Advanced Exergy Analysis in the Dynamic Framework for Assessing Building Thermal Systems

This work applies the Dynamic Advanced Exergy Analysis (DAEA) to a heating and domestic hot water (DHW) facility supplied by a Stirling engine and a condensing boiler. For the first time, an advanced exergy analysis using dynamic conditions is applied to a building energy system. DAEA provides insights on the components’ exergy destruction (ED) by distinguishing the inefficiencies that can be prevented by improving the quality (avoidable ED) and the ones constrained because of technical limitations (unavoidable ED). ED is related to the inherent inefficiencies of the considered element (endogenous ED) and those coming from the interconnections (exogenous ED). That information cannot be obtained by any other approach. A dynamic calculation within the experimental facility has been performed after a component characterization driven by a new grey-box modelling technique, through TRNSYS and MATLAB. Novel solutions and terms of ED are assessed for the rational implementation of the DAEA in building energy installations. The influence of each component and their interconnections are valuated in terms of exergy destruction for further diagnosis and optimization purposes.BMWi, 03ET1218B, Anwendung exergiebasierter Methoden zur Verbesserung von Gebäudeenergiesysteme

Holistic management of a smart city thermal energy plant with sewage heat pumps, solar heating, and grey water recycling

This article introduces a modern thermal energy plant consisting of sewage heat pumps, a biogas boiler, thermal solar collectors, and grey water recycling. It further discusses advanced methods to achieve energy efficiency in the plant operation. The project is a collaboration between the industrial plant designer, the municipal plant owner, and the local academic institution. The article presents the framework for the collaboration. The overall target is to investigate how the experience and competence of the three partners can lead to improved operation using data-driven methods and optimization strategies. The industrial partner can closely follow up on its design and increase its knowledge of artificial intelligence and data-driven methods. The municipal partner is given a “free-of-charge” system review. New knowledge and reduced life cycle costs and emissions are possible outcomes. The academic partner gets access to a “living green laboratory,” a unique dataset, and the opportunity to validate developed models and optimization strategies. The plant represents the state-of-the-art for a medium scaled, local thermal energy production system in an existing building cluster. The design energy and emission targets are presented and compared to the operational results. Though the municipal partner can report good agreement between targets and results, an evaluation of the day-to-day operation identified practical examples of system conditions that Artificial Intelligence may improve. The article concludes with a description of plans for future work and a broader discussion of the impacts of introducing data-driven methods to real-life systems.publishedVersio

IEA ECES Annex 31 Final Report - Energy Storage with Energy Efficient Buildings and Districts: Optimization and Automation

At present, the energy requirements in buildings are majorly met from non-renewable sources where the contribution of renewable sources is still in its initial stage. Meeting the peak energy demand by non-renewable energy sources is highly expensive for the utility companies and it critically influences the environment through GHG emissions. In addition, renewable energy sources are inherently intermittent in nature. Therefore, to make both renewable and nonrenewable energy sources more efficient in building/district applications, they should be integrated with energy storage systems. Nevertheless, determination of the optimal operation and integration of energy storage with buildings/districts are not straightforward. The real strength of integrating energy storage technologies with buildings/districts is stalled by the high computational demand (or even lack of) tools and optimization techniques. Annex 31 aims to resolve this gap by critically addressing the challenges in integrating energy storage systems in buildings/districts from the perspective of design, development of simplified modeling tools and optimization techniques

A User-Centred Methodology to Design and Simulate Smart Home Environments and Related Services

openI progressi nelle tecnologie di automazione e comunicazione all'interno degli edifici residenziali offrono molti interessanti vantaggi per lo sviluppo delle Smart Home, come l'aumento di efficienza energetica, il miglioramento il comfort per gli abitanti e la riduzione dei costi operativi per il proprietario. L'aggregazione e la condivisione dei dati all'interno delle reti possono essere garantite dal moderno approccio denominato Internet delle cose (IoT) e supportati dalle nuove tecnologie dell'informazione e della comunicazione (ICT). Tali tecnologie si stanno evolvendo e le abitazioni stanno diventando luoghi tecnologici popolati da una moltitudine di dispositivi in grado di raccogliere una grande quantità di dati e di cooperare in modo intelligente per controllare tutti i dispositivi connessi, come gli elettrodomestici, l'illuminazione, i sistemi di riscaldamento, ecc. Da un lato, l’intelligenza crescente dei dispositivi connessi produce una grande quantità di dati; dall'altro lato, la complessità di tali dati crea difficoltà di classificazione, trasmissione ed interpretazione delle informazioni utili. Entrambi gli aspetti possono ridurre drasticamente i potenziali vantaggi e limitare la diffusione dei cosiddetti dispositivi “smart”. Mentre a livello aziendale già esistono soluzioni di automazione affermate ed ampiamente utilizzate, le applicazioni per le abitazioni private sono ancora di difficile diffusione a causa della mancanza di standard di comunicazione e della presenza di dispositivi e sistemi altamente eterogenei e quindi di difficile integrazione. Inoltre, anche quando la connessione tra due dispositivi viene stabilita, renderli interoperabili è un’altra grande sfida a causa delle differenze nelle modalità di funzionamento e della difficoltà di integrazione dell'interfaccia. Infatti, le Smart Home non consentono ancora una elevata interoperabilità e gli studi fatti sono spesso fortemente orientati alla tecnologia e concentrati sulle potenzialità dei singoli sottosistemi, trascurando i benefici per gli utenti finali. A tale scopo, questo lavoro definisce un modello di gestione delle informazioni per ambienti domestici intelligenti con lo scopo di supportare la progettazione e la simulazione dei dispositivi “smart” nonché dei servizi sviluppati. Tale modello considera diverse tipologie di dispositivi, le relazioni esistenti tra loro, i flussi informativi e le modalità di interazione dell’utente per modellare correttamente l'ambiente e definirne il comportamento. Il modello sviluppato supporta la progettazione della Smart Home ed è in grado di simulare le funzionalità dei dispositivi con lo scopo finale di valutare i benefici dei servizi forniti.The advances in home automation and communication technologies offer several attractive benefits for the modern smart home, such as increased energy efficiency, improved residential comfort and reduced operative costs for the homeowner. Data aggregation and sharing within the networks can be guaranteed by modern Internet of Things (IoT) approaches and supported by available Information and Communication Technologies (ICT) tools. Such technologies are evolving and the private houses are becoming technological places populated by a multitude of devices able to collect a huge quantity of data and to cooperate in an intelligent way to control different domains, from household appliances to lighting or heating and ventilation. On one hand, the rising intelligence of smart devices makes a large amount of data available; on the other hand, data complexity creates difficulties in classifying, transmitting and interpreting essential data. Both aspects may drastically reduce the potential advantages and limit the diffusion smart devices. While in building automation proven solutions already exist, tailored applications for private houses and integration among heterogeneous devices and systems are still challenging due to the lack of standards and the variety of adopted communication protocols and data model schemas. Furthermore, even when the device connection and consolidation are achieved, making them cooperate in an interoperable way is another big challenge due to differences in usage paradigms, operation modes and interface integration. In fact, Smart Homes still lack of high interoperability and researches are often strongly technology-oriented and focused on single sub-system potentialities neglecting the expected benefits for the final users. For this purpose, the presented research defines an information management model for the smart home environment to support design and simulation of its devices as well as the enabled services. Such a model considers different device typologies, their mutual relationships, the information flows and the user interaction modalities in order to properly model the environment and define its behavior. It supports the design of the smart home by simulating the devices’ functionalities and estimating the expected performances.INGEGNERIA MECCANICA E GESTIONALEopenCapitanelli, AndreaCapitanelli, Andre

Application of Thermoeconomics in HVAC Systems

In order to achieve a sustainable society, the energy consumption in buildings must be reduced. The first step toward achieving this goal is to detect their weak points and analyze the energy-saving potential. to detect the units with higher consumption and cost. Exergy is very useful for analyzing pieces of equipment, systems or entire buildings. It measures not only the quantity of energy but also its quality. If the exergy is combined with economic analysis, this gives rise to thermoeconomics, and the system can be checked systematically and optimized from the perspective of economics. In this work, exergy methods and thermoeconomic analysis were applied to a building thermal system. Due to its complexity, it is necessary to adapt some concepts to translate the exergy application from industry to buildings. The purpose of this work is to overcome these shortcomings and to deal with energy-saving actions for buildings. To this end, a thermoeconomic study of a facility that covers the heating and domestic hot water (DHW) demands of 176 dwellings in Vitoria-Gasteiz (Basque Country) using two boilers and two cogeneration engines was analyzed. The irreversibility associated with each piece of equipment was quantified, and the costs associated with resources, investment and maintenance were calculated for each flow and, consequently, for the final flows, that is, electricity (11.37 c€/kWh), heating (7.42 c€/kWh) and DHW (7.25 c€/kWh). The results prove that the boilers are the lesser efficient components (with an exergy efficiency of 15%). Moreover, it is demonstrated that micro-cogeneration engines not only save energy because they have higher exergy efficiency (36%), but they are also economically attractive, even if they require a relatively high investment. Additionally, thermoeconomic costs provide very interesting information and underscore the necessity to adapt the energy quality in between the generation and demand

An arctic low-energy house as experimental setup for studies of heat dynamics of buildings

AbstractThis paper addresses the difficulties in pinpointing reasons for unexpectedly high energy consumption in construction, and in low-energy houses especially. Statistical methods are applied to improve the insight into the energy performance and heat dynamics of a building based on consumption records and weather data. Dynamical methods separate influences from outdoor temperature, solar radiation, and wind on the energy consumption in the building. The studied building is a low-energy house in Sisimiut, Greenland. Weather conditions like large temperature differences between indoors and outdoors throughout long winters, strong winds, and very different circumstances regarding solar radiation compared to areas where low-energy houses are usually built, make the location very interesting for modeling and testing purposes. In 2011 new measurement equipment was installed in the house, which will be used to develop more detailed models of the heat dynamics and energy performance in relation to different meteorological variables, heating systems, and user behavior. This type of models is known as a graybox model and is been introduced in this paper

Data-Driven Virtual Replication of Thermostatically Controlled Domestic Heating Systems

Thermostatic load control systems are widespread in many countries. Since they provide heat for domestic hot water and space heating on a massive scale in the residential sector, the assessment of their energy performance and the effect of different control strategies requires simplified modeling techniques demanding a small number of inputs and low computational resources. Data-driven techniques are envisaged as one of the best options to meet these constraints. This paper presents a novel methodology consisting of the combination of an optimization algorithm, two auto-regressive models and a control loop algorithm able to virtually replicate the control of thermostatically driven systems. This combined strategy includes all the thermostatically controlled modes governed by the set point temperature and enables automatic assessment of the energy consumption impact of multiple scenarios. The required inputs are limited to available historical readings from smart thermostats and external climate data sources. The methodology has been trained and validated with data sets coming from a selection of 11 smart thermostats, connected to gas boilers, placed in several households located in north-eastern Spain. Important conclusions of the research are that these techniques can estimate the temperature decay of households when the space heating is off as well as the energy consumption needed to reach the comfort conditions. The results of the research also show that estimated median energy savings of 18.1% and 36.5% can be achieved if the usual set point temperature schedule is lowered by 1 degrees C and 2 degrees C, respectively

Modelling, experimental characterization and simulation of stirling engine-based micro-cogeneration plants for residential buildings.

298 p.El sector de la edificación representa aproximadamente el 40% del consumo de energía y un 36% de las emisiones de CO2 en la Unión Europea. En la actualidad la investigación se dirige hacia la mejora de la eficiencia energética y la promoción de las energías renovables. Una de las tecnologías con mayor potencial para cubrir las necesidades energéticas del sector de forma eficiencia es la micro-cogeneración en base al motor Stirling.La presente tesis presenta la evaluación de la viabilidad de esta tecnología frente a otras opciones. A tal efecto, se aborda el modelado, caracterización experimental y simulación de plantas de micro-cogeneración en base a motor Stirling aplicado a edificios residenciales en la CAPV y España

Integrated model concept for district energy management optimisation platforms

District heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project