599 research outputs found

    SMART GRID ENERGY PRODUCTION AND TRANSMISSION SYSTEM MODELING AND COMPUTATIONAL ASSESSMENT METHODS

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    Based on the continuous growth of the economy, widespread adoption of intermittent renewable energy sources, and extensive use of information and communication technologies, conventional electric power systems are no longer able to meet the enormous demands of the information age. Diverse renewable energy technologies have been quickly developed to address the energy issue and environmental damage. However, since renewable energy sources are unpredictable and erratic, the widespread use of different renewable energy technologies has consequently put significant strain on the security and dependability of conventional power networks. The Smart Grid (SG) is a modernized electrical network that makes use of cutting-edge communication, control, and information technology to facilitate the integration of renewable energy sources, increase energy efficiency, and improve dependability and security. The invention of computational modeling and evaluation methodologies for SG energy transmission and production networks is the main topic of the research. The Internet of Energy (IoE), which will eventually replace the conventional power production and distribution networks, increases the need to be familiar with the proper computing tools in order to conduct any future SG investigation. The software for simulation that is significant to the modeling and analysis of electrical power production, transmission, distribution, and related systems is examined in this research. The study's conclusions are anticipated to aid in the creation of power generation and transmission systems that are more effective, dependable, and sustainable

    2023-2024 Graduate School Catalog

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    You and your peers represent more than 67 countries and your shared scholarship spans 140 programs - from business administration and biomedical engineering to history, horticulture, musical performance, marine science, and more. Your ideas and interests will inform public health, create opportunities for art and innovation, contribute to the greater good, and positively impact economic development in Maine and beyond

    Bridging the Flexibility Concepts in the Buildings and Multi-energy Domains

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    paper aims to stimulate a discussion on how to create a bridge between the concept of flexibility used in power and energy systems and the flexibility that buildings can offer for providing services to the electrical system. The paper recalls the main concepts and approaches considered in the power systems and multi-energy systems, and summarises some aspects of flexibility in buildings. The overview shows that there is room to strengthen the contacts among the scientists operating in these fields. The common aim is to identify the complementary aspects and provide inputs to enhance the methodologies and models to enable and support an effective energy and ecologic transition

    Methodologies for the assessment of industrial and energy assets, based on data analysis and BI

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    In July 2020, post pandemic onset, Europe launched the Next Generation EU (NGEU) program. The amount of resources deployed to revitalize Europe has reached 750 billion. The NGEU initiative directs significant resources to Italy. These funds can enable our country to boost investment and increase employment. The missions of Italian Recovery and Resilience Plan (PNRR) include digitization, innovation and sustainable mobility (rail network investments, etc.). In this context, this doctorate thesis discusses the importance of infrastructure for society with a special focus on energy, railway and motorway infrastructure. The central theme of sustainability, defined by the World Commission on Environment and Development (WCDE) as ''development that meets the needs of the present generation without compromising the ability of future generations to meet their needs’’, is also highlighted. Through their activities and relationships, organizations contribute positively or negatively to the goal of sustainable development. Sustainability becomes an integrated part of corporate culture. First research in this thesis describes how Artificial Intelligence techniques can play a supporting role for both maintenance operators in tunnel monitoring and those responsible for safety in operation. Relevant information can be extracted from large volumes of data from sensor equipment in an efficient, fast, dynamic and adaptive manner and made immediately usable by those operating machinery and services to support rapid decisions. Performing sensor-based analysis in motorway tunnels represents a major technological breakthrough that would simplify tunnel management activities and thus the detection of possible deterioration, while keeping risk within tolerance limits. The idea involves the creation of an algorithm for detecting faults, acquiring real-time data from tunnel subsystem sensors and using it to help identify the tunnel's state of service. Artificial intelligence models were trained over a sixmonth period with a granularity of one-hour time series measured on a road tunnel forming part of the Italian motorway systems. The verification was carried out with 3 reference to a series of failures recorded by the sensors. The second research argument is relates to the transfer capacities of high-voltage overhead lines (HVOHL), which are often limited by the critical temperature of the power line, which depends on the magnitude of the current transferred and the environmental conditions, i.e. ambient temperature, wind, etc. In order to use existing power lines more effectively (with a view to progressive decarbonization) and more safely with respect to critical power line temperatures, this work proposes a Dynamic Thermal Rating (DTR) approach using IoT sensors installed on a number of HV OHL located in different geographical locations in Italy. The objective is to estimate the temperature and ampacity of the OHL conductor, using a data-driven thermomechanical model with a bayesian probabilistic approach, in order to improve the confidence interval of the results. This work shows that it might be possible to estimate a spatio-temporal temperature distribution for each OHL and an increase in the threshold values of the effective current to optimize the OHL ampacity. The proposed model was validated using the Monte Carlo method. Finally, in this thesis is presented study on KPIs as indispensable allies of top management in the asset control phase. They are often overwhelmed by the availability of a huge amount of Key Performance Indicators (KPIs). Most managers struggle In understanding and identifying the few vital management metrics and instead collect and report a vast amount of everything that is easy to measure. As a result, they end up drowning in data, thirsty for information. This condition does not allow good systems management. The aim of this research is help the Asset Management System (AMS) of a railway infrastructure manager using business intelligence (BI) to equip itself with a KPI management system in line with the AM presented by the normative ISO 55000 - 55001 - 55002 and UIC (International Union of Railways) guideline, for the specific case of a railway infrastructure. This work starts from the study of these regulations, continues with the exploration, definition and use of KPIs. Subsequently KPIs of a generic infrastructure are identified and analyzed, 4 especially for the specific case of a railway infrastructure manager. These KPIs are fitted in the internal elements of the AM frameworks (ISO-UIC) for systematization. Moreover, an analysis of the KPIs now used in the company is made, compared with the KPIs that an infrastructure manager should have. Starting from here a gap analysis is done for the optimization of AMS

    Supervised Learning in Time-dependent Environments with Performance Guarantees

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    In practical scenarios, it is common to learn from a sequence of related problems (tasks). Such tasks are usually time-dependent in the sense that consecutive tasks are often significantly more similar. Time-dependency is common in multiple applications such as load forecasting, spam main filtering, and face emotion recognition. For instance, in the problem of load forecasting, the consumption patterns in consecutive time periods are significantly more similar since human habits and weather factors change gradually over time. Learning from a sequence tasks holds promise to enable accurate performance even with few samples per task by leveraging information from different tasks. However, harnessing the benefits of learning from a sequence of tasks is challenging since tasks are characterized by different underlying distributions. Most existing techniques are designed for situations where the tasks’ similarities do not depend on their order in the sequence. Existing techniques designed for timedependent tasks adapt to changes between consecutive tasks accounting for a scalar rate of change by using a carefully chosen parameter such as a learning rate or a weight factor. However, the tasks’ changes are commonly multidimensional, i.e., the timedependency often varies across different statistical characteristics describing the tasks. For instance, in the problem of load forecasting, the statistical characteristics related to weather factors often change differently from those related to generation. In this dissertation, we establish methodologies for supervised learning from a sequence of time-dependent tasks that effectively exploit information from all tasks, provide multidimensional adaptation to tasks’ changes, and provide computable tight performance guarantees. We develop methods for supervised learning settings where tasks arrive over time including techniques for supervised classification under concept drift (SCD) and techniques for continual learning (CL). In addition, we present techniques for load forecasting that can adapt to time changes in consumption patterns and assess intrinsic uncertainties in load demand. The numerical results show that the proposed methodologies can significantly improve the performance of existing methods using multiple benchmark datasets. This dissertation makes theoretical contributions leading to efficient algorithms for multiple machine learning scenarios that provide computable performance guarantees and superior performance than state-of-the-art techniques

    Optimization of Sustainable Urban Energy Systems: Model Development and Application

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    Digital Appendix: Optimization of Sustainable Urban Energy Systems: Model Development and Applicatio

    Machine learning for the sustainable energy transition: a data-driven perspective along the value chain from manufacturing to energy conversion

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    According to the special report Global Warming of 1.5 °C of the IPCC, climate action is not only necessary but more than ever urgent. The world is witnessing rising sea levels, heat waves, events of flooding, droughts, and desertification resulting in the loss of lives and damage to livelihoods, especially in countries of the Global South. To mitigate climate change and commit to the Paris agreement, it is of the uttermost importance to reduce greenhouse gas emissions coming from the most emitting sector, namely the energy sector. To this end, large-scale penetration of renewable energy systems into the energy market is crucial for the energy transition toward a sustainable future by replacing fossil fuels and improving access to energy with socio-economic benefits. With the advent of Industry 4.0, Internet of Things technologies have been increasingly applied to the energy sector introducing the concept of smart grid or, more in general, Internet of Energy. These paradigms are steering the energy sector towards more efficient, reliable, flexible, resilient, safe, and sustainable solutions with huge environmental and social potential benefits. To realize these concepts, new information technologies are required, and among the most promising possibilities are Artificial Intelligence and Machine Learning which in many countries have already revolutionized the energy industry. This thesis presents different Machine Learning algorithms and methods for the implementation of new strategies to make renewable energy systems more efficient and reliable. It presents various learning algorithms, highlighting their advantages and limits, and evaluating their application for different tasks in the energy context. In addition, different techniques are presented for the preprocessing and cleaning of time series, nowadays collected by sensor networks mounted on every renewable energy system. With the possibility to install large numbers of sensors that collect vast amounts of time series, it is vital to detect and remove irrelevant, redundant, or noisy features, and alleviate the curse of dimensionality, thus improving the interpretability of predictive models, speeding up their learning process, and enhancing their generalization properties. Therefore, this thesis discussed the importance of dimensionality reduction in sensor networks mounted on renewable energy systems and, to this end, presents two novel unsupervised algorithms. The first approach maps time series in the network domain through visibility graphs and uses a community detection algorithm to identify clusters of similar time series and select representative parameters. This method can group both homogeneous and heterogeneous physical parameters, even when related to different functional areas of a system. The second approach proposes the Combined Predictive Power Score, a method for feature selection with a multivariate formulation that explores multiple sub-sets of expanding variables and identifies the combination of features with the highest predictive power over specified target variables. This method proposes a selection algorithm for the optimal combination of variables that converges to the smallest set of predictors with the highest predictive power. Once the combination of variables is identified, the most relevant parameters in a sensor network can be selected to perform dimensionality reduction. Data-driven methods open the possibility to support strategic decision-making, resulting in a reduction of Operation & Maintenance costs, machine faults, repair stops, and spare parts inventory size. Therefore, this thesis presents two approaches in the context of predictive maintenance to improve the lifetime and efficiency of the equipment, based on anomaly detection algorithms. The first approach proposes an anomaly detection model based on Principal Component Analysis that is robust to false alarms, can isolate anomalous conditions, and can anticipate equipment failures. The second approach has at its core a neural architecture, namely a Graph Convolutional Autoencoder, which models the sensor network as a dynamical functional graph by simultaneously considering the information content of individual sensor measurements (graph node features) and the nonlinear correlations existing between all pairs of sensors (graph edges). The proposed neural architecture can capture hidden anomalies even when the turbine continues to deliver the power requested by the grid and can anticipate equipment failures. Since the model is unsupervised and completely data-driven, this approach can be applied to any wind turbine equipped with a SCADA system. When it comes to renewable energies, the unschedulable uncertainty due to their intermittent nature represents an obstacle to the reliability and stability of energy grids, especially when dealing with large-scale integration. Nevertheless, these challenges can be alleviated if the natural sources or the power output of renewable energy systems can be forecasted accurately, allowing power system operators to plan optimal power management strategies to balance the dispatch between intermittent power generations and the load demand. To this end, this thesis proposes a multi-modal spatio-temporal neural network for multi-horizon wind power forecasting. In particular, the model combines high-resolution Numerical Weather Prediction forecast maps with turbine-level SCADA data and explores how meteorological variables on different spatial scales together with the turbines' internal operating conditions impact wind power forecasts. The world is undergoing a third energy transition with the main goal to tackle global climate change through decarbonization of the energy supply and consumption patterns. This is not only possible thanks to global cooperation and agreements between parties, power generation systems advancements, and Internet of Things and Artificial Intelligence technologies but also necessary to prevent the severe and irreversible consequences of climate change that are threatening life on the planet as we know it. This thesis is intended as a reference for researchers that want to contribute to the sustainable energy transition and are approaching the field of Artificial Intelligence in the context of renewable energy systems

    Solutions for Sustainable Economic Development - 4th Arctic Science Ministerial Meeting Report

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    Arctic Science Ministerial is a unique form of scientific cooperation that traditionally advocates preserving the Arctic region as a territory of peace, stability and constructive interaction focused on achieving concrete, practical results in the interests of all people in the northern latitudes, including indigenous peoples. The Russian Federation continues the coordinating functions within the ASM adopted from previous coordinators on June 16, 2021 at the final ASM3 webinar, and on October 14, 2021 in Reykjavik, Iceland at the annual international Arctic Circle Assembly, based on the continuity of previous ASM and the increasing relevance of scientific research in the Arctic. This book provides an overview of past events - webinars, participation in conference roundtables - with the aim of sharing scientific experience of Arctic research and forming informational materials to support science and higher education activities through international organizations and forums in the Arctic zone, supporting and updating the database of Arctic research projects carried out by scientific and educational organizations, including jointly, as well as through international. The information base for this work was the results of feedback assessment from Russian and foreign scientific and educational organizations, data on international projects in the Arctic, materials from the websites of the Arctic Council https://arctic-council.org/ and the working groups of the Arctic Council. In addition, climate, geological, biological, sociological, and technological research was used as the basis for developing strategies for sustainable economic development in the Arctic that take into account the interests of all stakeholders, including indigenous peoples, environmental organizations, industry, and government agencies

    Book of Abstracts:9th International Conference on Smart Energy Systems

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    Organizational resilience as a response to the energy crisis: Systematic literature review

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    In this paper, we provide a literature review on the topic of organizational resilience, in relation to the energy crisis. The concept of organizational resilience refers to the capability to respond and adapt to shocks. Undoubtedly, the recent energy crisis may be considered an external shock, as it has raised energy prices and exerts a significant pressure on decision makers. Although the energy crisis has impacted organizations significantly in recent months, there is little knowledge on how companies should respond to this threat. Thus, to fill in this research gap, we apply the method of a systematic literature review (SLR), combined with text mining tools, to map the topics covered by 124 works in the field. Based on our results, we uncover several important gaps in the existing studies. We also provide suggestions on relevant future research directions that could broaden the scope of the management of energy crisis, in line with the concept of organizational resilience.Web of Science162art. no. 70
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