Assessment of the Impact of a Battery Energy
Storage System on the Scheduling and Operation of
the Insular Power System of Crete
Stylianos I. Vagropoulos, Christos K. Simoglou, Anastasios G. Bakirtzis, Emmanouil J. Thalassinakis and Antiopi Gigantidou
1
Abstract- This paper examines the impact that the installation
of battery energy storage systems (BESSs) has on the daily
scheduling and operation of the insular power system of Crete.
An optimization model for the integration of BESS in the dayahead unit commitment problem of isolated power systems is
developed and annual simulations are carried out for BESSs of
different dimensioning regarding power, capacity and efficiency.
Simulation results for load leveling and reserve provision from
BESS are presented and thoroughly discussed.
Psdch,min
ηsch
Minimum discharging power of BESS unit s in
discharging mode, in MW
Maximum discharging power of BESS unit s in
discharging mode, in MW
Charging efficiency of BESS unit s, in p.u
ηsdch
Discharging efficiency of BESS unit s, in p.u
Index Terms—Battery energy storage system, insular power
system, load leveling, redox flow battery, reserve provision
Esfin
I. NOMENCLATURE
A. Sets and Indices
i ∈I
Index (set) of conventional thermal units
s∈S
Index (set) of BESS units
t ∈T
Index (set) of hourly time intervals
m ∈ M Index (set) of reserve type, where m=1±: primary
up(+)/down(-), m=2±: secondary up(+)/down(-),
m=3S: tertiary spinning, and m=3NS: tertiary nonspinning reserve
Rsm
C(mi/ s )t
Minimum energy storage level of BESS unit s at
the end of the scheduling horizon, in MWh
Maximum contribution of BESS unit s in reserve
type m, in MW
Additional cost for the procurement of reserve
Dt
type m from thermal unit i (/BESS unit s) in time
interval t, in €/MWh
System load in time interval t, in MW
B. Parameters
Esini
Initial energy storage level of BESS unit s, in
MWh
max
Es
Maximum energy storage capacity of BESS unit s,
in MWh
Esmin
Minimum energy storage capacity of BESS unit s,
in MWh
Psch,min Minimum charging power of BESS unit s in
charging mode, in MW
ch ,max
Ps
Maximum charging power of BESS unit s in
charging mode, in MW
Psdch,max
RESt
TC
C. Variables
Est
Energy storage level of BESS unit s in time
interval t, in MWh
pit
Power output of unit i in time interval t, in MW
pstdch
pstch
r(mi/ s )t
ustch
1
This work was supported by the EU Seventh Framework Programme
FP7/2007-2013 under grant agreement no. 309048 (Project SiNGULAR) and
the State Scholarships Foundation of Greece in the context of the “IKY
Fellowships of Excellence for Postgraduate studies in Greece – Siemens
Program”.
Stylianos I. Vagropoulos, Christos K. Simoglou and Anastasios G.
Bakirtzis are with the Department of Electrical & Computer Engineering,
Aristotle University of Thessaloniki, 54124, Thessaloniki, GREECE (e-mail,
[email protected],
[email protected],
[email protected]).
Emmanouil J. Thalassinakis and Antiopi Gigantidou are with the Hellenic
Electricity Distribution Network Operator S.A. (HEDNO), Iraklion, Crete,
Greece (e-mail: [email protected], [email protected]).
‹,(((
Aggregated total renewable energy production
(wind and photovoltaic) in time interval t, in MW
Total production cost of thermal units, including
variable, no-load, start-up and shut-down cost, in
€
ustdch
ust3 NS
Discharging power of BESS unit s in discharging
mode in time interval t, in MW
Charging power of BESS unit s in charging mode
in time interval t, in MW
Contribution of thermal unit i (BESS unit s) in
reserve type m during time interval t, in MW
Binary variable that is equal to 1 if storage unit s
is in charging mode during time interval t
Binary variable that is equal to 1 if storage unit s
is in discharging mode during time interval t
Binary variable that is equal to 1 if storage unit s
provides tertiary non-spinning reserve during
time interval t.
II. INTRODUCTION
Electrical Energy Storage Systems (EESSs) stand as key
elements for promoting the integration of renewable energy
in power systems and securing power system operation by
effectively balancing supply with demand. Focusing on
insular power systems, where frequency regulation and
reliable operation is a challenging task, the large Renewable
Energy Sources (RES) share in the generation mix causes
frequent thermal generator cycling, necessary to account for
the variability and unpredictability of RES production.
Therefore, Battery Energy Storage Systems (BESSs), which
are very flexible assets, can be considered as a candidate
solution for both minimizing system operational cost through
load levelling and increasing reliable system operation
through their ability to alter quickly between charging and
discharging state and rate.
In a broader sense, EESSs will enable the Smart Grid
concept to become a reality. Although most of currently
operational EESSs are hydro storage facilities, an intensive
ongoing research on these systems is carried out focusing on
new technologies and their market potential [1], [2].
However, most of the emerging storage technologies (i.e.
advanced batteries, superconducting Magnetic Energy
Storage (SMES)) are still in demonstration phase. Anticipated
improvements in the cost and commercial availability of
energy storage technologies could accelerate the storage
integration in the future power systems.
The BESS technology under investigation is the flow
battery that is considered as a suitable storage system for
large-scale applications [3]-[4]. The philosophy of the flow
battery is reversible from the conventional batteries (i.e. leadacid, li-ion, etc.). Two different aqueous electrolytic solutions
are contained in separate tanks. During the normal operation
of the battery, these aqueous solutions are pumped through
the electrochemical cell where the reactions occur. During the
charging process, the first electrolyte is oxidized at the anode,
while the second electrolyte is reduced at the cathode. The
discharging cycle consists of the reverse process. A deeper
insight on the redox flow battery technology can be found in
[3]-[6].
One of the major advantages of flow batteries is that their
energy capacity is easily scalable since it depends on the
volume of the stored electrolyte and, therefore, energy and
power capacity are independent each other. In addition, they
are able to become fully discharged without any damage and
they present a very low self-discharge rate. Generally, they
are systems with a long life and low maintenance and able to
store energy over long periods of time [3].
There are few studies that focus on storage integration in
isolated or insular power systems. In [7] an economic
assessment of EESS that provide primary reserve and peakshaving in two isolated Spanish power systems is carried out
and the savings are determined. The authors assess the
economic benefit that can be achieved by employing EESSs
in the primary frequency regulation reserve and peak-shaving
generation in two small isolated Spanish power systems
(Grand Canaria and La Gomera) by developing an
optimization model of the weekly economic operation. They
conclude that the EESS could be an economic alternative to
scheduling spare generation capacity in the on-line units and
the installation in La Gomera island could yield to an internal
rate of return of 8%. In [8] the unit commitment (UC)
problem for the isolated system of Taipower is solved based
on the concept of load-frequency sensitivity index and the
results are compared with the Lagrangian relaxation-based
approach and the current Taipower operation practice.
According to that practice the regulating reserve is supplied
by synchronized pumped-storage units, the 10min spinning
reserve is provided by non-synchronized pumped-storage
units and the 30-min operating reserve is provided by nonsynchronized combined cycle units. Finally, in [9] a study on
the insular power system of Cyprus dictates that, although the
day-ahead scheduling does not encounter any significant
problem, without a proper revision of the current reserve
policies the system will not be able to facilitate wind power
integration without high levels of real-time wind curtailment
or load shedding. Changes in system’s flexibility along with
the impact that the fuel switch from gasoil to natural gas may
have on the system flexibility and costs are presented.
The contribution of this paper is the evaluation of the
impact that the integration of BESS has on the operation of
the real-world insular power system of Crete, Greece, which
is characterized by high production cost and high RES share
in the generation mix, based on the real operational data of
the year 2013. In this study, BESS contributes both in load
leveling and reserve provision.
The rest of the paper is organized as follows. In Section III
a description of the operating framework and the
mathematical modeling is presented. In Section IV the
examined case study along with the obtained results are
presented and discussed. Finally, conclusions are summarized
in Section VI.
III. OPERATING FRAMEWORK AND MATHEMATICAL
MODELING
In the non-interconnected Greek islands, there is no
wholesale electricity market established. However, the
operation of these systems should be managed according to
the recently enacted regulatory framework for the Greek noninterconnected (insular) power systems [10]. In this work,
storage units are considered to be centrally managed by the
System Operator (SO). In addition, storage units are
considered to operate with zero variable cost, thus being
assigned a dispatch priority by the SO with respect to the
conventional thermal units. The SO is aware of the
technical/operational constraints of the BESS and schedules
the appropriate charging and discharging programs. BESS
units are also considered to provide secondary and tertiary
reserves. The proposed mathematical model is formulated as
a Mixed Integer Linear Programming (MILP) problem. In
this context, the objective function to be optimized is as
follows:
§
·
°­
Min ®TC + ¦ ¦ ¨ ¦ Citm ⋅ ritm ¸ + ¦
°¯
t∈T i∈I © m∈M
¹ t∈T
· °½
Cstm ⋅ rstm ¸ ¾ (1)
s∈S © m∈M
¹ °¿
§
¦¨ ¦
Subject to the following constraints:
pit ∈ Wit
∀i ∈ I , t ∈ T
¦ pit + ¦
i∈I
s∈S
(2)
pstdch = ( Dt − RESt ) +
¦
pstch
s∈S
∀t ∈ T
(3)
Psch,min ⋅ ustch ≤ pstch ≤ Pstch,max ⋅ ustch
∀t ∈ T , s ∈ S
(4)
Psdch,min
∀t ∈ T , s ∈ S
(5)
⋅ ustdch
≤
pstdch
≤
Psdch,max
⋅ ustdch
Est = Es (t −1) + pstchη sch − pstdch / η sdch ∀t ∈ T , s ∈ S
(6)
Esmin ≤ Est ≤ Esmax
∀t ∈ T , s ∈ S
(7)
ustch + ustdch ≤ 1
∀t ∈ T , s ∈ S
(8)
Es (t =T fin ) ≥ Esfin
∀s ∈ S
(9)
pstdch − pstch − rst1− − rst2 − ≥ − Psch ,max
∀ s ∈ S,t ∈T
pstdch − pstch + rst1+ + rst2+ + rst3S ≤ Psdch,max
∀s ∈ S , t ∈ T
(10)
(11)
0 ≤ rst1+ ≤ Rs1+
∀s ∈ S, t ∈ T
(12)
0 ≤ rst1− ≤ R1s −
∀s ∈ S, t ∈ T
(13)
0 ≤ rst2+ ≤ Rs2+
∀ s ∈ S, t ∈ T
(14)
0 ≤ rst2− ≤ Rs2−
∀s ∈ S , t ∈ T
(15)
0 ≤ rst3S ≤ Rs3S
∀s ∈ S, t ∈ T
(16)
rst3 NS ≤ Rs3 NS ⋅ ust3 NS
∀s ∈ S, t ∈ T
(17)
ust3 NS ≤ 1 − ustch − ustdch
∀s ∈ S, t ∈ T
(18)
The objective of the model (1) is to minimize the total
production cost of all conventional units, including the
reserves provision cost of both thermal units and BESS.
Constraints (2) represent a block of equations referring to
the unit power output limits, ramp rates as well as the
minimum-up/down times and unit commitment processes
taking into account the different unit operating phases,
namely synchronization, soak, dispatchable and desynchronization. It should be noted that three different (i.e.
hot, warm, cold) start-up types are modelled depending on the
unit’s prior reservation time. The aforementioned constraints
have been analytically explained in [11]. Power balance
equation is described in (3), where thermal and BESS units
supply the net load (i.e. system load minus aggregated RES
generation) of the island in each time interval. Constraints (4)
and (5) define the associated energy charging and discharging
limits of the BESS unit.
For each scheduling day, the SO is aware of the available
energy of the BESS at the beginning of the scheduling
horizon, Evini . The stored energy level of the BESS is
calculated in (6), which increases when BESS charges and
decreases when BESS discharges, with the appropriate
efficiencies, ηvch and ηvdch , respectively. In (7), the energy
storage level is bounded between the upper and lower level.
Constraints (8) ensure that the BESS cannot charge and
discharge simultaneously and constraints (9) define the
minimum stored energy at the end of the scheduling horizon.
Constraints (10)-(18) enforce the power output and reserve
constraints for the storage unit. These constraints are
simplified as compared to those of the conventional units
because storage assets are considered to react very fast (no
ramp rates are included). During charging or discharging
operation the storage unit can contribute to all types of
reserves ( rst1+ , rst1− , rst2 + , rst2 − , rst3S ) exploiting the ability for
fast change between the two operational states
(charging/discharging). Reserve provision for both
operational states is constrained by (10) and (11) and is
graphically illustrated in Fig. 1. The combination of (17) and
(18) ensures that the BESS unit may provide tertiary nonspinning reserve ( rst3NS ) only when it is set to idle (no
charging or discharging).
Fig. 1 An example of reserve provision capability constrained by charging
and discharging BESS rate.
IV. CASE STUDY AND RESULTS
The above model was tested in the insular power system of
Crete, Greece, for the year 2013. The generation mix of the
island includes 26 conventional thermal units with exclusive
use of diesel and heavy fuel oil and a high share of RES,
including photovoltaic (PV) and wind parks. The generation
mix is presented in Table I. In Fig. 2 the annual electricity
consumption time series is presented. The peak system load
for 2013 was 579 MW. The hourly reserve requirements were
deterministically set equal to 20MW for secondary and 30
MW for tertiary reserve, for each time interval. The BESS
can supply at most 10MW of secondary reserve in all cases,
whereas tertiary reserve is constrained by the maximum
discharging power of BESS, Psdch,max , of each case (Table
II). The remaining 10MW of secondary reserve are supplied
by the CCGT unit, which remains always online to alleviate
voltage stability issues in the west side of the island where the
CCGT unit is located. Batteries are prioritized to participate
in reserve provision whenever possible to avoid extra wear
and tear of the conventional thermal units. To achieve that,
the cost for reserve provision is set to zero for batteries, while
each MW from thermal units is considered to be charged with
0.05 €/MW.
Consecutive daily simulations of hourly granularity are
carried out for the whole year 2013. Eighteen (18) scenarios
were created combining different values for the following
parameters (see Table II):
• BESS power level, equal to 16 MW, 32 MW or 48 MW.
• BESS capacity level, expressed by the number of hours
that the BESS is capable of discharging at rated
(maximum) discharging power. Capacity levels of 3
hours, 6 hours or 9 hours were considered.
• BESS efficiency, equal to 65% or 85%.
It is noted that the aforementioned 18 scenarios were
compared with the current status, where no BESS is
considered and the Crete power system comprises only
thermal and RES units (Base case). For each scenario,
Esini for the first day is set equal to 50% of Esmax , Esmin is set
to 10% of Esmax , Psch,min = Psdch,min = 0 , and Esfin is set equal
to Esmin .
TABLE I
GENERATION MIX OF CRETE
Capacity (MW)
196
142
110
299
184
94
1025
Fuel
Heavy fuel oil
Heavy fuel oil
Diesel
Diesel
-
Cost Reduction (€)
Technology
Steam
ICE
CCGT
OCGT
Wind
PV
Total
600
Consumption (MW)
The total annual cost of the thermal production (in the case
of no BESS integration) is equal to 244,858,830 €. However,
taking into account that RES production (PV and wind) is
compensated on the basis of fixed Feed-in-Tariffs (FiT), the
final total production cost to be undertaken by the consumers
increases significantly. The total RES FiT compensation is
equal to 106,395,725 €, which is due either batteries are
included in the case study or not. In Fig. 3 the cost reduction
due to the BESS integration in the power system operation
with respect to the base case (no BESS considered) is
presented both in absolute values (€) and as a percentage.
Useful results are derived regarding the economic impact of
BESS integration. For the same power level, increasing the
capacity of the battery leaves the cost reduction with respect
to the base case unaffected. The only exception is identified
for the larger battery scenario (i.e. 46 MW and 85%
efficiency) where the BESS capacity increase from 3 to 6
hours brings on a noticeable cost reduction. That means that
the storage value for load levelling is exhausted even with
few hours of storage capability, and, therefore, large capacity
systems, which increase the installation cost noticeably, are
not valuable. Moreover, Fig. 3 shows that for a given
capacity, the power level increase generally leads to a slightly
greater cost reduction. Finally, the intuitive result that greater
efficiency leads to higher cost reduction is verified.
500
400
9,000,000
3.50%
8,000,000
3.00%
7,000,000
2.50%
6,000,000
5,000,000
2.00%
4,000,000
1.50%
3,000,000
1.00%
2,000,000
0.50%
1,000,000
0.00%
0
300
x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9
200
16 MW
32 MW
48 MW
16 MW
Ș=0.65
100
32 MW
46 MW
Ș=0.85
Scenarios
1
382
763
1144
1525
1906
2287
2668
3049
3430
3811
4192
4573
4954
5335
5716
6097
6478
6859
7240
7621
8002
8383
0
Hour of the Year (h)
Fig. 2 Annual electricity consumption time series of the insular power
system of Crete for the year 2013.
TABLE II
BATTERIES SCENARIOS PARAMETERS
Scenarios
Both for
65% and 85%
overall
efficiency
Psch (/ dch ),max
Esmax
Rs2 + (/ −)
Rs3S (/3 NS )
(MW)
16
16
16
32
32
32
48
48
48
(MWh) (h)
48 (x3)
96 (x6)
144 (x9)
96 (x3)
192 (x6)
288 (x9)
144 (x3)
288 (x6)
432 (x9)
(MW)
10
10
10
10
10
10
10
10
10
(MW)
16
16
16
32
32
32
48
48
48
Fig. 3 Annual generation cost reduction per scenario with respect to the Base
case.
Fig. 4 and Fig. 5 illustrate the day-ahead scheduling of the
system for the peak-load day of the year. In Fig. 4, results
with no BESS are presented, while in Fig. 5 results including
BESS operation (48 MW, 3 hours, 85%) are presented. The
BESS discharges at peak-load hours (where highest marginal
production cost is observed), while charging takes place
during the morning valley hours (where lowest marginal
production cost takes place). The BESS charges using electric
energy provided by low-cost unit technologies (i.e. ICE and
steam units) and discharges to substitute the expensive
production of OCGT units. In fact, due to the non-perfect
charging/discharging cycle efficiency, the aggregated daily
generation of thermal units increases (as compared to the case
without BESS), but the shift from high-cost to low-cost
production leads to more economic operation of the system.
Another positive impact of the BESS integration is that the
CCGT units are allowed to decrease their power output level
by 10 MW, which are now mainly supplied by the BESS and
thus, more flexibility in the CCGT unit dispatching is
Power (MW)
500
400
300
200
25
20
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours (h)
R3S steam
R3S ICE
R3S CCGT
R3S OCGT
R3NS OCGT
Fig. 7 Peak day, Base Case (no BESS, thermal system only), tertiary reserve
provision from the thermal system
30
Tertiary Reserve (MW)
600
30
Tertiary Reserve (MW)
granted. This is also noticeable in Fig. 6, where the hourly
contribution of CCGT and BESS units in secondary reserve is
presented for the same day and scenario. In the base case
CCGT unit contributes 20 MW to secondary reserve, while in
the scenario shown in Fig. 6, 10 MW for secondary up and
down reserve are provided by BESS. However, when BESS
operates in discharging mode, its ability to contribute to
secondary up reserve diminishes (see Fig. 1) and the CCGT
unit increases the secondary up reserve provision. Similar
results for tertiary reserve contribution are also presented in
Fig. 7 and Fig. 8. When the BESS operation is considered,
tertiary reserve provision from thermal units decreases
drastically and is substituted by the BESS. However, when
BESS discharges, the related margins for tertiary reserve
provision are also diminished (see Fig. 1).
25
20
15
10
5
100
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
0
Hours (h)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
R3(N)S BESS
Hours (h)
Steam
ICE
CCGT
OCGT
Wind
PV
Consumption
Fig. 4 Peak-day production mix, Base Case (no BESS, thermal system only)
600
Power (MW)
500
400
300
200
100
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours (h)
Steam
Wind
ICE
PV
CCGT
BESS Discharge
OCGT
Consumption
Fig. 5 Peak-day production mix, BESS: 48 MW, 3 hours, 85%.
Secondary Reserve (MW)
40
35
30
25
20
15
10
5
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Hours (h)
R2dn CCGT
R2dn BESS
R2up BESS
R2up CCGT
Fig. 6 Peak day, secondary reserve provision from BESS and CCGT, BESS:
48 MW, 3 hours, 85%.
R3S Steam
R3S OCGT
Fig. 8 Peak day, BESS: 48 MW, 3 hours, 85%, tertiary reserve provision
from BESS and the thermal system,
As already mentioned, the charging of the BESS increases
the production of low-cost units, while its discharging
substitutes the production of more expensive thermal units.
The annual generation increase for the low-cost generation
technologies (i.e. steam and ICE) is presented in Fig. 9, while
the reduction of the annual energy production for the CCGT
and OCGT units for all scenarios is presented in Fig. 10 and
Fig. 11, respectively. By comparing the three figures, it is
obvious that the larger the BESS system is, the larger the
energy production shift from expensive to low-cost
generation technology is. The energy production increase of
steam units is relatively constant for all scenarios, whereas
for large BESS systems the larger share of charging energy
originates from ICE units (which on an annual basis
substitutes the energy production of CCGT and OCGT units,
see Fig. 10 and Fig. 11). The larger reduction of CCGT
production is not only due to the BESS discharge but also due
to the fact that the BESS integration substitutes in part the
provision of secondary down reserve by the CCGT unit, and
therefore, allows the operation of the CCGT unit in lower
power output levels. Finally, the OCGT production decreases
noticeably, especially in larger BESS scenarios, that
reduction reaches 27%.
Finally, in Fig. 12 annual secondary reserve contribution of
BESS (in MW-h) is presented. Whichever the scenario is,
BESS provides always the maximum capacity for down
reserve (10 MW). However, provision of up reserve is
scenario dependent on the BESS efficiency. For low
efficiencies, BESS participates less at load levelling and
remains in idle situation for more hours. In idle situation
reserve margins are greater and reserve provision is
prioritized against thermal units, as explained above. The
same behaviour is also valid for tertiary reserve provision.
9.00%
Annual Energy Increase (MWh)
80000
70000
Steam
8.00%
ICE
7.00%
60000
6.00%
50000
5.00%
40000
3.00%
20000
2.00%
10000
1.00%
0
0.00%
x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9
16 MW
32 MW
48 MW
Ș=0.65
16 MW
32 MW
46 MW
Ș=0.85
Scenarios
Annual Energy Increase (MWh)
Fig. 9 Annual energy increase of steam and ICE units production
-70000
-12.00%
-12.50%
-75000
-13.00%
-80000
-13.50%
-14.00%
-85000
In this paper the impact that the integration of a BESS has
on the day-ahead scheduling and operation of the real-world
insular power system of Crete were examined. Test results
showed that the BESS operation reduces the generation cost
by shifting energy production from expensive to low-cost
generating units. In addition, the BESS contributes to system
reserves and this operation offers more flexibility in thermal
units dispatching. Finally, the BESS efficiency is an
important parameter that determines whether the BESS
participates more in load levelling or reserve contribution.
The economic viability of such BESS investments will be the
scope of our future work.
-14.50%
-90000
REFERENCES
-15.00%
-15.50%
-95000
-16.00%
-100000
-16.50%
x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9
16 MW
32 MW
48 MW
16 MW
Ș=0.65
32 MW
46 MW
Ș=0.85
Scenarios
Fig. 10 Annual energy decrease of CCGT units production
Annual Energy Increase (MWh)
V. CONCLUSIONS
4.00%
30000
0
0.00%
-1000
-5.00%
-2000
-3000
-10.00%
-4000
-5000
-15.00%
-6000
-20.00%
-7000
-8000
-25.00%
-9000
-10000
-30.00%
x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9
16 MW
32 MW
48 MW
Ș=0.65
16 MW
32 MW
46 MW
Ș=0.85
Scenarios
Fig. 11 Annual energy decrease of OCGT units production
90000
Reserve Provision (MW-h)
The proposed models were implemented in GAMS version
24.0.2 along with CPLEX solver version 12.5 [12]. All tests
were performed on a 3.2 GHz Intel Core i7 processor with 64
GB of RAM, running 64-bit Windows. The typical execution
time for a single run of the algorithm (one day) varied
between 3 and 10 seconds while the optimality gap was set to
0.01%.
R2up
88000
R2dn
86000
84000
82000
80000
78000
76000
74000
x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9 x3 x6 x9
16 MW
32 MW
Ș=0.65
48 MW
16 MW
Scenarios
32 MW
46 MW
Ș=0.85
Fig. 12 Annual secondary up and down reserve provision from BESS
[1]
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