A one-dimensional model for the thermal conversion of thermally thick biomass particles is developed for the simulation of the fuel bed of biomass grate furnaces. The model can be applied for cylindrical and spherical particles. The particle is divided into four layers corresponding to the main stages of biomass thermal conversion. The energy and mass conservation equations are solved for each layer. The reactions are assigned to the boundaries. The model can predict the intra-particle temperature gradient, the particle mass loss rate as well as the time-dependent variations of particle size and density, as the most essential features of particle thermal conversion. When simulating the fuel bed of a biomass grate furnace, the particle model has to be numerically efficient. By reducing the number of variables and considering the lowest possible number of grid points inside the particle, a reasonable calculation time of less than 1 min for each particle is achieved. Comparisons between the results predicted by the model and by the measurements have been performed for different particle sizes, shapes and moisture contents during the pyrolysis and combustion in a single-particle reactor. The results of the model are in good agreement with experimental data which implies that the simplifications do not impair the model accuracy.

To optimize the combustion of biomass grate furnaces a sensitivity analysis is carried out by means of CFD simulation. The methodical procedure consists of a 3D packed bed biomass combustion model, which describes the most essential characteristics of the thermal conversion of biomass particles, such as the detailed consideration of drying, pyrolysis and char oxidation in parallel processes. Within the sensitivity analysis the following parameters have been investigated: distribution of false air, residence time of fuel on the grate and distribution of recirculated flue gas and primary air below the grate. To evaluate the influence of the varied parameters on the combustion process the focus lied on the position of the thermal conversion of the biomass and the CO at the outlet of the simulation domain. The results of the sensitivity analysis show a shift of the thermal conversion towards the grate end for increased false air as well as for reduced momentum of primary air/recirculated flue gas mixture. An increase of the fuel residence time leads to a shift of the thermal conversion towards the fuel inlet. Consequently a large region of the primary combustion zone is not used due to earlier release of CO inside the fuel bed.

Biomassefeuerungen spielen eine Schlüsselrolle in der Energiewende hin zu einem vollständig erneuerbaren Energiesystem. Allerdings müssen sie sich zukünftigen Herausforderungen stellen, um weiterhin relevant zu bleiben. Einerseits müssen Biomassefeuerungen mit dem höchstmöglichen Wirkungsgrad arbeiten, um wirtschaftlich rentabel zu bleiben während sie gleichzeitig eine hohe Lastmodulationsfähigkeit aufweisen müssen, um für eine breitere Palette von Anwendungen eingesetzt werden zu können. Andererseits müssen Biomassefeuerungen immer strengere Grenzwerte für Schadstoffemissionen einhalten und gleichzeitig in der Lage sein, neue und alternative Biomassebrennstoffe mit geringerer Qualität zu verbrennen.

In dieser Arbeit wird eine modellbasierte Regelungsstrategie entwickelt, die es Biomassefeuerungen ermöglicht, all diese Herausforderungen zu meistern. Diese Regelungsstrategie besteht aus drei Teilen, einer Verbrennungsregelung, einem Zustands- und Parameterschätzer und einer Methode zur CO-lambda-Optimierung. Alle drei Teile werden in dieser Arbeit hergeleitet und im Detail diskutiert, insbesondere im Hinblick auf ihre Implementierung an realen Biomassefeuerungen. Darüber hinaus werden alle drei Teile der modellbasierten Regelungsstrategie durch Simulationsstudien sowie durch eine Implementierung in realen Biomassefeuerungen verifiziert.

Als Grundlage für die modellbasierte Regelungsstrategie wird ein mathematisches Modell abgeleitet, welches das dynamische Verhalten der Prozesse in der Biomassefeuerungen einschließlich des Einflusses der Brennstoffeigenschaften beschreibt. Die berücksichtigten Brennstoffeigenschaften sind die Schüttdichte und die chemische Zusammensetzung einschließlich des Wasser- und Aschegehalts sowie der untere Heizwert.

Die Verbrennungsregelung nutz die Stellglieder der Biomassefeuerung um dessen stabilen Betrieb zu gewährleisten und schnelle Laständerungen zu ermöglichen. Diese modellbasierte Regelstrategie berücksichtigt durch ihre Formulierung, die auf dem oben genannten mathematischen Modell basiert, explizit alle relevanten Brennstoffeigenschaften. Dadurch reagiert sie gezielt auf Änderungen dieser Brennstoffeigenschaften und kompensiert direkt deren Einfluss auf den Betrieb der Biomassefeuerung. Gleichzeitig weist sie eine einfache Struktur auf und ist daher leicht zu implementieren und zu warten. Diese modellbasierte Verbrennungsregelung wird sowohl in Simulationsstudien als auch durch Experimente nach einer Implementierung an einer realen Biomassefeuerung verifiziert.

Es wird ein kombinierter Zustands- und Parameterschätzer entwickelt, der gleichzeitig die Brennstoffeigenschaften, die anschließend von der Verbrennungsregelung verwendet werden, und die Zustandsgrößen der Biomassefeuerungen in Echtzeit schätzt. Er basiert auf einem erweiterten Kalman-Filter, der das in dieser Arbeit vorgestellte mathematische Modell verwendet. Diese Methode wird für verschiedene Brennstoffeigenschaften sowohl in Simulationsstudien als auch durch Messdaten aus realen Biomassefeuerungen verifiziert. Die Ergebnisse dieser Verifikation zeigen, dass diese Methode in der Lage ist, die Brennstoffeigenschaften und Zustandsgrößen auch bei Last- oder Brennstoffwechseln genau zu bestimmen.

Um einen Betrieb der Biomassefeuerung mit möglichst hohem Wirkungsgrad und möglichst geringen Schadstoffemissionen zu gewährleisten, wird eine Methode zur CO-lambda-Optimierung entwickelt. Diese Methode verwendet einen erweiterten Kalman-Filter in Kombination mit Messdaten des Sauerstoffgehalts und des CO-Gehalts des Rauchgases zur Bestimmung eines optimalen Luftüberschussverhältnisses für den aktuellen Zustand der Biomassefeuerung. Diese Methode wird an einer realen Biomassefeuerung in einer Langzeitvalidierung über mehrere Monate verifiziert und validiert. Während dieser Langzeitvalidierung führte die Anwendung dieser Methode zur CO-lambda-Optimierung zu einer Wirkungsgradsteigerung von 3,8 %, einer Reduktion der CO-Emissionen um durchschnittlich 200 mg/m³ sowie einer Verringerung der Gesamtstaubemissionen um durchschnittlich 19 %.

Zusammenfassend ermöglicht die in dieser Arbeit vorgestellte modellbasierte Regelungsstrategie es, Biomassefeuerungen mit den geringstmöglichen Schadstoffemissionen und dem höchstmöglichen Wirkungsgrad zu betreiben und dabei ein hohes Maß an Brennstoffflexibilität und Lastmodulationsfähigkeit zu erreichen. Darüber hinaus weist die Regelungsstrategie eine geringe Komplexität auf und ist leicht in realen Biomassefeuerungen zu implementieren und zu warten. Dies ermöglicht den breiten Einsatz dieser Regelungsstrategie an bestehenden und zukünftigen Biomassefeuerungen. Dies unterstützt die weitere Verbreitung von Biomassefeuerungen im Energiesystem, was zur Reduzierung der CO2e-Emissionen beiträgt und auch die verstärkte Nutzung anderer, volatiler erneuerbarer Technologien, wie z.B. solarthermischer Anlagen, ermöglicht.

This paper proposes a novel design algorithm for nonlinear state observers for linear time-invariant systems. The approach is based on a well-known family of homogeneous differentiators and can be regarded as a generalization of Ackermann's formula. The method includes the classical Luenberger observer as well as continuous or discontinuous nonlinear observers, which enable finite time convergence. For strongly observable systems with bounded unknown perturbation at the input the approach also involves the design of a robust higher order sliding mode observer. An inequality condition for robustness in terms of the observer gains is presented. The properties of the proposed observer are also utilized in the reconstruction of the unknown perturbation and robust state-feedback control

Detailed reaction schemes and experimental data for the online release of pyrolysis volatiles are required to gain a more fundamental understanding of biomass pyrolysis, which would in turn allow the process to be controlled in a more precise way and the development of more targeted applications. A detailed online characterisation of pyrolysis products has been conducted in single particle experiments with spruce pellets at different temperatures, obtaining a good closure of the elemental mass balances. The yields and online release of CO, CO₂, H₂O, CH₄, other light hydrocarbons and total organic condensable species, as well as char yield and composition, can be predicted with a reasonable accuracy with the application of a single particle model, coupled with a detailed pyrolysis scheme, and a simple one-step scheme for tar cracking. In order to achieve it, improvements have been conducted in the pyrolysis scheme, mainly concerning the release of light hydrocarbons and char yield and composition. Deviations are still present in the different groups in which organic condensable species can be classified.

Energy management systems aiming for an efficient operation of hybrid energy systems with a high share of different renewable energy sources strongly benefit from short-term forecasts for the heat-load. The forecasting methods available in literature are typically tailor-made, complex and non-adaptive. This work condenses these methods to a generally applicable, simple and adaptive forecasting method for the short-term heat load. From a comprehensive literature review as well as the analysis of measurement data from seven different consumers, varying in size and type, the ambient temperature, the time of the day and the day of the week are deduced to be the most dominating factors influencing the heat load. According to these findings, the forecasting method bases on a linear regression model correlating the heat load with the ambient temperature for each hour of the day, additionally differentiating between working days and weekend days. These models are used to predict the future heat load by using forecasts for the ambient temperature from weather service providers. The model parameters are continuously updated by using historical data for the ambient temperature and the heat load, i.e. the forecasting method is adaptive. Additionally, the current prediction error is used to correct the prediction for the near future. Due to their simplicity, all necessary steps of the forecasting method, the update of the model parameters, the prediction based on linear regression models and the correction, can be implemented and computed with little effort. The final evaluation with measurement data from all seven consumers investigated leads to a Mean Absolute Range Normalized Error (MARNE) of 2.9% on average, and proves the general applicability of the forecasting method. In summary, the forecasting method developed is generally applicable, simple and adaptive, making it suitable for the use in energy management systems aiming for an efficient operation of hybrid energy systems.

The continuous increase of (volatile) renewable energy production and the coupling of different energy sectors such as heating, cooling and electricity have significantly increased the complexity of urban energy systems. Such multi-energy systems (MES) can be operated more efficiently with the aid of optimization-based energy management systems (EMS). However, most existing EMS are tailor-made for one specific system or class of systems, i.e. are not generally applicable. Furthermore, only limited information on the actual savings potential of the usage of an EMS under realistic conditions is available. Therefore, this paper presents a novel modular modeling approach for an EMS for urban MES, which also enables the modeling of complex system configurations. To assess the actual savings potential of the proposed EMS, a comprehensive case study was carried out. In the course of this the influence of different user behavior, changing climatic conditions and forecast errors on the savings potential was analyzed by comparing it with a conventional control strategy. The results showed that using the proposed EMS in conjunction with supplementary system components (thermal energy storage and battery) an annual cost savings potential of between 3 and 6% could be achieved.

Both the planning and operation of complex, multi-energy systems increasingly rely on optimization. This optimization requires the use of mathematical models of the system components. The model most often used to describe thermal storage, and especially in the common mixed-integer linear program (MILP) formulation, is a simple integrator model with a linear loss term. This simple model has multiple inherent drawbacks since it cannot be applied to represent the temperature distribution inside of the storage unit. In this article, we present a novel approach based on multiple layers of variable size but fixed temperature. The model is still linear, but can be used to describe the most relevant physical phenomena: heat losses, axial heat transport, and, at least qualitatively, axial heat conduction. As an additional benefit, this model makes it possible to clearly distinguish between heat available at different temperatures and thus suitable for different applications, e.g., space heating or domestic hot water. This comes at the cost of additional binary decision variables used to model the resulting hybrid linear dynamics, requiring the use of state-of-the-art MILP solvers to solve the resulting optimization problems. The advantages of the more detailed model are demonstrated by validating it against a standard model based on partial differential equations and by showing more realistic results for a simple energy optimization problem.

In this work a novel reaction mechanism for gas phase reactions has been developed to predict the formation of aromatic compounds from the pyrolysis products of woody biomass particles. The aromatic compounds are important for being main soot precursors as well as their toxic properties. The developed gas phase mechanism is validated with experimental data from literature as well as experimental data performed with a single particle reactor for three different pyrolysis temperatures, namely 550, 800 and 1000°C. A good agreement is achieved between model results and experimental data for the total yield of each main family of aromatic hydrocarbons, i.e. phenolics, BTXs and PAHs.

In modern waste management, the energetic utilization of waste is an important key technology. On the one hand, it allows the waste to be disposed of in an environmentally friendly manner and, on the other hand, makes it possible to reduce the use of other controversial energy sources, such as nuclear fission or fossil fuels. However, the efficient and clean incineration of waste is a challenging task due to the strong inhomogeneity of the waste.

In order to limit global warming to 1.5 °C compared to the pre-industrial temperature level, zero net CO2 emissions are needed on a global scale until 2050. A Chemical Looping (CL) process represents a technological system which is CO2-negative when using biomass as fuel and thus can substantially contribute to this target. In principle, the process uses a metal oxide as oxygen carrier material (OC) which is cyclically oxidized by air or steam and reduced by the fuel. Without air as the direct oxygen source for fuel conversion, high calorific product gases or pure carbon dioxide in case of combustion are obtained after the condensation of water vapor, which can then be stored or further utilized.
Within the funded project ”BIO-LOOP”, different Chemical Looping processes (for example combustion, gasification, hydrogen production) and reactors (fixed bed, fluidized bed) are investigated numerically and experimentally. An advanced process analysis tool based on mass and energy balances of the system considered will be presented. It provides data about the specific internal and external streams, process conditions and efficiencies. Within the analysis tool, various independent modular units describe individual process steps, e.g. mixing, chemical reaction or splitting. These components can be adjusted, combined and interconnected according to the flow chart of the system. The process model represents the first step towards a flexible Chemical Looping reactor simulation toolbox to analyze various process scenarios. Emphasis is put on the flexibility regarding the fuels and oxygen carriers, their conversion and possible process variations. The tool developed will support upcoming CFD modeling and further economic considerations.

This paper investigates the optimal planning of microgrids including the hydrogen energy system through mixed-integer linear programming model. A real case study is analyzed by extending the only microgrid lab facility in Austria. The case study considers the hydrogen production via electrolysis, seasonal storage and fueling station for meeting the hydrogen fuel demand of fuel cell vehicles, busses and trucks. The optimization is performed relative to two different reference cases which satisfy the mobility demand by diesel fuel and utility electricity based hydrogen fuel production respectively. The key results indicate that the low emission hydrogen mobility framework is achieved by high share of renewable energy sources and seasonal hydrogen storage in the microgrid. The investment optimization scenarios provide at least 66% and at most 99% carbon emission savings at increased costs of 30% and 100% respectively relative to the costs of the diesel reference case (current situation).

Current barriers to increase the use of bioenergy for different applications are first discussed. Then, recent advances are presented on gasification-based technologies to overcome these barriers that have been reached at TU Graz together with several partners. Gasification-based fuel bed concepts integrated in biomass combustion can significantly reduce emissions for bioheat production. Advances are presented for modern biomass boilers, significantly reducing nitrogen oxides and particle matter emissions as well as increasing the feedstock flexibility; and micro-gasifiers for traditional biomass utilization, significantly reducing the emissions of unburnt products. Gasification-based processes have as well the possibility to score high electrical efficiencies and to synthetize several products as second-generation biofuels. Advances are presented on measures for reducing the presence of contaminants as tars, including the catalytic use of char for tar cracking; and in applications of the producer gas, including gas cleaning and direct coupling with a solid oxide fuel cell to maximize electricity production. © 2021, ETA-Florence Renewable Energies.

The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.

Using solid oxide fuel cells in biomass gasification based combined heat and power production is a promising option to increase electrical efficiency of the system. For an economically viable design of gas cleaning units, fuel cell modules and further development of suitable degradation detection methods, information about the behavior of commercially available cell designs during short-term poisoning with H2S can be crucial. This work presents short-term degradation and regeneration analyses of industrial-relevant cell designs with different anode structure and sulfur tolerance fueled with synthetic product gas from wood steam gasification containing 1 to 10 ppmv of H2S at 750°C and 800°C. Full performance regeneration of both cell types was achieved in all operating points. The high H2O content and avoided fuel depletion may have contributed to a lower performance degradation and better regeneration of the cells. A strong influence of the catalytically active anode volume on poisoning and regeneration behavior was quantified, thereby outlining the importance of considering the anode structure besides the sulfur tolerance of the anode material. Hence, cells with less sulfur tolerant anode material but larger anode volume might outperform cells less sensitive to sulfur in the case of an early detection of a gas cleaning malfunction.

Coupling biomass gasification with high temperature Solid Oxide Fuel Cells (SOFCs) is a promising solution to increase the share of renewables and reduce emissions. The quality of the producer gas used can, however, significantly impact the SOFC durability and reliability. The great challenge is to ensure undisturbed operation of such system and to find a trade-off between optimal SOFC operating temperature and system thermal integration, which may limit the overall efficiency. Thus, this study focuses on experimental investigation of commercial SOFC single cells of industrial size fueled with different representative producer gas compositions of industrial relevance at two relevant operating temperatures. The extensive experimental and numerical analyses performed showed that feeding SOFC with a producer gas from a downdraft gasifier, with hot gas cleaning, at an operating temperature of 750 °C represents the most favorable setting, considering system integration and the highest fuel utilization. Additionally, a 120 h long-term test was carried out, showing that a long-term operation is possible under stated operating conditions. Local degradation took place, which can be detected at an early stage using appropriate online-monitoring tools.

To gain reliable data for the development of an empirical model for the prediction of the local high temperature corrosion potential in biomass fired boilers, online corrosion probe measurements have been carried out. The measurements have been performed in a specially designed fixed bed/drop tube reactor in order to simulate a superheater boiler tube under well-controlled conditions. The investigated boiler steel 13CrMo4-5 is commonly used as steel for superheater tube bundles in biomass fired boilers. Within the test runs the flue gas temperature at the corrosion probe has been varied between 625 °C and 880 °C, while the steel temperature has been varied between 450 °C and 550 °C to simulate typical current and future live steam temperatures of biomass fired steam boilers. To investigate the dependence on the flue gas velocity, variations from 2 m·s⁻¹ to 8 m·s⁻¹ have been considered. The empirical model developed fits the measured data sufficiently well. Therefore, the model has been applied within a Computational Fluid Dynamics (CFD) simulation of flue gas flow and heat transfer to estimate the local corrosion potential of a wood chips fired 38 MW steam boiler. Additionally to the actual state analysis two further simulations have been carried out to investigate the influence of enhanced steam temperatures and a change of the flow direction of the final superheater tube bundle from parallel to counter-flow on the local corrosion potential.

The direct combustion of the biomass is the most advanced and mature technology in the field of energetic biomass utilisation. The legislations on the amount of emitted pollutants and the plant efficiency of biomass combustion systems are continually being restricted. Therefore constant improvement of the plant efficiency and emission reduction is required Numerical modelling is gaining increasing importance for the development of biomass combustion technologies. In this paper an overview about the numerical modelling efforts deal with the most relevant phenomena in biomass grate firing systems is given. The numerical modelling results in a deeper understanding of the underlying processes in biomass combustion plants. Therefore, it leads to a faster and safer procedure of development of a new technology.

Measuring the producer gas from biomass gasification is very challenging and the use of several methods is required to achieve a complete characterization. Various techniques are available for these measurements, offering very different affordability or time demand requirements and the reliability of these techniques is often unknown. In this work an assessment of commonly employed measuring methods is conducted with a round robin. The main permanent gases, light hydrocarbons, tars, sulfur and nitrogen compounds were measured by several partners employing a producer gas obtained from fluidized bed gasification of wood and miscanthus with steam. Online and offline methods were used for this purpose and their accuracy, repeatability and reproducibility are here discussed. The results demonstrate the reliability of gas chromatography for measuring the main permanent gases, light hydrocarbons, benzene and H2S, validating the obtained results with other methods. An online method could also measure NH3 with a reasonable accuracy, but deviations were present for compounds at even lower concentrations. Regarding tar sampling and analysis, the main source of variability in the results was the analysis of the liquid samples, especially for heavier compounds. The presented work pointed out the need for a complementary use of several techniques to achieve a complete characterization of the producer gas from biomass gasification, and the suitability of certain online techniques as well as their limitations.

Fixed-bed biomass gasification coupled with internal combustion engines allows an efficient exploitation of biomass for the combined production of heat and power (CHP) at small scale with increased economic viability with respect to combustion-based CHP systems. The main barrier on the way towards a wider market distribution is represented by the fact that a robust practical operation of state-of-the-art fixed-bed biomass gasification systems is limited to very specific fuel properties and steady-state operation. The aim of this work is twofold. On the one hand, it presents the results of a series of test runs performed in a monitored commercial plant under different process conditions, in order to assess its behaviour during load modulation and fuel property variations. On the other hand, an in-house developed thermodynamic equilibrium model was applied to predict the behaviour of the gasification reactor. This gasification model could be used for the development of a model-based control strategy in order to increase the performance of the small-scale gasification system. To assess the general operational behaviour of the whole gasification system an experimental one-week-long test run has been performed by BIOENERGY 2020+ and the Free University of Bozen-Bolzano as round robin test. The plant has been tested under different operating conditions, in particular, varying the load of the engine and the moisture content of the feedstock. The outcomes shown in the present work provide a unique indication about the behaviour of a small-scale fix-bed gasifier working in conditions different from the nominal ones.

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.

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.

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.

The Area Automation and Control at BEST - Bioenergy and Sustainable Technologies GmbH focuses on the optimal operation of sustainable biorefinery and renewable energy systems, the optimal interaction of different technologies and systems and the highly automated operation management
by new digital services.

Workshop of the research project ÖKO-OPT-QUART (Ökonomisch optimiertes Regelungs- und Betriebsverhalten komplexer Energieverbünde zukünftiger Stadtquartiere)

List of presentations:

Biorefineries

• Learnings from biomass combustion towards future bioenergy applications (M. Schwabl)
• Green Carbon perspectives for regional sourcing and decarbonization (E. Wopienka)
• Bioconversion processes for renewable energy and/or biological carbon capture and utilisation (B. Drosg)
• Second generation biomass gasification: The Syngas Platform Vienna – current status and outlook (M. Kuba)
• Utilization of syngas for the production of fuel and chemicals – recent developments and outlook (G. Weber)

Digital methods, tools and sustainability

• Evaluation of different numerical models for the prediction of NOx emissions of small-scale biomass boilers (M. Eßl)
• Digitalization as the basis for the efficient and flexible operation of renewable energy technologies (M. Gölles)
• Smart Control for Coupled District Heating Networks (V. Kaisermayer)
• Integrated energy solutions for a decentral energy future - challenges and solutions (P. Liedtke)
• Wood-Value-Tool: Techno-economic assessment of the forest-based sector in Austria (M. Fuhrmann)

Warum ist es sinnvoll, Wärmenetze zu verbinden?

• Erläuterung am Beispiel des Projekts Thermaflex
• Drei Wärmenetze bei Leibnitz in der Steiermark.
• Sind gewachsen und haben die Grenzen ihrer Nachbar-Wärmenetze erreicht.
• Die Wärmenetze werden durch zwei getrennte Eigentümer betrieben.

The current bioenergy uses and conversion technologies as well as future trends for the production of heat, power, fuels and chemicals from biomass are reviewed. The focus is placed in Austria, which is selected due to its high bioenergy utilization, providing 18.4% of the gross energy final consumption in 2017, and its strong industrial and scientific position in the field. The most common bioenergy application in Austria is bioheat with 170 PJ in 2017 mainly obtained from woody biomass combustion, followed by biofuels with 21 PJ and bioelectricity with 17 PJ. Bioheat has a stable market, where Austrian manufacturers of boilers and stoves have a strong position exporting most of their production. Future developments in bioheat production should go in the line of further reducing emissions, increasing feedstock flexibility and coupling with other renewables. For bioelectricity and biofuels, the current framework does not promote the growth of the current main technologies, i.e. combined heat and power (CHP) based on biomass combustion or biogas and first generation biofuels. However, an increase in all bioenergy uses is required to achieve the Austrian plan to be climate neutral in 2040. The current initiatives and future possibilities to achieve this increase are presented and discussed, e.g. mandatory substitution of old oil boilers, production of biomethane and early commercialization of CHP with a high efficiency or demonstration of advanced biofuels production based on gasification.

The large variations found in literature for the activation energy values of main biomass compounds (cellulose, hemicellulose and lignin) in pyrolysis TGA raise concerns regarding the reliability of both the experimental and the modelling side of the performed works. In this work, an international round robin has been conducted by 7 partners who performed TGA pyrolysis experiments of pure cellulose and beech wood at several heating rates. Deviations of around 20 – 30 kJ/mol were obtained in the activation energies of cellulose, hemicellulose and conversions up to 0.9 with beech wood when considering all experiments. The following method was employed to derive reliable kinetics: to first ensure that pure cellulose pyrolysis experiments from literature can be accurately reproduced, and then to conduct experiments at different heating rates and evaluate them with isoconversional methods to detect experiments that are outliers and to validate the reliability of the derived kinetics and employed reaction models with a fitting routine. The deviations in the activation energy values for the cases that followed this method, after disregarding other cases, were of 10 kJ/mol or lower, except for lignin and very high conversions. This method is therefore proposed in order to improve the consistency of data acquisition and kinetic analysis of TGA for biomass pyrolysis in literature, reducing the reported variability.

Char originating from biomass can be used as a sustainable carbon additive in the production of polymer compounds with enhanced characteristics.

With respect to the climate objectives Chemical Looping (CL) processes constitute a promising alternative to traditional thermochemical conversion routes. Through the application of solid materials, so-called oxygen carriers (OC), instead of air as oxygen supply, CO2 can be easily separated from the flue gas. By this, biomass can be used for hydrogen production (Chemical Looping Hydrogen, CLH) or it can be burnt without CO2 emissions (Chemical Looping Combustion, CLC).

The continuous increase of (volatile) renewable energy production and the development of energy-efficient buildings have led to a transformation of city districts’ energy systems. Their complexity has increased significantly due to the coupling of the different energy sectors like heating, cooling and electricity. Such complex multi-energy systems can be operated more efficiently and reliably if knowledge of their specific components (in terms of mathematical models) as well as knowledge of weather forecasts is incorporated in a high-level controller, which is typically referred to as an Energy Management System (EMS). However, still little comprehensive information on the costs and the practical advantages of such systems is available. For this reason, a simulation environment to estimate the real costs and advantages of the use of such an EMS is required. Consequently, this work focuses on the development of an EMS for future city districts’ energy systems and the development of a co-simulation environment in order to demonstrate the benefits of the use of the developed EMS in comparison to a conventional control strategy. The co-simulation is implemented with the aid of the co-simulation platform Building Controls Virtual Test Bed (BCVTB) and consists of the following parts: a non-linear, thermoelectric model and a control block containing either the conventional control strategy or the EMS. The thermoelectric model is built up using the well-established simulation tools TRNSYS and IDA-ICE, simulating the energy hub of the city district and the districts’ buildings, respectively. The control block is simulated using MATLAB, where IBM ILOG CPLEX is used for solving the resulting mixed-integer linear program (MILP) of the EMS. Finally, an economic model for financial (and ecological) assessment of the operation is simulated with the aid of the software package Dymola. To put the developed EMS and the co-simulation into practise a case study based on a new city district in Graz, Austria, which is currently in the planning stage, is carried out. The integration of the responsible planners and investors in the modelling process guarantees the models’ practical applicability. In the case study the performance of the originally planned conventional control strategy is compared with the performance of the developed EMS using annual simulations with a simulation time step of 1 minute, and a 24 hour prediction horizon and a 15 minute time step for the EMS. For a more robust and realistic comparison both control strategies are simulated for different scenarios considering current and future (2060) climate conditions, medium and high energy demands (load), ideal and real load prediction methods and varying import prices for electricity from the electricity grid. The results show that the use of the developed EMS strategy results in reduced annual total costs (considering operational and investment costs of additionally suggested distributed energy resources) in comparison to the conventional control strategy. Furthermore, the annual CO2-emissions could be reduced by increasing the self-consumption of the installed (renewable) energy resources and thus decreasing the necessary energy imports from the electricity and the heating grid.

Slides of the talk "Co-Simulation of an Energy Management System for Future City District Energy Systems"

The CO/CO₂ release ratio obtained during char combustion of single biomass particles has been analysed in this work experimentally and by modelling. Experiments have been conducted with spruce, straw and Miscanthus pellets at different temperatures. Furthermore, these experiments have been modelled with a single particle model coupled with a CFD model of the single particle reactor. The results show that the CO/CO₂ ratio strongly depends on the feedstock, being lower for spruce than for straw or Miscanthus. Furthermore, the most commonly employed correlations for this ratio in literature are not adequate, as they either under- or over-predict it.

A new model-based control strategy for biomass grate boilers is presented in this paper. Internal model control is used to control four outputs of the plant and to achieve a control structure with fewer control parameters needing to be experimentally tuned. A nonlinear state–space model describing the essential behaviour of the biomass grate boiler is used for controller design. The inverse system dynamics representing the main part of internal model control are designed with the help of this model. In doing so the properties of differentially flat systems are used. Due to a time delayed input, the inverse system is determined only for three input output channels. The stabilization of the inverse system dynamics, however, is a challenging task. A stabilization method with the help of the time delayed input is suggested and a stability analysis is given. The new control strategy has only three parameters to be tuned, representing a major reduction of complexity in comparison to existing model-based approaches. Finally, experimental results of the implemented control strategy on representative biomass grate boiler with a nominal capacity of 180 kW are presented and compared to an existing model-based control strategy based on input output linearization. The experimental evaluation proves that it is possible to operate the biomass boiler in all load ranges with high efficiency and low pollutant emissions.

Overview on different approaches for the control of the heat distribution networks in case of the integration of large-scale solar thermal systems, and different possibilities for the reduction of the operating temperatures in DH systems.

Overview on the control of large-scale thermal plants, limited to plants feeding into DH networks as well as theirkey components, i.e. the actual collector circuit and the heat exchanger between primary and secondary circuit.

Absorption heat pumping devices (AHPDs, comprising absorption heat pumps and chillers) are devices that use thermal energy instead of electricity to generate heating and cooling, thereby facilitating the use of waste heat and renewable energy sources such as solar or geothermal energy. Despite this benefit, widespread use of AHPDs is still limited. One reason for this is partly unsatisfactory control performance under varying operating conditions, which can result in poor modulation and part load capability. A promising approach to tackle this issue is using dynamic, model-based control strategies, whose effectiveness, however, strongly depend on the model being used. This paper therefore focuses on the derivation of a viable dynamic model to be used for such model-based control strategies for AHPDs such as state feedback or model-predictive control. The derived model is experimentally validated, showing good modeling accuracy. Its modeling accuracy is also compared to alternative model versions, that contain other heat transfer correlations, as a benchmark. Although the derived model is mathematically simple, it does have the structure of a nonlinear differential–algebraic system of equations. To obtain an even simpler model structure, linearization at an operating point is discussed to derive a model in linear state space representation. The experimental validation shows that the linear model does have slightly worse steady-state accuracy, but that the dynamic accuracy seems to be almost unaffected by the linearization. The presented new modeling approach is considered suitable to be used as a basis for the design of advanced, model-based control strategies, ultimately aiming to improve the modulation and part load capability of AHPDs.

The authors want to acknowledge, that during the production of the final version of the publication the image for Figure 9 has been replaced with the image for Figure 12, however without changing the content of the paper. This issue is resolved in the current version of the publication.

The major problem of fluidized bed biomass gasification is the high tar contamination of the producer gas which is associated with the complex and time-consuming sampling and analysis of these tars. Therefore, correlations to predict the tar content are a helpful tool for the development and operation of biomass gasifiers. Correlations between tars and gas composition as well as reactor temperature derived for a steam-blown lab-scale bubbling fluidized bed gasifier are investigated in this study to assess their applicability. A comprehensive data set containing over 80 experimental points was obtained for various operation conditions, including variations in temperature from 700 to 800 °C, feedstock, amount of steam for fluidization, as well as the addition of oxygen. Linear correlations between tar and permanent gases show good accuracy for H2 and CH4 when using pure steam. However, experiments conducted with steam-oxygen mixtures show high deviations for the CH4-based correlation and smaller but still significant deviations for the H2-based correlation. No relation between tar and CO or CO2 was found. The correlation between tar and temperature shows highest accuracy, including good agreement with the steam-oxygen experiments. All tar correlations showed useful results over a broad operating range. However, significant deviations can be obtained when considering just one gas compound. Therefore, a combination of different correlations considering gas components and temperature seems to be the best method of tar prediction. This leads to a powerful tool for fast online tar monitoring for a broad range of operating conditions, once a calibration measurement was conducted.

Winter tourism is an energy-intensive branch of industry. The aim of the FFG funding project Clean Energy for Tourism is to support Salzburg’s skiing areas on the way to decarbonization by developing technologies and business models. In this article, the developed ICT infrastructure, the optimization algorithms and the business models are presented.

This publication focuses on the experimental investigation of a novel small-scale fuel flexible biomass combustion technology with a fixed-bed employing a low oxygen concentration. It was obtained through a low primary air ratio and the additional supply of recirculated flue gas. The plant was operated with spruce wood chips, which contained three different mass fractions of water, and miscanthus pellets. All relevant components of the released gas above the fixed-bed were measured, as well as the 3D bed temperature distribution. The balances confirmed a high experimental data consistency. Therefore, it was possible to determine the location of the four different conversion zones inside the fixed-bed: drying, pyrolysis, char gasification and char oxidation. The reduction of CO2 to CO in the char reduction zone worked efficiently across the entire grate area. Furthermore, the results showed that the water mass fraction of the fuel did not influence the dry product gas composition, but significantly affected the location for the release of pyrolysis products such as tars. It was found that the low oxygen concentration in the fixed-bed combined with flue gas recirculation was an effective method to reduce bed temperatures and therefore its inorganic emissions while significantly increasing feedstock flexibility. The investigations provided fundamental findings on the conversion and release behavior of the new technology under real operating conditions and are very useful for further experimental work and CFD simulations targeting the reduction of PM and NOX emissions.

State-of-the-art packed bed models supply continuous concentration profiles as boundary conditions for subsequent CFD simulations of gas phase, leading to pre-mixed combustion conditions. However, in reality the “porous” nature of the packed bed leads to streak formation influencing gas mixing and combustion. Therefore, in the present work, in order to account for the influence of the streaks on gas phase combustion, a gas streak model based on a correlation between the local gas residence time and a mixing time has been developed based on numerical simulations. Finally, the streak model was linked with an in-housed developed hybrid gas phase combustion model suitable for laminar to highly turbulent flow conditions and applied for an under-feed pellet stoker furnace (20 kW_th) concerning the simulation of gas phase combustion and NO_x formation. The results in comparison with a simulation without the streak formation model show that the flue gas species prediction can be improved with the proposed streak formation model. Especially, in the region above the fuel bed (in the primary combustion chamber), this is of special importance for NO_x reduction by primary measures.

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.  

Annex TS3: Hybrid Energy Networks

The aim of the IEA DHC Annex TS3 „hybrid energy networks" is to promote opportunities and to overcome challenges for district heating and cooling (DHC) networks in an integrated energy system context, focusing on the coupling to the electricity and the gas grid.

Ash related problems such as slagging, fouling, and high temperature corrosion in biomass fired boilers are still insufficiently explored due to the complexity of the underlying processes. High temperature corrosion of low alloy steels like 13CrMo4-5 has already been investigated in plants firing chemically untreated wood chips. In this earlier work it has been suggested that the oxidation of the steel is the dominating mechanism in the material temperature range between 450 and 550 °C. Unfortunately the exponential dependence of the material degradation on the flue gas temperature also found within this work cannot be explained with the proposed corrosion mechanism. To determine the dominating corrosion mechanism, additionally test runs have been carried out in a specially designed drop tube reactor. To investigate the time-dependent corrosion behavior of 13CrMo4-5, a newly developed mass loss probe was applied under several constant parameter setups. In addition to these measurements, the time-dependent oxidation of 13CrMo4-5 under air was investigated in a muffle furnace. To gain relevant information regarding the corrosion mechanism prevailing, the deposits as well as the corrosion products have been examined subsequently to the test runs by means of scanning electron microscopy and energy dispersive X-ray analyses. With the experimental data gained it could be shown that the dominating corrosion mechanism strongly depends on the conditions prevailing (e.g., steel temperature, flue gas temperature, and velocity) and can either be the oxidation of the steel by gaseous O₂ and H₂O or a combination of oxidation and active Cl-induced oxidation.

Absorptionskälteanlagen können einen wesentlichen Beitrag zur Verringerung von CO2-Emissionen leisten, wenn Wärme aus regenerativen Energieträgern oder Abwärme aus industriellen Prozessen zum Antrieb verwendet wird. Absorptionskälteanlagen weisen bereits jetzt eine hohe Effizienz auf, bei veränderlichen Betriebsbedingungen kann diese je nach vorhandenen Stellgliedern weiter gesteigert werden. Dazu werden im Rahmen des Forschungsprojektes „Heat Pumping Systems Control (HPC)“ zwei Absorptionskälteanlagen – eine mit der Stoffpaarung Ammoniak/Wasser (NH3/H2O) und eine mit der Stoffpaarung Wasser/Lithiumbromid (H2O/LiBr) – untersucht, um für unterschiedliche Anwendungen optimale Betriebsstrategien zu entwickeln. Zur Berücksichtigung der Zustandsänderungen in der Absorptionskälteanlage, werden dynamische Simulationsmodelle in der Modellierungssprache Modelica entwickelt und mit Messdaten validiert.

Im Rahmen dieses Konferenzbeitrags werden Komponentenmodelle für die NH3/H2O-Absorptionskälteanlage und Simulationsrechnungen bei veränderlichen Randbedingungen präsentiert, sowie ein Vergleich mit Messdaten diskutiert.

A one-dimensional single particle model is utilised to investigate the effects of radiation temperature, moisture content, particle size and biomass physical properties on the heating rate in biomass particles during pyrolysis. The model divides the particle into four layers - drying, pyrolysis, char and ash layer - corresponding to the four main stages of biomass thermal conversion. The average of the time derivative of the pyrolysis layer centre temperature weighted by the pyrolysis rate is introduced as an appropriate indicator for the heating rate in the particle during pyrolysis. The influencing parameters on the heating rate are summarised in the Biot number and the thermal time constant, to make the investigation of their effects easier. The heating rate is inversely proportional to the thermal time constant. The effect of a variation of the Biot number on the heating rate is negligible in comparison to the thermal time constant. Therefore, the thermal time constant can be sufficiently used to specify the heating rate regimes during pyrolysis. It is found that for thermal time constants of more than 50 s, pyrolysis takes place in a low heating rate regime, i.e. less than 50 K/min. Additionally, the heating rate during pyrolysis of various biomass types under a wide range of thermal conversion conditions has been examined, in order to classify the heating rate regime of pyrolysis in state-of-the-are combustion/gasification plants. The pyrolysis of wood dust and wood pellets is found to happen always in high heating rate regimes. Therefore, the kinetic parameters obtained by conventional TGA systems (typically with heating rates lower than 50 K/min) are not applicable for them. On the contrary, the pyrolysis of wood logs always happens in low heating rate regimes, which indicates that kinetic parameters obtained by conventional TGA systems can be applied. However, pyrolysis of wood chips can undergo low or high heating rate regimes depending on their particle size. Concerning the moisture content, it can be stated that it does not strongly influence the heating rate regime of certain biomass particles. © 2011 Elsevier Ltd. All rights reserved.

Introduction and motivation
A key objective for the operation of biomass boilers is to achieve the highest possible efficiency while emitting the lowest possible pollutant emissions. In order to automate this task, CO-lambda optimization methods have been proposed in literature that ensure that the biomass boiler is operated at the lowest excess air ratio at which no relevant pollutant emissions occur, maximizing efficiency as a result. Since this optimal excess air ratio depends on various external factors, such as fuel properties, CO-lambda optimization methods continuously incorporate new measurements of the excess air ratio and the carbon monoxide content of the flue gas and estimate a new optimal excess air ratio during operation.
While achieving promising results in lab-scale tests, none of the CO-lambda optimization methods presented in literature has yet been able to gain practical acceptance. Either they are not robust enough and provide inaccurate estimates of the optimal excess air ratio or they are too slow and do not allow the optimal excess air ratio to be tracked sufficiently quickly. With the goal of providing a method that is fit for practical application, this publication presents a new modular approach for CO-lambda optimization that determines the optimal excess air ratio robustly and quickly, i.e. in real time.

Method
The new approach for CO-lambda optimization approximates the correlation between the excess air ratio and the carbon monoxide content of the flue gas, the CO-lambda characteristic, with a continuous, algebraic, non-linear model function. For this purpose, it uses a recursive-least-squares algorithm to continuously identify the model function’s parameters that lead to the optimal fit with the measured data, which are the excess air ratio and carbon monoxide content of the flue gas. From these model parameters, the optimal excess air ratio is calculated and defined as a desired value for the biomass boiler’s existing controller. This existing controller then ensures, that the biomass boiler is operated with this desired optimal excess air ratio, thus, maximizing efficiency and decreasing pollutant emissions. As a result, this new approach for CO-lambda optimization is entirely modular and can be applied to any biomass boiler with an existing control strategy capable of accurately adjusting the excess air ratio. For the measurement of the carbon monoxide content of the flue gas, a separate sensor has to be used. For this study the commercially available and proven in-situ exhaust gas sensor “KS1D” provided by the company LAMTEC has been used.

Long-term verification
The new approach for CO-lambda optimization was tested and validated at a biomass boiler with a nominal capacity of 2.5 MW that supplies a local heating network and combusts wood chips with a water content ranging from 30 w.t.% to 50 w.t.%. The long-term validation took place over an entire heating period, i.e. 5 months from November to March, during which the biomass boiler was operated alternately with the new approach for CO-lambda optimization and the standard control strategy, which means a constant desired residual oxygen content. In total the new approach for CO-lambda optimization was active for 1155 operating hours while the standard control strategy was active for 1310 operating hours. Compared to the standard control strategy, the new approach for CO-lambda optimization increased the biomass boiler’s efficiency by 3.8%, decreased total dust emissions by 19.5% and reduced carbon monoxide emissions on average (median) by 200 mg/m³. This demonstrates that the new approach for CO-lambda optimization is not only robust enough to run over a long period of time, it also leads to significant improvements in the biomass boiler’s operation. In addition, following these results, this new approach for CO-lambda optimization has also successfully been implemented and demonstrated at another biomass boiler with a nominal capacity of 1 MW where it has already been active for several months. This contribution presents the new approach to CO-lambda optimization in detail and discusses its technological and economic impact.

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice.

With energy systems, the problem of economic planning is decisive in the design of a low carbon and resilient future grid. Although several tools to solve the problem already exist in literature and industry, most tools only consider a single “typical year” while providing investment decisions that last around a quarter of a century. In this paper, we introduce why such an approach is limited and derive two approaches to correct this. The first approach, the Forward-Looking model, assumes perfect knowledge and makes investment decisions based on the full planning horizon. The second novel approach, the Adaptive method, solves the optimization problem in single year iterations, making incremental investment decisions that are dependant on previous years, with only knowledge of the current year. Comparing the two approaches on a realistic microgrid, we find little difference in investment decisions (maximum 21% difference in total cost over 20 years), but large differences in optimization time (up to 12000% time difference). We close the paper by discussing implications of forecasting errors on the microgrid planning process, concluding that the Adaptive approach is a suitable choice.

Around 2.7 billion people worldwide have no access to clean cooking equipment, which leads to major health problems due to high emissions of unburned products (VOC, CO and soot). A top-lit updraft gasifier cookstove with forced draft was identified as the technology with the highest potential for reducing harmful emissions from incomplete combustion in simple cookstoves. The basic variant of the stove was equipped with a fan for efficient mixing of product gas with air and fired with pellets to increase the energy density of low-grade residues. The development was conducted based on water boiling test experiments for wood and rice hull pellets and targeted CFD simulations of flow, heat transfer and gas phase combustion with a comprehensive description of the reaction kinetics, which were validated by the experiments. Emphasis was put on the reduction of CO emissions as an indicator for the burnout quality of the flue gas. The optimisation was carried out in several steps, the main improvements being the design of a sufficiently large post-combustion chamber and a supply of an appropriate amount of primary air for a more stable fuel gasification. The experiments showed CO emissions <0.2 g/MJdel for wood and rice hull pellets, which corresponds to a reduction by a factor of about 15 to 20 compared to the basic forced draft stove concept. Furthermore, these values are between 5 and 10 times lower than published water boiling test results of the best available cookstove technologies and are already close to the range of automatic pellet furnaces for domestic heating, which are considered to be the benchmark for the best possible reduction of CO emissions.

Der Leitfaden „Energiegemeinschaften im Tourismus“ zeigt, welche Möglichkeiten Energiegemeinschaften für Tourismusbetriebe, ihre Beschäftigten und Menschen, die in Tourismusregionen leben, bieten können und wie eine Energiegemeinschaft ins Leben gerufen werden
kann.

Four different models for heat transfer to the particles immersed in a fluidized bed were evaluated and implemented into an existing single particle model. Pyrolysis experiments have been conducted using a fluidized bed installed on a balance at different temperatures and fluidization velocities using softwood pellets. Using a heat transfer model applicable for fluidized beds, the single particle model was able to predict the experimental results of mass loss obtained in this study as well as experimental data from literature with a reasonable accuracy. A good agreement between experimental and modeling results was found for different reactor temperatures and configurations as well as different biomass types, particle sizes – in the typical range of pellets - and fluidization velocities when they were higher than . However, significant deviations were found for fluidization velocities close to minimum fluidization. Heat transfer models which consider the influence of fluidization velocity show a better agreement in this case although differences are still present.

Assessing the operational behaviour of biomass gasification systems is a crucial basis for further improvements in terms of operational behaviour and robustness in order to increase the technologies’ operational and economic viability. However, in most fixed-bed biomass gasification systems not all parameters required for the assessment can be measured directly. Typically, unknown parameters are determined by using as many balance equations as parameters have to be determined neglecting the additional information provided by other available but not chosen balance equations. Thus, these approaches do not incorporate all measurement data available resulting in a lack of reliability in their results. A detailed analysis of these approaches emphasises that even small deviations in the measurement data can lead to significant deviations in the calculated parameters, demonstrating that individual choices of equations can be highly sensitive regarding measurement uncertainties.

Therefore, an adjusted weighted least squares approach is developed utilizing an overdetermined system of equations incorporating all balance equations simultaneously. Thus, all measurement data available is taken into account, minimizing the influences of measurement uncertainties on the determined parameters. A comprehensive analysis shows that this approach is less sensitive to measurement uncertainties, allowing for a more reliable and accurate assessment of fixed-bed biomass gasifiers.

Keywords: fixed-bed, gasification, mass balance, performance assessment

The majority of renewable energy technologies are volatile in nature. External factors such as weather conditions lead to fluctuations in their produced electricity and heat. This results in a demand either not being covered or dissatisfied since too much electricity and heat is produced in the energy system. Although energy storages can counteract these fluctuations, renewable energy technologies that are capable of producing energy on demand are needed as well. As such, technologies based on the thermochemical conversion of biomass are especially relevant as they are considered to be CO2-neutral. Although most existing implementations are based on combustion of biomass, fixed-bed biomass gasification is of growing relevance due to higher overall efficiencies and low pollutant emissions. Currently, fixed-bed biomass gasifiers are usually operated at steady-state operation to produce the maximum amount of energy possible. This contribution investigates, whether they can be used as a technology for demand-oriented electricity and heat production

The operation characteristics of thermally driven absorption refrigeration systems (ARS) are strongly dependent on their hydraulic integration. Therefore, varying operating conditions of the hydraulic supply have a great influence on the behaviour of ARS and lead to dynamic operation, which can affect the efficiency and is largely unexplored so far. To enable a simple investigation of ARS integration considering their dynamic behaviour and to develop modern, efficiency-enhancing control strategies, dynamic simulation models of ARS are developed in Modelica Code.

In this paper, a dynamic simulation model of an ARS with the working pair ammonia/water (NH3/H2O) is presented. The parameterization and the physical correlations of selected components of the simulation model are described. Afterwards, the simulation model is verified by comparing simulation results with measurement data of the NH3/H2O-ARS. Finally, the capabilities of the simulation model are demonstrated by performing a simulation-based analysis of the temperature glide of the refrigerant in the evaporator.

There is a need to reduce NO_x emissions, which can only be achieved through a detailed understanding of the mechanisms for their formation and reduction. In this work the release of the NO_x precursors, NH₃ and HCN, for different fuels is experimentally analysed and modelled in typical fixed-bed combustion conditions. It is shown that NH₃ and HCN are released during the main devolatilization phase and the NH₃/HCN ratio increases for fuels with a higher nitrogen content. A simplified two-steps model for their release is presented. The model can predict with a reasonable accuracy the release for fuels with a low nitrogen content, however deviations are present for fuels with a high nitrogen content, which probably arise due to a reduction of NH₃ and HCN taking place already in the bed.

Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications.

The so-called “layer model” or “interface-based model” is a simplified single particle model, originally developed for shorter computation time during computational fluid dynamics (CFD) simulations. A reactive biomass particle is assumed to consist of successive layers, in which drying, pyrolysis and char conversion occur sequentially. The interfaces between these layers are the reaction fronts. The model has already been validated for drying, pyrolysis and char oxidation. Layer models in the literature have commonly employed surface reactions at the reaction front to describe char conversion. In this work, the suitability of this surface reaction concept is assessed when gasifying biochar. It is shown that a particular layer model, already available, which originally employed surface reactions, was unable to adequately describe the mass loss during gasification of a biochar. In order to overcome this incapability, the model was extended to consider volumetric reactions in the char layer. The influence of intraparticle diffusion was considered through an effectiveness factor. The model is easily adaptable for different gas-solid kinetic rate laws, while still allowing for comparably fast solutions of the model equations. The extended model was validated using theoretical calculations and experimental measurements from literature. It was demonstrated that intraparticle diffusion can significantly slow down the biochar gasification process. A general guideline for when to employ volumetric reactions, rather than surface reactions, and when to consider intraparticle diffusion is provided based on the Thiele modulus as the criterion.

Fault-Detection (FD) is essential to ensure the performance of solar thermal systems. However, manually analyzing the system can be time-consuming, error-prone, and requires extensive domain knowledge. On the other hand, existing FD algorithms are often too complicated to set up, limited to specific system layouts, or have only limited fault coverage. Hence, a new FD algorithm called Fault-Detective is presented in this paper, which is purely data-driven and can be applied to a wide range of system layouts with minimal configuration effort. It automatically identifies correlated sensors and models their behavior using Random-Forest-Regression. Faults are then detected by comparing predicted and measured values.

The algorithm is tested using data from three large-scale solar thermal systems to evaluate its applicability and performance. The results are compared to manual fault detection performed by a domain expert. The evaluation shows that Fault-Detective can successfully identify correlated sensors and model their behavior well, resulting in coefficient-of-determination scores between R²=0.91 and R²=1.00. In addition, all faults detected by the domain experts were correctly spotted by Fault-Detective. The algorithm even identified some faults that the experts missed. However, the use of Fault-Detective is limited by the low precision score of 30% when monitoring temperature sensors. The reason for this is a high number of false alarms raised due to anomalies (e.g., consecutive days with bad weather) instead of faults. Nevertheless, the algorithm shows promising results for monitoring the thermal power of the systems, with an average precision score of 91%.

Gas-fermentation is the conversion of gaseous feedstocks (e.g. CO2-rich off gases, CO, H2) into
valuable products such as organic acids and alcohols by microorganisms such as clostridia.
By supplying electrical energy (an alternative source of reducing/oxidizing energy), the fermentation
environment can be further optimized, resulting in products with higher purity, a broader product
spectrum and higher cell densities.

Kernthema dieses Beitrags ist die ganzheitliche Konzeption von Mikronetze, die sich auf die Reduzierung von Kosten und CO2-Emissionen konzentriert. Mikronetze, oder auch Microgrids, ermöglichen die koordinierte Energieerzeugung von dezentralen Energieressourcen, die Speicherungen der produzierten Energie und ein Lastmanagement zum Ausgleich von Wärme-, Kälte- und Elektrizitätsdienstleistungen. Mikronetze können vom breiteren Versorgungsnetz getrennt werden, können diverse Dienstleistungen erbringen und/oder selbst Energie erzeugen sowie in Überschusszeiten speichern und bei Bedarf wieder Kosten- oder Stabilitäts-orientiert freigeben.
Die mathematische Optimierung dient als unvoreingenommene Alternative für eine gesamtheitliche Planung von dezentralen Energietechnologien. Dieses Kriterium wird bei einer Kosten- oder CO2-Reduktion vor allem dann essentiell, wenn vielfältigen Kombinationen von Technologien und Kapazitäten möglich sind. Modernste Ansätze betrachten jedoch einen quasistatischen Aufbau unter Verwendung linearisierte Modelle und Mixed Integer Linear Optimization (MILP), wobei dynamische Effekte vernachlässigt werden. Unter Berücksichtigung von Lasten, geografischen, ökonomisch-ökologischen und tariflichen Daten sind mathematische Optimierungsalgorithmen in der Lage, verschiedene Anwendungsfälle zu beurteilen, wobei Effekte wie Vorwärmung, Sollwertänderungen oder kurzfristige Sonnenschwankungen unberücksichtigt bleiben. Dies bedeutet, dass die in quasistatischen Ansätzen verwendete Wärme- und Strombilanzen ungenau sein können (eventuell können physikalische Randbedingungen sogar verletzt werden, was zu suboptimalen Ergebnissen bei der Planung führen würde).
Die Notwendigkeit besteht quasistatische Optimierung mit einer weiteren Modellierungsart zu vergleichen und die Auswirkungen auf traditionelle quasistatische Ansätze, wie sie in DER-CAM oder ReOpt eingesetzt werden, aufzudecken. Um Abweichungen - bestehend aus dynamischen oder sogar Rebound Effekten - zu erkennen, werden mit TRNSYS Gebäude- und Anlagensimulationen für eine geplante Siedlungsanlage erstellt und ein Energiekonzept mit dem mathematischen Optimierungsprogramm OptEnGrid berechnet. Der Ansatz wird für vier Doppelhäuser und ein Mehrfamilienhaus getestet. Die Gebäude werden in TRNSYS simuliert und bieten thermische Lastdaten für den Referenzfall. Auch die Stromerzeugung mit PV-Modellen und der elektrische Verbrauch mit synthetischen Lastprofilen sind sowohl in der Optimierung als auch in der Simulation beteiligt. In der elektrischen Stromerzeugung zeigt die mathematische Optimierung bereits eine Abweichung von mehr als 5% auf Jahresbasis zur TRNSYS-Simulation. Ergebnisse im thermischen Energiebereich folgen nach weiterer Auswertung.

Iron production via blast furnace utilizes coal and coke to reduce iron oxides resulting in high greenhouse gas emissions. This important issue for the iron and steel industry may be mitigated by application of biomass-based reducing agents (bioreducer).

Mikro-Netze (Microgrids), ein Unterbereich der Intelligenten Strom/Energie-Netze (Smartgrids),
die sich durch eine enge räumliche Bindung von Energieerzeugungseinheiten und Verbraucher
auszeichnen wird international ein sehr starkes Wachstum zugeschrieben. Microgrids sind kleine,
lokale Energienetze für Strom, Wärme und Kälte, die Haushalte, Betriebe und Gemeinden mit
Energie versorgen. Diese lokalen und regionalen Konzepte der Energieversorgung können in
Zukunft einen wesentlichen Beitrag in Richtung Energieunabhängigkeit und effizientere
Integration von Erneuerbaren in das Energiesystem leisten. Sie können ihren Energiebedarf
selbstständig aus erneuerbaren Energien oder anderen Energieformen decken, etwa Biomasse,
Wärmepumpen, PV, Windräder oder Kraftwärmekopplungen. Diese können nach den
individuellen Zielen der Gemeinden, Haushalte oder der Betriebe gesteuert werden, um
Kostenreduktionen, CO2 Einsparungen oder eine Erhöhung des Unabhängigkeitsgrades zu
realisieren. Sie berechnen den aktuellen und zukünftigen Verbrauch und können Energie im
Bedarfsfall dorthin verlagern, wo sie gerade benötigt wird, oder sie reduzieren den
Energieverbrauch direkt.

Durch die vermehrte Einbindung von Absorptionswärmepumpen und -kältemaschinen in bestehende und zukünftige Energiesysteme des Kälte- und Wärmesektors kann der Anteil erneuerbarer Energien deutlich gesteigert werden. Um dies erfolgreich umsetzen zu können, müssen die Betriebsstrategien und Regelungen dieser Systeme jedoch in der Lage sein, auch mit dynamischen und stark variierenden Betriebsbedingungen umgehen zu können. Dieser Herausforderung hat sich das von der FFG geförderte Projekt HPC – heat pumping system control gewidmet. Im Rahmen dieses Workshops sollen die Ergebnisse und deren Nutzen für die Praxis präsentiert und diskutiert werden.