Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network

Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network Rajib Baran Roy, Sanath Alahakoon, Piet Janse Van Rensburg, Shantha Jayasinghe Arachchillage This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5985265/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The global maritime industry significantly contributes to greenhouse gas emissions in marine environments. To address this, there is a growing global initiative to adopt renewable-powered electric marine vessels, where storage plays a crucial role. This study delves into the potential impacts of charging electric ferries in coordinated mode on local distribution network by metaheuristic optimization. Using Gladstone Marina in Queensland, Australia, as a test case, the research employs DIgSILENT PowerFactory for power flow analysis, based on actual load data. The simulated network includes four BESSs (Battery Energy Storage Systems) representing proposed charging stations. For analysis, MATLAB Simulink based BESS’s dynamic model is included in the simulated network of DIgSILENT. A novel control algorithm is used for controlling and optimizing the operation of BESSs according to load demand and status of network’s system parameters. Python based control algorithm implements a balanced hybrid GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization) metaheuristic optimization which ensures sequential operation of BESSs according to their SOC (State of Charge) in coordinated mode. Initially, power flow analysis is conducted without BESS integration, termed as the base case, at 50% and 80% load capacities of transformers. For impact analysis, power flow analysis is performed by integrating BESSs to simulate fully utilized charging stations at 50% and 80% load increment in coordinated charge-discharge and only charge coordinated modes. Results show a 1%-1.5% increase in bus voltages in coordinated modes as load escalates. Transformer loading decreases by 3%-4% in coordinated charge-discharge mode, while line loading drops by 2.5%-3.5%, contributing to reduced system current and power. The transformer loading and line loading remain same to base case in only charge coordinated mode. The findings from the time-based quasi-dynamic mode in DigSilent suggest that coordinated charge-discharge imposes beneficial effect on the system parameters of the test network. By aligning charge-discharge times with load demand, coordinated mode enables BESSs to participate in peak shaving of the test distribution network. This peak shaving strategy indicates that electric ferries at dockyards can serve as a spinning reserve for the shore-side distribution network. Physical sciences/Energy science and technology/Energy storage Physical sciences/Energy science and technology/Renewable energy Physical sciences/Engineering/Electrical and electronic engineering Electric Ferry BESS Coordinated Mode DIgSILENT PowerFactory Control Algorithm Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 Figure 16 Figure 17 Figure 18 Figure 19 Figure 20 Figure 21 Figure 22 1. Introduction The maritime industry significantly contributes to greenhouse gas emissions, [ 1 ] accounting for about 3% of the total [ 1 ] global emissions, which amounts to approximately 1 billion tons of CO 2 and other greenhouse gases [ 1 , 2 ]. The IMO (International Maritime Organization) enforces regulations targeting air pollution from conventional diesel vessels to combat this environmental issue. Without intervention, projections suggest that CO 2 emissions can increase to an alarming level by 2050 [ 2 ]. As global concerns over environmental sustainability grow, both the IMO and the United Nations are strongly advocating for the adoption of environmentally responsible policies in the maritime industry. The IMO's resolution MEPC.304(72) outlines a target to reduce CO2 emissions from international shipping by at least 50% by 2050, relative to 2008 levels [ 3 ]. This ambitious goal underscores the pressing need for alternative energy solutions, such as hybrid and renewable energy-powered electric vessels, to curb emissions. Advances in energy storage technologies, including batteries and super-capacitors, are expanding the options for cleaner maritime power sources. By combining renewable energy systems like solar PV (photovoltaic) panels and fuel cells with efficient storage solutions, it is possible to significantly reduce CO2 emissions from the shipping sector. However, despite their environmental benefits, concerns still need to be addressed about [ 1 ] the financial viability of these alternative electricity options [ 1 ] due to their high initial costs. Optimizing the use of fuel cells and batteries in response to varying load demands, while incorporating advanced optimization methods that account for operational limitations, presents promising solutions for overcoming current challenges. These strategies can help speed up the integration of hybrid and alternative energy-powered electric ferries into the maritime sector. The transition to electric vessels offers a promising solution to address environmental concerns linked to traditional diesel-powered ships. However, ensuring stability becomes crucial as power systems encounter growing demands and nonlinear loads [ 1 ]. Incorporating EFs (electric ferries) into current power grids demands thorough planning, as it may pose challenges to grid stability. While many studies have explored the effects of EV (Electric Vehicle) charging on electrical networks [ 4 ], research specifically focused on EF charging remains scarce [ 5 ]. This section reviews key research on EV charging, drawing parallels to the potential impacts of EF charging [ 5 ]. Findings indicate that EV charging can influence various grid parameters, leading to changes in system dynamics, higher energy losses, and diminished efficiency and lifespan of grid equipment [ 6 – 8 ]. Crozier et al. investigate the impacts of EV charging on the UK’s power system, which demonstrates uncontrolled EV charging in the UK can increase peak demand by 8 GW, but this can be mitigated through controlled charging, thereby eliminating the need for additional generation infrastructure [ 9 ]. This research also indicates that upgrading the electricity network, ranging from 9–28% is required for uncontrolled charging at the distribution level, while controlled charging reduces these requirements significantly [ 9 ]. Research is conducted by Khan et al. on the potential impact of EV charging on grid demand, which implies that EV charging can potentially augment the grid demand, particularly during peak charging periods [ 10 ]. This research also identifies that the rise in peak load is influenced by factors such as the number of EVs, charging patterns and the availability of charging infrastructure [ 10 ]. This research suggests that the widespread integration of EVs in existing power systems may lead to a notable increase in peak electricity demand [ 10 ]. Jones et al. simulate two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on ten actual distribution feeders supporting residential, commercial, and industrial loads using GPS data to reflect actual driving patterns for simulating power flow across feeders [ 11 ]. Results show modest voltage impacts (less than 0.01 p.u.), noticeable line loading increases (up to 15%), and slight shifts in peak load times (about 1 hour) for residential and mixed feeders, while no shift for industrial and 8 hours shift for commercial feeders [ 11 ]. Wang et al. conduct research on power quality challenges due to fast EV charging of DC fast chargers, which demonstrates that fast charging poses challenges, including voltage profile deterioration, power losses and excessive harmonic emissions which arise due to high-charging power, centralized load demand and pulsating charging nature [ 12 ]. This research concludes that real-time charging, proper management of charging behavior, integration of energy storage systems to compensate for pulsating loads, design of filters with various damping techniques for harmonic mitigation and impedance-based approaches for controlling fast charger front-end converters can potentially offset the adverse impacts of fast EV charging [ 12 ]. Olcay et al. assess the impact of rising EV use on electricity grids, focusing on how EV charging stations affect power systems by examining issues like phase imbalances, harmonic formation, and energy quality using IEEE 6 bus, IEEE 14 bus and IEEE 30 bus test systems [ 13 ]. The research predicts how increased EV charging and grid loads affect harmonic effects and overall grid performance, using artificial neural networks and proposes solutions for managing these impacts on power networks [ 13 ]. Mowry et al. investigate how highway electric vehicle charging impacts the grid and explore energy storage as a potential solution, emphasizing the significance of HFC (Highway Fast Charging) infrastructure in tackling range anxiety and advancing economy-wide decarbonization [ 14 ]. This research uses a detailed grid operations model for analyzing the effects of expanded HFC infrastructure on the 2033 Texas power system under different EV penetration scenarios [ 14 ]. Results reveal increased operational costs and highlight transmission congestion on feeder lines serving HFC stations as a critical issue[ 14 ]. Purnell et al. examine the effects of charging electric ferries [ 1 ] and public transit on energy use in New South Wales [ 1 ], Australia, which employs [ 1 ] a tool to model BEPT (Battery Electric Transit) impact on grid energy [ 1 ] by revealing significant strain on low voltage networks [ 15 ]. The study suggests a BEPT shift may increase annual peak demand at local substations by up to 17% and intensify evening peak periods by 20–30% [ 15 ]. Additionally, a complete transition to BEPT in NSW can raise annual electricity consumption by 1.28–1.34% and peak demand by 1–3% [ 15 ]. Limput et al. demonstrate that residential slow charging poses a more positive impact on the distribution grid than fast charging and strategies like uncoordinated charging and off-peak charging while exploring to understand their grid impact [ 16 ]. The research implies that fast charging reduces EV hosting capacity by up to 10%, whereas grid-friendly slow charging significantly increases capacity, which may offset fast charging effects [ 16 ]. This research emphasizes coordinated charging strategies and the benefits of EV-based peak shaving for managing grid impact, noting that fast charging infrastructure requirements are less significant than slow charging [ 16 ]. Sehimi et al. show that substituting traditional DC fast chargers with V2V ( Vehicle to Vehicle) chargers significantly reduces overall losses [ 17 ]. The research reports a 12.42% reduction in losses in the primary distribution system and a 14.08% reduction in the second secondary distribution system [ 17 ]. Furthermore, the study notes that V2V's impact on losses in LV grids is minimal, with line losses being more prominent in DCFC (DC Fast Charging) due to their correlation with line length [ 17 ]. Replacing conventional DC chargers with V2V chargers does not necessitate grid integration, lowering costs and overall losses on the distribution grid [ 17 ]. A study is conducted by Mojumder et al. for identifying impacts of integrating EVs and V2G (Vehicle-to-Grid) technology, revealing that V2G can optimize power demand and enhance innovative grid sustainability [ 18 ]. However, comprehensive research on its operation, EV types, policies, business models and implementation challenges is limited [ 18 ]. The study highlights V2G opportunities, necessary policies, business models and the challenges of integrating V2G with power grids and EV batteries [ 18 ]. It detects significant challenges, including the lack of a solid V2G business model, insufficient incentives, stress on EV batteries, inadequate bidirectional chargers and potential unscheduled V2G practices [ 18 ]. Bayeni et al. explore the shift to electric transportation, covering its environmental impact, battery advancements, sustainability and effects on consumers, utilities, and the economy [ 19 ]. Key findings emphasize the need for cleaner electricity, the cost-effectiveness of EVs driving adoption and the challenge of EV charging rates [ 19 ]. While short-term grid impacts are minimal, long-term higher EV penetration requires charging management [ 19 ]. Bi-directional V2G (Vehicle-to-Grid) systems enhance grid [ 18 ] efficiency and reliability and V2V (Vehicle-to-Vehicle) charging helps manage high EV loads [ 19 ]. Nutkani et al. address the urgent need to understand and maximize EV hosting capacity on the grid without significant orchestration [ 20 ]. Considering steady-state and quasi-dynamic simulations on real urban and rural networks [ 20 ], the study estimates EV grid impact and hosting capacity across various scenarios, including customer loads, regulated and unregulated EV charging and seasonal variations [ 20 ] in load and solar PV output [ 20 ]. The analysis [ 20 ] highlights the impact of EVs on existing distribution networks and [ 21 ] demonstrates how load management and regulated charging can manage this impact effectively [ 20 ]. Gomez Ramirez et al. [ 1 ] outline a methodology for evaluating [ 1 ] the impact of EVs on [ 22 ] Costa Rica's power grid, considering EV penetration [ 1 ], user preferences, charging habits, and fleet growth [ 1 ] by using ETAP software [ 23 ], which simulates power flow, demand and voltage levels up to 2040 [ 22 , 23 ]. Key findings indicate voltage declines and demand increases after 2030, especially in distribution grids and highlight infrastructure issues starting in 2030, the need for stability assessments, public policy-driven infrastructure investments and the importance of incorporating EV growth and infrastructure improvements in system planning [ 23 ]. Deilami et al. explore electric vehicles EVs in the smart grid, providing solutions for EV charging strategies to manage large-scale adoption by reviewing EV battery infrastructure and the impacts of uncontrolled charging and presenting solutions for integrating EVs into the grid [ 24 ]. The study includes simulations of two controlled strategies, MSS (Maximum Sensitivity Selection) and GA (Genetic Algorithm) optimization [ 25 ] on a modified IEEE 23 kV medium voltage distribution system and a low voltage residential network [ 24 , 25 ]. This comparative analysis validates these approaches, demonstrating their effectiveness in optimizing EV charging strategies[ 24 ]. Gilleran et al. evaluate the impact of EV charging stations on the power demand of [ 26 ]a retail big box grocery store by considering various station sizes, charging power levels and utilization factors across different climates and seasons [ 27 ]. As EV penetration rises and fast chargers with power levels up to 350 kW become common, the effect on the grid becomes significant [ 27 ]. This research uses three distinct rate structures to measure changes in monthly peak power demand, electricity usage and annual electricity bills [ 27 ]. Results indicate that EV stations can increase a store's peak power demand by over 250%, including cold climates combined with rate structures featuring high demand charges, which are most susceptible, potentially raising annual electricity bills by up to 88% [ 27 ]. This research seeks to assess the potential effects of EF charging on the distribution network by using a test network in regional Australia [ 24 ]. Given that EF implementation is still in its early stages in Australia, actual charge and discharge data for EF operations are not available. The Gladstone marina distribution network, a component of the larger Gladstone, Queensland network, is selected as the test case, incorporating four proposed charging stations. A simulated distribution network is developed using daily load demand data from the [ 1 ] 11 kV marina feeder, with four BESSs representing [ 1 ] the storage capacities of the charging stations [ 1 ]. BESSs play a crucial role in powering EFs amidst nonlinear propulsion loads affected by sea weather and integrated electronic devices, requiring robust handling of rapid power demand changes [ 24 ]. DIgSILENT PowerFactory software analyzes the [ 1 , 4 ] probable impacts of EF charging [ 1 , 4 ] on the test network [ 1 ]. The DLL (Dynamic Link Library) of MATLAB Simulink based BESS’s model is linked to the synchronous generator block of DIgSILENT for impact analysis. Python based a balanced hybrid metaheuristic optimization algorithm is used to control and optimize the operation of BESSs according to load demand, SOC of BESSs, and [ 1 ] system parameters of the test distribution network [ 1 ]. Power flow analysis is conducted under various load demand scenarios, revealing insights into system parameters such as transformer loading, line loading and voltage [ 8 ]. This research examines how the storage charge and discharge cycles of EFs affect the distribution network when operating in a coordinated mode [ 24 ]. A comprehensive analysis is conducted to assess the impact on various network parameters [ 28 ]. Although the analysis relies on simulations, the results provide useful guidance for the deployment of EF charging stations in portside distribution networks [ 1 , 29 ]. The formation [ 1 ] of the test distribution network [ 1 ] according to the [ 1 ] collected network information for probable impact analysis is discussed in section 2 . The network model is based on the daily load demand [ 1 ] data from the 11 kV marina feeder [ 1 ], with four BESSs used to simulate the [ 1 ] capacities of the proposed charging stations [ 1 ]. Section 3 elaborates on dynamic modelling and hybrid metaheuristic optimization driven control algorithm for managing the charge-discharge of BESS in accordance with load demand. The specifications of Corvus Orcha ESS are used for BESS modeling in MATLAB Simulink. This section discusses the formation of DLL for integrating the BESS model to a simulated DIgSILENT PowerFactory network. The construction of a balanced hybrid GA-PSO-BFO metaheuristic optimization-based control algorithm is also depicted in this section. Sections 4 and 5 address the impacts [ 1 ] on system parameters due to EF charging by [ 1 ] increasing load demand to 50% and 80% of the test network and considering the maximum charging capacities of the proposed charging stations [ 1 ] in coordinated mode. The analysis emphasizes the impacts of transformer loading, line loading and bus voltage. A discussion on research findings is included in section 6 which highlights the relative comparison among simulation results of different optimization algorithms. The overall research procedure is concluded in section 7 , which also highlights the limitations of this research. The direction of future research by utilizing actual EF charging patterns and [ 1 ] the impacts of [ 1 ] uncoordinated EF charging is [ 1 ] emphasized in this section. 2. Test Distribution Network Analyzing the impact of rapid-charging electric ferries [ 1 ] on the electricity network’s parameters [ 1 ] involves assessing a test distribution network near Gladstone Marina [ 1 ]. This network mirrors the load demand of the 11 kV Marina feeder [ 1 ], with 129 electricity buses [ 1 ] allocated based on distribution voltage levels of 11 kV and 0.415 kV [ 1 ]. Google Earth Pro is utilized to determine [ 1 ] the positions and capacities of distribution transformers [ 1 ], owing to its high-resolution imagery and advanced measurement capabilities [ 1 ]. Leveraging a detailed [ 1 ] 11 kV network image from Google Earth Pro [ 1 ] aided in pinpointing transformers and [ 1 ] proposed charging stations in Gladstone Marina [ 1 ]. The test distribution network [ 1 ] is anchored on the 11 kV marina feeder [ 1 ], originating from the Clinton 33 kV/11 kV distribution substation [ 1 , 30 ]. From this substation, five 11 kV feeders [ 1 ] extend outward: Clinton Park feeder, Callemondah Drive feeder, Hason Road feeder, Kin Kora feeder, and Marina feeder [ 1 ]. The schematic diagram of the test distribution network [ 1 ] is illustrated in Fig. 1 [ 1 , 28 ], with the network depicted by red and blue lines [ 1 , 31 ]. Figure 1 showcases the distribution network, highlighting four proposed charging stations intended [ 1 ] to integrate into existing ferry terminals [ 1 ], such as MIPEC, Sea Link, Curtis Ferry services [ 1 ] and Heron Island Ferry Terminal [ 1 ]. These figures are generated using Google Earth Pro [ 1 ]. Data on the electrical power consumption of the 11 kV Marina feeder [ 1 ] is obtained from Ergon Energy [ 1 ], a primary electricity provider in Australia that supplies electricity to communities in urban and regional Queensland. The daily load profile of the test distribution network [ 1 ] is represented by 2 which is prepared according to the [ 1 ] 11 kV Marina feeder-based electricity network [ 1 ] data without inclusion of EF charging station load. The peak real and reactive power demand of the 11 kV Marina feeder [ 1 ] is proportionally divided among loads of the [ 1 ] test distribution network according to the respective [ 1 ] transformer’s rated capacity. The capacities of transformers are collected from Ergon Energy and the standard PQ load is imitated as load in the test network. In order to distinguish voltage levels for buses, A and B notation [ 1 ] are used for 11 kV and 415 V respectively [ 1 ]. The 11 kV/0.415 kV [ 1 ] distribution transformers are placed between [ 1 ] 11 kV and 415 V buses to connect the load to the 415 V buses. Four probable charging stations are [ 1 ] associated with 415 V buses, which are [ 1 ] CQUni_BJDFerryTerminal_Opp, Marina_Ave_Pioneer_Seafoods, BryanJordanDr_MarinaAveue [ 1 ] and Marina_Ave_Slipway to identify the impacts of EF charging. The optimal capacities of four charging stations [ 1 ] are 300 kWh (200 kW), 250 kWh (150 kW), 400 kWh (300kW) and 400 kWh (300kW) respectively [ 1 ]. The capacities [ 1 ] of these charging stations are determined [ 1 ] based on the respective distribution transformers' capacities [ 1 ]. The charging stations add additional load to the test network besides the daily load demand of Fig. 2 . The optimal capacities of BESSs are defined by considering distribution network constraints so that the key system parameters such as bus voltage, line current and power loss remain within the operational limits of the distribution network. The distribution network constraints are defined according to the Australian NER (National Electricity Rule), such as system voltage should remain within + 10% and − 6% of its nominal value of 1.0 p.u. (per unit), line current should not exceed its rated capacity and power load should be kept within 10% of total power consumption [ 32 , 33 ]. The terminology of buses is defined according to the location of distribution transformers in Google Earth Pro. The type of conductors and cables which are used for the marina feeder are Pluto (19/3.75 AAC), Wasp (7/0.173 AAC), Moon (7/4.75 AAC) and U/G 11 kV 185 mm 2 Aluminum Triplex XLPE PVC/HDPE. In designing the test distribution network, the line parameters and respective lengths are coordinated according to the type of conductors and cables. The length of each line is approximated by considering the distance from the respective bus, which is taken from Google Earth Pro. Figures 3 , 4 and 5 represent the simulated test network, which is designed by using DIgSILENT PowerFactory. The overall test distribution network is demonstrated by three interconnected networks, shown in those figures. In Fig. 5 , the four BESSs are shown as circles, symbolizing the combined charging capacities of the four proposed charging stations at Gladstone Marina. The charging and discharging schedules of the BESSs are examined through quasi-dynamic power flow analysis to evaluate the potential effects of EF charging on the test distribution network [ 1 , 28 ].The impact analysis is [ 1 ] performed by increasing load demand to 50% and 80% of the actual load of the test network. Two operational modes are selected: one is only charged coordinated mode and another is coordinated charge-discharge mode. “Initially, the power flow analysis is conducted without integrating BESSs, which is termed as base case” [ 1 ]. Afterwards, power flow analysis [ 1 ] is conducted in [ 1 ] coordinated modes by incorporating BESSs into the test network, with base case results [ 1 ] serving as a reference for [ 1 ] relative comparison. The charging and discharging time of BESSs are aligned with the load demand of the test distribution network in coordinated modes. In this mode, charging occurs during off-peak hours and discharging during peak hours to [ 34 ] maximize system efficiency [ 35 ]. The charging time is set at 1 am to 5 pm (off-peak hours) and the [ 36 ] discharging time is set at 9 am to 4 pm (peak hours), aligned with the daily load pattern of the 11 kV marina feeder. The maximum charging time is defined at 1 am as 3 am [ 37 ] and the maximum discharging time is defined as 1 pm to 2 pm [ 37 ] according to the off-peak and peak demand of the test network [ 1 ] respectively. The SOC is selected to vary from 20–80%, so BESSs cannot charge over 80% SOC and discharge below 20% SOC. A control algorithm is formulated to control and optimize the charge and discharge of [ 1 ] BESSs according to load demand [ 1 ], BESS’s SOC and the permissible limit of the test network’s system parameters. MATLAB Simulink-based BESS dynamic model and Python-based proposed control algorithm are integrated into the simulated test distribution network of DIgSILENT Power Factory to keep synchronism of coordinated charge-discharge with load demand and system parameters. 3. BESS Modelling and Control Algorithm In this study, MATLAB Simulink is used to model four BESSs based on the specifications of the Corvus Orca ESS, which is a popular energy storage solution for marine applications. The specification of the single module Corvus Orcha ESS is shown in Table 1 [ 38 ]. Various combinations of Corvus Orcha ESS modules can represent the capacities of four BESSs. The dynamic attributes of BESSs are defined according to the dynamic properties of the lithium-ion battery. The dynamic properties of BESSs are included in the MATLAB model so that it can respond according to the load demand of the network during coordinated charge-discharge. The selected dynamic properties are mentioned in tabular form in Table 2 . The specific series parallel configurations to meet each BESS’s kWh and kW capacities are 54 modules (9 modules in series, 6 parallel strings) for BESSs A and B with 400 kWh (300kW) capacity, 36 modules (9 modules in series, 4 parallel strings) for BESS C with 300 kWh (200 kW) capacity and 27 modules (9 modules in series, 3 parallel strings) for BESS D with 250 kWh (150 kW). Table 3 elaborates the calculation to determine the number of modules with series-parallel combination for BESSs. To mimic the actual BESS’s response in DigSilent, its dynamic properties are included in the simulated model. Figure 6 depicts the block diagram of the MATLAB Simulink-based BESS model. The conventional steps for DLL formation are represented in Fig. 7 . Figure 7 illustrates the integration process of the BESS model, specifically the dynamic characteristics of the Corvus Orcha ESS with an external application through the creation of a DLL. The process begins with the DLL Generator, where the BESS model is designed and validated in the MATLAB environment and later, it is prepared for compilation. The MATLAB compiler then converts this validated BESS model into a DLL, ensuring that the model’s functionalities are preserved in a format that external applications can access. The DLL user program represents any software capable of utilizing DLLs, providing a way to integrate the BESS model outside MATLAB. The external application, in this case, DIgSILENT PowerFactory, can load and call the DLL functions and thereby integrate the BESS model into its simulation environment. The DLL interface/API defines how the external application interacts with the DLL, which ensures proper communication and functionality. Finally, in the load/call DLL function step, DIgSILENT PowerFactory dynamically loads the DLL and invokes its functions to incorporate the detailed dynamic characteristics of the BESS model into the simulated distribution network. The overall process demonstrates how DLL formation allows the BESS model, encapsulated with detailed dynamic characteristics from MATLAB, to be seamlessly integrated into larger power distribution network simulations, enhancing the accuracy and performance of energy storage system analyses. The overall process demonstrates how DLL formation allows the BESS model, with its detailed dynamic characteristics from MATLAB, to be seamlessly integrated into the simulated power distribution network of DIgSILENT PowerFactory, enhancing the accuracy and performance of storage system analyses. Table 1 Specification of single module Corvus Orcha ESS Property Type/Value Battery Cell Chemistry Lithium ion NMC / graphite Single Module Size / Increments 5,6 kWh / 50 VDC Single Module Capacity 128 Ah Single Pack Range 38–136 kWh / 350–1200 VDC Max Gravimetric Density - Pack 77 Wh/kg | 13 kg/kWh Max Volumetric Density - Pack 88 Wh/l Table 2 Dynamic Properties of BESS Dynamic Property Value SOC limit 20%-80% Internal Resistance 0.013 ohm Charge Cycle 2500 Fully charged voltage 438 V Nominal discharge current 130.5 A Cut off Voltage 311 V Charge and discharge efficiency 90% Self-discharge rate 0.1% Response time 1 second Table 3 Selection of Corvus Orcha ESS module combination according to BESS capacity BESS Capacity Mathematical Calculation BESS A 400 kWH (300 kW) BESS B 400 kWH (300 kW) Capacity Calculation : Total kWh required: 400 kWh Single module capacity: 5.6 kWh Number of modules required: \(\:\frac{400\:\text{k}\text{W}\text{h}}{5.6\:\text{k}\text{W}\text{h}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}=71.43\approx\:72\:\:\) Power Calculation : Total kW required: 300 kW Operating voltage: 438 V Current requirement: \(\:\frac{300\:\text{k}\text{W}}{438\:\text{V}}\:\approx\:685\:\text{A}\:\) Nominal discharge current per module: 130.5 A Number of parallel strings: \(\:\frac{685\:\text{A}}{130.5\:\text{A}/\text{s}\text{t}\text{r}\text{i}\text{n}\text{g}}\) \(\:\approx\:\) 5.25 \(\:\approx\:\) 6 Series Configuration : Module voltage: 50 V Modules in series: \(\:\frac{438\:\text{k}\text{W}\text{h}}{50\:\text{V}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}\) \(\:\approx\:\) 9 Final Configuration : Modules in series: 9 Parallel strings: 6 Total modules: 9X 6 = 54 BESS C 300 kWh (200 kW) Capacity Calculation : Total kWh required: 400 kWh Single module capacity: 5.6 kWh Number of modules required: \(\:\:\frac{300\:\text{k}\text{W}\text{h}}{5.6\:\text{k}\text{W}\text{h}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}=53.57\approx\:54\) Power Calculation : Total kW required: 300 kW Operating voltage: 438 V Current requirement: \(\:\frac{200\:\text{k}\text{W}}{438\:\text{V}}\:\approx\:456.62\:\text{A}\) Nominal discharge current per module: 130.5 A Number of parallel strings: \(\:\frac{456.62\:\text{A}}{130.5\:\text{A}/\text{s}\text{t}\text{r}\text{i}\text{n}\text{g}}\:\approx\:3.5\:\approx\:4\:\) Series Configuration : Module voltage: 50 V Modules in series: \(\:\frac{438\:\text{V}}{50\:\text{V}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}\:\approx\:9\) Final Configuration : Modules in series: 9 Parallel strings: 4 Total modules: 9X 6 = 36 BESS D 300 kWh (200 kW) Capacity Calculation : Total kWh required: 400 kWh Single module capacity: 5.6 kWh Number of modules required: \(\:\frac{250\:\text{k}\text{W}\text{h}}{5.6\:\text{k}\text{W}\text{h}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}=44.64\approx\:45\) Power Calculation : Total kW required: 300 kW Operating voltage: 438 V Current requirement: \(\:\frac{150\:\text{k}\text{W}}{438\:\text{V}}\:\approx\:342.47\:\text{A}\) Nominal discharge current per module: 130.5 A Number of Parallel Strings: \(\:\frac{342.\:47\:\text{A}}{130.5\:\text{A}/\text{s}\text{t}\text{r}\text{i}\text{n}\text{g}}\:\approx\:2.62\:\approx\:3\) Series Configuration : Module voltage: 50 V Modules in series: \(\:\frac{438\:\text{V}}{50\:\text{V}/\text{m}\text{o}\text{d}\text{u}\text{l}\text{e}}\:\approx\:9\) Final Configuration : Modules in series: 9 Parallel strings: 3 Total modules: 9X 3 = 27 A control algorithm is formulated by Python in order to sequentially charge and discharge four BESSs according to their SOCs and [ 1 ] load demand of the test distribution network [ 1 ]. This algorithm is integrated into DIgSILENT’s simulated test network. The controller checks [ 1 ] the load demand of the network [ 1 ] in time-based simulation and allows the BESSs to charge and discharge according to time settings in coordinated mode. The control algorithm retains a synchronism between the load demand and BESS’s SOC. It keeps the BESS’s SOC within 20–80% so that the BESSs cannot charge above 80% and discharge below 20%. Moreover, it checks the network’s system voltage and power factor at each hour for daily power flow analysis to limit them near unity. BESS can provide significant voltage and power factor support to an electricity network. By injecting or absorbing reactive power, BESS assists in maintaining voltage levels within desired limits and corrects the power factor, ensuring efficient power usage. It can respond quickly to fluctuations due to its power electronics-based inverter, making real-time adjustments possible. This automated control enhances grid stability and reliability, especially during peak and off-peak hours [ 39 ], thereby reducing losses and optimizing power delivery [ 39 ]. The adjust_voltage and adjust_power_factor functions in the Python-based proposed control algorithm assist DIgSILENT PowerFactory in checking and adjusting system voltage and power factor according to hourly load demand in each power flow iteration. The adjust_voltage function monitors the system voltage at critical points and compares it to the target nominal voltage, 1 per unit. If deviations are detected, the BESS injects or absorbs reactive power to correct the system voltage. Similarly, the adjust_power_factor function monitors the power factor to keep it near unity for efficient power utilization. If the power factor deviates from the target, the BESS adjusts by providing the necessary reactive power. Both functions operate sequentially to ensure the network remains within optimal operating conditions, thereby preventing instability. Since the BESSs charge during off-peak hours and discharge during peak hours in coordinated mode, the control algorithm works appropriately, which can be identified from the power flow results [ 35 ]. The pseudo-code of the control algorithm is represented by code A. A balanced hybrid GA-PSO-BFO [ 40 ] metaheuristic optimization is incorporated in the control algorithm for optimal use of storage capacities of BESSs according to load demand so that the system parameters of the network can [ 1 ] remain within the permissible limit. Integrating these algorithms aims to harness their distinct exploration and exploitation mechanisms for improved convergence and solution quality. The hybrid approach can leverage their complementary strengths for more robust optimization performance. The balanced GA-PSO-BFO optimization enhances traditional GA-PSO-BFO methods by maintaining equilibrium among each optimization algorithm. Unlike conventional approaches that blend these techniques without specific balance, the balanced method dynamically adjusts each algorithm's influence based on the problem's requirements. This customization improves adaptability and performance, facilitating more effective exploration and exploitation of solution spaces. By finely tuning the contributions of GA, PSO, and BFO, balanced optimization aims to accelerate convergence, enhance solution accuracy, and bolster overall robustness in solving complex optimization problems. Critical aspects of implementing a balanced approach include integrating the algorithms seamlessly, defining adjustable parameters for dynamic contribution tuning, designing a robust fitness function aligned with optimization goals, employing adaptive control mechanisms to balance algorithm influence, and validating the approach against power flow-based scenarios to ensure superior performance in diverse optimization challenges. Code B represents the pseudo code of a Python-based balanced hybrid optimization algorithm. The block diagram of Fig. 8 represents the relationship between the MATLAB-based BESS Model and Python-based control algorithm to simulate the selected network in DIgSILENT. The iteration graph of Fig. 9 depicts the performance of the hybrid [ 41 ] GA-PSO-BFO optimization algorithm over 100 iterations [ 41 ]. The fitness values oscillate widely for the first 99 iterations, indicating exploration and instability. At the 100th iteration, the fitness value stabilizes, signifying that the algorithm has converged to an optimal or near-optimal solution. The evolution of the pareto front depicted in Fig. 10 shows how the algorithm manages the trade-offs between bus voltage, line current, and power through successive iterations of power flow analysis. Each point on the graph signifies a non-dominated solution, demonstrating the algorithm's capacity to balance multiple objectives without prioritizing one excessively over the others. As the algorithm progresses, the curve illustrates the migration of these solutions toward an optimal pareto front, highlighting the variety and quality of the solutions produced. The broad distribution of points indicates the algorithm's strong exploration abilities, covering a wide array of potential solutions. Over time, the movement of these points toward a clearer front suggests that the algorithm is converging toward a set of optimal trade-offs, where improving one objective does not greatly affect the others. This curve is a key visualization tool, showcasing the optimization process and providing insight into the algorithm’s effectiveness in achieving a well-distributed and convergent set of optimal solutions in the network's power flow analysis. Code A: Pseudo Code of BESS controller Function initialize_storage_controller(distribution_network, battery_storage): storage_controller = Create new StorageController object storage_controller.distribution_network = distribution_network storage_controller.battery_storage = battery_storage storage_controller.off_peak_hours = [0, 6] # Off-peak hours (midnight to 5 AM) storage_controller.peak_hours = [ 10 , 18 ] # Peak hours (10 AM to 4 PM) storage_controller.soc_threshold_high = 0.8 # 80% State of Charge threshold storage_controller.soc_threshold_low = 0.2 # 20% State of Charge threshold Return storage_controller Function check_load_demand(storage_controller): current_time = Get current hour from system time load_demand = storage_controller.distribution_network.get_load_demand() voltage = storage_controller.distribution_network.get_voltage() power_factor = storage_controller.distribution_network.get_power_factor() If current_time in range(*storage_controller.off_peak_hours): If storage_controller.battery_storage.soc < storage_controller.soc_threshold_high: charging_power = storage_controller.battery_storage.charge() storage_controller.distribution_network.reduce_load_demand(charging_power) Elif current_time in range(*storage_controller.peak_hours): If storage_controller.battery_storage.soc > storage_controller.soc_threshold_low: discharging_power = storage_controller.battery_storage.discharge() storage_controller.distribution_network.increase_load_demand(discharging_power) If voltage > 1: storage_controller.distribution_network.adjust_voltage(1) If power_factor > 1: storage_controller.distribution_network.adjust_power_factor(1) # Define functions for DistributionNetwork class methods Function get_load_demand(): # Method to get current load demand Pass Function get_voltage(): # Method to get current voltage Pass Function get_power_factor(): # Method to get current power factor Pass Function reduce_load_demand(amount): # Method to reduce load demand by specified amount Pass Function increase_load_demand(amount): # Method to increase load demand by specified amount Pass Function adjust_voltage(target_voltage): # Method to adjust voltage to target value Pass Function adjust_power_factor(target_power_factor): # Method to adjust power factor to target value Pass # Define functions for BatteryStorage class methods Function initialize_battery_storage(max_capacity): battery_storage = Create new BatteryStorage object with max_capacity battery_storage.soc = 0.5 # Initial State of Charge Return battery_storage Function charge(battery_storage): charging_power = Minimum of ((battery_storage.max_capacity - battery_storage.soc) * battery_storage.max_capacity, 1.0) battery_storage.soc + = charging_power / battery_storage.max_capacity Return charging_power Function discharge(battery_storage): discharging_power = Minimum of (battery_storage.soc * battery_storage.max_capacity, 1.0) battery_storage.soc -= discharging_power / battery_storage.max_capacity Return discharging_power # Main program logic distribution_network = Initialize DistributionNetwork object battery_storage = initialize_battery_storage(max_capacity) storage_controller = initialize_storage_controller(distribution_network, battery_storage) While True: check_load_demand(storage_controller) Code B: Pseudo code of Python based balanced hybrid optimization algorithm Initialize BESSController with parameters (max_soc, min_soc, charge_rate, discharge_rate) Initialize Load Demand, System Voltage Function charge(amount): Increment current_soc by amount Ensure current_soc = min_soc Function optimize_charge(load_demand, off_peak_hours, peak_hours): Set GA parameters (ga_pop_size, ga_generations, ga_mutation_rate) Set PSO parameters (pso_swarm_size, pso_iterations, pso_c1, pso_c2) Set BFO parameters (bfo_swim_length, bfo_tumble_count, bfo_population_size) Initialize population with random SOC values within [0, max_soc] Repeat for ga_generations times: Perform GA selection, crossover, and mutation Evaluate fitness of GA population Select top solutions for PSO from GA population Initialize PSO parameters Repeat for pso_iterations times: Update PSO velocity and position Evaluate fitness of PSO population Update PSO best position Initialize BFO population with random SOC values within [0, max_soc] Repeat for bfo_swim_length times: Perform chemotaxis and reproduction Evaluate fitness of BFO population Merge GA, PSO, and BFO populations Choose solution with lowest fitness value If off_peak_hours: Charge BESS according to charge_rate Else if peak_hours: Discharge BESS according to discharge_rate Main program logic: load_demand, voltage, power_factor = get_distribution_network_data() Call optimize_charge(soc, max_soc, off_peak_hours, peak_hours, load_demand) Adjust voltage and power factor: If voltage > target_voltage: Adjust voltage to target_voltage If power_factor > target_power_factor: Adjust power_factor to target_power_factor 4. Impact Analysis with 50% Loading It is observed that the transformers are loaded to 15%-20% according to [ 1 ] the daily load demand of the test [ 1 ] distribution network [ 1 ]. The load demand [ 1 ] increased to 50% [ 1 ] according to each transformer’s capacity for identifying [ 1 ] the probable impacts of [ 1 ] charging electric ferry [ 1 ]. The capacities of four connected BESSs represent the capacities of the proposed electric ferry charging stations [ 1 ]. In impact analysis, the charging capacities [ 1 ] of the proposed stations are [ 1 ] supposed to be fully utilized by electric ferries in coordinated mode. The test distribution network [ 1 ] without integrating BESSs is termed [ 1 ] the base case [ 1 ]. The power flow [ 1 ] results in the coordinated mode of the network with BESSs are compared with those of the base case for relative comparison. Figure 11 shows the hourly loading of the BryanJordanDrive_MarinaAvnue transformer, which is connected to the buses where a BESS of 250 kWh capacity is installed. The figure shows the loading of that transformer in various operational modes of the BESSs. In the base case, the transformer’s maximum and minimum loadings [ 42 ] are 49.21% and 26.38% respectively, the transformer’s maximum and minimum loadings [ 42 ] remain the same in the only charge coordinated mode. The maximum and minimum loadings for coordinated charge-discharge mode are reduced to 46.26% and 27.55% respectively. These results indicate that the coordinated modes do not impose additional loading on the transformers, instead they reduce loading by discharging power during peak hours of the network. A similar decrement in maximum loading occurs in coordinated modes for 11 kV/ 0.415 kV transformers [ 1 ] where the BESSs are connected to the low voltage side [ 1 ]. Figure 12 shows the maximum loading of transformers where BESSs are connected for the base case, only charge coordinated mode and coordinated charge-discharge mode respectively. Three lines, Pluto_1, Moon_A and Moon_Begin are considered for identifying the loading impacts on lines of the test network. The reason for selecting these lines is that they consume the highest loading among other network lines. Figure 13 illustrates the hourly load variations of the Pluto_1 line under three scenarios: the base case, coordinated charge-discharge mode, and a mode where only charging occurs. In the base case, the load fluctuates between a maximum of 50.33% and a minimum of 25.53%. In the mode with only charging, the load ranges from a maximum of 50.33% to a minimum of 26.30%. Meanwhile, in the coordinated charge-discharge mode, the load varies from a maximum of 49.56% to a minimum of 26.30%. This result implies that the coordinated charge-discharge mode reduces the maximum load to the line. Figure 14 represents the maximum loading of three selected lines for the base case, coordinated charge-discharge mode and only charge coordinated mode. The figures depict the decrement in the loading of selected lines in coordinated charge-discharge mode concerning that in the base case. In only charge coordinated mode, the load of selected lines remains the same as those in the base case. The impact on bus voltages can be identified by checking the voltages of buses where the BESSs are connected. The bus voltages of four selected buses [ 1 ] are presented to check the impact on bus voltages due to coordinated modes. Figure 15 shows the maximum and minimum bus voltages [ 43 ] of selected buses in base mode. The maximum and minimum bus voltages [ 43 ] of those buses in coordinated modes are represented in Fig. 16. The bus voltages remain the same in only charge coordinated mode relative to those in coordinated charge-discharge mode. For example, the maximum and minimum bus voltages of Marina_Ave_Slipway_B bus are 0.979 p.u. and 0.955 p.u. respectively [ 44 ] for the base case. When a BESS of 300 kW capacity is connected to this bus [ 45 ], the maximum and minimum bus voltages are [ 46 ] 1.009 and 0.987 respectively, in coordinated modes respectively [ 47 ]. An increment of 1.12%-1.15% in bus voltages occurs in coordinated modes as compared to those [ 48 ] in the base case. 5. Impact Analysis with 80% Loading For probable impact analysis of electricity ferry charging, the load demand [ 1 ] of the test distribution network is increased to [ 1 ] 80% of each transformer’s capacity while [ 1 ] BESSs are connected to the network [ 1 ]. Figure 17 illustrates the hourly loading status of the BryanJordanDrive_MarinaAvenue transformer, linked to the buses hosting a 250 kWh BESS. The diagram displays the transformer's loading under various operational modes. In the base case, the transformer's maximum and minimum loadings stand at 79.55% and 41.96% respectively. The maximum loading remains unchanged in only charge coordinated mode, whereas the minimum loading becomes 43.83%. In the coordinated charge-discharge scenario, the maximum and minimum loadings are 74.61% and 43.83%, respectively. These findings indicate that coordinated operating modes do not enhance the transformer’s maximum loading capacity. A similar pattern of reduced maximum loading is also observed in 11 kV/0.415 kV transformers [ 1 ] when Battery Energy Storage Systems (BESS) are connected to the low-voltage side [ 1 ]. Figure 18 illustrates the peak loading of these transformers under the base case, the charge-only coordinated mode, and the charge-discharge coordinated mode. Figure 19 shows the hourly loading profile of the Pluto_1 line across different modes, which highlights the impact of these modes on the line’s loading. In the base case, the loading fluctuates between 82.69% and 41.37%. In the charge-only coordinated mode, it remains steady between 82.69% and 42.15%, whereas in the charge-discharge coordinated mode, the loading ranges from 81.88–42.15%, showing a reduction in peak line loading. Figure 20 compares the maximum loading of three selected lines under different operating modes. It is evident that the loading decreases in the charge-discharge mode, while it remains unchanged in the charge-only mode when compared to the base case. To assess the effect on bus voltages, four buses were chosen for analysis in coordinated modes. Figures 21 and 22 present the maximum and minimum bus voltages for these buses under the base case and the coordinated modes. The voltage levels in both the charge-only and charge-discharge coordinated modes remain nearly identical. For example, at the Marina_Ave_Slipway_B bus, the maximum and minimum voltages are 0.966 p.u. and 0.927 p.u., respectively, in the base case without BESS. After a 300 kW BESS is integrated into this bus, the maximum and minimum voltages in the coordinated modes increase slightly to 0.976 p.u. and 0.937 p.u., respectively [ 49 ]. Around 1-1.12% [ 50 ] increment in bus voltages occurs in coordinated modes compared to those in the base case. 6. Discussion on Research Findings The simulation results imply that the proposed control topology keeps the bus voltage, line loading and transformer loading of the selected distribution network within the allowable limit according to the Australian grid regulation constraints for coordinated operational mode of electric ferry charging stations. In Australia, the National Electricity Rules (NER) establishes standards for voltage levels, transformer loading, and line loading to maintain stability and reliability in the electrical network. For normal operating conditions, the voltage should remain within + 10% and − 6% of the nominal voltage (1.0 p.u.), [ 51 ] leading to a maximum of 1.10 p.u. and a minimum of 0.94 p.u [ 33 ]. Transformers need to operate efficiently between 40–80% of their rated load [ 52 ]. Similarly, line loading should also be managed to avoid excessive current that can lead to overheating and potential damage to the infrastructure [ 52 , 53 ]. The impact of these voltage, transformer loading, and line loading standards on electric ferry charging and BESS operational performance is significant. Under voltage conditions below 0.94 p.u., which generally occurs during peak hours, results in inadequate power supply to the charging system, prolonging charging times and potentially leading to incomplete charges, thus disrupting ferry schedules. For BESS, low voltage affects charging efficiency, leading to incomplete cycles and reduced energy storage capacity, increasing internal resistance and heat generation that accelerates degradation. Conversely, over-voltage conditions, which generally arise during off-peak hours exceeding 1.10 p.u., can cause excessive current draw by the ferry charging system, leading to overheating and potential damage to the equipment. This instance can also trigger over-voltage protection mechanisms, interrupting the charging process and reducing efficiency. For BESS, high voltage accelerates battery cell degradation, reducing lifespan and efficiency and poses safety risks such as thermal runaway. High loading conditions, exceeding 80% of rated capacity for transformers and lines, can cause voltage drops along distribution lines, exacerbating under-voltage issues at charging stations. Overloading transformers and lines lead to overheating, increasing maintenance costs, and raising the risk of equipment failure. The overloading forces the BESS to discharge more frequently to support the grid, leading to faster cycling and reduced battery lifespan. Conversely, low loading conditions, below 50% of rated capacity, though less immediately harmful, can result in inefficient grid infrastructure utilization and economic inefficiencies, affecting the cost-effectiveness of charging infrastructure. For BESS, low loading conditions can extend operational life due to less frequent cycling but may also cause capacity fade due to irregular charge-discharge cycles. The peak shaving approach of BESSs manages low bus voltage and high loading of the test distribution network by discharging during peak hours. The valley filling approach of BESSs regulates over bus voltage and low loading by charging during off-peak hours. So, the storage of EF can be utilized as a spinning reserve for the shore side electricity network. A balanced hybrid GA-PSO-BFO algorithm represents an innovative approach that combines three well-established optimization techniques. While these optimization methods are extensively used individually in various optimization problems, their specific combination into a hybrid algorithm for optimizing the BESS’s charge and discharge cycles in a simulated distribution network is relatively novel. This hybridization leverages the strengths of each method, GA's robustness in exploring the global search space, PSO's convergence speed and simplicity and BFO's local search capabilities and fine-tuning to address the limitations of each technique, resulting in a more powerful and effective optimization tool. The application of this hybrid algorithm to BESS in distribution networks, particularly for managing system voltage, current, power factor, transformer loading and line loading, represents a new and innovative use case. Although hybrid algorithms are employed in other optimization problems, their specific use in this domain is rare and demonstrates a novel approach. While similar hybrid approaches may be used for other optimization problems or different contexts, optimizing BESS operations by considering critical parameters like voltage regulation, power factor correction and load management is likely a recent development. The DIgSILENT PowerFactory simulation results underscore this control approach’s performance in managing [ 1 ] the load demand of the test distribution network [ 1 ] with BESS by retaining system parameters within permissible limits. The power flow analysis of the [ 1 ] BESSs integrated test distribution network [ 1 ] with 80% load is done in coordinated charge-discharge mode by using individual GA, PSO and BFO optimization algorithms in order to identify the performance of the hybrid GA-PSO-BFO optimization algorithm. Table 4 represents a comparative result of voltage and percentage loading of a selected bus, transformer and line respectively which identifies the better performance of the hybrid optimization algorithm than the individual optimization algorithm. Table 4 Relative comparison among power flow results among various optimization algorithms Bus_BryanJDr_MarinaAveue_B Bus Voltage (p.u.) Transformer_BryanJorDr_MarinaAT Loading (%) Line_Pluto_1 Loading (%) Maximum Minimum Maximum Minimum Maximum Minimum GA 0.971 0.933 75.96 45.18 82.86 43.13 PSO 0.967 0.929 76.14 45.36 83.65 43.92 BFO 0.973 0.935 75.83 45.05 83.17 43.44 GA-PSO-BFO 0.988 0.950 74.61 43.83 81.88 42.15 7. Conclusion This study explores the potential effects of electric ferry charging on the distribution network through power flow analysis. The analysis is conducted using a coordinated approach on a test distribution system based in Gladstone Marina, Queensland. The network model incorporates the daily load profile of the 11 kV Marina feeder, along with four proposed charging stations, each represented by BESSs. It's important to note that this model may not fully reflect the technical characteristics and capabilities of the future EF charging infrastructure in Gladstone. The findings indicate a slight increase in bus voltages, ranging from 1.12–1.15%, as the load grows in coordinated modes. This increase in voltage could enhance the efficiency and voltage regulation of the network. In coordinated charge-discharge mode, transformer loading decreases by 3%-4%, while line loading experiences a reduction of 2.5%-3.5%. These reductions significantly contribute to decreased system current and power consumption. Notably, transformer and line loadings remain unchanged in only charge coordinated mode relative to the base case. The results indicate that the initiation of the proposed metaheuristic optimization-based control algorithm in coordinated modes appropriately manages the charge-discharge pattern of EF storage with its SOC and distribution network’s load demand by retaining system parameters within the permissible limits. The peak shaving and valley filling approaches of EF storage during peak and off-peak hours respectively, can serve as spinning reserves for the distribution network along the shoreline. The coordinated modes have a beneficial effect on the system performance of the test distribution network, which is integrated into a robust grid infrastructure. The Gladstone grid network is reliable, resilient and efficient with modern infrastructure, advanced monitoring, flexible operations and sufficient transmission capacity for managing load demand and the ability to integrate electric ferry charging stations. The research findings may not apply to a distribution system which is prone to outages, inefficiencies due to outdated infrastructure, lack of advanced monitoring, vulnerability to disruptions, challenges integrating renewable energy and inadequate coordination and regulatory support. As a result, the findings from this impact analysis may not be directly relevant to other areas with varying infrastructure, load patterns, and operational conditions. The proposed control algorithm may need some modifications before being used in other similar applications. When adapting the control algorithm for controlling system parameters of different distribution networks, fine-tuning hyperparameters like population size, crossover and mutation rates for GA, cognitive, social parameters and inertia weight for PSO and chemotactic steps, swim length and reproduction steps for BFO ensure effective search behavior. Seamless integration and appropriate weighting of each algorithm's fitness contributions and incorporating domain-specific knowledge and constraints can significantly enhance the algorithm's performance and reliability. However, these research findings may be a guideline for implementing electric ferry charging stations in regional Australia. Future research may consider the actual charging patterns of electric ferries in various operational modes to identify their potential impacts on the shore-side distribution network. 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Roy","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAt0lEQVRIiWNgGAWjYNCCCgglQYKWMyRrYWwjRYt5A+/Dx7zz7GQ3HGA+eJuHwS6xgZAWmQPsxsa825KNNxxgS7bmYUgmrEWCgY1NmnfbgcQNB3jMpHkYmInVMgekhf8bUEs9sVoawLawAbUcJkILMxuz4ZxjycYzD7MZW84xOG5MWAt7G+ODNzV2sn3Hmx/eeFNRLUtQCwMzAwMTDzBqGphBPAOC6iGA8QdIC5GKR8EoGAWjYAQCADYjMffqoKc3AAAAAElFTkSuQmCC","orcid":"","institution":"Central Queensland University","correspondingAuthor":true,"prefix":"","firstName":"Rajib","middleName":"Baran","lastName":"Roy","suffix":""},{"id":418799139,"identity":"acfa9988-e219-4744-a06c-092f5c056320","order_by":1,"name":"Sanath Alahakoon","email":"","orcid":"","institution":"Central Queensland University","correspondingAuthor":false,"prefix":"","firstName":"Sanath","middleName":"","lastName":"Alahakoon","suffix":""},{"id":418799140,"identity":"4d77f114-4f7a-48d2-b298-6227b418b610","order_by":2,"name":"Piet Janse Van 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21","display":"","copyAsset":false,"role":"figure","size":19199,"visible":true,"origin":"","legend":"\u003cp\u003eMaximum bus voltages of selected buses without BESSs and coordinated modes\u003c/p\u003e","description":"","filename":"21.png","url":"https://assets-eu.researchsquare.com/files/rs-5985265/v1/d5ea00dd3354c4d48de11838.png"},{"id":77134371,"identity":"0a1829ab-864e-4e16-b386-f96d7be1cb98","added_by":"auto","created_at":"2025-02-25 12:41:43","extension":"png","order_by":22,"title":"Figure 22","display":"","copyAsset":false,"role":"figure","size":20383,"visible":true,"origin":"","legend":"\u003cp\u003eMinimum bus voltages of selected buses without BESSs and coordinated modes\u003c/p\u003e","description":"","filename":"22.png","url":"https://assets-eu.researchsquare.com/files/rs-5985265/v1/45a1a196c21bc46bfb260b02.png"},{"id":99789014,"identity":"0026eb83-e2f8-4e9a-b55e-878d4188b1f8","added_by":"auto","created_at":"2026-01-08 12:48:32","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3582661,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5985265/v1/f3d8fb87-1e31-424f-ae28-997f431b5d94.pdf"},{"id":77132823,"identity":"2f6b752b-5bdb-4b96-a7f2-629a784c48af","added_by":"auto","created_at":"2025-02-25 12:33:42","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":57147,"visible":true,"origin":"","legend":"","description":"","filename":"AuthorInfo.docx","url":"https://assets-eu.researchsquare.com/files/rs-5985265/v1/aea306630bf4578c47be2e13.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe maritime industry significantly contributes to greenhouse gas emissions, [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] accounting for about 3% of the total [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] global emissions, which amounts to approximately 1\u0026nbsp;billion tons of CO\u003csub\u003e2\u003c/sub\u003e and other greenhouse gases [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The IMO (International Maritime Organization) enforces regulations targeting air pollution from conventional diesel vessels to combat this environmental issue. Without intervention, projections suggest that CO\u003csub\u003e2\u003c/sub\u003e emissions can increase to an alarming level by 2050 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As global concerns over environmental sustainability grow, both the IMO and the United Nations are strongly advocating for the adoption of environmentally responsible policies in the maritime industry. The IMO's resolution MEPC.304(72) outlines a target to reduce CO2 emissions from international shipping by at least 50% by 2050, relative to 2008 levels [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This ambitious goal underscores the pressing need for alternative energy solutions, such as hybrid and renewable energy-powered electric vessels, to curb emissions. Advances in energy storage technologies, including batteries and super-capacitors, are expanding the options for cleaner maritime power sources. By combining renewable energy systems like solar PV (photovoltaic) panels and fuel cells with efficient storage solutions, it is possible to significantly reduce CO2 emissions from the shipping sector. However, despite their environmental benefits, concerns still need to be addressed about [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] the financial viability of these alternative electricity options [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] due to their high initial costs. Optimizing the use of fuel cells and batteries in response to varying load demands, while incorporating advanced optimization methods that account for operational limitations, presents promising solutions for overcoming current challenges. These strategies can help speed up the integration of hybrid and alternative energy-powered electric ferries into the maritime sector. The transition to electric vessels offers a promising solution to address environmental concerns linked to traditional diesel-powered ships. However, ensuring stability becomes crucial as power systems encounter growing demands and nonlinear loads [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Incorporating EFs (electric ferries) into current power grids demands thorough planning, as it may pose challenges to grid stability. While many studies have explored the effects of EV (Electric Vehicle) charging on electrical networks [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], research specifically focused on EF charging remains scarce [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This section reviews key research on EV charging, drawing parallels to the potential impacts of EF charging [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Findings indicate that EV charging can influence various grid parameters, leading to changes in system dynamics, higher energy losses, and diminished efficiency and lifespan of grid equipment [\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCrozier et al. investigate the impacts of EV charging on the UK\u0026rsquo;s power system, which demonstrates uncontrolled EV charging in the UK can increase peak demand by 8 GW, but this can be mitigated through controlled charging, thereby eliminating the need for additional generation infrastructure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. This research also indicates that upgrading the electricity network, ranging from 9\u0026ndash;28% is required for uncontrolled charging at the distribution level, while controlled charging reduces these requirements significantly [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Research is conducted by Khan et al. on the potential impact of EV charging on grid demand, which implies that EV charging can potentially augment the grid demand, particularly during peak charging periods [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This research also identifies that the rise in peak load is influenced by factors such as the number of EVs, charging patterns and the availability of charging infrastructure [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. This research suggests that the widespread integration of EVs in existing power systems may lead to a notable increase in peak electricity demand [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Jones et al. simulate two EV charging scenarios (home- and work-dominant) under potential 2030 EV adoption levels on ten actual distribution feeders supporting residential, commercial, and industrial loads using GPS data to reflect actual driving patterns for simulating power flow across feeders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Results show modest voltage impacts (less than 0.01 p.u.), noticeable line loading increases (up to 15%), and slight shifts in peak load times (about 1 hour) for residential and mixed feeders, while no shift for industrial and 8 hours shift for commercial feeders [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Wang et al. conduct research on power quality challenges due to fast EV charging of DC fast chargers, which demonstrates that fast charging poses challenges, including voltage profile deterioration, power losses and excessive harmonic emissions which arise due to high-charging power, centralized load demand and pulsating charging nature [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This research concludes that real-time charging, proper management of charging behavior, integration of energy storage systems to compensate for pulsating loads, design of filters with various damping techniques for harmonic mitigation and impedance-based approaches for controlling fast charger front-end converters can potentially offset the adverse impacts of fast EV charging [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Olcay et al. assess the impact of rising EV use on electricity grids, focusing on how EV charging stations affect power systems by examining issues like phase imbalances, harmonic formation, and energy quality using IEEE 6 bus, IEEE 14 bus and IEEE 30 bus test systems [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The research predicts how increased EV charging and grid loads affect harmonic effects and overall grid performance, using artificial neural networks and proposes solutions for managing these impacts on power networks [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMowry et al. investigate how highway electric vehicle charging impacts the grid and explore energy storage as a potential solution, emphasizing the significance of HFC (Highway Fast Charging) infrastructure in tackling range anxiety and advancing economy-wide decarbonization [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. This research uses a detailed grid operations model for analyzing the effects of expanded HFC infrastructure on the 2033 Texas power system under different EV penetration scenarios [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Results reveal increased operational costs and highlight transmission congestion on feeder lines serving HFC stations as a critical issue[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Purnell et al. examine the effects of charging electric ferries [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and public transit on energy use in New South Wales [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], Australia, which employs [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] a tool to model BEPT (Battery Electric Transit) impact on grid energy [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] by revealing significant strain on low voltage networks [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The study suggests a BEPT shift may increase annual peak demand at local substations by up to 17% and intensify evening peak periods by 20\u0026ndash;30% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Additionally, a complete transition to BEPT in NSW can raise annual electricity consumption by 1.28\u0026ndash;1.34% and peak demand by 1\u0026ndash;3% [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Limput et al. demonstrate that residential slow charging poses a more positive impact on the distribution grid than fast charging and strategies like uncoordinated charging and off-peak charging while exploring to understand their grid impact [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The research implies that fast charging reduces EV hosting capacity by up to 10%, whereas grid-friendly slow charging significantly increases capacity, which may offset fast charging effects [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This research emphasizes coordinated charging strategies and the benefits of EV-based peak shaving for managing grid impact, noting that fast charging infrastructure requirements are less significant than slow charging [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Sehimi et al. show that substituting traditional DC fast chargers with V2V ( Vehicle to Vehicle) chargers significantly reduces overall losses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. The research reports a 12.42% reduction in losses in the primary distribution system and a 14.08% reduction in the second secondary distribution system [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Furthermore, the study notes that V2V's impact on losses in LV grids is minimal, with line losses being more prominent in DCFC (DC Fast Charging) due to their correlation with line length [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Replacing conventional DC chargers with V2V chargers does not necessitate grid integration, lowering costs and overall losses on the distribution grid [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A study is conducted by Mojumder et al. for identifying impacts of integrating EVs and V2G (Vehicle-to-Grid) technology, revealing that V2G can optimize power demand and enhance innovative grid sustainability [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. However, comprehensive research on its operation, EV types, policies, business models and implementation challenges is limited [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The study highlights V2G opportunities, necessary policies, business models and the challenges of integrating V2G with power grids and EV batteries [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. It detects significant challenges, including the lack of a solid V2G business model, insufficient incentives, stress on EV batteries, inadequate bidirectional chargers and potential unscheduled V2G practices [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBayeni et al. explore the shift to electric transportation, covering its environmental impact, battery advancements, sustainability and effects on consumers, utilities, and the economy [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Key findings emphasize the need for cleaner electricity, the cost-effectiveness of EVs driving adoption and the challenge of EV charging rates [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. While short-term grid impacts are minimal, long-term higher EV penetration requires charging management [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Bi-directional V2G (Vehicle-to-Grid) systems enhance grid [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] efficiency and reliability and V2V (Vehicle-to-Vehicle) charging helps manage high EV loads [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Nutkani et al. address the urgent need to understand and maximize EV hosting capacity on the grid without significant orchestration [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Considering steady-state and quasi-dynamic simulations on real urban and rural networks [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], the study estimates EV grid impact and hosting capacity across various scenarios, including customer loads, regulated and unregulated EV charging and seasonal variations [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] in load and solar PV output [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The analysis [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] highlights the impact of EVs on existing distribution networks and [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] demonstrates how load management and regulated charging can manage this impact effectively [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Gomez Ramirez et al. [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] outline a methodology for evaluating [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] the impact of EVs on [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] Costa Rica's power grid, considering EV penetration [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], user preferences, charging habits, and fleet growth [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] by using ETAP software [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which simulates power flow, demand and voltage levels up to 2040 [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Key findings indicate voltage declines and demand increases after 2030, especially in distribution grids and highlight infrastructure issues starting in 2030, the need for stability assessments, public policy-driven infrastructure investments and the importance of incorporating EV growth and infrastructure improvements in system planning [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Deilami et al. explore electric vehicles EVs in the smart grid, providing solutions for EV charging strategies to manage large-scale adoption by reviewing EV battery infrastructure and the impacts of uncontrolled charging and presenting solutions for integrating EVs into the grid [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The study includes simulations of two controlled strategies, MSS (Maximum Sensitivity Selection) and GA (Genetic Algorithm) optimization [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] on a modified IEEE 23 kV medium voltage distribution system and a low voltage residential network [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. This comparative analysis validates these approaches, demonstrating their effectiveness in optimizing EV charging strategies[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Gilleran et al. evaluate the impact of EV charging stations on the power demand of [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]a retail big box grocery store by considering various station sizes, charging power levels and utilization factors across different climates and seasons [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. As EV penetration rises and fast chargers with power levels up to 350 kW become common, the effect on the grid becomes significant [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. This research uses three distinct rate structures to measure changes in monthly peak power demand, electricity usage and annual electricity bills [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Results indicate that EV stations can increase a store's peak power demand by over 250%, including cold climates combined with rate structures featuring high demand charges, which are most susceptible, potentially raising annual electricity bills by up to 88% [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis research seeks to assess the potential effects of EF charging on the distribution network by using a test network in regional Australia [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Given that EF implementation is still in its early stages in Australia, actual charge and discharge data for EF operations are not available. The Gladstone marina distribution network, a component of the larger Gladstone, Queensland network, is selected as the test case, incorporating four proposed charging stations. A simulated distribution network is developed using daily load demand data from the [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] 11 kV marina feeder, with four BESSs representing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] the storage capacities of the charging stations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. BESSs play a crucial role in powering EFs amidst nonlinear propulsion loads affected by sea weather and integrated electronic devices, requiring robust handling of rapid power demand changes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. DIgSILENT PowerFactory software analyzes the [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] probable impacts of EF charging [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] on the test network [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The DLL (Dynamic Link Library) of MATLAB Simulink based BESS\u0026rsquo;s model is linked to the synchronous generator block of DIgSILENT for impact analysis. Python based a balanced hybrid metaheuristic optimization algorithm is used to control and optimize the operation of BESSs according to load demand, SOC of BESSs, and [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] system parameters of the test distribution network [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Power flow analysis is conducted under various load demand scenarios, revealing insights into system parameters such as transformer loading, line loading and voltage [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This research examines how the storage charge and discharge cycles of EFs affect the distribution network when operating in a coordinated mode [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. A comprehensive analysis is conducted to assess the impact on various network parameters [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Although the analysis relies on simulations, the results provide useful guidance for the deployment of EF charging stations in portside distribution networks [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe formation [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] of the test distribution network [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] according to the [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] collected network information for probable impact analysis is discussed in section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The network model is based on the daily load demand [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] data from the 11 kV marina feeder [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], with four BESSs used to simulate the [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] capacities of the proposed charging stations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Section \u003cspan refid=\"Sec3\" class=\"InternalRef\"\u003e3\u003c/span\u003e elaborates on dynamic modelling and hybrid metaheuristic optimization driven control algorithm for managing the charge-discharge of BESS in accordance with load demand. The specifications of Corvus Orcha ESS are used for BESS modeling in MATLAB Simulink. This section discusses the formation of DLL for integrating the BESS model to a simulated DIgSILENT PowerFactory network. The construction of a balanced hybrid GA-PSO-BFO metaheuristic optimization-based control algorithm is also depicted in this section. Sections \u003cspan refid=\"Sec4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e5\u003c/span\u003e address the impacts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] on system parameters due to EF charging by [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] increasing load demand to 50% and 80% of the test network and considering the maximum charging capacities of the proposed charging stations [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] in coordinated mode. The analysis emphasizes the impacts of transformer loading, line loading and bus voltage. A discussion on research findings is included in section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e6\u003c/span\u003e which highlights the relative comparison among simulation results of different optimization algorithms. The overall research procedure is concluded in section \u003cspan refid=\"Sec7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, which also highlights the limitations of this research. The direction of future research by utilizing actual EF charging patterns and [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] the impacts of [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] uncoordinated EF charging is [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] emphasized in this section.\u003c/p\u003e"},{"header":"2. Test Distribution Network","content":"\u003cp\u003eAnalyzing the impact of rapid-charging electric ferries [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] on the electricity network\u0026rsquo;s parameters [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] involves assessing a test distribution network near Gladstone Marina [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. This network mirrors the load demand of the 11 kV Marina feeder [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], with 129 electricity buses [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] allocated based on distribution voltage levels of 11 kV and 0.415 kV [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Google Earth Pro is utilized to determine [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] the positions and capacities of distribution transformers [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], owing to its high-resolution imagery and advanced measurement capabilities [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Leveraging a detailed [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] 11 kV network image from Google Earth Pro [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] aided in pinpointing transformers and [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] proposed charging stations in Gladstone Marina [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is anchored on the 11 kV marina feeder [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], originating from the Clinton 33 kV/11 kV distribution substation [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. From this substation, five 11 kV feeders [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] extend outward: Clinton Park feeder, Callemondah Drive feeder, Hason Road feeder, Kin Kora feeder, and Marina feeder [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The schematic diagram of the test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is illustrated in Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e], with the network depicted by red and blue lines [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e showcases the distribution network, highlighting four proposed charging stations intended [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] to integrate into existing ferry terminals [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], such as MIPEC, Sea Link, Curtis Ferry services [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] and Heron Island Ferry Terminal [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. These figures are generated using Google Earth Pro [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Data on the electrical power consumption of the 11 kV Marina feeder [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is obtained from Ergon Energy [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], a primary electricity provider in Australia that supplies electricity to communities in urban and regional Queensland. The daily load profile of the test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is represented by 2 which is prepared according to the [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] 11 kV Marina feeder-based electricity network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] data without inclusion of EF charging station load. The peak real and reactive power demand of the 11 kV Marina feeder [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is proportionally divided among loads of the [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] test distribution network according to the respective [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] transformer\u0026rsquo;s rated capacity. The capacities of transformers are collected from Ergon Energy and the standard PQ load is imitated as load in the test network.\u003c/p\u003e\n\u003cp\u003eIn order to distinguish voltage levels for buses, A and B notation [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] are used for 11 kV and 415 V respectively [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The 11 kV/0.415 kV [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] distribution transformers are placed between [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] 11 kV and 415 V buses to connect the load to the 415 V buses. Four probable charging stations are [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] associated with 415 V buses, which are [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] CQUni_BJDFerryTerminal_Opp, Marina_Ave_Pioneer_Seafoods, BryanJordanDr_MarinaAveue [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] and Marina_Ave_Slipway to identify the impacts of EF charging. The optimal capacities of four charging stations [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] are 300 kWh (200 kW), 250 kWh (150 kW), 400 kWh (300kW) and 400 kWh (300kW) respectively [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The capacities [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] of these charging stations are determined [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] based on the respective distribution transformers\u0026apos; capacities [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The charging stations add additional load to the test network besides the daily load demand of Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The optimal capacities of BESSs are defined by considering distribution network constraints so that the key system parameters such as bus voltage, line current and power loss remain within the operational limits of the distribution network. The distribution network constraints are defined according to the Australian NER (National Electricity Rule), such as system voltage should remain within +\u0026thinsp;10% and \u0026minus;\u0026thinsp;6% of its nominal value of 1.0 p.u. (per unit), line current should not exceed its rated capacity and power load should be kept within 10% of total power consumption [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. The terminology of buses is defined according to the location of distribution transformers in Google Earth Pro. The type of conductors and cables which are used for the marina feeder are Pluto (19/3.75 AAC), Wasp (7/0.173 AAC), Moon (7/4.75 AAC) and U/G 11 kV 185 mm\u003csup\u003e2\u003c/sup\u003e Aluminum Triplex XLPE PVC/HDPE. In designing the test distribution network, the line parameters and respective lengths are coordinated according to the type of conductors and cables. The length of each line is approximated by considering the distance from the respective bus, which is taken from Google Earth Pro.\u003c/p\u003e\n\u003cp\u003eFigures \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e represent the simulated test network, which is designed by using DIgSILENT PowerFactory. The overall test distribution network is demonstrated by three interconnected networks, shown in those figures. In Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the four BESSs are shown as circles, symbolizing the combined charging capacities of the four proposed charging stations at Gladstone Marina. The charging and discharging schedules of the BESSs are examined through quasi-dynamic power flow analysis to evaluate the potential effects of EF charging on the test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e].The impact analysis is [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] performed by increasing load demand to 50% and 80% of the actual load of the test network. Two operational modes are selected: one is only charged coordinated mode and another is coordinated charge-discharge mode. \u0026ldquo;Initially, the power flow analysis is conducted without integrating BESSs, which is termed as base case\u0026rdquo; [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Afterwards, power flow analysis [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] is conducted in [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] coordinated modes by incorporating BESSs into the test network, with base case results [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] serving as a reference for [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] relative comparison. The charging and discharging time of BESSs are aligned with the load demand of the test distribution network in coordinated modes. In this mode, charging occurs during off-peak hours and discharging during peak hours to [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e] maximize system efficiency [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The charging time is set at 1 am to 5 pm (off-peak hours) and the [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e] discharging time is set at 9 am to 4 pm (peak hours), aligned with the daily load pattern of the 11 kV marina feeder. The maximum charging time is defined at 1 am as 3 am [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] and the maximum discharging time is defined as 1 pm to 2 pm [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e] according to the off-peak and peak demand of the test network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] respectively. The SOC is selected to vary from 20\u0026ndash;80%, so BESSs cannot charge over 80% SOC and discharge below 20% SOC. A control algorithm is formulated to control and optimize the charge and discharge of [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] BESSs according to load demand [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e], BESS\u0026rsquo;s SOC and the permissible limit of the test network\u0026rsquo;s system parameters. MATLAB Simulink-based BESS dynamic model and Python-based proposed control algorithm are integrated into the simulated test distribution network of DIgSILENT Power Factory to keep synchronism of coordinated charge-discharge with load demand and system parameters.\u003c/p\u003e"},{"header":"3. BESS Modelling and Control Algorithm","content":"\u003cp\u003eIn this study, MATLAB Simulink is used to model four BESSs based on the specifications of the Corvus Orca ESS, which is a popular energy storage solution for marine applications. The specification of the single module Corvus Orcha ESS is shown in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. Various combinations of Corvus Orcha ESS modules can represent the capacities of four BESSs. The dynamic attributes of BESSs are defined according to the dynamic properties of the lithium-ion battery. The dynamic properties of BESSs are included in the MATLAB model so that it can respond according to the load demand of the network during coordinated charge-discharge. The selected dynamic properties are mentioned in tabular form in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. The specific series parallel configurations to meet each BESS\u0026rsquo;s kWh and kW capacities are 54 modules (9 modules in series, 6 parallel strings) for BESSs A and B with 400 kWh (300kW) capacity, 36 modules (9 modules in series, 4 parallel strings) for BESS C with 300 kWh (200 kW) capacity and 27 modules (9 modules in series, 3 parallel strings) for BESS D with 250 kWh (150 kW). Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e elaborates the calculation to determine the number of modules with series-parallel combination for BESSs. To mimic the actual BESS\u0026rsquo;s response in DigSilent, its dynamic properties are included in the simulated model. Figure \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e depicts the block diagram of the MATLAB Simulink-based BESS model. The conventional steps for DLL formation are represented in Fig. \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e. Figure \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e illustrates the integration process of the BESS model, specifically the dynamic characteristics of the Corvus Orcha ESS with an external application through the creation of a DLL. The process begins with the DLL Generator, where the BESS model is designed and validated in the MATLAB environment and later, it is prepared for compilation. The MATLAB compiler then converts this validated BESS model into a DLL, ensuring that the model\u0026rsquo;s functionalities are preserved in a format that external applications can access. The DLL user program represents any software capable of utilizing DLLs, providing a way to integrate the BESS model outside MATLAB. The external application, in this case, DIgSILENT PowerFactory, can load and call the DLL functions and thereby integrate the BESS model into its simulation environment. The DLL interface/API defines how the external application interacts with the DLL, which ensures proper communication and functionality. Finally, in the load/call DLL function step, DIgSILENT PowerFactory dynamically loads the DLL and invokes its functions to incorporate the detailed dynamic characteristics of the BESS model into the simulated distribution network. The overall process demonstrates how DLL formation allows the BESS model, encapsulated with detailed dynamic characteristics from MATLAB, to be seamlessly integrated into larger power distribution network simulations, enhancing the accuracy and performance of energy storage system analyses. The overall process demonstrates how DLL formation allows the BESS model, with its detailed dynamic characteristics from MATLAB, to be seamlessly integrated into the simulated power distribution network of DIgSILENT PowerFactory, enhancing the accuracy and performance of storage system analyses.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSpecification of single module Corvus Orcha ESS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eProperty\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eType/Value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBattery Cell Chemistry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLithium ion NMC / graphite\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle Module Size / Increments\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5,6 kWh / 50 VDC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle Module Capacity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e128 Ah\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle Pack Range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38\u0026ndash;136 kWh / 350\u0026ndash;1200 VDC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax Gravimetric Density - Pack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77 Wh/kg | 13 kg/kWh\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMax Volumetric Density - Pack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e88 Wh/l\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eDynamic Properties of BESS\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDynamic Property\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eValue\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSOC limit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20%-80%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternal Resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.013 ohm\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharge Cycle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFully charged voltage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e438 V\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNominal discharge current\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e130.5 A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCut off Voltage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e311 V\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCharge and discharge efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e90%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-discharge rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.1%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eResponse time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1 second\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"left\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSelection of Corvus Orcha ESS module combination according to BESS capacity\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eBESS Capacity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMathematical Calculation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBESS A 400 kWH (300 kW)\u003c/p\u003e\n \u003cp\u003eBESS B 400 kWH (300 kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCapacity Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kWh required: 400 kWh\u003c/p\u003e\n \u003cp\u003eSingle module capacity: 5.6 kWh\u003c/p\u003e\n \u003cp\u003eNumber of modules required: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{400\\:\\text{k}\\text{W}\\text{h}}{5.6\\:\\text{k}\\text{W}\\text{h}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}=71.43\\approx\\:72\\:\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePower Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kW required: 300 kW\u003c/p\u003e\n \u003cp\u003eOperating voltage: 438 V\u003c/p\u003e\n \u003cp\u003eCurrent requirement: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{300\\:\\text{k}\\text{W}}{438\\:\\text{V}}\\:\\approx\\:685\\:\\text{A}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eNominal discharge current per module: 130.5 A\u003c/p\u003e\n \u003cp\u003eNumber of parallel strings: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{685\\:\\text{A}}{130.5\\:\\text{A}/\\text{s}\\text{t}\\text{r}\\text{i}\\text{n}\\text{g}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\)\u003c/span\u003e\u003c/span\u003e 5.25 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\)\u003c/span\u003e\u003c/span\u003e 6\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSeries Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModule voltage: 50 V\u003c/p\u003e\n \u003cp\u003eModules in series: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{438\\:\\text{k}\\text{W}\\text{h}}{50\\:\\text{V}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}\\)\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\approx\\:\\)\u003c/span\u003e\u003c/span\u003e 9\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFinal Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModules in series: 9\u003c/p\u003e\n \u003cp\u003eParallel strings: 6\u003c/p\u003e\n \u003cp\u003eTotal modules: 9X 6\u0026thinsp;=\u0026thinsp;54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBESS C 300 kWh (200 kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCapacity Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kWh required: 400 kWh\u003c/p\u003e\n \u003cp\u003eSingle module capacity: 5.6 kWh\u003c/p\u003e\n \u003cp\u003eNumber of modules required: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{300\\:\\text{k}\\text{W}\\text{h}}{5.6\\:\\text{k}\\text{W}\\text{h}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}=53.57\\approx\\:54\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePower Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kW required: 300 kW\u003c/p\u003e\n \u003cp\u003eOperating voltage: 438 V\u003c/p\u003e\n \u003cp\u003eCurrent requirement: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{200\\:\\text{k}\\text{W}}{438\\:\\text{V}}\\:\\approx\\:456.62\\:\\text{A}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eNominal discharge current per module: 130.5 A\u003c/p\u003e\n \u003cp\u003eNumber of parallel strings: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{456.62\\:\\text{A}}{130.5\\:\\text{A}/\\text{s}\\text{t}\\text{r}\\text{i}\\text{n}\\text{g}}\\:\\approx\\:3.5\\:\\approx\\:4\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSeries Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModule voltage: 50 V\u003c/p\u003e\n \u003cp\u003eModules in series: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{438\\:\\text{V}}{50\\:\\text{V}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}\\:\\approx\\:9\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFinal Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModules in series: 9\u003c/p\u003e\n \u003cp\u003eParallel strings: 4\u003c/p\u003e\n \u003cp\u003eTotal modules: 9X 6\u0026thinsp;=\u0026thinsp;36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBESS D 300 kWh (200 kW)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCapacity Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kWh required: 400 kWh\u003c/p\u003e\n \u003cp\u003eSingle module capacity: 5.6 kWh\u003c/p\u003e\n \u003cp\u003eNumber of modules required: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{250\\:\\text{k}\\text{W}\\text{h}}{5.6\\:\\text{k}\\text{W}\\text{h}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}=44.64\\approx\\:45\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePower Calculation\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eTotal kW required: 300 kW\u003c/p\u003e\n \u003cp\u003eOperating voltage: 438 V\u003c/p\u003e\n \u003cp\u003eCurrent requirement: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{150\\:\\text{k}\\text{W}}{438\\:\\text{V}}\\:\\approx\\:342.47\\:\\text{A}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003eNominal discharge current per module: 130.5 A\u003c/p\u003e\n \u003cp\u003eNumber of Parallel Strings: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{342.\\:47\\:\\text{A}}{130.5\\:\\text{A}/\\text{s}\\text{t}\\text{r}\\text{i}\\text{n}\\text{g}}\\:\\approx\\:2.62\\:\\approx\\:3\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSeries Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModule voltage: 50 V\u003c/p\u003e\n \u003cp\u003eModules in series: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{438\\:\\text{V}}{50\\:\\text{V}/\\text{m}\\text{o}\\text{d}\\text{u}\\text{l}\\text{e}}\\:\\approx\\:9\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFinal Configuration\u003c/span\u003e:\u003c/p\u003e\n \u003cp\u003eModules in series: 9\u003c/p\u003e\n \u003cp\u003eParallel strings: 3\u003c/p\u003e\n \u003cp\u003eTotal modules: 9X 3\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA control algorithm is formulated by Python in order to sequentially charge and discharge four BESSs according to their SOCs and [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] load demand of the test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. This algorithm is integrated into DIgSILENT\u0026rsquo;s simulated test network. The controller checks [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] the load demand of the network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] in time-based simulation and allows the BESSs to charge and discharge according to time settings in coordinated mode. The control algorithm retains a synchronism between the load demand and BESS\u0026rsquo;s SOC. It keeps the BESS\u0026rsquo;s SOC within 20\u0026ndash;80% so that the BESSs cannot charge above 80% and discharge below 20%. Moreover, it checks the network\u0026rsquo;s system voltage and power factor at each hour for daily power flow analysis to limit them near unity. BESS can provide significant voltage and power factor support to an electricity network. By injecting or absorbing reactive power, BESS assists in maintaining voltage levels within desired limits and corrects the power factor, ensuring efficient power usage. It can respond quickly to fluctuations due to its power electronics-based inverter, making real-time adjustments possible. This automated control enhances grid stability and reliability, especially during peak and off-peak hours [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e], thereby reducing losses and optimizing power delivery [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]. The adjust_voltage and adjust_power_factor functions in the Python-based proposed control algorithm assist DIgSILENT PowerFactory in checking and adjusting system voltage and power factor according to hourly load demand in each power flow iteration. The adjust_voltage function monitors the system voltage at critical points and compares it to the target nominal voltage, 1 per unit. If deviations are detected, the BESS injects or absorbs reactive power to correct the system voltage. Similarly, the adjust_power_factor function monitors the power factor to keep it near unity for efficient power utilization. If the power factor deviates from the target, the BESS adjusts by providing the necessary reactive power. Both functions operate sequentially to ensure the network remains within optimal operating conditions, thereby preventing instability. Since the BESSs charge during off-peak hours and discharge during peak hours in coordinated mode, the control algorithm works appropriately, which can be identified from the power flow results [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The pseudo-code of the control algorithm is represented by code A.\u003c/p\u003e\n\u003cp\u003eA balanced hybrid GA-PSO-BFO [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e] metaheuristic optimization is incorporated in the control algorithm for optimal use of storage capacities of BESSs according to load demand so that the system parameters of the network can [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] remain within the permissible limit. Integrating these algorithms aims to harness their distinct exploration and exploitation mechanisms for improved convergence and solution quality. The hybrid approach can leverage their complementary strengths for more robust optimization performance. The balanced GA-PSO-BFO optimization enhances traditional GA-PSO-BFO methods by maintaining equilibrium among each optimization algorithm. Unlike conventional approaches that blend these techniques without specific balance, the balanced method dynamically adjusts each algorithm\u0026apos;s influence based on the problem\u0026apos;s requirements. This customization improves adaptability and performance, facilitating more effective exploration and exploitation of solution spaces. By finely tuning the contributions of GA, PSO, and BFO, balanced optimization aims to accelerate convergence, enhance solution accuracy, and bolster overall robustness in solving complex optimization problems. Critical aspects of implementing a balanced approach include integrating the algorithms seamlessly, defining adjustable parameters for dynamic contribution tuning, designing a robust fitness function aligned with optimization goals, employing adaptive control mechanisms to balance algorithm influence, and validating the approach against power flow-based scenarios to ensure superior performance in diverse optimization challenges. Code B represents the pseudo code of a Python-based balanced hybrid optimization algorithm. The block diagram of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e represents the relationship between the MATLAB-based BESS Model and Python-based control algorithm to simulate the selected network in DIgSILENT. The iteration graph of Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003e depicts the performance of the hybrid [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e] GA-PSO-BFO optimization algorithm over 100 iterations [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]. The fitness values oscillate widely for the first 99 iterations, indicating exploration and instability. At the 100th iteration, the fitness value stabilizes, signifying that the algorithm has converged to an optimal or near-optimal solution. The evolution of the pareto front depicted in Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e10\u003c/span\u003e shows how the algorithm manages the trade-offs between bus voltage, line current, and power through successive iterations of power flow analysis. Each point on the graph signifies a non-dominated solution, demonstrating the algorithm\u0026apos;s capacity to balance multiple objectives without prioritizing one excessively over the others. As the algorithm progresses, the curve illustrates the migration of these solutions toward an optimal pareto front, highlighting the variety and quality of the solutions produced. The broad distribution of points indicates the algorithm\u0026apos;s strong exploration abilities, covering a wide array of potential solutions. Over time, the movement of these points toward a clearer front suggests that the algorithm is converging toward a set of optimal trade-offs, where improving one objective does not greatly affect the others. This curve is a key visualization tool, showcasing the optimization process and providing insight into the algorithm\u0026rsquo;s effectiveness in achieving a well-distributed and convergent set of optimal solutions in the network\u0026apos;s power flow analysis.\u003c/p\u003e\n\u003cp\u003eCode A: Pseudo Code of BESS controller\u003c/p\u003e\n\u003cp\u003eFunction initialize_storage_controller(distribution_network, battery_storage):\u003c/p\u003e\n\u003cp\u003estorage_controller\u0026thinsp;=\u0026thinsp;Create new StorageController object\u003c/p\u003e\n\u003cp\u003estorage_controller.distribution_network\u0026thinsp;=\u0026thinsp;distribution_network\u003c/p\u003e\n\u003cp\u003estorage_controller.battery_storage\u0026thinsp;=\u0026thinsp;battery_storage\u003c/p\u003e\n\u003cp\u003estorage_controller.off_peak_hours = [0, 6] # Off-peak hours (midnight to 5 AM)\u003c/p\u003e\n\u003cp\u003estorage_controller.peak_hours = [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e] # Peak hours (10 AM to 4 PM)\u003c/p\u003e\n\u003cp\u003estorage_controller.soc_threshold_high\u0026thinsp;=\u0026thinsp;0.8 # 80% State of Charge threshold\u003c/p\u003e\n\u003cp\u003estorage_controller.soc_threshold_low\u0026thinsp;=\u0026thinsp;0.2 # 20% State of Charge threshold\u003c/p\u003e\n\u003cp\u003eReturn storage_controller\u003c/p\u003e\n\u003cp\u003eFunction check_load_demand(storage_controller):\u003c/p\u003e\n\u003cp\u003ecurrent_time\u0026thinsp;=\u0026thinsp;Get current hour from system time\u003c/p\u003e\n\u003cp\u003eload_demand\u0026thinsp;=\u0026thinsp;storage_controller.distribution_network.get_load_demand()\u003c/p\u003e\n\u003cp\u003evoltage\u0026thinsp;=\u0026thinsp;storage_controller.distribution_network.get_voltage()\u003c/p\u003e\n\u003cp\u003epower_factor\u0026thinsp;=\u0026thinsp;storage_controller.distribution_network.get_power_factor()\u003c/p\u003e\n\u003cp\u003eIf current_time in range(*storage_controller.off_peak_hours):\u003c/p\u003e\n\u003cp\u003eIf storage_controller.battery_storage.soc\u0026thinsp;\u0026lt;\u0026thinsp;storage_controller.soc_threshold_high:\u003c/p\u003e\n\u003cp\u003echarging_power\u0026thinsp;=\u0026thinsp;storage_controller.battery_storage.charge()\u003c/p\u003e\n\u003cp\u003estorage_controller.distribution_network.reduce_load_demand(charging_power)\u003c/p\u003e\n\u003cp\u003eElif current_time in range(*storage_controller.peak_hours):\u003c/p\u003e\n\u003cp\u003eIf storage_controller.battery_storage.soc\u0026thinsp;\u0026gt;\u0026thinsp;storage_controller.soc_threshold_low:\u003c/p\u003e\n\u003cp\u003edischarging_power\u0026thinsp;=\u0026thinsp;storage_controller.battery_storage.discharge()\u003c/p\u003e\n\u003cp\u003estorage_controller.distribution_network.increase_load_demand(discharging_power)\u003c/p\u003e\n\u003cp\u003eIf voltage\u0026thinsp;\u0026gt;\u0026thinsp;1:\u003c/p\u003e\n\u003cp\u003estorage_controller.distribution_network.adjust_voltage(1)\u003c/p\u003e\n\u003cp\u003eIf power_factor\u0026thinsp;\u0026gt;\u0026thinsp;1:\u003c/p\u003e\n\u003cp\u003estorage_controller.distribution_network.adjust_power_factor(1)\u003c/p\u003e\n\u003cp\u003e# Define functions for DistributionNetwork class methods\u003c/p\u003e\n\u003cp\u003eFunction get_load_demand():\u003c/p\u003e\n\u003cp\u003e# Method to get current load demand\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction get_voltage():\u003c/p\u003e\n\u003cp\u003e# Method to get current voltage\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction get_power_factor():\u003c/p\u003e\n\u003cp\u003e# Method to get current power factor\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction reduce_load_demand(amount):\u003c/p\u003e\n\u003cp\u003e# Method to reduce load demand by specified amount\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction increase_load_demand(amount):\u003c/p\u003e\n\u003cp\u003e# Method to increase load demand by specified amount\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction adjust_voltage(target_voltage):\u003c/p\u003e\n\u003cp\u003e# Method to adjust voltage to target value\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003eFunction adjust_power_factor(target_power_factor):\u003c/p\u003e\n\u003cp\u003e# Method to adjust power factor to target value\u003c/p\u003e\n\u003cp\u003ePass\u003c/p\u003e\n\u003cp\u003e# Define functions for BatteryStorage class methods\u003c/p\u003e\n\u003cp\u003eFunction initialize_battery_storage(max_capacity):\u003c/p\u003e\n\u003cp\u003ebattery_storage\u0026thinsp;=\u0026thinsp;Create new BatteryStorage object with max_capacity\u003c/p\u003e\n\u003cp\u003ebattery_storage.soc\u0026thinsp;=\u0026thinsp;0.5 # Initial State of Charge\u003c/p\u003e\n\u003cp\u003eReturn battery_storage\u003c/p\u003e\n\u003cp\u003eFunction charge(battery_storage):\u003c/p\u003e\n\u003cp\u003echarging_power\u0026thinsp;=\u0026thinsp;Minimum of ((battery_storage.max_capacity - battery_storage.soc) * battery_storage.max_capacity, 1.0)\u003c/p\u003e\n\u003cp\u003ebattery_storage.soc\u0026thinsp;+\u0026thinsp;=\u0026thinsp;charging_power / battery_storage.max_capacity\u003c/p\u003e\n\u003cp\u003eReturn charging_power\u003c/p\u003e\n\u003cp\u003eFunction discharge(battery_storage):\u003c/p\u003e\n\u003cp\u003edischarging_power\u0026thinsp;=\u0026thinsp;Minimum of (battery_storage.soc * battery_storage.max_capacity, 1.0)\u003c/p\u003e\n\u003cp\u003ebattery_storage.soc -= discharging_power / battery_storage.max_capacity\u003c/p\u003e\n\u003cp\u003eReturn discharging_power\u003c/p\u003e\n\u003cp\u003e# Main program logic\u003c/p\u003e\n\u003cp\u003edistribution_network\u0026thinsp;=\u0026thinsp;Initialize DistributionNetwork object\u003c/p\u003e\n\u003cp\u003ebattery_storage\u0026thinsp;=\u0026thinsp;initialize_battery_storage(max_capacity)\u003c/p\u003e\n\u003cp\u003estorage_controller\u0026thinsp;=\u0026thinsp;initialize_storage_controller(distribution_network, battery_storage)\u003c/p\u003e\n\u003cp\u003eWhile True:\u003c/p\u003e\n\u003cp\u003echeck_load_demand(storage_controller)\u003c/p\u003e\n\u003cp\u003eCode B: Pseudo code of Python based balanced hybrid optimization algorithm\u003c/p\u003e\n\u003cp\u003eInitialize BESSController with parameters (max_soc, min_soc, charge_rate, discharge_rate)\u003c/p\u003e\n\u003cp\u003eInitialize Load Demand, System Voltage\u003c/p\u003e\n\u003cp\u003eFunction charge(amount):\u003c/p\u003e\n\u003cp\u003eIncrement current_soc by amount\u003c/p\u003e\n\u003cp\u003eEnsure current_soc\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;max_soc\u003c/p\u003e\n\u003cp\u003eFunction discharge(amount):\u003c/p\u003e\n\u003cp\u003eDecrement current_soc by amount\u003c/p\u003e\n\u003cp\u003eEnsure current_soc\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;min_soc\u003c/p\u003e\n\u003cp\u003eFunction optimize_charge(load_demand, off_peak_hours, peak_hours):\u003c/p\u003e\n\u003cp\u003eSet GA parameters (ga_pop_size, ga_generations, ga_mutation_rate)\u003c/p\u003e\n\u003cp\u003eSet PSO parameters (pso_swarm_size, pso_iterations, pso_c1, pso_c2)\u003c/p\u003e\n\u003cp\u003eSet BFO parameters (bfo_swim_length, bfo_tumble_count, bfo_population_size)\u003c/p\u003e\n\u003cp\u003eInitialize population with random SOC values within [0, max_soc]\u003c/p\u003e\n\u003cp\u003eRepeat for ga_generations times:\u003c/p\u003e\n\u003cp\u003ePerform GA selection, crossover, and mutation\u003c/p\u003e\n\u003cp\u003eEvaluate fitness of GA population\u003c/p\u003e\n\u003cp\u003eSelect top solutions for PSO from GA population\u003c/p\u003e\n\u003cp\u003eInitialize PSO parameters\u003c/p\u003e\n\u003cp\u003eRepeat for pso_iterations times:\u003c/p\u003e\n\u003cp\u003eUpdate PSO velocity and position\u003c/p\u003e\n\u003cp\u003eEvaluate fitness of PSO population\u003c/p\u003e\n\u003cp\u003eUpdate PSO best position\u003c/p\u003e\n\u003cp\u003eInitialize BFO population with random SOC values within [0, max_soc]\u003c/p\u003e\n\u003cp\u003eRepeat for bfo_swim_length times:\u003c/p\u003e\n\u003cp\u003ePerform chemotaxis and reproduction\u003c/p\u003e\n\u003cp\u003eEvaluate fitness of BFO population\u003c/p\u003e\n\u003cp\u003eMerge GA, PSO, and BFO populations\u003c/p\u003e\n\u003cp\u003eChoose solution with lowest fitness value\u003c/p\u003e\n\u003cp\u003eIf off_peak_hours:\u003c/p\u003e\n\u003cp\u003eCharge BESS according to charge_rate\u003c/p\u003e\n\u003cp\u003eElse if peak_hours:\u003c/p\u003e\n\u003cp\u003eDischarge BESS according to discharge_rate\u003c/p\u003e\n\u003cp\u003eMain program logic:\u003c/p\u003e\n\u003cp\u003eload_demand, voltage, power_factor\u0026thinsp;=\u0026thinsp;get_distribution_network_data()\u003c/p\u003e\n\u003cp\u003eCall optimize_charge(soc, max_soc, off_peak_hours, peak_hours, load_demand)\u003c/p\u003e\n\u003cp\u003eAdjust voltage and power factor:\u003c/p\u003e\n\u003cp\u003eIf voltage\u0026thinsp;\u0026gt;\u0026thinsp;target_voltage:\u003c/p\u003e\n\u003cp\u003eAdjust voltage to target_voltage\u003c/p\u003e\n\u003cp\u003eIf power_factor\u0026thinsp;\u0026gt;\u0026thinsp;target_power_factor:\u003c/p\u003e\n\u003cp\u003eAdjust power_factor to target_power_factor\u003c/p\u003e"},{"header":"4. Impact Analysis with 50% Loading","content":"\u003cp\u003eIt is observed that the transformers are loaded to 15%-20% according to [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] the daily load demand of the test [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The load demand [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] increased to 50% [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] according to each transformer\u0026rsquo;s capacity for identifying [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] the probable impacts of [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] charging electric ferry [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The capacities of four connected BESSs represent the capacities of the proposed electric ferry charging stations [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. In impact analysis, the charging capacities [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] of the proposed stations are [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] supposed to be fully utilized by electric ferries in coordinated mode. The test distribution network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] without integrating BESSs is termed [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] the base case [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. The power flow [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] results in the coordinated mode of the network with BESSs are compared with those of the base case for relative comparison. Figure \u003cspan class=\"InternalRef\"\u003e11\u003c/span\u003e shows the hourly loading of the BryanJordanDrive_MarinaAvnue transformer, which is connected to the buses where a BESS of 250 kWh capacity is installed. The figure shows the loading of that transformer in various operational modes of the BESSs. In the base case, the transformer\u0026rsquo;s maximum and minimum loadings [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] are 49.21% and 26.38% respectively, the transformer\u0026rsquo;s maximum and minimum loadings [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e] remain the same in the only charge coordinated mode. The maximum and minimum loadings for coordinated charge-discharge mode are reduced to 46.26% and 27.55% respectively. These results indicate that the coordinated modes do not impose additional loading on the transformers, instead they reduce loading by discharging power during peak hours of the network. A similar decrement in maximum loading occurs in coordinated modes for 11 kV/ 0.415 kV transformers [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] where the BESSs are connected to the low voltage side [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Figure 12 shows the maximum loading of transformers where BESSs are connected for the base case, only charge coordinated mode and coordinated charge-discharge mode respectively.\u003c/p\u003e\n\u003cp\u003eThree lines, Pluto_1, Moon_A and Moon_Begin are considered for identifying the loading impacts on lines of the test network. The reason for selecting these lines is that they consume the highest loading among other network lines. Figure \u003cspan class=\"InternalRef\"\u003e13\u003c/span\u003e illustrates the hourly load variations of the Pluto_1 line under three scenarios: the base case, coordinated charge-discharge mode, and a mode where only charging occurs. In the base case, the load fluctuates between a maximum of 50.33% and a minimum of 25.53%. In the mode with only charging, the load ranges from a maximum of 50.33% to a minimum of 26.30%. Meanwhile, in the coordinated charge-discharge mode, the load varies from a maximum of 49.56% to a minimum of 26.30%. This result implies that the coordinated charge-discharge mode reduces the maximum load to the line. Figure \u003cspan class=\"InternalRef\"\u003e14\u003c/span\u003e represents the maximum loading of three selected lines for the base case, coordinated charge-discharge mode and only charge coordinated mode. The figures depict the decrement in the loading of selected lines in coordinated charge-discharge mode concerning that in the base case. In only charge coordinated mode, the load of selected lines remains the same as those in the base case. The impact on bus voltages can be identified by checking the voltages of buses where the BESSs are connected. The bus voltages of four selected buses [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] are presented to check the impact on bus voltages due to coordinated modes. Figure \u003cspan class=\"InternalRef\"\u003e15\u003c/span\u003e shows the maximum and minimum bus voltages [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] of selected buses in base mode. The maximum and minimum bus voltages [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e] of those buses in coordinated modes are represented in Fig.\u0026nbsp;16. The bus voltages remain the same in only charge coordinated mode relative to those in coordinated charge-discharge mode. For example, the maximum and minimum bus voltages of Marina_Ave_Slipway_B bus are 0.979 p.u. and 0.955 p.u. respectively [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e] for the base case. When a BESS of 300 kW capacity is connected to this bus [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e], the maximum and minimum bus voltages are [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e] 1.009 and 0.987 respectively, in coordinated modes respectively [\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e]. An increment of 1.12%-1.15% in bus voltages occurs in coordinated modes as compared to those [\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e] in the base case.\u003c/p\u003e"},{"header":"5. Impact Analysis with 80% Loading","content":"\u003cp\u003eFor probable impact analysis of electricity ferry charging, the load demand [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] of the test distribution network is increased to [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] 80% of each transformer\u0026rsquo;s capacity while [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] BESSs are connected to the network [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e17\u003c/span\u003e illustrates the hourly loading status of the BryanJordanDrive_MarinaAvenue transformer, linked to the buses hosting a 250 kWh BESS. The diagram displays the transformer\u0026apos;s loading under various operational modes. In the base case, the transformer\u0026apos;s maximum and minimum loadings stand at 79.55% and 41.96% respectively. The maximum loading remains unchanged in only charge coordinated mode, whereas the minimum loading becomes 43.83%. In the coordinated charge-discharge scenario, the maximum and minimum loadings are 74.61% and 43.83%, respectively. These findings indicate that coordinated operating modes do not enhance the transformer\u0026rsquo;s maximum loading capacity. A similar pattern of reduced maximum loading is also observed in 11 kV/0.415 kV transformers [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e] when Battery Energy Storage Systems (BESS) are connected to the low-voltage side [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Figure \u003cspan class=\"InternalRef\"\u003e18\u003c/span\u003e illustrates the peak loading of these transformers under the base case, the charge-only coordinated mode, and the charge-discharge coordinated mode. Figure \u003cspan class=\"InternalRef\"\u003e19\u003c/span\u003e shows the hourly loading profile of the Pluto_1 line across different modes, which highlights the impact of these modes on the line\u0026rsquo;s loading. In the base case, the loading fluctuates between 82.69% and 41.37%. In the charge-only coordinated mode, it remains steady between 82.69% and 42.15%, whereas in the charge-discharge coordinated mode, the loading ranges from 81.88\u0026ndash;42.15%, showing a reduction in peak line loading. Figure \u003cspan class=\"InternalRef\"\u003e20\u003c/span\u003e compares the maximum loading of three selected lines under different operating modes. It is evident that the loading decreases in the charge-discharge mode, while it remains unchanged in the charge-only mode when compared to the base case. To assess the effect on bus voltages, four buses were chosen for analysis in coordinated modes. Figures \u003cspan class=\"InternalRef\"\u003e21\u003c/span\u003e and 22 present the maximum and minimum bus voltages for these buses under the base case and the coordinated modes. The voltage levels in both the charge-only and charge-discharge coordinated modes remain nearly identical. For example, at the Marina_Ave_Slipway_B bus, the maximum and minimum voltages are 0.966 p.u. and 0.927 p.u., respectively, in the base case without BESS. After a 300 kW BESS is integrated into this bus, the maximum and minimum voltages in the coordinated modes increase slightly to 0.976 p.u. and 0.937 p.u., respectively [\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e]. Around 1-1.12% [\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e] increment in bus voltages occurs in coordinated modes compared to those in the base case.\u003c/p\u003e"},{"header":"6. Discussion on Research Findings","content":"\u003cp\u003eThe simulation results imply that the proposed control topology keeps the bus voltage, line loading and transformer loading of the selected distribution network within the allowable limit according to the Australian grid regulation constraints for coordinated operational mode of electric ferry charging stations. In Australia, the National Electricity Rules (NER) establishes standards for voltage levels, transformer loading, and line loading to maintain stability and reliability in the electrical network. For normal operating conditions, the voltage should remain within +\u0026thinsp;10% and \u0026minus;\u0026thinsp;6% of the nominal voltage (1.0 p.u.), [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] leading to a maximum of 1.10 p.u. and a minimum of 0.94 p.u [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Transformers need to operate efficiently between 40\u0026ndash;80% of their rated load [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Similarly, line loading should also be managed to avoid excessive current that can lead to overheating and potential damage to the infrastructure [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The impact of these voltage, transformer loading, and line loading standards on electric ferry charging and BESS operational performance is significant. Under voltage conditions below 0.94 p.u., which generally occurs during peak hours, results in inadequate power supply to the charging system, prolonging charging times and potentially leading to incomplete charges, thus disrupting ferry schedules. For BESS, low voltage affects charging efficiency, leading to incomplete cycles and reduced energy storage capacity, increasing internal resistance and heat generation that accelerates degradation. Conversely, over-voltage conditions, which generally arise during off-peak hours exceeding 1.10 p.u., can cause excessive current draw by the ferry charging system, leading to overheating and potential damage to the equipment. This instance can also trigger over-voltage protection mechanisms, interrupting the charging process and reducing efficiency. For BESS, high voltage accelerates battery cell degradation, reducing lifespan and efficiency and poses safety risks such as thermal runaway. High loading conditions, exceeding 80% of rated capacity for transformers and lines, can cause voltage drops along distribution lines, exacerbating under-voltage issues at charging stations. Overloading transformers and lines lead to overheating, increasing maintenance costs, and raising the risk of equipment failure. The overloading forces the BESS to discharge more frequently to support the grid, leading to faster cycling and reduced battery lifespan. Conversely, low loading conditions, below 50% of rated capacity, though less immediately harmful, can result in inefficient grid infrastructure utilization and economic inefficiencies, affecting the cost-effectiveness of charging infrastructure. For BESS, low loading conditions can extend operational life due to less frequent cycling but may also cause capacity fade due to irregular charge-discharge cycles. The peak shaving approach of BESSs manages low bus voltage and high loading of the test distribution network by discharging during peak hours. The valley filling approach of BESSs regulates over bus voltage and low loading by charging during off-peak hours. So, the storage of EF can be utilized as a spinning reserve for the shore side electricity network.\u003c/p\u003e \u003cp\u003eA balanced hybrid GA-PSO-BFO algorithm represents an innovative approach that combines three well-established optimization techniques. While these optimization methods are extensively used individually in various optimization problems, their specific combination into a hybrid algorithm for optimizing the BESS\u0026rsquo;s charge and discharge cycles in a simulated distribution network is relatively novel. This hybridization leverages the strengths of each method, GA's robustness in exploring the global search space, PSO's convergence speed and simplicity and BFO's local search capabilities and fine-tuning to address the limitations of each technique, resulting in a more powerful and effective optimization tool. The application of this hybrid algorithm to BESS in distribution networks, particularly for managing system voltage, current, power factor, transformer loading and line loading, represents a new and innovative use case. Although hybrid algorithms are employed in other optimization problems, their specific use in this domain is rare and demonstrates a novel approach. While similar hybrid approaches may be used for other optimization problems or different contexts, optimizing BESS operations by considering critical parameters like voltage regulation, power factor correction and load management is likely a recent development. The DIgSILENT PowerFactory simulation results underscore this control approach\u0026rsquo;s performance in managing [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] the load demand of the test distribution network [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] with BESS by retaining system parameters within permissible limits. The power flow analysis of the [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] BESSs integrated test distribution network [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] with 80% load is done in coordinated charge-discharge mode by using individual GA, PSO and BFO optimization algorithms in order to identify the performance of the hybrid GA-PSO-BFO optimization algorithm. Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e represents a comparative result of voltage and percentage loading of a selected bus, transformer and line respectively which identifies the better performance of the hybrid optimization algorithm than the individual optimization algorithm.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRelative comparison among power flow results among various optimization algorithms\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eBus_BryanJDr_MarinaAveue_B Bus Voltage (p.u.)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eTransformer_BryanJorDr_MarinaAT Loading (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eLine_Pluto_1\u003c/p\u003e \u003cp\u003eLoading (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e82.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePSO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e75.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e43.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGA-PSO-BFO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e74.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e43.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e81.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e42.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study explores the potential effects of electric ferry charging on the distribution network through power flow analysis. The analysis is conducted using a coordinated approach on a test distribution system based in Gladstone Marina, Queensland. The network model incorporates the daily load profile of the 11 kV Marina feeder, along with four proposed charging stations, each represented by BESSs. It's important to note that this model may not fully reflect the technical characteristics and capabilities of the future EF charging infrastructure in Gladstone. The findings indicate a slight increase in bus voltages, ranging from 1.12\u0026ndash;1.15%, as the load grows in coordinated modes. This increase in voltage could enhance the efficiency and voltage regulation of the network. In coordinated charge-discharge mode, transformer loading decreases by 3%-4%, while line loading experiences a reduction of 2.5%-3.5%. These reductions significantly contribute to decreased system current and power consumption. Notably, transformer and line loadings remain unchanged in only charge coordinated mode relative to the base case. The results indicate that the initiation of the proposed metaheuristic optimization-based control algorithm in coordinated modes appropriately manages the charge-discharge pattern of EF storage with its SOC and distribution network\u0026rsquo;s load demand by retaining system parameters within the permissible limits. The peak shaving and valley filling approaches of EF storage during peak and off-peak hours respectively, can serve as spinning reserves for the distribution network along the shoreline. The coordinated modes have a beneficial effect on the system performance of the test distribution network, which is integrated into a robust grid infrastructure. The Gladstone grid network is reliable, resilient and efficient with modern infrastructure, advanced monitoring, flexible operations and sufficient transmission capacity for managing load demand and the ability to integrate electric ferry charging stations.\u003c/p\u003e \u003cp\u003eThe research findings may not apply to a distribution system which is prone to outages, inefficiencies due to outdated infrastructure, lack of advanced monitoring, vulnerability to disruptions, challenges integrating renewable energy and inadequate coordination and regulatory support. As a result, the findings from this impact analysis may not be directly relevant to other areas with varying infrastructure, load patterns, and operational conditions. The proposed control algorithm may need some modifications before being used in other similar applications. When adapting the control algorithm for controlling system parameters of different distribution networks, fine-tuning hyperparameters like population size, crossover and mutation rates for GA, cognitive, social parameters and inertia weight for PSO and chemotactic steps, swim length and reproduction steps for BFO ensure effective search behavior. Seamless integration and appropriate weighting of each algorithm's fitness contributions and incorporating domain-specific knowledge and constraints can significantly enhance the algorithm's performance and reliability. However, these research findings may be a guideline for implementing electric ferry charging stations in regional Australia. Future research may consider the actual charging patterns of electric ferries in various operational modes to identify their potential impacts on the shore-side distribution network. To achieve a comprehensive impact analysis, the uncoordinated mode of ferry charging should be considered, and this aspect may be explored in future research.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial and non-financial competing interest’s declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during this study are not publicly available due to copyright restrictions. However, they are available from the corresponding author upon reasonable request\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eRoy, R.B.; Alahakoon, S.; Van Rensburg, P.J.; Arachchillage, S.J. Impact Analysis of Uncoordinated Electric Ferry Charging on Distribution Network. \u003cem\u003ee-Prime - Advances in Electrical Engineering, Electronics and Energy\u003c/em\u003e \u003cstrong\u003e2024\u003c/strong\u003e, \u003cem\u003e10\u003c/em\u003e, 100783, doi:10.1016/j.prime.2024.100783.\u003c/li\u003e\n \u003cli\u003eStoumpos, S.; Bolbot, V.; Theotokatos, G.; Boulougouris, E. 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In \u003cem\u003eEngineering Materials for Efficient Energy Storage and Conversion\u003c/em\u003e; IGI Global Scientific Publishing, 2024; pp. 303\u0026ndash;326 ISBN 9798369327982.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electric Ferry, BESS, Coordinated Mode, DIgSILENT PowerFactory, Control Algorithm","lastPublishedDoi":"10.21203/rs.3.rs-5985265/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5985265/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe global maritime industry significantly contributes to greenhouse gas emissions in marine environments. To address this, there is a growing global initiative to adopt renewable-powered electric marine vessels, where storage plays a crucial role. This study delves into the potential impacts of charging electric ferries in coordinated mode on local distribution network by metaheuristic optimization. Using Gladstone Marina in Queensland, Australia, as a test case, the research employs DIgSILENT PowerFactory for power flow analysis, based on actual load data. The simulated network includes four BESSs (Battery Energy Storage Systems) representing proposed charging stations. For analysis, MATLAB Simulink based BESS\u0026rsquo;s dynamic model is included in the simulated network of DIgSILENT. A novel control algorithm is used for controlling and optimizing the operation of BESSs according to load demand and status of network\u0026rsquo;s system parameters. Python based control algorithm implements a balanced hybrid GA-PSO-BFO (Genetic Algorithm-Particle Swarm Optimization-Bacterial Foraging Optimization) metaheuristic optimization which ensures sequential operation of BESSs according to their SOC (State of Charge) in coordinated mode. Initially, power flow analysis is conducted without BESS integration, termed as the base case, at 50% and 80% load capacities of transformers. For impact analysis, power flow analysis is performed by integrating BESSs to simulate fully utilized charging stations at 50% and 80% load increment in coordinated charge-discharge and only charge coordinated modes. Results show a 1%-1.5% increase in bus voltages in coordinated modes as load escalates. Transformer loading decreases by 3%-4% in coordinated charge-discharge mode, while line loading drops by 2.5%-3.5%, contributing to reduced system current and power. The transformer loading and line loading remain same to base case in only charge coordinated mode. The findings from the time-based quasi-dynamic mode in DigSilent suggest that coordinated charge-discharge imposes beneficial effect on the system parameters of the test network. By aligning charge-discharge times with load demand, coordinated mode enables BESSs to participate in peak shaving of the test distribution network. This peak shaving strategy indicates that electric ferries at dockyards can serve as a spinning reserve for the shore-side distribution network.\u003c/p\u003e","manuscriptTitle":"Metaheuristic Optimization based Coordinated Electric Ferry Charging Impacts on Distribution Network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-25 12:33:37","doi":"10.21203/rs.3.rs-5985265/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b84fd143-0714-463a-bca1-56cb46485dba","owner":[],"postedDate":"February 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":44654092,"name":"Physical sciences/Energy science and technology/Energy storage"},{"id":44654093,"name":"Physical sciences/Energy science and technology/Renewable energy"},{"id":44654094,"name":"Physical sciences/Engineering/Electrical and electronic engineering"}],"tags":[],"updatedAt":"2026-01-01T05:08:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-25 12:33:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5985265","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5985265","identity":"rs-5985265","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}