1,218 research outputs found

    Adaptive vehicular networking with Deep Learning

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    Vehicular networks have been identified as a key enabler for future smart traffic applications aiming to improve on-road safety, increase road traffic efficiency, or provide advanced infotainment services to improve on-board comfort. However, the requirements of smart traffic applications also place demands on vehicular networks’ quality in terms of high data rates, low latency, and reliability, while simultaneously meeting the challenges of sustainability, green network development goals and energy efficiency. The advances in vehicular communication technologies combined with the peculiar characteristics of vehicular networks have brought challenges to traditional networking solutions designed around fixed parameters using complex mathematical optimisation. These challenges necessitate greater intelligence to be embedded in vehicular networks to realise adaptive network optimisation. As such, one promising solution is the use of Machine Learning (ML) algorithms to extract hidden patterns from collected data thus formulating adaptive network optimisation solutions with strong generalisation capabilities. In this thesis, an overview of the underlying technologies, applications, and characteristics of vehicular networks is presented, followed by the motivation of using ML and a general introduction of ML background. Additionally, a literature review of ML applications in vehicular networks is also presented drawing on the state-of-the-art of ML technology adoption. Three key challenging research topics have been identified centred around network optimisation and ML deployment aspects. The first research question and contribution focus on mobile Handover (HO) optimisation as vehicles pass between base stations; a Deep Reinforcement Learning (DRL) handover algorithm is proposed and evaluated against the currently deployed method. Simulation results suggest that the proposed algorithm can guarantee optimal HO decision in a realistic simulation setup. The second contribution explores distributed radio resource management optimisation. Two versions of a Federated Learning (FL) enhanced DRL algorithm are proposed and evaluated against other state-of-the-art ML solutions. Simulation results suggest that the proposed solution outperformed other benchmarks in overall resource utilisation efficiency, especially in generalisation scenarios. The third contribution looks at energy efficiency optimisation on the network side considering a backdrop of sustainability and green networking. A cell switching algorithm was developed based on a Graph Neural Network (GNN) model and the proposed energy efficiency scheme is able to achieve almost 95% of the metric normalised energy efficiency compared against the “ideal” optimal energy efficiency benchmark and is capable of being applied in many more general network configurations compared with the state-of-the-art ML benchmark

    Photovoltaics, Batteries, and Silicon Carbide Power Electronics Based Infrastructure for Sustainable Power Networks

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    The consequences of climate change have emphasized the need for a power network that is centered around clean, green, and renewable sources of energy. Currently, Photovoltaics (PV) and wind turbines are the only two modes of technology that can convert renewable energy of the sun and wind respectively into large-scale power for the electricity network. This dissertation aims at providing a novel solution to implement these sources of power (majorly PV) coupled with Lithium-ion battery storage in an efficient and sustainable approach. Such a power network can enable efficiency, reliability, low-cost, and sustainability with minimum impact to the environment. The first chapter illustrates the utilization of PV- and battery-based local power networks for low voltage loads as well as the significance of local DC power in the transportation sector. Chapter two focuses on the most efficient and maximum utilization of PV and battery power in an AC infrastructure. A simulated use-case for load satisfaction and feasibility analysis of 10 university-scale buildings is illustrated. The role of PV- and battery-based networks to fulfill the new demand from the electrification of the surface transportation sector discussed in Chapter three. Chapter four analyzes the PV- and battery- based network on a global perspective and proposes a DC power network with PV and complementary wind power to fulfill the power needs across the globe. Finally, the role of SiC power electronics and the design concept for an SiC based DC-to-DC converter for maximum utilization of PV/wind and battery power through enabling HVDC transmission is discussed in Chapter six

    SET2022 : 19th International Conference on Sustainable Energy Technologies 16th to 18th August 2022, Turkey : Sustainable Energy Technologies 2022 Conference Proceedings. Volume 4

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    Papers submitted and presented at SET2022 - the 19th International Conference on Sustainable Energy Technologies in Istanbul, Turkey in August 202

    Economic and Social Consequences of the COVID-19 Pandemic in Energy Sector

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    The purpose of the Special Issue was to collect the results of research and experience on the consequences of the COVID-19 pandemic for the energy sector and the energy market, broadly understood, that were visible after a year. In particular, the impact of COVID-19 on the energy sector in the EU, including Poland, and the US was examined. The topics concerned various issues, e.g., the situation of energy companies, including those listed on the stock exchange, mining companies, and those dealing with renewable energy. The topics related to the development of electromobility, managerial competences, energy expenditure of local government units, sustainable development of energy, and energy poverty during a pandemic were also discussed

    Sustainable Design of Industrial Energy Supply Systems - Development of a model-based decision support framework

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    Energy and media supply systems and related infrastructure at industrial sites have grown historically and is largely dependent on the use of fossil fuels. High fuel prices and the emission reduction targets of companies challenge existing supply concepts. Supply concepts usually remain in place for decades due to the long-lived nature of generation technologies and distribution systems. Today's investment decisions are therefore confronted with a changing environment in which the share of volatile renewables from solar and wind is continuously increasing. The long planning horizons make design decisions very complex. Optimization-based design approaches automatically derive cost- or carbon-optimal selections of generation technologies and procurement tariffs. Thus, they enable faster and more accurate planning decisions in techno-economic feasibility studies. In this work, a novel optimization model for techno-economic feasibility studies in industrial sites is developed. The optimization model uses a generic technology formulation with base classes, which takes into account the large variety of technologies and procurement tariffs at industrial sites. The optimization model also includes two reserve concepts: an operating reserve concept for short-term disruptions and a redundancy concept for long-term plant failures. The two concepts ensure security of supply for production-related energy requirements and thereby contributes to avoidance of costly production outages. The optimization model is integrated into an optimization framework to effectively calculate decarbonization strategies. The framework uses time series aggregation and heuristic decomposition techniques. Time series aggregation is performed by an integer program and results in a robust selection of representative days. The selection of representative days is used in a multi-year planning model to derive transformation roadmaps. Transformation roadmaps analyze the evolution of energy supply systems to long-term trends and consider adaptive investment decisions. A transformation strategy with myopic foresight (MYOP) solves the multi-year planning problem sequentially and is solved up to 98 % faster than a transformation approach with perfect foresight (PERF). The high uncertainties in early planning phases and the resulting need for detailed sensitivity analysis make this approach the preferred choice for many feasibility studies. The newly developed optimization framework is used in numerous research and consulting projects for urban districts, microgrids and factories. In this work, the capabilities of the framework are demonstrated for three use cases (automotive, pharmaceutical, dairy) of factory sites in southern Germany. In the use cases, decarbonization strategies for electricity, steam, heating and cooling supply are analyzed. Simulation evaluations identify changing operating patterns of combined heat and power (CHP) plants along the 15-year planning horizon. In addition, electrification of heating demand leads to a significant increase of total electricity demands. The results derived with the framework provide decision makers in industrial companies a clear view of the long-term impact of their investment decisions on decarbonization strategies
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