Recent Trends on Liquid Air Energy Storage: A Bibliometric Analysis

The increasing penetration of renewable energy has led electrical energy storage systems to have a key role in balancing and increasing the e ciency of the grid. Liquid air energy storage (LAES) is a promising technology, mainly proposed for large scale applications, which uses cryogen (liquid air) as energy vector. Compared to other similar large-scale technologies such as compressed air energy storage or pumped hydroelectric energy storage, the use of liquid air as a storage medium allows a high energy density to be reached and overcomes the problem related to geological constraints. Furthermore, when integrated with high-grade waste cold/waste heat resources such as the liquefied natural gas regasification process and hot combustion gases discharged to the atmosphere, LAES has the capacity to significantly increase the round-trip efficiency. Although the first document in the literature on the topic of LAES appeared in 1974, this technology has gained the attention of many researchers around the world only in recent years, leading to a rapid increase in a scientific production and the realization of two system prototype located in the United Kingdom (UK). This study aims to report the current status of the scientific progress through a bibliometric analysis, defining the hotspots and research trends of LAES technology. The results can be used by researchers and manufacturers involved in this entering technology to understand the state of art, the trend of scientific production, the current networks of worldwide institutions, and the authors connected through the LAES. Our conclusions report useful advice for the future research, highlighting the research trend and the current gaps.This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades de España (RTI2018-093849-B-C31—MCIU/AEI/FEDER, UE). This work was partially funded by the Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación (AEI) (RED2018-102431-T). The authors at the University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group GREiA (2017 SGR 1537). GREiA is a certified agent TECNIO in the category of technology developers from the Government of Catalonia. This work was partially supported by ICREA under the ICREA Academia program

First Principles NMR Study of Fluorapatite under Pressure

NMR is the technique of election to probe the local properties of materials. Herein we present the results of density functional theory (DFT) \textit{ab initio} calculations of the NMR parameters for fluorapatite (FAp), a calcium orthophosphate mineral belonging to the apatite family, by using the GIPAW method [Pickard and Mauri, 2001]. Understanding the local effects of pressure on apatites is particularly relevant because of their important role in many solid state and biomedical applications. Apatites are open structures, which can undergo complex anisotropic deformations, and the response of NMR can elucidate the microscopic changes induced by an applied pressure. The computed NMR parameters proved to be in good agreement with the available experimental data. The structural evaluation of the material behavior under hydrostatic pressure (from --5 to +100 kbar) indicated a shrinkage of the diameter of the apatitic channel, and a strong correlation between NMR shielding and pressure, proving the sensitivity of this technique to even small changes in the chemical environment around the nuclei. This theoretical approach allows the exploration of all the different nuclei composing the material, thus providing a very useful guidance in the interpretation of experimental results, particularly valuable for the more challenging nuclei such as $^{43}$Ca and $^{17}$O.Comment: 8 pages, 2 figures, 3 table

TiReX : tiled regular eXpressions matching architecture

LAUREA MAGISTRALENegli ultimi anni, con l'avanzamento tecnologico e con la produzione di sistemi dotati di una grande potenza computazionale, il mondo dell'informatica ha incominciato a trovarsi di fronte a quella che oggigiorno è chiamata epoca dei Big Data. Ogni giorno, viene prodotta un'enorme mole di dati e vi è un crescente bisogno di analizzarli in maniere sempre più efficienti e in tempi ragionevoli. Sono disponibili diversi approcci per l'estrazione di informazioni dai dati, e uno di questi sfrutta le Espressioni Regolari (RE), usate per trovare pattern definiti dall'utente tra svariati tipi di dati. Le RE possono essere applicate in diversi campi, che vanno da semplici funzionalità di ricerca e sostituzione negli editor di testo fino alle queries alle basi di dati, dall'analisi del DNA al Packet Inspection per fornire protezione ai sistemi IT. Nonostante la diversità, questi scenari pongono sfide molto simili per quanto riguarda le performance. Il DNA contiene fino a 3 miliardi di caratteri, mentre i pacchetti di rete viaggiano a più di 10Gb/s. Pertanto, vi è la necessità di avere un sistema in grado di riconoscere i pattern in tempo reale, e le RE non sono in grado di affrontare tali requisiti tramite soluzioni puramente software. D'altra parte, le soluzioni hardware proposte fino ad ora, per le quali i pattern sono inseriti direttamente nella logica circuitale, non sono fattibili in alcuni scenari dove le RE sono impiegate. Ecco perchè abbiamo deciso di progettare un'architettura basata su un processore personalizzato che effettua il pattern matching e in cui le RE sono compilate via software in istruzioni da eseguire sui dati. Le istruzioni possono essere aggiornate facilmente così da cambiare l'RE che deve essere analizzata in modo molto flessibile. La soluzione è stata implementata su FPGA per accelerare l'intero processo di riconoscimento delle RE. Inoltre, la nostra soluzione prevede un sistema multi-core che può incrementare considerevolmente le performance. Abbiamo validato l'architettura attraverso una comparazione in termini di performance con la soluzione software più performante.In the last few years, with the advancement of the technology and the production of systems provided with a great amount of computational power, the world of informatics has begun to face what we nowadays call Big Data. Every day huge amounts of data are produced, and, since these data carry relevant information, there is the need to analyze them in efficient ways and, most importantly, in reasonable amounts of time. Among the various approaches to extract information and patterns from data, Regular Expressions(REs) are used to find user-defined patterns from a large variety of data sources and for many different purposes. REs can be applied in many different fields, ranging from simple find and replace functionality in text editors to database querying, from DNA analysis to Deep Packet Inspection to provide protection to IT-systems. Despite their diversity, these scenarios have similar performance challenges. For example, the DNA contains up to 3 billion characters, while the packets travel at more than 10 Gb/s through the network. Therefore, there is the necessity to have a system able to recognize patterns in real time, and pure-software solutions are often unable to meet such requirements. On the other hand, the hardware-based solutions proposed so far, which typically embed the patterns in the circuit logic, are not adequate for several scenarios where REs are employed. Therefore, we propose an architecture based on a matching core, where REs are software-compiled into instructions and run against input data. The instructions can be easily updated to change the RE that has to be analyzed in a very flexible way. The architecture is implemented on an FPGA device, able to accelerate the whole matching process. We produce a multi-core system which can proportionally increase the performance, since the number of the matching cores can easily scale up with the available resources. We evaluate the proposed architecture by comparing its performance against the best performing software solution

Assessing battery degradation as a key performance indicator for multi-objective optimization of multi-carrier energy systems

The Pareto frontier is extensively adopted in multi-objective optimization, especially in multi-carrier energy system modeling. Despite the various methodologies available to derive the frontier, it represents different optimal solutions, making the final selection non-trivial. The modeler's expertise is crucial in determining the weight factors assigned to each objective for selecting the final solution from the Pareto frontier. This study proposes a novel approach to support such decision-making, introducing an additional key performance indicator, the state of health of the battery, evaluated through physical battery modeling. By comparing different scheduling schemes in multi-objective multi-carrier energy systems, each with its distinct battery operational strategy, this newly introduced indicator has deployed to automatically identify the ultimate solution from the Pareto frontier, without additional weighting coefficients. Such an approach, therefore, automates the decision process, which supports easy engineering, especially for the small scale multi-energy systems such as smart homes, like the case study presented in this work that has four distinct energy carriers, adopting the 12 V 128 Ah LFP chemistry Li-ion battery modules, demonstrates the effectiveness of this automated selection process. Furthermore, when compared to the maximum values across the entire frontier, the automatically chosen solution exhibits reductions of 27.96% in CO2 emissions and 3.67% reduction in overall costs. Over long-term operation, this approach has the potential to extend battery lifespan by up to 26.67%, directly impacting the economics of multi-carrier energy systems.</p

Energy flexible CHP-DHN systems: Unlocking the flexibility in a real plant

The purpose of this paper is to identify and analyze the impact of flexibility enablers in cogeneration and district heating network (CHP-DHN) plants by means of a real case study located in central Italy. A wider definition of energy flexibility applicable to the entire energy supply chain (i.e. production, transport and usage) is used in this analysis. In particular the flexibility is intended as the capability of each part of the system to produce a variation in its load curve, while ensuring the required performance. In this sense energy efficiency technologies, the use of energy storage and advanced control techniques can be seen as flexibility enablers potentially available in each section of the energy system. The innovative contribution of this work is to propose flexibility strategies in compliance with the constraints imposed by both the managers and users. The study aims to show possible ways to activate flexibility services to be used with known instruments and to quantify their impact with a simulation-based approach. In particular, three different flexibility instruments are identified in different sections of the plant: (i) the use of a thermal energy storage (TES) in the generation side, (ii) the optimal management of the DHN supply temperature (energy distribution side) and (iii) the management of the thermostatically controlled loads (TCLs) of the final users (demand side) connected to the network. Through the implementation of simulation models calibrated with available measurements, the influence of these flexibility instruments on the energy/environmental performance is evaluated in comparison to the current configuration of the plant. Results confirm the great impact of the TES to increase the CHP working hours and, as a consequence, a primary energy saving increase is obtained in mid-season and in summer season. Whereas the optimal management of the water supply temperature in the DHN allows to obtain 1% fuel reduction in a typical winter week and 2% in a typical summer week. As far as the activation of the demand side flexibility is concerned, the effect of the management of TCLs on energy conservation is demonstrated: 1 °C reduction of the setpoint of all the residential users during a typical winter day produces a 7.3% reduction of the DHN thermal demand. However, its impact on the generation side (i.e. to reduce the electricity/thermal production of the CHP at specific times) is limited due to the characteristics of the considered CHP plant (the CHP engine is sized to cover only the thermal baseload and it scarcely affected by thermal demand variations). The analysis proposed helps to obtain valuable hints on unlocking the energy flexibility in CHP-DHN plants useful for a better management of such systems

Micro Gas Turbines

This work describes the research activity conducted by the authors to enhance micro gas turbines performance, focusing on inlet air cooling, bottoming organic Rankine cycles, micro STIG and trigeneration

improving liquefaction process of microgrid scale liquid air energy storage laes through waste heat recovery whr and absorption chiller

Abstract Liquid air energy storage systems (LAES) store liquid air produced by a liquefaction cycle and convert it into electric/cooling power when needed. A small-scale Liquid air energy storage system represents a sustainable solution in microgrid and distributed generation, where small energy storage capacities are required. The main drawback of these systems though, is the low round trip efficiency due to a high specific consumption of the liquefaction cycle. In this work, a single-effect absorption chiller using a Water-Lithium Bromide solution is integrated with a small air liquefier with a liquid air production capacity of 0.834 t/h. In the proposed solution, the waste heat of the compression phase of the liquefaction cycle is recovered and used to drive the absorption cycle, where the resulting cooling power is used to decrease the specific consumption and improving the exergy efficiency of the system. The operative parameters of the absorption chiller reflect the specifications of the most common commercial models available in the market and the size has been selected to maximize the heat power recovered. The results of simulation of the absorption chiller integration show a reduction of the specific consumption of around 10% (537 kWh/t to 478 kWh/t) and an increase of exergy efficiency of around 11.5%

Assessment of a NaOH-based alkaline electrolyser’s performance:System modelling and operating parameters optimisation

Most of the scientific research is focused on KOH-based alkaline electrolysers, while NaOH-based ones are unexplored although they present interesting features. This paper presents a semi-empirical model developed in the Python environment to predict a NaOH-based alkaline electrolyser’s performance to cover such a research gap and perform an optimisation procedure of electrochemical parameters. A sensitivity analysis has been carried out to study how its performance changes while varying the: i) NaOH content, ii) pressure, and iii) both. Separately, the best result has been obtained with a NaOH content and an operating pressure of 8% and 6.5 bar, respectively. Furthermore, the same values have been recorded even by varying both the NaOH content and the operating pressure. Specifically, a maximum average efficiency increase of 3.57% at 35 ◦C, 0.17% at 40 ◦C, and 3.74% at 35 ◦C in the case of NaOH content, pressure, and both, respectively

effects of viscosity on the performance of hydraulic power recovery turbines hprts by the means of computational fluid dynamics cfd simulations

Abstract Centrifugal pumps are used for increasing the energy content of a liquid: this technology is used in chemical processes with liquids having specific chemical and physical characteristics. Most of the processes are closed-loop, meaning that the liquid is reused after a proper physical or chemical washing treatment is performed. Therefore, the pressure of the liquid has to be decreased by means of a lamination valve or a Hydraulic Power Recovery Turbine (HPRT) with the advantage of recovering energy. HPRTs are generally tested in both pump and turbine modes using water as working fluid. The technical report ISO/TR 17766 indicates the procedure to evaluate the performance of centrifugal pumps handling viscous liquids by supplying correction factors with respect to water, but no indications are given in turbine mode. This work provides correction factors able to evaluate also the performance of HPRTs handling viscous fluids in turbine mode by varying the proposed formulae in the technical report. Computational Fluid Dynamics (CFD) simulations of two tested HPRTs are performed using, at first, water as working fluid for validating the experimental results and, subsequently, the SELEXOL® solvent. Results show that the original correction factors are still valid for the HPRT B that has a parameter B, which is the main one to be involved in the evaluation of the correction factors, lower than 1. A better accuracy, instead, is achieved by modifying the correction factors of the HPRT A, having a value of B higher than 1

A Design Approach of Off-grid Hybrid Electric Microgrids in Isolated Villages: A Case Study in Uganda

Abstract Rural electrification in isolated areas of developing countries can be considered a pivotal factor for economic and social growth, moreover the absence of electricity grid in villages leads to an elevated usage of diesel generators that entails large costs and high CO 2 emissions. This paper presents a design methodology and economical evaluation to implement a hybrid power system composed of a photovoltaic power plant, electrical storage and a backup system of diesel generators in an isolated village in Uganda named Ntoroko. Results show that the usage of battery storage is economically crucial, particularly in areas with a low daily electrical consumption and peak loads increasing in the early morning and late evening when the solar radiation is lower and PV array has a reduced power production. Results disclose that the optimal configuration of the hybrid system (PV-storage-diesel generators), despite its high investment cost, presents an economic benefit of 25.5 and 22.2% compared to the usage of only PV array and diesel generators and only diesel generators and a reduction of fuel consumption equal to 74.7 and 77%, respectively