Energy Transport In Systems Research Articles

Socially Enhanced Defense in Energy-Transportation Systems

Socially Enhanced Defense in Energy-Transportation Systems

Research and design of high gain DC-DC converter based on coupled inductor

Abstract A high-gain DC-DC converter with soft-switching characteristics is proposed to meet the application requirements of new energy transportation and power systems. By using a coupled inductor boost structure and adding a voltage doubling unit, the boost capability of the converter is further enhanced. By constructing a resonant cavity, the switching transistor can operate in ZVS (Zero Voltage Switching), and all diodes can operate in ZCS (Zero Current Switching), significantly reducing device losses. The coupled inductor boost structure can achieve a higher voltage output at a lower switching transistor voltage stress and only requires control by a single switching transistor. The article analyzes a 300W experimental prototype of the converter, which can achieve an efficiency of 90% at a 10-fold voltage gain, verifying the feasibility and superiority of the converter.

Advancements in Machine Learning for Pipeline Integrity Management: A Comprehensive Review of Predictive and Optimization Techniques

Oil and gas pipeline networks are crucial component of energy infrastructure. However, as the integral elements of the energy transportation system are evolving towards Industry 4.0 and Smart Manufacturing, pipelines network are progressively shifting towards intelligence and digitization. The fields of asset integrity management, operation inspection, corrosion prevention, leak and intrusion detection, and flow assurance are just a few of the areas where machine learning and artificial intelligence (AI) algorithms are becoming more and more important. The predictive and optimization capabilities of these Models/ Algorithms have been leveraged by various Pipeline Network Integrity Management systems to avert pipeline failure, reduce environmental damage, and effectively allocate resources. This study reviews and gives a summary of current advancements in intelligent techniques, encompassing heuristic computations, mathematical programming, and machine learning techniques used in a pipeline network integrity management to enhance various operational and maintenance aspects. This work makes two intellectual accomplishments. Firstly, it offers a technical review of literatures, systematically synthesizing different computational intelligence and machine learning approaches previously applied, along with their methodologies, contributions, drawbacks, and comparisons. Secondly, the study recommends some nature-inspired meta-heuristic algorithms, which encompasses Evolutionary and the Swarm Intelligence Algorithms to be applied for future directions and challenges. Finally, the study underscores the potential of computational intelligence and algorithms for learning methods in predicting and optimizing the efficiency of pipeline network operation and management, emphasizing the necessity for further development of models/algorithms and application to enhance more intricate interactions between pipeline infrastructure monitoring, maintenance, and management.

Mission profile oriented energy efficiency and CO2 footprint assessment of naval vessels at conceptual design phase: A comparative case study of conventional and hybrid energy systems

CO2 is the main green house gas (GHG) produced in fossil fuel burning engines on ships. The growth of activities of sea-going vessels, have been increasing interest and concerns over ship-born CO2 emissions. Consequently, numerous studies have been introduced for emission estimation. Each estimation approach has its own assumptions, thus uncertainty with data of vessel activities, vessel attributes, and calculating methods. The conceptual design phase is the first phase of ship design. One of the issues addressed at this phase is the assessment of energy system alternatives of relevant ship. A ship energy system provides propulsion and electrical power. The assessment of energy system alternatives for a naval vessel is a very complicated business that has a significant impact and interaction on overall vessel integration. Naval vessels and their energy systems constitute a noteworthy part of the total world ship fleet. The present study aims to present a realistic methodology for estimating efficiency, hence CO2 emissions, for candidate energy systems of surface vessels at conceptual design phase. A frigate is selected as the case (baseline) vessel. Three energy systems, namely CODAD, CODOG, and CODLOG-CC, are proposed as alternatives for the case vessel. Instead of general emission estimation approaches, the present study proposes a mission profile-oriented estimation methodology which provides more precise and accurate results. The Energy Efficiency Design Index (EEDI) defines efficiency and sets limitation during design and construction of specific merchant vessels, but since this index does not comply with unique characteristics of mission profile of surface vessels, the novel efficiency design index defined as integral warship EEDI (IWEEDI) is implemented for the case vessel. According to the analysis, CODAD is found to be the most efficient alternative, followed by CODLOG-CC and CODOG, respectively.

Collaborative Optimization Framework for Coupled Power and Transportation Energy Systems Incorporating Integrated Demand Responses and Electric Vehicle Battery State-of-Charge

The growing adoption of electric vehicles (EVs) and advancements in dynamic wireless charging (DWC) technology have strengthened the interdependence between power distribution networks (PDNs) and electrified transportation networks (ETNs), leading to the emergence of coupled power and transportation energy systems (CPTESs). This development introduces new challenges, particularly as DWC technology shifts EV charging demand from residential plug-in charging to charging-while-driving during commuting hours, causing simultaneous congestion in both ETNs and PDNs during peak times. The present work addresses this issue by developing a collaborative optimization framework for CPTESs that incorporates integrated demand responses (IDRs) and EVs battery state-of-charge (SOC). In the ETN, a multiperiod traffic assignment model with time-shiftable traffic demands (MTA-TSTD) is established to optimize travelers’ routes and departure times while capturing traffic flow distribution. Meanwhile, effective path generation models with EVs battery SOC are proposed to optimize charging energy during driving and construct the effective path sets for MTA-TSTD. In the PDN, a multiperiod optimal power flow model with time-shiftable power demands (MOPF-TSPD) is formulated to schedule local generators and flexible power demands while calculating the power flow distribution. To enhance temporal and spatial coordination in CPTESs, a distributed coordinated operation model considering IDRs is proposed, aiming to optimize energy consumption, alleviate congestion, and ensure system safety. Finally, an adaptive effective path generation algorithm and an ETN–PDN interaction algorithm are devised to efficiently solve these models. Numerical results on two test systems validate the effectiveness of the proposed models and algorithms.

Assessing the reliability of a ship energy performance simulation tool through on-board data

To mitigate the environmental impact of shipping on climate change, it is nowadays urgent to explore energy efficiency and cost-effective measures by means of reliable digital tools. These are becoming essential for assisting the design of new ships and the refurbishment of the existing ones, as well as for assessing and optimizing the energy performance of a ship during its entire life-cycle. This paper focuses on the verification of the reliability of a novel ship energy dynamic simulation tool, developed for the calculation of the energy, economic, and environmental performance of large ships, the design and optimization of energy system layouts and operational logics, as well as the definition of design and management guidelines for ships. The reliability of the developed model was verified through the use of data measured onboard an existing sample cruise ship. The verification procedure is conducted for assessing the reliability and for exploiting the potentiality of dynamic analysis for a more precise evaluation of ships energy loads, temperature levels, and necessary sizes of possible saving technologies. The validated tool will enable reaching two different goals: i) to evaluate different energy system layouts and to define the optimal one for achieving the present imposed targets and constraints, ii) to obtain a large amount of data for allowing the implementation of the digital twin approach in the maritime sector. The model validation was successfully achieved and very low or negligible deviations, lower than 4%, of numerical data vs. measurements were observed. Through the validated simulation tool approximately 23.4 GWh of primary energy from polluting fuels over two weeks is computed. In addition, the model provided insights into the flow rate, temperature levels, and energy flows of the ship energy system, necessary for identifying the amount of thermal energy to be recovered as well as potential solutions to enhance the energy efficiency of the entire system.

INCREASING THE STABILITY OF ORGANISATIONAL AND TECHNICAL SYSTEMS THROUGH THE USE OF MACHINE LEARNING

The stability of technical and organisational-technical systems is an integral factor in the functioning of a wide range of complex technical and organisational-technical systems, such as energy systems, industrial facilities, transport systems, and others. While it depends on various factors, there are currently no means to support decision-making for automated dispatch control systems users in case of failures. The article analyses the effects of failures and breakdowns in complex technical and organisational-technical systems. It provides examples of major technogenic disasters. The study examines the human factor influence on the emergence of technogenic disasters. Currently, automated dispatch control systems do not have any means to facilitate decision-making by providing the dispatcher with a clear plan of action to troubleshoot and eliminate a possible disaster. The authors propose a concept of support means for decision-making in response to crises using automated dispatch systems for managing complex organisational-technical systems based on machine learning and artificial intelligence. The means is based on machine learning methods and has a two-component structure: an artificial neural network that defines the type of failure and a programme component that selects the algorithm. The neural network is implemented in the Python programming language, and the user interface is in the C# language. The neural network fulfils the classification task defining the failure based on an array of logical variables that demonstrate the change of state of system elements. The programme component uses the database created with SQL language to define the algorithm of actions for the dispatcher. The authors describe the dataset for the model in case of its use in energy systems. There is also a description of the model components. The model’s distinguishing features are adaptivity due to machine learning methods and the possibility of further model training to increase the classification accuracy based on its use cases. Keywords: machine learning, automated dispatch control systems, decision-making support, system stability.

Enhancing renewable energy utilization and energy management strategies for new energy yachts

Hydrogen energy, due to its clean and efficient nature, has shown great potential during the current transition period in the shipbuilding industry. However, the application of hydrogen energy in ship energy systems is influenced by variations in operational load and the integration of new energy sources during actual navigation. To address these issues, this paper focuses on optimizing and scheduling the operation of ships under various navigation conditions, considering the distributed nature of hydrogen energy. System simulations were conducted to model the photovoltaic (PV), proton exchange membrane fuel cells (PEMFCs), lithium batteries (LIBs), electrolytic cells (ECs), and energy storage modules of yacht energy systems. Component boundaries and objective functions were set, and two cases (excess photovoltaic state and constant power state) were designed to optimize and regulate the energy balance of hydrogen-powered yachts, enhancing their comprehensive utilization of renewable energy. By comparing the changes in ship energy under the two cases, it was concluded that case 1 ensures the maximum utilization of renewable energy. When photovoltaic power generation is insufficient, the PEMFC and LIB in the system provide the required power to achieve a supply-demand balance. Moreover, when PV power generation is sufficient, hydrogen energy is used to store renewable energy. The optimization method designed in this study can, to some extent, maximize the application of renewable energy in new energy yachts, ensuring the efficiency of the comprehensive energy system of new energy yachts, reducing emissions, and improving the sustainability and economic efficiency of the ships.

Some aspects of reliability prediction of chemical industry and hydrogen energy facilities (vessels, machinery and equipment) operated in emergency situations and extreme conditions

This work is aimed at a comprehensive solution to the problem of reliable and safe operation of a transport energy system with a high energy concentration based on a universal energy carrier - cryogenic liquid hydrogen.The article discusses the possibility of using various methods and techniques to assess the reliability of machines and equipment operated in emergency situations and extreme conditions. The obtained results are analyzed.Currently, the oil and gas complex pays great attention to the development of hydrogen technologies, as well as hydrogen energy in connection with the relevance of the Climate Agenda. In this regard, hydrogen energy facilities are of the greatest interest: cryogenic hydrogen reservoirs, cryogenic hydrogen pipelines, cryogenic oxygen reservoirs and cryogenic oxygen pipelines, as well as cryogenic reservoirs and pipelines for storing process nitrogen gas. An important role for global energy exchange is played by LH2 tankers for transporting cryogenic hydrogen. For example, Australia and Japan built the first LH2 tanker to transport hydrogen from Australia to Japan. In addition, another 85 LH2 tankers are expected to be built.After transportation, cryogenic hydrogen is stored in cryogenic hydrogen storages, usually also representing cryogenic hydrogen tanks with piping in the form of cryogenic pipelines, as well as cryogenic nitrogen tanks for storing process nitrogen gas. Further, hydrogen is used in road transport, aviation, ship fleet, industry, and energy. The main elements of mobile, stationary and airborne hydrogen storage systems are under critical loads and are in the area of increased study and attention.In this regard, we considered the functions of changing the main operational characteristics, made proposals on the possibility of predicting the development of accumulated faults and proposals for ensuring safety and extending the life of objects, taking into account the determination of local and integral damage to cryogenic tanks and pipelines.

A Novel Optimal Planning and Operation of Smart Cities by Simultaneously Considering Electric Vehicles, Photovoltaics, Heat Pumps, and Batteries

A smart city (SC) includes different systems that are highly interconnected. Transportation and energy systems are two of the most important ones that must be operated and planned in a coordinated framework. In this paper, with the complete implementation of the SC, the performance of each of the network elements has been fully analyzed; hence, a nonlinear model has been presented to solve the operation and planning of the SC model. In the literature, water treatment issues, as well as energy hubs, subway systems (SWSs), and transportation systems have been investigated independently and separately. A new method of subway and electric vehicle (EV) interaction has resulted from stored energy obtained from subway braking and EV parking. Hence, considering an SC that simultaneously includes renewable energy, transportation systems such as the subway and EVs, as well as the energy required for water purification and energy hubs, is a new and unsolved challenge. In order to solve the problem, in this paper, by presenting a new system of the SC, the necessary planning to minimize the cost of the system is presented. This model includes an SWS along with plug-in EVs (PEVs) and different distributed energy resources (DERs) such as Photovoltaics (PVs), Heat Pumps (HPs), and stationary batteries. An improved grey wolf optimizer has been utilized to solve the nonlinear optimization problem. Moreover, four scenarios have been evaluated to assess the impact of the interconnection between SWSs and PEVs and the presence of DER technologies in the system. Finally, results were obtained and analyzed to determine the benefits of the proposed model and the solution algorithm.

Oxyfuel combustion based carbon capture onboard ships

Reducing greenhouse gas emissions in the shipping sector is a challenging task. While renewable fuels stand out as the most promising long-term solution, their near- and mid-term viability is hampered by limited availability and high costs. An alternative approach is onboard carbon capture, which can reduce emissions from new ships as well as retrofitted vessels. This paper examines the techno-economic potential of oxyfuel combustion based carbon capture on ships. The oxyfuel concept uses an oxygen-rich atmosphere in the combustion process, resulting in a mixture of carbon dioxide and water. After the condensation of water, the carbon dioxide rich gas can be directly stored on board. Various onboard oxygen supply concepts are investigated, including different technologies for onboard air separation and liquid oxygen bunkering. Influences on the ship energy system are studied by system simulation of a deep-sea container vessel. Benchmarked against a technologically mature post-combustion carbon capture system, the results show that the oxyfuel concepts have limited competitiveness because of reduced engine efficiencies and high energy demands for onboard oxygen supply. Avoiding onboard oxygen supply by using liquefied oxygen as a byproduct from onshore electrolysis increases energy efficiency and the competitiveness of oxyfuel combustion but requires additional storage space. Sensitivity analyses highlight that the engine combustion concept and engine efficiency are the most critical influences on the techno-economic performance.

A weak leak location method based on signal attenuation mechanism for energy transportation system

Traditional leak location methods based on signal saltation points make it hard to locate the position of the weak leak event in the energy transportation system. To address it, a data-driven method based on signal attenuation mechanism is proposed in this paper. First, a wavelet adaptive threshold denoising procedure is proposed to obtain the pure signal from the collected data, and the decimal-coded genetic mechanism is designed to calculate the optimal threshold of wavelet decomposition for different layers. Second, the two-stage leak location method is proposed to calculate the position of the leak point through the combination analysis of pressure data change of both ends of the pipeline. Through the presented pressure signal attenuation mechanism, the leak location result is obtained from the data changes of both ends of the pipeline, not the time difference of arrived signal. With the above signal denoising and analysis way, the proposed method has the 1% location deviation of the leak point, which is superior to leak location methods based on signal saltation points such as wavelet transform. Finally, different experiment results are studied, and the analysis results show the effectiveness of the proposed method for the leak location problem.

Strategies and Perspectives on Enhancing Energy Density in Lithium-Ion Batteries

The rapidly growing demand for clean energy transportation and storage systems emphasizes the need for the development of large-scale lithium-ion batteries. Achieving this goal requires a comprehensive approach that considers not only technical aspects but also social, environmental, and economic perspectives. Specifically, the next-generation batteries should exhibit high energy and power densities, long-term cycle stability, and the efficient utilization of low-cost, abundant raw materials. The cathode material is the key determinant of these requirements. This presentation will introduce progress and strategies for enhancing cathode materials and their corresponding cell performances. These strategies include goals to enable reversible redox reaction processes through elemental and compositional choices, as well as crystal structural variations ranging from order to disorder. Furthermore, we combine these with advanced characterization techniques to understand the correlation between material properties and electrochemical behavior at multiple length and depth scales. The discussion also delves into a deeper understanding of the fundamental mechanisms of performance, coupled with advancements in new materials. Overall, the presentation aims to provide valuable insights into the strategy for developing the next generation of lithium-ion batteries.

ResNet diagnosis of rotor faults in oil transfer pumps

To address rotor imbalance and misalignment in oil transfer pumps, an innovative diagnostic framework using Residual Network (ResNet) is proposed. The model incorporates advanced signal processing algorithms and strategic sensor placement to enhance diagnostic efficacy. A fault simulation test rig captured vibration signals from eight key measurement points on the pump. One-dimensional and multi-dimensional signal processing techniques generated comprehensive datasets for training and validating the model. Sensor placement optimization, focusing on the bearing seat's axial direction, inlet flange's vertical direction, and outlet flange's axial direction, increased rotor fault sensitivity. Time-frequency data processed via Short-Time Fourier Transform (STFT) achieved the highest diagnostic accuracy, surpassing 98 %. This study highlights the importance of optimal signal processing and precise sensor placement in improving the accuracy of diagnosing rotor faults in oil transfer pumps, thus enhancing the operational reliability and efficiency of energy transportation systems.

A resilience-oriented pre-positioning approach for electric vehicle routing and scheduling in coupled energy and transport sectors

High-impact low-probability (HILP) events have occurred more frequently than before and caused severe damage to conventional power systems. Energy hubs (EHs) consist of various distributed energy resources (DERs) for energy generation, conversion and storage across different sectors. The high energy integration of EH systems makes them more robust under extreme events than traditional power systems. In addition, the large penetration of electric vehicles (EVs) has been witnessed in modern energy systems due to their significant benefits in accelerating transport electrification and reducing carbon emissions. Due to the characteristics of mobility and flexibility, EVs can be utilised as mobile energy resources in coupled energy and transport sectors and shift energy between different regions via effective routing and scheduling behaviours. In this context, this paper proposes a resilient-oriented pre-positioning approach for the routing and scheduling of multiple EVs in a coupled energy-transport system, where the energy system includes multiple EHs across both electricity and heat sectors. To simulate real-world scenarios, uncertainties associated with renewable generation, load profiles, and EV commuting time as well as contingencies related to component failures are incorporated into the proposed pre-positioning approach via stochastic programming. Extensive case studies are carried out based on an EH system including three EHs and five EVs, which illustrate that EVs can coordinate with static DERs in the system and perform energy shifting between different EHs via appropriate routing and scheduling behaviours. Additionally, results demonstrate that the proposed pre-positioning approach can achieve higher resilience level and ensure the supply continuity of critical loads.

Data-driven robust optimization for a sustainable steel supply chain network design: Toward the circular economy

Steel is the indispensable cornerstone of modern life and its remarkable strength and adaptability render it a fundamental pillar of worldwide advancement and economic growth. However, the steel sector faces significant challenges, including the need to incorporate sustainable practices and circular economy principles, especially enhancing occupational safety, optimizing water and energy resource utilization, and effectively utilizing by-products within the steel supply chains. This paper addresses these gaps by presenting a bi-objective data-driven optimization model formulated to design a sustainable forward-reverse steel supply chain network. Sustainable development is studied by examining total cost, occupational safety, and environmental impacts of water and energy consumption and transportation system, concurrently. Incorporating circular economy principles, the collection of steel scrap under take-back policies and utilizing steel slag, a by-product of steel production, is considered. To address uncertainties, a data-driven robust optimization method is employed which harnesses historical data to construct a dynamic uncertainty set using support vector clustering. The model is applied to a real case study in the Iranian steel sector. Results demonstrate the effectiveness and robustness of the proposed method, showing that, on average, a 5 % increase in costs correlates with a 7 % improvement in environmental impacts.

Rapid prediction of water hammer characteristics in liquid hydrogen storage and transportation systems: A theoretical model

A renewable energy liquid hydrogen storage and transportation system is a very complex system, so water hammering due to valve closure cannot be ignored. A theoretical model for the rapid prediction of the water hammer effect in liquid hydrogen pipeline valves was developed in this work. The impact of valve closure characteristics and subcooling on the peak pressure water hammer was analyzed. The results indicate that as the subcooling temperature of liquid hydrogen decreases, the density of subcooled liquid hydrogen increases, leading to larger peak pressures in the water hammer transient. At subcooling temperatures of 14, 16, 18, and 20 K, the maximum instantaneous pressures for the water hammer fluctuation are 51563.02, 50846.76, 50082.40, and 48510.70 Pa, respectively. Under identical boundary conditions, the density of subcooled liquid hydrogen emerges as the primary factor influencing the peak pressure of water hammer. The theoretical model exhibits a significant advantage in terms of computational time cost, saving approximately 9.99 h. However, the accuracy of the theoretical model is slightly inferior to that of the CFD model. The results of the study are expected to provide a technical basis for the design of refueling systems for storage and transportation, as well as densification technology of supercooled renewable liquid hydrogen and safety protection.

Quantifying Flexibility for a Ship Power and Energy System Design

The pace of technology maturation and the uncertainty in magnitude and characteristics of future load types on Navy ships drive the need for robust power and energy system architectures that can adapt to future perturbations in requirements. The Naval design community needs a consistent method for evaluating ship system flexibility in the early design stages when informed decision making provides the greatest opportunity to influence the system’s performance and lifecycle cost. The research presented herein develops quantitative, measurable metrics and applies them to applicable case studies for Naval power and energy system flexibility: the capability of the system to accommodate change in response to perturbations in requirements.

Sustainable Tourism Infrastructure Development

This study looks at the development of sustainable tourist infrastructure, focusing only on physical factors. In terms of resource efficiency and environmental sustainability, planning, design, and management techniques are assessed through a review of the literature and case studies. In order to pinpoint problems and offer viable solutions, the research carefully examines how much energy, water, garbage, and transportation systems are used. The report promotes minimising environmental effect and optimising resource conservation in tourism infrastructure projects by highlighting eco-friendly technology and design ideas. Innovative strategies, such as the use of sustainable construction materials, integrated renewable energy, water-saving techniques, and effective transportation systems, are demonstrated in successful case studies. The research prioritises physical factors to ensure lowest ecological impact and long-term environmental resilience, advocating for sustainable tourist infrastructure by integrating theoretical and practical insights. The findings contribute to advancing sustainable development goals within the tourism sector, offering actionable recommendations for policymakers, planners, and developers to create destinations in harmony with natural surroundings. Keywords— Sustainable tourism, Tourism infrastructure, Physical parameters, Water usage, Waste management, Transportation systems

How can emerging economies meet development and climate goals in the transport-energy system? Modelling co-developed scenarios in Kenya using a socio-technical approach

Transport-energy transitions pose complex challenges that have been extensively studied in high-income countries in response to national mandates for climate action. Low- and middle-income countries, however, have low but rapidly growing motorisation rates and face very different challenges in adopting new technologies to foster economic development and ensure equitable access to clean transportation. Here, we present a set of narrative scenarios for the future of the Kenyan transport-energy system co-developed through engagement with 41 local experts and decision-makers. Through the co-development of a Kenyan transport-energy system model, we present a decision-support tool, populated with those scenarios, to assist policymakers at regional, national and international levels in building policy and investment pipelines to support low-carbon economic growth. We find that Kenya’s transport-energy system can meet both development and climate goals, but this demands strong policy support for efficient public transport and targeted support for road vehicle electrification. Increased support for non-motorised transport is essential to provide equitable access to services and economic opportunities. Favourable pathways result in significant e-mobility uptake, which is anticipated to increase electricity demand by 5%–56% from 2023 to 2040, relative to the IEA Kenya Energy Outlook’s Stated Policies scenario, representing a 2.7–3.9x increase in Kenya’s total electricity demand over the same period. From a macro-fiscal perspective, results show that e-mobility has two important consequences for Kenya. Firstly, under high e-mobility scenarios, there is a negative fiscal impact that taxation revenues from the sale of transport fuels reduce by up to 41% relative to the low e-mobility scenario (though, notably, they still increase marginally from the 2023 level because of increasing transport demand). Secondly, high e-mobility scenarios have a positive impact on balance of payments by reducing the fuel import bill by up to 69% relative to the low e-mobility baseline. This corresponds to a reduction in foreign exchange requirement of up to $4.2bn annually by 2050.