefit of

deferring new investment in transmission asset and that it does not generate energy on its own but

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merely withholds energy from the system to subsequently releases it back (Bubik, 2014). Klinkenborg

(2014) is of the opinion that their operation is in no way different than that of conventional generators

when discharging and another opinion is that they act as either load or generator (Cheng, 2014). The

fact that their operational modes can be regarded as either load or generation has led to suggestions that

the current tariff structure for demand and supply may be suitable. Several studies have been conducted

to estimate the potential profitability of arbitrage operation in various electricity markets. The existing

literature has covered deregulated electricity markets in Europe, North America, and elsewhere.

The study by Walawalkar et al. ( 2007) investigated the economics of energy storage operation in the

electricity market of New York by using market data from 2001 to 2005. The study shows that operation

of Sodium Sulphur battery and flywheel for arbitrage and regulation operation in the New York City

region has high probability of positive net present value. In the Electricity Reliability Council of Texas

(ERCOT) market, the maximum potential revenue obtainable from operating a hypothetical 8MW,

32MWh battery connected to HB_Houston node is estimated by Byrne and Silva-Monroy (2015), for

both energy arbitrage and regulation. This study emphasized how largely dependent potential revenue

is on market price fluctuation. The study by Fertig and Apt (2011) investigated the economics of pairing

Compressed Air Energy Storage (CAES) with wind farm in Houston using 2008 hourly ERCOT

electricity market price. Results showed that pairing CAES with a wind farm to smooth dispatchable

power from the farm or storing energy from the wind farm for arbitrage opportunity is not economically

viable. Considering performance-based regulation and battery life cycle, He et al. (2015) proposed an

optimal bidding strategy for a battery energy storage system to maximize profit in markets that have

implemented performance-based regulation (PBR) such as PJM. The study shows that incorporating

PBR and battery life cycle modeling could significantly improve overall economics of Battery Energy

Storage System (BESS).

Using five-year historical data, Adebayo et al. (2016) examined the economic viability of arbitrage

operation of battery in Alberta electricity market with a case study of 30MW, 120 MWh Vanadium

Redox Battery (VRB) considered large enough to have impact on the pool price of electricity. Taking

both impact of the battery operation on price and 2020 projected capital cost estimate into consideration,

the study showed that with a 34% reduction of capital cost, the case study considered could become

economically viable. Another study of Canada's second electricity market by Khani and Dadash Zadeh

(2015) assessed the economic viability of arbitrage operation of cryogenic energy storage with 60%

round-trip efficiency in Ontario electricity market, showing that the system cannot return expected

revenue and proposes a price modulation algorithm to competitively offer subsidy to the merchant.

In Europe, two different studies by Kazempour et al. (2009) and Moghaddam and Saeidian explore the

profitability of arbitrage operation of two different battery technologies; Sodium Sulphur (NaS) and

Vanadium Redox Battery (VRB) in the electricity market of Mainland Spain and reached a similar

conclusion. Findings by Kazempour et al. (2009) show that the 10MW, 70MWh NaS operating in

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energy, regulating, and spinning reserve market cannot generate a return up to the minimum acceptable

return and thus proposed support mechanisms in form of tax benefits and gratuitous loan to potential

merchant. Similarly, Moghaddam and Saeidian (2010) concluded that a VRB of equivalent power rating

and storage capacity is also not economically viable.

Optimal operational strategy for an energy storage system to maximize arbitrage profit in the real-time

electricity market of Denmark is investigated in the study by Hu et al. (2010), with a comparison of two

battery technologies; VRB and Polysulfide-bromine(PSB). Numerical results from this study show that

PSB is a better investment choice as it has shorter payback time than VRB. With a special focus on

Finland in the Nordic electricity market, Zakeri and Syri (2014) examined the economics of various

energy storage technologies, noting that the ESS considered will require additional benefit to become

economically attractive. Ippolito et al. (2015) analyzed the economic viability of operating customer-

side NaS battery in the Italian electricity market and concluded that at the current hourly price, it is

currently not economically viable due to high initial investment cost. The economics of operating

compressed air energy storage in Turkish power market using probabilistic price estimation to obtain

annual profit from 2011 to 2041 is examined in a study by Yucekaya (2013). Based on net present value

and payback period estimates, this study shows that investment in such a project can be economically

viable. Steffen (2012) investigated the economic prospect of operating Pumped hydro storage system

in Germany using estimates of arbitrage profit from year 2002 to 2010. Internal rate-of-return (IRR)

estimates from this study are noted to be below average industry requirement but increase in renewable

energy penetration could expand opportunity.

Policies in different jurisdictions may affect the economics and general operation of EES. In this paper,

we investigate the impact of transmission tariff policy on the economics of arbitrage operation of ESS

in the Alberta electricity market. Using the AESO’s tariff policy documents accessible on the AESO

website, we incorporate all the potential tariff structures applicable to the ESS operation into a Mixed

Integer Linear Programming (MILP) self-scheduling optimization model to obtain operating profit for

ESS large enough to impact the pool price and another one considered to be small scale with negligible

impact on price. The contributions of this paper are to:

• Formulate a price-maker model using actual historical data from hourly supply curve in Alberta

electricity market;

• Incorporate Alberta's transmission tariff into an economic dispatch model for both price taker

and price maker ESSs; and

• Analyze the impact of energy storage operation on price volatility in Alberta electricity market.

The paper is structured as follows. Section two gives an overview of the tariff structure in Alberta

applicable to ESSs. The energy storage operation model is described in section three. Section four gives

details of the case study considered and section five provides the results.

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2.0 THE TRANSMISSION TARIFF IN ALBERTA

Alberta’s electric system is legislated to be congestion free (AlbertaEnergy, 2012), requiring continuous

investment in transmission facilities to ensure that sufficient capacity will be available for any device

to connect to the grid irrespective of location. The costs incurred to meet this legislative requirement

are significant and are recovered from the participants in the electricity market through transmission

tariffs. In the following sections, the existing tariff rates that may be applicable to ESS are described

(AESO, 2015b).

2.1 Demand Transmission Service (DTS) Rate

This tariff is applicable to all demand and may be applied to a storage facility during charging operation,

given that an ESS draws power from the grid just like any other load. This tariff comprises the

following:

Connection Charge: The breakdown of the connection charge components is presented in Table 1. The

various charges can be divided into their fixed and variable components. The variable component is

comprised of the metered energy component of Bulk and Local system charges. These components vary

based on operation pattern of the ESS, and thus must be included in the self-scheduling optimization

model later described in section three. The most significant portion of the fixed component is the

coincident-metered demand. This charge is applied to the metered demand that occurred during the 15-

minute interval of peak system demand in each month. This portion is also based on the operation

pattern of the ESS but cannot be included in the optimization model because it is an a-posterioric charge.

In this analysis, we assume that the energy storage facilities have their own substation thus we neglect

Point-of-Delivery charge in the formulation.

Operating Reserve Charge: The AESO estimates this as the sum of the product of the metered energy

and 7.98% of the hourly pool price over all hours of the settlement period. This portion has to be

included in the optimization model since the system can adjust its operation to optimize this cost.

Voltage Control Charge: This is estimated by the AESO as the product of metered energy and

$0.03/MWh over the settlement period. Because this also varies based on the ESS operation, it is

included in the optimization model discussed in Section 3.

Other System Support Charge: This tariff is estimated as the product of the highest metered demand in

the settlement period and $20/MW/month.

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Table 1: DTS tariff –Connection charge breakdown (AESO, 2015b)

Volume in Settlement Period Charge

Bulk System charge

(a) Coincident metered demand $5,033.00/MW/month

(b) Metered energy $1.68/MWh

Local System Charge

(c) Billing capacity $1,243.00/MW/month

(d) Metered energy $0.70/MWh

Point-of-Delivery Charge

(e) Substation fraction $10,926.00/month

(f) First (7.5 × substation fraction) MW of billing

capacity

$7,401.00/MW/month

(g) Next (9.5 × substation fraction) MW of billing

capacity

$2,732.00/MW/month

(h) Next (23 × substation fraction) MW of billing

capacity

$1,655.00/MW/month

(i) All remaining MW of billing capacity $907.00/MW/month

2.2 Supply Transmission Service (STS) Rate

The Supply Transmission Service rate is applicable to system access service at points of supply. The

tariff applies to all system generators, so may be considered to apply to ESS while discharging. It is

calculated as the product of metered energy sold to the grid over the settlement period, pool price and

loss factor which is determined by the System Operator. Loss factors are location dependent, and due

to the complexity involved, an average loss factor is considered. The average loss factor was 3.39%

between the periods of January and December 2014(Yu et al., 2013). The actual loss factor may be

higher or lower than this, depending on the location where the ESS is sited.

3.0 ENERGY STORAGE OPERATION MODEL

In this section, the formulation of a generic ESS model to obtain optimal operating profit from arbitrage

operation in the energy market is presented. This model is used for small-scale ESS (small enough to

have negligible impact on pool price). For large-scale storage, the formulation is adjusted to account

for impact on pool price as a result of the ESS operation.

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3.1 Formulation for the Price Taker

The equations below describe a generic model for a small-scale ESS. Additional technical constraints

and choice of parameters are used to depict possible unique features of each technology.

The objective function (1) is to maximize the arbitrage operating profit, that is, the difference between

revenue and cost from participation in the energy market, considering fixed and variable operation and

maintenance costs. It ensures the facility would rather remain idle if the summation of revenue obtainable

from selling power is not greater or equal to the cost of buying power to charge the system and other cost

component. Equation (2) is the revenue obtainable from selling discharge power 𝑃𝑑𝑐ℎ𝑡 at price 𝜆𝑡 while

equation (3) is the costs of buying power 𝑃𝑝𝑐ℎ𝑡 at 𝜆𝑡, variable operation & maintenance cost, trading

charge 𝑇𝑅𝑐, variable component of rate DTS 𝐶𝐷𝑇𝑆, operating reserve charge 𝑂𝑅𝑐 , and rate STS which is

the product of load factor 𝐿𝑓, pool price and power discharged. The variable component of rate DTS

𝐶𝐷𝑇𝑆 comprises bulk system charge, regional system charge and voltage control charge.

𝑀𝑎𝑥 ( 𝑅𝑒𝑣𝑒𝑛𝑢𝑒 − 𝐶𝑜𝑠𝑡) (1)

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = ∑ 𝜆𝑡𝑇𝑡=1 𝑃𝑑𝑐ℎ

𝑡 (2)

𝐶𝑜𝑠𝑡 = ∑ 𝜆𝑡𝑃𝑐ℎ𝑡𝑇

𝑡=1 + (𝑉𝑂𝑀𝑐 + 𝑇𝑅𝑐)(𝑃𝑑𝑐ℎ𝑡 + 𝑃𝑐ℎ

𝑡 ) + 𝐶𝐷𝑇𝑆 . 𝑃𝑐ℎ𝑡 + 𝜆𝑡(𝑃𝑐ℎ

𝑡 . 𝑂𝑅𝑐 + 𝐿𝑓 . 𝑃𝑑𝑐ℎ𝑡 ) (3)

Equation (4) ensures that the system can discharge (xt=1) or charge (ut =1), but not at the same hour. It

can however be in an idle mode when xt= ut=0.

𝑥𝑡 + 𝑢𝑡 ≤ 1 (4)

Equations 5 and 6 ensure the quantity of power sold and purchased are within the rated capacity of the

system while (7) gives the state of charge (SOC), that is, available energy in the system at any time t.

𝑢𝑡𝑃𝑚𝑖𝑛 ≤ 𝑃𝑐ℎ𝑡 ≤ 𝑢𝑡𝑃𝑚𝑎𝑥 (5)

𝑥𝑡𝑃𝑚𝑖𝑛 ≤ 𝑃𝑑𝑐ℎ𝑡 ≤ 𝑥𝑡𝑃𝑚𝑎𝑥 (6)

𝑆𝑡 = 𝛾𝑠𝑆𝑡−1 + 𝛾𝑐𝑃𝑐ℎ𝑡 − 1

𝛾𝑐⁄ 𝑃𝑑𝑐ℎ

𝑡 (7)

Equation (8) limits the SOC at any time between the depth of discharge and the storage capacity while

(9) makes the operation a continuous self-scheduling such that the SOC at the end of day 1 is the SOC

at the beginning of day 2.

(1 − 𝐷𝑜𝑑)𝑆𝑚 ≤ 𝑆𝑡 ≤ 𝑆𝑚 (8)

𝑆1 = 𝑆0 (9)

3.2 Formulation for the Price Maker

To formulate a price-maker arbitrage model, the impact of ESS operation on pool price must be

accounted for. This impact is accounted for by price sensitivity curves to additional net demand for

hourly interval across the five-year study period. The price sensitivity curve applied in the formulation

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is a simulation of historical hourly supply curves. Real-market data capturing hourly price-quantity offers

for every hour of the five year period (January 2010 to December 2014) considered was downloaded,

stacked in increasing order of price and used to generate supply curve, also known as the merit order

curve. The historical generators’ offer data for Alberta electricity market is publicly accessible after two

months. In a similar way, demand curve for each hour can be generated from the price-quantity bids

submitted. However, while sizeable market participants are allowed to submit bids to buy energy, the

demand is largely inelastic. The demand curve for each hour is generated by determining equivalent

demand from the clearing price and the actual supply curve. This equivalent demand, with the knowledge

of clearing price, is then implored to estimate adjusted price for steps of increase in equivalent demand.

Price sensitivity curve is a stepwise monotonically decreasing or increasing curve that expresses the

impacted market price as a function of decreasing or increasing market equivalent net demand by small

steps, say 10 or 20 MW. To illustrate this, Figure 1 below shows that the actual market price was

$103/MWh for this particular hour. As the equivalent demand is reduced (through discharge operation

of ESS), the would-be market prices decrease.

Figure 1: Sample price sensitivity curves to additional supply

Another price sensitivity curve shown in Figure 2 illustrates the price for an hour when the net equivalent

market demand is increased (as a result of charging of the ESS) by 10 MW increments.

0

20

40

60

80

100

120

0 10 20 30

Pri

ce (

$/M

Wh

)

Additional Generation (MW)

10

Figure 2: Sample curve of price sensitivity to additional demand

Equations (10) - (20) describe the mixed integer linear programming (MILP) model for obtaining optimal

operating profit from arbitrage operation in energy market for a large-scale ESS. Similar to (1), the

objective is to maximize arbitrage operating profit from participation in energy market. Equation (10) is

the summation of the hourly revenue, which is the product of the clearing price 𝜆𝑡(𝑃𝑑𝑐ℎ𝑡 ) and power

discharge 𝑃𝑑𝑐ℎ𝑡 . The clearing price is now a function of power discharged by the facility 𝑃𝑑𝑐ℎ

𝑡 Total cost

of operation is expressed in (11) and accounts for the variable operation and maintenance cost, trading

charge, transmission tariff charges, and the cost of buying power 𝑃𝑐ℎ𝑡 at the price 𝜆𝑡( 𝑃𝑐ℎ

𝑡 ).

𝑅𝑒𝑣𝑒𝑛𝑢𝑒 = ∑ (𝜆𝑡(𝑃𝑑𝑐ℎ𝑡 ) 𝑃𝑑𝑐ℎ

𝑡 )𝑇𝑡=1 (10)

𝐶𝑜𝑠𝑡 = ∑ ( 𝜆𝑡(𝑃𝑐ℎ𝑡 ) 𝑃𝑐ℎ

𝑡 + (𝑉𝑂𝑀𝑐 + 𝑇𝑅𝐷𝑐 )(𝑃𝑐ℎ𝑡 + 𝑃𝑑𝑐ℎ

𝑡 ) + 𝐶𝐷𝑇𝑆 . 𝑃𝑝𝑐ℎ𝑡 + 𝜆𝑡(𝑃𝑝𝑐ℎ

𝑡 . 𝑂𝑅𝑐 +𝑇𝑡=1

𝐿𝑓 . 𝑃𝑑𝑐ℎ𝑡 )) (11)

Equations (12) and (13) give the hourly discharge power sold and charge power purchased respectively.

Figure 3 below shows how the model solves for discharge power; 𝑃𝑑𝑐ℎ𝑡 (a similar approach) is used to

solve for 𝑃𝑐ℎ𝑡 . 𝑂𝑓

𝑠 and 𝐵𝑑𝑘 represent the offer and bid blocks of 10MW each denoted by step size s and k

respectively. 𝑂𝑓𝑠 and 𝐵𝑑

𝑘 range from 0 to the rated power capacity. For example, when s= 1, 𝑂𝑓𝑠 = 0MW,

when s=2, 𝑂𝑓𝑠=10MW, when s=3, 𝑂𝑓

𝑠=20MW and vice versa for𝐵𝑑𝑘.

𝑃𝑑𝑐ℎ𝑡 = ∑ (𝑂𝑡,𝑠 + 𝑂𝑓

𝑠. 𝑋𝑡,𝑠)𝑁𝑠𝑠=1 (12)

𝑃𝑐ℎ𝑡 = ∑ (𝐵𝑡,𝑘 + 𝐵𝑑

𝑘 . 𝑈𝑡,𝑘)𝑁𝑘𝑘=1 (13)

43.544

44.545

45.546

46.547

47.548

48.549

0 10 20 30

Pri

ce (

$/M

Wh

)

Additional Demand (MW)

11

Figure 3: Generic price sensitivity quota curve showing stepwise MW blocks added to the grid

Equations (14) and (15) give the range of possible integers 𝑂𝑡,𝑠 or 𝐵𝑡,𝑘 (1 to 9) that can be added to the

optimal block size for a particular hour. The values for 𝑂𝑡,𝑠 and 𝐵𝑡,𝑘 are integers because the minimum

bid quantity in the energy market is 1MW. For example, if the optimal value for 𝑃𝑑𝑐ℎ𝑡 = 27MW, then

s=3, thus 𝑂𝑓𝑠 =20MW and 𝑂𝑡,𝑠 =7MW. At each hour, the data in the price sensitivity quota curve is

tabulated, giving price 𝜆𝑡,𝑠 as discrete functions of s, which is a value of 3 in this example. A similar

procedure is followed for 𝑃𝑐ℎ𝑡 but the corresponding price this time around will be 𝜆𝑡,𝑘.

0 ≤ 𝑂𝑡,𝑠 ≤ 9. 𝑋𝑡,𝑠 (14)

0 ≤ 𝐵𝑡,𝑘 ≤ 9. 𝑈𝑡,𝑘 (15)

𝑋𝑡,𝑠 and 𝑈𝑡,𝑘 are binary variables associated with on and off status of the facility at time t and also

determines the s or k value of the MW blocks to select to achieve optimal solution. Equations (16) and

(17) ensure that only one of the block sizes is selected every hour while equation (18) ensures that the

system can only be in any of the three possible states (offline, on and off).

∑ 𝑋𝑡,𝑠𝑁𝑠𝑠=1 ≤ 1 (16)

∑ 𝑈𝑡,𝑘𝑁𝑘𝑘=1 ≤ 1 (17)

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∑ 𝑋𝑡,𝑠𝑁𝑠

𝑠=1 + ∑ 𝑈𝑡,𝑘𝑁𝑘𝑘=1 ≤ 1 (18)

Equations (19) and (20) ensure that the energy storage facility operates within its rated power capability,

(10) is the state of charge (SOC) at any time t considering the power conversion efficiency while (11)

ensures that the system operates within the maximum storage capacity.

0 ≤ 𝑃𝑑𝑐ℎ𝑡 ≤ ∑ 𝑋𝑡,𝑠𝑁𝑠

𝑠=1 . 𝑃𝑟𝑎𝑡𝑒𝑑 (19)

0 ≤ 𝑃𝑐ℎ𝑡 ≤ ∑ 𝑈𝑡,𝑘𝑁𝑘

𝑘=1 . 𝑃𝑟𝑎𝑡𝑒𝑑 (20)

Equations (8) and (9) are also applicable to price-maker formulation, (8) gives the energy level available

at any time t while (9) is to ensure that energy level at the end of day 1 is the energy level at the beginning

of day 2.

4.0 CASE STUDY

To have a typical representation of arbitrary energy storage operation, ESS small enough to have no

impact on pool price and one large enough to impact the pool price of electricity are considered. Based

on the hourly supply curve, it is noted that ESS with ratings less than or equal to 10MW have negligible

impact on price while the ones greater than 10MW have impact on price which is estimated as discussed

in section 3.2. For every hour of the five-year period considered, impact on price is estimated and these

prices are input to the optimization model. Parameters of the ESS are shown in Table 2 below. It is

expected that the result of this model is technology independent. This is because capital cost and life

cycles that could be significantly different for each technology is not required in the analysis to predict

operating profit.

Five-year (2010- 2014) historical Pool price data are input to the mixed integer linear programming

(MILP) model. To estimate the impact of tariff, the model is run with and without the transmission

tariff. After careful consideration of computational burden with respect to the merit, an optimization

window of 168 hours (one week) is considered. Based on another run, we also found that optimizing

operating profit over 24-hour window will always make the system fully discharge irrespective of the

pool price in hour 24; these results depend on the specific time of day that the window is initialized.

13

Table 2: Parameters of the ESS considered

Energy Storage Parameters (Price Taker)

Round trip efficiency 80%

Self-discharge loss 1% per month

Power rating 10MW

Storage capacity 50MWh

Variable O&M cost $7/MWh

Energy Storage Parameters (Price Maker)

Round trip efficiency 75%

Power rating 100MW

Storage capacity 1200MWh

Variable O&M cost $1/MWh

5.0 RESULTS AND ANALYSIS

To show a generic operating profile of the storage facility, the self-schedule operation output from the

arbitrage model is averaged over a 168-hour window (1 week) and plotted in Figure 4. As intuitively

expected, ESS charges in period of low demand and discharges in period of high demand. The figure

shows that the system withdraws electricity from the grid during overnight hours and supplies it back

during hours from mid-morning to late evenings. The ESS operation schedule for the first week of the

260-week period is plotted against the system demand and shown in Figure 5. While the system is idle

for several hours, the ESS generally charges at hours when the system demand is low which usually

coincides with hours with low pool price, and discharges when the system demand is high, which in

most cases is when the pool price is high. Note that energy supply to the grid is considered positive

while energy removal from the grid is considered negative in all illustrations.

14

Figure 4: Five-year 168-hour summary of operation schedule

Figure 5: Power schedule against system demand for week one-100MW price maker

There are some instances when the ESS charges (at fractions of its rated power) in periods of high

demand. To show these instances, a plot of frequency count for various ranges of operation within

fraction of rated capacity with respect to quintiles of system demand is presented in Figure 6. These

instances could be as a result of low price in period of high demand, which could be as a result of wind

facilities operating at their maximum capacity and bidding into the electricity market at $0 and thus

bringing down the pool price of electricity, despite high demand. The chart also shows that ESS

operating on arbitrage basis are idle a significant number of times (over 60% of the period considered).

-100

-50

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50Po

wer

(M

W)

0

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4000

6000

8000

10000

12000

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50

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(MW

)

ESS

Po

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(MW

)

Power Demand

15

These idle periods can be carefully explored for additional profit through participation in ancillary

service (spinning and non-spinning) market.

Figure 6: Charge and discharge frequency with respect to quintiles of system demand for a 100MW price

maker

Using results from the optimal operation schedule also, an evaluation of how often the ESS operate at

above 75% of its rated capacity (in charging or discharging mode), is examined, compared with charge

and discharge operating profile over the 260-week period considered. Shown in Figures 7 and 8, it is

noted that the 10MW price taker ESS most constantly operate close to its rated power while the 100MW

price-maker facility usually operate at a fraction of its rated power because it withholds a portion of its

power in order not to dilute the arbitrage opportunity by charging or discharging at rated power. The

chart shows impact of ESS size on operation and confirms that the model works as intended. Even

though the operation pattern looks similar, it can be seen that the 100MW ESS spends smaller time

operating above 75% of its rating as larger energy volume reduces the price, the model limits the energy

volume so as to not dilute arbitrage opportunity as a result of its impact on price.

0

2

4

6

8

10

12

14

16

< =20 20 to 40 40 to 60 60 to 80 > 80

% f

req

uen

cy

Charge: 75%<P<=100%

Charge: 50%<P<=75%

Charge: 25%<P<=50%

Charge: 0%<P<=25%

idle

Discharge: 0%<P<=25%

Discharge: 25%<P<=50%

Discharge: 50%<P<=75%

Discharge 75%<P<=100%

16

Figure 7: Operation frequency at a range of fraction of rated capacity-10MW price taker

Figure 8: Operation frequency at a range of fraction of rated capacity-100MW price maker

0

20

40

60

80

100

120

140

160

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mb

er o

f W

eeks

Hours Charging Hrs Charge > 75%

Hours Dischrg Hrs Dischrg > 75%

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er o

f W

eeks

Hours Charging Hrs Charge > 75%Hours Dischrg Hrs Dischrg > 75%

17

The plots in Figures 7 and 8 for scenarios when DTS was considered, and when it was not, look similar.

This is because the plot is aggregated over five-year period. In the next subsections, the impact of

transmission tariffs on ESS operation is investigated in detail.

5.1 Impact on Operation

The following section assesses the impact of the transmission tariff on the operation schedule and

volume of energy traded (summation of power flow over a period). Using self-schedule operation output

from the arbitrage model over the period considered, Figures 9 and 10 below are presented to show the

impact of the variable components of DTS and STS in the operation pattern of the price-maker and the

price-taker ESSs respectively averaged over a five-year period.

Figure 9: Five-year summary of operation pattern for the price taker considering the transmission tariff

-10.0

-5.0

0.0

5.0

Pow

er )

MW

)

No DTSWith DTS

18

Figure 10: Five-year summary of operation pattern for a 100MW price maker considering the

transmission tariff

Figure 1: Five-year summary of operation pattern for a 100MW price taker considering transmission

tariff

The charts show that irrespective of tariff, the ESS generally charges at night but discharges

midafternoon towards the evening and performs arbitrage operation more during weekdays than

weekends. For both price-taker and price-maker scenarios, the power flow is impacted by the variable

component of the tariff. In the price-maker case, the ESS reduces its power flow while charging in order

-100

-50

0

50

Pow

er (

MW

)

No DTSWith DTS

-100

-50

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50

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er (

MW

)

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19

to minimize all the variable components of the DTS. Also while discharging, it reduces its power flow

but only slightly as the variable component of rate STS is a function of the power flow and pool price.

The higher the flow, the lower the pool price. Rate STS has lower impact on discharge pattern compared

to rate DTS on charging pattern. In the price-taker scenario, the discharge power flow is impacted more

than the price-maker scenario because the pool price remains constant so the only way it has to minimize

rate STS is to reduce discharging power flow. To confirm this, it is important to consider an additional

scenario to isolate all other factors, most importantly, the price-maker assumption and ESS parameters.

To isolate the impact of price-maker assumption, a third scenario that considers the 100MW, 1200MWh

storage facility as a price taker with same parameters presented in Table 2 is included. As shown in

Figures 10 and 11, both the 100MW, 1200MWh price taker and price maker ESS reduce their power

flow while charging to minimize the impact of the variable component of DTS. The reduction in power

flow is more evident in the price taker scenario as with no DTS, it operates at higher power flow since

its operation is assumed and modeled to have no impact on pool price. Irrespective of the level of power

it draws from the system, the pool price remains the same. With DTS factored in, it responds to the

variable component by reducing its power draw from the system.

For a price maker, the ESS is strategically mindful of diluting arbitrage opportunity by reducing its

power draw from the system, as increase in power draw (charging) will lead to increase pool price. And

when DTS is considered, it further reduces power draw to minimize DTS penalty. This explains the

difference in charge level patterns between the two scenarios.

While the ESS supplies power to the system, with no STS, the price maker operates such that it does

not reduce arbitrage opportunity by discharging power at a level that would severely reduce pool price.

When STS is considered, instead of reducing the level further, it increases the power level slightly as

this reduces the pool price and thus STS penalty as it is a function of pool price. For the price taker

however, with no STS the system supplies power at a relatively higher level as it has no impact on pool

price. With rate STS is included, the system reduces its power flow to minimize STS penalty. Hence

the notable difference in discharge power level pattern between the two scenarios.

The total energy-traded volume with tariff compared to the case when tariff is not included is examined.

The percentage monthly reduction in energy-traded volume (input and output) is seen to vary across the

60 months considered; the percentage decrease in energy input and energy output was about equal in

all the 60 months for both price-taker and price-maker scenario, as shown in Figure 12 and 13 below.

Energy-traded volume is the summation of power flow over the period of consideration. The average

decline in energy traded for the price taker was 18% while it was 12% for the price maker. The tariff

has more impact on small-scale than large-scale ESSs.

20

Figure 2: Percentage decrease in energy input volume as a result of the transmission tariff

Figure 3: Percentage decrease in energy output volume as a result of the transmission tariff

5.2 Impact on Operating Cost

Irrespective of the operation pattern of the ESS, it always incurs the fixed component of the transmission

tariff. These costs include billing capacity (which is 1,243/MW every month), system support charge

and point-of-delivery charge if the ESS merchant does not own the substation. A very significant portion

0%

10%

20%

30%

40%

50%

60%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

% decrease in Energy Input-Price maker % decrease in Energy Input-Price taker

0%

10%

20%

30%

40%

50%

60%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

% decrease in Energy Output-Price maker % decrease in Energy Output-Price taker

21

of this charge is the coincident peak charge. Coincident peak charge is the product of coincident-

metered demand and $5,033 every month. The coincident-metered demand is estimated by analyzing

the result of the self-schedule optimization to check if the ESS is contributing to the load in the 15-

minute interval overall peak of the month. Historical data on coincident peak period is confidential to

the AESO; thus the exact period is not included in this paper. Other fixed components of the tariff are

the billing capacity, which depends on the power rating of the ESS and substation fraction but is

completely waived if the ESS has its own substation. It is assumed in this analysis that ESS merchant

owns the substation; thus substation fraction component is neglected. Throughout the five-year period

considered, the ESS in the two scenarios was seen to charge only once in the coincident peak period

(CPP). In a typical occurrence, even though the system demand is at its peak, the pool price is relatively

low. This usually occurs on a very windy day when production from wind facilities are at their highest.

Most of the wind farms are located in southern Alberta, so this peak production would be common to

majority of the wind farms. As wind generation has a negligible variable cost, they are offered to the

market at 0$/MWh and thus would always get dispatched. This in turn leads to a lower pool price for

that hour.

Overall, this implies that the portion of the rate DTS might not be much of a threat to profitability of an

ESS that avoids charging in that period but can have huge impact if the ESS charges during CPP. Just

like the coincident peak charge, all the other fixed component of the tariff is not directly included in the

optimization model as it is a post-operation charge. An estimate of the percentage increase in operating

cost as a result of the transmission tariff is presented in Figure 14. Operating cost can be seen to increase

up to fivefold in some months with minimal arbitrage operation. This is because fixed cost contributed

majorly to the operating costs in these months. This is common to the price-taker scenario too, but due

to its lower power rating, it incurs much lower increase in operating cost.

Figure 14: Percentage increase in operating cost as a result of the transmission tariff

0%

50%

100%

150%

200%

250%

300%

350%

400%

450%

500%

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

Price maker Price taker

22

5.3 Impact on Price

With the assumption that other generators do not change their bidding strategy while the ESS MW bids

and offers move around the generated price sensitivity supply curve, an estimate of average pool price

for all the 60 months (five-year period) is estimated. The 100MW ESS brings down the average monthly

pool price by an average of 6% over the five-year period. This implies that when additional ESS is

added into the system, the average hourly price may drop further. Shown in Figure 15, comparing this

to the result when tariff is considered, there is negligible impact on the change in average pool price.

This shows that even though the variable component of the tariff impacts the energy-traded volume,

this impact is not strong enough to significantly change the operation pattern and strategy.

Figure 15: Monthly average pool price, impacted price based on ESS operation with and without tariff

for the 100MW price-maker case

To estimate the impact of ESS operation on energy cost to consumers, the hourly system demand is

multiplied by the hourly pool price (with no ESS, with tariff and with no tariff). Note that the energy

cost estimated here does not capture exact total cost paid by consumers as there are some other costs by

the distribution company not factored in. As shown in Table 3, the overall cost of energy to consumers

over the 5-year period will have reduced by $1.85 billion if a 100MW, 1200MWh storage facility was

participating in the market. This translates to a $308k per annum reduction in the cost of energy to

consumers per every MWh of ESS connected to the grid. Considering the impact of the transmission

tariff on the ESS, savings on energy cost is reduced to $1.79 billion. This 3.2 % decrease in cost is as a

result of the impact of both STS and DTS tariff.

0

20

40

60

80

100

120

140

160

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

$/M

Wh

Months(Jan 2010- Dec 2014)

No ESS With ESS ESS with tarif

23

Table 3: Impact of the transmission tariff on the cost of energy to consumers over the 5-year period

considered

Scenario Energy Cost ($bn)

No ESS 24.89

With 100MW ESS 23.04

100MW ESS with tariff 23.10

Net Impact of ESS 1.85

Resulting Impact of tariff 1.79

Using volatility indices from the methodologies explored in (Zareipour et al., 2007) and (Alvarado and

Rajaraman, 2000), hourly price fluctuation i.e. changes in price from one hour to the other for each

month is estimated. The result of index of volatility for price prior to adding ESS to the system, if ESS

is added with no transmission tariff and when the transmission tariff is considered are compared and

shown in Figure 16 for monthly prices for the 60 months considered. It is noted that for all the months

considered except May 2011 and June 2012, addition of ESS reduces the price volatility and the

transmission tariff slightly inhibits ESS from bringing down the price volatility. In May 2011, prices

were relatively low and did not vary significantly hence low price volatility. MW bids and offer

movement across supply curve is seen to be significant in this month. This translates to low arbitrage

opportunity and this shows in the net operating profit, which happened to be the lowest in 2011.

Operational profitability is noted to be directly proportional to average pool price and price volatility.

The correlation between the monthly profits and average price and price volatility were 0.67 and 0.94.

respectively. The facility was idle for about 520 out of the 720 hours in the month, and when the

transmission tariff is considered, it was idle for about 540 hours. In June 2012, prices were generally

low, with a zero price for about 30 hours out of the 744 hours. This is why volatility index was highest

in this month. In estimating the logarithmic return in this period, prices of 0.1$/MWh is assumed for all

the hours with zero prices, so as to get a numerical value for the return. Operation of ESS (withdrawal

and injection of power) during the period only increases the variation and thus the volatility.

24

Figure 16: Impact of the 100MW price-maker operation and the transmission tariff on monthly price

volatility

5.4 Impact on Operating Profit

The operating profit is seen to decline significantly with the consideration of the transmission tariff.

This is expected as the operating cost is increased while the volume of energy traded is reduced as a

result of the transmission tariff. The percentage decrease in profit is seen to vary across the 60-month

period considered. Figure 17 below shows the % annual decrease in profit as a result of the transmission

tariff. The impact on annual operating profit is noted to be quite significant and could be as high as 40%

per annum, driven by the increase in operating cost and reduction in energy-traded volume. While this

reduction in profitability is seen as a loss by ESS merchant, a large percentage of this from a neutral

stand point, is simply a value transfer from one party to another; in this case to the AESO. Part of this

is what the AESO use to invest or recoup investment in transmission facilities to ensure the system runs

congestion free as legislated. The other component is a loss from any perspective as this is the reduction

in operating profit because of the decrease in energy-traded volume due to the variable component of

the tariff. For the five-year period considered, of the 21% reduction in total operating income because

of the transmission tariff, 4% reduction in operating profit is due to lost value as a result of reduction in

energy-traded volume.

The impact of the tariff is less significant when the ESS is more active and more significant when the

ESS is less active (2010 and 2014). This is evident in the quantity of energy injected and withdrawn

from the grid in those years. For instance, the 100MW ESS withdrew 72,498MWh and 80,118MWh in

Years 2010 and 2014 (with most significant impact) respectively compared to 104,511MWh and 123,

278MWh in Years 2011 and 2013(least significant impact) respectively.

0

0.2

0.4

0.6

0.8

1

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59

Mo

nth

ly P

rice

Vo

lati

lity

Ind

ex

Months(Jan2010- Dec 2014)

no ESS with ESS ESS with tariff

25

Figure 17: Annual percentage decline in operating profit as a result of the transmission tariff

To examine the profitability of small-scale and large-scale ESSs, IRR was estimated using the 2020

projected capital cost and life cycle by Viswanathan et al. (2013) and five-year average operating profit.

For the price maker, IRR stands at 8.47% with no tariff consideration, but with tariff considered, IRR

reduces to 6.56%. IRR estimates for the price taker show no economic sense at all. With no tariff

considered, IRR is -1.4% and -3.35% when tariff is factored in. As expected, the transmission tariff

significantly affects the rate of return for both price taker and price maker. The IRR estimate shows that

despite not factoring the impact of deep discharge on lifecycle of small-scale ESS (specifically

batteries), they may not be best suited for investment solely for arbitrage operating profit in Alberta

electricity market. This could change when market rules allow participation in the ancillary service

market, through which more value can be captured, especially converting the very high idle period into

market participation.

5.5 Sensitivity

To understand the dependence of the model on certain parameters, we carried out a sensitivity analysis.

The analysis is only carried out only on the 10MW case. Each sensitivity case is run as a new

optimization, to illustrate the impact of various input parameters. Sensitivity data is presented as

percentage change relative to the initial simulation with no rate DTS charges.

As shown in Table 4, the sensitivity analysis indicates that the conversion efficiency has a significant

impact on operating profit, with 10% increase in efficiency resulting in 6% increase in operating profit;

and 10% decrease in efficiency a 5% reduction in operating profit. Increasing either of power rating or

storage capacity also results in a profit increase by 5% variable operation and maintenance (O&M) costs

have little impact on profit, with a 10% change only impacting profit by 1%.

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

2010 2011 2012 2013 2014

Price maker Price taker

26

Application of rate DTS to the system has more significant impact, which in this case is a 20% reduction

in profit. If the rate DTS tariff were to be 10% higher, the operating profit is reduced by 22% relative

to the no rate DTS case. Comparing to the Base Case with rate DTS, a 10% increase in rate DTS

incrementally reduces operating profit by 3%.

Table 4: Summary of sensitivity analysis

6.0 CONCLUSION

The transmission tariff has a very significant impact on the operational profitability of ESS based on

arbitrage, irrespective of facility scale. This impact does not change overall operation pattern of ESS

but affects the energy-traded volume. Also affected is the operating cost, which directly brings down

the operating profit. The fixed component of the tariff charge has the most significant impact on the

economics of ESS. This cost is incurred monthly and does not depend on operation strategy or volume

of energy traded by the merchant. While this can be considered a loss by an ESS merchant, overall it is

Energy Input

(MWh)

Energy Output

(MWh)

Revenue Cost Profit

Base Case no Rate DTS 0 0 0 0 0

Efficiency Increased 10% 5% 16% 6% -8% 6%

Efficiency Decreased 10% -5% -15% -6% 7% -5%

Power Rating Increased 10% 5% 5% 5% -7% 5%

Capacity Increased 10% 5% 5% 5% -2% 5%

OM Cost Increased 10% -5% -5% -1% 2% -1%

Base Case with Rate DTS -15% -16% -3% -55% -20%

Rate DTS Increased by 10% -17% -17% -4% -58% -22%

Additional Impact of Rate DTS Increase

(Above Base Case with Rate DTS)

-2% -2% -1% -2% -3%

27

actually a value transfer; that is, money transferred to the system operator, the AESO in this case, to

maintain Alberta’s congestion free policy by investing in transmission facility. The variable component

of the tariff drives the actual loss, evident in the reduction of energy-traded volume as a result of the

tariff.

operational strategy or volume of energy traded by the merchant. Monthly average pool price and

volatility is seen to reduce as a result of price-maker ESS operation. The decline would become

increasingly relevant as multiple large-scale ESSs are added to the system. There comes a point when

arbitrage opportunity would be highly diluted, leading to minimal difference in hourly pool price and

thus operational profitability.

This analysis quantifies the impact of the transmission tariff on operational profitability based on

arbitrage in Alberta's energy-only market. It does not take into account the ancillary service market as

it remains unclear at the moment whether ESS will be allowed to participate in the ancillary service

market and what sort of policy would be introduced to guide its operation. In future works, there is a

need to compare how policies in various electricity markets in North America and elsewhere impact the

operation of ESS both in energy and ancillary service markets. Our findings emphasize the relevance

of transmission tariff policy in making a business case for ESS in Alberta. ESS merchant was assumed,

in this work, to own the substation. If otherwise, point-of-delivery tariff component of rate DTS would

be applied and this is expected to further increase the operating cost, depending on the rated power of

the ESS. The load factor value used in this work is the average load factor in 2014 published by AESO,

actual load factor value is location dependent. The lower the load factor, the lower the STS rate incurred.

7.0 ACKNOWLEDGEMENT

We extend our sincere gratitude to the management of NRGSTREAM for granting us access to their

software, which provided us the refined price data and hourly supply curve for our models. This work

was supported by Alberta’s Department of Energy.

28

8.0 REFERENCES

Adebayo, A.I., Zareipour, H., Knight, A.M., 2016. Economic Viability of Price Arbitrage Operation

of Vanadium Redox Battey in Alberta ’ S Energy Market. IET Conf. Publ.

AESO, 2015a. Energy Storage Integration-RECOMMENDATION PAPER [WWW Document]. URL

http://www.aeso.ca/downloads/Energy_Storage_Integration_Recommendation_Paper.pdf

(accessed 4.29.16).

AESO, 2015b. ISO Tariff [WWW Document]. URL

http://www.aeso.ca/downloads/AESO_2015_ISO_Tariff_(2016-01-01).pdf (accessed 2.19.16).

AlbertaEnergy, 2012. Alberta’s Electricity Industry [WWW Document]. URL

http://www.energy.alberta.ca/Electricity/pdfs/RMRC_Appendix3_Industry.pdf (accessed

4.30.16).

Alvarado, F.L., Rajaraman, R., 2000. Understanding price volatility in electricity markets. Proc. 33rd

Annu. Hawaii Int. Conf. Syst. Sci. 00, 1–5.

Bradbury, K., Pratson, L., Patino-Echeverri, D., 2014. Economic viability of energy storage systems

based on price arbitrage potential in real-time U.S. electricity markets. Appl. Energy 114, 512–

519.

Bubik, P., 2014. Stakeholder Comment and AESO Replies Matrix Discussion Paper – Energy Storage

Integration [WWW Document].

Byrne, R.H., Silva-Monroy, C.A., 2015. Potential revenue from electrical energy storage in ERCOT:

The impact of location and recent trends. IEEE Power Energy Soc. Gen. Meet. 2015-Septe.

Chen, J., 2013. Energy Storage Initiative Issue Identification [WWW Document]. URL

http://www.aeso.ca/downloads/Formatted_ES_IS_Paper_Final_20130613.pdf (accessed

10.5.16).

Cheng, J., 2014. Stakeholder Comment and AESO Replies Matrix Discussion Paper – Energy Storage

Integration [WWW Document]. URL http://www.aeso.ca/downloads/Discussion_Paper_-

_Energy_Storage_Integration_-_Reply_Matrix_-_IPCAA_comments.pdf (accessed 4.29.16).

Fertig, E., Apt, J., 2011. Economics of compressed air energy storage to integrate wind power: A case

study in ERCOT. Energy Policy 39, 2330–2342.

He, G., Chen, Q., Kang, C., Pinson, P., Xia, Q., 2015. Optimal Bidding Strategy of Battery Storage in

Power Markets Considering Performance-Based Regulation and Battery Cycle Life. IEEE Trans.

Smart Grid 1–9.

Hu, W., Chen, Z., Bak-Jensen, B., 2010. Optimal operation strategy of battery energy storage system

to real-time electricity price in Denmark. IEEE PES Gen. Meet. PES 2010 1–7.

Ippolito, M.G., Favuzza, S., Sanseverino, E.R., Telaretti, E., Zizzo, G., Palermo, U., 2015. Economic

Feasibility of a Customer-side Energy Storage in the Italian Electricity Market 1–6.

29

Kazempour, S.J., Member, S., Moghaddam, M.P., 2009. Economic Viability of NaS Battery Plant in a

Competitive Electricity Market 453–459.

Khani, H., Dadash Zadeh, M.R., 2015. Real-time optimal dispatch and economic viability of

cryogenic energy storage exploiting arbitrage opportunities in an electricity market. IEEE Trans.

Smart Grid 6, 391–401.

Kintner-Meyer, M., 2014. Regulatory policy and markets for energy storage in North America. Proc.

IEEE 102, 1065–1072.

Klinkenborg, H., 2014. Stakeholder Comment and AESO Replies Matrix Discussion Paper – Energy

Storage Integration [WWW Document]. URL

http://www.aeso.ca/downloads/ATCO_Discussion_Paper_-_Energy_Storage_Integration_-

_Reply_Matrix.pdf (accessed 4.29.16).

Masiello, R.D., Roberts, B., Sloan, T., 2014. Business models for deploying and operating energy

storage and risk mitigation aspects. Proc. IEEE 102, 1052–1064.

Moghaddam, I.G., Saeidian, A., 2010. Self Scheduling Program for a VRB Energy Storage in a

Competitive Electricity Market.

Steffen, B., 2012. Prospects for pumped-hydro storage in Germany. Energy Policy 45, 420–429.

Viswanathan, V., Balducci, P., Jin, C., 2013. National Assessment of Energy Storage for Grid

Balancing and Arbitrage Phase II Volume 2: Cost and Performance Characterization. Pnnl 2.

Walawalkar, R., Apt, J., Mancini, R., 2007. Economics of electric energy storage for energy arbitrage

and regulation in New York. Energy Policy 35, 2558–2568.

Yu, H., John, K., Wang Peng, Ritter Fred, 2013. 2014 Loss factor [WWW Document]. URL

http://www.aeso.ca/downloads/2014_Loss_Factor_Posting_Letter.pdf (accessed 1.1.16).

Yucekaya, A., 2013. The operational economics of compressed air energy storage systems under

uncertainty. Renew. Sustain. Energy Rev. 22, 298–305.

Zakeri, B., Syri, S., 2014. Economy of electricity storage in the Nordic electricity market: The case

for Finland. Int. Conf. Eur. Energy Mark. EEM.

Zareipour, H., Bhattacharya, K., Canizares, C.A., 2007. Electricity market price volatility: The case of

Ontario. Energy Policy 35, 4739–4748.