Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management

Abstract

Hybrid renewable energy systems (HRESs) are being incorporated and evaluated within seaports to realize efficiencies, reduce dependence on grid electricity, and reduce operating costs. The paper adopts a genetic algorithm (GA)-based optimization framework to assess four energy management scenarios that embed wind turbines (WTs), photovoltaic energy (PV), an energy storage system (ESS), and an energy management system (EMS). The scenarios were developed based on different levels of renewable energy integration, energy storage utilization, and grid dependency to optimize cost and sustainability while reflecting the actual port energy scenario as the base case. Integrating HRES, ESS, and EMS reduced the port’s levelized cost of energy (LCOE) by up to 54%, with the most optimized system (Scenario 3) achieving a 53% reduction while enhancing energy stability, minimizing grid reliance, and maximizing renewable energy utilization. The findings show that the HRES configuration provides better cost, sustainability, and resiliency than the conventional grid-tied system. The unique proposed EMS takes it a step further, optimizing not just the energy flow but also the cost, making the overall system more efficient—and less costly—for the user. ESS complements energy storage and keeps it functional and reliable while EMS makes it completely functional by devising ways to reduce costs and enhance efficiency. The study presents the technical and economic viability of HRES as an economic and operational smart port infrastructure through its cost-effective integration of renewable energy sources. The results reinforce the move from conventional to sustainable autonomous port energy systems and lay the groundwork for forthcoming studies of DR-enhanced port energy management schemes. While prior studies have explored renewable energy integration within ports, many lack a unified, empirically validated framework that considers HRES, ESS, and EMS within real-world port operations. This research addresses this gap by developing an optimization-driven approach that assesses the techno-economic feasibility of port energy systems while incorporating real-time data and advanced control strategies. This study was conducted to enhance port infrastructure and evaluate the impact of HRES, ESS, and EMS on port sustainability and autonomy. By bridging the gap between theoretical modeling and practical implementation, it offers a scalable and adaptable solution for improving cost efficiency and energy resilience in port operations.

1. Introduction

The urgent need to mitigate climate change and reduce GHG emissions has accelerated the global transition toward sustainable energy systems [1]. Seaports have a pivotal role in critical infrastructures, requiring transformation due to their high energy demands and significant environmental impacts [2]. As key nodes in global trade, ports must transition from conventional fossil fuel-based energy systems to greener alternatives, integrating renewable energy sources and advanced energy storage solutions [3].

HRES combined with ESS offers a promising pathway for decarbonizing port operations [4]. By leveraging a mix of renewable energy sources alongside efficient storage technologies, ports can achieve a more reliable and self-sustaining energy supply [5]. Among the various strategies for achieving sustainability with HRES and ESS, one effective approach involves the combined use of HRES-generated energy and grid electricity, taking advantage of lower nighttime energy costs to supplement renewable energy production [6]. This method, in conjunction with ESS integration, has a crucial role in reducing the port’s LCOE while enhancing both the sustainability and autonomy of HRES-based energy generation. Excess energy produced by HRES can be stored in ESS for later use during periods of reduced renewable energy production [7,8]. Additionally, during nighttime hours when electricity costs are lower [9], ESS can be charged from the grid, ensuring energy availability for future operations when HRES output is insufficient or in cases of grid power shortages or outages [10]. This strategic utilization of ESS not only enhances energy resilience but also optimizes cost efficiency, contributing to the overall sustainability of port operations [11].

While the transition to sustainable port operations has gained increasing attention, only a limited number of studies have focused on the concept of nearly zero-energy ports and the integration of HRES and ESS in conjunction with grid energy utilization [12]. The majority of previous research has primarily examined the optimization of specific port services, such as container terminal operations, lighting systems, and mooring infrastructure, rather than addressing the comprehensive energy transformation of an entire port facility [13]. This study aims to bridge this gap by exploring a holistic approach to port decarbonization, emphasizing the synergies between renewable energy generation, storage solutions, and strategic grid energy usage to enhance efficiency, sustainability, and energy autonomy. Recent research highlights the growing global momentum toward transforming traditional ports into sustainable, green, and blue hubs. From hydrogen-powered infrastructure [14,15,16,17] and digital innovations [16] to eco-tourism models like Skyros’ blue port [18,19,20], studies emphasize decarbonization, smart energy systems, stakeholder collaboration, and regulatory policy as key enablers of port sustainability. Whether through onshore power supply, circular economy practices, or community-driven governance, ports are rapidly evolving into critical nodes for achieving environmental and economic goals in the maritime sector [21].

Genetic algorithms (GAs), inspired by natural selection, have become a prevalent metaheuristic optimization technique for tackling complex problems within energy systems [22,23,24,25,26]. Their ability to navigate non-linear and non-convex search spaces makes them particularly well suited for the intricacies of optimizing energy management in dynamic environments like ports, which often involve variable energy demands and the integration of intermittent renewable energy sources [27,28]. GAs operate on a population of potential solutions, representing chromosomes, which are iteratively improved through processes mimicking natural selection, including selection based on a fitness function, crossover (recombination), and mutation [29,30]. This evolutionary approach allows GAs to effectively explore a wide range of solutions to identify optimal or near-optimal configurations for energy systems, considering multiple objectives such as cost reduction, emission minimization, and reliability enhancement [31,32]. The versatility of GAs has led to their application in various energy-related challenges, including the planning of distributed generation, the optimization of power transfer limits for enhanced system stability, and the overall management of power system operations [33].

This research introduces a custom-developed GA-based assessment framework, specifically engineered for the holistic optimization of HRES design, sizing, and operational control, fully integrated with ESS within port environments. A core innovation is the framework’s ability to simultaneously optimize system components alongside smart energy management strategies, including cost-driven nighttime grid charging via EMS, to enhance energy autonomy, system stability, and cost effectiveness. Applied to the Mediterranean Port of Souda, Crete, using granular, real-world hourly energy demand data and specific component costs, the framework is validated for its effectiveness and adaptability for ports seeking sustainable power solutions. This data-driven, empirically validated approach yields practical, actionable insights, directly bridging the gap between theoretical optimization and real-world feasibility. Consequently, this study establishes a scalable and replicable model tailored for advancing renewable energy integration in Mediterranean maritime infrastructures and similar contexts.

Furthermore, a defining novelty of this work lies fundamentally in its data-driven approach. Unlike purely theoretical models, this study utilizes actual hourly energy consumption data obtained directly from the Port of Souda authority for the year 2020, providing a realistic, high-resolution demand profile. This real-world demand, combined with site-specific renewable resource data (wind speed and solar clearness index from established databases like Copernicus, NASA POWER, PVGIS) and current market-based component costs for PV panels, wind turbines, and energy storage systems, forms the empirical backbone of the analysis. By grounding the custom-developed GA optimization framework in these specific, real-world parameters, the study generates accurate, practical insights rather than generalized estimations. These insights include optimized HRES configurations (specific kW capacities for PV and WT), defined ESS capacities, and quantifiable techno-economic outcomes, and grid energy usage reductions under various operational scenarios. This thorough use of actual operational data and costs effectively bridges the gap between theoretical optimization and real-world feasibility. It demonstrates not just the potential but the practical viability and specific economic benefits of implementing such systems within the constraints and conditions of an actual port. Consequently, the methodology—integrating real data inputs with GA optimization and techno-economic analysis—establishes a scalable and replicable model. While tailored to Souda, the framework’s structure allows for adaptation to other maritime infrastructures by substituting relevant local data, thus advancing the practical application of renewable energy integration in the sector.

The structure of the research is organized to provide a comprehensive analysis of the research topic. Section 2 presents the state of the art, offering a critical review of previous studies while identifying existing research gaps that necessitate further investigation. Section 3 details the methodology employed in this study, beginning with an examination of the case study location, followed by an assessment of its energy demands and the potential for HRES. The section further details the system design by analyzing the scenarios simulated to evaluate performance, specifying the components and their characteristics, and addressing the cost specifications of each simulated system to assess economic feasibility. Finally, the mathematical modeling of the system components is presented, establishing a rigorous analytical framework for the study. Section 4 presents the results of the study, providing a detailed analysis of each of the four simulated scenarios. This section examines the performance of the proposed systems, comparing their effectiveness in meeting the energy demands of the case study location while considering technical, economic, and environmental factors. Following the presentation of results, a discussion interprets the findings in the context of the research objectives and existing literature, highlighting the implications, limitations, and potential applications of the study. Finally, the conclusion synthesizes the key insights derived from the analysis, emphasizing the contributions of this research and offering recommendations for future studies and practical implementations.

2. State of the Art

Ports play a crucial role in global trade and economic development, serving as essential hubs for transporting goods and passengers [34,35,36]. These facilities require substantial energy to sustain their operations, including cargo handling, logistics, and auxiliary services [37]. Traditionally, ports have relied on conventional fossil fuel-based energy sources, contributing to high carbon emissions and environmental degradation. With increasing global concerns over climate change and sustainability, ports are transitioning toward greener energy solutions to enhance efficiency and reduce environmental impact [38]. They require significant energy due to their extensive operational requirements, including cargo handling equipment, fuel bunkering operations, logistics services, and lighting. The increasing scale of global trade has led to a rise in port activities, causing an upsurge in energy consumption, leading to higher port throughput and extended operational hours [39]. Such demands present challenges that lead to high dependency on fossil fuels, variability in energy demand due to fluctuating shipping activities, and the need for a reliable and resilient energy supply to ensure uninterrupted operations [40]. To address these challenges, ports are exploring alternative energy solutions, including electrification, renewable energy integration, and advanced energy management strategies [41,42]. The integration of renewable energy sources within port operations has gained significant attention [43].

The growing concern over fossil fuel depletion and the environmental impact of conventional energy generation has increased interest in HRES. Due to the stochastic nature of renewable energy sources, multiple sources are combined to enhance reliability. Optimizing the size, cost, and power production of HRES is crucial for effective planning. This study reviews the optimization of tools, constraints, and storage/backup systems that are essential for improving HRES performance [44].

Despite the benefits that renewable energy sources provide, they are inherently intermittent, requiring advanced strategies for energy management and storage to ensure a stable energy supply [45]. Due to the volatility of renewable energy sources, seaports face unstable electricity production, necessitating further actions to ensure continuous and reliable operations [46]. One way to succeed in a stable energy supply is by combining multiple renewable energy sources with complementary characteristics to enhance efficiency and reliability [47]. Such systems can mitigate the intermittency of individual renewable technologies and optimize energy production, offering enhanced energy security through diversified energy sources [9]. HRES is evolving with the integration of intelligent optimization techniques to enhance efficiency, reliability, and cost-effectiveness. Such smart algorithms analyze multiple variables, including energy generation, storage capacity, and operational constraints, to minimize costs while ensuring stable power supply. By leveraging these optimization techniques, HRES can achieve a more adaptive and intelligent approach to sustainable energy generation [48].

HRES combined with ESS could be a viable and feasible optimal sustainable solution [9]. ESS has a pivotal role in stabilizing the energy supply by storing excess energy during peak energy production periods and releasing it during high demand. Port authorities have started using ESS to provide grid stability, and support load balancing, ensuring a continuous and efficient energy supply for port operations [49,50]. Ports rely on continuous power for critical operations, including cargo handling, refrigeration, security, and navigation systems. A power outage could lead to financial losses, operational delays, and safety risks [51]. ESS acts as an uninterrupted power supply, ensuring smooth operations even during grid failures or natural disasters. In locations prone to power losses and extreme, unstable weather conditions that may cause HRES stoppage, ESS provides a resilient energy source, allowing ports to recover quickly and maintain emergency response systems.

At the same time, EMS is being incorporated to optimize ESS and HRES operations, coordinating energy generation, storage, and consumption [52]. EMS enhances efficiency by strategically dispatching stored energy to alleviate peak loads while prioritizing renewable energy utilization to achieve economic and environmental benefits [53]. During off-peak hours, when electricity demand is lower, grid energy prices typically drop. An interesting approach to reduce costs and better calculate or serve energy is the off-peak use of energy and storage, where ports take advantage of lower tariffs by charging ESS at a reduced cost during the night and strategically using the stored energy during the day when demand—and electricity prices—are higher [9,54]. This approach, known as load shifting, not only reduces operational costs but also decreases reliance on fossil fuel-based energy sources during peak hours [55]. By integrating EMS with dynamic algorithms, ports can automate this process, ensuring maximum cost savings and energy efficiency while maintaining a stable and reliable power supply [56]. Although extensive research has been conducted on individual renewable energy technologies and their integration into port systems, studies specifically examining the integration of HRES with EMS in port environments remain limited [57,58].

Recent advancements have further refined the application of GAs in energy systems, particularly in the optimization of HRES and ESS [59]. Studies have employed GAs to determine the optimal sizing and placement of renewable energy sources and storage, enhance the efficiency and reliability of microgrids, and develop effective load scheduling and demand response strategies within smart grids [60,61,62]. Hybrid approaches, combining GAs with other optimization techniques, like particle swarm optimization (PSO), or integrating them with machine learning models for improved forecasting and adaptive control, represent promising directions for achieving even greater performance in complex energy management scenarios [63,64,65]. Multi-objective genetic algorithms are also increasingly utilized to address the inherent trade-offs between economic, environmental, and technical objectives in the design and operation of sustainable energy systems [66,67,68].

Seaports, which account for roughly 3% of global greenhouse gas emissions, have increasingly embraced HRES and GA-based optimization to achieve green port objectives [69,70,71,72,73]. GA-driven frameworks have been applied to port energy system design. For example, a two-stage GA approach optimized a Chinese container port’s hybrid wind, storage, and shore-power system, minimizing both capital investments and operating costs. Beyond capacity planning, evolutionary algorithms also inform operational strategies. Recent studies coordinated port microgrid dispatch with container handling schedules using improved multi-objective GA techniques to minimize total energy cost [45]. GA-based optimizations have similarly been employed to curtail emissions in ports—one study utilized a hybrid GA–simulated annealing method for crane scheduling, achieving notable reductions in fuel consumption and carbon emissions [74]. These approaches can incorporate demand response and other flexibilities; including such measures in optimization significantly cuts port energy expenditures [75], and GA-optimized energy management has been shown to outperform rule-based controls in lowering operational costs [76]. Real-world case studies validate GA effectiveness in port settings, with integrated energy–logistics optimization yielding substantial economic gains in a Shanghai port pilot.

Advanced optimization algorithms, such as neural networks and genetic algorithms, play a crucial role in determining the optimal configuration of HRES, ESS, and EMS [77,78,79,80,81,82,83]. As demonstrated in this study, the integration of GA-neural network (NN) optimization enhances the techno-economic analysis by minimizing installation costs, CO₂ emissions, and loss of power supply probability (LPSP) [84]. These smart algorithms enable a more efficient and cost-effective deployment of renewable energy solutions, ensuring improved reliability and energy independence across different climate conditions [84].

One effective strategy to achieve such sustainability objectives is by transforming ports into nearly zero-energy ports (nZEPs). The transformation of a conventional port into an nZEP is a crucial step toward reducing greenhouse gas emissions and enhancing energy efficiency [20,85,86,87,88]. This transition is driven by the need to minimize reliance on fossil fuels while simultaneously lowering operational costs for end-users, thereby playing a pivotal role in the global economy [89]. Achieving nZEP status requires the integration of renewable energy sources, the adoption of advanced energy storage systems, and the modernization of outdated infrastructure with innovative, energy-efficient technologies [90,91,92,93]. However, several challenges hinder this development, including financial constraints and the persistence of traditional management practices [94]. Overcoming these barriers is essential to ensuring the successful implementation of autonomous energy ports, aligning with global sustainability objectives, and fostering a more resilient and environmentally responsible maritime industry [9,43,95].

Despite the growing emphasis on renewable energies in port infrastructures, research remains fragmented, often focusing on isolated technologies rather than fully integrated solutions. As per

Table 1. Summary, insights, and relevance of the most relevant articles.

Ref.	Year	Summary	Relevance to This Study	Novel Contribution
[37]	2006	This study examines port investment, highlighting key paradigms, stakeholder dynamics, and critical factors like profitability and financing to enhance decision making and efficiency.	Addresses financial and stakeholder considerations, aligning with the study’s focus on assessing the economic viability of HRES, ESS, and EMS integration for LCOE reduction and sustainability in ports.	Expands port investment strategies by integrating HRES, ESS, and EMS, aligning with the study’s focus on enhancing decision making and efficiency through sustainable energy solutions. By optimizing energy costs and resilience, it provides a techno-economic framework that supports long-term profitability and smarter infrastructure investment.
[38]	2024	This study evaluates the energy and peak power demand of ships for onshore power supply (OPS) and alternative fuels, using the Port of Plymouth as a case study to assess feasibility, emissions reduction, and fuel requirements.	Examines power demand and clean energy solutions for ports, supporting the study’s exploration of HRES and ESS integration to enhance energy efficiency and sustainability.	Builds on energy demand assessments by integrating HRES, ESS, and EMS, aligning with the study’s focus on optimizing port energy systems for efficiency and sustainability. By reducing grid reliance and enhancing renewable energy utilization, it offers a comprehensive approach to managing OPS and alternative fuel integration in ports.
[39]	2004	This study explores the impact of transport costs and port efficiency on trade, revealing that inefficient ports increase shipping expenses and market barriers, while improvements significantly enhance trade.	Highlights the role of port efficiency in reducing costs, reinforcing the study’s goal of optimizing energy management through HRES, ESS, and EMS to lower operational expenses and improve sustainability.	Connects port efficiency and trade impact with energy optimization by integrating HRES, ESS, and EMS, aligning with the study’s focus on reducing operational costs and improving infrastructure. By enhancing energy reliability and sustainability, it supports cost-effective port operations that contribute to lower transport costs and increased trade efficiency.
[40]	2020	This study examines future port infrastructure demands, highlighting the need for expansion and adaptation to sea-level rise under different climate scenarios, with significant trade growth driving increased port capacity requirements.	Emphasizes the necessity of future-proofing port infrastructure, aligning with the study’s focus on integrating sustainable energy solutions like HRES, ESS, and EMS to support long-term resilience and efficiency.	Addresses future port infrastructure demands by integrating HRES, ESS, and EMS, aligning with the study’s focus on sustainable expansion and resilience. By optimizing energy management and reducing reliance on conventional power sources, it supports climate-adaptive port development while enhancing capacity and efficiency.
[41]	2023	This study explores the transformation of ports into energy hubs through renewable energy-based polygeneration systems, presenting a dynamic simulation model to optimize energy and economic impacts, with a case study on the Port of Naples.	Supports the concept of ports as energy hubs, aligning with the study’s investigation of HRES and ESS integration to optimize energy use, reduce LCOE, and enhance sustainability through advanced energy management strategies.	Advances the concept of ports as energy hubs by integrating HRES, ESS, and EMS, aligning with the study’s focus on optimizing energy and economic performance. By employing an optimization-driven approach, it enhances renewable energy utilization, cost efficiency, and system resilience, providing a scalable solution for smart port energy management.
[42]	2020	This study analyzes the use of a hybrid renewable energy system for electricity generation at Banjul Port to reduce costs and greenhouse gas emissions. By integrating renewable sources, the research highlights the potential for improving energy efficiency and sustainability in port operations.	Showcases the potential of HRES in ports, emphasizing cost reduction and emission mitigation, which align with the study’s goal of enhancing energy efficiency and sustainability.	Builds on the analysis of HRES for port energy by incorporating ESS and EMS, aligning with the study’s focus on optimizing cost, efficiency, and sustainability. By enhancing energy management and storage strategies, it provides a comprehensive framework for reducing reliance on conventional power and maximizing renewable energy utilization in port operations.
[43]	2021	This study reviews and analyzes existing research on nearly zero-energy ports, identifying key opportunities, challenges, and gaps in energy management strategies while proposing a framework for future sustainable port development.	Investigates nearly zero-energy ports, providing valuable insights into energy management challenges and opportunities that complement the study’s approach to optimizing port energy systems for greater sustainability and efficiency.	Enhances the nearly zero-energy port concept by integrating HRES, ESS, and EMS, aligning with the study’s focus on advancing sustainable port energy management. By optimizing energy flow, reducing costs, and improving resilience, it provides a practical framework for implementing efficient and autonomous renewable energy solutions in port operations.
[44]	2018	This study examines the optimization of HRES, highlighting key challenges, methodologies, and storage solutions. By reviewing optimization tools and constraints, it explores strategies to enhance size, cost, and reliability in HRES planning. The findings contribute to improving the efficiency and sustainability of renewable energy integration.	Highlights the importance of optimizing HRES and ESS, aligning with the study’s focus on enhancing energy efficiency, reliability, and sustainability through advanced integration and management strategies.	Enhances HRES optimization by integrating ESS and EMS within a port environment, aligning with the study’s focus on improving renewable energy efficiency. By addressing unique port energy demands, optimizing storage solutions, and implementing real-time energy management, it provides a practical framework for cost-effective, reliable, and sustainable HRES deployment in maritime operations.
[46]	2023	This study analyzes the integration of renewable energy and storage systems in the Kaliningrad Seaport to optimize power use. A software-based approach shifts peak loads, enhancing sustainability and economic feasibility.	Demonstrates practical implementation of HRES and ESS in a port setting, offering insights into load management strategies and the role of software in optimizing energy use, which can inform EMS integration for improved efficiency.	Enhances renewable energy and storage system integration in port environments by implementing HRES, ESS, and EMS with real-time energy management. Aligning with the study’s focus on optimizing power use, it introduces advanced control strategies to improve efficiency, reduce costs, and enhance resilience. This framework supports sustainable and economically viable energy solutions tailored for dynamic port operations.
[47]	2020	This study explores microgrid integration in ports, proposing a framework using Smart Port Index metrics to enhance sustainability. A two-stage stochastic model optimizes investment and operations, improving efficiency, safety, and reliability.	Provides a structured approach to microgrid deployment in ports, highlighting key performance metrics and optimization strategies. The use of a stochastic model offers valuable insights for assessing EMS impact on operational efficiency, investment decisions, and overall energy reliability.	Expands port microgrid integration by incorporating HRES, ESS, and EMS, aligning with the study’s focus on sustainability and operational efficiency. By implementing real-time energy management and optimization strategies, it improves investment planning, cost effectiveness, and system resilience. This framework supports the development of smart, reliable, and sustainable port energy solutions.
[9]	2021	This study examines the optimization of a seaport’s hybrid renewable energy system to improve efficiency and reduce emissions. By comparing dispatch strategies and energy storage options, the results show that peak shaving enhances energy management, lowers costs, and supports a nearly zero-energy port concept for sustainability.	Offers valuable insights into HRES optimization and the role of ESS in cost reduction and emission control. The comparison of dispatch strategies aligns with evaluating EMS effectiveness in enhancing energy management and achieving sustainable port operations.	Expands the optimization of a seaport’s hybrid renewable energy system by integrating HRES, ESS, and EMS, aligning with the study’s goal of improving efficiency and reducing emissions. By implementing advanced dispatch strategies and real-time energy management, it enhances peak shaving, lowers operational costs, and strengthens system resilience. This framework supports the nearly zero-energy port concept, promoting sustainable and autonomous energy solutions in port operations.
[48]	2022	This study investigates optimizing hybrid renewable energy systems using advanced algorithms to minimize costs and ensure reliability, by comparing SSA, GWO, and IGWO techniques through MATLAB R2022b simulations.	Provides optimization insights relevant to HRES deployment in ports, showcasing algorithmic approaches to cost minimization and reliability improvement.	Expands the study by introducing a custom Python 3.13.2-based genetic algorithm (GA) approach for system configuration, offering an alternative to the MATLAB-based simulations using SSA, GWO, and IGWO. This distinct methodology broadens the scope of optimization techniques, contributing to more effective hybrid renewable energy system design for cost minimization and reliability.
[49]	2018	This study reviews battery sizing criteria and methods for renewable energy systems, emphasizing their role in managing solar and wind variability. By categorizing applications based on energy system type, it highlights how system characteristics influence optimal battery sizing and selection methods.	Offers critical insights into ESS sizing for HRES in ports, helping to assess the impact of storage on energy reliability and cost-effectiveness. The categorization of applications provides a foundation for selecting optimal battery configurations to enhance ESS performance.	Expands the study by not only focusing on battery storage but also incorporating an EMS simulation scenario. This approach extends beyond battery sizing to enhance energy storage utilization and system autonomy, providing a more comprehensive framework for managing solar and wind variability in renewable energy systems.
[50]	2018	This study examines a large-scale solar PV installation in Singapore’s Jurong Port grid, analyzing its impact under various loading scenarios. The findings show PV integration reduces congestion, minimizes transmission losses, and supplies surplus power to the grid during low demand.	Provides practical insights into large-scale PV integration in port energy systems, demonstrating its effects on grid performance, congestion reduction, and surplus energy management. These findings can inform the assessment of HRES feasibility and EMS strategies for optimizing port energy distribution.	Expands the study by not only focusing on solar PV but also on wind integration and incorporating port infrastructure, ESS, and EMS. This broader approach provides a more comprehensive analysis of renewable energy integration in port operations, enhancing grid stability, reducing congestion, and optimizing energy distribution under various loading scenarios.
[51]	2014	This study examines the role of energy management in ports, highlighting the benefits of active energy strategies through the experiences of Hamburg and Genoa. The findings suggest that improved energy coordination enhances efficiency, supports sustainability, and strengthens port competitiveness.	Demonstrates the real-world impact of EMS in port settings, showing how strategic energy coordination improves efficiency and sustainability.	Enhances the analysis of energy management by incorporating energy storage with HRES. By integrating these advanced systems, it provides a more comprehensive strategy for optimizing energy flow, enhancing grid stability, reducing congestion, and improving resilience while simultaneously leveraging HRES and promoting sustainability.
[54]	2016	This study analyzes battery control strategies to reduce peak electricity demand and costs in South Australia. Simulations using real-time data show that combining solar PV with energy storage improves efficiency, lowers expenses, and supports the growing adoption of small-scale storage technologies.	Provides valuable insights into ESS control strategies for peak demand reduction, which is directly relevant to optimizing HRES in ports. The findings on cost savings and efficiency improvements can inform EMS implementation to enhance energy management and reduce LCOE.	Enhances the analysis of battery control strategies by optimizing ESS management to regulate charging through the grid during peak demand when HRES output is at its lowest. This approach improves energy efficiency, reduces costs, and strengthens system resilience.
[56]	2018	This study examines load shifting in hybrid power systems to reduce electricity costs and optimize storage capacity. By considering energy losses in real conditions, the proposed strategy minimizes storage size and cost while effectively distributing peak demand to off-peak hours.	Offers practical insights into load management strategies for HRES in ports, demonstrating how EMS can optimize storage use and reduce electricity costs. The focus on real-condition energy losses strengthens the understanding of ESS sizing and peak demand distribution for improved efficiency.	Enhances the analysis of load shifting in hybrid power systems by optimizing ESS management to regulate charging through the grid during peak demand when HRES output is at its lowest. This approach improves cost efficiency, optimizes storage utilization, and enhances energy distribution.
[57]	2020	This study reviews energy management systems for integrating renewables, electric vehicles, and storage into the grid. It analyzes key frameworks, optimization techniques, and challenges in balancing supply and demand, offering insights for improving efficiency and reliability across various sectors.	Provides a broad perspective on EMS capabilities, highlighting optimization techniques and challenges that are relevant to integrating HRES and ESS in ports. The focus on balancing supply and demand offers valuable insights for enhancing energy reliability and efficiency in maritime energy systems.	Expands the idea of EMS by not only delivering excess energy to the grid but also enabling controlled charging from the grid during periods of low HRES output. This approach enhances grid stability, optimizes energy management, and ensures a more balanced and resilient power distribution.
[58]	2022	This study explores energy storage solutions for managing peak loads in ports, particularly for cranes and cold ironing systems. By integrating energy storage and microgrid approaches, ports can optimize demand, enhance efficiency, and reduce costs while supporting renewable energy integration.	Highlights the role of ESS in managing high-demand port operations, such as cranes and cold ironing, which are critical for energy optimization. The integration of microgrids aligns with EMS strategies for improving efficiency, reducing costs, and enhancing renewable energy utilization in ports.	Expands energy storage and microgrid approaches by incorporating ESS and EMS, similar to the study, but also integrates PV and wind-based HRES to actively support renewable energy implementation in port infrastructure. This approach enhances demand optimization, improves efficiency, and strengthens the role of sustainable energy in port operations.
[84]	2022	This study explores the optimization of HRES in buildings, highlighting the role of GA and neural network (NN) techniques in enhancing system efficiency. By simulating different configurations across four cities in Iran, the research integrates PV panels, wind turbines, and hydrogen storage, using GA-NN optimization to minimize costs, CO2 emissions, and power supply loss.	Aligns with this study’s focus on optimizing HRES configurations using GA-NN algorithms, enhancing techno-economic analysis to improve cost efficiency, reliability, and sustainability across diverse climate conditions.	Takes a similar approach to GA usage for simulating HRES and ESS configurations to enhance efficiency but focuses on port infrastructure instead of buildings. By optimizing energy management in ports, it aims to improve sustainability, reduce costs, and ensure reliable power distribution within maritime operations.
[96]	2024	This study proposes a bi-layer energy management and capacity allocation method for hybrid energy storage in ports to balance hydrogen and electricity supply and demand. By optimizing scheduling and minimizing costs, the approach enhances efficiency, reduces emissions, and lowers overall operational expenses.	Offers a structured approach to hybrid energy storage management, providing insights into balancing multiple energy carriers in ports. The focus on cost minimization and efficiency aligns with strategies for optimizing HRES and ESS, contributing to emission reduction and economic viability.	Takes a similar approach by integrating HRES and ESS into ports but expands on energy management by incorporating EMS to optimize energy flow, including battery charging through the grid. This study further enhances sustainability and resilience by improving the coordination of renewable energy sources within port infrastructure.

, while previous studies have explored HRES, ESS, and EMS individually, few have examined their combined implementation in a real-world port setting. Moreover, many existing studies lack empirical validation, failing to capture the dynamic interactions between renewable energy generation, storage, and intelligent energy management. This research takes a bold step forward by introducing a holistic, data-driven approach to optimizing port energy systems, integrating HRES, ESS, and EMS within a unified optimization framework. By leveraging real-world energy demand profiles, cost parameters, and advanced control strategies, this study provides tangible, scalable, and actionable insights that can transform ports into self-sufficient energy hubs. The findings establish a blueprint for the next generation of resilient, cost-effective, and sustainable ports, ensuring they can not only meet but outpace the growing energy demands of global trade while driving the transition toward a decarbonized maritime sector.

Most existing studies fail to deliver an empirically validated framework that concurrently integrates HRES, ESS, and EMS under real port operating conditions. This research addresses that gap by implementing a unified, data-driven optimization architecture tailored to the dynamic demands of port infrastructures. The proposed framework incorporates real-time consumption data, adaptive ESS scheduling through EMS, and GA-based configuration control to maximize techno-economic performance. Unlike previous models constrained by theoretical assumptions or limited component integration, this study operationalizes a fully functional, simulation-validated system. The result is a replicable methodology that elevates sustainability, autonomy, and cost control in port energy systems beyond the benchmarks set by prior literature.

3. Materials and Methods

In this research, an optimization study of HRES for a Mediterranean port in Crete is conducted, focusing on the optimal WT-PV configuration and the port’s energy autonomy potential through the implementation of ESS. Four scenarios are examined, assessed based on their LCOE and the degree of autonomy they provide.

3.1. Case Study Selection

The Port of Souda was selected as the case study location due to Crete’s significant solar energy potential and the island’s growing reliance on wind energy [97,98]. This small port serves as an example application, demonstrating how the proposed methodology can be adapted and applied to other port facilities. A key factor in choosing this case was the high scalability of the proposed energy system, making it a viable model for similar ports seeking to transition toward sustainable energy solutions.

To ensure the accuracy of the study, port energy demand data were obtained from local authorities, reflecting real consumption patterns. Notably, the Port of Souda experiences higher energy demand during nighttime hours due to extensive outdoor lighting requirements. Additionally, solar and wind energy data were sourced from reputable European and international databases, including Copernicus, NASA POWER, and PVGIS, providing reliable input parameters for the analysis [99,100,101].

3.2. Energy Demand and Renewable Energy Potential

Figure 1 illustrates the port’s hourly energy demand in 2020, revealing distinct seasonal consumption patterns. The data, provided by the local port authorities for this study, indicated that energy usage was notably lower during the summer months and daytime hours, while demand peaked at night and in the winter months. This trend is largely driven by shorter daylight hours in winter, resulting in extended nighttime periods and increased electricity consumption for outdoor lighting.

Figure 2 and Figure 3 provide insights into the port’s renewable energy potential by depicting the wind and solar energy data. The solar energy graph, which represents the clearness index, demonstrates higher values during the summer months and lower values in winter. This trend aligns with Crete’s hot summers and high sun exposure, making solar energy a more abundant resource in warmer months. By contrast, the wind speed graph exhibits more fluctuating values but maintains relative stability throughout the year, with fewer extreme spikes. These findings are essential for optimizing the integration of HRES, ensuring a balanced and efficient renewable energy supply for port operations.

3.3. System Design and Scenario Conseptualisation

The modeling of the system consists of four distinct scenarios, each designed to evaluate different levels of renewable energy integration and energy autonomy at the port. The base case scenario depicts the current state of the port, where all energy demands are met exclusively through grid electricity without any integration of renewable energy sources or energy storage systems. This scenario represents a zero-autonomy baseline, relying entirely on conventional power supply without contributions from wind or solar energy. In the first scenario, the system maintains no autonomy but incorporates a combination of renewable sources (WT and PV) alongside the grid, aiming to optimize renewable energy usage. The second scenario incorporates an ESS designed to provide eight hours of autonomy, minimizing reliance on the grid during peak periods and enhancing self-sufficiency by charging the ESS exclusively through the HRES. Finally, the third scenario expands upon the third by integrating an EMS into the existing ESS, which strategically charges the battery at night when grid electricity costs are lower, further enhancing cost efficiency and energy independence. The model used to simulate and optimize these scenarios is based on a genetic algorithm, which dynamically determines the optimal configuration of the WT-PV HRES and the two distinct ESS configurations. This approach ensures that the system is designed to minimize costs while maximizing renewable energy utilization and autonomy potential.

Since the base case scenario represents the conventional energy usage the port currently operates, it is not directly studied but serves as a reference point to determine whether the proposed HRES configurations offer improvements. The other three simulated scenarios were selected to assess the feasibility of HRES in port applications and to evaluate the roles of ESS and EMS in improving energy management. By analyzing these configurations, the study aims to determine how integrating renewable energy and smart storage solutions impacts the port’s economic and environmental performance. The results provide insights into cost reductions and overall system efficiency, demonstrating the benefits of hybrid renewable energy adoption in port infrastructures.

The Python 3.13.2-based model and genetic algorithm (GA) used in the simulations were tailored to the Port of Souda and the specific renewable energy devices analyzed. However, by modifying the input data—such as the climatic conditions (e.g., wind speed and clearness index) and energy demand profiles of another port—the model remained fully functional and adaptable for different locations. The GA-simulated scenarios were tested across various climatic conditions and historical datasets from previous years, demonstrating the validity of the simulation. The minimal disturbances in the results confirmed the accuracy of the data and the algorithm’s capability to manage uncertainties associated with the intermittent generation of renewable energy. Consequently, this case study of the Port of Souda can serve as a reference for other ports, provided that the necessary data adjustments are made.

Figure 4 outlines the custom Python 3.13.2-based modeling and optimization framework developed in this research.

Figure 4 details the workflow from the initial creation of distinct HRES, ESS, and EMS classes to their integration within the main simulation code. A core novelty demonstrated is the integrated GA optimization process, which utilizes imported real-world data and iterates through generations to identify optimal HRES capacities. The framework explicitly incorporates scenario-specific logic, simulating ESS and advanced EMS functionalities (including unique charging strategies) when autonomy is required. Finally, the computation of financial metrics and validation steps underscore the framework’s capability to deliver techno-economically optimized and feasible energy solutions for port environments, representing a key contribution of this study.

Components of Each Scenario Examined
Case	Grid	PV	WT	ESS	EMS
0	✓	x	x	x	x
1	✓	✓	✓	x	x
2	✓	✓	✓	✓	x
3	✓	✓	✓	✓	✓

The scenarios are schematically represented into Figure 5.

3.4. System Components Specification and Characteristics

The technical specifications of the PV and the WT systems are presented in Table 2 and Table 3, respectively. The technical specifications of the ESS are presented in Table 4.

Table 2. Technical specifications of the Longi Solar LR-60PH PV panel, LONGI Solar Technology Co., Xi’an, China [102,103].

Parameter	Value
Power per module (kW)	0.31
Optimal operating temperature (°C)	40
Efficiency (%)	19
Lifetime (y)	25
Temperature power coefficient (%/°C)	−0.038

Table 2. Technical specifications of the Longi Solar LR-60PH PV panel, LONGI Solar Technology Co., Xi’an, China [102,103].

Parameter	Value
Power per module (kW)	0.31
Optimal operating temperature (°C)	40
Efficiency (%)	19
Lifetime (y)	25
Temperature power coefficient (%/°C)	−0.038

The combined cost of the photovoltaic panel and the inverter is estimated at 1300 € per kW.

Table 3. Technical specifications of the EW16 Thetis wind turbine, Eunice Group, Maroussi, Greece [102,103].

Parameter	Value
Rated power (kW)	54
Rated wind speed (m/s)	12
Minimum sufficient wind speed (m/s)	3
Maximum wind speed (m/s)	20
Hub height (m)	22.03
Lifetime (y)	25
Efficiency (%)	90

Table 3. Technical specifications of the EW16 Thetis wind turbine, Eunice Group, Maroussi, Greece [102,103].

Parameter	Value
Rated power (kW)	54
Rated wind speed (m/s)	12
Minimum sufficient wind speed (m/s)	3
Maximum wind speed (m/s)	20
Hub height (m)	22.03
Lifetime (y)	25
Efficiency (%)	90

The combined cost of the wind turbine is estimated at 4200 € per kw [44,46].

Table 4. Technical specifications of the Galaxy 3420 ESS, FFD Power, Brescia, Italy [102,103].

Parameter	Value
System capacity per unit (kwh)	3421
Efficiency (%)	86
Cycles	≥8000
Rated voltage (V)	3.2
Charging current (A)	150
Discharging current (A)	150

Table 4. Technical specifications of the Galaxy 3420 ESS, FFD Power, Brescia, Italy [102,103].

Parameter	Value
System capacity per unit (kwh)	3421
Efficiency (%)	86
Cycles	≥8000
Rated voltage (V)	3.2
Charging current (A)	150
Discharging current (A)	150

The combined cost of the ESS is estimated at 106 € per kW.

3.5. System Cost Specification

The final part of the methodology involves the consideration of grid energy costs, which vary based on the time of use. Specifically, the cost of electricity is 0.36 €/kWh during the day and 0.20 €/kWh at night, with the reduced nighttime tariff applied between 12:00 a.m. and 6:00 a.m. This provides different operational possibilities for ESS and the overall system configuration. Among the four scenarios analyzed, only the fourth scenario, which incorporates an ESS that charges at night, actively leverages the reduced nighttime tariff. This allows for cost optimization by storing electricity at a lower price and utilizing it during peak hours. For all other scenarios, the grid electricity cost remains constant at 0.36 €/kWh, as there is no strategic nighttime charging implemented. Additionally, it is important to note that O&M costs are not considered in this study. The economic evaluation focuses solely on the purchase costs of the system components, ensuring that the analysis remains centered on capital investment and grid energy expenditures.

3.6. Mathematical Modeling

After a thorough analysis of the specifications and characteristics of all components in the case study, each element was imported into the developed GA to initiate the simulation. Mathematical models for each component were formulated based on the methodologies and equations in HOMER Pro version 3.14 [9] software and were also integrated into the custom Python code containing the GA. This ensured an accurate and optimized simulation for each scenario. The mathematical models and equations used in this study are presented below.

According to HOMER, the PV energy output is influenced by several factors, including the rated capacity of the PV array, the derating factor, and the incident solar radiation at the given time step relative to standard test conditions. Additionally, the temperature coefficient of power accounts for variations in PV cell temperature, impacting overall performance. The derating factor is applied to account for potential power losses caused by soiling, wiring inefficiencies, and aging of the PV arrays. The PV energy output is calculated using Equation (1):

$$P_{PV}=Y_{PV}\times f_{PV}\times \left({\displaystyle \frac{G_T}{G_T,STC}}\right)\times \left[1+a_{\rho }\times \left(T_c-T_{C,STC}\right)\right]$$

(1)

The WT power output is calculated using Equation (2), where the rated power is determined based on the rated wind speed, cut-in speed, and cut-out speed. The actual wind speed, which varies based on the wind turbine’s hub height, is determined using Equation (3). It is derived from the wind speed measured at the base height, with an exponent that accounts for climatic factors such as temperature, season, time of day, and surface roughness. Under steady wind conditions, this exponent typically takes a value of 0.143. Additionally, the power output is adjusted by a density ratio, which represents the actual air density relative to the standard air density under specified temperature and pressure conditions, as expressed in Equation (4) [104]:

$$P_{WT}=\left\{\begin{array}{r}0 V<V_{cut-in} and V\ge V_{cut-out}\\ {\displaystyle \frac{P_r\times \left(V-V_{cut-in}\right)}{\left(V_r-V_{cut-in}\right)}} V_{cut-in}\le V\le V_r\hfill \\ P_r V_r\le V<V_{cut-out} \end{array}\right.$$

(2)

$$V=V_i{\left({\displaystyle \frac{H}{H_i}}\right)}^a$$

(3)

$$P_{w_T}=\left({\displaystyle \frac{\rho }{\rho o}}\right)R_{WT,STP}$$

(4)

Two different configurations of ESS are analyzed: a conventional ESS and an EMS-integrated ESS. Both systems store renewable energy surplus and support the PS technique. The energy stored in the ESS is determined using Equation (5), which considers the initial charge, battery voltage, and ESS current [6]:

$$Q_{ESS}=Q_{ESS,0}+{\int }_a^t{V}_{ESS}{I}_{ESS}dt$$

(5)

The required state of charge (SoC) of the ESS is determined using Equation (6), with the maximum allowable charge power estimated through the kinetic battery model, as described in Equations (7) through (9):

$$B_{SOC}={\displaystyle \frac{Q_{ESS}}{Q_{ESS,max}}}$$

(6)

$${\displaystyle \frac{Q_{ESS,max}=\min \left(P_{ESS},max,kbm, P_{ESS},max,mcr, P_{ESS},max,mcc\right)}{{\eta }_{ESS,c}}}$$

(7)

$$P_{ESS,max,kbm }={\displaystyle \frac{\left(k \ast Q_1\ast e^{-k \ast \Delta t}+Q \ast k \ast c \ast \left(1-e^{-k \ast \Delta t}\right)\right)}{\left(1-e^{k \ast \Delta t}+c \ast \left(k \ast \Delta t-1+e^{-k \ast \Delta t}\right)\right)}}$$

(8)

$$P_{ESS,max,mcr }=\left(1-e^{-{\alpha }_c\ast \Delta t}\right)\ast {\displaystyle \frac{\left(Q_{ESS},\max -Q_{ESS}\right)}{\Delta t}}$$

(9)

Table 5. Mathematical modeling equations’ symbols and expressions.

PV	PPV	PV energy output
	YPV	Rated capacity (kw)
	ƒPV	Photovoltaic derating factor (%)
	Gt	Incident solar radiation under standard test conditions (Kw/m2)
	αρ	Temperature coefficient of power (%/°C)
	Tc	Photovoltaic cell temperature (°C)
	Tc,stc	Cell temperature under standard test conditions (typically 25 °C)
WT	Pwt	WT adjusted power output
	V	Actual windspeed (m/s)
	Vcut-in	Minimum wind speed for power production (m/s)
	Vcut-out	Maximum wind speed the turbine shuts down (m/s)
	Pr	Rated power
	Vr	Rated windspeed
	Vi	Wind speed at reference height Hi
	H	WT hub height
	Hi	Reference height
	α	Power law exponent
	ρ	Actual air density
	ρ0	Air density under standard temperature and pressure (1.225 kg/m3)
ESS	BSOC	Available battery capacity
	c	Energy storage system’s capacity ratio
	IESS	Energy storage system’s current (A)
	Imax	Energy storage system’s maximum charge current (A)
	k	Constant for storage (h−1)
	NESS	Number of energy storage systems
	nESS,c	Charge storage efficiency
	PESS,max,kbm	Kinetic battery model
	PESS,max,mcr	Maximum charge rate
	Q	Initial available energy (kWh)
	Q1	Energy storage system’s available energy (kWh)
	QESS,0	Initial energy storage system charge (kWh)
	QESS,max	Total maximum available energy stored (kWh)
	VESS	Energy storage system’s voltage (V)
	Vnom	ESS’s nominal voltage
	αc	Energy storage system’s maximum charge rate (A/Ah)
	Δt	Time step duration (h)

includes the meanings of all the parameters’ symbols.

3.7. Limitations and Assumptions

To simulate HRES within a computational framework, this study establishes specific assumptions to bridge the gap between digital models and their real-world counterparts. These fundamental assumptions serve as a foundation for exploring feasible configurations of sustainable energy systems, as outlined below:

• The optimization process is based on a genetic algorithm, which may not always find the absolute global optimum due to computational constraints;
• The economic analysis does not account for potential fluctuations in energy prices, maintenance costs, or unexpected operational expenses;
• The study does not consider regulatory or policy constraints that may affect the feasibility of implementing HRES, ESS, and EMS in port operations;
• The impact of integrating additional port electrification measures is not assessed within the scenarios;
• PV and WT energy alone may not be the most ideal sources to meet the port’s total energy demand, making integration with other energy resources essential;
• The study does not consider possible fluctuations in electricity demand that may arise due to variations in grid supply and HRES energy generation over time;
• Public perception and regulatory authorization of the proposed energy systems are beyond the scope of this analysis and are not included in the evaluation criteria;
• The collected data, although approved by the European Union and global institutions, may not fully capture local climate conditions, leading to potential discrepancies in renewable energy generation estimations;
• The analysis does not account for potential financing challenges or high capital investment requirements;
• Variability in grid integration policies, restrictions on selling excess energy, and differing governmental incentives or subsidies are not considered;
• The study does not assess potential grid stability issues and energy storage limitations;
• The current model utilizes only solar and wind energy; additional sources may be necessary to enhance variability and reliability. Larger applications might require increased energy storage, leading to higher costs and system complexity.

These fundamental assumptions are essential to ensuring that the computational models employed in the study accurately reflect real-world conditions and generate meaningful, applicable insights.

3.8. Business Model for the Suggested System

The optimization of HRES, ESS, and EMS in port operations focuses on enhancing energy management and reducing the levelized cost of energy (LCOE). By integrating ESS and EMS, ports can improve energy efficiency, ensure grid stability, and optimize cost-effective energy distribution. The inclusion of ESS allows for better utilization of excess renewable energy, while the EMS strategically manages energy storage and consumption, further minimizing reliance on high-cost grid electricity. A proposed business model for port applications emphasizes a collaborative approach between port authorities, energy providers, and stakeholders to facilitate the integration of HRES with ESS and EMS. This model ensures efficient investment in renewable infrastructure, promotes sustainable energy independence, and enhances operational resilience. Through strategic partnerships and optimized energy management, ports can transition into nZEPs, aligning with global sustainability goals and reducing their environmental impact.

4. Results

A comprehensive techno-economic analysis of the four studied scenarios—HRES without storage, HRES with a basic ESS, and HRES with an ESS incorporating EMS—is conducted based on the port’s actual energy demand data from 2020, its energy pricing structure, and the current market costs of system components. This study seeks to analyze the effects of the four examined scenarios on system efficiency, emphasizing the significance of EMS in optimizing HRES performance. Furthermore, it provides a comprehensive assessment of feasible solutions that ports can implement in their transition toward sustainability.

The baseline scenario of the proposed system relies solely on the electricity grid, with an LCOE of 0.36 €/kWh and a net present cost (NPC) of €4,070,367. The total grid energy supplied amounts to 874,623.636 kWh, which corresponds to an average daily consumption of approximately 2396 kWh. As the port’s entire energy demand is met by the grid, the grid energy usage plot directly reflects the port’s total energy consumption.

HRES and ESS are widely recognized for their implementation in port energy systems, providing effective solutions for optimizing energy management. A common approach to further reducing costs involves increasing grid energy consumption during nighttime hours when electricity rates are lower, with the LCOE during these off-peak periods reaching 0.20 €/kWh. This strategy allows ports to cover their energy demand cost-effectively while minimizing overall expenditure.

4.1. HRES 0 Autonomy (Scenario 1)

In Scenario 1, an HRES consisting of PV panels and WT was evaluated through 500 iterations of the GA, which identified the configuration with the optimal LCOE of 0.1636. The selected HRES configuration included 139 kW of PV panels and 216 kW of wind turbines.

The daily and seasonal PV output, as illustrated in Figure 6a,b, demonstrated a significant increase in energy production during the summer months, with greater fluctuations in the winter. This variation was attributed to higher sunlight availability and temperature due to increased values of the clearness index. The highest PV energy output was observed in spring and summer. Conversely, the wind turbine energy output, shown in Figure 6c,d, exhibited more stable variations, with small peaks occurring in February and late November. The highest wind energy production was recorded in winter and summer. These findings indicated that PV and WT technologies can effectively complement each other in the studied location, as they provide substantial energy production during different seasons, with fall exhibiting slightly lower output levels.

As depicted in Figure 7, the HRES energy production significantly exceeded the port’s energy demand, leading to large amounts of excess renewable energy exported to the grid. The system was strategically designed to generate surplus renewable energy, ensuring a reliable supply during periods of low generation while maintaining grid connectivity for supplemental energy needs and exporting excess production when necessary, as illustrated in Figure 8b, which displays the daily HRES energy export to the grid. At the same time, as shown in Figure 8, the port’s dependency on the grid increased despite the overproduction of renewable energy, which was influenced by the Port of Souda’s energy production potential.

While the integration of HRES significantly reduced the LCOE compared to the conventional approach of meeting energy demand solely through grid electricity, the substantial excess energy remaining unused presented a challenge (Figure 8b). This inefficiency highlighted the need for further optimization, which is explored in subsequent scenarios to improve the system’s overall performance.

4.2. HRES 8 h Autonomy ESS (Scenario 2)

In Scenario 2, an HRES with an integrated ESS was designed to provide eight hours of autonomy for the port. After 500 iterations, the GA identified the optimal configuration based on the lowest LCOE of 0.1805. The selected HRES configuration consisted of 360 kW of PV panels and 108 kW of WT, while the ESS capacity was set at 3420 kW to ensure reliable energy supply.

The HRES output in Figure 9 followed the same trends and principles observed in Scenario 1, with the only distinction being the configuration differences determined by the GA algorithm. While the specific setup varied, leading to potential differences in overall output, the general trends and characteristics of the diagrams remained consistent.

Observing the energy output from the HRES with the port’s energy demand, as shown in Figure 10, a noticeable reduction in energy output was observed in comparison to Scenario 1 due to the integration of ESS. This occurred because the ESS stores excess energy during peak production periods from the HRES, allowing it to be utilized later. As a result, the HRES configuration selected by the GA required fewer PV panels and wind turbines, leading to a reduction in overall HRES energy production and subsequently lowering the cost of PV and wind turbine components.

To maintain stability, 50% of the battery capacity remained charged at all times, serving as a reserve for periods when no energy was generated from renewable sources and the grid supply was unavailable. The remaining 50% of the ESS capacity facilitated hourly energy transfer, dynamically balancing charging and discharging based on HRES output fluctuations (Figure 11c).

Furthermore, as illustrated in Figure 11b, there was a visible decrease in energy exported to the grid, as the ESS captured and stored excess energy that would otherwise be returned to the grid. This reduced energy export further explains how ESS contributes to lowering the LCOE of HRES by maximizing the utilization of locally generated renewable energy and reducing the need for an oversized PV and WT configuration. This is also directly connected to the energy grid usage depicted in Figure 11a, which was reduced in comparison to Scenario 1, as the battery utilized the excess energy to cover demand during periods of low energy generation.

Additionally, Figure 11c illustrates the daily battery discharge pattern, which predominantly took place during the early hours of the day and at night. This discharge compensated for the lack of energy generation from PV panels during these periods, ensuring a stable and continuous power supply. Meanwhile, battery charging occurred primarily around midday, aligning with peak solar energy production. This trend was driven by the deployment of PV installations, which surpassed wind energy generation during this period.

While it may be expected that the total LCOE of the system would decrease compared to Scenario 1—since integrating ESS with HRES can lower energy production costs—the increased cost of the ESS itself, along with the fact that half of the battery capacity was reserved for emergency use rather than active utilization, contributed to a slight overall increase in the system’s total LCOE.

4.3. HRES 8 h Autonomy with Optimized ESS with Time-Shifted Charging (Scenario 3)

In Scenario 3, an HRES incorporating an ESS and an EMS with nighttime battery charging was designed to provide eight hours of autonomy to the port. In this case, the system configuration was manually selected to match that of Scenario 2, consisting of 360 kW of PV panels and 108 kW of WT, while the ESS capacity was set at 3420 kW to ensure a reliable energy supply. This approach aimed to evaluate the impact of EMS on the overall HRES performance and the LCOE of the system. Following the selection and simulation of the manually set configuration, the resulting LCOE was 0.1682.

The battery setup remained the same as in Scenario 2, ensuring 50% of the capacity was reserved for stability while the remaining 50% facilitated hourly energy transfer, dynamically balancing charging and discharging based on HRES output fluctuations. The PV, WT and total HRES output, as illustrated in Figure 12, were identical to those observed in Scenario 2, as the configuration remained unchanged. The only difference in the ESS between Scenario 2 and Scenario 3 was the implementation of nighttime charging through the grid in Scenario 3. In this scenario, the EMS was utilized when the state of charge was between 50% and 60%, while for higher states of charge, the ESS operated as in Scenario 2. These thresholds were selected to prevent overcharging from the grid, ensure no excess HRES energy was exported, and minimize reliance on grid energy during daytime demand periods.

As shown in Figure 13, the results mirror those in Figure 7, since the same HRES configuration is used, resulting in excess renewable energy being exported to the grid. Furthermore, as illustrated in Figure 14b, energy exported to the grid was further reduced in Scenario 3 due to the ESS capturing and storing excess energy, along with the implementation of nighttime charging. This resulted in low energy export compared to the previous scenarios. At the same time, grid energy usage during the day was nearly zero, while at night, grid usage increased to facilitate battery charging (Figure 14a).

Figure 14c illustrates the daily battery discharge pattern, which primarily occurred during the early morning and nighttime hours. This discharge offset the absence of PV energy production, ensuring a stable power supply. By contrast, battery charging followed a dual pattern: during midday, the HRES charged the battery, driven by high solar energy availability, like in Scenario 2, while at night, grid charging took place, as dictated by the EMS settings. This coordinated charging strategy optimized energy utilization and minimized dependency on daytime grid energy.

Similar to Scenario 2, the slightly increased cost in Scenario 3 was attributed to the initial investment in the ESS, as well as the system upgrade that enabled eight hours of autonomy, albeit at the cost of reserving 50% of the battery capacity.

4.4. Comparative Analysis of This Study’s Results with Past Research Studies

As far as the research team is aware, this study is among the first to analyze HRES integration within port infrastructures while considering both electrical and thermal energy requirements. Additionally, a key limitation identified during the literature review was the scarcity of studies focusing on the techno-economic analysis of HRES using GA-based optimization in port operations, particularly in the Mediterranean region.

As far as the research team is aware, this study is among the first techno-economic analyses to integrate HRES with ESS and EMS, optimizing ESS charging during nighttime using a GA-based approach to enhance port operations, particularly in the Mediterranean region. Unlike conventional studies relying solely on HOMER Pro, this research optimizes the HOMER Pro approach through a custom GA-based Python code, a methodology rarely explored in the existing literature. As a result, the port’s LCOE experienced a significant reduction from 0.36 €/kWh to 0.1682 €/kWh with HRES-EMS integration, representing an approximate 53.28% decrease. Furthermore, the lowest LCOE achieved was 0.1636 €/kWh with HRES alone, marking a 54.44% reduction compared to the initial grid-dependent scenario. These findings highlight the effectiveness of the proposed HRES-EMS framework in improving port energy efficiency and cost-effectiveness.

Table 6. Comparison of the study’s outcomes with the relevant literature.

Ref.	LCOE (diff. %)	RF (%)	PP (y)
[105]	N/G	100	N/G
[106]	N/G	94.4	2.04
[107]	N/G	N/G	N/G
[108]	N/G	N/G	6.90
[109]	N/G	95.0	N/G
[110]	N/G	>75.0	N/G
[111]	N/A	100	>9
[9]	51.8	>60	>8.5
[112]	81.7	100	N/G
[7]	N/G	N/G	N/G
[113]	>65	85	3.17
Our	53.28	89.31	9.82

N/G stands for Not Given, N/A stands for Not Available.

and

Table 7. Simulated scenario results.

Scenario	LCOE (€/kWh)	Payback Period (y)	Initial Capital (€)	Grid Energy Usage (kW)	Exported Energy (kW)	HRES Contribution (%)
0	0.36	-	-	874,623	-	-
1	0.1636	6.33	1,087,900	349,993	410,195	72.759
2	0.1805	10.49	1,646,852	124,989	80,230	88.207
3	0.1682	9.82	1,646,852	111,789	62,780	89.319

presents a comparison of similar studies on this topic, analyzing key parameters such as LCOE difference percentage (LCOE Δ%), which represents the reduction in levelized cost of energy, renewable fraction (RF%), which indicates the percentage contribution of renewable energy, and payback period (PP), which refers to the time required to recover the initial investment.

This research establishes a new benchmark in port energy optimization by delivering a data-driven, empirically validated framework that integrates HRES, ESS, and EMS through an advanced GA-based optimization approach. Unlike previous studies that relied on predefined simulation tools, our custom Python-based GA framework ensures a more adaptive and precise optimization process, achieving LCOE reductions surpassing 53%, a level of efficiency rarely demonstrated in prior research. By considering real-world energy consumption, thermal and electrical demands, and dynamic energy pricing, this study provides a practical and scalable model for port decarbonization. The robustness of our results is reinforced by direct empirical validation against actual port energy demand profiles, ensuring that our conclusions are not just theoretical but implementable on a scale. Given the substantial cost reductions, enhanced autonomy, and proven system efficiency, this research pushes the boundaries of existing work, offering a replicable and future-proof framework that can drive the transition toward fully sustainable, self-sufficient port infrastructures.

5. Discussion

As shown in Table 7, the results for each scenario are presented, with Scenario 1 proving to be the most cost-effective solution, followed closely by Scenario 3. By contrast, Scenario 2 exhibits the highest LCOE; however, it still represents an improvement over the conventional grid-only baseline, Scenario 0. It is evident that the integration of HRES, when designed effectively and optimized appropriately, can serve as a viable replacement for the conventional energy system. This approach not only enhances economic feasibility but also provides a more sustainable and efficient energy solution.

Moreover, the scenarios differ in their impact on grid dependence and energy security. While Scenario 1 achieves the lowest LCOE, it still relies partially on grid electricity, making it susceptible to fluctuations in energy prices and potential supply constraints. By contrast, Scenario 3, despite its slightly higher LCOE, demonstrates a more balanced energy profile by effectively leveraging EMS strategies to reduce reliance on external power sources by approximately 68% compared to Scenario 1

Although the total LCOE of the ESS-EMS system is slightly higher than that of the HRES-only configuration, its benefits—such as extended autonomy, improved energy stability, and minimized excess energy export—are evident. In Scenario 3, the EMS effectively further reduced energy exports, addressing a key inefficiency observed in other cases. This highlights the crucial role of an optimized EMS in enhancing overall system performance, surpassing the capabilities of a standalone ESS while simultaneously improving stability and sustainability.

A key financial consideration is the difference in initial capital investment between Scenarios 1 and 3. Scenario 1 requires approximately 600,000 € less in upfront capital compared to Scenario 3, yet the difference in LCOE is only 2.81%, highlighting the minimal cost increase relative to the significant investment gap. This initial capital disparity arises primarily due to the additional expenses associated with battery storage in Scenario 3. However, despite the higher initial investment, Scenario 3 provides considerable advantages, including enhanced energy reliability, greater energy autonomy, and optimized storage utilization. The integration of ESS with a well-optimized EMS enables more effective energy distribution, minimizes waste, and significantly reduces dependence on external power sources. Given the small difference in LCOE compared to the substantial variation in initial capital, Scenario 3 proves to be a highly efficient and strategic choice, offering a more resilient, flexible, and future-proof energy solution for port applications. Ultimately, while Scenario 3 exhibits a slightly higher LCOE than Scenario 1, its contributions to energy reliability and efficiency are substantial. This underscores the fact that achieving sustainability and stability extends beyond hardware optimization—it also relies on effective resource allocation, strategic energy management, and advanced software optimization.

In acknowledging potential worst-case scenarios, it is essential to emphasize that the proposed HRES with ESS and EMS has been specifically optimized to effectively manage realistic adverse conditions. While prolonged low renewable energy availability, unexpected ESS performance degradation, or spikes in grid energy costs represent substantial operational risks, the robustness of the genetic algorithm-based optimization ensures substantial mitigation of these impacts. The strategic sizing and configuration of the renewable generation components and storage capacity, combined with intelligent EMS charging and discharging schedules, significantly enhance the system’s resilience and operational stability under stress conditions. Thus, although worst-case events may pose temporary performance challenges, the rigorous optimization and scenario analyses embedded within our methodology provide confidence that the proposed system can adequately absorb and respond effectively to realistic, adverse operational scenarios.

6. Conclusions

In conclusion, this research has effectively demonstrated the potential for integrating RES and ESS within a port infrastructure. Through the design, modeling, and analysis of four distinct scenarios, all three HRES configurations successfully reduced grid dependency and lowered the LCOE, surpassing Scenario 0 in all performance metrics. Each optimized scenario enhanced the port’s energy efficiency, demonstrating the feasibility of renewable integration. An optimal setup was identified, guided by a GA that dynamically determined the best WT-PV HRES configuration. The EMS-equipped scenario, in particular, provided autonomy and achieved one of the lowest LCOE values, emphasizing the critical role of system optimization and battery management in maximizing efficiency and cost-effectiveness.

By employing advanced optimization techniques, this research has taken an innovative step in modeling the combined impact of WT-PV systems, as well as the strategic use of storage solutions. The SA conducted underscores the robustness of the proposed system, demonstrating its adaptability to fluctuations in energy demand and market conditions. A key academic contribution of this study is the integration of smart energy management strategies within port infrastructures, enhancing the evaluation of HRES performance in maritime applications.

While acknowledging the constraints of the GA model, including its reliance on predefined parameters and assumptions about energy pricing, this research establishes a strong foundation for future studies. Expanding the model to incorporate additional RES technologies and real-time EMS adaptability could further improve the sustainability and efficiency of port energy systems.

Recommendations for future research endeavors include:

• Explore the integration of emerging RES, such as tidal and wave energy, to complement WT and PV systems in maritime environments, creating a more diversified and resilient energy mix;
• Investigate AI-driven EMS and real-time adaptive control algorithms to optimize battery management, load balancing, and demand-side response, enhancing system efficiency and reducing costs;
• Expand the GA model to incorporate MOO techniques, enabling a more comprehensive trade-off analysis between economic, environmental, and operational performance metrics;
• Study the integration of multiple ESS technologies (e.g., lithium-ion, flow batteries, hydrogen storage) to determine optimal storage solutions for varying operational needs;
• Conduct comprehensive life cycle assessments (LCAs) and carbon footprint analyses of HRES deployments to quantify long-term sustainability benefits.

The study makes a significant contribution to the domain of HRES implementation in industrial settings, particularly within port infrastructures, demonstrating pathways toward sustainable and economically viable energy solutions. By adopting such innovative energy practices, ports can not only reduce their environmental footprint but also enhance their operational efficiency and cost effectiveness. The broad-scale implementation of HRES is crucial for addressing the challenges of energy sustainability, reducing reliance on conventional power sources, and accelerating the transition toward a greener, more resilient maritime sector.