Optimization-Based Energy Management for Multi-energy Maritime Grids

This open access book discusses the energy management for the multi-energy maritime grid, which is the local energy network installed in harbors, ports, ships, ferries, or vessels. The grid consists of generation, storage, and critical loads. It operates either in grid-connected or in islanding modes, under the constraints of both power system and transportation system. With full electrification, the future maritime grids, such as all-electric ships and seaport microgrids, will become “maritime multi-energy system” with the involvement of multiple energy, i.e., electrical power, fossil fuel, and heating/cooling power. With various practical cases, this book provides a cross-disciplinary view of the green and sustainable shipping via the energy management of maritime grids. In this book, the concepts and definitions of the multi-energy maritime grids are given after a comprehensive literature survey, and then the global and regional energy efficiency policies for the maritime transportation are illustrated. After that, it presents energy management methods under different scenarios for all-electric ships and electrified ports. At last, the future research roadmap are overviewed. The book is intended for graduate students, researchers, and professionals who are interested in the energy management of maritime transportation

Measuring & Mitigating Electric Vehicle Adoption Barriers

Transitioning our cars to run on renewable sources of energy is crucial to addressing concerns over energy security and climate change. Electric vehicles (EVs), vehicles that are fully or partially powered by batteries charged from the electrical grid, allow for such a transition. Specifically, if hydro, solar, and wind generation continues to be integrated into the global power system, we can power an EV-based transportation network cleanly and sustainably. To this end, major car manufacturers are now producing and marketing EVs. Unfortunately, at the time of this writing, drivers are slow to adopt EVs due to a number of concerns. The two greatest concerns are range anxiety—the fear of being stranded without power and the fear that necessary charging infrastructure does not exist—and the unknown return on investment of EVs over their lifetime. This thesis presents computational approaches for measuring and mitigating EV adoption barriers. Towards measuring the barriers to adoption, we build a sentiment analysis system for programmatically mining detailed perceptions towards EVs from ownership forums. In addition, we design the most comprehensive electric bike trial to date, which allows us to study several aspects of electric vehicles, including range anxiety, at a much lower cost. Towards mitigation, we develop algorithms for managing a network of gasoline vehicles to be used by EV owners when a planned trip exceeds the range of their EV. Further, we design a model for taxi companies to compute whether it is profitable to transition a fraction of their fleet to EVs. To summarize our findings, we find that sentiments towards EVs are very positive, especially regarding performance and maintenance, but there are concerns over range anxiety and the higher initial price of EVs. There is a delicate balance between these two adoption barriers. Larger batteries cost more, so alleviating range anxiety with larger batteries leads to pricier vehicles. Conversely, EVs with low range capabilities can also induce costs, because drivers and fleets that own EVs may have to often acquire (or own as an additional vehicle) a gasoline vehicle to fully meet their mobility demands. As a result, EVs are best suited for drivers and fleets that are able to make long-term return on investment calculations, and whose mobility patterns do not include many very long trips. Fleets can greatly reduce their operating costs by adopting EVs because they have the capital to make upfront investments that are profitable long-term. We show that even under conservative assumptions about revenue loss due to battery depletion, EVs are already profitable (the company saves more than enough money to recoup all initial investments) for a large taxi company in San Francisco. Similarly, EVs can be profitable for two-car families (those who already have a gasoline car) and for those who can easily acquire a gasoline vehicle when needed, hence our work on sizing networks of gasoline-vehicle pools for EV owners. Finally, we find that not only are electric bikes and EVs operationally similar, the sentiments towards the two technologies are as well. Advancements made in the battery sector, especially those that reduce costs or weight, are likely to accelerate sales in both markets. The results presented in this thesis, as well as in prior work, suggest that EVs are suitable for many drivers and will hence serve a role in our eventual transition away from fossil fuels

A Temporal Pyramid Pooling-Based Convolutional Neural Network for Remaining Useful Life Prediction

Remaining Useful Life (RUL) prediction is a key issue in Prognostics and Health Management (PHM). Accurate RUL assessments are crucial for predictive maintenance planning. Deep neural networks such as Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) have been widely applied in RUL prediction due to their powerful feature learning capabilities in dealing with high-dimensional sensor data. The sliding time window method with a predefined window size is typically employed to generate data samples to train such deep neural networks. However, the disadvantage of using a fixed-size time window is that we might not be able to apply the resulting predictive model to predict new sensor data whose length is shorter than the predetermined time window size. Besides, as the length of sensor data varies, the traditional unchanged and subjectively set time window size may be inappropriate and impair the prediction model’s performance. Therefore, we propose a Temporal Pyramid Pooling-Based Convolutional Neural Network (TPP-CNN) to increase model practicability and prediction accuracy. With the temporal pyramid pooling module, we can generate data samples of arbitrary time window sizes and use them as inputs of CNN. In the training phase, CNN can learn to capture temporal dependencies of different lengths since we feed in samples with different time window sizes. In this novel manner, the learned model can be used to test data with arbitrary sizes, and its predictive ability is also improved. The proposed TPP-CNN model is validated on the C-MPASS turbofan engine dataset, and the experiments have demonstrated its effectiveness

Data-driven model-based approaches to condition monitoring and improving power output of wind turbines

The development of the wind farm has grown dramatically in worldwide over the past 20 years. In order to satisfy the reliability requirement of the power grid, the wind farm should generate sufficient active power to make the frequency stable. Consequently, many methods have been proposed to achieve optimizing wind farm active power dispatch strategy. In previous research, it assumed that each wind turbine has the same health condition in the wind farm, hence the power dispatch for healthy and sub-healthy wind turbines are treated equally. It will accelerate the sub-healthy wind turbines damage, which may leads to decrease generating efficiency and increases operating cost of the wind farm. Thus, a novel wind farm active power dispatch strategy considering the health condition of wind turbines and wind turbine health condition estimation method are the proposed. A modelbased CM approach for wind turbines based on the extreme learning machine (ELM) algorithm and analytic hierarchy process (AHP) are used to estimate health condition of the wind turbine. Essentially, the aim of the proposed method is to make the healthy wind turbines generate power as much as possible and reduce fatigue loads on the sub-healthy wind turbines. Compared with previous methods, the proposed methods is able to dramatically reduce the fatigue loads on subhealthy wind turbines under the condition of satisfying network operator active power demand and maximize the operation efficiency of those healthy turbines. Subsequently, shunt active power filters (SAPFs) are used to improve power quality of the grid by mitigating harmonics injected from nonlinear loads, which is further to increase the reliability of the wind turbine system

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies

Data-Driven and Hybrid Methods for Naval Applications

The goal of this PhD thesis is to study, design and develop data analysis methods for naval applications. Data analysis is improving our ways to understand complex phenomena by profitably taking advantage of the information laying behind a collection of data. In fact, by adopting algorithms coming from the world of statistics and machine learning it is possible to extract valuable information, without requiring specific domain knowledge of the system generating the data. The application of such methods to marine contexts opens new research scenarios, since typical naval problems can now be solved with higher accuracy rates with respect to more classical techniques, based on the physical equations governing the naval system. During this study, some major naval problems have been addressed adopting state-of-the-art and novel data analysis techniques: condition-based maintenance, consisting in assets monitoring, maintenance planning, and real-time anomaly detection; energy and consumption monitoring, in order to reduce vessel consumption and gas emissions; system safety for maneuvering control and collision avoidance; components design, in order to detect possible defects at design stage. A review of the state-of-the-art of data analysis and machine learning techniques together with the preliminary results of the application of such methods to the aforementioned problems show a growing interest in these research topics and that effective data-driven solutions can be applied to the naval context. Moreover, for some applications, data-driven models have been used in conjunction with domain-dependent methods, modelling physical phenomena, in order to exploit both mechanistic knowledge of the system and available measurements. These hybrid methods are proved to provide more accurate and interpretable results with respect to both the pure physical or data-driven approaches taken singularly, thus showing that in the naval context it is possible to offer new valuable methodologies by either providing novel statistical methods or improving the state-of-the-art ones

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines

Offshore renewable energy (ORE) sources, such as offshore wind turbines, wave energy converters, and tidal and current turbines, have experienced rapid growth in the past decade. The combination of wave, wind, and current energy devices in hybrid marine platforms that use synergies through proper combinations has been a recent scientific focus. The new concepts and structures being investigated require developing new design and analysis approaches that implement novel numerical modeling tools and simulation methods, thus advancing science, technology, and engineering. ORE structures may be subject to complex loads and load effects, which demand comprehensive and accurate numerical modeling representations of the physics underpinning the problem. Important factors that affect design, functionality, structural integrity, and performance of offshore structures include (but are not limited to): fluid–structure interactions, controller actions, intense dynamic effects, nonlinear loadings, extreme and harsh weather conditions, and impact pressure loads. Furthermore, these factors cannot be considered in isolation, since each factor is potentially coupled with another, requiring fully coupled models. To enable further growth in reliable ORE technologies, more advanced numerical tools and nonlinear modeling are needed

Assessment and Nonlinear Modeling of Wave, Tidal and Wind Energy Converters and Turbines

The Special Issue “Assessment and Nonlinear Modeling of Wave, Tidal, and Wind Energy Converters and Turbines” contributes original research to stimulate the continuing progress of the offshore renewable energy (ORE) field, with a focus on state-of-the-art numerical approaches developed for the design and analysis of ORE devices. Particularly, this collection provides new methodologies, analytical/numerical tools, and theoretical methods that deal with engineering problems in the ORE field of wave, wind, and current structures. This Special Issue covers a wide range of multidisciplinary aspects, such as the 1) study of generalized interaction wake model systems with elm variation for offshore wind farms; 2) a flower pollination method based on global maximum power point tracking strategy for point-absorbing type wave energy converters; 3) performance optimization of a Kirsten–Boeing turbine using a metamodel based on neural networks coupled with CFD; 4) proposal of a novel semi-submersible floating wind turbine platform composed of inclined columns and multi-segmented mooring lines; 5) reduction of tower fatigue through blade back twist and active pitch-to-stall control strategy for a semi-submersible floating offshore wind turbine; 6) assessment of primary energy conversion of a closed-circuit OWC wave energy converter; 7) development and validation of a wave-to-wire model for two types of OWC wave energy converters; 8) assessment of a hydrokinetic energy converter based on vortex-induced angular oscillations of a cylinder; 9) application of wave-turbulence decomposition methods on a tidal energy site assessment; 10) parametric study for an oscillating water column wave energy conversion system installed on a breakwater; 11) optimal dimensions of a semisubmersible floating platform for a 10 MW wind turbine; 12) fatigue life assessment for power cables floating in offshore wind turbines