3,092 research outputs found

    A Cooperative Resilience-Oriented Planning Framework for Integrated Distribution Energy Systems and Multi-Carrier Energy Microgrids Considering Energy Trading

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    Integrated distribution systems (IDSs) and multi-carrier energy microgrids (MCEMs) can play a crucial role in enhancing distribution energy systems’ overall efficiency and flexibility. By cascading energy usage and cooperating through energy trading, IDSs and MCEMs can reduce overall system costs and provide more flexibility for system operators. Adding resilience to the planning problem of IDSs can reduce planning costs in the long term, as proactive preparedness is key to coping with high-impact rare (HR) events. Adding resilience to the planning problem of IDSs can reduce the planning costs in the long term since proactive preparedness is a key necessity to cope with high-impact rare (HR) events. This paper proposes a resilience-oriented stochastic tri-level and two-stage cooperative expansion planning of IDSs and MCEMs, considering energy trading between IDSs and MCEMs. The first stage comprises two levels; the first level minimizes the investment and operation costs of IDSs and MCEMs, while the second level desires to maximize the energy exchange profit for MCEMs and thus reduce the overall costs. The second stage includes the third level problem involving two objective functions: resilience cost minimization and resilience index (RI) maximization. The multi-objective problem in the second stage is converted into a single-objective problem using the min–max regret method. The DC and AC configurations for the power distribution system (PDS) and power microgrids (PMGs) are studied to identify the optimal configuration of these networks in the expansion planning problem. A new framework is proposed based on an aggregator-agent splitting solution using the aggregator coupling coordinator unit (ACC) responsible for coordinating IDNs and MCEMs. The studied large-scale complex optimization problem is efficiently solved computationally by introducing a combined adaptive dynamic programming (ADP) and linearized alternating direction method of multipliers with parallel splitting (LADMMPSAP) algorithm. Three cases are studied to demonstrate the effectiveness of the proposed model and method. The results depict that MCEMs help reduce expansion planning costs and improve the system’s resilience. Adding resilience to the expansion planning problem enhances the resilience of the whole system and simultaneously reduces the costs by 2.7%. The expansion planning costs for the AC and DC configuration are close, and the AC is the optimal choice in all case studies. By increasing the planning horizon from 5 to 10 years, DC will be the optimal solution since network reinforcement costs and power losses are significantly lower.<br/

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Nonlinear Modeling of Power Electronics-based Power Systems for Control Design and Harmonic Studies

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    The massive integration of power electronics devices in the modern electric grid marked a turning point in the concept of stability, power quality and control in power systems. The evolution of the grid toward a converter-dominated network motivates a deep renovation of the classical power system theory developed for machine-dominated networks. The high degree of controllability of power electronics converters, furthermore, paves the way to the investigation of advanced control strategies to enhance the grid stability, resiliency and sustainability. This doctoral dissertation explores four cardinal topics in the field of power electronics-based power systems: dynamic modeling, stability analysis, converters control, and power quality with particular focus on harmonic distortion. In all four research areas, a particular attention is given to the implications of the nonlinearity of the converter models on the power system

    A review of solar hybrid photovoltaic-thermal (PV-T) collectors and systems

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    In this paper, we provide a comprehensive overview of the state-of-the-art in hybrid PV-T collectors and the wider systems within which they can be implemented, and assess the worldwide energy and carbon mitigation potential of these systems. We cover both experimental and computational studies, identify opportunities for performance enhancement, pathways for collector innovation, and implications of their wider deployment at the solar-generation system level. First, we classify and review the main types of PV-T collectors, including air-based, liquid-based, dual air–water, heat-pipe, building integrated and concentrated PV-T collectors. This is followed by a presentation of performance enhancement opportunities and pathways for collector innovation. Here, we address state-of-the-art design modifications, next-generation PV cell technologies, selective coatings, spectral splitting and nanofluids. Beyond this, we address wider PV-T systems and their applications, comprising a thorough review of solar combined heat and power (S–CHP), solar cooling, solar combined cooling, heat and power (S–CCHP), solar desalination, solar drying and solar for hydrogen production systems. This includes a specific review of potential performance and cost improvements and opportunities at the solar-generation system level in thermal energy storage, control and demand-side management. Subsequently, a set of the most promising PV-T systems is assessed to analyse their carbon mitigation potential and how this technology might fit within pathways for global decarbonization. It is estimated that the REmap baseline emission curve can be reduced by more than 16% in 2030 if the uptake of solar PV-T technologies can be promoted. Finally, the review turns to a critical examination of key challenges for the adoption of PV-T technology and recommendations

    On factor models for high-dimensional time series

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    The aim of this thesis is to develop statistical methods for use with factor models for high-dimensional time series. We consider three broad areas: estimation, changepoint detection, and determination of the number of factors. In Chapter 1, we sketch the backdrop for our thesis and review key aspects of the literature. In Chapter 2, we develop a method to estimate the factors and parameters in an approximate dynamic factor model. Specifically, we present a spectral expectation-maximisation (or \spectral EM") algorithm, whereby we derive the E and M step equations in the frequency domain. Our E step relies on the Wiener-Kolmogorov smoother, the frequency domain counterpart of the Kalman smoother, and our M step is based on maximisation of the Whittle Likelihood with respect to the parameters of the model. We initialise our procedure using dynamic principal components analysis (or \dynamic PCA"), and by leveraging results on lag-window estimators of spectral density by Wu and Zaffaroni (2018), we establish consistency-with-rates of our spectral EM estimator of the parameters and factors as both the dimension (N) and the sample size (T) go to infinity. We find rates commensurate with the literature. Finally, we conduct a simulation study to numerically validate our theoretical results. In Chapter 3, we develop a sequential procedure to detect changepoints in an approximate static factor model. Specifically, we define a ratio of eigenvalues of the covariance matrix of N observed variables. We compute this ratio each period using a rolling window of size m over time, and declare a changepoint when its value breaches an alarm threshold. We investigate the asymptotic behaviour (as N;m ! 1) of our ratio, and prove that, for specific eigenvalues, the ratio will spike upwards when a changepoint is encountered but not otherwise. We use a block-bootstrap to obtain alarm thresholds. We present simulation results and an empirical application based on Financial Times Stock Exchange 100 Index (or \FTSE 100") data. In Chapter 4, we conduct an exploratory analysis which aims to extend the randomised sequential procedure of Trapani (2018) into the frequency domain. Specifically, we aim to estimate the number of dynamically loaded factors by applying the test of Trapani (2018) to eigenvalues of the estimated spectral density matrix (as opposed to the covariance matrix) of the data

    Sizing of Energy Storage Systems for Photovoltaic–Wind Power Plants

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    As the share of highly volatile photovoltaic (PV) and wind power generation increases in power grids, there is an increasing need to level their power fluctuations. The power fluctuations from PV and wind power plants can be extremely rapid, and the stability of the power grid is endangered. To prevent these problems, some countries have set ramp rate (RR) limits that the output powers of power plants must not exceed. One solution to mitigate the power fluctuations of a power plant is to equip the power plant with an energy storage system (ESS). The topic of this thesis is to investigate the power fluctuations of PV–wind power plants and to discover the required sizes for the ESSs that are coupled with the power plants. The goal of this thesis was to investigate how the size of the centralized ESS for the PV–wind power plant differs from the sizes of the ESSs for the separate PV and wind power plants. This investigation was done for the small- and large-scale PV–wind power plants. It was also investigated that how the different levels of nominal PV and wind power affect the required size of the centralized ESS. The ESSs of this thesis were virtual and the output powers of the power plants were modeled based on climatic measurements done at Tampere University Solar PV Power Station Research Plant in Finland. The measured quantities were irradiance, PV module backside temperature, wind speed and ambient temperature. The modeled PV and wind powers seemed to match the literature well. It was found that the required size of the ESS for the PV power plant is larger than for the wind power plant in the small- and large-scale investigations. It was also found that on general, the relative size of the centralized ESS of the PV–wind power plant is smaller than the relative sizes of the separate ESSs of the PV and wind power plants. When the nominal PV power was scaled in contrast to the nominal wind power, it was found that the required relative energy and power capacities of the centralized ESS are the smallest when the scaling coefficients for the nominal PV power were 0.45 and 0.35, respectively

    An empirical survey on wireless inductive power pad and resonant magnetic field coupling for in-motion EV charging system

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    EVs are the recent emerging automotive technology in the transportation sector to reduce the CO2 emission from the internal combustion engine. The issues in EVs technology development are battery tube capacity, heavy-size batteries, fast charging, and safe charging infrastructure. The dynamic wireless charging technology shows a suitable alternative to address the charging system-related issues in EV. However, a limited number of review studies are conducted to specifically address the wireless charging pad design challenges. The wireless inductive power pad and magnetic coupling circuit design are the main factors to decide the performance of the DWPT system. This review analyzes the current developments and challenges associated with wireless charging pad design. Further, this study investigates the potential parameters which improve the performance of a DWPT system to increase the distance traveled (mileage). First, this paper discusses WRIPT technology for DWPT EV charging application, and several parameters affecting the PTE are examined. Also, the aids factors considered for designing the DWPT power pad and different magnetic resonance coupling topologies are presented. In addition, the performance evaluation of the WRIPT power pad, with in-motion testing from the major findings in earlier studies is discussed. Finally, the challenges and opportunities of the WRIPT power pad for in-motion EV charging applications are also addressed. The current state of the art of DWPT and its future directions to make DWPT EV charging systems a full-fledged method are highlighted.Web of Science114693466

    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
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