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

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

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%

parametric performance maps for design and selection of liquid air energy storage system for mini to micro grid scale applications

Abstract This paper aims to deliver new performance maps for "microgrid scale" Liquid Air Energy Storage system with a liquid air production of 1000 kg/h. By means of the performance maps, the impact of the main Liquid Air Energy Storage operative parameters, as well as the effect of the cold/warm thermal energy storage utilization factor, over the key performance indicators has been assessed and analyzed. The thermodynamics and sub-processes of the Liquid Air Energy Storage system are described in details and simulated by means of the software Aspen Hysys. Each performance map has been modelled by means of a sensitivity analysis carried out for the system operative parameters. Such a new methodology allows to select Liquid Air Energy Storage size and its related performance by means of a simple tool without the implementation of any complex numerical model

planning tool for polygeneration design in microgrids

Abstract This work suggests a methodology to assist the designer during the planning phase of microgrids and eco-districts. A mixed integer linear programming model is designed to mathematically describe the different energy systems and the physical relations among them. Given the different electrical/thermal demand profiles, the micro grid's topology and a set of boundary conditions, the model can identify the optimum mix of (poly-)generation units and energy storage systems, as well as the necessary district heating/cooling infrastructure. Both economic and energetic cost functions are defined to explore the problem from different perspectives. The described tool is applied to study an actual district of the NTU campus in Singapore, comprising 5 multi-purpose buildings and a district cooling network supplied by centralized electrical chillers. The planning tool was run to assess the optimal configuration that minimizes the overall cost (initial investment and OM the outcome results presented a layout and a mix of energy systems different from the present one. In particular, the optimal configuration results to be a district cooling system served by a mix of electrical chiller plant, trigeneration distributed energy system and sensible cold thermal energy storage

Low Order Grey-box Models for Short-term Thermal Behavior Prediction in Buildings

Abstract Low order grey-box models are suitable to be used in predictive controls. In real buildings in which the measured quantities are few the reliability of these models is crucial for the control performance. In this paper an identification procedure is analyzed to investigate the accuracy of different order grey-box models for short-term thermal behavior prediction in a real building, part of a living smart district. The building has a low number of zones and a single indoor temperature measuring point. The models are identified on the data acquired in 31 days during the winter 2015. The second order model shows the best performance with a root-mean-square error (RMSE) less than 0.5°C for a prediction horizon of 1-hour and a RMSE less than 1 °C for a prediction horizon of 3-hours

Compressed Air Energy Storage—An Overview of Research Trends and Gaps through a Bibliometric Analysis

Electrical energy storage systems have a fundamental role in the energy transition process supporting the penetration of renewable energy sources into the energy mix. Compressed air energy storage (CAES) is a promising energy storage technology, mainly proposed for large-scale applications, that uses compressed air as an energy vector. Although the first document in literature on CAES appeared in 1976 and the first commercial plant was installed in 1978, this technology started to gain attention only in the decade 2000–2010, with remarkable scientific production output and the realization of other pre-commercial demonstrators and commercial plants. This study applies bibliometric techniques to draw a picture of the current status of the scientific progress and analyze the trend of the research on CAES and identify research gaps that can support researchers and manufacturers involved in this entering technology. Recent trends of research include aspects related to the off-design, the development of thermal energy storage for adiabatic CAES, and the integration of CAES with combined heating and cooling systems

preliminary assessment of waste heat recovery solution orc to enhance the performance of liquid air energy storage system

Abstract Liquid Air Energy Storage (LAES) is a novel energy storage system that stocks up energy by means of air liquefaction and recovers the cryogenic energy when required. The performance of LAES is actually limited both by the inefficiencies of liquefaction and discharge section leading to lower value of round trip efficiency compared to other energy storage solutions. This work investigates the thermodynamic feasibility of an integrated energy system consisting of a LAES system and Organic Rankine Cycle (ORC) in order to recover the waste heat released by the compression phase. To further improve the round trip efficiency of LAES, different integrated LAES-ORC system configurations have been modelled by means of the numerical software EES-Engineering Equation Solver v.10, which allows to compute the thermo-physical properties of the working fluids throughout the whole cycles. The LAES-ORC integrated systems are compared in terms of different performance indices such electric power output, round trip efficiency of stand-alone and integrated systems and recover efficiency of ORC. Moreover, since the potential benefits of waste heat recovery by means of ORC introduces a new capital and operative cost, an economic analysis has been carried out in order to determine the impact of ORC introduction in LAES economy. The results show that a tight integration between LAES and ORC allows to significantly improve the round efficiency (up to 20%) and reduce the pay-back period of stand-alone LAES as high as 6 %

Effect of the Nano-Ca(OH)2 addition on the Portland clinker cooking efficiency

A new technology was tested to improve the cooking efficiency of the raw mixture for Portland clinker production by the use of nano-Ca(OH)2. A decrease in the free lime concentration after the firing of approximately 35% and 55% in the nano-added clinkers burned at 1350 °C and 1450 °C, respectively, with respect to the standard Portland clinkers was observed. Moreover, in the nano-added clinkers, a slight decrease in alite (C3S), of approximately 2-4 wt%, and increase in belite (C2S), of approximately 5-6 wt%, were observed. Despite these variations, the C2S and C3S abundance lies within the ranges for standard Portland clinkers. The results showed that the nano-addition leads to an increase of the raw mixtures' cooking efficiency. The relatively low energy required for the clinker firing could be used to increase the plant productivity and decrease the CO2 emissions during clinker burning. The decrease of the work index of the clinkers produced by the use of the nano-Ca(OH)2 also contributes to the energy saving during clinker grinding. Differences were also found in the pore size distribution among nano-added clinkers and the standard Portland clinker. The smallest porosities with the modal volume lying in the class of 3 × 10-6 mm3 were found to increase by the use of nano-Ca(OH)2. However, the pore volumes higher than 2.0 × 10-5 mm3 decreased in the nano-added clinkers. © 2019 by the authors

An experimental and numerical method for thermal characterization of phase change materials for cold thermal energy storage

This paper seeks to establish a methodology which predicts the phase change duration and this assists the design of an optimized container sizing for cold thermal energy storage systems. The thermal characterization with numerical methods is widely used due to their versatility and low cost when compared to the experimental methods, but, to obtain reasonable results, the numerical model needs to be calibrated and validated with real data. In this work an experimental rig has been designed for phase change materials with low temperature applications. The results, obtained with pure water as PCM, have been used to validate a 1-D numerical model based on the effective capacity method and solved by MATLAB software.NRF (Natl Research Foundation, S’pore)Published versio