For mitigating the harmful environmental effects of greenhouse gas (GHG), which is emitted by conventional fossil fuels, the utilization of renewable resources is increasing day by day. The transport sector is one of the prime emitters of GHG and was responsible for 35% of the total energy consumed in 2014, of which 21% corresponded to passenger transport with an average consumption of 1.9 MJ/km [
1]. According to the International Maritime Organization (IMO), the carbon dioxide emission from shipping is equal to 2.2% of the global human made emissions in 2012, which is expected to rise to 50–250% by 2050 if no action is taken for reducing emission [
2,
3]. The battery powered electric fleet (EF) can be an alternate option for conventional diesel engine ferry for reduction of greenhouse gas. Like electric vehicles (EV), further research on charging technologies of EFs is required. There are basically three EV charging setups which can also be applicable for EF. The first setup is of 120 V AC with 6.5 A per hour charging, the second setup comprises of 220/240 V AC with up to 30 A per hour charging, and the third setup is up to 800 V DC with up to 300 A per 20–30 min charging [
4,
5]. Generally, the DC charging is called fast charging, and various charging methods and algorithms have been proposed for improving charging time, efficiency, and life cycle of different batteries in various studies. These methods include Constant Current (CC), Constant Voltage (CV), CC-CV, double loop control, fuzzy logic control, boost charge, pulse charge, droop control, quadratic optimization, gradient optimization, linear programming, game theory, and artificial neural network [
6,
7]. The most recommended and popular method for battery charging is CC-CV charging, which consists of trickle charge, constant current, constant voltage, and charge termination [
8,
9].
Like EV, when EF is connected to a power outlet, it can operate in two modes, (1) charging mode, which is called Grid to Ferry (G2F) mode, and (2) discharging mode, which is called Ferry to Grid (F2G) mode [
10]. However, in case of using EF in F2G mode depends on availability of EF in cold ironing stage. Like EV, EF can act as a fast charging load for G2F mode, whereas it acts as a power source in case of F2G mode [
11,
12]. The impacts of fast charging EF load on local grid mainly depends on number of connected EF to grid and charging and discharging characteristics of battery. The battery is charged either in coordinated mode or uncoordinated mode [
13]. In coordinated charging, charging is done during off peak hours of the day when the load demand is low whereas in uncoordinated charging, the charging is done irrespective of load demand during any time of the day [
14,
15]. Moreover, smart charging infrastructure is used by coordinated charging which helps in minimizing load burden on existing distribution system [
16]. Like Vehicle to Grid (V2G), F2G helps in retaining system stability by regulation of active power, supporting reactive power, load balancing, peak load shaving, and minimization of harmonics [
17,
18]. With increasing integration of green electricity from renewable sources to grid, a battery energy storage system (BESS) provides ancillary services like spinning reserve, voltage, and frequency control [
19]. Degradation of battery, upgradation of the existing grid, and extensive communication between EF and grid are challenging issues for F2G [
20,
21]. For uncoordinated charging, a fast charging battery generally consumes a large amount of power over a short time, and probable impacts of that on the grid are an increase of peak load demand with fluctuations of system voltage and frequency, which affect voltage regulation [
22], increment of power system losses [
23], and overloading of distribution transformers, distribution lines, and cables [
24,
25]. The power system stability greatly depends on the nature of the load, therefore an accurate load model for a fast charging battery is required to identify its impact on the grid [
26].
Though several research works have so far been conducted on impacts of fast charging of EV on electricity grid, very few similar research works have been conducted for EF. In this paper, a simulated model of grid interactive marine electrical system is designed by MATLAB Simulink in order to identify probable impacts of fast uncoordinated charging on electricity grids. Since the BESS of EF is charged without a smart coordinated charging schedule, the fast-charging consumes large amounts of power within a short time, which may create instability in the power system.
Section 2 discusses the probable impacts on power system stability due to uncoordinated fast charging in terms of the simulated model. The intermittent green electricity from photovoltaic (PV) and uncoordinated fast charging of EF causes instability in the electricity grid. The probable impacts on system stability due to sudden load increment is analogous to the inclusion of uncoordinated fast charging load to the power system is analyzed by simulated models of 5 bus and 7 bus networks. In
Section 2.1, the variation in system voltage and frequency are investigated in terms of voltage unbalance factor and total harmonic distortion (THD) by a MATLAB Simulink model of an IEEE 5 bus system. The transient stability, voltage real power (V-P) and voltage reactive power (V-Q) sensitivity analysis for different contingency scenarios are performed for a Power World Simulator based 7 bus system in
Section 2.2. A probabilistic simulation analysis is performed in this paper for identifying impacts of uncoordinated fast charging on electrical power systems. Finally, the paper ends with a discussion in
Section 3, where the findings of this paper are included, along with shortfalls and future work.