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Journal ArticleDOI

A Battery Energy Management Strategy for U.K. Enhanced Frequency Response and Triad Avoidance

22 Mar 2018-IEEE Transactions on Industrial Electronics (IEEE)-Vol. 65, Iss: 12, pp 9509-9517

TL;DR: This paper describes a control algorithm to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission while managing the state of charge of the BESS to optimize the availability of the system.

AbstractThis paper describes a control algorithm for a battery energy storage system (BESS) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission (NGET)—the primary electricity transmission network operator in the U.K.—while managing the state of charge of the BESS to optimize the availability of the system. Furthermore, this paper investigates using the BESS in order to maximize triad avoidance benefit revenues while layering other services. Simulation using a 2 MW/1 MWh lithium–titanate BESS validated model is carried out to explore possible scenarios using the proposed algorithms. Finally, experimental results of the 2 MW/1 MWh Willenhall Energy Storage System verify the performance of the proposed algorithms.

Topics: Energy storage (53%), Frequency grid (52%), State of charge (52%)

Summary (1 min read)

Introduction

  • Battery energy storage; enhanced frequency response; frequency stability; grid support; lithium-titanate; triad avoidance; Willenhall energy storage.
  • BESSs using various battery chemistries are installed around the world for grid support [4].
  • Maintaining the grid at a nominal frequency (i.e. 50 Hz for the UK) requires the management of many disparate generation sources against varying loads.
  • In Section III, three different EFR service models are developed to evaluate control strategies for delivering a real-time response to deviations in the grid frequency.
  • Finally, the change in power output per time step (1 second) for each zone is determined using the given ramp-rate limits given in [4].

A. Simulation results of EFR Model-1

  • In order to show the performance of the reported EFR algorithm in Section III, the real grid frequency data for the 21st of October of 2015 [23] is employed herein, as this particular day is known to have a large period of under frequency.
  • Calculated power dictated by EFR specification, also known as *CPower.
  • Because of the SOC reaching 0% and therefore there is no power available for delivery to the grid.
  • This non-conformance would cause a penalty in the SPM and hence it is necessary to improve the EFR control algorithm to minimise such occurrences.

A. Simulation Results of EFR Model-2

  • Model-2 introduces the extended grid frequency event timer and cuts the EFR power output after 15 minutes (Fig. 3).
  • The same frequency data is injected into Model-2 capturing 13 15- minute extended frequency events (Fig. 5(d)).
  • Therefore, the BESS is 100% available for providing power according to the EFR specification.

B. Simulation Results for EFR Model-3

  • The EFR algorithm implemented in Model-3 allows for the charge/discharge of the battery during the 30-minute rest period (Fig. 3).
  • The model is simulated with the 21st October 2015 grid frequency data [23] as shown in Fig.
  • This is a substantial achievement in terms of maximising the utilisation of the BESS stored energy.

C. Results Analysis

  • It was shown that, for the historical dataset considered, the basic EFR algorithm, Model-1, would not be able to manage the extended 15-minute grid frequency events, thus, causing the battery’s SOC to drop to 0%, which would incur a service performance penalty charge.
  • T. Feehally et al., "Battery energy storage systems for the electricity grid: UK research facilities," in IET Int. Conf. Power Electron., Mach.
  • He is a Senior Lecturer in the Department of Electrical and Electronic Engineering, the University of Sheffield, with particular interest for research into energy storage and management, power electronics, and intelligent systems.

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This is a repository copy of A Battery Energy Management Strategy for UK Enhanced
Frequency Response and Triad Avoidance.
White Rose Research Online URL for this paper:
http://eprints.whiterose.ac.uk/129204/
Version: Accepted Version
Article:
Mantar Gundogdu, B., Nejad, S., Gladwin, D.T. orcid.org/0000-0001-7195-5435 et al. (2
more authors) (2018) A Battery Energy Management Strategy for UK Enhanced Frequency
Response and Triad Avoidance. IEEE Transactions on Industrial Electronics, 65 (12). pp.
9509-9517. ISSN 0278-0046
https://doi.org/10.1109/TIE.2018.2818642
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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Abstract This paper describes a control algorithm for a
battery energy storage system (BESS) to deliver a
charge/discharge power output in response to changes in
the grid frequency constrained by the National Grid
Electricity Transmission (NGET) the primary electricity
transmission network operator in the UK whilst managing
the state-of-charge (SOC) of the BESS to optimise the
availability of the system. Furthermore, this paper
investigates using the BESS in order to maximise Triad
avoidance benefit revenues whilst layering other services.
Simulation using a 2 MW/1 MWh lithium-titanate BESS
validated model are carried out to explore possible
scenarios using the proposed algorithms. Finally,
experimental results of the 2MW/1MWh Willenhall Energy
Storage System (WESS) verify the performance of the
proposed algorithms.
Index Terms Battery energy storage; enhanced
frequency response; frequency stability; grid support;
lithium-titanate; triad avoidance; Willenhall energy storage.
I. INTRODUCTION
ITH increasing environmental concerns about climate
change and burning fossil fuels, and the requirement for
a more sustainable grid, renewable energy sources (RES) play
an essential role in energy continuity for today’s electricity
supply grid [1],[2]. Increased penetration of uncertain and
intermittent RES on power grids causes many challenges for
grid operators including increased frequency fluctuations,
power quality reduction, reduced reliability and voltage
transients [3]. Energy storage systems (ESSs) are one of the
efficient ways to deal with such issues by decoupling energy
generation from demand. Moreover, ESSs can be used to tackle
the power quality concerns, especially in the UK, by providing
ancillary services such as 15-minute fast frequency response,
frequency regulation, Triad avoidance, load levelling and peak
shaving [4], [5].
There are various types of existing ESSs such as pumped
hydro, hydrogen, fuel cells, cryogenic, compressed air,
flywheel and superconducting magnetic storage [6]. In
comparison to such ESSs, the battery energy storage system
(BESS) has numerous advantages including faster response
time compared to conventional energy generation sources,
energy efficiency, storage size, long cycle life, low self-
discharge rate, high charging/discharging rate capability, and
low maintenance requirements [7], [8]. The cost of batteries has
been decreasing in recent years and therefore there is now
potential for profitable large-scale grid application. BESSs
mostly participate in balancing demand and supply through
frequency response services, voltage support and peak power
lopping [9], [10] BESSs using various battery chemistries are
installed around the world for grid support [4].
In power distribution networks, the frequency changes
continuously due to the imbalance between total generation and
demand; if demand surpasses generation, a decrease in the
frequency will occur and vice versa [4], [11] Maintaining the
grid at a nominal frequency (i.e. 50 Hz for the UK) requires the
management of many disparate generation sources against
varying loads. The National Grid Electricity Transmission
(NGET) the primary electricity transmission network operator
in the UK has introduced a new faster frequency response
service, called Enhanced Frequency Response (EFR), to assist
with maintaining the grid frequency closer to 50 Hz under
normal operation [12]. A BESS is an ideal choice for delivering
such a service to the power system due to its rapid response and
its capability to import/export [4]. In the UK, there are limited
numbers of installed BESS facilities which are suitable for
providing grid support. In 2013, The UK’s first grid-tie lithium-
titanate BESS, the Willenhall Energy Storage System (WESS),
was installed by the University of Sheffield to enable research
on large scale batteries and to create a platform for research into
grid ancillary services [4], [8], [13].
In the UK, the “Triadrefers to the three half-hour settlement
periods with the highest system demand between the months of
November and February, separated by at least ten clear days.
The timing of these peaks is typically one period between
1600hrs to 1800hrs. These three periods are not known in
advance and therefore are determined from the measured data
analysed in March of every year. Half-hourly metered (HHM)
Post Conference Paper
A Battery Energy Management Strategy for
UK Enhanced Frequency Response and
Triad Avoidance
B. Gundogdu, S. Nejad, D. T. Gladwin, M. P. Foster, and D. A. Stone
W
Manuscript received Month xx, 2xxx; revised Month xx, xxxx;
accepted Month x, xxxx.
This work was supported in part by the UK Engineering and
Physical Sciences Research Council under Grant EP/N032888/1.
Authors are with the department of Electronic and Electrical
Engineering at the University of Sheffield, U.K. (emails:
bmantar1@sheffield.ac.uk; shahab.nejad@sheffield.ac.uk;
d.gladwin@sheffield.ac.uk; m.p.foster@sheffield.ac.uk;
d.a.stone@sheffield.ac.uk)

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
electricity customers in the UK pay charges proportional to
their consumption during the Triad; this is called the
Transmission Network Use of Service (TNUoS). The HHM
customers can minimise their TNUoS charges by reducing their
demand during Triad periods. Many commercial customers
have an energy storage device or back-up generators to ensure
the maintenance of critical supplies in case of a failure that can
also be engaged to decrease Triad demand; this is known as
‘Triad avoidance’ [14]-[19]. It is also possible for generating
assets such as BESSs to export power to the grid during the
Triad, this results in a payment from the electricity supplier
known as the Triad Avoidance Benefit (TAB). It is a complex
task to predict the Triad periods in advance, however, many
electricity suppliers offer Triad prediction services based on
insufficient system margin (NISM) provided by NG and other
factors such as the weather forecast [16].
Since the EFR is introduced as a new UK grid balancing
service published in the late of 2016, in literature there are only
a few papers about EFR service delivery for grid support. In
[20], a new EFR control algorithm implemented in the DC/AC
converter of a BESS was developed to fulfil the NGET EFR
service requirements, however in this paper EFR control is
achieved with battery energy management system rather than
controlling the energy storage converter. The study [20]
compares the performance of the EFR Sevice-1 (wide
deadband) and Service-2 (narrow dead-band), and it was stated
that the narrow service is technically more challenging, likely
requiring four time the storage capacity of the wide service.
That control algorithm does not cover the 15-mins frequency
event control to be able to increase the availability of the BESS,
especially with the narrow dead-band. However, this paper
extends the basic EFR control algorithm with the two different
extended 15-mins frequency event controls to achieve a
maximum BESS availability for delivering EFR service. In
addition, in [20], the algorithm manages the SOC of the BESS,
maintaining at 49-51%. But, the SOC band should not be kept
at less than 5% SOC band in order to reduce battery degradation
and hence prolong its lifetime.
In [21], Cooke et al. present a method of providing the new
EFR service to avoid the necessity of holding more FFR in
reserve when system inertia falls. That study also introduced
several alternative response curves which indicate that if
arresting the fall in grid frequency in the event of a drop in
generation is an important aspect of the control design, then a
stepped response may provide a better service. An energy
storage strategy based on PI control can help with restoration
and damping of frequency. However, that response time will be
slower than a stepped response so that stability can be ensured.
In [22], the authors investigate the possible performance of a
BESS in EFR provision, by simulating its response to grid
frequency according to the EFR service requirements, and this
evaluating its ability to exchange energy for the service, a
service performance indicator, and the possible aging related to
battery cycling. Different EFR power versus frequency
characteristics, BESS technologies and BESS energy capacities
are considered in [22]. It was also assumed that the BESS are
connected to the UK or to the Continental Europe (CE)
synchronous area; therefore, for the CE system those
requirements are adjusted according to the CE frequency
behaviour. However, a major specification of the EFR service
is to consider ramp-rate limits in the UK requirements, it was
not considered in [22] for simplicity; power exchange rate
limits internal to the batteries was also neglected. In addition,
that study did not cover an extended 15-min frequency event
control in order to increase the batteries availability.
In contrast to other recent works in the field; the main
contribution of this paper is to present a novel control algorithm
that enables BESSs to provide a bi-directional power in
response to changes in the grid frequency, whilst managing the
SOC of the BESS to optimise availability of the system.
Moreover, this study introduces a strategy to generate
additional revenues from ancillary services such as Triad
Avoidance only available during the winter season.
Moreover, this paper considers layering the new UK grid
frequency balancing service, EFR, with Triad Avoidance in
order to maximise the system’s availability and profitability. It
should be noted that the previous basic study [4] presented
initial three EFR control methodologies with their simulation
results; and this paper extends to show how this can be used to
maximise profits from other services such as Triad Avoidance.
This paper also includes experimental validation with a
2MW/1MWh lithium-titanate BESS, commissioned and
operated by the University of Sheffield, which is the largest
research only platform for grid-tie energy storage applications.
This paper is organised as follows. In Section II, the technical
specification of the new UK EFR service is described. In
Section III, three different EFR service models are developed
to evaluate control strategies for delivering a real-time response
to deviations in the grid frequency. The first model introduces
a control algorithm designed to meet the technical requirements
of NGET specifications [12]. The second model addresses the
EFR service design with an extended 15-minute frequency
event control, in order to optimise the use of the available stored
energy. The third model extends the EFR control algorithm to
include a dynamic SOC target to maximise the energy stored on
predicted Triad days. In Section IV the simulation results based
on the 2 MW / 1 MWh BESS are analysed to verify the transient
performance of the proposed control strategy. In Section V, the
performance of the EFR service delivery through TAB is
quantified and the performance of the proposed EFR control
algorithm is verified experimentally with the 2MW / 1MWh
WESS in Section VI.
II. EFR SERVICE TECHNICAL SPECIFICATIONS
EFR is introduced as a new fast frequency response service
for grid balancing that can deliver full-scale active power within
one second of registering a grid frequency deviation. NGET
prepared an EFR specification to facilitate a tender competition
for 200 MW of support provision to be distributed amongst
potential energy storage providers in 2016 [12], which is
described as follows.

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Fig. 1. NGET specifications (a) EFR envelope and (b) power zones [12].
TABLE I
EFR ENVELOPE FREQUENCY AND POWER BOUNDARIES [12]
Power (%)
Ref.
Point
Service-1
Service-2
Ref.
Point
Service-1
Service-2
A
B
C
D
E
F
49.5
49.75
49.95
50.05
50.25
50.5
49.5
49.75
49.985
50.015
50.25
50.5
t
u
v
w
x
y
z
100
44.44444
9
0
-9
-44.44444
-100
100
48.4536
9
0
-9
-48.4536
-100
Energy storage providers must respond to deviations in
nominal frequency (50 Hz) by decreasing or increasing their
power output. Specifically, energy storage devices must
provide power to the grid to respond to deviations in frequency
outside of a dead band (DB). Providers must deliver continuous
power to the grid as described in one of the two EFR service
envelopes (Service-1, Service-2) of Table I [12]. As seen in
Error! Reference source not found.(a), the power level must
remain within the upper and lower envelopes at all times; power
provided outside the envelope will decrease the service
performance measurement (SPM), and thus reduce the income
revenue [12]. In DB, the reference power profile is at zero MW
output and hence providers do not have to respond to changes
in the grid frequency. The BESS can be freely operated to
charge/discharge in DB, however, the maximum export/import
power must not exceed 9% of the BESS’s full-scale range [12].
Providers may operate anywhere within the upper and lower
envelopes to deliver a continuous service to the power system,
with respect to the specified limitations on ramp rates as given
in [4],[12]. For a BESS, this effectively provides some control
over state-of-charge (SOC) of the battery. For the zones A, C, D
in Error! Reference source not found.(b), the ramp rate must
obey the specified values in [4], [12]. Operation in zones C and
D will result in payments at a lower SPM. Hence, in such cases,
EFR power output has to return to the specified envelope with
respect to the ramp-rate limits given in [4]. Ramp-rate zone B is
described as being the area between the upper and lower
envelopes, excluding the DB, and extends to achieve the full
power capability at ±0.5 Hz [12]. The allowable ramp rates
within zone B depend on the rate of change of frequency. For
EFR Service-1 and Service-2, the ramp rate limitations for all
frequencies in zone B are shown in [4]. With these ramp limits,
output power changes proportionally to changes in grid
frequency, whilst allowing the energy storage providers some
flexibility [12] to manage the battery SOC.
III. EFR DESIGN ALGORITHM
A BESS model is developed in MATLAB/Simulink and
verified against experimental operation of the WESS. An EFR
control algorithm is then implemented on the model to deliver
a grid frequency response service to the NGET specification.
Fig. 2 presents the EFR control scheme implemented in EFR
Model-1 [4], where the inputs are real-time grid frequency ()
and battery SOC, and the output is the required EFR power.
Fig. 2. EFR control scheme implemented in EFR Model-1 [4].
The algorithm starts by detecting the position of the
measured grid frequency with respect to the zones bounded by
vertical lines A’ to ‘F’ in Fig. 1 (a). This is achieved by the
‘EFR Power Calculation’ block (labelled ‘1’), where the
required EFR response envelopes are calculated. In the 2 MW
BESS model, the frequency and power bounds are calculated as
a function of the limits denoted in Fig. 1 (a), with their values
declared in Table I. The power output is restricted to ±180 kW
(i.e. 9% of 2 MW) within the DB and both services include an
upper, base line and lower power line denoted, and ,
respectively. Block 2 selects the required power line with the
decision being based on the measured SOC. For example, if the
current SOC is below the desired SOC range, the demanded
power is calculated using the equations derived for the upper
line (). This has the effect of either importing energy to charge
the battery or minimising the exported energy to maintain a
desired SOC range. ‘Zone Assignment’ (Block 3) is responsible
for identifying the current operating zone (refer to Fig. 1(b)) for
the calculation of the power-output levels.
t
u
v
w
x
y
z
A
B
C
D
E
F
Upper
limit
Lower
limit
Base line
Output power
Frequency
DB
(a)
(b)
EFR Power
Calculation
f (Hz)
U, Z, L
Ramp-
Rate
Limiter
SOC
Power Setpoint
- +
Measured
Power
Zone
df / dt
f (Hz)
f (Hz)
SOC
error
State
Power
Output
State
Assignment
&
EFR Power
Set Point
Zone
Assignment
1
2
3
4

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Finally, the change in power output per time step (1 second)
for each zone is determined using the given ramp-rate limits
given in [4]. In this study, battery SOC is calculated using (1)
[4], where SOC

, and

represent initial SOC, Watt-hour
capacity and instantaneous battery power, respectively.
SOC
out
SOC
init
batt


(1)
Fig. 3. Flow chart showing the structure of the two proposed battery energy
management strategies for enhanced frequency response in the UK [4].
The EFR specification defines frequency outside DB for
longer than 15 minutes as an extended event, whereby after the
15 minutes, it is optional to deliver power for up to 30 minutes
post the grid frequency returning to DB. In order to increase the
availability of the BESS in Model-1, by avoiding SOC limits,
an extended 15-minute frequency event control algorithm is
implemented in EFR Model-2 and Model-3, as given in Fig. 3.
EFR Model-2 introduces a timed control block, which measures
the length of time that the grid frequency is continuously
outside of the DB. If this block measures a value higher than 15
minutes, then the BESS’s output power is set to zero. The BESS
remains in this state until the system frequency returns within
DB, at which point a second timer starts timing for 30 minutes
and the output power stays at zero until the timer expires, at
which point, the EFR control is reset back to operating as EFR
Model-1. EFR Model-3 allows the BESS to manage its SOC
between its upper (SOC

) and lower limits (SOC

) during the
30-minute rest period by charging and discharging the battery
within the ±9% power limits.
IV. SIMULATION RESULTS OF EFR MODELS
Using a real-time frequency data set obtained from NGET
[23], the three EFR models are simulated in
MATLAB/Simulink. The simulation results presented in this
paper are all based on a 1 MWh BESS model, which has been
experimentally validated on the WESS plant in the UK, with a
maximum EFR power of ±2 MW. Table V shows the
parameters used in the EFR models.
A. Simulation results of EFR Model-1
In order to show the performance of the reported EFR algorithm
in Section III, the real grid frequency data for the 21
st
of October
of 2015 [23] is employed herein, as this particular day is known
to have a large period of under frequency.
TABLE I
SYSTEM PARAMETERS [12]
Parameter
Value
Nominal frequency
Low/high DB
Max/min EFR power limit
Battery rated power/capacity
Battery initial SOC (SOC

)
SOC band (SOC

- SOC

)
Inverter efficiency (

)
Battery charge/discharge efficiency (
/
)
50 Hz
±0.015 Hz (Service-2)
±2 MW
2 MW/1 MWh
50%
45-55%
97%
94%
Fig. 4 shows the simulation results of Model-1 for a ‘Service-
2’ EFR with a target SOC band of 45-55%. On the frequency
plot, the DB (±0.015 Hz) is shown by the green lines. It is clear
that the SOC sharply drops, reaching 0% at 11:00, and stays
there for ~30mins due to the grid frequency demands at that
time. As the frequency stabilises, the EFR algorithm charges
the battery when it is permissible (frequency in DB) and returns
the SOC to within the specified band of 45-55%. The power
response versus frequency plot of EFR Model-1 for 21
st
October 2015 is shown in Fig. 7(a). The red lines represent the
upper, reference and lower EFR power lines. It can be seen that
the EFR power (blue circles) does not remain within the
required zones of ‘A’ and ‘B’. As outlined in Fig. 1, this is
*CPower: Calculated
power dictated by
EFR specification.
Start
Measure frequency
Frequency in
DB?
PowerOut = CPower
Event Counter=0
Timer1=0
Timer2=0
End
Yes
No
Start/Continue Timer1
Timer1≥15 mins
No
PowerOut = CPower
Yes
M2/M3
M2
Start/Continue Timer2
Timer2≥30 mins
PowerOut=0
No
Yes
PowerOut = CPower
End
Model 2
Event Counter=1
Stop Timer1 & Timer2
M3
Start/Continue Timer2
Timer2≥30 mins
PowerOut = CPower
Yes
End
No
Frequency in
DB?
Yes
SOC<SOClow?
No
SOC>SOCup?
Yes
Yes
No
PowerOut=0
PowerOut = CPower
(charging battery)
PowerOut=0
No
Model 3
PowerOut = CPower
(discharging battery)
Event Counter=1
Stop Timer1 & Timer2

Citations
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Journal ArticleDOI
TL;DR: An optimization process with nondominated sorting genetic algorithm II (NSGA-II) is proposed to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test and the degradation features in this article are the knee points at the transfer instants of the voltage.
Abstract: As a favorable energy storage component, lithium-ion (Li-ion) battery has been widely used in the battery energy storage systems (BESS) and electric vehicles (EV). Data driven methods estimate the battery state-of-health (SOH) with the features extracted from the measurement. However, excessive features may reduce the estimation accuracy and also increases the human labor in the lab. By proposing an optimization process with nondominated sorting genetic algorithm II (NSGA-II), this article is able to establish a more efficient SOH estimator with support vector regression (SVR) and the short-term features from the current pulse test. NSGA-II optimizes the entire process of establishing a SOH estimator considering both the measurement cost of the feature and the estimation accuracy. A series of nondominated solutions are obtained by solving the multiobjective optimization problem, which also provides more flexibility to establish the SOH estimator at various conditions. The degradation features in this article are the knee points at the transfer instants of the voltage in the short-term current pulse test, which is fairly convenient and easy to be obtained in real applications. The proposed method is validated on the measurement from two LiFePO4/C batteries aged with the mission profile providing the primary frequency regulation (PFR) service to the grid.

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Journal ArticleDOI
TL;DR: The results demonstrate that the proposed control scheme can ensure the stable operation of the system under a larger sampling period so as to reduce the communication network burden and show that the controller designed by a large exponential decay rate can provide a fast frequency response to alleviate the impact of thesystem’s frequency response due to the high penetration of wind power.
Abstract: Load frequency control employs communication networks to transmit measurements and control signals. The controller is usually designed in continuous-mode and discretized in implementation with a large sampling period, which may result in a degraded dynamic performance or even cause system instability. On the other hand, high penetration of wind power reduces the inertia of the power system, leading to a faster frequency response and larger frequency deviation after a contingency, and desires a fast load frequency control. Therefore, this paper presents a discrete-mode load frequency control scheme considering a large sampling period of control/measurement signals via sampled-data control, and introduces an exponential decay rate as a new performance index to guide a design of load frequency control scheme with desired faster frequency response. The proposed scheme is evaluated on a one-area power system, a traditional two-area power system with wind power and a deregulated three-area power system with wind power. Using the proposed scheme and the state-of-the-art schemes, the frequency response performance and the tolerance to sampling period of power systems are analyzed. The results demonstrate that the proposed control scheme can ensure the stable operation of the system under a larger sampling period so as to reduce the communication network burden. Also, the results show that the controller designed by a large exponential decay rate can provide a fast frequency response to alleviate the impact of the system’s frequency response due to the high penetration of wind power.

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TL;DR: A dual-consensus-based approach is presented for distributed frequency control for power systems with multiple distributed battery energy storage systems (BESSs), which consists of tuning of the BESS control gain and design of control signals as inputs to BESSs for proportional use of the preserved energy.
Abstract: Intermittent renewable energy sources are being increasingly integrated into modern power networks. This leads to severe frequency fluctuations in the networks. Energy storage systems can be used for frequency restoration due to their capability to provide in-time active power compensations. This paper examines the frequency control problem for power systems with multiple distributed battery energy storage systems (BESSs). A dual-consensus-based approach is presented for distributed frequency control. It consists of three main components: 1) tuning of the BESS control gain; 2) design of control signals as inputs to BESSs for proportional use of the preserved energy; and 3) estimation of BESS parameters for control implementation. A static parameter and a time-varying parameter are defined, and their average values are estimated through static average consensus (SAC) and dynamic average consensus (DAC) algorithms, respectively. Case studies are conducted to demonstrate our dual-consensus control approach.

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Cites background or methods from "A Battery Energy Management Strateg..."

  • ...In both case studies, our dual-consensus control approach is compared with two recent and relevant methods [13], [24] in which the PBi − f characteristic is also adopted....

    [...]

  • ...Moreover, a strategy is proposed in [13] which enables BESSs to generate additional revenues from ancillary services such as triad avoidance....

    [...]


Journal ArticleDOI
TL;DR: Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller, which include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.
Abstract: The increased adoption of renewable energy generation is reducing the inertial response of the Great Britain (GB) power system, which translates into larger frequency variations in both transient and pseudo-steady-state operation. To help mitigate this, National Grid, the transmission system operator in GB, has designed a control scheme called enhanced frequency response (EFR) specifically aimed at energy storage systems (ESSs). This study proposes a control system that enables the provision of EFR services from a multi-electrical ESS and at the same time allows the management of the state of charge (SOC) of each ESS. The proposed control system uses a Fuzzy Logic Controller to maintain the SOC as near as possible to the desired SOC of each ESS while providing EFR. The performance of the proposed controller is validated in transient and steady-state domains. Simulation results highlight the benefits of managing the SOC of the energy storage assets with the proposed controller. These benefits include a reduced rate of change of frequency and frequency nadir following a loss of generation as well as an increase in the service performance measure which renders into increased economic benefits for the service provider.

20 citations


Journal ArticleDOI
01 May 2020-Energy
TL;DR: An improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service is proposed.
Abstract: This paper proposed an improved vehicle-to-grid (V2G) scheduling approach for the frequency control with the advantage of protecting the batteries hence saving the battery lifetime during grid connected service. The proposed methodology is improved in two ways. Firstly, to give a prediction of the available electric vehicle (EV) battery capacity in the control time-step for the V2G service, a deep learning based prediction is developed. Secondly, this study advances the previous V2G method by adding the quantitative analysis of the battery cycle life into the V2G optimization. The accurate prediction of the schedulable battery capacity based on the LSTM algorithm is shown very effective in the power system frequency control. Also, compared with the previous method that without battery lifetime control, the proposed method benefits in the reduction of charge/discharge cycles.

19 citations


References
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Journal ArticleDOI
Abstract: The penetration of renewable sources (particularly wind power) in to the power system network has been increasing in the recent years. As a result of this, there have been serious concerns over reliable and satisfactory operation of the power systems. One of the solutions being proposed to improve the reliability and performance of these systems is to integrate energy storage devices into the power system network. Further, in the present deregulated markets these storage devices could also be used to increase the profit margins of wind farm owners and even provide arbitrage. This paper discusses the present status of battery energy storage technology and methods of assessing their economic viability and impact on power system operation. Further, a discussion on the role of battery storage systems of electric hybrid vehicles in power system storage technologies had been made. Finally, the paper suggests a likely future outlook for the battery technologies and the electric hybrid vehicles in the context of power system applications.

1,447 citations


"A Battery Energy Management Strateg..." refers background in this paper

  • ...dro, hydrogen, fuel cells, cryogenic, compressed air, flywheel, and superconducting magnetic storage [6]....

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Journal ArticleDOI
Abstract: The critical challenges to enlarge the market share of electric vehicles (EVs) are cost, performance, reliability and safety. These issues are closely linked to the energy storage system in the EVs. Lithium-ion batteries have revolutionized the EV industry to become the preferred battery choice for EVs. This is due to their outstanding characteristics including high energy density, high voltage, low self-discharge rate, long cycle life, high charging and discharging rate capability. In EVs, Lithium-ion cells are connected in series and/or parallel to deliver the required power to the traction motor and auxiliary systems. However, due to the operating environment of EVs, the Lithium-ion battery may not perform to the best of its ability when integrated into an EV battery pack. In this work, the integration of Lithium-ion battery into an EV battery pack is investigated from different aspects, namely different battery chemistry, cell packaging, electric connection and control, thermal management, assembly and service and maintenance. In addition, benchmarking study using different cell packaging of Lithium Iron Phosphate cell as an energy storage system is conducted. This study provides a basic guideline for cell selection and integration of cell for the EVs battery pack.

189 citations


Journal ArticleDOI
TL;DR: Novel statistical techniques have been devised to quantify the design and operational requirements of ESS providing frequency regulation services, demonstrated via an illustrative service design and high-resolution frequency data from the Great Britain transmission system.
Abstract: Energy Storage Systems (ESS) are expected to play a significant role in regulating the frequency of future electric power systems. Increased penetration of renewable generation, and reduction in the inertia provided by large synchronous generators, are likely to increase the severity and regularity of frequency events in synchronous AC power systems. By supplying or absorbing power in response to deviations from the nominal frequency and imbalances between supply and demand, the rapid response of ESS will provide a form of stability which cannot be matched by conventional network assets. However, the increased complexity of ESS operational requirements and design specifications introduces challenges when it comes to the realisation of their full potential through existing frequency response service markets: new service markets will need to be designed to take advantage of the capabilities of ESS. This paper provides new methods to analyse and assessing the performance of ESS within existing service frameworks, using real-time network simulation and power hardware in the loop. These methods can be used to introduce improvements in existing services and potentially create new ones. Novel statistical techniques have been devised to quantify the design and operational requirements of ESS providing frequency regulation services. These new techniques are demonstrated via an illustrative service design and high-resolution frequency data from the Great Britain transmission system.

165 citations


"A Battery Energy Management Strateg..." refers background or methods or result in this paper

  • ...In [20], a new EFR control algorithm implemented in the dc–ac converter of a BESS was developed to fulfill the NGET EFR service requirements; however, in this paper, EFR control is achieved with the battery energy management system rather than controlling the energy storage converter....

    [...]

  • ...In addition, in [20], the algorithm manages the state-of-charge (SOC) of the BESS, maintaining at 49–51%....

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  • ...The study [20] compares the performance of the EFR Sevice 1 (wide deadband) and Service 2 (narrow deadband), and it was stated that the narrow service is technically more challenging, likely requiring four times the storage capacity of the wide service....

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Journal ArticleDOI
Abstract: The fast growing expansion of renewable energy increases the complexities in balancing generation and demand in the power system. The energy-shifting and fast-ramping capability of energy storage has led to increasing interests in batteries to facilitate the integration of renewable resources. In this paper, we present a two-step framework to evaluate the potential value of energy storage in power systems with renewable generation. First, we formulate a stochastic unit commitment approach with wind power forecast uncertainty and energy storage. Second, the solution from the stochastic unit commitment is used to derive a flexible schedule for energy storage in economic dispatch where the look-ahead horizon is limited. Analysis is conducted on the IEEE 24-bus system to demonstrate the benefits of battery storage in systems with renewable resources and the effectiveness of the proposed battery operation strategy.

140 citations


"A Battery Energy Management Strateg..." refers background in this paper

  • ...W ITH increasing environmental concerns about climate change and burning fossil fuels, and the requirement for a more sustainable grid, renewable energy sources (RES) play an essential role in energy continuity for today’s electricity supply grid [1], [2]....

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Journal ArticleDOI
TL;DR: This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates and response effectiveness of the BESS with different levels of disturbances is analyzed.
Abstract: With the increased environmental concern, the photovoltaic (PV) generation capacity is growing in today’s power systems. As the PV penetration rate increases, the intermittency and uncertainty of PV systems will cause frequency regulation issues. When rapid fluctuations take place, the system requires fast responding regulation to recover the frequency within a short period of time. Traditional power plants with slow dynamics are less capable of tracking the fast-changing regulation signal. In this context, a battery energy storage system (BESS) is considered as an effective regulation source to respond immediately to frequency deviations. This paper addresses the sizing issue of an aggregated BESS by a series of system level performance tests with different BESS penetration rates. The evaluation criteria are the control performance standards 1 and 2. Response effectiveness of the BESS with different levels of disturbances is also analyzed, with comparison to that of the traditional power plants. The proposed BESS aggregation controller is also validated using software simulations and a hardware testbed.

101 citations


Additional excerpts

  • ...BESSs mostly participate in balancing demand and supply through frequency response services, voltage support, and peak power lopping [9], [10] BESSs using various battery chemistries are installed around the world for grid support [4]....

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Frequently Asked Questions (1)
Q1. What are the contributions in this paper?

This paper describes a control algorithm for a battery energy storage system ( BESS ) to deliver a charge/discharge power output in response to changes in the grid frequency constrained by the National Grid Electricity Transmission ( NGET ) – the primary electricity transmission network operator in the UK – whilst managing the state-of-charge ( SOC ) of the BESS to optimise the availability of the system. Furthermore, this paper investigates using the BESS in order to maximise Triad avoidance benefit revenues whilst layering other services.