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Thermodynamic cycles requiring high operating temperatures (≥750 °C up to 1200 °C) are currently being explored to improve the sun-to-electricity conversion efficiency of Concentrating Solar Power (CSP) plants. This is calling for the design of new efficient high-temperature (≥750 °C) Thermochemical Energy Storage (TCES) systems, which are fundamental for supplying power on demand during off-sun periods. Recently, Fe-doped CaMnO3 oxides have been proposed as TCES candidate materials, and the determination of their thermodynamics properties via thermogravimetric (TG) analysis allowed evaluation of their heat storage capacity at a very small scale (mg scale). A 10 % Fe-doped CaMnO3 composition (CaMn0.9Fe0.1O3-δ – CMF91) emerged as optimum candidate material for TCES application due to its large heat storage capacity complemented by enhanced thermal stability over multiple oxidation/reduction cycles. To advance in the thermal characterization of these materials at a multigram scale, here we carried out bench-scale reactor tests using CMF91 under conditions considered representative of future CSP plants. The redox-active material has been extruded in the form of porous pellets through a simple production method that required the use of carboxymethylcellulose as a removable binder and water. With the bench-scale reactor tests, the CMF91 pellets showed fully reversible reduction-oxidation in cycles between 500 and 1100 °C under relevant operating pO2 conditions without any deterioration of the pellet's structural integrity. Remarkably, the material exhibited the same δ(T, pO2) profile at this significantly larger scale (~40 g) than the one derived from thermodynamics. Nevertheless, slight differences in oxygen release/uptake profiles between cooling and heating branches can be tracked down to an excess heat generation in the perovskite bed not efficiently extracted by the carrier gas. These results demonstrate that CMF91 oxide is ideally suited for thermal energy storage applications with a large total (thermochemical and sensible) heat storage capacity (~ 916 kJ/kgABO3 or ~ 400 kWh/m3) and good scalability. © 2022 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska- Curie grant agreement N◦ 74616. Support of the ACES2030-CM from “Comunidad de Madrid” and European Structural Funds to (P2018/EMT-4319), and the Spanish “Ministerio de Economía y Competitividad” through Research Challenges project ARROPAR-CEX (ENE2015-71254-C3-1-R) are also fully acknowledged. M. S´anchez is grateful to Spanish “Ministerio de Economía y Competitividad” by funding through internship FPI (BES-2016-077031). It is greatly acknowledged the Technical Research Support Unit of the Institute of Catalysis and Petroleum Chemistry (ICP-CSIC). The authors fully appreciate the advice provided by Prof. Pedro Avila and Dr. Raquel Portela from the SpeICat group of ICP-CSIC, about the procedure for pellets preparation. Supporting Information Peer reviewed

The safeguard of power equipments is assuming a major role in the deregulated electricity market, where a malfunctioning power system could be responsible for serious damages to a large number of system operators having access to the shared energy resource. In this context thermal monitoring of power cables represents a key issue in evaluating the risks associated with a given load management policy especially during emergency conditions. In particular this demands for reliable assessment models that should be able to predict the transient thermal behaviour when the load exceeds the nameplate value. In this connection the application of both simplified and detailed power cable thermal models is often complicated by the presence of uncertainties affecting the tolerances of the input data. The effect of these uncertainties could influence the final solution to a considerable extent. A comprehensive tolerance analysis is therefore required in order to incorporate the effect of data uncertainties into the thermal modelling process. To address this problem, in the paper the employment of interval mathematic is proposed. Several numerical results are presented and discussed in order to assess the effectiveness of the proposed methodology which offer a rough qualitative insight of the solution in a very short time, comparable to the time required for a deterministic numerical simulation.

In recent years, the occurrence and damages of the blackouts in power systems saw an increase. Many of them are due to the impacts of climate change. However, tracing all blackouts that happened in the world and associating them with specific causes is quite challenging due to the huge amount of information over the Internet. In this paper, we proposed a framework for automatically analyzing the information collected from Internet based on the Ontological Approach coupled with Artificial Intelligence (AI) tailored for Natural Language Processing, which permits information extraction from reports to better understand the major entities of a power outage, their role, and the connections among them. However, our test shows that, despite the promising results of Named Entity Recognition in other fields, the lack of a standard ontology for blackout analysis is both the proof and the cause for the missed chance of exploiting this AI branch.

Recently, a multiple-resonator structure was proposed as a robust and computationally efficient tool for harmonic analysis. Two trivial cases have been previously observed. The first case exhibits good out-of-band suppression and elimination of unwanted harmonics, but with a high latency. In the second case, a phase frequency response around the passband centre is zero-flat, that provides fast estimates. However, in this case, resonant peaks at the ends of the passband and high interharmonic gains cause large overshoots. In general, the basic algorithm performance requirements: selectivity and speed, are contradictory which makes it impossible to completely fulfil both of them. In this paper, the Constrained Linear Least-Squares (CLLS) optimization method is used to obtain a compromise solution. As a result, the resonant peaks in the passband are avoided and side lobes are mitigated, simultaneously minimizing the group delay in the middle of the passband. Performed simulations confirmed the effectiveness of the proposed algorithm.

This paper proposes a device of sensing that could be integrated into the instruments of any surgical robot. Despite advances in robot-assisted laparoscopic surgery, the tools currently supplied to surgical robots have limited functions, due to the absence of sensorization. With this motivation, we present a preliminary work based on the design, development, and early stages of experimentation with smart and multifunctional devices of sensing for surgical tools. The proposed device of sensing has a proximity sensor, colorimetric sensor, and BLE connection for different surgical instruments to connect to each other. The proximity feedback allows the surgeon to know the distance of the instrument from a particular tissue, to operate in conditions of greater safety. With the colorimetric feedback, on the other hand, we intend to proceed to the identification of specific tissue areas with characteristics that are not typical of the physiological tissue. The results show that the device is promising and can be further developed for multiple clinical needs in robotic procedures. This system can effectively increase the functionality of surgical instruments by overcoming the sensing limitations introduced by using robots in laparoscopic surgery.

AbstractThis article describes a control approach for large‐scale electricity networks, with the goal of efficiently coordinating distributed generators to balance unexpected load variations with respect to nominal forecasts. To mitigate the difficulties due to the size of the problem, the proposed methodology is divided in two steps. First, the network is partitioned into clusters, composed of several dispatchable and nondispatchable generators, storage systems, and loads. A clustering algorithm is designed with the aim of obtaining clusters with the following characteristics: (i) they must be compact, keeping the distance between generators and loads as small as possible; (ii) they must be able to internally balance load variations to the maximum possible extent. Once the network clustering has been completed, a two layer control system is designed. At the lower layer, a local model predictive controller is associated to each cluster for managing the available generation and storage elements to compensate local load variations. If the local sources are not sufficient to balance the cluster's load variations, a power request is sent to the supervisory layer, which optimally distributes additional resources available from the other clusters of the network. To enhance the scalability of the approach, the supervisor is implemented relying on a fully distributed optimization algorithm. The IEEE 118‐bus system is used to test the proposed design procedure in a nontrivial scenario.

The Sustainable Development Goals (SDGs) of the United Nations state that cities and human settlements need to be more inclusive, safe and resilient. In Europe cities have experienced dramatic physical, social and economic changes during the last decades while historic centres of European cities, among the most important assets of the European cultural heritage, are living paradoxes. They are defined as “a collection of beauty, icon of well-being, model of sustainability, but abandoned”. This study investigates the changes in the urban landscape of Nicosia, a particular historical centre in the Mediterranean region (Cyprus). The city centre is characterised by exceptionally well-preserved Venetian fortifications. Due to political circumstances, the capital of Cyprus, Nicosia, is still divided and has been ruled by two different administrations for several decades. This study used optical multi-spectral satellite datasets processing, like the Landsat and the most recent Sentinel-2 products, to detect, identify and characterise significant morphological transformations within the walled city and around it. This paper’s central thesis promotes a more systematic use of earth observation products and derivatives in decision-making processes that regard planning, use and management of urban resources in Europe, especially in support of urban planning strategies of historic cities.