Battery Energy Storage Systems for Ancillary Services Provision

154 Pages·2017·3.4 MB·English

POLITECNICO DI MILANO
Scuola di Ingegneria Industriale e dell’Informazione
Dipartimento di Energia
Corso di Laurea Magistrale in Ingegneria Energetica

Battery Energy Storage Systems
for Ancillary Services Provision

Relatore:
Prof. Marco MERLO
Co-relatore:
Ing. Davide FALABRETTI

Tesi di Laurea Magistrale di:
Giuliano RANCILIO Matr. 854880

Anno Accademico 2016 – 2017
2
The scope of this study is to model a Battery
Extended abstract
Energy Storage System (BESS) able to provide
multiple services in the framework of an
Ancillary Services Market (ASM) and simulating
Introduction
its operation to provide both technical and
economic analysis of the performance. ASMs
Global demand of energy will expand by 30%
have always been “conservative” environments
among today and 2040 [1]. The growth rate is
and grid services have been only provided by
decreasing compared to recent past, but far from
large-scale conventional units. In fact, units
stopping. In the same time horizon world
suitable for providing power Reserves on ASM
population is expected to rise to 9 billion people.
must be totally reliable and conventional units
Growth in demand is due to both rise of
guarantee years of experience and 24/7 control.
population and increase in consumptions per
Nevertheless, in a system where RES increased,
capita: energy demand is usually related to wealth
the size of necessary Reserves increased, too. The
and social wellness, therefore its increase per
lack in new conventional plants to enter ASM
capita should not be distressful. The concern
obliged TSOs to refine the rules of ASM in many
should be in having clean power to feed that
ways with the purpose of attracting new actors in
energy need. In New Policies Scenarios foreseen
it.
by International Energy Agency (IEA), new loads
will be powered in large part by Renewable
Since the interest of the study is both in modeling
Energy Sources (RES) and predominantly by
the battery system and introducing it in a market,
wind and PV. Since these are non-programmable
a deep analysis on both the modeling of the
intermittent sources, this scenario arises new
complex electrochemistry of the battery cell and
issues for the security of power systems. Power
the structures and evolution of ASMs is one of the
systems needs to preserve a stable balance among
peculiarities of this work.
production and consumption of energy. In AC
systems, the most common type of power Battery modeling
networks, main marker of grid balance is stability
Modeling a system means approximating its
of frequency. Frequency must have average value
operation using a mathematical representation
exactly corresponding to nominal value (50 Hz in
considering all relevant aspects of that system.
Europe, 60 Hz in US) and stay always as close as
Several models with different degrees of
possible to this value. Larger share of RES in
accuracy are in place for batteries: accuracy is
power networks leads to more unbalances due to
usually directly proportional to computational
their non-programmability and to possibility of
effort. This study will focus on electric and
forecast errors. Furthermore, rise of inverter-
analytical models, whose accuracy and
based systems as wind and PV generators causes
computational effort stay halfway in the
a loss in inertia, affecting the capability of
panorama.
network to react to unbalances by keeping
frequency variation small. Inertia of a network is
Electric models
proportional to the overall mass of spinning
turbines grid-connected directly by transformers. Electric models simplify batteries considering
Consequently, the energetic scenario evolution is that useful parameters for describing their
going to be more and more bounded by an operation are the ones that can be seen at the
effective management of the so called Ancillary terminals of electric circuit: a potential difference
services. Ancillary services provide support in and a current. Consequently, they depict battery
guaranteeing reliable operation of power as an equivalent electric circuit (Equivalent-
network. Circuit Models, ECM) with a voltage source (or
a capacitor) and an impedance in series. By
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performing experimental campaign, a value for Analytical models
every circuital element is found. The number of
The huge amount of simulations performed
the circuital elements defines the level of
during the study led to interest towards faster-
approximation of the model: the more elements
computing models. Analytical models have been
are present, the more every element can be
a tool largely widespread in battery modeling for
representative either of a phenomenon or of an
years. They are based on equation no more
element of the electrochemical cell. Outputs of
dealing with physics of cell but just
interest are: terminal voltage (V), current (i) and
approximating its behavior mathematically. One
Open-Circuit Voltage (OCV). This last output is
or more equations are developed to model
used to compute State of Charge (SOC), the
efficiency, voltage, OCV or SOC of the battery
operating parameter that indicates the amount of
from experiments. Most widespread among this
energy still present in a cell with respect to total
type of models are empirical models. They
energy. In particular, a passive impedance-based
approximate steady-state operation of the battery:
model developed in Politecnico di Milano has
a value of each parameter of interest (OCV, V, i,
been adopted for this application. Passive means
efficiency) has been given for each condition
that it presents a capacitor as non-ideal voltage
experimented. Model is built on these
source for measuring OCV. Impedance-based
measurements. Empirical models act runtime by
means that the experimental campaign to
computing the actual energy flow through the
characterize its circuit used diagnostic techniques
battery over a given time step to update SOC.
in frequency-domain. The model is implemented
Their accuracy becomes poorer when BESS
in a MATLAB® Simulink® tool called
perform long periods close to 0 and 100% SOC
BESS4PCR [2]. This model was built after an
(SOC saturation limits), at high power requested
experimental campaign on the cell Swing® 5300
or when operating conditions are far from
by Boston-Power. This is a Lithium Nickel
experimented ones (low or high temperatures).
Cobalt Oxide (LNCO) battery. The equivalent
With respect to electric model described before,
circuit of this model is presented in Figure 0-1. It
the one selected for this study is not able to deal
uses OCV for the approximation of SOC.
with voltage SOA. Therefore, some divergences
Moreover, it implements a simplified Battery
in the behavior can be detected at SOC close to
Management System (BMS) limiting the terminal
saturation. Despite lower accuracy, empirical
voltage within a Safe Operating Area (SOA).
models are the most widespread tool for battery
When SOC gets close to saturation levels, power
sizing, thanks to their velocity.
delivered by cell can be reduced to stay within the
voltage window.
State of Health

Scope of battery models is estimating not only
State of Charge but also State of Health (SOH).

This latter is useful to compute battery lifetime.
SOH definition for this study (many are valid) is
the ratio among battery capacity at time t and
initial battery capacity. Lifetime definition in this
study is the moment in which battery reaches
80% of SOH. Batteries undergo cycle ageing,
when charging and discharging, and calendar
ageing, when idle. For cycle ageing, this study
used a diagram fitting Swing® 5300. 12 years
have been selected instead as maximum lifetime
Figure 0-1 Equivalent circuit of BESS4PCR
due to calendar ageing, following data from study
electric model
4
[3]. Therefore, in the study, lifetime will be is increasing. Evolution is in the direction of
selected as the minimum among cycle-ageing involving New Actors, to increase the size of the
lifetime and 12 years. Reserves. The introduction of New Services can
be then of interest, to exploit qualities of these
Ancillary Services Market
Actors. BESS present higher precision in
following steep power ramps and sudden
Ancillary Services Market (ASM) is the
setpoints, therefore enhanced services can be
electricity market where power Reserves to face
designed. On the other side, BESS limits must be
unbalances of grid are gathered. Marker of
taken in account. It is the case of Enhanced
balance of grid is the frequency: average value
Frequency Response (EFR) in UK: it is
equal to nominal value and as small as possible
exploiting fast-response of BESS while allowing
deviation are requested. Three main Reserves,
SOC restoration. SOC restoration is the
managed by three main Ancillary Services, are
operation of taking back SOC to a target value
always present in markets. Primary Reserve is
(usually 50%) when SOC is getting close to
associated to Primary Control Regulation (PCR)
saturation limits. Asymmetry of services is
and pairs the unbalance to lead frequency back to
another valuable feature for ancillary services.
a steady value. Every unit involved in PCR
This term defines the possibility of offering
follows a control law requesting a power output
different regulating bands for positive and
proportional to frequency variation with respect
negative Reserves of same service. This would
to nominal value. This control law is called droop
allow batteries to better manage regulating band
control curve, since droop is the value of the ratio
offered with respect to SOC.
among frequency variation and required power
output: it is the absolute value of slope of line in
Multiple services provision
Figure 0-2. Secondary Reserve is linked to
Secondary Control Regulation (SCR) and creates Multiple services provision is indicated in studies
temporarily an unbalance in opposite direction as the main road to provide positive economic
with respect to the one solved by PCR, to take return for investment on BESS operating on ASM
back frequency to nominal value. In Italy, TSO is [4]. This allows in fact to increase the power
sending a signal (Segnale di Livello) each minute cycled by batteries, increase cash flows per
containing the fraction of regulating band to be month and decrease time idle. With convenient
used by each unit selected for provision. Tertiary combination of services, also SOC restoration
Reserve is providing Tertiary Control Regulation can be provided by grid services with no energy
(TCR) to restore the margins of Secondary flows on purpose.
Reserve that gets ready for next eventual
Methodology
unbalance. ASM is evolving since its importance
The methodology proposed is simulating a period
of 30 days of Ancillary Services provision. The
simulations have been performed in Simulink
environment. The outputs of simulation were
then fed to a spreadsheet and elaborated. The
work of the Author included the construction of
the controller of the Simulink model for
implementation of grid services; the choice of the
services to provide within the simulation; the
sizing of the battery; the evaluation of
performance and of economic return of the
battery in the context of two ASMs. Italian ASM
Figure 0-2 Droop control curve
5
was used as reference case, German one was used Validation of that model entered the aims of the
for economic return comparison. The scheme of study. This empirical model comes from a
the process includes the reception of inputs campaign of experiments on same Swing® 5300
necessary for simulation (frequency data for cell and operates by assigning a variable
PCR, regulation signal log for SCR), the efficiency as a function of power requested in per
simulation performed by the BESS model, the unit. The roundtrip efficiency formula is a
collection and elaboration of outputs. The process polynomial of degree 3 whose coefficients a, b
is presented as flow chart in Figure 0-3. and c come from experiments. Instantaneous
efficiency is the square root of roundtrip
efficiency:
𝜂 (∆𝑃̇ )= √1−𝑎 ∆𝑃̇ +𝑏 ∆𝑃̇ 2 −𝑐 ∆𝑃̇ 3
𝑐𝑒𝑙𝑙 𝑒𝑚𝑝 𝑟𝑒𝑞 𝑟𝑒𝑞 𝑟𝑒𝑞
Eq. 1
Real power in per unit is therefore obtained as:
∆𝑃̇  (∆𝑃̇ ) 𝑖𝑓 ∆𝑃̇ <0
∆𝑃̇ = { 𝑐𝑒𝑙𝑙 𝑒𝑚𝑝
𝑟𝑒𝑎𝑙 ∆𝑃̇ /  (∆̇𝑃) 𝑖𝑓 ∆𝑃̇ ≥0
𝑐𝑒𝑙𝑙 𝑒𝑚𝑝
Eq. 2
And SOC update is consequently:
∫𝑡+1∆𝑃̇ 𝑃 𝑑𝑡 ∫𝑡+1∆𝑃̇ 𝑑𝑡
∆𝑆𝑂𝐶 = 𝑡 𝑟𝑒𝑎𝑙 𝑛 = 𝑡 𝑟𝑒𝑎𝑙
𝐸 𝐸𝑃𝑅
𝑛
Figure 0-3 Flow chart of the process utilized in Eq. 3
the study
Where Energy to Power Ratio (EPR) is the ratio
among nominal energy and nominal power (P ).
n
Inputs The two models deal with SOC estimation, but
neglect SOH estimation. SOH estimation is
Frequency data were measured directly in
computed, in terms of capacity-fade only, after
Politecnico di Milano without interruptions in
simulation, by using an empirical diagram
2017, in the period February 15 – March 16, in
tailored on same cell and linking capacity-fade
framework of the IoT-Storage Lab. Frequencies
were stored in a 1 Hz sampling-rate array.
0,016%
Regulation signal for SCR in Italian framework
(Segnale di livello) was taken by Italian TSO ]elcy 0,014%
c/% 0,012%
website. Data for markets come from the Italian [
ya 0,010%
energy markets manager website. ce
d
y 0,008%
tic
Simulation ap 0,006%
a
c
The electric model receives as input the power yrettaB 00,,000024%%
setpoint requested and updates SOC level as a
0,000%
function of its OCV. It gives as output updated 0 0,2 0,4 0,6 0,8 1
Mean C-rate
SOC level. During the development of the study,
the large number of simulations to perform foster Figure 0-4 Empirical diagram for capacity-
the interest in use of a faster-computing model. fade estimation
6
per cycle with average c-rate in the period (Figure - Market Time-period block allows just a
0-4). single update of regulating band value at the
beginning of each market session.
Both cell models are completed with Inverter
- In Allowing block the market is simulated.
model, introducing its efficiency, and Controller.
The output of this block is a Boolean value: 1
The Controller is the section of the model where
if unit is selected by market, 0 if rejected.
implementation of services is active.
Allowing switch works as door for regulating
Implementation of services and therefore
band: if result of market is positive (1), then
Controller construction has been developed
regulating band is transmitted downstream
during this study. Ancillary Services present
the switch. Otherwise, 0 is transmitted.
similar characteristics and needs. Their modelling
- Input Management block is the block
can be done by defining a common process, to be
receiving the inputs for the provision of the
fitted then on the specific characteristic of each
service. These inputs define share of
service. A comprehensive procedure was
regulating band asked to unit by network
described and then implemented as a tool for
each instant. Output of this block is exactly
multiple services provision in Simulink: it is the
that share, and it is multiplied by regulating
Controller of the model. It receives as input all
band to obtain power setpoint requested to
data for the provision of the service. It returns the
BESS.
power output requested from grid to BESS every
instant. It is presented in the scheme in Figure Outputs and analysis
0-5. It is based on the functional blocks here
After simulation, outputs are collected to files and
described:
analyzed. Analysis of performance aims to define
- In Band Management block is implemented the security guaranteed by BESS in grid services
the strategy for building the regulating bands provision. The index used is Loss of Regulation
for the service. The output is the value of (LOR), returning the share of energy not provided
regulating band (battery side). (E ) on the total energy requested (E ) for each
np req
- In Efficiency Computer block the efficiency service. Definition of LOR for service i is:
of the system is estimated. The output value
𝐸
of this block is multiplied to the output of 𝐿𝑂𝑅 = 𝑛𝑝,𝑖
𝑖 𝐸
Band Management block, to obtain 𝑟𝑒𝑞,𝑖
regulating band offered on market (grid-
Eq. 4
side).
Economic analysis has been implemented using
Net Present Value (NPV) at 20 years, Payback
Figure 0-5 Controller layout on Simulink, based on comprehensive procedure for multiple services
implementation developed
7
Time (PBT) and Profitability Index (PI) as main is then multiplied by power requested, obtained
indexes. NPV is the sum of Net Cash Flows from droop relation by frequency.
(NCF) for each year added of Residual Values
Secondary Control Regulation
(RV) of goods at year 20 and subtracted of cost
of investment of batteries (C ):
inv SCR is implemented in the model following
Italian rules. It is a symmetric service traded on
20
𝑁𝐶𝐹(𝑦)
𝑁𝑃𝑉[€]=−𝐶 +∑ +𝑅𝑉(20) market every 4 hours for next 4 hours. It is
𝑖𝑛𝑣 (1+𝑟)𝑦
regulated by regulation signal sent by TSO,
𝑦=0
updated every minute, indicating the share of
Eq. 5
regulating band to be provided (Segnale di
PBT is the first year in which NPV is positive, livello). Band Management block is less trivial
while PI is the ratio among NPV and Cinv. here. Band offered is updated every 4 hours and
is aimed to avoid hitting of SOC saturation limits
Services implementation
in next four hours. Since there are two SOC
The core of the work is the implementation of saturation limits (0 and 100%), there will be two
Ancillary Services in the Controller of the model. bands (respectively P* , P* ) as output
reg,SCR+ reg,SCR-
They are implemented based on Italian Grid of Band Management block. They are obtained
Code. by dividing the energy contribution needed for
hitting each of the thresholds in 4 hours:
Primary Control Regulation
(𝑆𝑂𝐶 +𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 −𝑆𝑂𝐶 ) 𝐸𝑃𝑅
First implemented grid service was PCR. PCR 𝑃𝑟∗𝑒𝑔,𝑆𝐶𝑅+ = 𝑖𝑛𝑖𝑡 + 𝑚𝑖𝑛 ⦁
100 ⦁ 𝑎𝑣𝑔 4
follows fixed droop control law, that provides a 𝑠𝑒𝑔𝑛𝑎𝑙𝑒 𝑑𝑖 𝑙𝑖𝑣𝑒𝑙𝑙𝑜+
unique correspondence among frequency Eq. 7
variation from nominal frequency and power
(𝑆𝑂𝐶 −𝑆𝑂𝐶 −𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 ) 𝐸𝑃𝑅
requested to cell. There is no market since PCR is 𝑃𝑟∗𝑒𝑔,𝑆𝐶𝑅−= 1𝑚0𝑎0𝑥 ⦁ 𝑎𝑣𝑔 𝑖𝑛𝑖𝑡 − ⦁ 4
𝑠𝑒𝑔𝑛𝑎𝑙𝑒 𝑑𝑖 𝑙𝑖𝑣𝑒𝑙𝑙𝑜−
mandatory in Italy. Recalling the layout of
Eq. 8
Controller (see Figure 0-5), the input of Input
management block is the measured frequency,
Where SOC is SOC at the beginning of the 4
init
samples are detailed each second. To get the
hours and the * is present since these values must
output, droop control curve in Figure 0-2 is used.
be updated with system efficiency. Variation
+
Dead band is ±20 mHz. Maximum power output
and variation are the contribution to SOC
-
is regulating power (P ,in per unit). Droops
reg,PCR variation from PCR provision in the 4 hours.
value used during simulation are function of
Avg and avg are the
segnalde di livello+ segnalde di livello-
P :
reg,PCR maximum 4 hours average values in direction of
[0.03−0.075] either positive or negative contributions that the
𝑑𝑟𝑜𝑜𝑝[%]= regulating signal for SCR can have. Both couple
𝑃
𝑟𝑒𝑔,𝑃𝐶𝑅
of values come from statistical analysis
Eq. 6
performed during this work on historical data of
These droop values have been selected to comply frequency and Segnale di livello. They are
with the Italian Grid Code requirements (2-5% of utilized to better exploit the SOC margins while
droop on 0.015 regulating band in per unit), just setting regulating bands. These outputs are
rescaled on actual regulating band. Regulating multiplied by Efficiency Computer block output
band is a constant value subject neither to market and just the minimum one is offered on market:
nor to market period: the output from Allowing

switch is always that constant value of user’s 𝑃∗
choice. This band coming from Allowing switch 𝑃𝑟𝑒𝑔,𝑆𝐶𝑅=min(𝑃𝑟∗𝑒𝑔,𝑆𝐶𝑅+ ⦁ 𝜂𝑠𝑦𝑠+, 𝑟𝜂𝑒𝑔,𝑆𝐶𝑅−)
𝑠𝑦𝑠−
Eq. 9
8
This is the limit of symmetric service that are not
exploiting the whole ΔSOC residual in the two
directions taking instead the smaller one for
defining regulation band (red bars in Figure 0-6).
The SCR market is simulated in the Allowing
block of the Controller. It uses real prices from
Italian ASM market session in the simulated
period and sets price offered each hour, for each
Reserve (positive and negative). BESS is selected
for the provision of service in positive and
negative Reserve if its bid is lower than the
maximum accepted on real market for both
Reserves. To bid consistently, a statistical
analysis was performed on prices in February-
Figure 0-6 SOC residual margins exploiting
March 2016. Allowing block acts each hour by
comparison (red for symmetric, green for
returning a 0 or a 1. Input Management block
asymmetric SCR)
output is the regulating signal expressed as share

of regulating band. It is multiplied by the band of
nominal power and 2 h of Energy to Power Ratio
the correspondent Reserve selected in that hour to
(EPR) was selected. 1 MW is an arbitrary choice,
obtain power setpoint requested from grid to cell.
useful to provide a significative term of reference
Relaxing (in order to simulate viable market and to be scaled up. EPR is the ratio among
conditions for BESS) the hypothesis of symmetry nominal energy and nominal power in a battery.
on SCR provision, asymmetric SCR is EPR must reflect relation among energy and
implemented. The procedure is the same as for power requirements of services provided. The
symmetric SCR, but the bands offered on market aim is exploiting both energy band and power
are: band to maximize performance. A statistical
analysis has been performed for a battery offering
𝑃 = 𝑃∗ ⦁ 𝜂 ,
𝑟𝑒𝑔,𝑆𝐶𝑅+ 𝑟𝑒𝑔,𝑆𝐶𝑅+ 𝑠𝑦𝑠+ PCR and SCR and the range found for EPR
𝑃∗
𝑟𝑒𝑔,𝑆𝐶𝑅− consistent is 1.03-1.93 h. The value 2 h used is
𝑃 =
𝑟𝑒𝑔,𝑆𝐶𝑅− 𝜂𝑠𝑦𝑠− taken as upper limit of this interval. It is a
conservative value to endorse security of grid on
Eq. 10
return of investment: a larger cost of investment
The exploiting of SOC margins in this case is (investment is proportional to nominal energy) is
reported in Figure 0-6 with green lines. justified by lower LOR expected (larger energy
band implies SOC more stable). Droop value is
Two Allowing blocks manage markets for each
0.075%/P , upper limit of interval shown in
regPCR
Reserve, and each one returns 1 or 0. Regulating
Eq. 6, selected following same reasoning.
signal is multiplied by positive band if requiring
positive power, and vice versa. First simulation (CASE A) involved just PCR
provision, with P = 1. It highlighted high
regPCR
Simulations and results LOR (13.2%) and NPV highly negative, due to
low remuneration of PCR in Italy. Regulating
The aim of performing simulations with the
band for PCR can be increased, but this would
described model is finding a satisfactory setup of
lead to higher LOR.
PCR and SCR provision guaranteeing
simultaneously security to grid and economic The second simulation (CASE B) implemented
return of investment. A battery of 1 MW of simultaneously provision of PCR and of
9
symmetric SCR, following requirements by it is close to saturation. SOC restoration dedicated
Italian Grid Code. Since SCR is more mechanisms imply:
remunerative on Italian ASM than PCR, SCR
- More costs for energy purchased.
band was set larger. The simulation on PCR and
- Regulatory issues since the BESS operates as
symmetric SCR took to results similar to previous
consumer and producer of energy.
ones in terms of LOR. LOR is high since SCR
- Increased average c-rate due to new energy
band is large when far from SOC saturation. It
flows, and consequent increase of cycle
brings SOC far from 50% fast, towards either
ageing and decreased lifetime.
100% or 0. In following market session
regulating band offered to SCR decrease to zero Therefore, SOC restoration dedicated
for both reserves, because of SOC. Since avgsegnale mechanisms are not considered during this study.
di livello+ and avgsegnale di livello- are both around 0.4 Instead, asymmetric service provision is
and maximum variations of SOC for PCR introduced.
provision statistically expected in 4 hours in the
Third simulation (CASE C) is provision of
two directions are
asymmetric SCR only. This CASE took the first
−23.6%
result of interest. SOC restoration provided by
𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 = ⦁ 𝑃 ,
+ 𝐸𝑃𝑅 𝑟𝑒𝑔𝑃𝐶𝑅 asymmetric service worked and the simulation
26.2%
took to good results: LOR at 4.5% and positive
𝑣𝑎𝑟𝑖𝑎𝑡𝑖𝑜𝑛 = ⦁ 𝑃
− 𝐸𝑃𝑅 𝑟𝑒𝑔𝑃𝐶𝑅
NPV. The battery is well exploiting energy band:
Eq. 11 SOC log highlighted repeated cycles featuring
large Depth of Discharge (DOD), that is the
SCR band offered, computed as in Eq. 9, gets to
difference among maximum and minimum SOC
zero if either SOC > 93% or SOC < 6%.
init init within same cycle. With respect to CASE B,
Therefore, SOC gets fast to limits and then
energy exchanged is doubled and LOR is less
behaves as CASE A. LOR value is above 11%
than halved. The comparison among SOC
and still there is no PBT within 20 years. The
evolution is found in Figure 0-7, where the
multiple service provision with symmetric
difference among the exploiting of SOC margins
services only resulted an unviable strategy for
can be checked.
BESS since no one of the services is providing
SOC restoration. A SOC restoration dedicated Fourth simulation (CASE D) has been dedicated
strategy would be necessary. This would mean to multi-service provision (providing both PCR
energy purchased and sold on market with the and SCR). PCR is in fact mandatory in Italy for
purpose of getting SOC back towards 50%, when large-scale programmable plants. Furthermore,
the main trends in ASM over Europe are leading
towards an increasing importance of Primary
Regulation, also presenting improved versions of
it as EFR in UK. PCR and Asymmetric SCR
provision were performed simultaneously. This
setup gave satisfactory results with very low LOR
(<2%) and positive NPV. Asymmetric provision
of SCR worked as passive SOC restoration
mechanism: SOC is restored when it gets close to
saturation limits without implying costly energy
flows but using the provision of service itself.
This setup was taken as reference for many
Figure 0-7 SOC evolution in CASE B (red) vs
simulations aiming to verify possibility for
CASE C (blue)
10