DISORDERS OF CONSCIOUSNESSA Brain–Computer Interface Contr.docx

DISORDERS OF CONSCIOUSNESS
A Brain–Computer Interface Controlled
Auditory Event-Related Potential (P300)
Spelling System for Locked-In Patients
Andrea Kübler,a,b Adrian Furdea,b,c Sebastian Halder,b
Eva Maria Hammer,b Femke Nijboer,b
and Boris Kotchoubeyb
aClinical and Health Psychology Research Centre, School of
Human and Life Sciences,
Roehampton University, London, UK
bInstitute of Medical Psychology and Behavioural
Neurobiology, University of Tübingen,
Tübingen, Germany
cGraduate Institute of Technology, University of Arkansas at
Little Rock, Little Rock, Arkansas 72204, USA
Using brain–computer interfaces (BCI) humans can select
letters or other targets on a
computer screen without any muscular involvement. An
intensively investigated kind
of BCI is based on the recording of visual event-related brain
potentials (ERP). However,
some severely paralyzed patients who need a BCI for
communication have impaired
vision or lack control of gaze movement, thus making a BCI

depending on visual input
no longer feasible. In an effort to render the ERP–BCI usable
for this group of patients,
the ERP–BCI was adapted to auditory stimulation. Letters of the
alphabet were assigned
to cells in a 5 × 5 matrix. Rows of the matrix were coded with
numbers 1 to 5, and columns
with numbers 6 to 10, and the numbers were presented
auditorily. To select a letter, users
had to first select the row and then the column containing the
desired letter. Four severely
paralyzed patients in the end-stage of a neurodegenerative
disease were examined.
All patients performed above chance level. Spelling accuracy
was significantly lower
with the auditory system as compared with a similar visual
system. Patients reported
difficulties in concentrating on the task when presented with the
auditory system. In
future studies, the auditory ERP–BCI should be adjusted by
taking into consideration
specific features of severely paralyzed patients, such as reduced
attention span. This
adjustment in combination with more intensive training will
show whether an auditory
ERP–BCI can become an option for visually impaired patients.
Key words: locked-in syndrome; brain-computer interface;
event-related potentials;
P300; auditory; amyotrophic lateral sclerosis; communication
Introduction
Brain–computer interfaces (BCI) are devices
that directly connect human (or animal) brain
activity with artificial effectors, such as commu-

nication programs or neuroprostheses. They al-
Address for correspondence: P.D. Dr. Andrea Kübler, Ph.D.,
Clinical
and Health Psychology Research Centre, School of Human and
Life Sci-
ences, Roehampton University, Whitelands College, Holybourne
Avenue,
London SW15 4JD, UK. [email protected]
low patients with lost movement ability after
injury or disease to replace a part of the lost
motor function (for recent reviews, see Refs.
1–3). The most commonly used neural signal
for the purpose of BCI control is electroen-
cephalography (EEG).4 The EEG signals are
acquired, digitized, filtered, and transformed
by various algorithms into an output signal that
controls an effector. Depending on the type of
BCI, users are required to either learn regula-
tion of a specific brain response, for example,
Disorders of Consciousness: Ann. N.Y. Acad. Sci. 1157: 90–100
(2009).
doi: 10.1111/j.1749-6632.2008.04122.x C© 2009 Association
for Research in Nervous and Mental Disease.
90
Kübler et al.: BCI Auditory Event-Related Potential Spelling
System 91
slow cortical potentials (SCP)5,6 or sensorimo-
tor rhythms (SMR),7,8 or to attend to sensory
stimulation. This stimulation elicits a character-

istic EEG pattern referred to as event-related
potential (ERP). In particular, the already exist-
ing BCI use steady state–evoked potentials9,10
or the late ERP component P300.11,12 The lat-
ter is a broad centroparietal electropositive de-
flection usually observed in response to target
stimuli (in particular, rare targets) starting about
300 ms poststimulus.
One target group for BCI use is patients who
lose the ability to communicate due to progres-
sive neurodegenerative disease, such as amy-
otrophic lateral sclerosis (ALS). This disease of
the first and second motor neurons is character-
ized by a progressive motor paralysis, with no
curative treatment available. If life-sustaining
treatment is withheld or withdrawn, death due
to respiratory failure occurs, on average, three
to five years after the first symptoms. In several
studies it has been shown that patients with ALS
can control an effector using different kinds of
BCI.5,6,13 However, patients examined in those
studies retained at least minimal control of gaze
movements. Artificially (via tracheostoma) ven-
tilated patients can lose the ability to control
ocular muscle activity, which may further re-
sult in the decline of visual attention14,15; such
completely paralyzed (including gaze paralysis)
patients are referred to as being in a complete
locked-in state (CLIS).3,14
Recently, efforts have been undertaken to use
nonvisual stimulation for BCI control. Audi-
tory feedback of SCP and SMR was incorpo-
rated into BCI.16,17 Both the SMR– and the

SCP–BCI require learning to regulate a brain
response that is achieved by means of neuro-
feedback. In both studies, the task, target EEG
response, and the results were presented and
fed back to the participants via auditory stim-
uli. The data showed that healthy participants
need more time to achieve BCI control with
auditory as compared to visual feedback. Vi-
brotactile feedback of SMR was realized by
Cincotti and colleagues,18 and selection accu-
racies were comparable to those achieved with
visual presentation. Müller-Putz and colleagues
used vibratory stimulation of left and right hand
finger tips to elicit somatosensory steady state–
evoked potentials (SSSEP).19 In each trial, both
index fingers were stimulated simultaneously at
different frequencies, and participants were in-
structed by arrows on a computer screen to
which finger they should pay attention. Hill
and colleagues20 used ERP to auditory stim-
uli consisting of 50-ms square-wave beeps with
different frequencies, grouped in two different
streams, one presented to the left ear and the
other to the right ear. Following an oddball
paradigm,21 each stream contained rare target
and frequent nontarget beeps. The users were
able to direct conscious attention to one of the
stimulus streams, and the BCI could detect the
stream and the target the user attended to.
Almost all of the just-summarized studies in-
cluded healthy volunteers only. However, per-
formance of patients with severe neurological
disease cannot be inferred from healthy sub-
jects.1 In a study that included ALS patients,

Sellers and colleagues used an auditory odd-
ball paradigm in which stimuli were the words
“yes,” “no,” “pass,”and “end” presented in a
random sequence.12 The participants’ task was
to count the number of times the target (ei-
ther yes or no) was presented. The authors
showed that with a target probability of 25%, a
P300 response to these stimuli was reliably de-
tected, and that the response remained stable
in healthy volunteers as well as in ALS patients
over a period of 10 sessions, although in patients
selection accuracy was lower.
Some studies have found alteration in la-
tency of auditory and somatosensory evoked
potentials in ALS patients,22,23 whereas oth-
ers have not.24,25 Cosi and colleagues investi-
gated somatosensory-evoked potentials (SEP)
in ALS patients and found a delay of cer-
vical and cortical SEP, namely the N13 and
subsequent components, accompanied by de-
crease of amplitude, which suggested a slowing
of conduction along the central sensory path-
ways.23 Alterations in auditory processing were
reported by Pekkonen and colleagues, who
92 Annals of the New York Academy of Sciences
TABLE 1. Background Data of Patients
Artificial
Participants Age Sex Time since diagnosis Nutrition Ventilation
Degree of Impairment

1 39 F 10 years No No Major
2 51 F 3 years Yes Yes LIS
3 49 F 4 years Yes Yes LIS
4 39 M 8 years Yes Yes LIS/CLIS
ABBREVIATIONS: LIS = locked-in state; CLIS = complete
locked-in state; M = male; F = female.
used magnetoencephalography.22 The authors
suggest that these abnormalities in stimulus
detection and subsequent memory-based com-
parison processes in ALS patients, with ad-
vanced symptoms, might be due to hyperac-
tivity of the cortical excitatory glutamatergic
system. Palma and colleagues also found a re-
duction of N13 amplitude and a prolonged
P22 latency in SEP recordings, but on the ba-
sis of a source analysis they concluded that
these changes do not implicate the involve-
ment of somatosensory pathways.24 Gil and
colleagues compared late ERP components in
healthy subjects and patients with ALS and did
not find significant differences between patients
with ALS and a control group in the amplitudes
of N100, P200, N200, and P300.25 Taken to-
gether, the results of studies on auditory and
SEP and the P300 component in ALS patients
are ambiguous. We assume that at least in most
patients auditory and somatosensory pathways
remain functional, offering a possible alterna-
tive for stimulus and feedback presentation for
BCI control.
All the presented studies with auditory BCI
provided the user with the possibility to com-

municate a yes or no response. To allow users a
more flexible communication, we recently de-
veloped an oddball-based spelling system with
auditory stimulation, referred to as auditory
ERP–BCI, and tested it in healthy volunteers.26
Following the setup of the visual ERP–BCI, as
described initially by Farwell and Donchin and
later by Sellers and Donchin, users were pre-
sented with a 5 × 5 matrix of characters.11,12
Each row and column was coded with numbers
1 to 5 and 6 to 10, respectively, and numbers
were presented acoustically. The users’ task was
to spell the English word BRAINPOWER. To
do this, they had to attend first to the number
of the row, and after row selection, to the num-
ber of the column (see methods section for de-
tailed description). Auditory spelling accuracy
of 14 healthy volunteers was worse, letter se-
lection needed more time, and peak latency of
the ERP was delayed as compared to the visual
ERP–BCI. Nevertheless, 9 of the 14 partici-
pants achieved spelling accuracies above 70%,
which is the lower limit for spelling with an
assistant device for communication.6,27
In the current study, we explored whether
patients in the late stages of ALS in the locked-
in state (LIS), or in the LIS with periods of
CLIS, were able to spell words using the audi-
tory ERP–BCI.26
Methods
Participants

Background data of patients are listed in
Table 1. Three patients were diagnosed with
the sporadic and one with the hereditary form
of ALS; all patients had the spinal form of ALS.
Three patients lived at home and were cared
for either by family members or professional
caregivers; one patient lived in a nursing home.
Three patients lived at a maximum distance
of 160 km from our institute and had been
trained with the visual P300–BCI since the be-
ginning of 2006.28 BCI training was conducted
System 93
exclusively at the patients’ home at a maximum
rate of two visits per week. Patient 1’s major im-
pairment included tetraplegia and almost in-
comprehensible speech.14 Residual speech and
head movement were used for communica-
tion. Patients 2 and 3 were artificially ven-
tilated via tracheostoma and fed via PEG.
Communication was established using residual
movement of facial muscles and head move-
ments. All three patients additionally used PC-
based programs for assistant communication
and for surfing the Internet. Patient 4 lived
at a distance of 300 km, and therefore was
trained in weekly blocks, twice a year since early
2006. Gradually decreasing residual muscular
control of the right thumb and of the right
corner of the mouth were his only means of
communication. To the moment of writing this

report, he has lost thumb movement and mouth
twitches. Some days the patient can still use
slight horizontal eye movement to indicate yes
or no.
Procedure
Patients were seated in their wheelchairs (pa-
tients 1, 2, and 3) or bed (patient 4). Audi-
tory stimuli were presented with two separate
loudspeakers, positioned in front of the par-
ticipants at a distance of approximately 1.2 m.
The experimental session started with a calibra-
tion run to determine classification weights (see
Data Analysis); within this run the participants
did not receive any feedback. Data of the fol-
lowing runs were collected in the copy spelling
mode of the spelling system. In this mode, the
software prompts participants to spell a given
word character by character, and feedback of
the selected letter is provided after each selec-
tion.6 We used a 5 × 5 spelling matrix con-
taining all letters of the alphabet, except the
letter Z.26 The task of each run was to spell a
word preset by the trainer. Patients had to select
each letter of the word by attending to acous-
tically presented number words. A short break
followed each run; its duration was determined
by the patients.
Figure 1. Design of the acoustically presented
matrix. Rows were coded with numbers 1–5, and
columns with numbers 6–10. Speakers indicate that
numbers were presented auditorily by a male voice.
The word to be spelled is depicted in the topmost
line—in this case WINTER. The letter in parenthesis

following the word indicates the letter to be spelled
in each specific trial.
Each character’s position in the matrix was
coded by two acoustically presented numbers,
that is, words spoken by a male voice: one for
the row and one for the column (Fig. 1). To
select one target letter, the patients had to first
attend to the number that coded the row (num-
bers 1–5; see Fig. 1) containing the target letter.
In the trial following row selection, numbers
6 to 10 were presented, and patients had to
attend to the number that coded the column
that contained the target letter. After comple-
tion of the second trial a letter was selected. To
facilitate focusing of attention in each trial, pa-
tients were asked to count how often the num-
bers referring to the target coordinates were
presented. Since counting during number pre-
sentation may be difficult, we also instructed
patients to have an “aha-effect” whenever they
heard the target number. This design consti-
tutes an oddball paradigm, because in one trial
of stimuli presentations, only one number out
of five refers to the target letter. The rare events
in the context of the other irrelevant stimuli
were shown in healthy participants to elicit a
P300-like event-related response.26 In addition
to auditory presentation of numbers, a “visual
support matrix” was displayed on a monitor to
help the participants to remember the coordi-

nates for the target letter, but no visual stimula-
tion occurred.26
One trial consisted of 150 stimuli presenta-
tions (30 targets, 120 nontargets), thus each au-
ditory stimulus was presented 15 times per trial.
First, 75 stimuli (15 targets) were presented, in-
dicating the numbers that coded the rows; sec-
ond, the next 75 stimuli (15 targets) were the
numbers coding the columns. Each auditory
stimulus lasted 450 ms, followed by an inter-
stimulus interval (ISI) of 175 ms. Row codes
were separated from column codes by an inter-
val of 1875 ms. Total time needed for selection
of one character was 1.5 minutes.
The 5 × 5 visual support matrix was displayed
on a 19-inch monitor located about 1.2 m in
front of a participant to facilitate identifying
the coordinates of the target letter; no visual
stimulation occurred. Patient 4 could not see
the matrix, because he could not focus gaze.
However, due to previous training sessions with
the visual P300–BCI, he knew the matrix by
heart.
Patients 1 to 3 were presented with the task to
spell a 5-character word in each of 3 runs. The
task of Patient 4 was to spell in 5 runs one 5-
character and four 3-character words that were
suggested by him. The first run was used for
calibration (see Data Analysis).
Data Acquisition
Stimulus presentation and data collection

were controlled by the BCI2000 software29
(http://www.bci2000.org/). The EEG was
recorded using a tin electrode cap (Electro-Cap
International, Inc., Eaton, Ohio, USA) with 16
or 8 channels [(F3), Fz, (F4), (T7), (T8), (C3), Cz,
(C4), (Cp3), (Cp4), P3, Pz, P4, PO7, PO8, and
Oz; channels in parenthesis were only available
with the 16-channel cap] based on the modi-
fied 10–20 system of the American Electroen-
cephalographic Society.30 Each channel was
referenced to the right mastoid and grounded
to the left mastoid. The EEG was amplified us-
ing a g-tec 16- or 8-channel amplifier, sampled
at 256 Hz band-pass filtered between 0.01 and
30 Hz. Fifty-hertz noise was filtered using the
notch filter implemented in the BCI2000 soft-
ware. Data processing, storage, and on-line dis-
play of the participants’ EEG were conducted
using an IBM ThinkPad laptop.
Data Analysis
We used the stepwise-linear discriminant
analysis method (SWLDA) for classification and
weight generation.11,31 The method, an ex-
tension of the Fisher’s Linear Discriminant
(FLD), is established as a successful classifica-
tion method for EEG data in general, and more
recently for BCI data, for which rapid classifica-
tion is essential. Previous studies of classification
methods demonstrated that SWLDA provides
good overall performance in classifying both
visual P30011,12,21,31 and auditory P300 within
the frame of BCI.26

For each of the recorded channels, 1200-ms
poststimulus data segments were extracted and
averaged. A moving average filter was then ap-
plied to the segments that were further deci-
mated by a factor of 20. The feature vector that
resulted from concatenating the data segments
was then used to train the classifier.
For each of the 16 channels an r2 coefficient
was computed to indicate the portion of signal
variance that was due to whether the row or
column did (oddball/target) or did not contain
the desired character (standard). The r2 was
calculated on the base of a point–biserial cor-
relation between the ERP signal and the task.
Peak latency and amplitude were calculated for
the electrode with the highest r2 value.
To quantify the ability to select characters,
we determined selection accuracy as a percent-
age of correctly selected letters as compared
with the total number of letters to be spelled
in each run. Selection accuracy comprises cor-
rect selection of rows and columns. Classifica-
tion accuracy was determined as percentage of
correctly selected rows or columns per run.
The data obtained using the visual ERP–
BCI has already been presented elsewhere.28
System 95

TABLE 2. Results with the Auditory
SA CA Peak amp Peak lat Number of
Patient [%] [%] Loc [μV] [ms] Chars Target Spelled Stimuli
1 25 58.33 Cp4 2.9 457.03 12 WINTER WJNJBS 360 Targets
total
WINTER WSOEOI 1440 Nontargets total
2 0 25 Po8 2.5 578.03 12 WINTER FEUHGH 1800 Total
WINTER UKOJJM
3 0 25 Po8 1.68 941.31 12 WINTER RRKSLB
WINTER LFUYCB
BRAIN LAIEK 510 Targets total
UHR IMQ 2040 Non-targets total
4 23.53 41.17 Pz 0.91 574.12 22 BAD BSI 2550 Total
MAI NAI
EVA ESM
ERP–BCI. SA = selection accuracy; CA = classification
accuracy; Loc = electrode position at which peak
amplitude and latency were calculated; amp = amplitude; lat =
latency; Chars = characters to be spelled
The method is sufficiently described in the lit-
erature (e.g., Refs. 12, 32, 33).
Results
Selection and classification accuracies, elec-
trode position of peak amplitude, latency, the
word to be spelled, selected characters, and the
number of presented stimuli per run are pre-
sented in Table 2. As can be seen from this

table, performance was generally low. Taking
into account the design of the auditory ERP–
BCI (5 × 5 matrix), the performance at chance
level would be 4% for selection accuracy and
20% for classification accuracy. Therefore, pa-
tients 2 and 3 performed at, or very close to,
chance level. Patient 1 who had the highest
peak amplitude and the shortest peak latency,
also performed better than the other patients,
although her performance was also not high
enough to allow meaningful communication,
which requires a selection accuracy of above
70%.6 Interestingly, the same patient also had
the largest P3 amplitude with the visual ERP–
BCI.28 On the other hand, the results of pa-
tient 4 indicate that even a low peak amplitude
of around 1 μV may be sufficient for above-
chance classification.
Our previous data indicate that the same pa-
tients were much more successful when using
a visual ERP–BCI.28 They achieved selection
accuracy of at least 70%. Patients 1–3 commu-
nicated messages of considerable length.
Figure 2 shows ERP to auditory (left column)
and visual (right column) stimulation averaged
across all runs separated according to whether
rows and columns did or did not contain the
target letter. In both modalities and all patients,
the ERP response to target and nontarget stim-
uli can be clearly distinguished. However, in
three patients (except GR) the configuration
of target responses is more distinct in the vi-
sual BCI than in the auditory BCI. The size
of the target/nontarget difference is lower and

the peak latency larger in the auditory than in
the visual ERP–BCI.
Discussion
We presented four patients with ALS with
a spelling system based on auditory ERP. Two
of the patients were in LIS and one was at
the border of CLIS. All patients were previ-
ously trained with an oddball-based ERP–BCI
using visual stimulation and were thus well fa-
miliar with this type of BCI. With the visual
Figure 2. Event-related potentials (ERP) to auditory (left) and
visual (right) stimulation
with the ERP–BCI (brain–computer interface). Solid line
depicts averaged EEG responses to
targets, and dashed line to nontarget stimuli. In the auditory
ERP–BCI for all patients peak
amplitudes were lower and peak latencies delayed as compared
to the visual ERP–BCI.28
ERP–BCI, all patients achieved spelling ac-
curacies above 70%, some even above 90%,
clearly showing that the patients could concen-
trate on the task that elicited highly classifiable
brain responses to stimulation (for a detailed
report of patients 1, 2, and, 3 see Ref. 28).
In the auditory BCI, however, the patients’
performance was very poor. Although it might

System 97
be suggested that any modification in the
functioning BCI would result in a negative
performance change, the degree of the ob-
served impairment calls for additional explana-
tion. Healthy control subjects performed only
slightly worse in the auditory ERP–BCI than
in the visual ERP BCI.26
It should be taken into account that each
number word in the coding system used in the
auditory BCI referred to five different letters,
whereas in the visual BCI, the target letter it-
self served as a stimulus. Thus, the auditory
system imposed an additional effort for con-
stantly maintaining the target number in short-
term memory. Three patients stated that it was
very difficult for them to focus their attention
on the numbers, and that the visual support
matrix was absolutely necessary. ERP studies
of working memory clearly indicate that an
increasing workload results in a delay of P3
peak latency and the decrease of P3 amplitude
(for review, see, e.g., Refs. 34 and 35). Accord-
ingly, Figure 2 shows that the target/nontarget
differences were of a smaller size and of a
larger latency for the auditory than for the
visual BCI.
Although the amplitudes were decreased in
all patients, two of them achieved classifica-

tion accuracy quite above chance level, and we
may assume that with training and adaptation
to auditory stimulation patients’ performance
might improve. It is important to note that pa-
tient 4 was one of these two patients despite his
low peak amplitude. This patient immediately
learned the matrix by heart and wanted to con-
tinue using the auditory ERP–BCI. Due to the
lack of control over eye movement, the patient
may have already learned to rely more on audi-
tory input during his daily life, a phenomenon
also seen in visually impaired or blind people
(e.g., Ref. 36).
Kübler and Kotchoubey suggested a hierar-
chical approach to the detection of cognition
and consciousness using BCI in nonresponsive
patients. The approach comprises five steps4,37
(Fig. 3). Step 1: recording of the rest EEG
and auditory-evoked potentials to exclude pa-
tients whose dominant EEG frequency is below
4 Hz and those with hearing loss.38 Step 2: pas-
sive auditory stimulation paradigms with EEG
recording, including auditory oddballs to as-
sess preattentive cortical orientation (mismatch
negativity), and deeper cortical analysis of the
physical stimulus properties (P300); and second,
semantic stimulation leading to N400 or P600
components. Step 3: presentation of the same
tasks as in Step 2, with additional instructions
to pay attention to specific target stimuli. With
participants who are able to understand and
follow the instruction, this results in a consid-
erable increase of the P300 and N400 effects

(e.g., Refs. 39 and 40). Step 4: volitional tasks
that require the subject to actively imagine one
of two actions according to two corresponding
auditory stimuli,16,41 or to focus attention on a
specific target in a paradigm designed to elicit
ERP (as described in this study and Refs. 12
and 26). If a nonresponsive patient performed
above chance level in any of these volitional
paradigms, it could be inferred on the presence
not only of cognitive processing and conscious
awareness but also on active volition.4 Step 5:
decision making with a BCI. On the most ba-
sic level, this would include answering yes/no
questions with the BCI. A more sophisticated
level would include communication and inter-
action with the environment.5,6
The technology of auditory BCI is the nec-
essary prerequisite for the use of this approach.
Nonresponsive patients may be in a CLIS due
to total paralysis of peripheral muscles as a re-
sult of degeneration of motor neurons or due to
neuronal loss in brain areas required for con-
scious attention and awareness, initiation of ac-
tion or motor execution, and goal-directed be-
havior.4,37 The present data show that we do
not yet have an auditory system that would ful-
fill the requirements of such patients. Patients
who are already deprived of visual input may
benefit more of the auditory ERP–BCI than do
patients with intact vision. However, the feasi-
bility of the auditory ERP–BCI must be con-
siderably improved before it becomes an option
for patients approaching or being in the CLIS.

Figure 3. Flow chart of the hierarchical approach to the use of
brain–computer interface
(BCI) for the detection of conscious awareness and cognitive
function in completely locked-in
patients. Each level involves higher demands on cognitive
processing: Step 1: EEG record-
ing, exclusion of patients with a rest EEG below 4 Hz; Step 2:
Passive stimulation; Step 3:
instruction to focus attention on target stimuli in passive-
stimulation tasks;. Step 4: presentation
of volitional tasks such as a 4-choice oddball paradigm (EEG
recording) or mental imagery
(fMRI or EEG recording); Step 5: decision making by means of
a BCI. On each specific
step, performance above chance level is required in order to
proceed to the next level of the
hierarchy.
Several possible lines of such modification can
be proposed. First, numbers can be replaced by
more interesting stimuli such as musical tones
varying in pitch and timbre. Second, such stim-
uli can be presented from different spatial po-
sitions corresponding to the position of the sig-
nified unit (e.g., the leftmost position for the
leftmost column of the matrix). Third, a BCI
based on the self-regulation of the SMR13 also
can be adapted to auditory presentation, which
might yield better results because the SMR-
based BCI can have lower memory load than

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the ERP-based BCI.16,42 Future studies should
test these options.
Acknowledgments
This work was supported by the Deutsche
Forschungsgemeinschaft (German Research
Council) SFB 550/TB 5. The authors are
grateful to the patients who participated in the
study. We thank Slavica von Hartlieb for her
support in data acquisition.
Conflicts of Interest
The authors declare no conflicts of interest.
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Developmental mechanism for the resistance change effect in
perovskite
oxide-based resistive random access memory consisting
of Bi2Sr2CaCu2O81d bulk single crystal
A. Hanada,1 K. Kinoshita,1,2,a) K. Matsubara,1 T. Fukuhara,1
and S. Kishida1,2
1Graduate School of Engineering, Tottori University, 4-101
Koyama-Minami, Tottori 680-8552, Japan

2Tottori University Electronic Display Research Center, 2-522-
2 Koyama-Kita, Tottori 680-0941, Japan
(Received 2 April 2011; accepted 7 September 2011; published
online 24 October 2011)
Resistive random access memory (ReRAM) structures of
M/Bi2Sr2CaCu2O8þd (Bi-2212) bulk
single crystal/Pt (M¼Al, Pt) were prepared and their memory
characteristics and superconducting
properties were evaluated. The resistance change effect
developed only in the Al/Bi-2212/Pt
structure and was enhanced with decreasing critical temperature
by annealing in Ar atmosphere.
Due to the large resistance anisotropy of bulk Bi-2212 single
crystals, the resistance change effect
was confirmed to occur at the interface between the Al
electrode and the Bi-2212 single crystal.
These results indicate that introduction of an oxygen-depleted
layer to the Bi-2212 single crystal is
required to develop the resistance change effect, which could be
achieved by the deposition of
electrodes with low Gibbs free energy and raising the
temperature to exceed the activation energy
for oxygen ions to move from Bi-2212 to the electrode. A model
is proposed to explain the resistive
switching of perovskite oxide-based ReRAM by

generation/recovery of the oxygen-depleted layer.
The resistance change effect developed also in the Pt/Bi-
2212/Au structure annealed in hydrogen
gas, in which an oxygen-depleted layer is formed with the
assistance of catalytic effect of Pt on the
surface of the Bi-2212 at the Pt/Bi-2212 interface, proving the
validity of the model. VC 2011
American Institute of Physics. [doi:10.1063/1.3651465]
I. INTRODUCTION
Resistive random access memory (ReRAM) has a struc-
ture where a transition metal oxide (TMO) is sandwiched
between top and bottom electrodes. This simple structure
enables high integration and has attracted attention as a non-
volatile memory to replace flash memory. The resistance
change effect of perovskite oxide-based ReRAM, in which
perovskite oxides such as Pr1�xCaxMnO3 (PCMO)
1–5
and
SrTiO3 (Refs. 6 and 7) are used as the TMO layer, is thought
to be related to the migration of oxygen ions. However, the
switching mechanism is yet to be clarified.

One factor that hinders elucidation of the mechanism
could be attributed to the use of thin films. The presence of
grain boundaries may affect electrical conduction and the
migration of oxygen ions when polycrystalline thin films are
used.
3,4,6,8
Mismatching with a substrate introduces stress
into the film and complicates elucidation of the mechanism,
even if epitaxially grown TMO films are used.
1,5,7,9,10
Therefore, introduction of a bulk single crystal as the
TMO layer would be effective for elucidation of the switch-
ing mechanism. In addition, the superconducting critical
temperature (Tc) for a single crystal of the high temperature
Bi2Sr2CaCu2O8þd (Bi-2212) superconductor is strongly
dependent on the oxygen content.
11,12
Therefore, the depend-
ence of the resistance change effect on the oxygen content
can be obtained by evaluating the relation between the

resistance change effect and Tc. The large crystalline anisot-
ropy of Bi-2212 single crystal enables cleavage of the crystal
into thin plates with clean and flat surfaces.
13,14
Above all,
for elucidation of the resistance change effect, it is most
important to specify where the resistance change occurs.
In this paper, the resistance change effect of ReRAM
was investigated using M/Bi-2212 single crystal/Pt (M¼Al,
Pt) structures. It was clarified that the resistance change
effect was caused at the interface of Al and Bi-2212 in the
Al/Bi-2212/Pt structure and was enhanced by annealing the
structure. This suggested that the resistance change effect
occurs in the oxygen-depleted layer of the Bi-2212 single
crystal formed at the interface between an electrode with low
Gibbs free energy and a Bi-2212 single crystal.
II. EXPERIMENTAL
Bi-2212 single crystals were grown using the vertical
Bridgman method
15,16

and were cleaved under the ambient
atmosphere to produce thin plates with typical dimensions of
2.0�1.5�0.02 mm3. All the cleaved crystals were annealed
in flowing O2 at 500
�C for 20 min to induce uniform oxygen
content. Al and Pt electrodes were deposited, respectively,
on both of the surfaces (a-b plane) of the Bi-2212 single
crystals by sputtering to produce M-TE/Bi-2212/Pt-BE
(M¼Al, Pt) structures, where TE and BE represent top and
bottom electrodes. The size and thickness of both the TE and
BE were 1.0�1.0 mm2 and 100 nm, respectively. Current-
voltage (I-V) characteristics were measured using a semicon-
ductor parameter analyzer (Agilent 4155 C).
The BE was grounded and a bias voltage was applied to
the TE. The compliance current was set to 60 mA, where the
compliance current is a limiting value to which a
currenta)Electronic mail: [email protected]
0021-8979/2011/110(8)/084506/5/$30.00 VC 2011 American
Institute of Physics110, 084506-1
JOURNAL OF APPLIED PHYSICS 110, 084506 (2011)
http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465

http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465
http://dx.doi.org/10.1063/1.3651465
flow through the M-TE/Bi-2212/Pt-BE structure is limited
during the set process [resistive switching from a high
resistance state (HRS) to a low resistance state (LRS)]. The
samples were annealed in 100% Ar atmosphere at 300 and
400 �C for 60 min to control Tc, and the correlation between
the resistance change effect and Tc was investigated. In addi-
tion, a Ag electrode, used for monitoring interface resistance
between the M-TE and the Bi-2212 crystal and between the
Pt-BE and the Bi-2212 crystal, was formed on the same side
of the Bi-2212 single crystal as the TE. The Pt-TE/Bi-2212/
Au-BE structure was annealed in ArþH2 (Ar:H2¼19:1)
atmosphere at 400 �C for 10 min to introduce an oxygen-
depleted layer into the surface of the Bi-2212 single crystal
in the vicinity of the Pt-TE by reduction effect of H2 gas
with the assistance of the catalytic effect of Pt.
17–19
III. RESULTS AND DISCUSSION
Figure 1(a) shows the I–V characteristics of the as-

prepared Pt-TE/Bi-2212/Pt-BE structure and those annealed
in Ar atmosphere at 300 �C for 60 min and at 400 �C for
60 min. The voltage was ramped up from 0 V to +1.0 V, and
then down to 0 V in steps of 10 mV. The voltage was then
ramped down from 0 V to �1.0 V, and then back to 0 V in
steps of 10 mV. No resistance change effects and no signifi-
cant differences were observed in the I–V characteristics,
independent of annealing temperature.
Figure 1(b) shows the I–V characteristics of the as-
prepared Al-TE/Bi-2212/Pt-BE structure and those annealed
in Ar atmosphere at 300 �C for 60 min and at 400 �C for
60 min. Set and reset switching occurred by application of
positive and negative voltages, respectively, where reset rep-
resents a switching from LRS to HRS. The ratio of RHRS to
RLRS (RHRS/RLRS) increased with increasing annealing
temperature, where RHRS and RLRS represent the resistances
in the HRS and LRS, respectively. RHRS/RLRS of the as-
prepared sample was 2, whereas those annealed at 300 and
400 �C were increased to 10 and 20, respectively. In addi-
tion, the initial resistances of the as-prepared Pt-TE/Bi-2212/
Pt-BE and Al-TE/Bi-2212/Pt-BE structures were 5.1 X and
1.3 kX, respectively, and those of the Al-TE/Bi-2212/Pt-BE
structures annealed at 300 and 400 �C were 2.5 and 12.7 kX,
respectively. The resistance of the as-prepared Al-TE/
Bi-2212/Pt-BE structure is higher than that of the as-

prepared Pt-TE/Bi-2212/Pt-BE structure, and the resistance
of the Al-TE/Bi-2212/Pt-BE structure increased with the
annealing temperature. The Gibbs free energies of Pt and Al
at 300 (600) K are 92.852 (9.356) kJ/mol and �1690.973
(�1717.192) kJ/mol,20 respectively; therefore, the increase
in the initial resistance was caused by reduction of Bi-2212
due to oxidation of the Al electrode. This is consistent with
the large oxygen diffusion coefficient of Bi-2212
(1.6�10�17 cm2/s), even at 300 K.21 These results suggest
that reduction of Bi-2212 is required for the development of
the resistance change effect and that the effect was enhanced
by the extent of reduction.
To specify where the resistance change effect occurs,
two extreme cases shown in Figs. 2(a) and 2(b) are discussed
for TE/TMO/BE structures which have a monitoring elec-
trode (ME) on the same side of the TMO layer as TE. Figure
2(a) shows the TE/TMO/BE structure using the TMO layer
with small resistance anisotropy. When bias voltage is
applied between the TE and ME, current dominantly flows

through the BE. This is due to the fact that a film thickness
of a TMO layer is, in general, much smaller than a TE-ME
distance. Therefore, we can regard Fig. 2(a) as the circuit
given by connecting the ME/TMO/BE and BE/TMO/TE
structures in series. In this case, resistances of the TE/TMO
and TMO/BE interfaces cannot be measured independently
because current flows through the both interfaces. On the
other hand, Fig. 2(b) shows the TE/TMO/BE structure using
the TMO layer with large resistance anisotropy such as a
Bi-2212 bulk single crystal as the TMO layer. When
bias voltage is applied between the TE and ME, a current
FIG. 1. (Color online) I-V characteristics of as-prepared and
annealed (a)
Pt-TE/Bi-2212/Pt-BE and (b) Al-TE/Bi-2212/Pt-BE structures.
The device
structures are shown in the insets.
FIG. 2. (Color online) Schematics to explain current paths in
TE/TMO/BE
structures which have ME for the TMO layers with (a) small
and (b) large
resistance anisotropies.

084506-2 Hanada et al. J. Appl. Phys. 110, 084506 (2011)
dominantly flows along the surface of the Bi-2212 (a-b
plane) between TE and ME due to its large resistance anisot-
ropy (qc/qab > 10
3
).
13
Therefore, the resistances of the TE/
TMO and TMO/BE interfaces can be obtained directly by
the measurement of resistances between TE-ME and ME-
BE, respectively.
To specify where the resistance change of the Al-TE/Bi-
2212/Pt-BE structure takes place, a sample with a Ag-ME
(D) on the same side of the Bi-2212 single crystal as the TE
was prepared, as shown in Fig. 3(a). Set voltages (þV) and
reset voltages (�V) were alternately applied between
terminals A and C (A-C). At the same time, resistances
between terminals A and D (A-D) and between terminals C
and D (C-D) were measured. Terminal B was located at a
different position than terminal A on the Al electrode, and

the resistance between terminals A and B (A-B) was also
measured. The results are shown in Fig. 3(b). The resistance
between A-C was alternately switched between low and high
resistance by application of +V and �V, respectively. The
resistance between A-D changed in accordance with the
change of resistance between A-C. In contrast, the resistan-
ces between A-B and C-D were invariably independent of
the resistance between A-C. The results indicate that the
resistance change of the Al-TE/Bi-2212/Pt-BE structure
occurs at the Al-TE/Bi-2212 interface.
Figure 4(a) presents the resistivity-temperature (q-T)
characteristics of an as-prepared Bi-2212 single crystal and
those annealed in Ar atmosphere at 300 �C for 60 min and
400 �C for 60 min. The q-T measurement was performed
using four-terminal method with four Pt electrodes. The inset
shows four Pt electrodes formed side by side on the surface
of the Bi-2212 single crystal, where the pairs of outer and
inner electrodes were used as the current and voltage termi-
nals, respectively. An enlarged view of the q-T characteris-
tics around Tc is shown in the inset. No significant change in
the q-T characteristics was observed, independent of the

annealing temperature. The same measurements were per-
formed for a sample on which an Al electrode was deposited
between the voltage terminals and the results are shown in
Fig. 4(b). The Tc of the as-prepared Bi-2212 single crystal
with the Al electrode was 84 K, which was lower than the Tc
of 88 K for the sample without the Al electrode. In addition,
the Tc of the Bi-2212 single crystal with the Al electrode was
decreased to 83 and 75 K when annealed in Ar atmosphere
at 300 and 400 �C for 60 min, respectively. It is well known
that both the resistivity in the normal conducting state and Tc
are strongly dependent on the oxygen content of the Bi-2212
crystal;
11,12
the resistivity increases with decreasing oxygen
content, whereas Tc decreases. An increase in the resistivity
and decrease in Tc were observed only in the sample with the
Al electrode, which suggests that the Al electrode removes
oxygen from the Bi-2212 single crystal and an oxygen-
depleted layer is formed in the Bi-2212 single crystal in the
vicinity of the Al electrode.
Free energies for the states of AlþBi2Sr2CaCu2O8þd1
(a) and AlOxþBi2Sr2CaCu2O8þd2 (b) are represented in
Fig. 5. A shift of the energy state from a to b corresponds to

a reduction of the Bi-2212 single crystal due to oxidation of
the Al electrode by oxygen diffusion from the Bi-2212 single
crystal to the Al electrode. The reaction rate, v ! exp(-Ea/
kBT), is dependent on the annealing temperature T, and it is
necessary to exceed the activation energy Ea, for the reaction
from a to b to proceed. Accordingly, the resistance of the
Al-TE/Bi-2212/Pt-BE structure is increased with increasing
T. In contrast, oxygen ions will not move at the interface
between the Pt electrode and the Bi-2212 crystal, because Pt
is not easily oxidized. The reason for the higher resistance
measured in the as-prepared Al-TE/Bi-2212/Pt-BE structure
FIG. 3. (Color online) (a) Al-TE/Bi-2212/Pt-BE structure with a
Ag-ME
(terminal D) and (b) resistances measured between A-B, A-D,
and D-C
when the resistance between A-C was changed.
FIG. 4. (Color online) q-T characteristics of Bi-2212 single
crystal meas-
ured using the four-terminal method with four Pt electrodes (a)
before and
(b) after Al deposition between the voltage terminals. Enlarged
views of the
q-T characteristics around Tc are shown in the insets.

than that in the as-prepared Pt-TE/Bi-2212/Pt-BE structure is
attributed to the sputtering energy during Al deposition. The
radiant heat of the Ar plasma and kinetic energy of sputter-
ing particles raised the temperature of the sample, which
enabled partial reaction from a to b. Shono et al.3 reported
that a 10 nm thick TiOx layer was naturally formed at the as-
deposited interface between Ti and PCMO without annealing
of the sample. Therefore, the reaction from a to b is caused
by deposition of an electrode with low Gibbs free energy
onto the Bi-2212 and heating it to a temperature correspond-
ing to the activation energy. No resistance change occurred
in Pt-TE/Bi-2212/Pt-BE structure, which suggests that the
introduction of an oxygen-depleted layer into Bi-2212 is
required for the development of the resistance change effect.
Figure 6 shows schematics that indicate how the resist-
ance change effect develops and how the resistive switching
between the LRS and HRS occurs. First, an electrode with
low Gibbs free energy (Al) receives oxygen from the

Bi-2212 single crystal, and an oxygen-depleted layer is
formed in Bi-2212 in the vicinity of the Al-TE, which results
in the HRS [Fig. 6(a)]. Second, by applying positive voltage
to the TE, oxygen ions in the bulk of the single crystal are
drawn to the TE side by coulombic forces with the assistance
of Joule heat. Therefore, the oxygen-depleted layer is partly
recovered and the LRS is attained [Fig. 6(b)]. Application of
negative voltage to the TE results in the movement of oxy-
gen ions into the partly recovered oxygen-depleted layer of
the single crystal bulk and the oxygen-depleted layer is
formed again to give the HRS [Fig. 6(c)]. If the resistance
change occurred due to redox reaction of the Al-TE at the
Al-TE/Bi-2212 interface, the relationship between the resist-
ance change and the bias polarity opposite to that observed
in this study should be observed.
3,4
Therefore, the resistance
change effect is caused by generation/recovery (reduction/

oxidation) of the oxygen-depleted Bi-2212 layer formed at
the interface between Al and Bi-2212. The most direct way
to prove the validity of the proposed resistance change model
is to show the development of the resistance change effect
simply by inserting the oxygen-depleted Bi-2212 layer at the
interface between a high Gibbs free energy electrode such as
Pt and Bi-2212 layer. Utilizing the catalytic effect of Pt that
drastically enhances reduction reaction of hydrogen,
17
we
can introduce the oxygen-depleted Bi-2212 layer into the Pt/
Bi-2212 interface by annealing a Pt-TE/Bi-2212/Au-BE
structure in H2 atmosphere. Since the Au is resistant to
hydrogen and has high Gibbs free energy of �42.447 kJ/mol
at 300 K,
20
the Bi-2212 layer is not reduced at the Bi-2212/
Au interface during the H2 annealing. Figure 7 shows the I-V
characteristics of the as-prepared Pt-TE/Bi-2212/Au-BE
structure and those annealed in Ar and ArþH2 atmospheres
at 400 �C for 60 and 10 min, respectively. Resistance change

did not occur in the as-prepared sample and that annealed in
Ar atmosphere. On the other hand, the resistance change
effect was observed in the sample annealed in ArþH2 atmos-
phere. Here, set and reset switching occurred by application
of positive and negative voltages, respectively.
To specify where the resistance change of the Pt-TE/Bi-
2212/Au-BE structure takes place, a sample with a Ag-ME
(D) was prepared, as shown in Fig. 8(a). Set voltages (þV)
and reset voltages (�V) were alternately applied between
A-C. The results are shown in Fig. 8(b). The resistance
between A-C was alternately switched between low and high
resistance by application of of þV and �V respectively. The
resistance between A-D changed in accordance with the
change of resistance between A-C. In contrast, the resistan-
ces between A-B and C-D were invariably independent of
the resistance between A-C. The results indicate that the
FIG. 5. (Color online) Free energies for the states of
AlþBi2Sr2CaCu2O8þd1 a and AlOxþBi2Sr2CaCu2O8þd2 b in
the interface
between Al-TE and Bi-2212 and the activation energy for
oxygen ions to
move from Bi-2212 to the electrode.

FIG. 6. (Color online) Schematics to indicate the oxygen
movement until
stabilization of the initial state (a) and during the resistive
switching to LRS
(b) and to HRS (c).
FIG. 7. (Color online) I-V characteristics of the as-prepared Pt-
TE/Bi-2212/
Au-BE structure and those annealed in Ar and ArþH2
atmospheres at
400 �C for 60 and 10 min, respectively. The device structure of
Pt/Bi-2212/
Au is shown in the inset.
resistance change of the Pt-TE/Bi-2212/Au-BE structure
occurs at the Pt-TE/Bi-2212 interface. In addition, resistive
switching did not be observed in a Pt-TE/Bi-2212/Au-BE
structure in which Pt-TE was deposited after ArþH2 anneal-
ing. Therefore, it was shown that an oxygen-depleted
Bi-2212 layer was formed in the Bi-2212 single crystal in
the vicinity of the Pt-TE due to the reduction effect of the
ArþH2 annealing18,19 and the catalytic effect of the
Pt-TE.

17
These results as well as the bias polarity depend-
ence of the resistance change indicate that the resistance
change is caused by generation/recovery of the oxygen-
depleted Bi-2212 layer as shown in Fig. 6. A similar
resistance change effect was also reported in Ag/PCMO
structures.
2
The resistance change of Ag/PCMO structures
was reported to be caused by destruction/repair of the con-
ductive Mn-O chain caused by a change in the concentration
of oxygen ions near the Ag/PCMO interface, and which was
enhanced in a PCMO film grown under oxygen deficient
conditions compared with that grown under oxygen rich
condition. Accordingly, a reduction of oxide ions near the
structure interface plays a key role in the development of the
resistance change effect by providing space for oxygen ions
to migrate. A similar scenario may also be applicable to
other perovskite oxides, independent of whether the TMO is

a single crystal or poly crystalline. Considering the Gibbs
free energies of the oxides and electrodes, the resistance
change effect of perovskite oxide-based ReRAM can be con-
trolled by application of an appropriate annealing tempera-
ture that exceeds the activation energy for oxygen diffusion
from the TMO to the electrode.
IV. CONCLUSION
Perovskite oxide-based ReRAM was prepared using
Bi2Sr2CaCu2O8þd bulk single crystal for the TMO layer. The
resistance change effect was observed in the Al-TE/Bi-2212/
Pt-BE structure. An introduction of the Bi-2212 bulk single
crystal enabled clarification that the resistance change effect
occurs at the Al/Bi-2212 interface. An increase in the
resistivity and decrease in the Tc with increased annealing
temperature were confirmed, and the resistance change effect
(RHRS/RLRS) was enhanced by increased annealing
temperature.
These results indicate that the introduction of an oxygen-
depleted layer into the Bi-2212 single crystal is required for

the development of the resistance change effect, which can
be achieved by deposition of a low Gibbs free energy elec-
trode in order to form an oxygen-depleted layer at the TMO
surface. The resistance change effect developed even in the
Pt-TE/Bi-2212/Au-BE structure, which has high Gibbs free
energy electrodes, by inserting the oxygen-depleted layer
into the surface of the Bi-2212 single crystal at the vicinity
of the Pt-TE. This excludes the possibility that resistance
change occurs due to the oxidation/reduction of the Al-TE.
The resistance change of ReRAM is caused by the migration
of oxygen ions under application of an intense electric field,
and it is thought that the set/reset processes are caused by re-
covery/generation of the oxygen-depleted layer. This resist-
ance change model provides a guideline for the selection of
oxide and electrode materials for perovskite oxide-based
ReRAM.
ACKNOWLEDGMENTS
This study was supported by Grant-in-Aid for Young

Scientists B (No. 23760313).
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Challenges 2014, 5, 473-503; doi:10.3390/challe5020473
OPEN ACCESS
challenges
ISSN 2078-1547
www.mdpi.com/journal/challenges
Article
Optimisation of Storage for Concentrated Solar Power Plants
Luigi Cirocco 1,*, Martin Belusko 2, Frank Bruno 2, John
Boland 1, Peter Pudney 1
1 Centre for Industrial and Applied Mathematics, School of
Information Technology and Mathematics,
University of South Australia, Mawson Lakes Boulevard,
Mawson Lakes, SA 5095, Australia;

E-Mails: [email protected] (J.B.); [email protected] (P.P.)
2 Barbara Hardy Institute, University of South Australia,
Mawson Lakes Boulevard, Mawson Lakes,
SA 5095, Australia; E-Mails: [email protected] (M.B.);
[email protected] (F.B.)
* Author to whom correspondence should be addressed; E-Mail:
[email protected];
Tel.: +61-8-8302-5781; Fax:+61-8-8302-5785
External Editor: Andreas Manz
Received: 10 October 2014; in revised form: 1 December 2014 /
Accepted: 2 December 2014 /
Published: 12 December 2014
Abstract: The proliferation of non-scheduled generation from
renewable electrical energy
sources such concentrated solar power (CSP) presents a need for
enabling scheduled
generation by incorporating energy storage; either via directly
coupled Thermal Energy
Storage (TES) or Electrical Storage Systems (ESS) distributed
within the electrical network
or grid. The challenges for 100% renewable energy generation
are: to minimise
capitalisation cost and to maximise energy dispatch capacity.
The aims of this review article
are twofold: to review storage technologies and to survey the
most appropriate optimisation
techniques to determine optimal operation and size of storage of
a system to operate in the
Australian National Energy Market (NEM). Storage
technologies are reviewed to establish
indicative characterisations of energy density, conversion

efficiency, charge/discharge rates
and costings. A partitioning of optimisation techniques based on
methods most appropriate
for various time scales is performed: from “whole of year”,
seasonal, monthly, weekly and
daily averaging to those best suited matching the NEM bid
timing of five minute dispatch
bidding, averaged on the half hour as the trading settlement spot
price. Finally, a selection of
the most promising research directions and methods to
determine the optimal operation and
sizing of storage for renewables in the grid is presented.
Challenges 2014, 5 474
Keywords: concentrated solar power; energy storage systems;
thermal energy
storage; optimisation
1. Research Background
Australia’s and the world’s use of fossil fuels is unsustainable
with Australia having an abundance of
renewable energy sources, particularly solar irradiation. We
need to imagine, design and transition to a
future where electricity is effectively generated from Renewable
Energy (RE) sources. The solution will
require a mix of scalable renewable generation technologies,
demand management and energy storage.
Concentrated Solar Power (CSP) plants are a form of RE
generation where solar radiation falling
on a large area ranging from a few hundred to hundreds of
thousands of square metres is focused or

concentrated onto a significantly smaller receiver area, up to
several thousand times smaller than the
collection area. At the receiver this concentrated energy can be
harnessed as heat to generate steam
for the production of electrical power via a steam turbine driven
generator, as would any traditional
fuel driven steam-electric power plant such as a coal fired or
nuclear power station. Although there are
many realisations of CSP plants based on this simple design
principle throughout the world, research is
still required to enhance the designs for operation within a
specific region. This requirement is driven by
variances in the intermittent solar resource and energy demands,
as well as on the economic environment
and the opportunities for profit making within the energy
market framework of the specific region.
In Australia CSP has the potential to be a substantial
contributor to electricity generation as the output
from such plants correlates well to the daytime electricity
demand. In order to meet demand when the
sun is obscured by cloud and some or all of the night time
demand, a CSP plant can be designed with
Thermal Energy Storage (TES) where excess energy at the
receiver may be re-directed to storage, via
intermediary systems, for later use in steam generation. Once
again there are realisations of CSP with
TES worldwide but few if any have been fine tuned to meet
their particular climatic, energetic and
economic environments. Further research is required in
determining not only the optimal sizing but also
the specific operational paradigms and control systems for such
plants in the Australian context.
With growing levels of RE penetration and with the further
distribution of variable generation

as imposed by the growth of residential rooftop Photo-Voltaic
(PV) into the electrical supply grid,
maintaining grid stability is emerging as a critical problem in
the short term. With research into this
problem being in its infancy in countries such as the US and
Spain, the opportunity exists for directed
research to address this problem in the Australian context.
To date, studies involving generation from 100% RE depend on
arguably excessive and economically
incredulous amounts of storage. As such studies primarily
involve meeting energetic demand under
variable generation in RE supply, research is required to
investigate an optimal and robust control strategy
before the determination of the minimum quantity of storage
such that local demand is met and grid
stability is maintained.
Furthermore, research into hybridised storage for CSP where
TES and Electrical Storage Systems
(ESS) are controlled from the one plant is also lacking despite
extensive research into ESS storage
options for other VG RE sources such as wind and PV
generation. ESS such as utility scale batteries
or Pumped Hydro Storage (PHS) where water is moved back
into the upper reservoir lake when supply
energy exceeds demand would greatly enhance the options
available to a CSP plant for storage. Future
research should consider the incorporation of ESS with CSP as
it may prove advantageous despite there
being no identified literature considering a CSP with both TES

and ESS.
Figure 1 as adapted from Figure 2.1 [1] and Figure 16.6 [2],
depicts a generalised CSP plant that
incorporates TES with electrical heaters and ESS for electricity
buy-back in the plant design. Such
a plant would then participate in the supply of electricity to the
grid within the Australian National
Energy Market (NEM), bidding for the generation of power and
associated services in accordance with
the National Electricity Rules [3]. Essentially this market
requires bids for the dispatch of energy to be
placed every 5 minutes, with the spot price to be paid to
suppliers by consumers being the average of the
last six bids as cleared every half hour.
Figure 1. General Overview of Energy Conversion Blocks for
Concentrated Solar Power,
adapted [1,2]
Solar
Radiation
Input
Solar
Concen-
trator
Receiver
Media
Energy
Transport

Media
Thermal
Energy
Storage
Power
Conversion
Electrical
Grid
Demand
Electrical
Storage
Systems
Electrical
Buy-back
TES
Heaters
Thermal
Storage
Concentrator
Block
SUN
Power
Block
Electrical
Storage

GRID
The remainder of this article will present relevant works used to
formulate the research problem and
present possible future research to be undertaken in order to
develop optimal robust operation and control
with minimal sizing of CSP with storage.
2. Survey of Relevant Works
2.1. Introduction
This section presents a survey of the literature conducted in
order to achieve an understanding of
the problem domain and to set the research direction. The
section is in three major parts: the first will
identify background information relating renewable energy
technologies and frame the problem for CSP
plants within this context; the second part will then discuss
research into electricity generation with CSP
with TES and ESS with the goal to provide a concise index to
the elements for the operation of a CSP
system and the environment in which it would operate (What to
model and why?); the third part presents
a review of the modelling and optimisation methods applicable
to CSP with storage within the renewable
energy domain (How to model and optimise.).
2.1.1. Renewable Energy Domains: An Overview

In order to achieve an appropriate operational model for CSP
plant with storage, the RE landscape and
the varied RE technologies need to be acknowledged, even if
they are not incorporated into the model
explicitly, this knowledge allows for a more holistic
perspective. The collection preface by Sayigh [4]
presents an overview of the RE research landscape worldwide,
identifying an eight volume partitioning
of various RE domains, namely:
Photo-Voltaic,
Wind,
Solar Thermal,
Fuel Cells and Hydrogen Technology,
Biomass and Biofuels,
Hydro,
Geothermal and
Oceanic Power
Within this partitioning CSP with storage is one element of the
“Solar thermal” grouping offering
utility scale electricity generation in the order of hundreds of
mega Watts (MW) using the same or
similar generator technology as conventional fossil fuel or
nuclear powered power stations, without the
ongoing fuel costs, Green House Gas (GHG) emissions and
waste disposal issues.
As CSP plants continue to use conventional power generation
from heated steam some designs have
hybridised the heat sources currently incorporating heating
elements that burn either a mineral gas or
biofuel such that the plant can operate for a larger proportion of
time especially during times of low
irradiation, i.e., winter days; an analysis of such a plant is
presented by Usaola [5].

Furthermore when considering ESS for CSP storage there is a
large body of knowledge in the PV and
Wind domains which could be applied to investigations for CSP.
Hydro-electric power generation also has a considerable body of
knowledge relating to it and bears
some similarity to the operation of CSP with storage, albeit the
supply of rain water is on an annual
cycle rather than a daily one for sunlight. When TES with buy-
back heating is considered for a CSP with
storage the plant then resembles PHS, where energy can be
bought in at favourable prices, stored and
then used to generate at some later time when market conditions
are more favourable (or profitable) thus
enabling an arbitrage mechanism for profit making (buy low,
sell high), this mechanism is investigated
in Kim and Powell [6] for the operation of wind farms with
associated PHS and more recently in
Cruise et al. [7] where optimal control of PHS was studied
based on detailed mathematical models of
both the store and market dynamics when operating in the UK’s
National Grid.
2.1.2. CSP with Storage: Framing the Problem
Traditionally the terms “baseload” and “peaking” have been
used in the electrical power generation
industry to refer to an operational paradigm where grid security
is achieved through having enough
generation installed to meet the average (or baseload) and

spurious peaks (or peaking) in demand that
may occur for only a small percentage of the time in a yearly
cycle. With the advent of greater penetration
of semi-scheduled RE generators into the grid, namely large
scale wind farms and residential PV, and
improved Demand Side Participation (DSP) for Commercial and
Industrial (C&I) participants and of the
residential consumer on the horizon the AEMC report “Power of
choice review—giving consumers
options in the way they use electricity” [8] this paradigm is
being challenged and a new common
vocabulary for power distribution needs to be established. In
order to provide a reliable energy supply
grid Denholm et al. [9] suggest the existing “variability” in
demand needs to be better matched to that
from RE sources by increasing their “dispatchability” as is
made possible by CSP with TES.
Within Australia a recent publication from the Australian
Bureau of Resources and Energy Economics
(BREE) “Australian Energy Projections to 2049-50” Table 11
[10], projects the supply of electricity from
RE sources to be 51% of the total projected demand by 2050
with wind to provide 21%, solar (both PV
and thermal) 16%, geothermal about 8% and hydro output about
5%, as the capacity of hydro is expected
to remain constant at current levels.
The categorisation of power generating plants is presented as
being either “base-load”, “peak-load”
and “mid-merit” or “intermediate”, [11] Box 1.
Base-load plants can generate electricity at lower cost per unit
generation, typically stated in dollars
per Watt-hours ($/Wh) but lack the ability to respond to large
changes in demand, either increasing or

decreasing. In the context of energy market trading base load
plants may bid at negative spot prices when
demand decreases rapidly in order to maintain output stability
in the short term.
Peak-load plants, as the name suggests are often implemented as
gas fired turbines (GFT) and are
able to power up in a matter of minutes to meet large spikes in
demand. Within the context of energy
market trading these plants can also attract premium prices for
meeting these upward spikes with peak
plants typically only operating for short periods and thus
incurring only minimal fuel, operation and
maintenance costs.
Hydro power is often categorised as mid-merit or intermediate,
as such plants can respond rapidly
to changes in demand in both directions but often have
objectives and constraints unrelated to energy
generation including supplying potable water and controlling
flood water outflow to downstream
waterways. Such plants are often play a role in stabilizing grid
frequency and assist in meeting ramp rate
changes in the ancillary services market of the NEM framework.
In scenarios with higher and higher penetration of RE sources
such as PV and wind, one possible
avenue of investigation is that CSP with TES can offer a service
akin to that provided by outflow
only hydroelectric plants not only provide scheduled generation
but more importantly, grid stabilisation
ancillary services through an ability to rapidly respond to
changing system dynamics. In addition, when
coupled with some level of ESS or TES fitted with buyback
heaters such plants would have the added
versatility of providing a utility size regenerative breaking

element capable of harnessing energy that
would otherwise be wasted from either traditional fossil fuel
based or other RE systems, especially in
the event of sharp dips in demand occurring, thus likening the
complete CSP with TES and ESS system
to a fast dynamic, PHS plant. In a recent review by Akinyele
and Rayudu [12] the authors present both
the qualitative and quantitative properties of energy storage
media and systems in general, with one
section highlighting the limitations of some PHS in performing
frequency regulation.
It can be argued that a CSP with TES and ESS offers grid
security by enabling such plants to supply
robust grid stabilisation with infinitely variable and
dispatchable power when other sources that cannot
otherwise supply the demand effectively (akin to peaking-power
plants) and like the PHS paradigm,
could also capture excess energy when it’s available from the
grid thus enabling a better matching to
sharp changes in grid demand.
Figure 2 presents a generalised model for investigations into the
optimal partitioning of multiple TES
and ESS elements. This model assumes that the TES is co-
located with the collector and generator
blocks but does not impose the same restriction on the ESS
blocks. The ESS is assumed to be in the
direct control of the CSP plant but could be implemented as
downstream distributed storage within either
the transmission or distribution networks.

Figure 2. A generalised system for the realisation of a
concentrated solar power (CSP)
with multiple tuned Thermal Energy Storage (TES) and
Electrical Storage Systems (ESS)
storage elements.
HEXs TES2
TES1
direct heat
...
TESn
HEXg Generator T/Fg ESS2
ESS1
direct electrical
...
ESSm
T/Fd
Heat Grid
TES Buy-Back
With reference to Figure 2 it is envisaged that a number or
combinations of TES and ESS may
be investigated for a particular CSP plant with the salient
aspects for the formulation of the problem
as follows:

QTESi Max � QTESi+1 Max hierarcy of TES capacities
TTESi Max � TTESi+1 Max hierarcy of TES operating
temperatures
Q
̇ TESi Max � Q
̇ TESi+1 Max hierarcy of TES responsivness
COSTTESi � COSTTESi+1 hierarcy of TES cost
QESSj Max � QESSj+1 Max hierarcy of ESS capacities
Q
̇ ESSj Max � Q
̇ ESSj+1 Max hierarcy of ESS responsivness
COSTESSj � COSTESSj+1 hierarcy of ESS cost
n∑
i=0
QTESi Max �
m∑
i=0
QESSj Max comparative TES and ESS sizes
where the subscripts i and j are the indexes for each of the TES
and ESS blocks of Figure 2 respectively
with m representing the total number of TES blocks and n
indicating the total number of ESS blocks.
The different TES and ESS blocks would typically have
different trade-offs between capacity, cost and
performance alluded to with the hierarchical set of relationships
above.
The aim of the research is to identify existing works applicable
to demonstrating the energetic and
economic benefits of a CSP system as depicted in Figure 2 not

only to meet the present demands (if the
opportunity should arise to implement such a system in the near
future) but a number of “on the horizon”
scenarios with varying RE and demand side management
penetration being the motives for developing
this article.
2.2. Elements of Concentrating Solar Power with Storage
2.2.1. Solar Concentrators
Figure 3, as taken from Figure 1 of [13], depicts the four main
approaches for implementing the solar
concentrator element of CSPs for thermal power generation.
They are listed here in the order of current
commercial deployment levels [14]:
(i) parabolic trough,
(ii) central receiver tower,
(iii) linear Fresnel and
(iv) paraboloidal dishes
The list above excludes configurations involving the focussing
of DNI onto PV cells, referred to as
Concentrated PV (CPV), achieved with a clear polycarbonate
Fresnel lens in front of the PV cell or
the mounting of PV cell receivers at the focus of paraboloidal
dishes. As these configurations generate
electricity directly without the opportunity to store the heat
energy for delayed generation using TES
devices, CPV configurations currently only use ESS storage
although research into the use of TES for
CSP may be transferable to CPV systems despite the current
status of it only being compatible with
ESS storage.

The low ecological impact of CSP does come with its own
challenges, as stated before solar irradiation
can be variable and unlike PV, which generates electricity from
global or diffused solar radiation,
CSP is limited to utilizing only the direct normal irradiation
(DNI) component hence candidate sites
should have low smog and dust levels as well as a high
percentage of clear days as highlighted by
Lovegrove and Stein [14]. A discussion relating to location in
Section 3.06.7.1 of Hoffschmidt et al. [15]
the authors present a lower bound for total DNI of 2000
kilowatt hours (kWh) per square meter,
annually; sites achieving up to 2800 kWh·m−2·year−1 are
termed “premium sites”, with sites
receiving <6 kWh·m−2·day−1 being excluded outright, although
this is circa 2200 kWh·m−2·year−1,
it presents a lowest acceptable daily limit as presented by
Leitner and Owens [16]. Further constraints
are configuration specific with CSP utilising solar trough or
linear Fresnel concentrators requiring an
area minimum of 1 km2 with 1% flatness. Central tower
receivers with heliostats can be erected smaller
areas, “even in hilly regions” but cannot be operated at times of
high wind hence precluding sites with
frequent excessive wind gusts.
Figure 3. Schematics of the Four CSP Approaches for Power
Generation (taken from
Figure 1 [13]).

2.2.2. Theoretical Limits on CSP System Efficiency
Details of the theoretical and practical limits to efficiencies
achievable with the various
realisations of solar concentrators for the generation of
electrical power are presented in detail by
Lovegrove and Pye [1] and Hoffschmidt et al. [17] with the
salient features presented herein. With
reference to Figure 1 the overall system efficiency of a CSP
plant using an adaptation of Equation 2.1
from [1] is given as:
ηsystem = ηoptical ×ηreceiver ×ηtransport ×ηthermal storage
×ηconversion ×ηelectrical storage (1)
where
ηoptical
is the optical efficiency of the concentrator, which includes all
losses up to but not including the
receiver
ηreceiver
is the receiver element efficiency, which includes any losses
associated with the absorption and
transfer of the concentrated energy available at the receiver
ηtransport
is the transport media efficiency, including any losses in

downstream heat exchangers where
different heat transfer fluids (HTF) are interfaced
ηthermal storage
is the combined charging and discharging efficiency of any TES
element(s)
ηconversion
is the heat to electrical power conversion efficiency
theoretically limited to the Carnot cycle
efficiency for thermal-mechanical systems but in practice
accounts for mechanical and electrical
losses in the power block.
ηelectrical storage
is the combined charging and discharging efficiency of any EES
element(s), if used, incorporating
any conversion losses for AC-DC and DC-AC conversion.
A simplified analysis for the theoretical limit for achieving
maximum efficiency of a CSP is often
presented as being a product of the receiver efficiency and the
Carnot efficiency of the power block with
all other efficiencies being ignored (or assumed to be 100%). In
a CSP system optimisation problem the
nuances of each element and their interactions should not be
ignored and as stated in [1]: the optimal
system efficiency is not achievable by maximising each of the
efficiencies expressed in Equation (1)
separately. The following information is presented to provide a
generalised qualitative overview on the
theoretical limits imposed on CSP technologies.
Carnot efficiency (ηCarnot ≈ ηconversion) for the conversion of

heat to mechanical work will
constrain the overall system efficiency of any thermal-
mechanical system. It is derived from
the Second Law of Thermodynamics and can be expressed as a
function of the highest (THigh)
conversion process temperature and lowest or ambient (TLow)
temperature in degrees Kelvin (K) for
the thermal-mechanical system:
ηCarnot = 1−
TLow
THigh
(2)
With simplified assumptions relating to the various losses in the
receiver, its efficiency can be
presented as given in [17]:
ηreceiver = αeffective −
�σsT
4
CS
(3)
where
αeffective is the effective absorptivity of the receiver
� is the emission coefficient for the receiver
σs is the Stefan-Boltzmann constant, 5.670×10−8 W ·m−2 ·K−4
T is the receiver temperature in K
C is the concentration factor given by:

C =
collector aperature area
receiver area
(4)
S is the solar input in W · tm−2
The relationship between typical concentration factors,
temperature at the receiver (or absorber)
and maximum receiver efficiencies for different concentrator
types is given in Table 1 and taken from
observations made of Figure 1 of Hoffschmidt et al. [17].
Theoretical limits on concentration factors
have also been described by Lovegrove and Pye [1] as being
215 for 2 dimensional line concentrators,
such as solar trough or linear Fresnel receivers and 46,250 for 3
dimensional point-focus concentrators
such as solar towers and paraboloid disc concentrators. These
theoretical limits are then compromised
from the ideal from factors such as geometry variances, non-
ideal reflectivity of the collector material
and variations in the shape of the solar disc as a result of
dispersion from atmospheric particulates.
Table 1. Indicative values of concentration factor, receiver
temperature at maximum
efficiency for different concentrator types (as observed from
adapting Figure 1 [17]).
Reflector Type Concentration Factor Tabsorber = Tprocess(K)
(at ηmax) ηmax

Flat plate collector 1 300 K 0.08
Parabolic Trough ≈100 720 K 0.50
Central Tower ≈1000 1120 K 0.65
Parabolic Dish ≈4000 1480 K 0.72
2.2.3. Power Block Operation
The text “Power Systems Analysis” by Saadat [18] presents the
basic principles of power system
generation, distribution, dispatch, fault tolerance, stability and
control with MATLAB code for use
with MATLAB, SIMULINK and Control System Toolbox.
Chapters 1–6 deal with the basics of power
generation and distribution. Chapter 7 deals directly with the
cost to dispatch energy from a traditional
fuel fired power station, stating that the operational cost of such
a power plant is a quadratic function of
the fuel cost; with CSP the cost of generation is largely driven
by operation and maintenance activities.
Chapters 8–10 present transient analysis methods and fault
condition management. Chapter 11
introduces the swing equation for output power (Pe) vs. power
angle (δe) and equal area method for
evaluating system stability capabilities, this analysis method
may prove useful for sanity checking a
design resulting from a stochastic analysis of solar resource,
grid demand and market price perspectives.
Finally Chapter 12 deals with power systems control
applications and theory.
In Hoffschmidt et al. [17] the authors further detail the topology
of a number of power block
technologies as listed below:

Steam Cycle
the most common form of power generation using a variety of
heat transfer fluids to generate
steam for a generator turbine operating to a Rankine Cycle or
variant thereof, 100s of MW of
generation are possible in this configuration, with typical
commercial CSP installations being
50 MW and above.
Organic Rankine Cycle
use organic materials as the working fluid at lower working
temperatures whilst still operating to
a Rankine Cycle or variant thereof, for generation up to 10 MW.
Gas Turbines
use a tower CSP in combination with a fuel fired combustion
chamber to heat high pressure
compressed air in order to drive a Brayton cycle power turbine,
through the expansion of this
heated air. The power turbine drives a primary generator and the
air compressor used to provide the
high pressure air. The expanded exhaust air is then used to heat
a secondary steam cycle Rankine
Cycle or variant thereof, steam turbine driven generator. No
realisations of CSP augmented
systems have been undertaken but conventional gas fired
turbines used in this combined cycle
configuration typically achieve 100s of MW of generation at the
high conversion efficiencies.
Solar Dish Sterling

due to their high theoretical operation efficiencies paraboloid
dishes have typically been
implemented with Stirling cycle engines using hydrogen as a
working fluid fitted directly to the
receiver.
It is imagined that the details of the power block operation
could be abstracted to a characterisation
of the conversion efficiency (ηconversion) and a quasi-static
continuous time based function for energy
conversion (Qe(t)). Such a model would accurately reflect the
change of state for a power block over
the time frames of interest (say 5 minute time intervals) without
having to account for more dynamic
transient response, in say the 10 second time frame.
2.2.4. CSP Examples
Example of implementations of CSP are presented in [17] with a
partitioning of commercial and
research plants being presented. A database of all current CSP
Projects worldwide is maintained by the
National Renewable Energy Laboratory (NREL) and
SolarPACES organisations and is accessible at the
website [19].
2.2.5. Heat Transfer Fluids
In order to transfer the heat available at the receiver block to
the conversion block, either directly or
indirectly through TES, heat transfer fluids (HTF) are used. A
HTF can be a liquid, gas or in as is the
case with steam for power generation or the refrigerant in a
household refrigerator both, depending on
where in the cycle you happen to look.

Heat exchangers (HEX) are required for the transfer heat from
one HTF to another, say between
the receiver media and the transfer media of the TES in Figure
1. Heat exchangers are constructed to
maximise the heat transfer area whilst keeping each of the HTF
separated and are usually constructed of
highly conductive metals such as copper.
A common approach in CSP with two tank molten salt TES is to
use the molten salt for the multiple
purposes of receiver and transfer HTF as well as TES with only
one set of heat exchangers between the
molten salt HTF and the steam generators for the conversion
block. This minimisation of heat exchanger
elements is one of the common optimisation strategies employed
in order to achieve greater conversion
efficiencies as heat exchangers are material (and hence cost)
intensive devices [1].
2.2.6. Thermal Energy Storage
Recent reviews of TES systems for use with CSP are presented
in detail in both by Cabeza [20] and
Steinmann [21]. Formulas pertaining to the thermodynamic
behaviour of TES using Number of Transfer
Units (NTU) are given in [20].
From these reviews there are three main categories of thermal
energy storage: Sensible, latent
and chemical.
Sensible storage involves materials storing heat in a given state

be it solid or liquid. Solid concrete
blocks and two tank molten salt systems are examples of
sensible TES.
Phase Change Materials (PCM) offer a higher energy density
(expressed in kWh/m3) than sensible
(or constant phase) TES. There are a number of possible phase
change materials, these can involve a
solid-solid transition as can occur in some polymer materials
where chain alignment decreases as energy
is stored; solid-liquid where the melting of a material
constitutes energy storage and liquid-gas which
often requires containment of the gas in a pressure vessel. A
review of high temperature PCM suitable
for CSP TES is given by Liu et al. [22], further more detailed
formulations relating to the behaviour
of PCM TES are given in Tay et al. [23] and Amin et al. [24],
which builds on the work done by
Belusko et al. [25].
�-NTU based characterisation approaches offer a simple
method to modelling the turnaround
efficiency (ηtransport × ηthermal storage) of PCM TES
elements and could be used in the formulation of
TES storage characterisation. In Tay et al. [23] the average
effectiveness � ∝ ηtransport.ηthermal storage
of a PCM thermal storage unit with phase change temperature
TPCM is presented as follows:
� =
Tinlet −Toutlet
Tinlet −TPCM
= 1−e−NTU (5)
where NTU can then be expressed as follows:

NTU =
UA
ṁCp
=
1
RTṁCp
(6)
where
U is the overall heat transfer coefficient in W ·m−2 ·K−1
A is the heat transfer area in m2
ṁ is the mass transfer rate of the (HTF) in kg ·s−1
Cp is the specific heat of the HTF kJ ·kg−1 ·K−1
RT is the total thermal resistance W ·K−1
where RT = RHTF + Rwall + RPCM
i.e., the sum of the HTF, heat exchange wall material and PCM
resistances
From the above equations effectiveness � = 1 − e−UA/ṁCp so
in order to keep storage effectiveness
and hence efficiency high, mass flow rates must be minimised
whilst maximising the heat transfer area
between the HTF and PCM, with the implication that additional
transfer area requires more material to
implement and hence incurs greater cost.

There is potential for even higher energy densities from
thermochemical storage which is still a
topic of broad research. In this method heat is used to enact the
endothermic reaction of a reversible
chemical process, when the heat is later required the separated
reagents are brought together to enact
the exothermic process and recoup the stored energy. The
separation of ammonia (NH3) is discussed at
length in [26].
Thermochemical storage could be analysed in detail, though a
simplified generic model of storage
considering turnaround efficiency (ηtransport × ηthermal
storage) and continuous time based function for
energy storage state of charge (SoC, Qthermal store(t)) may be
sufficient.
2.2.7. Electrical Storage Systems
As stated above PV and wind based RE can only make use of
ESS, Díaz-González et al. [27] and
Chen et al. [28] provide a detailed reviews of ESS technologies
which include capital costs as well
as technical details, with Fthenakis and Nikolakakis [29]
providing a more generalized overview of
the technologies.
The broad categories of ESS are as follows:
Electro-mechanical electrical energy is used to perform
mechanical work to store the energy, examples

include:
Pumped Hydro Systems (PHS) Water from a lower level
reservoir lake is pumped up to the
higher reservoir for later use in the generation of energy
Compressed Air Energy Storage (CAES) Natural (air tight)
caverns are pumped with
compressed air for later energy recovery via decompression to
atmospheric pressure
Flywheel Energy Storage Systems (FESS) A flywheel is spun-up
to store energy and this
increase in rotational momentum is then harvested to recoup the
energy.
Electro-chemical energy is stored via changing the level of
ionisation of a chemical electrolyte,
examples include:
Battery Energy Storage Systems (BESS) there is a wide variety
of battery technologies both in
production and as topics of research. The main challenges with
battery storage relate to
limited useful life and the trade-off between the amount of
storage used vs. battery life as is
typical with Lead Acid batteries where the greater the Depth of
Discharge (DoD) in any one
cycle, the shorter the expected lifespan of the battery as a whole
(expressed as the expected
number of charge-dicharge cycles). Batteries offer large good
energy storage capacity but are
limited in the amount of power they can deliver. Key parameters
for batteries are useful life
(of the order of 1000s of cycles), capacity (Wh), maximum
charge and discharge rates and

the impact of DoD on useful life.
Capacitor and Super-Capacitor Storage Systems Capacitors
offer a greater power capacity
than batteries with only a limited amount of energy storage, the
advent of super capacitors
allows for utility sized solutions to be implemented. The useful
life of super capacitors is of
the order of 106 cycles.
Electro-magnetic Superconductor Magnetic Energy Storage
(SMES) stores via energising a
superconductor coil.
The use of ESS could be captured with a simplified generic
model of storage considering turnaround
efficiency (ηelectrical storage) and continuous time based
function for energy storage state of charge (SoC),
stated as follows:
SoCe(t) = Qe Stored(t)/Qe max (7)
with
Q
̇ e Stored = αQe Stored for some constant α < 0 (8)
Q
̈ e Stored > 0 when discharging and (9)
Q
̈ e Stored < 0 when charging. (10)
where

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