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1 INTRODUCTION
Modern maritime transport is facing strict
requirements regarding energy efficiency, greenhouse
gas emission reduction, and operational safety, in the
context of complex propulsion systems and variable
operating conditions. These requirements are
reinforced at global level by regulatory frameworks
and strategic initiatives promoted by the International
Maritime Organization (IMO), Tier II/III and GHG
Strategy, which establishes ambitious targets,
including the objective of achieving net-zero emissions
by or around 2050 [1]. At European level, these efforts
are further reinforced by regulatory instruments such
as the European Union Emissions Trading System EU
ETS [2] and the Fit for 55 legislative package [3],
including the FuelEU Maritime regulation [4]. In
addition, the European Union has strengthened its
Monitoring, Reporting, and Verification (MRV)
Regulation, requiring companies to submit GHG
emissions annual report through EMSA portal [5]. This
regulation obliges vessels to monitor, report, and verify
emissions of carbon dioxide (CO₂), methane (CH₄), and
nitrous oxide (N₂O), in addition to collecting
operational data such as transported cargo, distance
travelled, and time spent at sea [6]. These reporting
requirements have been extended to offshore vessels
from January 2025 onwards [7].
While current decarbonisation strategies primarily
emphasise the adoption of alternative fuels and
advanced propulsion technologies, growing attention
is being directed towards operational optimisation as
an immediate and cost-effective pathway for emissions
reduction.
In this context, the present paper focuses on
improving the efficiency of onboard power generation
systems, particularly through optimised load
Operational Optimization of Parallel Diesel Generator
Systems for Enhanced Fuel Efficiency and Emissions
Reduction in DP Vessels
N. Acomi & A. Preda
Constanta Maritime University, Constanța, Romania
ABSTRACT: The present paper analyses the optimisation of the parallel operation of diesel generator (DG)
systems on board dynamically positioned (DP) vessels, using the Kongsberg Challenger III model vessel as a
reference platform. The research focuses on identifying the optimal balance between energy efficiency, reduction
of pollutant emissions (CO₂, NOₓ, and SO₂), and the maintenance of an adequate power reserve (spinning reserve)
to ensure navigational safety. The study adopts a simulation-based approach, employing linear interpolation to
determine specific fuel consumption and conducting a comparative analysis of various load configurations. The
results demonstrate that operating generators within the efficiency “sweet spot” (75–90% of rated load) maximises
thermodynamic efficiency, reaching approximately 37%, while also preventing premature engine degradation
associated with the wet stacking phenomenon. Furthermore, the study highlights the critical role of power
management systems (PMS) in automating generator coupling, thereby ensuring enhanced operational stability
under conditions of variable external loads.
http://www.transnav.eu
the International Journal
on Marine Navigation
and Safety of Sea Transportation
Volume 20
Number 2
June 2026
DOI: 10.12716/1001.20.02.20
470
distribution and generator management, which
represents a critical opportunity to reduce fuel
consumption, minimise emissions, and enhance
overall system performance without requiring major
technological modifications. In the case of specialized
vessels, such as Dynamic Positioning, the demand for
power is fluctuating, being influenced by
environmental factors such as wind, waves and
currents [8]. To investigate these aspects, a
representative diesel-electric power system is
considered, using the Kongsberg Challenger III model
vessel as a reference platform. The analysed system
comprises seven identical diesel-generating units, each
with a capacity of 2000 kW, configured to operate in
parallel. The main challenge in managing such a
system is to avoid undercharging (below 30%), which
leads to incomplete combustion and carbon deposits,
and overloading, which eliminates the safety reserve
[9]. The introduction of the Spinning Reserve concept
of at least 15% is essential to prevent blackout incidents
in the event of load shocks caused by the start-up of
large consumers or sudden engine failure [10],[11].
This paper explores the computational algorithms
needed to transform raw technical data into
sustainable operating strategies.
2 METHODOLOGY
The study considers a diesel-electric power system
installed on a dynamically positioned (DP) vessel,
comprising seven identical diesel generator (DG) units,
each with a rated capacity of 2000kW. The generators
are configured to operate in parallel and are managed
through a Power Management System (PMS), which
ensures load sharing, system stability, and operational
safety. The analysis is conducted using the Kongsberg
simulator [12], which provides a controlled
environment for evaluating different loading scenarios
and generator configurations under variable
operational conditions.
The specific fuel consumption behaviour of the
diesel generators is modelled using a linear
interpolation approach between two reference
operating points obtained from the manufacturer’s
technical documentation [13]: 75% load (1500kW):
393l/h and 100% load (2000kW): 509l/h. Assuming a
linear relationship between fuel consumption and
power output within this operating range, the fuel
consumption is expressed as function of generator load
P[kW]. This approximation is considered valid within
the high-efficiency operating region (75–100% load),
where diesel engine performance exhibits near-linear
behaviour [14]. The thermodynamic efficiency
of
each generator is calculated as the ratio between the
electrical power output and the chemical energy input
of the fuel. Pollutant emissions are estimated using
standard emission factors derived from the literature
and international guidelines: CO₂ emissions: 2.64 kg
per litre of diesel fuel [15]; NOₓ emissions: 10 g/kWh
(typical value for medium-speed marine diesel
engines) [16] and SO₂ emissions: calculated based on a
sulphur content of 0.1%, resulting in approximately 1.7
g per litre of fuel [17]. These values provide a
simplified but widely accepted approach for
estimating emissions in simulation-based studies,
where direct exhaust measurements are not available.
To evaluate the performance of the system under
realistic operating conditions, two representative load
scenarios are considered: medium-high load: 8500kW,
and medium load: 5000kW. For each scenario, different
generator configurations are analysed by varying the
number of active units, while maintaining system
constraints related to operational safety and efficiency.
The optimization of generator operation is based on
identifying the number of active units that ensures
operation within the optimal load range (75–90% of
rated capacity), minimization of total fuel
consumption, reduction of pollutant emissions,
maintenance of a minimum spinning reserve of 15% of
the total available power.
The initial number of required generators N is
estimated using the formula N= (Ptotal)/(Punit), meaning
total system load over the rated power of a single
generator (2000kW). This value is subsequently
adjusted to ensure compliance with the optimal
loading range and spinning reserve requirements. The
final configuration is selected based on a comparative
evaluation of fuel consumption, efficiency, and
emissions for each feasible operating scenario.
The study employs a quantitative approach based
on mathematical modelling of the performance of the
Cummins Centrum C2000D6E engine [13]. The
methodology is structured on three pillars:
consumption modelling, calculation of
thermodynamic efficiency and quantification of
emissions, each applied for two types of loads: 8500kW
and 5000kW. In the first step, the optimal operating
configuration for the first load scenario is determined.
The analysis is based on the following initial
conditions: 7 units of 2000kW, and the total load is
8500kW. If all seven units are operated, the load per
unit is 8500/7 ≈ 1214kW (approx. 60%). If only six
units are operated, the load per unit is 1417kW (approx.
71%). Considering five units results in a load per unit
of 1700kW (85%). The use of only four units cannot
cover the required load.
The configuration with 5 units is the most efficient
because diesel engines reach maximum efficiency in
the 75-90% load range, avoiding the "low-load
operation" pitfalls identified in modern maritime
engineering [14].
In this study, linear interpolation is used between
the reference points of 75% (1500kW- 393l/h) and 100%
(2000kW-509l/h). The consumption slope (m) is: m =
(509 – 393)/(2000 – 1500) = 0.23l/k. Consumption for one
unit at 1700kW Cunit=393+0.23×(1700-1500)=439l/h and
the total consumption for 5 units is Ctotal=5×439=2195l/h.
Efficiency is calculated by relating electrical power
to the chemical energy of the diesel (average density
0.85 kg/l) LHV=11.83 kWh/kg. In this case mf =
439l/h×0.85kg/l = 373.15kg/l and the efficiency
=
1700/(373.15×11.83) × 100 = 38.51%.
To calculate pollutant emissions, standard emission
factors were applied: 2.64 kg/l for CO2, and an average
value of 10 g/kWh for NOx. This results in CO2 and NOx
emissions of ECO2=2280l/h×2.64kg/l=6019.2kg/h and
ENOx=8500kW×10g/kWh=85kg/h.
471
Table 1. Comparative table with the operation of 5 units for
a load of 8500Kw
Parameter
5 units (60%
load)
5 units (85%
load)
Difference/Advantage
Unit
consumption
320 l/h
456 l/h
Total
Consumption
2240 l/h
2280 l/h
Similar consumption
Energy
efficiency
32-34%
37.07%
More complete
combustion
CO2
emissions/h
5913 kg/h
6019 kg/h
Engine status
Risk of Wet
Stacking
Optimal
Increased reliability
The second step involves determining the optimal
operating configuration for a load of 5000kW. The
system consists of seven units, each with a capacity of
2000kW. If three units are operated, the load per unit is
1666kW, corresponding to 83.3% of rated capacity.
When four units are operated, the load per unit is
1250kW (62.5% of capacity), while operating five units
results in a load of 50% per unit.
The most efficient option is to use 3 units, as this
keeps the engines in an optimal combustion regime
(>80%), avoiding premature wear caused by low loads.
To determine the fuel consumption, the previously
established consumption rate is used (0.23 l/kW
between 75% and 100%): Consumption for 1 unit at
1666.6kW. In this case Cunit=393l/h + 0.23×(1666.6-1500)
431.18l/h and the total consumption for 3 units is Ctotal
=3×431.18=1293.54 1294l/h.
The efficiency (η) is calculated based on fuel mass
flow rate, mf = 431.18l/h × 0.85kg/l = 366.5kg/h. The
resulting efficiency is η = 1666.6 / (366.5 × 11.83) × 100
38.43%.
Applying the same methodology for emissions
calculation, the resulting CO₂ and NOₓ emissions are as
follows: of ECO2 = 1294l/h × 2.64kg/l = 3416.16kg/h and
ENOx = 5000kW × 10g/kWh = 50kg/h (estimated at 10
g/kWh).
Table 2: Comparative table with the operation of 3 or 5 units
for a load of 5000kW
Indicator
5 units
(50% load)
3 units
83.30% load)
Advantages 3 units
Consumption per
unit
262 l/h
431.18 l/h
Total
consumption
1310 l/h
1294 l/h
Economy 16 l/h
Energy efficiency
31-32%
38.43%
Increased thermal
efficiency with 6.43%
CO2 emissions/h
3458 kg
3416.16 kg
Reduced carbon
emissions
Maintenance
Wet Stack
Risk
No risk
Lower service costs
The implemented algorithm determines the optimal
number of generators (N) by the ceil function (Total
Load / 2000kW), subsequently adjusted to comply with
the 85% load threshold imposed by the power reserve.
3 DATA ANALYSIS AND RESULTS
The data analysis reveals a direct correlation between
the load of the units and the overall efficiency of the
system. For a load of 8500 kW, configurations with 5, 6
and 7 units were compared. The data indicates that the
use of 5 units results in an 85% charge, reaching an
efficiency of 37.07% [18]. In contrast, using 7 units
reduces load to 60%, decreasing efficiency in the range
of 32-34% and increasing the risk of wet stacking a
condition where unburned fuel and carbon accumulate
in the exhaust system [19].
A similar situation is observed at the 5000kW load,
where the configuration with 3 units (83.3% load) is
clearly superior to the 5 units (50% load). The
difference in total consumption between these two
scenarios is 10l/h (1340l/h vs 1350l/h), but the real gain
lies in increasing thermal efficiency by 15 percent and
reducing the carbon footprint.
Table 3. Optimized operating table with power reserve
(15%)
Total load
(kW)
Nr. Of
Active Gen
Load/Gen
(kW)
Unit load
(%)
System
reserve
(kW)
Total
consumptio
n (l/h)
Emissions
Co2 (kg/h)
Emissions
Nox (kg/h)
Emissions
SO2 (kg/h)
500
1
500
25%
1500
128
316.8
5
0.22
1000
1
1000
50%
1000
255
686.4
10
0.43
1500
1
1500
75%
500
309
1056
15
0.53
1750
2
875
43.70%
2250
445.375
1188
17.5
0.76
2000
2
1000
50%
2000
509
1372.8
20
0.87
2500
2
1250
62.50%
1500
636.25
1742.4
25
1.08
3000
2
1500
75%
1000
763.5
2112
30
1.30
3400
3
1133
56.60%
2600
865.3
2349.6
34
1.47
4000
3
1333
66.60%
2000
1018
2798.4
39
1.73
4500
3
1500
75%
1500
1145.25
3168
45
1.95
5000
3
1667
83.30%
1000
1272.5
3537.6
50
2.16
5100
4
1275
63.70%
2900
1297.95
3558.7
51
2.21
6000
4
1500
75%
2000
1527
4224
60
2.60
7000
4
1750
87.50%
1000
1781.5
4963.2
70
3.03
7500
5
1500
75%
2500
1908.75
5280
75
3.24
8000
5
1600
80%
2000
2036
5649.6
80
3.46
8500
5
1700
85%
1500
2163.25
6019.2
85
3.68
9000
6
1500
75%
3000
2290.5
6336
90
3.89
10000
6
1667
83.30%
2000
2545
7075.2
100
4.33
11000
6
1833
91.60%
1000
2799.5
7814.4
110
4.76
11500
7
1643
82.10%
2500
2926.75
8131.2
115
4.98
12000
7
1714
85.70%
2000
3054
8500.8
120
5.19
13000
7
1857
92.80%
1000
3308.5
9240
130
5.62
14000
7
2000
100%
0
3563
9979.2
140
6.06
The interpretation of the optimization table (Table
3) (0-14,000 kW) shows that inflection points (where
the PMS starts a new generator) are critical for
maintaining the "Spinning Reserve" and grid
frequency stability [20]. For example, at 5100 kW,
starting the fourth unit increases the consumption from
1340 l/h to 1348 l/h, but the power reserve jumps from
1000 kW to 2900 kW. This 'sacrifice' of minimum
consumption in favour of safety is a key element in the
interpretation of data for real maritime applications.
The study demonstrated that active load management
allows the generators to be kept in an optimal
operating mode (75-90%), achieving an average
efficiency of over 37%. Consumption and emissions are
within the expected level, and the implementation of
the 15% reserve ensures the stability of the system
without drastically penalizing energy efficiency.
Based on the data collected from the Kongsberg
Simulator, two types of graphical representations were
obtained. The first one presents the cumulative
consumption and activation stages and the second one
shows the environmental footprint (CO2, SO2 and
NOx).
472
Figure 1. Load versus consumptions and generators
Figure 2. Load versus emissions
A linear increase is directly proportional to fuel
consumption. At maximum load, CO2 emissions reach
almost 10 tons/h, and SO2 emissions exceed 6 kg/h due
to the 0.1% sulphur in diesel. The data clearly
demonstrates that the strategy of using fewer
generators at high load (e.g. 5 units at 85% for 8500kW)
is superior to using the entire fleet at low load (7 units
at 60%), saving fuel and reducing mechanical wear.
From a long-term perspective, predictive maintenance
should be considered, including lube oil analysis,
vibration monitoring, gas analysis (Tier III
compliance), blackout recovery tests, and governor
checks. All mentioned above should be part of a very
well-thought-out plan.
4 CONCLUSIONS
Optimizing the parallel operation of diesel generators
on DP ships represents a trade-off between
thermodynamic efficiency and operational safety.
From the analyses carried out, it emerges that
operating the units at high loads (over 75%) is essential
to ensure complete combustion and extend the life of
the equipment by minimizing corrective maintenance.
The automatic system (PMS) should be calibrated to
start additional units only when the 85-90% threshold
is reached, thus providing a power reserve to protect
the vessel against sudden environmental variations.
Although using a larger number of generators at low
loads might seem safer, the negative impact on
emissions and engine wear invalidates this strategy. In
conclusion, the use of linear calculation algorithms and
KPI monitoring provides a solid foundation for
reducing the carbon footprint in the maritime industry.
Based on the simulations carried out and the
analysis of thermodynamic performance it is very
important to take into consideration a few factors. The
first is Power Management System Calibration with
Coupling Thresholds, only when the load exceeds 85-
90%, and maintaining a power reserve (Spinning
Reserve) of at least 15%. The Anti-Wet Stacking
protocol and intelligent rotation of diesel generators
during start-up should also be considered.
Future research should focus on identifying
efficient methods to support the transition from the use
of diesel engines to electric ones, as well as a gradual
replacement of those already in operation.
DISCLAIMER
The work has been carried out within GreenPort Alliance -
Funded by the European Union. However, the views and
opinions expressed are those of the authors only and do not
necessarily reflect those of the European Union or the
European Education and Culture Executive Agency
(EACEA). Neither the European Union nor the granting
authority can be held responsible for them (Project number:
101139879).
ACKNOWLEDGMENTS
The authors gratefully acknowledge the financial support
provided by Constanta Maritime University for the
publication of this article, through its research funding
programme.
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https://ieeexplore.ieee.org/document/7272043
