/src/Controller/ProjectscontentController.php (line 206)
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'id' => (int) 377,
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'longtitle_de' => 'UserGRIDS: User-Centered Smart Control and Planning of Sustainable Microgrids',
'longtitle_en' => 'User-GRIDS: User-Centered Smart Control and Planning of Sustainable Microgrids',
'content_de' => '<p>Der Klimaschutz erfordert die massive Reduktion der durch den Gebäudebestand bedingten Treibhausgas-­Emissionen. Die neuen Möglichkeiten der Digitalisierung versprechen, mittels „<strong>Digital Energy Services</strong>“ (DES) Energiesysteme mit stark fluktuierender Nutzung und großen Anteilen volatiler Energiequellen zielgerichteter betreiben und planen zu können.</p>
<p>Im Projekt <strong>UserGRIDs</strong> werden Methoden entwickelt und erprobt, die den Betrieb und die Planung von Quartiers-Energiesystemen nutzerzentriert und effizient unterstützen. Als Grundlage dient der INNOVATION DISTRICT INFFELD. Dieser Forschungs- und Lehrcampus ist mit 125.000 m² Bruttogeschoßfläche und einer Mischung aus Büro-, Lehr- und projektgetriebenem Laborbetrieb ein ideales Beispiel für stark fluktuierende Nutzungsanforderungen.</p>
<p>Die Basis bildet eine <strong>ICT-Plattform</strong>, in der alle für die Energieperformance rele­vanten Daten zusammenfließen. Dazu gehören Messdaten aus den Energiesystemen (Temperaturen, Leistungen, etc.) und Daten anderer digitaler Systeme (Raumbelegung, Wetterdaten, Preissignale, etc.). Die Plattform stellt den DES „Energiemanagement“ und „Energie­strukturplanung“ laufend Daten zur Verfügung und übermittelt deren Feedback zurück an den Campus. Zudem wird den Nutzer*innen ermöglicht, in Echtzeit mit den DES zu interagieren.</p>
<p>Das DES <strong>Energiemanagement</strong> verbindet die Regelungen der Gebäude zu einem umfassenden, selbstlernenden Gesamtkonzept. Nutzer*innendaten fließen in Prognosen und Zielsetzungen des Systems ein. Ziel ist der emissionsminimierte ökonomische Betrieb durch optimale Bewirtschaftung der Speicher und Einbindung volatiler Quellen. Externe Kommunikation sichert die intelligente Einbindung in übergeordnete urbane Versorgungssysteme.</p>
<p>In der <strong>Energiestrukturplanung</strong> werden detaillierte Modelle des Energiesystems der Gebäude und der übergeordneten Campus-Infrastruktur entwickelt, validiert und für die Bewertung struktureller Weiterentwicklungen eingesetzt. Es werden alle Stakeholder einbezogen und Key Performance Indicators (KPIs) definiert. Simulationen bewerten die KPIs unterschiedlicher Entwicklungsvarianten.</p>
<p>Dabei werden sowohl System-Transformationen, wie die Einbeziehung von Energiespeichern oder der Austausch von Energietechnologien, als auch der Ausbau der Photovoltaik am Campus bewertet. Ebenso wird das abzusehende Wachstum auf 185.000 m² Brutto­geschoßfläche analysiert.</p>
<p>Die Entwicklungen werden als Musterlösungen formuliert, die als Basis für die Entwicklung von Geschäftsmodellen herangezogen werden. Der INNOVATION DISTRICT INFFELD versteht sich als ein Vorreiter des Einsatzes neuer DIGITALER ENERGY SERVICES, um die Nutzungszufriedenheit eines Stadtquartiers zu steigern, den Betrieb des energie­technischen Systems optimal zu regeln und den Ausbau konsequent in Richtung eines Nullemissions-Quartiers voranzutreiben.</p>
<p><a href="https://greenenergylab.at/projects/nutzungszentrierte-planung-und-regelung-komplexer-nachhaltiger-quartiers-energiesysteme/" target="_blank"><img alt="" src="/webroot/files/image/Projektseite/GreenEnergyLab.png" style="float:left; height:83px; margin-left:10px; margin-right:10px; width:114px" /></a></p>
<p> </p>
<p> </p>
<p> </p>
<p> </p>
',
'content_en' => '<p>Climate protection requires a massive reduction in greenhouse gas emissions from existing buildings. Modern digital systems promise more targeted operation and planning of energy systems with strongly fluctuating use and large shares of volatile energy sources by means of new "<strong>Digital Energy Services</strong>" (DES).</p>
<p>The <strong>UserGRIDs</strong> project is developing and testing methods for operating urban district energy systems in a user-centred and efficient manner, as well as for the further planning of the energy infrastructure. The INNOVATION DISTRICT INFFELD serves as the basis for development. With 125 000 m² of gross floor area and its mixture of office, teaching and project-based laboratory operations, the campus is an ideal example of strongly fluctuating usage requirements.</p>
<p>The basis is an <strong>ICT platform</strong> that brings together all energy related data, including measurements from the energy systems (temperatures, performance data, etc.) and data from other digital systems (room occupancy, weather and price forecasts, etc.). The platform makes the data available to the DES "Energy Management" and "Energy Structure Planning" and transmits their feedback back to the campus. Users are also given the opportunity to interact with the DES in real time.</p>
<p>The DES <strong>Energy Management</strong> extends the control systems of the buildings to an all-encompassing, self-learning control concept. User data flow into the forecasts and the objectives of the control system. The aim is to minimise emissions and ensure economical operation through optimum management of energy storages and the integration of renewable sources. External communication ensures intelligent integration into higher-level urban supply systems.</p>
<p>Within <strong>Energy Structure Planning</strong>, detailed transient models of the thermal and electrical energy system of the individual buildings and the superordinate campus infrastructure are developed and validated in order to be used for evaluation of further structural developments. Stakeholders are involved and key performance indicators (KPIs) are defined. Simulations evaluate the KPIs of different development variants.</p>
<p>Both system transformations, such as integrating energy storages or the exchange of energy technologies and the expansion of photovoltaic use on the campus, as well as the anticipated growth to 185 000 m² gross floor area are evaluated.</p>
<p>The developments are formulated as model solutions which are used as a basis for the development of business models. The INNOVATION DISTRICT INFFELD sees itself as a pioneer in the use of new DIGITAL ENERGY SERVICES in order to increase the user satisfaction of an urban district, to optimally operate the energy system and to consistently drive the expansion towards a zero-emissions district. </p>
<p><a href="https://greenenergylab.at/projects/nutzungszentrierte-planung-und-regelung-komplexer-nachhaltiger-quartiers-energiesysteme/" target="_blank"><img alt="" src="/webroot/files/image/Projektseite/GreenEnergyLab.png" style="float:left; height:83px; margin-left:10px; margin-right:10px; width:114px" /></a></p>
<p> </p>
<p> </p>
<p> </p>
<p> </p>
<p> </p>
',
'image_1' => '/webroot/files/image/USERGRIDS_Bild001.png',
'image_1_caption_de' => '',
'image_1_caption_en' => '',
'image_1_credits_de' => 'USERGRIDS',
'image_1_credits_en' => 'USERGRIDS',
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'image_2_caption_de' => '',
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'logos' => '<p>Institut für Wärmetechnik, TU Graz (Konsortialführung)<br />
Institut für Softwaretechnologie, TU Graz<br />
Gebäude und Technik, TU Graz<br />
Institut für Regelungs- und Automatisierungstechnik, TU Graz<br />
Institut für Bauphysik, Gebäudetechnik und Hochbau, TU Graz<br />
Institute of Interactive Systems and Data Science, TU Graz<br />
BEST - Bioenergy and Sustainable Technologies GmbH<br />
Bundesimmobiliengesellschaft m.b.H.<br />
EAM Systems GmbH<br />
Energie Steiermark AG<br />
EQUA Solutions AG<br />
Fronius International GmbH</p>
<p><img alt="" src="/webroot/files/image/Projektseite/TU_GRAZ.jpg" style="height:400px; width:800px" /> <img alt="" src="/webroot/files/image/Projektseite/BIG.jpg" style="height:55px; width:150px" /> <img alt="" src="/webroot/files/image/Projektseite/1200px-Energie_Steiermark_Logo.jpg" style="height:823px; width:800px" /> <img alt="" src="/webroot/files/image/Projektseite/EQUA.gif" style="height:29px; width:104px" /> <img alt="" src="/webroot/files/image/Projektseite/Fronius.jpg" style="height:58px; width:209px" /> <img alt="" src="/webroot/files/image/Projektseite/EAM.jpg" style="height:200px; width:200px" /></p>
<p> </p>
',
'finanzierung' => '<p><img alt="" src="/webroot/files/image/Projektseite/Klima_Energie_Fonds.jpg" style="height:86px; width:101px" /> <img alt="" src="/webroot/files/image/Projektseite/Vorzeigeregion.jpg" style="height:72px; width:242px" /> </p>
<p>FFG - 3rd Call – Energy Model Region (Vorzeigeregion Energie)<br />
Dieses Projekt wird aus Mitteln des Klima- und Energiefonds gefördert und im Rahmen der Vorzeigeregion Energie durchgeführt.</p>
<p> </p>
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'titel' => 'Automatic Thermal Model Identification and Distributed Optimisation for Load Shifting in City Quarters',
'subtitel' => '',
'autor' => 'Moser A, Kaisermayer V, Muschick D, Zemann C, Gölles M, Hofer A, Brandl D, Heimrath R, Mach T, Tugores C R, Ramschak, T',
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'citation' => 'Moser A, Kaisermayer V, Muschick D, Zemann C, Gölles M, Hofer A, Brandl D, Heimrath R, Mach T, Tugores C R, Ramschak, T. Automatic Thermal Model Identification and Distributed Optimisation for Load Shifting in City Quarters. 2nd International Sustainable Energy Conference: ISEC 2022. Graz, 07/04/2022. Oral presentation. ',
'abstract' => '<p>Modern buildings with floor heating or thermally activated building structures (TABS) offer a significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realized with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models describing the thermal characteristics of the individual thermal zones exist. Creating these by hand or from more detailed simulation models is infeasible for large numbers of zones; instead, they must be identified automatically based on measurement data. We present an approach using only open source tools based on the programming language Julia that allows to robustly identify simple thermal models for heating and cooling usable in MPC optimization. The resulting models are used in a distributed optimization scheme that co-ordinates the individual zones and buildings of a city quarter in order to best support an energy hub.</p>
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'titel' => 'Automatic thermal model identification and distributed optimisation for load shifting in city quarters',
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'autor' => 'Moser A, Kaisermayer V, Muschick D, Zemann C, Gölles M, Hofer A, Brandl D, Heimrath R, Mach T, Ribas Tugores C, Ramschak T',
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'citation' => 'Moser A, Kaisermayer V, Muschick D, Zemann C, Gölles M, Hofer A, Brandl D, Heimrath R, Mach T, Ribas Tugores C, Ramschak T. Automatic thermal model identification and distributed optimisation for load shifting in city quarters, International Journal of Sustainable Energy, 2023;42:1, 1063-1078, DOI: 10.1080/14786451.2023.2246079 ',
'abstract' => '<p>Buildings with floor heating or thermally activated building structures offer significant potential for shifting the thermal load and thus reduce peak demand for heating or cooling. This potential can be realised with the help of model predictive control (MPC) methods, provided that sufficiently descriptive mathematical models of the thermal characteristics of the individual thermal zones exist. Creating these by hand is infeasible for larger numbers of zones; instead, they must be identified automatically based on measurement data. In this paper an approach is presented that allows automatically identifying thermal models usable in MPC. The results show that the identified zone models are sufficiently accurate for the use in an MPC, with a mean average error below 1.5K for the prediction of the zone temperatures. The identified zone models are then used in a distributed optimisation scheme that coordinates the individual zones and buildings of a city quarter to best support an energy hub by flattening the overall load profile. In a preliminary simulation study carried out for buildings with floor heating, the operating costs for heating in a winter month were reduced by approximately 9%. Therefore, it can be concluded that the proposed approach has a clear economic benefit.</p>
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'id' => (int) 1389,
'titel' => 'Intelligent Building Control with User Feedback in the Loop',
'subtitel' => '',
'autor' => 'Kaisermayer V, Muschick D, Gölles M, Schweiger G, Schwengler T, Mörth M, Heimrath R, Mach T, Herzlieb M, Horn M.',
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'citation' => 'Kaisermayer V, Muschick D, Gölles M, Schweiger G, Schwengler T, Mörth M, Heimrath R, Mach T, Herzlieb M, Horn M. Intelligent Building Control with User Feedback in the Loop. 9th International Conference on Smart Energy Systems. Kopenhagen, Denmark, 12. September 2023. Oral Presentation. ',
'abstract' => '<p>Buildings account for 30% of the globally consumed final energy and 19% of the indirect emissions, i.e., from the production of electricity and heat. Air-conditioned office buildings have an especially high energy footprint. Retrofitting buildings with predictive control strategies can reduce their energy demand and increase thermal comfort by considering future weather conditions. One challenge lies in the required infrastructure, i.e., sensors and actuators. Another challenge is about satisfying the comfort requirements of the users, getting their feedback and reacting to it. We propose a predictive control strategy, where an optimization-based energy management system (EMS) controls the thermal zones of such office buildings. The approach uses a mathematical model of the building within an optimization problem to predict and shift thermal demand. The individual thermal zones are modelled using a grey-box approach, where the simultaneous state and parameter estimation is handled by an unscented Kalman filter (UKF). This minimizes the needed effort for deployment of the system, as the parameters are learned automatically from historical measurement data. The objective function ensures the users’ comfort based on a comfort model, penalizes unwanted behaviour such as frequent valve position changes, and minimizes the costs for heating and cooling supply. Since the offices are typically shared by multiple users, the internal comfort model is calibrated based on their feedback. Each feedback is viewed as a measurement from the internal comfort model, and an UKF updates the parameters of the model, thus lowering or increasing the temperature setpoint of the zone controller in a robust manner. As a case study, an office building at the “Innovation District Inffeld” is considered. The proposed predictive control strategy, together with the user feedback, is implemented. A central information and communication technology (ICT) handles all communication with the building automation system. We developed a simple web-based feedback system with a five-point Likert scale for user feedback integration. The presented ideas are evaluated based on both a preliminary simulation study and potential evaluation using the building modelling software IDA ICE, and a real-world implementation. A key requirement was to limit the number of new sensors and actuators, thus focusing on how much can be achieved with a retrofit measure with minimal hardware, but intelligent software. The presentation will give, an overview of the developed methods and first results of the real implementation will be given.</p>
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'titel' => 'Distributed Optimization Methods for Energy Management Systems',
'subtitel' => '',
'autor' => 'Valentin Kaisermayer',
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'citation' => 'Kaisermayer V. Distributed Optimization Methods for Energy Management Systems. 2023.',
'abstract' => '<p>Efficient control of energy systems is an important factor in achieving the CO2-emission goals. District heating (DH) networks are an especially relevant example of such energy systems. State-of-the-art control of small and medium-sized DH networks, however, still mainly relies on simple rule-based control concepts. Handling future challenges such as varying prices and intermittent renewable production is difficult to achieve with such control concepts. Optimization-based energy management systems (EMS) are a promising high-level control approach for the efficient operation of DH networks and complex energy systems in general. An especially interesting challenge arises when DH networks grow, as often the opportunity arises to interconnect them. However, if they operated by different owners, the control task becomes challenging, especially for optimization-based EMS. This is because, in the overall objective function, the cost and revenue for any exchange of energy would cancel out. This thesis presents a solution to this challenge. The main focus of this thesis is on the application of distributed optimization methods for EMS in the context of coupled energy systems, operated by multiple owners, especially interconnected DH networks. The presented methods and ideas are evaluated on a practical application of three DH networks in Austria. </p>
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'titel' => 'Predictive building energy management with user feedback in the loop',
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'autor' => 'Valentin Kaisermayer, Daniel Muschick, Martin Horn, Gerald Schweiger, Thomas Schwengler, Michael Mörth, Richard Heimrath, Thomas Mach, Michael Herzlieb, Markus Gölles',
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'citation' => 'Kaisermayer V, Muschick D, Horn M, Schweiger G, Schwengler T, Mörth M, Heimrath R, Mach T, Herzlieb M, Gölles M. Predictive building energy management with user feedback in the loop. Smart Energy. November 2024(16):100164.',
'abstract' => '<p>Retrofitting buildings with predictive control strategies can reduce their energy demand and improve thermal comfort by considering their thermal inertia and future weather conditions. A key challenge is minimizing additional infrastructure, such as sensors and actuators, while ensuring user comfort at all times. This study focuses on retrofitting with intelligent software, incorporating the users’ feedback directly into the control loop. We propose a predictive control strategy using an optimization-based energy management system (EMS) to control thermal zones in an office building. It uses a physically motivated grey-box model to predict and adjust thermal demand, with individual zones modelled using an RC-approach and parameter estimation handled by an unscented Kalman filter (UKF). This reduces deployment effort as the parameters are learned from historical data. The objective function ensures user comfort, penalizes undesirable behaviour and minimizes heating and cooling costs. An internal comfort model, automatically calibrated with user feedback by another UKF, further improves system performance. The practical case study is an office building at the ”Innovation District Inffeld”. Operation of the system for one year yielded significant results compared to conventional control. Thermal comfort was improved by 12% and thermal energy consumption for heating and cooling was reduced by about 35%.</p>
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UserGRIDS: User-Centered Smart Control and Planning of Sustainable Microgrids
Der Klimaschutz erfordert die massive Reduktion der durch den Gebäudebestand bedingten Treibhausgas-Emissionen. Die neuen Möglichkeiten der Digitalisierung versprechen, mittels „Digital Energy Services“ (DES) Energiesysteme mit stark fluktuierender Nutzung und großen Anteilen volatiler Energiequellen zielgerichteter betreiben und planen zu können.
Im Projekt UserGRIDs werden Methoden entwickelt und erprobt, die den Betrieb und die Planung von Quartiers-Energiesystemen nutzerzentriert und effizient unterstützen. Als Grundlage dient der INNOVATION DISTRICT INFFELD. Dieser Forschungs- und Lehrcampus ist mit 125.000 m² Bruttogeschoßfläche und einer Mischung aus Büro-, Lehr- und projektgetriebenem Laborbetrieb ein ideales Beispiel für stark fluktuierende Nutzungsanforderungen.
Die Basis bildet eine ICT-Plattform, in der alle für die Energieperformance relevanten Daten zusammenfließen. Dazu gehören Messdaten aus den Energiesystemen (Temperaturen, Leistungen, etc.) und Daten anderer digitaler Systeme (Raumbelegung, Wetterdaten, Preissignale, etc.). Die Plattform stellt den DES „Energiemanagement“ und „Energiestrukturplanung“ laufend Daten zur Verfügung und übermittelt deren Feedback zurück an den Campus. Zudem wird den Nutzer*innen ermöglicht, in Echtzeit mit den DES zu interagieren.
Das DES Energiemanagement verbindet die Regelungen der Gebäude zu einem umfassenden, selbstlernenden Gesamtkonzept. Nutzer*innendaten fließen in Prognosen und Zielsetzungen des Systems ein. Ziel ist der emissionsminimierte ökonomische Betrieb durch optimale Bewirtschaftung der Speicher und Einbindung volatiler Quellen. Externe Kommunikation sichert die intelligente Einbindung in übergeordnete urbane Versorgungssysteme.
In der Energiestrukturplanung werden detaillierte Modelle des Energiesystems der Gebäude und der übergeordneten Campus-Infrastruktur entwickelt, validiert und für die Bewertung struktureller Weiterentwicklungen eingesetzt. Es werden alle Stakeholder einbezogen und Key Performance Indicators (KPIs) definiert. Simulationen bewerten die KPIs unterschiedlicher Entwicklungsvarianten.
Dabei werden sowohl System-Transformationen, wie die Einbeziehung von Energiespeichern oder der Austausch von Energietechnologien, als auch der Ausbau der Photovoltaik am Campus bewertet. Ebenso wird das abzusehende Wachstum auf 185.000 m² Bruttogeschoßfläche analysiert.
Die Entwicklungen werden als Musterlösungen formuliert, die als Basis für die Entwicklung von Geschäftsmodellen herangezogen werden. Der INNOVATION DISTRICT INFFELD versteht sich als ein Vorreiter des Einsatzes neuer DIGITALER ENERGY SERVICES, um die Nutzungszufriedenheit eines Stadtquartiers zu steigern, den Betrieb des energietechnischen Systems optimal zu regeln und den Ausbau konsequent in Richtung eines Nullemissions-Quartiers voranzutreiben.
Projektlaufzeit
2021-03-01 - 2024-02-29
Finanzierung
FFG - 3rd Call – Energy Model Region (Vorzeigeregion Energie) 
Dieses Projekt wird aus Mitteln des Klima- und Energiefonds gefördert und im Rahmen der Vorzeigeregion Energie durchgeführt.
Projektpartner
Institut für Wärmetechnik, TU Graz (Konsortialführung)
Institut für Softwaretechnologie, TU Graz
Gebäude und Technik, TU Graz
Institut für Regelungs- und Automatisierungstechnik, TU Graz
Institut für Bauphysik, Gebäudetechnik und Hochbau, TU Graz
Institute of Interactive Systems and Data Science, TU Graz
BEST - Bioenergy and Sustainable Technologies GmbH
Bundesimmobiliengesellschaft m.b.H.
EAM Systems GmbH
Energie Steiermark AG
EQUA Solutions AG
Fronius International GmbH
Ansprechperson
Valentin KAISERMAYER
valentin.kaisermayer@best-research.eu
Area Management
Markus GÖLLES
markus.goelles@best-research.eu