Energetical analysis of two different configurations of a liquid-gas compressed energy storage

In order to enhance the spreading of renewable energy sources in the Italian electric power market, as well as to promote self-production and to decrease the phase delay between energy production and consumption, energy storage solutions are catching on. Nowadays, in general, small size electric storage batteries represent a quite diffuse technology, while air liquid-compressed energy storage solutions are used for high size. The goal of this paper is the development of a numerical model for small size storage, environmentally sustainable, to exploit the higher efficiency of the liquid pumping to compress air. Two different solutions were analyzed, to improve the system efficiency and to exploit the heat produced by the compression phase of the gas. The study was performed with a numerical model implemented in Matlab, by analyzing the variation of hermodynamical parameters during the compression and the expansion phases, making an energetic assessment for the whole system. The results show a good global efficiency, thus making the system competitive with the smallest size storage batteries

Mobile Platform of SRF Production and Electricity and Heat Generation

Abstract The technological frontier is ripe for action on the cycle of municipal waste at local level through the optimization of existing treatment processes, adapting to European Union directives. The study concerns the analysis of the waste cycle in order to rationalize the current paths of the waste by adapting to EU directives, with a view of the entire supply chain - from the delivery to the energy production (WtE, Waste to Energy) – with a intermediate stage of SRF (Solid Recovered Fuel) production. The DIMA has developed an innovative platform for MSW treatment (unsorted and not), based on newly developed technologies that enables its weight and volume reduction and the transformation in SRF high quality, by achieving consistent chemical-physical and particle size parameters through the innovative technology of mechanochemical micronization. This standardized fuel product is therefore suitable for energy recovery within the platform using the most advanced gasification process. The study aims at developing a mobile demonstration plant of 100-200 kWe for energy recovery from waste in cogeneration by conversion of MSW into SRF through a system of characterization, treatment and recycling based on a highly innovative mechanochemical refining system. The SRF is enhanced through more advanced gasification process and it can used for the production of electricity and thermal energy. The production, the gasification and the syngas combustion take place in modular units arranged in appropriate mobile units (containers) appropriately configured, to fully meet the objectives of a sustainable policy management and security of waste. b Unit 1 (waste treater - SRF producer) is developed to operate the transformation of industrial waste in SRF for subsequent gasification inside unit 2 (Boiler Gasifier). It carries out a pre-treatment and mechanochemical micronization waste treatment. The SRF is reduced into pellets to be introduced into the 2 (boiler gasifier) to its gasification (syngas production). The pellet (auxiliary unit 4, pellettizer) is gasified in the unit 2 and enriched in order to obtain synmethan gas for producing electricity in the cogeneration unit 3 (energies production)

ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out- performs all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners’ performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a plan- ner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Retrofit Proposals for Energy Efficiency and Thermal Comfort in Historic Public Buildings: The Case of the Engineering Faculty’s Seat of Sapienza University

The building sector greatly contributes to energy consumption and Greenhouse Gas emissions, relating to the whole building life cycle. Boasting a huge building heritage of historical and architectural value, Europe faces challenging retrofit perspectives, as the potential for high energy efficiency has to be exploited while preserving the buildings' original characteristics. The present work aims to feature the influence of a passive strategy on a heritage building in a mild climate. As historical its facade cannot be modified, its large glazing areas involve multiple issues, such as an increase in the heating (QH) and cooling (QC) energy demands and the risk of thermal discomfort. Thus, window replacement was proposed for retrofitting. A dynamic simulation model in TRNSYS was validated with experimental data collected by the continuous monitoring of walls of different thicknesses and orientations. Solutions from replacement with Double Glazing Units (DGUs) with improved thermal insulation, to internal shading activation were applied. All configurations were compared in terms of QH, QC, thermal performance of the building and user comfort (Fanger). Low-e DGU enabled the saving of up to 14% of the annual energy demand, and shading also offered good results in summer, reducing QC by 19%. In summer, DGU involved a maximum PPD reduction of 10 units

Energy refurbishment planning of Italian school buildings using data-driven predictive models

In the current practice, the design of energy refurbishment interventions for existing buildings is typically addressed by performing time-consuming software-based numerical simulations. However, this approach may be not suitable for preliminary assessment studies, especially when large building portfolios are involved. Therefore, this research work aims at developing simplified data-driven predictive models to estimate the energy consumption of existing school buildings in Italy and support the decision-making process in energy refurbishment intervention planning at a large scale. To accomplish this, an extensive database is assembled through comprehensive on-site surveys of school buildings in Southern Italy. For each school, a Building Information Modelling (BIM) model is developed and validated considering real energy consumption data. These BIM models serve in the design of suitable energy refurbishment interventions. Moreover, a comprehensive parametric investigation based on refined energy analyses is carried out to significantly improve and integrate the dataset. To derive the predictive models, firstly the most relevant parameters for energy consumption are identified by performing sensitivity analyses. Based on these findings, predictive models are generated through a multiple linear regression method. The suggested models provide an estimation of the energy consumption of the “as-built” configuration, as well as the costs and benefits of alternative energy refurbishment scenarios. The reliability of the proposed simplified relationships is substantiated through a statistical analysis of the main error indices. Results highlight that the building's shape factor (i.e., the ratio between the building's envelope area and its volume) and the area-weighted average of the thermal properties of the building envelope significantly affect both the energy consumption of school buildings and the achievable savings through retrofitting interventions. Finally, a framework for the preliminary design of energy refurbishment of buildings, based on the implementation of the herein developed predictive model, is proposed and illustrated through a worked example application. Worth noting that, while the proposed approach is currently limited to school buildings, the methodology can conceptually be extended to any building typology, provided that suitable data on energy consumption are available

Photovoltaics Noise Barrier: Acoustic and Energetic Study

AbstractIn the light of global warming, renewables such as solar photovoltaics (PV) are important to decrease greenhouse gas emissions. An important issue regarding implementation of solar panels on large scale, is the limited available area. Therefore, it can be interesting to combine PV with alternative applications, as a ways of not requiring “additional” space. One example is a photovoltaic noise barrier (PVNB), where a noise barrier located along a highway or railway is used as substructure for PV modules. Even though PVNB is not a novel concept, in this paper it is studied the best shape of the barrier to optimize the acoustic and energy properties

Aflatoxin occurrence in goat milk and supplied concentrate feed in farms of Veneto, Trentino and Friuli Venezia Giulia

Aflatoxin M1 (AFM1) is a probable human hepatocarcinogen (IARC, monographs on the evaluation of carcinogenic risks to human. Vol. 56, 1993) found in milk of animals that consume feeds contaminated with aflatoxin B1 (AFB1), produced by fungi of genus Aspergillus. There is little information about goat milk: the aim of this study was to examine the level of contamination of milk, and related concentrate feed, in goat farms of Veneto, Trentino and Friuli Venezia Giulia. In 2005 and 2006, during the lactation period, 79 samples of total daily milk and 125 concentrate feed samples (principally maize and concentrate feeds), collected in 17 goat farms of Triveneto, were analysed for the content of AFM1 and AFB1 respectively, by HPLC technique. Concerning the milk samples, only one-third of total samples exceed the analytical reliability level (3 ppt), 14 of which were positioned under the value of 9 ppt and only 1 sample was over the value of 27 ppt.With regard to the feed samples, the two-thirds of total samples exceed the analytical reliability level (0.1 ppb), 54 of which had a value lower than 1 ppb and only 1 had a value higher than 10 ppb. The relation between levels of aflatoxin in milk and feeds was also considered: there is a significant correlation that confirms the role of feeds in the contamination of milk. All the samples had values lower than the maximum limit established by Italian law concerning the content of aflatoxins in milk for human diet and the content of aflatoxin in the concentrates for the goat diet (AFM1: 50 ppt; AFB1: 20 ppb), showing a general situation of absence of risk for animal and human health, with only few cases to keep under control. The results are in accordance with the situation found in other regions of North Italy (Regione Lombardia, 2003-2005), where, also in the dairy cow sector, there was a reduction of aflatoxin contamination risk in 2005 after two years of high levels of contamination of the maize and of the milk

Static and Dynamic Portfolio Methods for Optimal Planning: An Empirical Analysis

Combining the complementary strengths of several algorithms through portfolio approaches has been demonstrated to be effective in solving a wide range of AI problems. Notably, portfolio techniques have been prominently applied to suboptimal (satisficing) AI planning. Here, we consider the construction of sequential planner portfolios for domainindependent optimal planning. Specifically, we introduce four techniques (three of which are dynamic) for per-instance planner schedule generation using problem instance features, and investigate the usefulness of a range of static and dynamic techniques for combining planners. Our extensive empirical analysis demonstrates the benefits of using static and dynamic sequential portfolios for optimal planning, and provides insights on the most suitable conditions for their fruitful exploitation

Efficient energy storage in residential buildings integrated with RESHeat system

The Renewable Energy System for Residential Building Heating and Electricity Production (RESHeat) system has been realized for heating and cooling residential buildings. The main components of the RESHeat system are a heat pump, photovoltaic modules, sun-tracking solar collectors and photovoltaic/thermal modules, an under-ground thermal energy storage unit, and a ground heat exchanger. One of the main novelties of the RESHeat system is efficient ground regeneration due to the underground energy storage unit. During a heating season, a large amount of heat is taken from the ground. The underground energy storage unit allows the restoration of ground heating capability and the heat pump's coefficient of performance (COP) to be kept high as possible for consecutive years. The paper presents an energy analysis for a residential building that is a RESHeat system demo site, along with integrating the RESHeat system with the building. The experimentally validated components coupled with the building model to achieve the system performance in TRNSYS software. The results show that the yearly average COP of the heat pump is 4.85 due to the underground energy storage unit. The RESHeat system is able to fully cover the heating demand of the building using renewable energy sources and an efficient underground energy storage system

D2D Communications for Large-Scale Fog Platforms: Enabling Direct M2M Interactions

To many, fog computing is considered the next step beyond the current centralized cloud that will support the forthcoming Internet of Things (IoT) revolution. While IoT devices will still communicate with applications running in the cloud, localized fog clusters will appear with IoT devices communicating with application logic running on a proximate fog node. This will add proximity-based machine-to-machine (M2M) communications to standard cloud-computing traffic, and it calls for efficient mobility management for entire fog clusters and energy-efficient communication within them. In this context, long-term evolution-advanced (LTE-A) technology is expected to play a major role as a communication infrastructure that guarantees low deployment costs, native mobility support, and plug-and-play seamless configuration. We investigate the role of LTE-A in future large-scale IoT systems. In particular, we analyze how the recently standardized device-to-device (D2D) communication mode can be exploited to effectively enable direct M2M interactions within fog clusters, and we assess the expected benefits in terms of network resources and energy consumption. Moreover, we show how the fog-cluster architecture, and its localized-communication paradigm, can be leveraged to devise enhanced mobility management, building on what LTE-A already has to offer