Table Of ContentSimulating Financial Returns of Arbitrage Opportunities using Lithium-
Ion Battery Storage Paired with Photovoltaic Systems in 3 U.S.
Wholesale Electricity Markets
by
Nicole R. Miller
Dr. Dalia Patino-Echeverri, Advisor
May, 2018
Masters project submitted in partial fulfillment of the Requirements of the
Master of Environmental Management degree in the Nicholas School of the
Environment
Duke University
2018
Executive Summary
Interest in battery storage as a means to support renewable energy integration has recently
increased as more developers continue to build utility-scale renewable energy resources.
Batteries offer a means to utilize renewable energy at every hour of the day despite production,
thus turning an intermittent resource into an on-demand one (or “dispatchable” as called in the
electricity industry). The ability to do so would allow the integration of renewables more fully
onto the electric grid without the concerns of ramping capabilities, supply and demand
balancing and more. Additionally, the integration of increased amounts of renewable energy
onto the electric grid may allow the gradual phase out of fossil fuels and the corresponding
reduction of air emissions and other negative lifecycle environmental impacts of coal and gas.
Yet, the ability of batteries to solve the world’s problems remains contingent on the bankability
of each project. Batteries are an expensive resource, which despite being deployed on an
increasing basis, are still limited and not yet common place in the renewable energy industry.
The ability for large-scale battery projects to be financially attractive depends on the means in
which value is extracted. This can take place mainly through participation of Battery Energy
Storage (BES) resources as providers of ancillary services, or through participation in energy
markets and engagement in energy price arbitrage.
This analysis seeks to determine whether a battery energy storage system paired with a
110MW utility-scale solar PV installation can result in net present value (NPV) positive returns
when engaging in electricity price arbitrage. The analysis examines the revenues and costs that
would be incurred under 12 different scenarios that vary in assumptions regarding the location
of the market- focusing on ERCOT, PJM and CAISO, the intra-day variations in electricity prices,
the energy storage capacity of the battery, and the length of the battery charge and discharge
periods- ranging from 2-6 hours.
Results indicated that a battery energy storage system paired with a 110 solar PV installation
participating in energy price arbitrage in the CAISO market, would achieve a positive net
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present value. Assuming an optimal schedule of charge and discharge, a small (1MW) battery
obtains a positive NPV if electricity prices are those observed in CAISO in year 2016 . Larger
batteries would only be cost effective if electricity price variability in the CAISO and PJM
markets (measured as the difference in prices for the lowest and highest cost hours)increased
200-400%. Instead, none of the assumptions made in the 12 scenarios examined, allowed a
battery energy storage project to obtain a positive NPV when the storage capacity is larger.
Overall, these results show that the financial viability of large battery storage projects receiving
revenues exclusively from electricity price arbitrage requires high intra-day price differentials
(e.g., requires large differences between the price of the hour with highest electricity prices and
the price of the hour with lowest electricity prices).
Hence, characterizing the volatility of electricity prices in markets with significant penetration
of renewable energy is a necessary step in any study looking to determine the financial viability
of projects relying on this revenue stream.
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Table of Contents
Section 1 .................................................................................................................................................... 5
1.1 Introduction .................................................................................................................................................................... 5
1.2 Business Case for Storage ............................................................................................................................................. 7
1.3 Objectives ............................................................................................................................................................................ 8
Section 2 .................................................................................................................................................... 9
2.1 Literature Review ............................................................................................................................................................ 9
Section 3 ..................................................................................................................................................11
3.1 Data.................................................................................................................................................................................... 11
3.2 Methods ............................................................................................................................................................................ 14
Section 4 ..................................................................................................................................................22
4.1 Major Findings ............................................................................................................................................................... 22
4.2 Limitations ...................................................................................................................................................................... 29
4.3 Conclusion........................................................................................................................................................................ 31
References ..............................................................................................................................................33
Appendix .................................................................................................................................................35
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Section 1
1.1 Introduction
Integrating renewable resources into the current electric grid is needed in order to reduce air
emissions and curb the effects of air pollution and climate change. Yet, renewables do not have
the ability to meet the demands of the grid on their own. For example, wind and solar only
produce when the wind is blowing or the sun is shining. Additionally, oversupply and instability
of power system frequency, including the need for increased ramping rates, are both risks that
are brought on by renewable energy (CAISO, 2016). These are disadvantages compared to
traditional electricity resources such as coal and natural gas, which are dispatchable and hence
can be called on to match the load that is needed at all hours of the day.
Despite this disadvantage, renewables continue to be deployed across the United States as
capital costs continue to decline and demand for renewable energy increases. The installed
capacity of renewable electricity generating technologies is projected to grow to 120 GW
globally by 2022 and to contribute a growing amount to power generation in the United States
(Greentech Media, 2018). As this trend occurs, the issues of renewable energy will be ones
which must be answered in order to maintain a reliable grid.
In areas with large percentages of intermittent resources, there can be large differences in
hourly prices. During the times in which renewables are producing, the cost of electricity is
depressed due to the large amount of energy being produced and the nonexistent price of fuel
for renewables. However, as soon as renewables are not producing, the electric prices may
increase dramatically in an effort to encourage traditional electric generators to increase their
production to match demand. This switch from major fuel sources partly through the day
contributes to the intra-day price variability and changes that have been seen in recent years.
In California, the inability of solar to be dispatched to meet demand whenever it is highest has
led to a power generation curve that aligns with solar production. This is known as the “duck
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curve” and leads to an increase in the amount of power generators that are needed which can
ramp up and down quickly to respond to needs of the grid and balance demand (CAISO, 2016).
Figure 1: “Duck Curve” in CAISO, Source: CAISO, 2016
This increase in renewable energy supply has also lead to an increase in curtailment during
which electric generators that could otherwise produce are asked not to produce. This is in
order to balance the supply and demand and reduce transmission congestion of the electric
grid. High volumes of curtailment are only expected to grow as more intermittent resources are
added to the grid. In California, renewable energy is projected to continue rapid growth due to
aggressive Renewable Portfolio Standards set by California-along with potential for curtailment
growth (CAISO, 2017). Assuming historic curtailment patterns continue, 2018 is poised to be a
record-breaking year when clean energy does not make it to the grid.
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CAISO Curtailment (TWh)
5
4.5
4
3.5
3
2.5
2
1.5
1
0.5
0
2014 2015 2016 2017
Figure 2: CAISO Curtailment, Source: CAISO 2017
1.2 Business Case for Storage
Wholesale power markets throughout the country have seen depressed electricity prices within
the last several years. However, average intraday price variability , measured as the difference
between the average of the highest priced hours and the lowest priced hours, has increased on
the Day-Ahead Market between 2014 and 2016. In CASIO, the average $/MWh on the
wholesale day ahead market has gone from $46.55 in 2014 to $27.98 in 2016 (CAISO, 2017).
Wholesale market participants, including renewable energy generators could be negatively
impacted by these depressed prices, especially if they are unable to take advantage of price
peaks later in the day. This is the case of solar PV projects, given that the timing of higher
$/MWh prices in the Wholesale Market are not aligning with the peak production of solar. In
fact, in markets in which solar is predominant, the highest percentage of solar produced can
align with the lowest average $/MWH (CAISO, 2017).
Battery storage is an option that can help generate additional revenue for wholesale market
participants through energy arbitrage, and the provision of services of voltage support, black
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start capabilities, frequency regulation, transmission deferral, transmission congestion relief,
and spinning reserves (Energy Storage Association, 2017). Given these revenue sources and
declining battery storage costs open the possibility for investments in battery storage to be
profitable.
In fact, the level of battery storage deployment has increased over the last 6 years and is
projected to grow rapidly in the future as costs come down (GTM, 2017).
U.S. Battery Storage Deployment Outlook (MW)
2500
2000
1500
1000
500
0
2012 2013 2014 2015 2016 2017 2018 2019 2020 2021
Figure 3: U.S. Battery Storage Deployment Outlook, Source: GTM, 2017
While there are a number of different storage options, in this analysis we assume the use of
Lithium-ion batteries. This assumption is made because this technology seem most promising
for electric power system applications, and is the focus of significant research and development
efforts to reduce costs and improve performance.
1.3 Objectives
The objective of this analysis is to identify the conditions under which a system of Lithium- Ion
battery storage modules, paired with a 110 MW solar photovoltaic installation may result in a
profitable investment for wholesale market participants engaged in electricity price arbitrage in
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the Day-Ahead market. Rather than selling all of the electricity when it is produced, this analysis
will determine the conditions that make it economic for the owner of the PV solar system, to
store electricity at periods of low prices to then be sold at times when prices are higher.
The analysis looks at the impacts that location, price volatility of day ahead market prices, the
energy capacity of the battery storage and the duration of the periods in which the battery
charges and discharges, have on profits. Each scenario will be compared based on the net
present value (NPV) calculated over a 15 year period.
This document is organized as follows: a review of the academic literature examining the
pairing of battery energy storage systems with PV solar projects and opportunities for price
arbitrage is presented in Section 2. Section 3 describes the data and methods, including the
introduction of the model used in this analysis for energy arbitrage opportunities. Section 4
provides results and findings, limitations and the conclusion for this paper.
Section 2
2.1 Literature Review
Utilizing energy storage for arbitrage is an attractive opportunity for any wholesale market
player for both the economic and environmental benefits. However, studies of the viability of
energy storage applications have found a dependence upon an economic profitability in order
to be successful. Energy storage can be utilized in various ways to regain value as has been
examined in the papers below.
Bradbury et. Al. (2013) discussed the necessity of profitable ESS to support increased
intermittent renewable energy integration onto the grid. Using a model that examined the
internal rate of return needed to make NPV nonnegative, the group experimented with
optimizing capacity sizes in real-time energy arbitrage markets across the United States. The
analysis found that Li-ion batteries were unable to achieve an IRR of over 0% in any of the 7
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markets in which it was modeled. The analysis also found that the optimal charge time was
under 4 hours for all types of batteries tested and predicted the greatest opportunity in capital
cost reductions and introducing ancillary service market potential.
Sioshanshi et. Al (2008) also utilized analyzed arbitrage opportunity, but by using historical
prices and perfect foresight to valuate ESS arbitrage opportunity within the PJM wholesale
market. Variables including location, market fuel-mix, and round-trip efficiency. While value at
times of high price peaks was found, Sioshanshi attributes this partly to the high natural gas
prices of the time which drove up electricity prices. Since the time of the study, the prices of
natural gas have fallen dramatically and therefore additional modeling within the PJM region
may be necessary to evaluate arbitrage potential with energy storage and without inflated
electric prices.
Hittenger (2012) determined the largest expense came from capital costs for Lithium-Ion
batteries and that once the battery was running the operational costs were minimal.
Additionally, the article states that the warn that a while a decrease in cost may seem
inevitable, that investments in such advancements are still a good means to improve the
viability of energy storage. The authors argue in that eliminating such obstacles as high capital
price can make the technology more prevalent and model a target of $250/kwh. Finally, the
article highlights the importance of the application for which the battery storage system may be
utilized and that ultimately “few universal statements” can be made regarding profitability.
McConnel (2015) identifies potential profitability of energy arbitrage in wholesale energy
markets utilizing the energy storage techniques, particularly in markets which have 5-minute
dispatch intervals. Further, battery storage may add value by serving as a means of replacing
peaker generation systems. Importantly, the research recognizes that as renewable energy
grows and wholesale energy prices have potential to become depressed, the opportunities for
arbitrage through batter storage are greater.
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Description:Interest in battery storage as a means to support renewable energy solar PV installation can result in net present value (NPV) positive returns.
