Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management
Abstract
:1. Introduction
2. State of the Art
3. Materials and Methods
3.1. Case Study Selection
3.2. Energy Demand and Renewable Energy Potential
3.3. System Design and Scenario Conseptualisation
Components of Each Scenario Examined | |||||
Case | Grid | PV | WT | ESS | EMS |
0 | ✓ | x | x | x | x |
1 | ✓ | ✓ | ✓ | x | x |
2 | ✓ | ✓ | ✓ | ✓ | x |
3 | ✓ | ✓ | ✓ | ✓ | ✓ |
3.4. System Components Specification and Characteristics
Parameter | Value |
---|---|
Power per module (kW) | 0.31 |
Optimal operating temperature (°C) | 40 |
Efficiency (%) | 19 |
Lifetime (y) | 25 |
Temperature power coefficient (%/°C) | −0.038 |
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 |
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 |
3.5. System Cost Specification
3.6. Mathematical Modeling
3.7. Limitations and Assumptions
- 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.
3.8. Business Model for the Suggested System
4. Results
4.1. HRES 0 Autonomy (Scenario 1)
4.2. HRES 8 h Autonomy ESS (Scenario 2)
4.3. HRES 8 h Autonomy with Optimized ESS with Time-Shifted Charging (Scenario 3)
4.4. Comparative Analysis of This Study’s Results with Past Research Studies
5. Discussion
6. Conclusions
- 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.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
GA | Genetic algorithm |
GHG | Greenhouse gas |
EMS | Energy management system |
ESS | Energy storage system |
HRES | Hybrid renewable energy system |
LCOE | Levelized cost of energy |
O&M | Operations and maintenance |
PV | Photovoltaic |
nZEP | Nearly zero-energy port |
WT | Wind turbine |
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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. |
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) |
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 |
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 |
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Cholidis, D.; Sifakis, N.; Savvakis, N.; Tsinarakis, G.; Kartalidis, A.; Arampatzis, G. Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management. Energies 2025, 18, 1941. https://doi.org/10.3390/en18081941
Cholidis D, Sifakis N, Savvakis N, Tsinarakis G, Kartalidis A, Arampatzis G. Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management. Energies. 2025; 18(8):1941. https://doi.org/10.3390/en18081941
Chicago/Turabian StyleCholidis, Dimitrios, Nikolaos Sifakis, Nikolaos Savvakis, George Tsinarakis, Avraam Kartalidis, and George Arampatzis. 2025. "Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management" Energies 18, no. 8: 1941. https://doi.org/10.3390/en18081941
APA StyleCholidis, D., Sifakis, N., Savvakis, N., Tsinarakis, G., Kartalidis, A., & Arampatzis, G. (2025). Enhancing Port Energy Autonomy Through Hybrid Renewables and Optimized Energy Storage Management. Energies, 18(8), 1941. https://doi.org/10.3390/en18081941