An Optimal Operation Strategy for Integrated Energy-Logistics System in Green Port

Shi, Zichuan; Fan, Feilong; Tai, Nengling; Chen, Qing; Meng, Yu; Guo, Rui

doi:10.1109/icpsasia55496.2022.9949685

Cited by 7 publications

(4 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where the superscripts M and m represent the upper and lower limits of variables, respectively; ∆t is the scheduling time interval. During the refrigeration, the temperature variation model of the RF is presented as follows [30]:…”

Section: Operation Structure Of Isesmentioning

confidence: 99%

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

Yang,

Sun,

Liang

et al. 2024

JMSE

View full text Add to dashboard Cite

With the rapid development of global trade, a large number of goods and resources are imported and exported via seaports. Multiple thermal loads and renewable energy merge into seaports, making the energy supply and demand structure increasingly complex. The traditional seaport becomes an integrated seaport energy system (ISES). Due to the complicated energy interaction of cooling, heating, electricity, and gas subsystems, the ISES urgently require reliable and secure operation. Hence, this paper proposes a new reliability assessment method for the ISES that considers thermal inertia. Firstly, the operational structure of the ISES is established considering multi-energy coupling relationships. Then, a two-stage optimal load curtailment model is constructed with multiple thermal inertias. In addition, the reliability assessment method for the ISES is proposed based on the sequential Monte Carlo simulation method. Simulations are presented to verify the effectiveness of the proposed method.

show abstract

Section: Operation Structure Of Isesmentioning

confidence: 99%

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

Yang,

Sun,

Liang

et al. 2024

JMSE

View full text Add to dashboard Cite

show abstract

“…Machine learning methodologies have been widely employed to enhance the efficiency and decisionmaking processes within energy hubs. For example, (Shi et al, 2022) proposed an energy hub framework based on machine learning algorithms to optimize the utilization of renewable energy resources in a smart port environment. Their results demonstrated the potential for improving energy efficiency and reducing costs through the integration of machine learning techniques.…”

Section: Introductionmentioning

confidence: 99%

Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology

Tawfik,

Shehata,

Hassan

et al. 2024

Preprint

View full text Add to dashboard Cite

The integration of renewable energy systems in port facilities is essential for achieving sustainable and environmentally friendly operations. This paper presents the implementation of an Optimal Energy Hub approach in smart green ports, with a focus on the case study of Egypt and the Middle East Oil Transmission & Pipelines Company (MIDTAP) company. The study explores the integration of photovoltaic (PV) and wind energy systems within the port's operations. Machine learning methodologies are utilized to optimize energy management and decision-making processes in the port. The proposed approach leverages historical data, weather forecasts, and real-time energy demand to predict and optimize the utilization of PV and wind energy resources. By utilizing machine learning techniques, the energy hub can efficiently balance energy supply and demand, ensuring optimal utilization of renewable sources while minimizing reliance on conventional energy sources. Furthermore, this paper discusses a scenario of increasing load and renewable energy generation in the port facility. A comprehensive energy flow-based mixed integer linear programming (MILP)-based optimization framework implemented in MATLAB is presented. This framework aims to minimize electricity consumption costs while providing considerable flexibility and adaptability to accommodate the changing energy landscape. The results of the study provide valuable insights into the implementation of sustainable energy solutions in smart green ports, specifically in the context of Egypt and the MIDTAP company. The integration of PV and wind energy systems, along with the proposed MILP-based optimization framework, offers a practical and efficient approach to minimize electricity consumption costs and reduce the carbon footprint of port operations. The findings demonstrate the flexibility and adaptability of the Optimal Energy Hub approach in accommodating increasing loads and renewable energy generation. The research findings encourage the adoption of similar energy hub approaches in other port facilities, contributing to the global transition towards greener and more sustainable practices. The utilization of machine learning methodologies and MILP-based optimization frameworks in smart green ports can facilitate cost-effective and environmentally friendly energy management, promoting a more sustainable future for port operations. Several scenarios were used to show how the proposed notion was perceived. The primary objective is to maximize the use of renewable energy sources while reducing expenses and emissions. The MIDTAP Company evaluated the effects of adding more renewable energy systems in smart green ports through a study involving six scenarios. Additionally, MIDTAP Company conducted research with six scenarios to assess the impact of increasing the number of renewable energy systems in smart green ports. In these scenarios, the renewable energy systems (PV + Wind) are increased by 10%, 20%, 30%, and 40%, while the load is increased by 25% and 50%. The primary objective of these scenarios is to maximize the use of renewable energy sources while reducing expenses and emissions, and the results provide valuable insights into the potential benefits and challenges of scaling up renewable energy systems in smart green ports. The significance of grid connectivity and sustainable energy solutions for smart green ports was also emphasized by the study. The findings show how economical and sustainable energy solutions may be achieved in smart green ports through the application of machine learning and optimization based on MILP. The selection of a 40% increase in renewable energy systems (PV + Wind) in the sixth scenario, which aims to explore the potential for further scaling up renewable energy infrastructure within the MIDTAP port, is based on the available area in MIDTAP. Considering the spatial limitations and resource availability of the port, careful assessment and planning are crucial to determine the optimal capacity for renewable energy systems. By selecting a 40% increase, the study takes into account the available area within MIDTAP and the potential for accommodating additional renewable energy installations. This approach ensures that the scenario aligns with the port's physical constraints while still pushing the boundaries of renewable energy integration. Evaluating this scenario provides insights into the feasibility and benefits of leveraging a larger share of renewable energy sources within the port, ultimately contributing to its sustainable and environmentally friendly operations.

show abstract

“…More research is needed on the collaborative optimization of port logistics and energy systems. Shi et al [29] propose a future port microgrid architecture that combines logistics and energy systems but does not provide a specific logistics-energy collaborative optimization method. A logistics and energy collaborative optimization strategy is constructed based on the demand response, and the results validate that the proposed strategy is effective for coordinating multi-energy and logistics scheduling and minimizing port operation cost [28].…”

Section: Introductionmentioning

confidence: 99%

“…A distributed algorithm based on Benders decomposition embedded with an improved non-dominated sorting genetic (INSGA-II) algorithm is designed to realize the optimal collaboration of a PIES's energy outputs and port container logistic system energy demands in a port [35]. Shi et al [29] use a mixed-integer linear programming model; numerical simulations were carried out verify that the proposed strategy can minimize the total cost of the integrated energy-logistics system and increase the utilization rate of renewable energy systems for green ports. Jiang et al [36] innovatively propose a nonlinear mixed-integer programming formula to obtain the optimal mode of berth and quay bridge collaborative optimization in ports under uncertain conditions.…”

Section: Introductionmentioning

confidence: 99%

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

Mo,

Zhang,

Xiong

et al. 2024

Mathematics

View full text Add to dashboard Cite

In order to achieve carbon peak and neutrality goals, many low-carbon operations are implemented in ports. Integrated energy systems that consist of port electricity and cooling loads, wind and PV energy devices, energy storage, and clean fuels are considered as a future technology. In addition, ports are important hubs for the global economy and trade; logistics optimization is also part of their objective, and most port facilities have complex logistics. This article proposes an energy–logistics collaborative optimization method to fully tap the potential of port-integrated energy systems. A logistics–energy system model is established by deeply examining the operational characteristics of logistics systems and their corresponding energy consumption patterns, considering ships’ operational statuses, quay crane distribution constraints, and power balances. To better represent the ship–energy–logistics optimization problem, a hybrid system modeling technique is employed. The case of Shanghai Port is studied; the results show that costs can be reduced by 3.27% compared to the traditional optimization method, and a sensitivity analysis demonstrates the robustness of the proposed method.

show abstract

An Optimal Operation Strategy for Integrated Energy-Logistics System in Green Port

Cited by 7 publications

References 19 publications

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

Reliability Assessment for Integrated Seaport Energy System Considering Multiple Thermal Inertia

Introducing Optimal Energy Hub Approach in Smart Green Ports based on Machine Learning Methodology

Energy–Logistics Cooperative Optimization for a Port-Integrated Energy System

Contact Info

Product

Resources

About