1,720,960 research outputs found
The Role of Inverter-based Generation in Future Energy Systems: An Oriented Decentralized Strategy for Reactive Power Sharing in Islanded AC Microgrids and a Techno-Economic Approach to Inertia Requirements Assessment of the Italian Transmission Network
No full textOne of the most impacting changes in the electricity energy scenario of the latest decades is the extensive increase of Distributed Energy Resources (DER) including Electrical Storage Systems (EES), fuel cells and Renewable Energy Sources (RES), such as Photovoltaic (PV) and Wind Turbines (WT). The integration of a rapidly increasing share of inverter-based generation poses relevant challenges in terms of frequency and voltage control both in Islanded Microgrids (MG) and traditional transmission networks. For the sake of complementarity, the thesis focuses on reactive power and voltage regulation in MG and frequency instability problems in a future Italian transmission network.
In MG with converter-based energy production, one of the main problems is the proper
reactive power sharing among DER and related voltage regulation. In this concern the most used approach is based on the conventional droop control; however, it presents some relevant drawbacks.
In SECTION A an Advanced Droop Control strategy (ADC) and an Advanced Boost Control strategy (ABC) are proposed, to approach primary voltage control and reactive power sharing among Grid-Supporting inverters in islanded MG.
The strategies are presented defining their control laws and the control schemes together with the relevant stability analysis. Then, an analytical procedure is developed for each control methods to set proper control parameters. Next, a comparison between the new strategies and droop conventional control is performed with simulations on a common benchmark MG, in order to show that new strategies, according to their specific control logics, are able to guarantee improved performance in terms of the combined regulation of voltage and reactive power.
Considering the traditional electric system, one of the main consequences of the increasing penetration of RES is, besides of the decrease of the system short-circuit power, the reduction of the electric system inertia: this could lead to frequency instability problems in case of severe perturbations, especially for what concerns the Rate of Change of Frequency (RoCoF)and the frequency nadir.
In SECTION B, the thesis provides a technical-economic methodology for the estimation of the amount of additional inertia that will be needed in the Italian Transmission Network in a prospective 2030 scenario, in order to limit the RoCoF within sustainable values. Moreover, the algorithm optimally commits synthetic inertia contribution from RES and Battery Energy Storage Systems (BESS) and installation of Synchronous Compensators (SC) among the Italian market areas. The method is designed to be sufficiently simple to process a relevant number of working scenarios in order to exploit the relevant quantity of information owned by the TSO. Results have shown to be highly accurate as outline by comparison with detailed time domain simulations
A Machine Learning Algorithm to Minimize Distribution Lines Overloads
No full textTo address the issue of line overloads arising from the increasing integration of Renewable Energy Sources (RES) in distribution networks, advanced grid management strategies are needed to dynamically optimize network configurations. Within this context, grid reconfiguration methods allow to define the best configuration of the network to minimize line overloads, improve voltage values within the network and reduce power losses. Hence, reconfiguration methods allow to enhance the operations and management of distribution networks hosting a high share of RES. In this framework, this paper proposes a grid reconfiguration tool based on a machine learning algorithm, aimed at minimizing line overloads and reducing the number of reclosures in a distribution system. The proposed methodology is validated on a 10-node test network with significant RES penetration. The outcomes obtained show that, by applying the reconfigurations proposed by the tool, 56% reduction in total overload occurrences is obtained. The computational time needed by the machine learning-based algorithm to output the best configuration among all the possible ones is less than 1 second, demonstrating the usefulness of the proposed tool to cope with (near)real-time network issues. This result demonstrates the effectiveness of the proposed algorithm in reducing line overloads and improving the system performance
Analytical load flow solution for radial distribution networks
No full textThis paper proposes an analytical method to solve the load flow problem for radial single and multi-feeder power distribution networks in three-phase balanced conditions. The Analytical Load Flow (ALF) formulation relies on a single assumption for the estimation of line losses and accounts for line susceptances. A set of comparative tests performed on a total active and passive 33 nodes benchmark network allowed showing that the accuracy of the proposed method is extremely high, if compared with the alternative numerical solution. Furthermore, a specific analysis is proposed to evaluate the impact of the approximation on the losses in the determination of the nodal voltage phasors. Finally, ALF is validated in a realistic scenario with high integration of Renewable Energy Sources (RESs), considering seasonal variations in production and consumption. In this context, it is shown that the proposed method outperforms existing approximate analytical approaches, such as the industrial voltage drop method. The ALF approach, being fully analytical, does not require any numerical solver and can be applied as a valid alternative to existing numerical and analytical methods in balanced multi-feeder networks
Reinforcement Learning Algorithms to Optimize the Integration of Electric Vehicle Services Into Power Systems
No full textElectric Vehicles (EVs) present an opportunity to enhance the flexibility of power systems through their integration with Renewable Energy Sources (RES) via Vehicle-to-Grid and Vehicle-to-Home services. However, EVs integration introduce challenges related to their management, influenced by factors such as user behaviour, fluctuating RES generation, grid or building requirements and battery degradation. Within this framework, this paper explores the applications of Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL) algorithms to solve optimization problems aimed at enhancing the flexibility services offered by EVs, maximizing the use of RES whenever possible, while accounting for battery degradation. Besides, limitations, potential solutions and new areas for further development are addressed. In particular, alternative solutions are proposed to address the challenge of requiring a large number of samples for the proper training of RL and DRL algorithms. Furthermore, to mitigate the models' dependence on stochastic variables-such as renewable energy production and load demand-the potential integration of forecasting models for these variables, as well as the implementation of virtual battery partitioning using RL and DRL algorithms is proposed
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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