762

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

Modeling and Real-Time Scheduling of DC
Platform Supply Vessel for Fuel
Efficient Operation
Kuntal Satpathi, Student Member, IEEE, VSK Murthy Balijepalli, Member, IEEE,
and Abhisek Ukil, Senior Member, IEEE

Abstract— DC marine architecture integrated with variable
speed diesel generators (DGs) has garnered the attention of
the researchers primarily because of its ability to deliver
fuel efficient operation. This paper aims in modeling and to
autonomously perform real-time load scheduling of dc platform
supply vessel (PSV) with an objective to minimize specific fuel oil
consumption (SFOC) for better fuel efficiency. Focus has been on
the modeling of various components and control routines, which
are envisaged to be an integral part of dc PSVs. Integration
with photovoltaic-based energy storage system (ESS) has been
considered as an option to cater for the short time load transients.
In this context, this paper proposes a real-time transient simulation scheme, which comprises of optimized generation scheduling
of generators and ESS using dc optimal power flow algorithm.
This framework considers real dynamics of dc PSV during
various marine operations with possible contingency scenarios,
such as outage of generation systems, abrupt load changes,
and unavailability of ESS. The proposed modeling and control
routines with real-time transient simulation scheme have been
validated utilizing the real-time marine simulation platform. The
results indicate that the coordinated treatment of renewablebased ESS with DGs operating with optimized speed yields better
fuel savings. This has been observed in improved SFOC operating
trajectory for critical marine missions. Furthermore, SFOC
minimization at multiple suboptimal points with its treatment
in the real-time marine system is also highlighted.
Index Terms— DC power flow, dc shipboard power system,
platform supply vessel (PSV), real-time simulation.

Pmech
Pload
uF
kpm
τpm
td
J
ωG
kloss
TG

N OMENCLATURE
Mechanical power at diesel engine (DE) shaft.
Load demand.
Fuel injection input signal.
Fuel injection system gain.
Fuel injection time constant.
Dead-time of DE.
DE rotor inertia moment.
DE and generator angular speed.
DE rotational loss.
Torque produced by the generator.

Manuscript received March 5, 2017; revised June 2, 2017; accepted
August 19, 2017. Date of publication August 24, 2017; date of current version
September 15, 2017. This work was supported by the National Research
Foundation Singapore through the Corporate Laboratory@University Scheme.
(Corresponding author: Kuntal Satpathi.)
The authors are with the School of Electrical and Electronics
Engineering, Nanyang Technological University, Singapore 639798 (e-mail:
kuntal001@e.ntu.edu.sg; vsk@ntu.edu.sg; aukil@ntu.edu.sg).
Digital Object Identifier 10.1109/TTE.2017.2744180

p
i M s, iT s
λ M s , λT s
TT
τT
dP
ωP
PT
CT
Cτ
SOC

Number of poles of generator.
M-T axis current of wound rotor synchronous
generator (WRSG) at stator flux reference
frame (SFRF).
M-T axis flux of WRSG at SFRF.
Thrust developed by the thrusters.
Torque developed by the thrusters.
Propeller diameter.
Speed of the propeller.
Power developed by the thrusters.
Thrust coefficient.
Torque coefficient.
State of charge.
I. I NTRODUCTION

P

LATFORM supply vessel (PSV) plays a major role in the
marine industry because of its ability to perform cruising
and dynamic positioning (DP) operation [1]. Development of
marine integrated power systems has enabled the marine loads
esp. the propulsion systems to be powered from the common
generation units [2], [3]. This resulted in the reduction of
the number of installed prime movers and offered designers
flexibility to place the generation system at any suitable
location. The future operation of the marine vessels depends on
the International Maritime Organization’s air pollution requirements [4], [5]. To comply with the requirements, it is pertinent
to develop fuel efficient marine vessels, hence limiting the
exhaust gas emissions. In the conventional ac marine vessels,
shaft electric machines have been proposed to minimize the
fuel consumption [6]. DC marine systems are also proposed as
they can operate with increased fuel efficiency as compared
with the corresponding ac marine vessels [7], [8]. The lack
of critical phase and frequency synchronizing parameters
allows the interfaced DEs to run at variable speeds depending
on total load demand, thus optimizing the specific fuel oil
consumption (SFOC) and increasing the fuel efficiency [9].
Other advantages constitute the ease of integration with the
energy storage systems (ESSs) [10]–[12], space and weight
reductions [13], and lower losses as compared with the corresponding ac systems.
As analogous to the land-based multiterminal dc systems,
dc marine vessels are envisaged to have a two-layer control
system [14], [15]. The primary control system comprises of

2332-7782 © 2017 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.
See http://www.ieee.org/publications_standards/publications/rights/index.html for more information.

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

the dc bus voltage control [16] and active/reactive power
control [17] with suitable protection and fault management
algorithms [18]. The secondary control system comprises of
the load forecasting, power flow algorithms, which will be
executed for a given state and requirements of the marine
missions [8], [19]. The secondary control system also helps
in optimized power flow by curbing out unintended power
consumption, hence minimizing the risk of blackout condition.
It also aids in proper coordination of the load and generation
system, which reduces the oversizing requirement of the
generation systems.
The generation scheduling for commercial land-based power
systems is usually done beforehand [8], which has been
extensively studied for land-based microgrids [19] and electric
vehicles [20]. Unlike land-based systems, the load profile
of marine vessels is continuously changing owing to variable propulsion load demands. Thus, the generation output
of marine vessels might be optimized for various operating scenarios. The optimized operation of the generation
systems in the marine vessel is relatively new topic with
a limited number of research attempts made in this area.
Zahedi et al. [21] proposed the operation of DE at minimum
SFOC with the help of integrated ESS where the authors
have considered only the steady-state analysis to arrive a
single point of minimized SFOC. Furthermore, much work
on SFOC has been reported in the domain of ac marine
vessels, such as shaft generator system integrated with the
diesel generators (DGs) for possible minimization in fuel
consumption [6], [22], agent-based real-time load management
to control the loads [23], and stochastic approaches [15], [24].
One of the key research gaps in the past research studies has
been the lack of modeling and implementation of realistic
marine loads and marine missions. Although, the researchers
have proposed various marine operating scenarios and SFOC
minimization by considering mixed integer linear programming approach for unit commitment [25]; such approaches
are NP-hard (nondeterministic polynomial) and may not be
preferred choice for real-time scheduling system, which is
the prime focus of this paper. Hence, this paper presents
modeling and control of the various components of marine
vessels incorporated in a real-time transient simulation scheme
with dc optimal power flow (OPF) algorithms utilizing reduced
bus-bar model for the scheduling of generation system. This
paper considers SFOC minimization at multiple suboptimal
points [26] as well to incorporate the real-time dynamics
of the dc shipboard systems. Furthermore, the performance
of dc OPF-based secondary control algorithm and SFOC
optimization with an option of ESS has been demonstrated
for various marine missions.
This paper considers PSV as an example of marine vessel,
which performs cruising operation for the logistics and DP
operation to support the offshore supply vessels. Apart from
the DGs, this paper also considers photovoltaic (PV)-based
ESS [10], [27] as a part of generation systems of dc PSV.
The focus is given on the integrated and automated generation
operation by incorporating dc OPF-based algorithms in the
proposed optimization framework to minimize the SFOC of
DGs with the help of scheduling of ESS. The traditional

763

analysis by offline simulations of the bigger and complex
emerging dc marine systems is expected to consume longer
time to generate results. This approach becomes quite cumbersome to analyze for the multiple test studies scenarios [28].
Thus, in this paper, the transient simulation framework for the
entire dc shipboard system has been developed by utilizing
the advantages of the real-time simulation platform. The
study of the dynamics of the full shipboard power system
along with the interaction of PV/ESS with the generation
systems during contingencies has been carried out with the
proposed approach. The real-time optimal power scheduling
for generators and ESSs has been carried out for various
marine missions under different contingencies, such as sudden
load changes and network faults, to prove the efficacy of the
proposed approaches. The trajectory of the SFOC of the DEs
during the various contingencies has been studied, which could
effectively be realized by the real-time simulation platform.
Moreover, this method could be useful to validate the hardware
design, converter control, and protection algorithms of the
future dc marine vessels. Hence, the smarter generation system
of the future dc PSV is proposed, which should be able to
operate autonomously, encompassing both the primary and the
secondary control system.
The rest of this paper is organized as follows. Section II
covers the modeling of dc PSV, which comprises of the
generation system and various marine loads. Section III comprises of the operating structure of the dc PSV considered
in this paper. The transient simulation scheme with dc OPF
algorithm handling ac/dc systems to minimize SFOC has
been implemented in Section IV. The optimized generation
scheduling applied with various marine operational scenarios
is discussed in Section V. The real-time load scheduling is
achieved using OPAL-RT OP5600-based simulator and this
paper is concluded in Section VI.
II. M ODELING OF DC P LATFORM S UPPLY V ESSEL
The bus-breaker model of the representative dc PSV is
shown in Fig. 1. The model is in coherent with the commercially available PSVs [29], [30] with two-bus architecture
to increase the reliability and survivability of the vessel.
The representative vessel comprises of four DEs coupled
with the synchronous generators (P DG ) and one PV-based
ESS (P ESS ). The total generation capacity can be illustrated
as per (1). The generators are interfaced with two level voltage
source converter (2L-VSC) acting as an active front end
rectifier and PV-based ESS is integrated via dc/dc converter
for dc bus voltage and active/reactive power control
PGen = {P DG n , P ESS ||n = 1 : 4}.

(1)

The nominal bus voltage of dc PSV is set at 1500 Vdc which
according to the IEEE Std 1709-2010 falls under medium
voltage dc shipboard architecture [31]. As compared with
the land-based power systems, marine vessels have loads
pertaining to different marine missions. Variable frequency
propulsion system (L propulsion) comprises of main propulsion (MP) systems to cater for the cruising loads (LC L );
tunnel thrusters (TTs) and retractable thrusters (RTs) to cater

764

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

Fig. 1.

Bus breaker model of representative dc PSV.

Fig. 2.

Control loop diagram for the speed regulation of the DE.

for the DP load (LDP ) [1]. The total connected propulsion
load is illustrated as L propulsion = {LC L , LDP }. The fixed
frequency hotel loads are required for air conditioning/lighting
systems, cranes/winches, small hotel motors, and so on. The
hotel loads are classified into high-power (LHLhigh ) and lowpower (LHLlow ) loads. The miscellaneous loads (Lmisc ) for
radar and pulsed load operation are also considered as it may
form the integral part of modern PSVs. The hotel loads can
be illustrated as L hotel = {LHLhigh , LHLlow , Lmisc } and the total
load L total can be expressed as follows:
L total = {LC L , LDP , LHLhigh , LHLlow , Lmisc }.

(2)

Fig. 3.
(a) BSFC chart for representative 2000-kW DE [34] and
(b) corresponding curve-fit of DE speed for optimized SFOC for various
loading conditions.

shown in (4)
kpm
Pmech (s)
=
e−td s
u F (s)
τpm s + 1
dωG
Pmech − Pload
+ klossωG =
J
.
dt
ωG
h pm (s) =

(3)
(4)

The power rating of the components and converter systems
is shown in Fig. 1. Derating factor of 125% has been used for
the selection of the converters, cables, and bus bars to cater for
the short time overload demands. The modeling and control
of various components are discussed in Section II-A, B, C.

Thus, the DE controls the synchronous generator by adjusting
the mechanical power output. By linearization of the power
flow equation (4) around the operating point ωG = ωGo ,
the transfer function reduces to

A. Generation System Modeling
1) Diesel Engine: DEs are used as prime movers for the
synchronous generators in dc PSV [3]. The prime-mover
model comprises of fuel injection system, dead time (td )
representing elapsed time until a cylinder produces torque
and inertia of the rotating parts. The dead-time approximation
of the prime-mover is realized by exponential delay and the
transfer function is given in (3) [32], [33]. The differential
equation governing the active power flow through the DE is


where
P = Pmech − Pload . The complete block diagram
for DE speed control is shown in Fig. 2. In the dc PSV, DE
should be able to operate at optimized SFOC by running at
optimized speed. Fig. 3(a) represents the brake specific fuel
consumption (BSFC) of the representative DE operating at
different powers for different operating speeds [34]. The cost
function of the optimized DE speed (C(ω)) in terms of DG
power output (P DG ) is calculated to understand the operating

1/ωGo
ωG (s)
h r (s) = 
=
J s + 2kloss
P

(5)

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

Fig. 4.

765

Complete control loop of the DG interfaced with 2L-VSC.

points of the DE. This is achieved with the help of curvefitting techniques and the cost function, C(ω) is derived as
shown in the following:

TABLE I
L OAD P RIORITIES FOR D IFFERENT M ARINE M ISSIONS

C(ω) = A0 + A1 P DG + A2 (P DG )2 + A3 (P DG )3
+ A4 (P DG )4 + A5 (P DG )5 (6)
where A0 = 720.93, A1 = 1.2591, A2 = −0.00292,
A3 = 3.8104 × 10−6 , A4 = −2.1716 × 10−9 , and
A5 = 4.5206 × 10−13 . The actual DE speed for various power
requirements and the curve-fit version is shown in Fig. 3(b).
The same cost function has been considered in the optimization framework presented in Section IV.
2) Diesel Generator–Rectifier Control: DG includes full
M − T model of WRSG at stator flux reference frame (SFRF)
with both the field and damper windings [35]. The output
torque of the shipboard DG is maintained by independently
controlling torque producing current, i T s and machine flux,
λ Ms , which is achieved by maintaining i Ms = 0 [16], [36].
With reference to [16], in this paper, the M-axis flux control is
done by implementing flux estimation-based method in which
the flux of the machine is estimated and suitably controlled for
varying operating conditions [16], [36]. The dc bus voltage and
line current control is realized by implementing PI regulator
having bandwidth of 100 and 1000 Hz, respectively, such a
range is suitable for simulating in the real-time operations
as well. The switching frequency of the VSC is chosen to
be 5 kHz and the VSC is modeled in both the average and
detailed switching models for comparative studies in the realtime simulation environment. The plant transfer function for
dc bus voltage control is calculated by equalizing the input
and output power flow while neglecting the line losses which
is shown in (7). The plant transfer function for current control
is calculated from the leakage inductance (L s ) and stator
resistance (Rs ) of the interfaced WRSG. The plant transfer
functions for voltage control loop (G V ) and current control
loop (G I ) are shown in (8). The combined control loop
representation of the DG system interfaced with 2L-VSC is
shown in Fig. 4
i T s λ Ms
dv dc
P DG = TG · ωG ⇒ 3 p
· ωr = C
· v dc +v dc · I L
4
dt
(7)
3 pλ Ms ωr
1
, GI =
.
(8)
GV =
4 sC Vdc + I L
s L s + Rs

B. Marine Loads
DC PSV comprises of propulsion systems, thruster systems,
hotel loads, and miscellaneous loads such as pulsed load
to undertake different marine missions. Prioritization of the
operation of these loads is dependent on the marine missions
undertaken by the vessel [37] and a sample priority table
considered in this paper is shown in Table I. Modeling of these
loads is required to understand the power consumption pattern,
which would eventually be necessary for the scheduling of
generation sources. The modeling and control of different
loads are discussed in Section II-B2 and II-B3.
1) Propulsion Systems: In the PSVs, the propulsion systems (L propulsion) are the main consumers of energy, which
undertake cruising and DP operation. The power requirement
during the cruising operation (LC L ) is dependent on the
operating speed of the PSV (ω P ) as shown in the following:
LC L ∝ ω3P .

(9)

The power requirement during DP operation (LDP ) carried
out by PSVs is primarily dependent on the environmental
forces and desired coordinate locations [38]. The sea current,
wind velocity, surge, and sway of the vessels have to be
balanced by the thrust produced by the thruster systems in
order to maintain the desired coordinates. The generalized
schematic of the DP system is shown in Fig. 5 [39]. The thrust
production of the propeller is dependent on the speed (ω P ),
propeller geometry (α), and hydrodynamic quantities (β). The
thrust (T ) and the torque (τ ) developed by the thrusters for
speed (ω P ) and diameter of the propeller (d P ) are given as
follows [38], [39]:
TT = gT (n, α, β) = C T ρd 4P ω2P

(10a)

τT = gτ (n, α, β) = Cτ ρd 5P ω2P

(10b)

766

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

TABLE II
T OTAL PV I NSTALLED C APACITY U SING S UNPOWER 305 S OLAR PANEL

In this paper, the pulsed load duration is selected to be 20 ms.
The pulsed power load can be illustrated by the following:

1 t2
Lpulse =
Po . dt.
(12)
T t1

Fig. 5.

Schematic of DP of PSV [38], [39].

where C T and Cτ are determined by open-water tests for
submerged vessels and are dependent on propeller advance
velocity. In this paper, Cτ = 0.56, density of water,
ρ = 997 kg/m3 , and d P = 3.5 m are considered. The power
consumed by the thrusters of DP system is shown in the
following:
LDP = 2πnτ = Cτ ρd 5P ω3P .

(11)

For the worst weather conditions, the power demanded by
the DP system to maintain desired coordinates would be
significantly higher than that of the power demand during the
calm weather condition. Direct torque control is chosen over
field-oriented control for propulsion motors and DP thrusters
for its fast and superior performance and limited dependence
on the machine parameters [40].
2) Hotel Loads: The percentage of the hotel loads (L hotel )
to the total load depends on the type of marine vessels.
In PSVs, installed hotel load is much lower than the propulsion
loads, but it is an important part of dc PSV. As shown
in Fig. 1, two types of house loads are considered in this
paper. The high power hotel loads (LHLhigh ) supplying power
to cranes/winches and air-conditioning/humidifiers have a
cumulative rating of 3200 kVA, 440 Vac, and operates at
60-Hz frequency. The low-power hotel loads (LHLlow ) have
a cumulative rating of 400 kVA, 230 Vac, and operates at
60-Hz frequency and are responsible for small hotel motors
and lighting loads. Two level voltage source inverter with a
constant output of 690 Vac, 60 Hz is utilized for the house
loads.
3) Miscellaneous Loads: Miscellaneous loads (Lmisc ) comprise of the pulsed and radar loads. Pulsed loads have presence
in the modern naval vessels, which are used in the electromagnetic guns, free electron lasers, radars and high energy lasers,
and draw huge amount of current lasting for short period
of time. This intermittent nature of the loads has effect on
the stability of the generation sources [41]. The pulsed load
duration may vary from few microseconds to milliseconds.

C. Energy Storage System
Future autonomous vessels are expected to be operated with
different forms of renewables. Here, in this paper, it is assumed
that battery-based ESS (BESS) units are supplied from the
PV-based renewable source, which are interfaced with the dc
PSV to cater for the short time load requirement and power
fluctuations of the shipboard system. Such ESS also acts as
reserve generation supply during the contingencies or sudden
change of load. Unlike land-based power systems where
energy is stored in the ESS when its cost is low and release the
same when the grid electricity cost is high [19], here, there is
no such variations in the cost. Electricity stored in the BESS
is based on the marginal cost of the power supplied from the
DGs. Cost function considered for the BESS is a constant price
with the maximum power transfer based on the state of SOC
as depicted in the following:
Cess = f p /kwh.

(13)

The operational capacity of the ESS is restricted to 10% of
the total installed generation capacity as illustrated in the
following:
P ESS = 0.1 ∗ PGen .

(14)

The PV-BESS is set to operate in optimal mode by limiting
the battery usage between 20% and 100% of total storage
capacity; furthermore, the scheduling constraints are imposed
accordingly. The selection, sizing, and schematic of the
PV-BESS are described in the following.
1) PV Energy Sources in Marine Vessels: As per the green
ship initiative, combination of PV-DG-based generation systems is expected to be part of future marine vessels [10]. The
rating and capacity of the PV panel are dependent on the
available space in the target marine vessel [27]. For marine
vessels undertaking longer voyages, e.g., liquefied natural
gas carriers having easier accessibility to roof top terrace;
proliferation of PV-based generation system with 20% capacity
of the total generation system has been suggested [10], [27],
[42]. This paper considers the PSV with size and power
rating comparable with the commercial available PSVs, such
as Rolls-Royce UT776 [29] and Viking Queen [30]. These two

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

TABLE III
PARAMETERS OF THE I NTERFACED BESS

Fig. 6. (a) Schematic of PV-based BESS (PV-BESS) and (b) representation
of the battery charging schemes.

PSVs are equal in size and rating according to the description
provided in the whitepaper [29], [30]. The deck area of the
PSV is of commercial interest and cannot be utilized for PV
installation [27]. Thus, the PSV has limited available space
for PV array installation. It has been assumed that the 600 m2
of the total available area of 1800 m2 [29], [30] is available
for installing PV arrays. Considering the parameters of the
commercially available Sunpower 305 Solar Panel [43] with
the available installation area of 600 m2 , the rating of total
installed PV capacity is described in Table II. The economic
analysis of the PV panel is dependent on several market parameters and specifications, but it is expected to be consistent
with the method provided in [42]. The comprehensive cost
analysis of PV panels in dc PSV is currently beyond the scope
of this paper.
2) Sizing of PV-BESS System: According to the IEEE
Std 1562-2007 [44] and the IEEE Std 1013-2007 [45], the sizing of the BESS connected to the PV is determined while
assuming that there is no power available from the PV system.
The BESS is installed in the dc Shipboard Power System with
the intention of fulfilling the intermittent loads and support the
generation system during various contingencies [8]. The PV
power is primarily used to charge the BESS and maintain its
SOC at maximum possible level. The selection of the BESS
has been done to minimize the weight and size constraints of
the dc marine vessels [8]. Furthermore, the 10% power level of
BESS is chosen to make it consistent with the trends of BESS
selection in commercially available marine vessels [46]. The
parameters of the BESS is chosen according to the commercially available SAFT Seanergy modules, which are suitable
for hybrid propulsion applications [47]. The parameters of the
battery module and the battery pack considering 10% of power
demand are shown in Table III.
3) Schematic of PV Interfaced BESS: The schematic of
the PV and BESS interfaced with the dc bus is shown as
per Fig. 6(a) [48]. To extract the maximum power, the PV
panel is interfaced with the unidirectional dc/dc-1 converter

767

which works on perturb and observe (P&O)-based maximum power point tracking (MPPT) algorithm [49] for the
proposed real-time transient simulation scheme. The modeling
of the PV generator and the MPPT algorithm is consistent
with strategy discussed in [49]. The dc/dc-2 converter is a
bidirectional converter used to interface PV-BESS system to
the dc bus. The modeling and control of dc/dc-2 is consistent with the approach provided in [7]. During the normal
operation, when the SOC of the battery is above threshold
limit (SOCmax ), the dc/dc-2 operates at boost conversion mode
supplying the power to the dc ship as fulfilling scheduled
generation requirements and complying with (15a)–(15h). The
variables in (15a)–(15h) are consistent with annotations shown
in Fig. 6(a)
PPV > 0

(15a)

PBatt > 0
PESS > 0

(15b)
(15c)

i PV_dc > 0

(15d)

i batt > 0
i dc_i = i batt + i PV_dc > 0

(15e)
(15f)

i ESS > 0
PESS = Pbatt + PPV .

(15g)
(15h)

When the SOC of the BESS is below lower threshold (SOCmin ), it could either be charged exclusively by
the PV system or by the combination of PV system and
dc/dc-2 converter. Since the power output of the PV array has
limitation owing to dependence on available irradiation, charging with PV panel would result in slow charging as shown
in Fig. 6(b). Constant current-based fast charging of the BESS
can be carried out by maintaining output current of dc/dc-2 at
desired charging rate suggested by the manufacturers. During
the charging operation supported by both PV panels and
dc/dc-2, (16a)–(16h) are satisfied
PPV > 0
PBatt < 0

(16a)
(16b)

PESS < 0
i PV_dc > 0

(16c)
(16d)

i batt < 0
i dc_i = i batt − i PV_dc < 0

(16e)
(16f)

i ESS < 0
Pbatt = PESS + PPV .

(16g)
(16h)

III. O PERATION OF DC P LATFORM S UPPLY V ESSEL
Installed generation capacity of the PSV is generally lower
than the total loads connected to the system. This is because
a defined set of loads are activated for particular marine
mission as indicated in Table I. For cruising operation, PSV
operates mostly in fixed speed condition and for DP mode,
the operating speed may change depending on the environmental conditions. Hence, the brake power of PSV for DP

768

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

TABLE IV
L INE PARAMETERS OF DC PSV

Fig. 7.

Brake power of PSV for DP operation.

TABLE V
B US D ATA OF DC PSV

Fig. 8.

Reduced bus-branch model of representative dc PSV.

operation is not constant as shown in Fig. 7, where the vessel
mostly operates between 20% and 80% of the total installed
brake power [34]. Thus, incorporating dc OPF algorithm and
operating the vessel at minimum SFOC can be implemented
to increase the fuel efficiency. For the dc OPF reduced busbranch model, segregating the ac and dc subsystems with
ac/dc-dc/ac boundary node is shown in Fig. 8. Generators
Gen-1 and Gen-3 are clubbed together and are connected to
bus B1. Similarly, the Gen-2 and Gen-4 are clubbed together
and connected to bus B2. The total generation capacity (PGen )
of the DGs and the ESS with the bus they are interfaced to is
illustrated in the following:

 Gen13
Gen24
ESS
PGen = P B1
.
(17)
, P B2
, P B14
The loads LC L , LDP , LHLhigh , LHLhigh , and Lmisc interfaced
with respective bus are depicted in the following:

 MP1
MP2
LC L = L B3
(18a)
, L B7

 TT1
TT2
RT
(18b)
LDP = L B12 , L B16 , L B5
 HL1

(18c)
LHLhigh = Ln ||n = B8 to B11
 HL2

LHLlow = Lm || m = B17 to B20
(18d)
 PL

FL
, L B15
.
(18e)
Lmisc = L B13

IV. P ROPOSED R EAL -T IME DC PSV P OWER
M ANAGEMENT S YSTEM
A. Problem Statement
As explained in Section I, operation of dc PSV demands
real-time scheduling mechanisms to tackle different loads and
generation sources in various operating conditions. The realtime transient simulation scheme with dc OPF is more suitable
for such conditions, which determines the power injections
of the DGs and ESS to minimize the SFOC in real time,
subjected to physical and operational constraints (relevant
data in Tables IV and V). Equality constraints include power
balance at each node and inequality constraints include the network operating limits, DG limits, ESS limits, and limits on the
other control variables. These control variables include active
power output of the generators, power electronic controls,
amount of load disconnected, and the status of storage devices.
Hence, subsequent to the modeling of dc PSV, real-time dc
OPF with the objective of minimizing SFOC considering
all control and state variables with real-time optimization
framework is the objective behind this paper.
B. Problem Formulation
The real-time transient simulation system for the generation
scheduling of the dc PSV is governed in such a way to

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

effectively utilize the available resources onboard to minimize
the SFOC of the DGs. In this process, the optimization
considers the set constraints as follows:
 Gen13 Gen24 ESS 
Minimize SFOC F P B1
, P B2 , P B14 = f (x, u) (19)
s.t. w(x, u) = 0

(20)

q(x, u) ≤ 0

(21)

where the cost function referring to the active power of the
energy resources is minimized while respecting the equality constraints w(x, u) and inequality constraints q(x, u).
These constraints can be viewed as linear and nonlinear
constraints

wnl (x, u)
w(x, u) =
(22)
Je (x, u) + oe

qnl (x, u)
q(x, u) =
(23)
Ji (x, u) + oi
where Je and Ji are constants and need to be calculated only
once. State variables of converter and dc network are set up to
this framework. Energy storage and dc side converter power
feed-in are mapped to the corresponding ac buses to satisfy
the Kirchhoff law.
The vector x consists of dependent variables such as fixed
parameters such as reference angles, noncontrolled generator or ESS outputs, noncontrolled loads, and line parameters.
The vector u consists of control variables, including real
power generation, PSV load shedding parameters/priorities,
ESS charging and discharging limits, ramp rates of the DG,
dc line flows, and converter control settings. The equality and
inequality constraints are, namely, power flow equations, limits
on all control variables, generation/load balance, branch flow
limits, and SOC limits. Considering Fig. 8, which represents
the reduced bus-bar model of dc PSV (having DGs, ESS, and
different types of loads), for an anticipated group of loads,
total system generation should be scheduled in such a way
to minimize the SFOC of DG. In such cases, the network
equality constraints are represented by the standard load flow
equations [14]. PSV load balance equation is as follows in the
real-time operation:
B

 Gen

ESS
PBi + PBi
−

i=1

D

 L

ESS
PDi + PDi
− PLosses = 0.

i=1

(24)
Inequality constraints limits are set accordingly, for example,
generator limits are set as
Gen
Gen
Gen
≤ PBi
≤ PBi
PBi
max
min

QGen
Bimin

≤

QGen
Bi

≤

QGen
Bimax .

(25)
(26)

Load shedding or load balancing limits have been set as
shed
≤ LBiDtotal .
0 ≤ LBi

(27)

Energy storage limits set as
ESS
ESS
ESS
≤ PBi
≤ PBi
.
PBi
max
min

(28)

769

Converter voltage limits on the ac side are nonlinear in nature
and can be set as
2
2
Vconv
≤ VR 2conv + VI 2conv ≤ Vconv
.
max
min

(29)

Converter filter side constraints are as follows, where V R and
VI are real and imaginary parts of the voltage:
2
2
Vfilter
≤ V R 2filter + VI 2filter ≤ Vfilter
max
min

(30)

and the limits on the converter current and dc voltages are
linear in nature and are as follows:
dc
dc
Vmin
≤ V dc ≤ Vmax

Iconv
min

≤I

conv

≤

Iconv
max .

(31)
(32)

q(x, u) in (23) is formed by (25)–(32) as stated previously.
It is to be noted that the voltage and branch flow limits are
the only nonlinear limits on the ac side. Options of setting
branch limits, and other operational limits in the dc PSV are
implemented as well, but skipped for better readability of this
paper.
Power generation schedules obtained from the optimization
framework have been fed to the SFOC calculation block to
calculate the speed (C(ω)) based on the equation derived in
Section II, where speed is derived as a function of generator
schedules
C(ω) = f (P DG ).

(33)

SFOC at each optimized speed point corresponding to the
power schedules has been calculated using the SFOC lookup
table available in the SFOC calculation block as shown
in Fig. 9(a).
C. Real-Time Transient Simulation of DC PSV
The architecture of the real-time transient simulation setup
comprising of generator scheduling scheme based on dc OPF
is shown in Fig. 9(a). Depending on the operating mode,
the scheduling block takes the input from the operating personnel. The available generation is also fed to calculate the
reserve generation capacity and setting the upper limits of the
generation system. Load estimation is the critical step where
the rate of the load changes has been assessed and passed as an
input to the proposed algorithm. Tabulation methods for electrical loads during marine missions which are proposed in [50]
are adopted in this paper. Line parameters (Table IV) and busbar parameters (Table V) are fed to ensure that the loading in
each line/bus stays within prescribed limits. The scheduled
generation is calculated and fed to the SFOC optimization
block to calculate the power demand and corresponding speed
set point of each of the in-line DGs. The scheduling block and
the SFOC algorithm are the part of the controller, while the rest
of the system of dc PSV system is divided into master/slave
computational subsystems and loaded into computing cores of
OPAL-RT OP5600-based real-time simulator. The segregation
of cores of the real-time simulation model has been realized
with the help of gyrators and the partitioning contours of the
divided subsystems are shown in Fig. 9(a). The description of
the real-time simulation system is described in the following.

770

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

Fig. 9. (a) Architecture of the real-time load scheduling of dc PSV. (b) Schematic in MATLAB/Simulink environment. (c) Representative OPAL-RT setup
for real-time simulation.

Fig. 10. (a) Representation of gyrators for partioning between subsystem-1
and subsystem-2. (b) Bond graph structure of a gyrator.

1) Overview of Real-Time Simulation System: The real-time
simulation is conducted on the OPAL-RT-based OP5600 realtime simulator which operates on RedHat Linux-based operating system and is interfaced with the host PC by TCP/IP
cable. The setup of the real-time simulation system is shown
in Fig. 9(c). OPAL-RT uses RT-LAB-based real-time platform,
which facilitates the conversion of MATLAB/Simulink models into real-time executable models [55]. It has dedicated
toolboxes, such as RT-Events, RTE-drive, and ARTEMiS,
to support the real-time simulation system [55], [56]. The
execution of the model is achieved by ARTEMiS solver, which
is a high-order time-step integration algorithm and is not prone
to numerical oscillations [55], [56]. The minimum time step
available for real-time simulation in the dc transient real-time
simulation model is 10 μs. To comply with such requirements,
all the interfaced converters are operated with a switching
frequency of 5000 Hz. Furthermore, all the results obtained
with the switching models are compared with the averaged
models to analyze the performance of VSCs under such time
step limitations as well. The partitioning of system using
gyrators helps in avoiding numerical inaccuracies by ensuring
parallel computation of the partitioned subsystems.
2) Gyrator-Based Partitioning of System: Gyrator is an
ideal energy transducer used for bond graph representation of a physical system [51]–[53]. This method has been

Fig. 11.

Gyrator-based system partitioning for “n” number of elements.

used for partitioning of the bigger marine dc power system into smaller subsystems for parallel computation in
real-time transient simulation framework. With reference
to Fig. 10(a) and (b), the bigger system is divided into
Subsystem-1 and Subsystem-2 with the help of gyrator, G r Y
while satisfying the following:
V1 = f (I2 )
V2 = f (I1 ).

(34a)
(34b)

From 34(a) and 34(b), it can be implied that current I2 in Subsystem-2 is dependent on the voltage V1 of
Subsystem-1 or vice versa. This approach can be realized
by implementing dependent current and voltage sources. The
partitioning of subsystems for “n” number of elements utilizing gyrator-based partitioning approach is shown in Fig. 11.
In Figs. 10(a) and 11, a very high value resistance (RT ) is
placed to ensure the numerical consistency of the simulation
and memory block is used to avoid algebraic loop errors.
The measured current and voltage between the computational
subsystems is transferred using OpComm block [55], [56]. With
the gyrator-based approach, the entire dc marine system is
divided into four subsystems (three computational subsystems

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

Fig. 12. Representative MATLAB/Simulink model into partitioned subsystems for real-time simulation in OPAL-RT.

and one console subsystem). The computational subsystems
comprise of one master (SM_Generator) and two slave
subsystems (SM_Bus1Load and SM_Bus2Load). The console subsystem (SC_Console) is the user interface for data
logging. The final partitioned executable file for transient realtime simulation is shown in Fig. 12.
3) Obtaining Results: The output results from the real-time
simulator have been obtained by: 1) monitoring scopes in the
console subsystem (SC_Scope); 2) by viewing the results in
the monitoring oscilloscope; and 3) by saving the data in .mat
file by OpWrite block [55], [56] for offline analysis. All the
results presented in this paper are obtained by processing
.mat files. The results obtained from the oscilloscope are
also presented in Section V for comparison with the offline
analyzed results.
D. Algorithm for Real-Time Transient Simulation
Pseudocode for real-time generation scheduling of dc PSV
for minimized SFOC is shown in Algorithm 1. It presents the
basis for real-time transient simulation scheme in the context
of handling various marine missions for minimized SFOC.
V. R EAL -T IME S IMULATION R ESULTS
With reference to the operational aspects of the proposed dc
PSV power management system, this section presents the simulation results of various cases of operating modes and associated contingencies in the real-time operation. The various
contingencies associated with dc PSV are listed in Table VI,
which has been prepared considering both availability and
unavailability of 10% ESS described in Section II-C. From
Table VI, it can be observed that the generator and ESS
output are marked in red for some specific contingencies. This
indicates the overload capabilities of the generation system
for supplying high power output for short durations [54].
In the proposed optimization framework, such relaxation has
been set for the PSV generation systems to emulate real-time
characteristics.
A. PSV Operating Modes and Associated Contingencies
1) Dynamic Positioning Operation: For the DP operation,
LC L is set to zero, while LHLhigh and LHLlow are set at
1000 and 270 kVA, respectively, and Lmisc is set to the
rated value. As described previously, the value of LDP is
dependent on the weather conditions, propeller design, and

771

Algorithm 1 Pseudocode of Scheduling for DC PSV
1: – Read generation data, network data, load estimation data,
static load data, SOC of ESS, and other PSV parameters.
2: – Define operating limits based on the shipboard real-time
marine missions.
 Eq. (23)-(30).
3: – Build initial Z-bus by handling isolated nodes, if any. 
Table II
4: – Create incidence matrix for the existing shipboard network.
5: - Initialize the proposed Optimization Suite.

Eq. (17)-(21)
6: while ((Error tolerance for power) < Set limit) do
7:
while (All options are not processed) do
8:
- Find X bus with power injection matrices
9:
- Calculate power flow, line outage conditions (if
any), SOC of ESS
10:
while (All scheduling options are not processed) do
11:
- Initialize power calculation process of each generator considering different sub-optimal points
12:
end while
13:
- Store optimized scheduling options
14:
- Treatment of sub-optimal points.  Section V-C
15:
- Evaluate objective function f
 Eq. (6), (13)
16:
end while
17:
for (Each optimized schedule Pi i = 1, 2, . . ., no. of
generators:P do
18:
(a) Create speed vector from scheduled generations Pi ;
19:
(b) Evaluate the schedules for minimized SFOC
20:
Check the error criterion to met. Otherwise, the appropriate speed is chosen from the corresponding generator speed vector.
21:
end for
22: end while
23: - Print the scheduling plans
characteristics. Under normal weather condition, LDP is set at
lower value with all the thrusters operating at lower loading
conditions as shown in 35(a). On the contrary at harsh weather
conditions, the thrusters are set to be operating at higher
loading conditions as shown in 35(b)

 TT1
TT2
RT
LDP low = L B12
, L B16
, L B5
= {−300 kW, −300 kW, −300 kW} (35a)

 TT1
TT2
RT
, L B16
, L B5
LDP high = L B12
= {−800 kW, −800 kW, −800 kW}. (35b)
Various contingency cases have been prepared, such
as loss of generation system and unavailability of ESS
(inadequate SOC) at low DP load and high DP load conditions.
Utilizing the transient simulation framework formulated in
Section IV, the desired power and corresponding operating
speed set point of the generation systems PGen for all the
contingency cases have been listed in Table VI. During the
Gen24 = 0) with harsh weather conditions,
fault at Bus-2 (P B2
power demand to the ESS (P ESS ) exceeds its rated capacity
which cannot be suitably fulfilled. This inadequate generation
availability can be mitigated by load shedding operation by

772

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

TABLE VI
C ONTINGENCY L IST

referring to Table I. However, the ESS helps in optimized generation scheduling when the Bus-2 is isolated during low load
DP operation and also during sudden gain/loss of DP loads,
which is indicated in blue in Table VI. The results pertaining
to sudden gain in DP load as per Case 1A of Table VI are
shown in Fig. 13. The path of transition of the operating point
of the optimized SFOC from initial set point to the final set
point has been traced in Fig. 13(d). The trajectory of the SFOC
for fixed speed operation is also plotted for comparison with
the proposed operation methodology. From Fig. 13(d), it can
be inferred that there is substantial reduction of SFOC with
the proposed method. The reduction in SFOC is 19% when
the DGs are allowed to operate in optimized speed rather
than fixed speed during low load DP mode. The operating
regime of the DG exceeds the prescribed contour of operation
because of the abrupt increase in the load. However, in the real
system, the load transition is expected to be smoother rather
than sudden abrupt changes. Nevertheless, the initial and final
SFOC is optimized and lies within stable region. The output
from the monitoring oscilloscope is presented in Fig. 13(e) for
comparison with the offline results.
2) Cruising Mode Operation: For the cruising operation,
LDP is set to zero, while LHLhigh , LHLlow , and Lmisc are set to
similar value that of DP operation. The LC L primarily depends
on the operating speed of the vessel. For low cruising speed,
the loading of the MP systems is given in 36(a), while for
higher speeds, the loading of the MP systems is given in 36(b)
 MP1

MP2
LC L low = L B3
, L B7
= {−1000 kW, −1000 kW}
LC L

high

(36a)
 MP1

MP2
= L B3 , L B7
= {−2500 kW, −2500 kW}.
(36b)

Similar to the DP operation, various contingency conditions
have been prepared for the cruising loads, which is described
in Table VI. In the contingencies associated with fault at
ESS exceeds its rated capacity and thus the impleBus-2, P B14
mentation of load shedding algorithm becomes impertinent.
However, when the Bus-2 is isolated and by setting maximum
load shedding by employing LHLhigh == 0 and Lmisc == 0,
ESS still exceeds the rated limits as highlighted in Case 10C
P B14
of Table VI. Thus, during this contingency, higher cruising
load cannot be supported by the optimized operation of PGen ,
and thus, the speed of the PSV needs to be slowed down to
prevent inadvertent black-out condition. However, scheduling
of ESS is helpful for the sudden gain/loss of the cruising
loads indicated with blue in Table VI. The real-time simulation
results during the sudden loss of cruising load are shown
in Fig. 14. The transition of SFOC operating point from initial
to final set point value is shown in Fig. 14(d). The same
operation is repeated when the vessel operates with fixed speed
generation system and the SFOC is compared in Fig. 14(d).
It can be established that the SFOC is minimized with the
proposed method. Although the power is abruptly decreased,
the operating point lies within the contours of the operating
limits of the DG. As discussed earlier, the change in load
would not be abrupt in real scenario, and hence, the DG would
operate with optimized SFOC in the stable region. The output
from the monitoring oscilloscope is presented in Fig. 14(e) for
comparison with the offline results.
B. Dynamics of the PV-Based BESS System
1) Charging of BESS: As discussed in Section II-C,
the charging of the BESS could be carried out either by
slow charging or fast charging schemes. Fig. 15(a) shows

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

773

Fig. 13. (a) Speed set point versus DG speed. (b) Power variation of DG-1. (c) Change in total load demand. (d) Transition of SFOC to optimized point in
real-time. (e) Output in monitoring oscilloscope during sudden gain in DP load as per Case 1A of Table VI.

Fig. 14. (a) Speed set point versus DG speed. (b) Power variation of DG-1. (c) Change in total load demand. (d) Transition of SFOC to optimized point in
real time. (e) Output in monitoring oscilloscope during sudden loss in cruising load as per Case 8A of Table VI.

the variation of irradiance and the output power of
the dc/dc-1 while operating at MPPT mode of operation. The PV current (i pv ) for charging the BESS is

shown in Fig. 15(b). For fast charging, the dc/dc-2
maintains battery charging current determined by the
set point (i batt−SP ) as shown in Fig. 15(c). With the

774

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

which the SFOC is calculated. Such an SFOC is dependent on
the operating speed of the DG and the ESS output. However,
it has been observed that the scheduling constraints are also
satisfied at the suboptimal locations with certain generator
schedules and corresponding SFOC values associated with it.
These set of points are termed as local optimum points.
To illustrate this, PGn is considered as generator schedule and f as the objective function used in Algorithm 1.
Equations 37(a) and 37(b) represent the conditions for
minima [26]
 
PGn ∈ R ⇒ f PGn = 0
(37a)
 n
> 0.
(37b)
f PG
Conditions for local minima (suboptimal point) is given in
the following:


(38)
f ∗ = f PGn∗
for local minimizer PGn∗ . This is the smallest function value
in some feasible neighborhood defined by the following:
PGn∗ ∈

.

(39)

There exists a δ > 0 such that

in PGn∗ ∈

Fig. 15. (a) Change in power availability from PV panels with change
in irradiation. (b) Variation of current output of PV panel while operating
it in MPPT mode. (c) Charging current characteristic from dc/dc-2. (d) Change
in SOC of BESS when it is charged from PV panel and both with PV panel
and dc/dc-2. (e) DC/DC-2 output for contingency 8A in Table VI.

dc/dc-1 current i pv , the variation of i dc_i and i batt with
change of irradiation and battery charging current set
point is shown in Fig. 15(c). Fig. 15(d) shows the
variation of SOC of the BESS when it is subjected to
slow charging and fast charging, respectively. The slow
charging of the BESS can be employed, while the PSV
is in dockyard. Alternatively, the fast charging schemes
might be employed by forecasting the nature of job to
be done by the PSV and if the job requires intermitted
power requirements.
2) Discharging of BESS: The discharging of the BESS
is explained with reference to Case 8A of Table VI.
During sudden change of the DP operation, the power
demand of the BESS decreases from 399.32 to 0.55 kW.
The power delivered by the PV-BESS system is shown
in Fig. 15(e).
C. Treatment of Suboptimal Points
Treatment of suboptimal points has been explained with
respect to the contingency scenario 8A of Table VI, where the
initial derived generation schedule of 1875.17 kW and ESS
of 399.32 kW are the points-1 in Fig. 16(a) and Table VII at



f ∗  f PGn∗ ∀ PGn

: |PGn − PGn∗ |  δ.

(40a)
(40b)

Thus, there can be many local minima, i.e., multiple suboptimal points, which are not global minima. Special properties
such as the convexity of feasible region “ ” and objective
function “ f ” imply that any local solution is a global solution.
It has been observed that during real-time simulations, such
suboptimal (local optimum points), which satisfy the given
constraints, has the potential to speed up the calculations of the
scheduling process while diligently treating the associated ESS
to arrive at better SFOC values than the corresponding SFOC
at global optima. This has been demonstrated using Table VII
corresponding to Fig. 16(a), where the ESS is varied at all
suboptimal locations to study the impact on the SFOC. The
absence of ESS during the initial state at PG1 would improve
the SFOC operating scenario (Point-2), however, the limits of
generator capacity (2048 kW) are enforced to consider the ESS
as an option (Point-1). Here, ESS is delivering at 399.32 kW
and the corresponding SFOC is 207 (Point-1). Now, with the
sudden loss of the cruising load, we could notice that before
arriving to the global optimum point PG2(g) (Point-5), it has
been passing through a local optima PG2(l) (Point-3) where
the corresponding SFOC is 205 against the global optimum
SFOC of 197 (PG2(g) , Point-5). During such a phase, it is
evident that ESS is no longer required to contribute to the load,
and accordingly, scheduling algorithm suggested the output
of ESS at +0.55 kW. However, considering the enforced
operation of ESS, at this instant, shall make the corresponding
SFOCs of PG2 and PG2(l) at 195 (Point-6) and 200 (Point-4),
respectively. Hence, instead of abrupt suspension of ESS
supply, slowly adjusting the ESS in such a way to yield the best
scenario of SFOC is a possibility and where the suboptimal
points can be diligently treated.

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

775

Fig. 16. (a) Trajectory of the SFOC during the sudden loss of cruising load with locations of various suboptimal points. (b) Surface plot of SFOC variation
for varying ESS and DG speed. (c) Comparison of the changing operating points. (d) Location of suboptimal points for DGs operating at optimized speed
and fixed speed.

TABLE VII
VARIATION OF SFOC AT S UBOPTIMAL P OINTS D URING
THE S UDDEN L OSS OF C RUISING L OAD

Table VII shows the variation of SFOC corresponding to
these suboptimal points considering ESS in operation even
after the sudden loss of load and at optimized speed of generators. However, it may not be prudent to consider such points in
all the contingency cases owing to operational constraints such
as availability and SOC of the interfaced ESS. So, it is evident
that the suboptimal point can yield better SFOC at optimized
speed and further it also has an upper hand in reducing the
time propagation of schedules for feeding to the shipboard
controllers.
So, in the proposed work, it was noticed that suboptimal
points can accommodate ESS for better performance of DGs.
To speed up the calculation in scheduling process and to treat
the consideration of ESS, suboptimal schedules can yield a
feasible solution.
Dependence of ESS output on suboptimal locations is
further demonstrated with the help of Fig. 16(b)–(d).
In Fig. 16(b), power extraction from ESS is varied from
0 to 300 kW to visualize the changing operating points
of the SFOC of DG while fulfilling constant load demand
of 1875 kW. The location of the global optima with minimized SFOC and local optima is highlighted in Fig. 16(b).
In Fig. 16(c) and (d), it is further compared with the DG
operating at constant speed. With reference to Fig. 16, the following observations can be cited as follows.
1) Fig. 16(b) shows the surface plot of the SFOC variation
with ESS and DG speed in the aforesaid treatment of
suboptimal points. The suboptimal scheduled points are
also marked in the figure.
2) Fig. 16(c) and (d) shows the comparative analysis
of the location of suboptimal points for fixed speed
and optimized speed operation of the DGs. It can be
observed that the minimum SFOC for the corresponding
suboptimal points for fixed speed DGs is achieved while

Fig. 17. (a) Variation of the operating point of SFOC for generator running
at 1800 rpm, 1600 rpm, and variable speed (optimized speed). (b) Comparison
of SFOC for generator running at 1800 rpm and optimized speed.

operating at rated conditions denoted by Poi nt F S A
in Fig. 16(d).
3) For the DGs operating at optimized speed, there are
multiple suboptimal points depending on ESS output
and operating speed of DG. These suboptimal points,
the variable speed DGs, and the suboptimal locations
are dependent on the injected power from ESS and DG
operating speed as shown in Fig. 16(d). The location
of the suboptimal points is shown as Poi nt O S A ,
Poi nt O S B , Poi nt O SC , and Poi nt O S D in Fig. 16(d).
D. Comparison With the Operation at Different Speeds
For all the operating cases in Table VI, SFOC is compared
when the generator is running at 1800 rpm and 1600 rpm
against the calculated optimized speed. The variation of the
operating points is shown in Fig. 17. It can be seen that SFOC
is significantly reduced when run at optimized speed, thus
highlighting the advantages of the proposed real-time transient
simulation scheme and corresponding optimization framework.
E. Influence of ESS on SFOC
From Section V-C, it is evident that ESS has strategic
influence on various marine missions not only on dealing with
short time-load transients for smooth operations but also on
improving the transient responses of DGs and corresponding
SFOC. The set point to ESS and DG output have been decided
based on the cost functions, cable loadings, and the realistic
criteria discussed in Section IV. From Fig. 17(b), it is evident

776

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

TABLE VIII
I NFLUENCE OF ESS ON SFOC D URING THE S UDDEN L OSS OF C RUISING L OAD

that at higher loads, the SFOC is almost constant for optimized
variable speed and constant speed operation with or without
ESS and the same has been shown in Table VIII. However,
having ESS during generators meeting higher loads can reduce
the initial speed deviations and torsional stress and further it
enables the option of catering any unexpected load demand.
VI. C ONCLUSION
This paper investigated the modeling and control approaches
of dc PSV with real-time transient simulation framework
to minimize the fuel consumption in terms of SFOC while
considering the treatment of suboptimal points along with an
option of utilizing onboard PV-based energy storage facility.
This sort of real-time operation is expected to be a key
ingredient of the future autonomous marine vehicles.
1) DC OPF-based algorithms have been applied for realtime scheduling of generation resources with an objective to minimize the fuel consumption. This operation is
within the framework of the proposed real-time transient
simulation setup and has been successfully demonstrated in this paper. Results obtained are promising and
strengthens the ability of the future dc marine vessels to
comply with the upcoming stringent laws on pollution
control. It has been shown that the output of ESS can
influence the generator set points particularly during
sudden load changes for DP and cruising missions.
2) Traditionally, the power sharing happens according to
generator ratings through droop control rather than optimal generation scheduling as per load demand for a
particular marine mission. Hence, an optimal scheduling
system based on dc OPF for tackling any specific
nature of marine missions has been demonstrated in this
paper and the results indicated such an approach will
enable the PSV to operate in fuel efficient regime with
improved transient responses.
3) Responses of the DGs to various contingencies are
studied and it has been found that unavailability of the
generation system due to fault at bus bar demands higher
requirement of ESS to satisfy the load demands. In such
cases, it is pertinent to have load shedding routines,
which are dependent on the marine missions to prevent
power blackout of the vessel. This highlights the necessity for considering such routines in future autonomous
dc PSVs. Some cases have been highlighted using the
proposed real-time transient simulation framework.
4) Higher fuel savings are noticed while the generators
are operated at optimized speed for low power demand.
In comparison with fixed speed operation, reduction in
SFOC of 19% has been reported when the PSV was
operating at low-power DP operation.

R EFERENCES
[1] A. Boveri, F. D’Agostino, A. Fidigatti, E. Ragaini, and F. Silvestro,
“Dynamic modeling of a supply vessel power system for DP3 protection
system,” IEEE Trans. Transport. Electrific., vol. 2, no. 4, pp. 570–579,
Dec. 2016.
[2] H. Ginn and R. Cuzner, “The shipboard integrated power system [about
this issue],” IEEE Electrific. Mag., vol. 3, no. 2, pp. 2–3, Jun. 2015.
[3] S. Y. Kim, S. Choe, S. Ko, and S. K. Sul, “A naval integrated power
system with a battery energy storage system: Fuel efficiency, reliability,
and quality of power.,” IEEE Electrific. Mag., vol. 3, no. 2, pp. 22–33,
Jun. 2015.
[4] T. J. McCoy, “Electric ships past, present, and future [technology
leaders],” IEEE Electrific. Mag., vol. 3, no. 2, pp. 4–11, Jun. 2015.
[5] International Maritime Organization (IMO), “Prevention of air pollution
from ships (MARPOL 73/78 annex VI),” IMO, London, U.K., Tech.
Rep., Aug. 2004.
[6] P. Michalopoulos, F. D. Kanellos, G. J. Tsekouras, and J. M. Prousalidis,
“A method for optimal operation of complex ship power systems
employing shaft electric machines,” IEEE Trans. Transport. Electrific.,
vol. 2, no. 4, pp. 547–557, Dec. 2016.
[7] B. Zahedi and L. E. Norum, “Modeling and simulation of all-electric
ships with low-voltage DC hybrid power systems,” IEEE Trans. Power
Electron., vol. 28, no. 10, pp. 4525–4537, Oct. 2013.
[8] E. Skjong, R. Volden, E. Rødskar, M. Molinas, T. A. Johansen, and
J. Cunningham, “Past, present, and future challenges of the marine
vessel’s electrical power system,” IEEE Trans. Transport. Electrific.,
vol. 2, no. 4, pp. 522–537, Dec. 2016.
[9] W. Koczara and G. Iwanski, “Variable-speed power generation,” in
Power Electronics for Renewable and Distributed Energy Systems—A
Sourcebook of Topologies, Control and Integration, S. Chakraborty,
M. G. Simoes, and W. E. Kramer, Eds. London, U.K.: Springer, 2013.
[10] M. R. Banaei and R. Alizadeh, “Simulation-based modeling and power
management of all-electric ships based on renewable energy generation
using model predictive control strategy,” IEEE Intell. Transp. Syst. Mag.,
vol. 8, no. 2, pp. 90–103, May 2016.
[11] W. H. Kumm, “Marine and naval applications of fuel cells for
propulsion: The process selection,” J. Power Sour., vol. 29, nos. 1–2,
pp. 169–179, 1990.
[12] C. C. Chan, A. Bouscayrol, and K. Chen, “Electric, hybrid, and fuelcell vehicles: Architectures and modeling,” IEEE Trans. Veh. Technol.,
vol. 59, no. 2, pp. 589–598, Feb. 2010.
[13] R. Soman, M. M. Steurer, T. A. Toshon, M. O. Faruque, and
R. M. Cuzner, “Size and weight computation of MVDC power equipment in architectures developed using the smart ship systems design
environment,” IEEE J. Emerg. Sel. Topics Power Electron., vol. 5, no. 1,
pp. 40–50, Mar. 2017.
[14] M. M. El-Marsafawy and R. M. Mathur, “A new, fast technique for
load-flow solution of integrated multi-terminal DC/AC systems,” IEEE
Trans. Power App. Syst., vol. PAS-99, no. 1, pp. 246–255, Jan. 1980.
[15] K. Rouzbehi, J. I. Candela, G. B. Gharehpetian, L. Harnefors, A. Luna,
and P. Rodriguez, “Multiterminal DC grids: Operating analogies to AC
power systems,” Renew. Sustain. Energy Rev., vol. 70, pp. 886–895,
Apr. 2017.
[16] K. Satpathi, N. Thukral, A. Ukil, and M. A. Zagrodnik, “Flux estimation
based DC bus voltage control in marine DC power system,” in Proc.
42nd Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Florence, Italy,
Oct. 2016, pp. 1815–1820.
[17] A. Yazdani and R. Iravani, Voltage-Sourced Converters in Power Systems: Modeling, Control and Applications. Hoboken, NJ, USA: Wiley,
2010.
[18] K. Satpathi, N. Thukral, A. Ukil, and M. A. Zagrodnik, “Directional
protection scheme for MVDC shipboard power system,” in Proc.
42nd Annu. Conf. IEEE Ind. Electron. Soc. (IECON), Florence, Italy,
Oct. 2016, pp. 3840–3847.

SATPATHI et al.: MODELING AND REAL-TIME SCHEDULING OF DC PSV FOR FUEL EFFICIENT OPERATION

[19] Y. Liu, Z. Qu, H. Xin, and D. Gan, “Distributed real-time optimal power
flow control in smart grid,” IEEE Trans. Power Syst., vol. 32, no. 5,
pp. 3403–3414, Sep. 2017.
[20] S. Barsali, C. Miulli, and A. Possenti, “A control strategy to minimize
fuel consumption of series hybrid electric vehicles,” IEEE Trans. Energy
Convers., vol. 19, no. 1, pp. 187–195, Mar. 2004.
[21] B. Zahedi, L. E. Norum, and K. B. Ludvigsen, “Optimized efficiency of
all-electric ships by DC hybrid power systems,” J. Power Sour., vol. 255,
pp. 341–354, Jun. 2014.
[22] A. G. Sarigiannidis, E. Chatzinikolaou, C. Patsios, and A. G. Kladas,
“Shaft generator system design and ship operation improvement involving SFOC minimization, electric grid conditioning, and auxiliary propulsion,” IEEE Trans. Transport. Electrific., vol. 2, no. 4, pp. 558–569,
Dec. 2016.
[23] X. Feng, K. L. Butler-Purry, and T. Zourntos, “Multi-agent system-based
real-time load management for all-electric ship power systems in DC
zone level,” IEEE Trans. Power Syst., vol. 27, no. 4, pp. 1719–1728,
Nov. 2012.
[24] E. A. Sciberras, B. Zahawi, D. J. Atkinson, A. Breijs, and
J. H. van Vugt, “Managing shipboard energy: A stochastic approach
special issue on marine systems electrification,” IEEE Trans. Transport.
Electrific., vol. 2, no. 4, pp. 538–546, Dec. 2016.
[25] E. Skjong, T. A. Johansen, M. Molinas, and A. J. Sørensen, “Approaches
to economic energy management in diesel–electric marine vessels,”
IEEE Trans. Transport. Electrific., vol. 3, no. 1, pp. 22–35, Mar. 2017.
[26] J. D. Pintér, Global Optimization in Action: Continuous and Lipschitz
Optimization: Algorithms, Implementations and Applications, vol. 6.
Dordrecht, The Netherlands: Springer, 2013.
[27] I. Kobougias, E. Tatakis, and J. Prousalidis, “PV systems installed in
marine vessels: Technologies and specifications,” Adv. Power Electron.,
vol. 2013, Jan. 2013, Art. no. 831560. [Online]. Available: http://dx.doi.
org/10.1155/2013/831560
[28] T. I. Bø et al., “Marine vessel and power plant system simulator,” IEEE
Access, vol. 3, pp. 2065–2079, 2015.
[29] Rolls-Royce, Fact Sheet-UT 776. Accessed on Jan. 8, 2016. [Online].
Available:
http://www.mokster.no/ShowFile.ashx?FileInstanceId=
8b75f34f-f294-4502-9908-3a443671fde1
[30] Bars Elekter, Viking Queen Project. Accessed on Feb. 12, 2017. [Online].
Available:
http://barselekter.com/public/documents/Viking_Queen
_datablad.pdf
[31] IEEE Recommended Practice for 1 kV to 35 kV Medium-Voltage
DC Power Systems on Ships, IEEE Standard 1709-2010, Nov. 2010,
pp. 1–54.
[32] A. J. Roscoe, I. M. Elders, J. E. Hill, and G. M. Burt, “Integration of a
mean-torque diesel engine model into a hardware-in-the-loop shipboard
network simulation using lambda tuning,” IET Elect. Syst. Transp.,
vol. 1, no. 3, pp. 103–110, Sep. 2011.
[33] T. Theubou, R. Wamkeue, and I. Kamwa, “Dynamic model of diesel
generator set for hybrid wind-diesel small grids applications,” in Proc.
25th IEEE Can. Conf. Elect. Comput. Eng. (CCECE), Montreal, QC,
Canada, May 2012, pp. 1–4.
[34] MTU Ffiedrichshafen GmbH, “Engine performance diagram,” in
Technical Project Guide Marine Application Part 1-General. Germany,
Jun. 2003, pp. 1–5.
[35] P. C. Krause, O. Wasynczuk, S. D. Sudhoff, and S. Pekarek, Analysis
of Electric Machinery and Drive Systems, vol. 75. Hoboken, NJ, USA:
Wiley, 2013.
[36] A. K. Jain and V. T. Ranganathan, “Modeling and field oriented
control of salient pole wound field synchronous machine in stator flux
coordinates,” IEEE Trans. Ind. Electron., vol. 58, no. 3, pp. 960–970,
Mar. 2011.
[37] M. Cupelli et al., “Power flow control and network stability in an
all-electric ship,” Proc. IEEE, vol. 103, no. 12, pp. 2355–2380,
Dec. 2015.
[38] A. J. Sørensen and Ø. N. Smogeli, “Torque and power control of
electrically driven marine propellers,” Control Eng. Pract., vol. 17,
no. 9, pp. 1053–1064, May 2009.
[39] A. J. Sørensen, “Marine Control Systems: Propulsion and Motion
Control of Ships and Ocean Structures,” Norwegian Univ. Sci. Technol.,
Trondheim, Norway, Tech. Rep. UK-13-76, Jan. 2013.
[40] D. Casadei, F. Profumo, G. Serra, and A. Tani, “FOC and
DTC: Two viable schemes for induction motors torque control,”
IEEE Trans. Power Electron., vol. 17, no. 5, pp. 779–787,
Sep. 2002.

777

[41] W. S. Im, C. Wang, L. Tan, W. Liu, and L. Liu, “Cooperative
controls for pulsed power load accommodation in a shipboard power
system,” IEEE Trans. Power Syst., vol. 31, no. 6, pp. 5181–5189,
Nov. 2016.
[42] K.-J. Lee, D. Shin, D.-W. Yoo, H.-K. Choi, and H.-J. Kim, “Hybrid
photovoltaic/diesel green ship operating in standalone and gridconnected mode in South Korea—Experimental investigation,” Energy,
vol. 49, pp. 475–483, Jan. 2013.
[43] Sunpower. 305 Solar Panel. Accessed on Jan. 10, 2017. [Online].
Available:
http://www.revolusun.com/documents/sunpowers-pr-305wht-en.pdf
[44] IEEE Guide for Array and Battery Sizing in Stand-Alone Photovoltaic
(PV) Systems, IEEE Standard 1562-2007, May 2008, p. I-22.
[45] IEEE Recommended Practice for Sizing Lead-Acid Batteries for StandAlone Photovoltaic (PV) Systems, IEEE Standard 1013-2007, Jul. 2007,
pp. 1–55.
[46] Energy Storage News. The First Offshore Vessel With a Battery Energy
Storage System in Operation. Accessed on Feb. 20, 2017. [Online].
Available:
https://www.energy-storage.news/blogs/the-first-offshorevessel-with-a-battery-energy-storage-system-in-operation
[47] SAFT Batteries. Seanergymodules: High Energy and High Power Li-Ion
Super-Iron Phosphate. Accessed on Feb. 20, 2017. [Online]. Available:
https://www.saftbatteries.com/force_download/4679_Seanergy+
Modules_final+low.pdf
[48] L. H. S. C. Barreto, P. Peixoto Praca, D. S. Oliveira, and
R. N. A. L. Silva, “High-voltage gain boost converter based on
three-state commutation cell for battery charging using PV panels in a
single conversion stage,” IEEE Trans. Power Electron., vol. 29, no. 1,
pp. 150–158, Jan. 2014.
[49] J. Ahmed and Z. Salam, “A modified P&O maximum power point
tracking method with reduced steady-state oscillation and improved
tracking efficiency,” IEEE Trans. Sustain. Energy, vol. 7, no. 4,
pp. 1506–1515, Oct. 2016.
[50] J. Wolfe and M. J. Roa, “Advanced methods for tabulation of electrical
loads during special modes of marine vessel operation,” IEEE Trans.
Ind. Appl., vol. 53, no. 1, pp. 667–674, Feb. 2017.
[51] F. E. Cellier and J. Greifeneder, Continuous System Modeling. New
York, NY, USA: Springer, 2013.
[52] B. Shenoi, “Practical realization of a gyrator circuit and RC-gyrator filters,” IEEE Trans. Circuit Theory, vol. 12, no. 3, pp. 374–380, Sep. 1965.
[53] J. Vlach and S. Kishore, Computer Methods for Circuit Analysis and
Design. New York, NY, USA: Springer, 1983.
[54] Understanding
the
Generator
Set
Limits.
Accessed
on
Feb. 17, 2010. [Online]. Available: http://www.mtuonsiteenergy.
com/../3156391_OE_TechnicalArticle_Reliability_2010.pdf
[55] OPAL-RT. OPAL-RT Technologies Official Website. Accessed:
Jan. 10, 2016. [Online]. Available: http://www.opalrt.com/
[56] S. Abourida, C. Dufour, J. Belanger, G. Murere, N. Lechevin, and B.
Yu, “Real-time PC-based simulator of electric systems and drives,”
in Proc. 17th Annu. IEEE Appl. Power Electron. Conf. Expo., vol. 1.
Mar. 2002, pp. 433–438.

Kuntal Satpathi (S’14) received the B.Tech. degree
in electrical engineering from the Haldia Institute
of Technology, Haldia, India, in 2011. He is currently pursuing the Ph.D. degree with the School
of Electrical and Electronic Engineering, Nanyang
Technological University, Singapore.
From 2011 to 2014, he was with Jindal Power
Ltd., Raigarh, India, specializing in power plant
operations. His current research interest includes
modeling, control and protection of dc grids, and
power electronics for dc distribution system.

778

IEEE TRANSACTIONS ON TRANSPORTATION ELECTRIFICATION, VOL. 3, NO. 3, SEPTEMBER 2017

VSK Murthy Balijepalli (M’06) received the Ph.D.
degree from IIT Bombay, Mumbai, India, in 2014.
He was with India Smart Grid Task Force,
Ministry of Power, New Delhi, India, the InterMinisterial Group on Smart Grids (The Future of
Energy), from 2014 to 2016, where he was involved
in the project execution and evaluation of smart
grid pilot projects in India. Since 2016, he has
been a Research Fellow with School of Electrical
and Electronic Engineering, Nanyang Technological
University, Singapore. His current research interests
include power system modeling and optimization, microgrid resiliency, policy
making, and model building for emerging power systems and smart grids.
Dr. Balijepalli was an Active Member of various technical panels under
the Bureau of Indian Standards (LITD-10), CIMug, and IEC PC118-Smart
Grid User Interface. He is the Founder of DesiSmartGrid.com, India’s first
Smart Grid Educational Portal. He was a recipient of the Massachusetts
Institute of Technology Young Innovator Award, the Department of Science
and Technology-Lockheed Martin Gold Medal, the Institute of Engineers
India Young Engineer Award (U.K. Royal Charter of Incorporation), and
the Gandhian Technological Edge Award for his outstanding research on
smart grids. He is currently serving on the expert committee of Sustainable
Energy to the United Nations Economic Commission for Europe, Geneva, and
Switzerland.

Abhisek Ukil (S’05–M’06–SM’10) received the
B.E. degree in electrical engineering from Jadavpur
University, Kolkata, India, in 2000, the M.Sc. degree
in electronic systems and engineering management
from the University of Bolton, Bolton, U.K., in 2004,
and the Ph.D. degree, with a focus on automated
disturbance analysis in power systems, from the
Tshwane University of Technology, Pretoria, South
Africa, in 2006.
From 2006 to 2013, he was a Principal Scientist
with the ABB Corporate Research Center, Baden,
Switzerland, where he led several projects on smart grid, protection, control,
condition monitoring, including the first worldwide prototype of directional
protection relay using only current for smart grid applications. Since 2013, he
has been an Assistant Professor with the School of Electrical and Electronic
Engineering, Nanyang Technological University, Singapore, where he has been
leading a group of 20 researchers with several industrial collaborations. He has
authored over 125 refereed papers, a monograph, and two chapters. He has
invented ten patents. His current research interests include smart grid, dc grid,
protection and control, renewable energy and integration, energy storage, and
condition monitoring.

