Identifying Energy Arbitrage Opportunities based on Statistical Analysis of Electricity Prices

63 Pages·2014·3.1 MB·English

eeh
power systems
laboratory
Eren C¸am
Identifying Energy Arbitrage
Opportunities based on Statistical
Analysis of Electricity Prices
Master Thesis
PSL1407
EEH – Power Systems Laboratory
Swiss Federal Institute of Technology (ETH) Zurich
Expert: Prof. Dr. G¨oran Andersson
Supervisor: Line Roald
Zurich, August 31, 2014
Abstract
Wholesaleelectricitypricesareexpectedtobecomeincreasinglymorevolatile
duetothegrowingshareofintermittentenergysourcesinthetotalelectricity
generation [1], [2]. This increased volatility, combined with further advances
in storage technologies, is likely to result in energy arbitrage becoming more
significant in the wholesale electricity markets in the near future.
To maximize the revenue obtained through energy arbitrage, it is impor-
tant to install the systems for energy arbitrage at locations with high arbi-
trage potential. For this reason, identifying most suitable interconnections
aswellastheindividualnodesrelativelyquicklywithoutdoingextensiveop-
timization calculations is of great importance. In this thesis, the main goal
is therefore to identify the statistical properties which are good indicators of
energy arbitrage potential. Furthermore, we also aim to observe the effect
of increasing wind penetration on energy arbitrage potential.
An energy storage system (ESS) optimization model was implemented
to calculate the upper bound on the arbitrage revenue at various nodes of
the following U.S. 5-minute interval real-time electricity markets: ISO-NE,
CAISO and MISO. The results of the model were compared with relevant
statistical methods and the impact of various factors on energy arbitrage
revenue was investigated. Finally, the potential impact of increasing share
of wind generation on electricity arbitrage potential in the MISO market
throughout the years 2008–2013 was investigated.
It was found that for a storage system with 0.1 hours of storage capacity
theaverage consecutive price difference isthebeststatisticalindicatorofthe
capturable arbitrage revenue potential. For storage capacities larger than
0.1 hours, the average intra-day price difference property introduced in this
thesis was found to be the best indicator. This property corresponds to the
summed up difference between a specified number of hours with the highest
prices and those with the lowest prices, where the number of hours are spec-
ified flexibly, taking into account the storage capacity and the round-trip ef-
ficiencyofthesystem. Moreover, itwasfoundthatlowpriceautocorrelation
at a node can indicate that a higher percentage of the available maximum
revenue can be captured by a small storage capacity (0.1–0.5 h) system. It
was also observed that storage devices with high self-discharge rates tend to
waste more of the arbitrage potential in percentage when placed at nodes
i
ii
withhighpriceautocorrelation. Regarding theimpactofwind, MISOnodes
locatedatstateswithhighwindpenetrationwerefoundtohaveconsistently
higher energy arbitrage revenues throughout the same period compared to
other nodes.
Acknowledgements
Foremost, I would like to express my sincere thanks to my supervisor Line
Roaldforhercontinuoussupportthroughoutthisthesis. Thisprojectwould
have been much harder to materialize without her guidance, precious feed-
back and her overall positive approach to problem solving. I also would like
to thank Dr. Johanna L. Mathieu for the fruitful discussions and her help-
ful suggestions throughout the thesis. Additionally, I would like to express
my gratitude to Prof. Dr. G¨oran Andersson for being my tutor and giving
me the opportunity to write my thesis at the Power Systems Laboratory.
I would also like to thank my dear friends Vassilis Saplamidis and Iason
Avramiotis for taking part in my brainstorming sessions and for their useful
tips. Finally, I would like to thank my family for their endless support and
encouragement.
iii
Contents
1 Introduction 1
1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Goal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Thesis Structure . . . . . . . . . . . . . . . . . . . . . . . . . 2
2 Energy Arbitrage Theory 3
2.1 Energy Storage Systems . . . . . . . . . . . . . . . . . . . . . 3
2.2 Demand Response . . . . . . . . . . . . . . . . . . . . . . . . 4
3 Energy Arbitrage Model 6
3.1 Storage Modeling . . . . . . . . . . . . . . . . . . . . . . . . . 6
3.2 Assumptions . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
3.3 Formulation of the Optimization Problem . . . . . . . . . . . 7
3.4 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
4 Analyzed Markets and Data Sets 11
4.1 Introduction to Locational Marginal Pricing and U.S. elec-
tricity markets . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.2 ISO New England (ISO-NE) . . . . . . . . . . . . . . . . . . . 12
4.3 California ISO (CAISO) . . . . . . . . . . . . . . . . . . . . . 14
4.4 Midcontinent ISO (MISO) . . . . . . . . . . . . . . . . . . . . 15
4.5 Data-Sets Comparison . . . . . . . . . . . . . . . . . . . . . . 17
5 Analysis of Storage Parameters 19
5.1 Energy Storage Capacity . . . . . . . . . . . . . . . . . . . . . 19
5.2 Round-trip Efficiency . . . . . . . . . . . . . . . . . . . . . . . 20
5.3 Self-discharge Rate . . . . . . . . . . . . . . . . . . . . . . . . 21
6 Statistical Analysis and Results 23
6.1 Investigated Statistical Properties. . . . . . . . . . . . . . . . 23
6.1.1 Standard Deviation . . . . . . . . . . . . . . . . . . . 24
6.1.2 Coefficient of Variation . . . . . . . . . . . . . . . . . 24
6.1.3 Kurtosis . . . . . . . . . . . . . . . . . . . . . . . . . . 24
6.1.4 Autocorrelation . . . . . . . . . . . . . . . . . . . . . . 25
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CONTENTS v
6.1.5 Average Intra-Day Price Difference . . . . . . . . . . . 25
6.1.6 Average Consecutive Price Difference . . . . . . . . . 27
6.2 Results of the Statistical Analysis . . . . . . . . . . . . . . . . 28
6.2.1 Case Study Set-Up . . . . . . . . . . . . . . . . . . . . 28
6.2.2 Price Statistics in Different Markets . . . . . . . . . . 29
6.2.3 CorrelationbetweenPriceStatisticsandArbitrageRev-
enue . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
6.2.4 Correlation between Price Differences and Arbitrage
Revenue . . . . . . . . . . . . . . . . . . . . . . . . . . 30
6.3 Effect of Autocorrelation . . . . . . . . . . . . . . . . . . . . . 34
6.3.1 Autocorrelation and the percentage of total arbitrage
potential captured . . . . . . . . . . . . . . . . . . . . 34
6.3.2 Autocorrelationimpactondeviceswithhighself-discharge
rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
6.4 Detecting Cyclic Patterns with Fourier Analysis . . . . . . . . 38
7 Wind Power Effect on Energy Arbitrage 42
7.1 Wind Power in the U.S. and the Analyzed Regions . . . . . . 42
7.2 Wind Power Penetration and Affected Price Statistics . . . . 43
7.3 Wind Power Impact on Energy Arbitrage Potential in MISO 45
8 Conclusions and Outlook 49
8.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
8.2 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . 50
Bibliography 51
List of Figures
3.1 Illustration of a generic energy storage system and its inter-
action with the grid . . . . . . . . . . . . . . . . . . . . . . . 9
3.2 A sample daily operation of a storage system with 5 hours of
storage at CAISO ARMSTRNG node . . . . . . . . . . . . . 10
4.1 ISO-NE operation areas [21] . . . . . . . . . . . . . . . . . . . 12
4.2 ISO-NE generation fuel-mix in 2000 and 2013. Plotted with
data from [22] . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
4.3 NaturalGaspriceimpactonreal-timeLMPsinISO-NE.Plot-
ted with data from [22] . . . . . . . . . . . . . . . . . . . . . 13
4.4 CAISO map and the three main trading hubs [24] . . . . . . 14
4.5 CAISOgenerationfuel-mixin2001and2013(calculatedfrom
gross energy generation values from [26]) . . . . . . . . . . . 15
4.6 MISO regions and the major hubs [29] . . . . . . . . . . . . . 16
4.7 MISO generation fuel-mix in 2009 and 2013. Plotted with
data from [30] . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
4.8 Marginal fuels in MISO in the years 2010–2013 [32] . . . . . . 17
5.1 Total yearly energy arbitrage revenue in 2013 with varying
energy storage capacity in hours (E /P ), using hourly
max max
and 5-minute interval LMPs . . . . . . . . . . . . . . . . . . . 20
5.2 Total yearly energy arbitrage revenue in 2013 with varying
round-trip efficiency for the cases of 0.1 and 12 hours storage
capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
5.3 Total yearly energy arbitrage revenue in 2013 with varying
self-discharge rate for the cases of 0.1 and 12 hours storage
capacity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
6.1 Weekly revenue for the CAISO, MISO and ISO-NE nodes
plotted against the average daily standard deviation for the
corresponding week for a storage capacity of 0.5 h . . . . . . 31
6.2 Realized energy arbitrage revenues for a storage capacity of
0.1hoursandtheadditionalarbitragepotentialavailablewith
1 and 9 hours of storage options . . . . . . . . . . . . . . . . 35
vi
LIST OF FIGURES vii
6.3 Weekly captured percentage of total arbitrage potential ver-
sustheaveragedailyautocorrelationfortherespectiveweeks,
with a storage capacity of 0.1 h . . . . . . . . . . . . . . . . . 35
6.4 Percentage of the weekly captured revenue potential plotted
against average daily autocorrelation a) for a self-discharge
rate of 0.2%, b) for a self-discharge rate of 1% . . . . . . . . 37
6.5 FFTplotfortheCAISOARMSTRNGnode,withafrequency
unit of [1/day] . . . . . . . . . . . . . . . . . . . . . . . . . . 39
6.6 Results of the FFT analysis for the investigated nodes; intra-
day cycle amplitude and the arbitrage revenue for 9 hours . . 39
6.7 Total yearly arbitrage revenue for the 1 h and 9 h of storage
capacity cases versus the intra-day cycle amplitude for the 19
nodes considered in this analysis . . . . . . . . . . . . . . . . 41
6.8 Total yearly arbitrage revenue for the 1 h and 9 h of storage
capacity cases versus the inter-day cycle amplitude for the 19
nodes considered in this analysis . . . . . . . . . . . . . . . . 41
7.1 Yearly additional wind capacity installed; 2014 value covers
the capacity installed only during the first quarter. Data
from [37] . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
7.2 Standard deviation plotted against the mean for the nodes
analyzed; note that blue indicates ISONE nodes, red mark-
ers stand for CAISO and yellow markers represent the MISO
nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
7.3 Coefficientofvariationplottedagainstthemeanforthenodes
analyzed; note that blue indicates ISONE nodes, red mark-
ers stand for CAISO and yellow markers represent the MISO
nodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
7.4 Normalizedarbitragerevenueinyears2008-2013plottedagainst
wind penetration for various MISO nodes . . . . . . . . . . . 48
List of Tables
4.1 Percentage of positive spikes, negative spikes, negative price
and zero price occurrences in the data-sets considered (for
2013) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
6.1 Statistical values for the investigated transmission regions . . 29
6.2 Correlations of the respective statistical properties with the
weekly arbitrage revenue for varying storage sizes . . . . . . . 30
6.3 Correlations of average consecutive price differences (Cons.
Diff.),andaverageintra-daypricedifferences forvarioushours
oft withtheweeklyenergyarbitragerevenuefortherespec-
H
tive storage sizes . . . . . . . . . . . . . . . . . . . . . . . . . 33
6.4 Correlation of captured arbitrage potential and autocorrela-
tion for varying storage capacities . . . . . . . . . . . . . . . . 36
6.5 Average inter- and intra-day cycle amplitudes for the investi-
gated markets . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
7.1 The states where the analyzed nodes are located and the
corresponding installed wind power capacity as well as wind
power share in generation . . . . . . . . . . . . . . . . . . . . 44
viii
Chapter 1
Introduction
1.1 Motivation
Wholesaleelectricitypricesareexpectedtobecomeincreasinglymorevolatile
due to the growing share of intermittent energy sources such as wind energy
in the total electricity generation [1], [2]. This new market structure is likely
to provide for more suitable circumstances for energy arbitrage possibilities,
i.e. buying and storing energy during low price periods and selling it back
when the prices are sufficiently high. Due to this increased price volatil-
ity phenomenon combined with further advances in storage technologies it
is expected that energy arbitrage will have an increased significance in the
wholesale electricity markets in the near future.
Electricity markets with nodal pricing are generally more suitable for
energy arbitrage since various individual nodes are likely to exhibit excep-
tional volatility compared to wholesale power markets with uniform pricing.
Furthermore, many nodal markets operate real-time markets with 5-minute
intervals, which are ideally better candidates for energy arbitrage due to in-
creased volatility. Therefore, in this thesis North American wholesale elec-
tricity markets employing nodal pricing with 5-minute interval real markets
are investigated.
1.2 Goal
To maximize the revenue potential obtained through energy arbitrage, it is
important to install the systems for energy arbitrage at locations with high
arbitrage potential. For this reason, identifying the most suitable intercon-
nection as well as the individual nodes with the highest arbitrage potential
is of utmost importance.
In previous literature, the focus has been generally on assessing the ar-
bitrage potential of various storage technologies at individual nodes by con-
ducting extensive optimization calculations. Relevant price statistics indi-
1