Decarbonising the transport sector is one of the key goals of national and international climate change mitigation policies. Rapid and effective market introduction of alternative fuels and vehicles is needed to reduce greenhouse gas emissions from the existing vehicle fleet as soon as possible and as extensively as possible.

However, experience with various attempts to introduce alternative fuels and vehicles to the market has shown that this is not always successful. Several participants in the Advanced Motor Fuels Technology Collaboration Program (AMF TCP) have therefore proposed an annex on lessons learned from market launch attempts.

The circumstances of the introduction of advanced motor fuels and the factors influencing their commercialization (resource, transport infrastructure, economic situation, etc.) in each country are different, and it is difficult to universally evaluate an advanced motor fuels policy.

For this reason, this annex clarifies the background and objective of the central government and local governments’ introduction policy and specific measures on advanced motor fuels in the past, and summarizes the effectiveness, successes, and lessons learned regarding the promotion of advanced motor fuels in each individual case of introduction and commercialization.

The participating countries Austria, China, Finland, Japan, Sweden and the USA conduct analyses of their own case studies on past market introductions taking into account specific framework conditions for each country:

Austria: low blend biofuels, CNG-driven cars, prevented introduction of E10

China: Ethanol

Finland: E10, E85, drop-in components for diesel, biogas

Japan: FAME, natural gas

Sweden: reduction obligation, high blend biofuels and biogas, E85

USA: low and high level blends of ethanol, methanol and FFVs, natural gas

The sum of the case studies is analysed and key drivers of successes and key barriers of failures are identified. Preliminary results from this work will be discussed in an expert workshop in 2020, and then the final lessons learned and recommendations will be derived. Policy briefs including key messages, best practices, lessons learned and avoided mistakes related to advanced motor fuels covering both fuels and related vehicle technologies will be developed and provided as recommendations for political decision makers.

Die Energiewende in Richtung dezentrale Energieversorgung und der stetige Ausbau
erneuerbarer Energieressourcen erfordert ein angepasstes Energienetz (Strom, Wärme und
Kälte) mit einem flexiblen, ausbau- und integrationsfähigen Regelungssystem, welches
bestehende EVU Systeme komplementiert, Netze entlastet und die Notwendigkeit des teuren
Netzausbaus verringert. Intelligente Mikro-Netze (Microgrids), ein Bereich der Strom- und
Energie-Netze (Smartgrids), erfüllen diese Anforderungen. Durch Microgrids werden lokale
Energiemärkte entstehen, welche lokale Ungleichgewichte von den Verbundnetzen
fernhalten und somit das Angebot und den Verbrauch bereits auf lokaler Ebene
ausbalancieren. Zusätzlich können die regionale Erzeugung und der Verbrauch von Strom
um die Wärme-, Kälte- und Gas-Seite ergänzt werden. Dies ergibt somit ein ganzheitliches
regionales Energiesystem, welches die gesamte Energieeffizienz erhöht und auch positive
Netzeffekte für den Energieversorger mit sich bringt. Microgrids liefern die Möglichkeit eine
100%ige dezentrale Energieversorgung zu erreichen.
Gegenwärtiger Forschungsbedarf und Gegenstand des Projektantrages
„Microgrid Lab 100%“ sind bestehende und neue wissenschaftliche Arbeiten und F&EErgebnisse
zu Microgrids (mathematische & physikalische Modellierung, modellbasierte
Steuerungsmethoden, Regelung mit künstlicher Intelligenz, Kommunikationsmethoden,
Datenerfassung und der Austausch zwischen Energieversorger, privaten Kunden und
Gebäudemanagementsystemen) in einem realen Umfeld zu evaluieren und auf
wissenschaftlicher Ebene weiter zu entwickeln. Da es derzeit keine vergleichbaren
Methoden, Verfahren oder Richtlinien gibt, ist ein hoher Innovationsgrad des beantragten
Projekts sichergestellt.
Projektinhalte und Projektziele sind die wissenschaftliche Planung und Inbetriebnahme des
Microgrid Forschungslabors, eine Nutzerbefragung, die Entwicklung von Testzyklen und ein
Monitoring, um mit den Ergebnissen die Optimierungsalgorithmen weiterzuentwickeln. Das
über das Projekt hinausgehende Ziel ist die Etablierung des Microgrid Forschungslabors für
verschiedene Wirtschaftszweige, um Planungs-, Steuerungs-, Integrations- und
Kommunikationskonzepte in Echtzeit zu entwickeln und für den Markt zu testen. Die
Involvierung von Industriepartner (u.a. COMET-Partner: EVN AG, Netz NÖ GmbH, Wien
Energie) bereits während der Projektlaufzeit und der Aufbau eines Kompetenznetzwerkes zu
Microgrids, mit Unterstützung des Bau.Energie.Umwelt Cluster und des
Technopolmanagement Wieselburg, tragen zu dieser Zielerreichung wesentlich bei.
Konkret umfasst das geplante Microgrid Forschungslabor das Umfeld des Technologie- und
Forschungszentrum (TFZ) Wieselburg-Land sowie das neue Feuerwehrhaus der
Stadtgemeinde Wieselburg und Gemeinde Wieselburg-Land. Zusätzlich wird die
Fachhochschule Wieselburg Daten (aus der Nutzerbefragung und einem eigenen
Monitoring) für die Weiterentwicklung der Optimierungsalgorithmen liefern.

Microgrids are local energy grids that (partly) cover their own energy demand. Decentralized renewable energy sources reduce energy costs and CO2 emissions in a microgrid. Various storage systems and strategies like load shift are employed to balance the volatile energy flows. Intelligent controllers improve the energy management of the micro and smart grids. BEST GmbH is the industry leader when it comes to biomass control systems in Austria. Thus, BEST GmbH is already combining this knowledge within the “OptEnGrid” (FFG 858815) and “Grundlagenforschung Smart- und Microgrid“ (K3-F-755/001-2017) research projects, which are based on the leading microgrid optimization tool DER-CAM from Lawrence Berkeley National Laboratory at the University of California. These two BEST GmbH basic research projects form the basis for new innovative microgrid controller concepts which will be implemented and tested in the presented Microgrid Research Lab in Wieselburg (project Microgrid Lab 100%). The Microgrid Research Lab will include the Technology- und Reseach Centre (tfz) Wieselburg-Land and the new firefighting department next to the tfz.

Microgrids, a research topic within the smart grids area, build on close relationships between demand and supply and will create a 170 Mrd. € market potential in 2020[1]. These individual markets are characterized by different technologies in use. For example, biogas will play a key role in microgrids in Asia compared to Photovoltaics, Combined heat and Power (CHP), as well as storage technologies in North America. All these different technologies need to be coordinated and controlled. BIOENERGY2020+ GmbH is the industry leader when it comes to biomass control systems in Austria. Thus, BIOENERGY2020+ GmbH is already combining this knowledge within the OptEnGrid and “Grundlagenforschung Smart- und Microgrid“ (K3-F-755/001-2017) research projects, which are based on the leading microgrid optimization tool DER-CAM from Lawrence Berkeley National Laboratory at the University of California in Berkeley. These two BIOENERGY2020+ GmbH basic research projects constitute the basis for new innovative microgrid controller concepts and these new microgrid controller will be implemented and tested in the suggested Microgrid Research Lab in Wieselburg. The Microgrid Research Lab will include the Technology- und Reseach Centre (tfz) Wieselburg-Land and the new firefighting department next to the tfz.

With the share of renewable energy sources increasing in heating and hot water applications, the role of hydraulic heat distribution systems is becoming more and more important. This is due to the fact that in order to compensate for the often fluctuating behaviour of the renewables a flexible heat transfer must be ensured by these distribution systems while also taking the optimal operating conditions (mass flow, temperature) of the individual components into consideration. This demanding task can be accomplished by independently controlling the two physical quantities mass flow and temperature. However, since there exists an intrinsic nonlinear coupling between these quantities this challenge cannot be handled sufficiently by decoupled linear PI controllers which are currently state-of-the-art in the heating sector. For this reason this paper presents a model-based control strategy which allows a decoupled control of mass flow and temperature. The strategy is based on a systematic design approach from models described in this contribution, which are validated by commercially available components from which most of them can be parametrized by the data sheet. The control strategy is designed for a typical hydraulic configuration used in heating systems, which will allow the accurate tracking of the desired trajectories for mass flows, temperatures and consequently heat flows. The controllers are validated experimentally and compared to well-tuned state-of-the-art (PI) controllers in order to illustrate their superiority and prove their decoupling of the control of mass flow and temperature in real world applications.

The release of potassium during biomass combustion leads to several problems as the emissions of particle matter or formation of deposits. K release is mainly described in literature in a qualitative way and this work aims to develop a simplified model to quantitatively describe it at different stages. The proposed model has 4 reactions and 5 solid species, describing K release in 3 steps; during pyrolysis, KCl evaporation and carbonate dissociation. This release model is coupled into a single particle model and successfully validated with experiments conducted in a single particle reactor with spruce, straw and Miscanthus pellets at different temperatures. The model employs same kinetic parameters for the reactions in all cases, while different product compositions of the reactions are employed for each fuel, which is attributed to differences in composition. The proposed model correctly predicts the online release at different stages during conversion as well as the final release for each case.

The increased biomass utilization leads to the need of an efficient and flexible usage of available sources. Therefore, it is necessary to combust low-cost biogenic residues, which inherently have higher nitrogen contents that lead to increased NOx emissions. In order to tackle this issue a new combustion technology with double air staging and flue gas recirculation is under development. The technology also features an increased fuel bed height and very low oxygen concentrations in the fuel bed to reduce fuel bed temperatures. This work focuses on the CFD simulation of the formation and reduction of NOx emissions of in a small scale boiler (35 kWth). Compared to previously applied models, major modification concerning the heat and mass transfer in the fuel bed as well as the subsequent conversion in the freeboard were made. The fuel bed is modelled via representative fuel particles with a Lagrangian approach and a thermally thick particle model considering intra-particle
gradients. Due to the increased fuel bed height and the relatively low oxygen concentration the formation and cracking of tars has to be considered in the simulation. This heavily influences the formation and reduction of NOx and its precursors. The fuel bound nitrogen is released via the particle model in the form of NO during char burnout and via a lumped tar species during pyrolysis. The cracking of the lumped tar species is modelled via two global gas phase reactions that releases the NOx precursors NH3 and HCN. The cracking reactions are added to a skeletal reaction mechanism with 28 species and 102 reactions that includes the fate of the N species. The simulation results are compared to experimental data from test runs with spruce wood chips and Miscanthus pellets as fuels. The comparison showed good agreement for the test runs with wood chips, where the temperature distribution inside the fuel bed and the released species above the fuel bed were predicted well. The test runs with Miscanthus showed a greater deviation between the measured and simulated values. For both fuels the NOx reduction that was experimentally observed in the secondary combustion zone could not be predicted with reasonable agreement. Therefore, it is necessary to further investigate the cracking of the tars and the subsequent formation of the NOx precursors. The presented work forms the basis for further improvements of the numerical models and subsequently the optimization of the new technology.

During transportation and storage of wood pellets various gases are formed leading to toxic atmosphere. Various influencing factors and measures reducing off-gassing have already been investigated. The present study aims at applying an antioxidant, acetylsalicylic acid (ASA), to reduce off-gassing from wood pellets by lowering wood extractives oxidation. Therefore, acetylsalicylic acid was applied in industrial and laboratory pelletizing processes. Pine and spruce sawdust (ratio 1:1) were pelletized with adding 0-0.8% (m/m) ASA. Glass flasks measurements confirmed off-gassing reduction by adding ASA for all wood pellets investigated.The biggest effect was achieved by adding 0.8% (m/m) ASA in the industrial pelletizing experiments where the emission of volatile organic compounds (VOC_tot) was reduced by 82% and a reduction of carbon monoxide (CO) and carbon dioxide (CO₂) emissions by 70% and 51%, respectively, could be achieved. Even an addition of 0.05% (m/m) ASA led to off-gassing reduction by >10%. A six week storage experiment to investigate the long-term effectivity of ASA addition revealed, that antioxidant addition was effective in reducing CO-, CO₂- and VOC_tot-release, especially during the first four weeks of the storage experiment, after which time the relative reduction effect was significantly decreased.

This study focuses on the determination of alkali release from wood and straw pellets during combustion. The aim is to expand the knowledge on the K and Na release behaviour and to adopt chemiluminescence-based sensors for online monitoring of alkali detection which can be applied for the prevention of fouling formation in low quality biomass combustion plants. Flame emission spectrometry (FES) was used for optical detection of chemiluminescence spectra of K and Na using optical bandpass filters mounted on an ICCD (Intensified Charge Coupled Device) camera. FES data were verified by additional experiments with a single particle reactor (SPR) coupled with an inductively coupled plasma mass spectrometer (ICP-MS). Using both techniques, the release profiles of K and Na during a single pellet combustion at 1000 °C were determined and obtained K* and Na* emission intensities directly correlated with the results from the ICP-MS. It was determined that the emission intensity of alkali radicals depends on alkali concentrations in the samples and K and Na radical emission intensities increase with increasing alkali amounts in the samples. The ICP-MS data revealed that the release of K and Na mainly takes place during the stage of devolatilization. During devolatilization, almost all potassium and sodium are released from wood samples, while only 65–90% of K and 74–90% of Na are released from straw samples. Based on the results, the flame emission spectroscopy technique is capable to fully detect released alkali metals in the gas phase during combustion and proves a possibility to use flame emission sensors for monitoring the release of alkali species from biomass during combustion processes.

Modern central heating systems with logwood boilers are comprised of the boiler, a buffer storage and solar thermal collectors. Conventional control strategies for these heating systems do not coordinate the utilization of all components. This can lead to a sub-optimal operation of the entire heating system resulting in a loss of efficiency and increased pollutant emissions. This contribution presents a control strategy which considers all components of the heating system including the user and forecasts for the solar yield and heat demand. It determines and carries out an optimal operating strategy that improves the user utility and maximizes the heating system efficiency while also ensuring a clean and efficient combustion. The control strategy continuously learns the user behavior and instructs the user when to refill the logwood boiler and how much fuel to use. The new control strategy was verified through test runs performed at an experimental setup consisting of a commercially available logwood boiler with a nominal capacity of 28 kW , two buffer storages with a capacity of 1.5 m³ each and a heating device with a thermal output of up to 12 kW simulating a solar thermal collector. During these test runs, the CO emissions were reduced 93.6 %by in the main combustion phase, 7.1 % more solar yield was utilized, the buffer losses were reduced by - 16.9 % and the overall efficiency was increased by 3.1 % . Thus, the application of this control strategy resulted in a significantly improved user utility and heating system efficiency.

Hydrogen production in 2010 was estimated to 50 Mt/a. 96 % of today’s hydrogen is produced by converting fossil fuels in thermochemical processes. As main conversion technology steam reforming of natural gas and naphtha has been established. Hydrogen is mainly used in refineries, for ammonia production and in several chemical production plants. Hydrogen is also seen as a promising alternative energy carrier for the transport sector. Therefor an increasing demand on hydrogen over the next years can be assumed.  
To substitute fossil produced hydrogen several renewable hydrogen routes have been established. Beside electrolysis of water also steam reforming of biogas, methane pyrolysis and gasification technologies have been developed. This work will focus on hydrogen production based on dual fluidized bed gasification of biomass.  
Dual fluidized bed gasification gives the possibility to establish a renewable hydrogen production route and substitute fossil fuels. A hydrogen production plant consisting of a dual fluidized bed gasifier, a water gas shift stage, a CO2 removal, a pressure swing adsorption and a steam reformer were erected and operated over 1000 h. The gathered data was validated and a model for up-scaling was developed. A benchmark size of 10 MW fuel input power was used as base for economic estimations. As described in previous work an overall efficiency of 55 % can be achieved, which is comparable to alternative technologies. Compared to other renewable routes, hydrogen production based on dual fluidized bed gasification gives the possibility of a fuel flexible system for continuous hydrogen production.  
Hydrogen production derived by DFB gasification of wood is a reliable process, which needs to be optimized due to economic reasons. Special attention has to be paid on the CO2 removal, to obtain an economic efficient process.  
In this study a parameter variation of the CO2 removal, which consists of absorption and desorption column, was done. Mono-ethanol-amine (MEA) was used as a solvent. One focus of the experimental investigations was the desorption at low temperatures to gain the possibility of using temperature levels which are common in district heat grids. For the experiments real synthesis gas with impurities was used. Over the gas cleaning steps of the hydrogen production plant, impurities were removed and hydrogen content was increased. To increase the efficiency of the CO2 removal and further the hydrogen production, a parameter study was done. A good correlation between separation efficiency and desorption temperature could be observed.  
Economics were calculated comparing natural gas steam reforming, electrolysis and hydrogen production based dual fluidized bed gasification. First results show a high potential for establishing the BioH2 plant as a commercial production plant. An economic plant operation with wood chips can be achieved at plant sizes of 20-30 MW fuel input power. A switch to lower quality biomass can reduce the economic feasible plant size even further.  
Keywords: hydrogen, up-scaling, economics, CO2 removal

Mixed Integer Linear Programming (MILP) optimization algorithms provide accurate and clear solutions for Microgrid and Distributed Energy Resources projects. Full-scale optimization approaches optimize all time-steps of data sets (e.g., 8760 time-step and higher resolutions), incurring extreme and unpredictable run-times, often prohibiting such approaches for effective Microgrid designs. To reduce run-times down-sampling approaches exist. Given that the literature evaluates the full-scale and down-sampling approaches only for limited numbers of case studies, there is a lack of a more comprehensive study involving multiple Microgrids. This paper closes this gap by comparing results and run-times of a full-scale 8760 h time-series MILP to a peak preserving day-type MILP for 13 real Microgrid projects. The day-type approach reduces the computational time between 85% and almost 100% (from 2 h computational time to less than 1 min). At the same time the day-type approach keeps the objective function (OF) differences below 1.5% for 77% of the Microgrids. The other cases show OF differences between 6% and 13%, which can be reduced to 1.5% or less by applying a two-stage hybrid approach that designs the Microgrid based on down-sampled data and then performs a full-scale dispatch algorithm. This two stage approach results in 20–99% run-time savings.

Integrated energy systems 1 couples power systems, district heating and cooling (DHC), and gas grids, thereby enabling the storage and distribution of energy across different infrastructure types. Supply and demand follow different patterns in these different domains, which can lead to synergies in generation, storage and consumption, if planned and managed as one energy system. An integrated approach has the potential to increase reliability, flexibility and supply safety and efficiency. Moreover, network coupling increases local utilization of renewables, avoiding problems in the distribution networks, as well as transmission losses. In addition, hybrid energy networks are a promising opportunity to manage and mitigate temporal imbalances of supply and demand in energy systems with a high share of volatile renewables, mainly PV and wind energy. The IEA DHC Annex TS3 provides a holistic approach for designing and assessing hybridization schemes, focusing on the district heating and cooling (DHC) networks and considering both technical (system configuration, operational strategy) and strategic aspects (business models, regulatory frame). These aspects will be discussed within the framework of the IEA DHC Annex TS3 in order to promote the benefits of DHC networks in an integrated energy system. Furthermore we can establish a common direction for the development and implementation of hybrid energy concepts. The IEA DHC Annex TS3 will connect existing national and international projects and thus benefit from interdisciplinary experience and exchange. The primary result of the IEA DHC Annex TS3 will be a guidebook including:  Analyses of available technologies and synergies / application areas  An overview of international case studies including simulation scenarios 1 Different alternative notations can be found in literature, e.g. multi-energy networks, hybrid energy networks, sector coupling, multi-domain networks, cross energy systems. However, since no standard definition is available, those notations are used synonymously.

Energy systems have increased in complexity in the past years due to the everincreasing integration of intermittent renewable energy sources such as solar thermal or wind power. Modern energy systems comprise different energy domains such as electrical power, heating and cooling which renders their control even more challenging. Employing supervisory controllers, so-called energy management systems (EMSs), can help to handle this complexity and to ensure the energy-efficient and cost-efficient operation of the energy system. One promising approach are optimization-based EMS, which can for example be modelled as stochastic mixed-integer linear programmes (SMILP). Depending on the problem size and control horizon, obtaining solutions for these in real-time is a difficult task. The progressive hedging (PH) algorithm is a practical way for splitting a large problem into smaller subproblems and solving them iteratively, thus possibly reducing the solving time considerably. The idea of the PH algorithm is to aggregate the solutions of subproblems, where artificial costs have been added. These added costs enforce that the aggregated solutions become non-anticipative and
are updated in every iteration of the algorithm. The algorithm is relatively simple to implement in practice, re-using almost all of a possibly existing deterministic implementations and can be easily parallelized.
Although it has no convergence guarantees in the mixed-integer linear case, it can nevertheless be used as a good heuristic for SMILPs. Recent theoretical results shown that for applying augmented Lagrangian functions in the context of mixed-integer programmes, any norm proofs to be a valid penalty function. This is not true for squared norms, like the squared L 2 -norm that is used in the classical progressive hedging algorithm. Building on these theoretical results, the use of the L 1 and L-infinity-norm in the PH algorithm is investigated in this paper. In order to incorporate these into the algorithm an adapted multiplier update step is proposed. Additionally a heuristic extension of the aggregation step and an adaptive penalty parameter update scheme from the literature is investigated. The advantages of the proposed modifications are demonstrated by means of illustrative examples, with the application to SMILP-based EMS in mind.

The incineration of waste wood is very often associated with ash-related problems (deposits, slagging and corrosion). This leads to short maintenance intervals, mainly needed to remove ash depositions, which result in significant power generation losses and high downtime costs. To avoid these problems, additives can be used, with particularly cost-effective additives being of great interest. On the one hand, the purpose of the additives is to reduce the Cl concentration in deposits on heat exchangers, which is the main cause for corrosion. On the other hand, the additives shall increase the ash melting temperature of deposits and hereby reduce deposit formation. In a first step the combustion behaviour of 3 different waste wood mixtures without and with the addition of various low-cost additives such as recycled gypsum, coal fly ash and iron sulphide with two different addition ratios were investigated in a laboratory reactor. Using the laboratory reactor allowed the determination of suitable additives and ratios of additivation for further investigations in the industrial plant. This approach represents a cost-effective and time-saving method for determining suitable additives and ratios of additivation. Based on the investigations carried out, the addition of 2% gypsum and 3% coal fly ash was recommended, since an improved ash melting behaviour can be expected with addition of gypsum and coal fly ash. These additives with the recommended mixing rates were then tested in a large scale CHP plant (a 40 MWth grate furnace with additional injection of wood dust above the grate). Extensive test runs were carried out without additive (as a reference), and with the additives focusing on dust formation (aerosols and total dust), deposit formation and the corrosion behaviour of superheaters. These investigations were accompanied by fuel and ash analyses (grate, cyclone and filter).

Recently, researchers have begun to study hybrid approaches to Microgrid techno-economic planning, where a reduced model optimizes the DER selection and sizing is combined with a full model that optimizes operation and dispatch. Though providing significant computation time savings, these hybrid models are susceptible to infeasibilities, when the size of the DER is insufficient to meet the energy balance in the full model during macrogrid outages. In this work, a novel hybrid optimization framework is introduced, specifically designed for resilience to macrogrid outages. The framework solves the same optimization problem twice, where the second solution using full data is informed by the first solution using representative data to size and select DER. This framework includes a novel constraint on the state of charge for storage devices, which allows the representation of multiple repeated days of grid outage, despite a single 24-h profile being optimized in the representative model. Multiple approaches to the hybrid optimization are compared in terms of their computation time, optimality, and robustness against infeasibilities. Through a case study on three real Microgrid designs, we show that allowing optimizing the DER sizing in both stages of the hybrid design, dubbed minimum investment optimization (MIO), provides the greatest degree of optimality, guarantees robustness, and provides significant time savings over the benchmark optimization.

This work presents a methodology to perform the scale-up of a solid fuel furnace to a higher heat output with maintaining or improving the burn-out quality. As basis to derive the scale-up concept, an example of a 35 kW screw burner for biomass fuels is investigated. Based on the Pi-theorem, the relevant dimensionless parameters are derived and similarity rules for the scale-up are proposed as follows: As initial conditions, the height to diameter ratio of the combustion chamber, the mean Reynolds number in the combustion chamber and the mean square velocity through the combustion chamber shall be kept constant or in the case of the Reynolds number may also increase. Additionally the effective momentum flux ratio between the secondary air injected in the combustion chamber and the gases from the pyrolysis and gasification section also shall be kept constant to maintain the mixing conditions between combustible gases and secondary air. Finally the thermal surface load on the screw also shall be kept constant. The influence of different scale-up approaches on thermal surface load, gas velocity, pressure losses, Reynolds number and height-to-diameter ratio are compared and discussed and a scaling approach to increase the heat output from 35 kW to 150 kW is described. For a theoretical validation of the scale-up, CFD simulations are performed to investigate the predicted pollutant emissions and the pressure loss for the scaled 150 kW furnace.

A key factor for the further distribution of biomass boilers in modern energy systems is the capability of changing the applied feedstock during normal plant operation. This is only possible with the application of advanced control strategies that utilize knowledge about the state variables and varying fuel properties. However, neither the state variables nor the fuel properties are measurable during plant operation and, thus, need to be estimated. This contribution presents a method for the simultaneous real-time estimation of the state variables and the fuel properties in fixed-bed biomass boilers which is a novel approach in the field of biomass boilers. The method bases on an Extended Kalman Filter using a nonlinear dynamic model and measurement data from the combustion process. The estimated variables are the masses of dry fuel and water in the fuel bed as well as the fuel's bulk density, water content, chemical composition and lower heating value. The proposed method is easy to implement and requires moderate computational effort which increases the potential of its application at actual biomass boilers. The proposed method is verified with simulation studies and by test runs performed at a representative small-scale fixed-bed biomass boiler. The estimation results show a good agreement with the actual values, demonstrating that the proposed method is capable of accurately estimating the biomass boiler's state variables and simultaneously its fuel properties. For this reason, the presented method is a key technology to ensure the further distribution of biomass boilers in modern energy systems.

A key factor for the further distribution of biomass boilers in modern energy systems is the capability of changing the applied feedstock during normal plant operation. This is only possible with the application of advanced control strategies that utilize knowledge about the state variables and varying fuel properties. However, neither the state variables nor the fuel properties are measurable during plant operation and, thus, need to be estimated. This contribution presents a method for the simultaneous real-time estimation of the state variables and the fuel properties in fixed-bed biomass boilers which is a novel approach in the field of biomass boilers. The method bases on an Extended Kalman Filter using a nonlinear dynamic model and measurement data from the combustion process. The estimated variables are the masses of dry fuel and water in the fuel bed as well as the fuel’s bulk density, water content, chemical composition and lower heating value. The proposed method is easy to implement and requires moderate computational effort which increases the potential of its application at actual biomass boilers. The proposed method is verified with simulation studies and by test runs performed at a representative small-scale fixed-bed biomass boiler. The estimation results show a good agreement with the actual values, demonstrating that the proposed method is capable of accurately estimating the biomass boiler’s state variables and simultaneously its fuel properties. For this reason, the presented method is a key technology to ensure the further distribution of biomass boilers in modern energy systems.

The flue gas mass flow is one of the fundamental quantities of the combustion process in biomass boilers. Since it directly relates to the enthalpy flow entering the heat exchanger, its knowledge is highly advantageous for a sophisticated load control of the biomass boiler. It also includes information regarding the primary and secondary air mass flows as well as the mass flows of potentially occurring leakage air and thermally decomposed fuel. However, in practical application it is not possible to obtain a reliable measurement of the flue gas mass flow. For this reason, this work presents a soft-sensor for the on-line estimation of the flue gas mass flow in biomass boilers. The approach is robust against fouling of the relevant boiler components and is based on standard measurements which are typically available in biomass boilers. In addition, the soft-sensor offers the possibility of monitoring the degree of heat exchanger fouling.

The present study aims to present a comprehensive characterization of the surface of ash-layered olivine bed particles from dual fluidized bed gasification. It is well known from operation experience at industrial gasification plants that the bed material is activated during operation concerning its positive influence on gasification reactions. This is due to the built up of ash layers on the bed material particles; however, the chemical mechanisms are not well understood yet. Olivine samples from long-term operation in an industrial-scale gasification plant were investigated in comparison to fresh unused olivine. Changes of the surface morphology due to Ca-enrichment showed a significant increase of their surface area. Furthermore, the Ca-enrichment on the ash layer surface was distinctively associated to CaO being present. The presence of CaO on the surface was proven by adsorption tests of carbon monoxide as model compound. The detailed characterization contributes to a deeper understanding of the surface properties of ash layers and forms the basis for further investigations into their influence on gasification reactions.

In the context of Austria´s and the EU´s ambitious goals to combat climate change by reducing the demand for fossil fuels in all sectors, many industries plan to increase the share of renewable energy in their production processes. Furthermore greenhouse gases shall be reduced by 36 % until 2030 (compared to 2005), which means another 14 Mio. tons CO2eq will have to be reduced per year in comparison to data from 2016. In doing so, some industries find it sufficient to use green electricity or green gas from the grid, but for some industries the use of biomass is particularly interesting. In particular, the wood-based economy as an essential part of the Austrian bio-based economy is needed to promote the development of sustainable production and sustainable energy generation. Besides the increasing demand for woody biomass, the supply side will also undergo substantial changes since increasing calamities (such as bark beetle infestation and windthrow) caused by climate change will affect the wood supply to a varying extend. Hence, within the project “BioEcon” the BIOENERGY 2020+ team together with industry partners analyses the effects of these developments on the wood-based economy and the corresponding supply chains in terms of economic and technological perspectives including econometric models to evaluate woody biomass supply and demand.
 

In the light of climate change, there is an urgent need to decarbonize our societies. The transport sector is specifically challenging, as transport demand is still growing, and so are the sector´s GHG emissions. Several countries have set ambitious national targets for GHG reduction in the transport sector. These are often backed with policy measures for implementation of both advanced renewable transport fuels and electrification.
In a project set up jointly by two Technology Collaboration Programmes of the International Energy Agency, namely the IEA Bioenergy TCP and the Advanced Motor Fuels TCP, the contribution that advanced renewable transport fuels should make to the decarbonisation of the transport sector is assessed by means of country-specific assessments.

The viability of thermochemical energy storage for a given application is often determined by the reaction kinetics under process conditions. For high exergetic efficiency the process needs to operate in close proximity to the reaction equilibrium. Thus, accurate kinetic models that include the effect of the reaction equilibrium are required.

In the present work, different parametrization methods for the equilibrium term in the General Kinetic Equation are evaluated by modeling the kinetics of two reaction systems relevant for thermochemical energy storage (CaC2O4 and CuO) from experimental data. A non-parametric modeling method based on tensor decompositions is used that allows for a purely data driven assessment of different parametrization methods.

Our analysis shows that including a suitable equilibrium term is crucial. Omitting the equilibrium term when modeling formation reactions can lead to seemingly negative activation energies. Our tests also show that for formation reactions, the reaction rate decreases much faster towards the equilibrium than theory predicts. We present an empirical modeling approach that can predict the reaction rate of gas-solid reactions, regardless of the shortcomings of theory. In this way, non-parametric modeling offers a powerful tool for applied research and may contribute to the advancement of the thermochemical energy storage technology.

A disconnect between real world financing and technical modeling remains one of the largest barriers to widespread adoption of microgrid technologies. Simultaneously, the optimal design of a microgrid is influenced by financial as well as technical considerations. This paper articulates the interplay between financial and technical assumptions for the optimal design of microgrids and introduces a design approach in which two financing structures drive an efficient design process. This approach is demonstrated on a descriptive test case, using well accepted financial indicators to convey project success. The major outcome of this paper is to provide a framework which can be adopted by the industry to relieve one of the largest hurdles to widespread adoption, while introducing multiple debt financing models to the literature on microgrid design and optimization. An equally important outcome from the test case, we provide several points of intuition on the impact of varying financing terms on the optimal solution.

The necessity of recycling anthropogenically used phosphorus to prevent aquatic eutrophication and decrease the economic dependency on mined phosphate ores encouraged recent research to identify potential alternative resource pools. One of these resource pools is the ash derived from the thermochemical conversion of sewage sludge. This ash is rich in phosphorus, although most of it is chemically associated in a way where it is not plant available. The aim of this work was to identify the P recovery potential of ashes from sewage sludge co-conversion processes with two types of agricultural residues, namely wheat straw (rich in K and Si) and sunflower husks (rich in K), employing thermodynamic equilibrium calculations. The results indicate that both the melting behavior and the formation of plant available phosphates can be enhanced by using these fuel blends in comparison with pure sewage sludge. This enhanced bioavailability of phosphates was mostly due to the predicted formation of K-bearing phosphates in the mixtures instead of Ca/Fe/Al phosphates in the pure sewage sludge ash. According to the calculations, gasification conditions could increase the degree of slag formation and enhance the volatilization of K in comparison with combustion conditions. Furthermore, the possibility of precipitating phosphates from ash melts could be shown. It is emphasized that the results of this theoretical study represent an idealized system since in practice, non-equilibrium influences such as kinetic limitations and formation of amorphous structures may be significant. However, applicability of thermodynamic calculations in the prediction of molten and solid phases may still guide experimental research to investigate the actual phosphate formation in the future.

Wood log stoves are a common residential heating technology that produce comparably high pollutant emissions. Within this work, a detailed CFD model for transient wood log combustion in stoves was developed, as a basis for its optimization. A single particle conversion model previously developed by the authors for the combustion of thermally thick biomass particles, i.e. wood logs, was linked with CFD models for flow and turbulence, heat transfer and gas combustion. The sub-models were selected based on a sensitivity analysis and combined into an overall stove model, which was then validated by simulations of experiments with a typical wood log stove, including emission measurements. The comparison with experimental results shows a good accuracy regarding flue gas temperature as well as CO2 and O2 flue gas concentrations. Moreover, the characteristic behavior of CO emissions could be described, with higher emissions during the ignition and burnout phases. A reasonable accuracy is obtained for CO emissions except for the ignition phase, which can be attributed to model simplifications and the stochastic nature of stove operation. Concluding, the CFD model allows a transient simulation of a stove batch for the first time and hence, is a valuable tool for process optimization.

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.

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

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.

Solar-assisted heating systems use the energy of the sun to supply consumers with renewable heat and can be found all over the world where heating of buildings is necessary. For these systems, both heat production and heat demand are directly related to the weather conditions. In order to optimally plan production, storage, and consumption, forecasts for both the future heat production of the thermal solar collectors as well as the future heat demand of the connected consumers are essential. For this reason, this contribution presents adaptive forecast methods for the solar heat production and the heat demand of consumers using weather forecasts. The developed methods are easy to implement and therefore practically applicable. The final verification of the methods shows good agreement between the predicted values and measurement data from a representative solar-assisted heating system.

n the course of this study the direct utilization of ammonia in different types of solid oxide fuel cells (SOFCs), such as anode- and electrolyte-supported SOFC, is investigated. Experiments in low fuel utilization, exhibited excellent performance of ammonia in SOFCs, although the power outputs of equivalent hydrogen/nitrogen fuels were not attained due to the incomplete endothermic ammonia decomposition. Next, the single cells were operated under high fuel utilization conditions and methane was added to the humidified ammonia stream, where they showed excellent ammonia- and methane conversions. The stability of the cells used was proven over a period of at least 48 hours with a variety of fuel mixtures. Post mortem scanning electron microscopy analysis of the anode micro-structures indicated nitriding effects of nickel, as microscopic pores and enlargements of the metallic parts occurred. Finally, a long-term test over 1,000 hours was carried out using a ten-layer stack consisting of electrolyte-supported cells.

The food and beverage industry offers a wide range of organic feedstocks for use in biogas production by means of anaerobic digestion (AD). Microorganisms convert organic compounds—solid, pasty, or liquid ones—within four steps to biogas mainly consisting of CH₄ and CO₂. Therefore, various conversion technologies are available with several examples worldwide to show for the successful implementation of biogas technologies on site. The food and beverage industry offer a huge potential for biogas technologies due to the sheer amount of process residues and their concurrent requirement for heat and power. The following study analyzes specific industries with respect to their implementation potential based on arising waste and heat and power demand. Due to their chemical composition, several feedstocks are resistant against microbiological degradation to a great extent. A combination of physical-, chemical-, and microbiological pretreatment are used to increase the biological availability of the feedstock. The following examples will discuss how to best implement AD technology in industrial processes. The brewery industry, dairy production, slaughterhouses, and sugar industry will serve as examples.

Several studies pointed out that emission release is related to the concentration of particular elements in the fuel. Fuel indexes were developed to predict emissions of biomass combustion based on the elemental composition of the fuel. This study focuses on emissions of different biomass combustion technologies for domestic heating. Based on combustion tests with a wide range of fuel qualities we validated fuel indexes from the literature. We calculated the values for predicting total suspended particulate (TSP) matter and nitrogen oxide (NO_x) emission of 39 biomass-derived fuels. Combustion tests conducted in 10 different small-scale appliances provided experimental data. The combustion technologies had a nominal load between 6 and 140 kW_th. We measured TSP and NO_x emissions during the stable phases of the experiments. The evaluation considered 529 combustion test intervals. All tested indexes for predicting the TSP corresponded well to the measured values. The correlation analysis confirmed that these indexes are associated with each other and are basically dominated by the concentration of potassium. The results regarding NO_x emissions confirm previous findings from the literature by showing the typical nonlinear relation between nitrogen content of the fuel and NO_x in the flue gas. Overall the comparison of the fuel indexes with the practical data indicated also an influence of the combustion technologies.

Several methods are available to predict the combustion behavior of fuels. Fuel indexes have been developed either for specific fuel types (e.g., coal, biomass) or their utilization in combustion technology (fluidized bed, grate systems). This study deals with the validation of fuel indexes for biomass fuels utilized in small-scale appliances for residential heating. Laboratory analysis data of 33 biomass-derived fuels were used for determining indexes for predicting slag formation tendencies. Indexes were selected that have been reported and previously applied in the literature. They vary in terms of their derivation: ratio or concentration of specific components that are relevant for ash chemistry, temperature-based indexes, and empirical correlations. Combustion tests with 9 different small-scale appliances were conducted to gain experimental data. The appliances had a nominal load between 6 kW_th and 140 kW_th. After each experiment, the fraction of fuel ash that formed slag was quantified. Because of several boiler–fuel combinations in total, data from 90 combustion experiments were available for evaluation. The comparison of the quantified slag with the calculated slagging indexes showed that the applicability was strongly dependent on the (chemical) background of the respective index. Also, the fuel composition (e.g., fuels rich in calcium, silicon or phosphorus) plays an important role. Thus, available indexes are not applicable without restrictions and require a closer look on fuel properties and possible ash transformation mechanisms. Overall, the comparison of the fuel indexes with practical data (slag formation) also indicated an influence of the combustion technologies and operation conditions. The comparison of indexes that predict particulate matter and nitrogen oxide emissions with data measured during combustion experiments was evaluated as well. These results will be described in the second part of the present work.

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.

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.

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

Fluidized bed gasification of sewage sludge is a promising method for its valorisation due to the fuel flexibility of the process. The main drawbacks are the impurities present in the producer gas, with a high tar content, and its low calorific value. In this study, sewage sludge and wood mixtures are gasified in a fluidized bed. A tar cracking reactor is used to reduce the amount of tars and to increase the calorific value of the producer gas. Sewage sludge char is employed for tar cracking with a real producer gas, showing the feasibility of the process with a tar conversion of about 80% at the beginning. The test was conducted for several hours and tar deactivation was observed, which lead to a decrease of tar conversion to about 35% after 5 hours. Reactivating the char with steam increases again the tar conversion up to 84%, however, the subsequent deactivation was found to be faster compared to the one for fresh char. First tests using char from the gasification process in the tar cracking unit also show promising results.

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"

Microalgal biomass as a feedstock for biogas production is linked to the parameters biomass productivity and biogas yield. Besides an easy-to-use strain for anaerobic digestion, the photobioreactor (PBR) design is important. A microalgae strain selection revealed Eustigmatos magnus (SAG 36.89) as the most promising strain yielding an average of 100 mg total suspended solids (TSS) L−1 day−1. The strain was tested in cost-effective sleevebag-PBR-systems of 10 cm, 20 cm and 30 cm diameter facing the light from the front or laterally. Highest mean productivity on a volumetric basis was measured in PBRs with the lowest diameter (104 and 117 mg L−1 day−1. The highest productivity per m−2 was achieved in 10 cm PBRs with front light configuration (9.35 g TSS m−2 day−1). The lateral light configuration of 10 cm PBRs had positive aspects such as the lowest mean water demand to produce 1 kg TSS (481 L−1 kg−1) and the lowest mean energy demand for medium separation of 1 kg TSS (106 Wh). The concentrated microalgal biomass was then subjected to ultrasonication and thermal pre-treatment (90 °C and 120 °C) and tested in BMP tests. Mesophilic anaerobic mono-digestion of untreated microalgae biomass led to a methane (CH4) yield of 343 L−1 kg−1 volatile solids (VS). Thermal pre-treatment at 120 °C resulted in significantly increased CH4 yields of 430 L−1 kg−1 VS. As thermal pre-treatment can be easily installed nearby a biogas plant it could be an interesting option for AD of microalgal biomass with only little investment.

The reduction of greenhouse gas emission is an important issue for steel industry. One possibility is to use biomass-based reducing agents, also called bioreducers, to replace a least partly the fossil reducer agents. To produce bioreducer we treated woody biomass in a lab-scale muffle furnace, we performed grinding experiments with a ball mill, we analyzed the particle size distribution with laser diffraction and we used a rotating device, the revolution powder analyzer, for flow behavior investigations. Our preliminary results show that treatment temperatures >250 oC bring adequate increased calorific value and improved grindability. For a certain treatment temperature the particle size distribution and as well the flow behavior shows similarities to lignite.

Climate change affects the frequency and intensity of extreme weather, the results of which include production losses and climate-induced crop productivity fluctuations.

Double-cropping systems (DCSs) have been suggested as a way to increase biomass-production while simultaneously delivering environmental benefits. In a three-year field-test, two DCSs based on maize and sorghum as the main crop and rye as the preceding winter crop were compared with each other and compared with 2 single-cropping systems (SCSs) of maize or sorghum; there were comparisons of growth dynamics, optimal harvesting and growing time as well as biomass and methane yield. In addition, the impact of variety and harvest time on the winter rye optimal biomass yield was studied.

The experiments clearly showed the superiority of the DCS over the SCS. Within the DCS, the rye/sorghum combination achieved significantly higher biomass yields compared to those of the rye/maize combination. The highest dry matter biomass yield was achieved during year 1 at 27.5 ± 2.4 t∙ha−1, during which winter rye contributed 8.3 ± 0.7 t∙ha−1 and sorghum contributed 19.2 ± 1.8 t∙ha−1. At the experimental location, which is influenced by a Pannonia climate (hot and dry), the rye/sorghum DCS was able to obtain average methane yields per hectare, 9300 m3, whereas the rye/maize combination reached 7400 m3. In contrast, the rye, maize and sorghum SCSs achieved methane yields of 4800, 6100 and 6500 m3 ha−1, respectively. The study revealed that the winter rye and sorghum DCS is a promising strategy to counteract climate change and thus guarantee crop yield stability.

The microalga Acutodesmus obliquus was investigated as a feedstock in semi-continuously fed anaerobic digestion trials, where A. obliquus was co-digested with pig slurry and maize silage. Maize silage was substituted by both 10% and 20% untreated, and 20% ultrasonicated microalgae biomass on a VS (volatile solids) basis. The substitution of maize silage with 20% of either ultrasonicated and untreated microalgae led to significantly lower biogas yields, i.e., 560 dm³ kg⁻¹ VS_corr in the reference compared to 516 and 509 dm³ kg^-1VS_corr for untreated and ultrasonicated microalgae substitution. Further, the viscosities in the different reactors were measured at an OLR of 3.5 g VS dm^-3 d^-1. However, all treatments with microalgae resulted in significantly lower viscosities. While the mean viscosity reached 0.503 Pa s in the reference reactor, mean viscosities were 53% lower in reactors where maize was substituted by 20% microalgae, i.e. 0.239 Pa s, at a constant rotation speed of 30 rpm. Reactors where maize was substituted by 20% ultrasonicated microalgae had a 32% lower viscosity, for 10% microalgae substitution a decrease of 8% was measured. Decreased viscosities have beneficial effect on the bioprocess and the economy in biogas plants. Nonetheless, with regard to other parameters, no positive effect on biogas yields by partial substitution with microalgae biomass was found. The application of microalgae may be an interesting option in anaerobic digestion when fibrous or lignocellulosic substances lead to high viscosities of the digested slurries. High production costs remain the bottleneck for making microalgae an interesting feedstock.

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.

The efficient and flexible conversion of solid biomass into energetic products will be an essential part of a future renewable, independent and reliable energy providing system. The main objective of the project Bio-CCHP is the development of a novel tri-generation system, including biomass gasification, gas cleaning, a Solid Oxide Fuel Cell (SOFC) and a cooling machine with the aim to produce electricity, heat and cold (CCHP), maximizing the efficiency and flexibility of the system. However, the employment of biomass derived product gas as fuel gas for SOFC is facing new challenges for gas quality assurance. For the evaluation of required dry high temperature gas cleaning processes the applied methods of gas characterization have to be accurate and reliable. Therefore, a comprehensive evaluation of analytical methods for the detection of SOFC harmful compounds is conducted within the ongoing project. First results of online and offline sampling and analysis methods employed at air- and steam-operated gasifiers are shown in this paper.

Although the number of studies investigating the contribution of anaerobic digestion facilities to greenhouse gas (GHG) emissions has increased during the last decade, reliable data with respect to gaseous process losses from these management practices, particularly at commercial scale, is scarce (Liebetrau et al., 2013, Reinelt et al., 2017, Hrad et al., 2015). The dynamic and fugitive nature of methane emissions, changing operating conditions, and different as well as not standardised measurement approaches compromise the precise quantification of the overall emissions from full-scale biogas plants. However, reliable and verifiable emission data from biogas or biomethane facilities are required in order to optimise and improve the plant-specific process efficiency as well as future technology developments. In addition, precise and comprehensive measurement data from full-scale waste treatment facilities are needed for more accurate emission factors (EFs) estimates, which are required for annual reporting according to the Intergovernmental Panel on Climate Change (IPCC) guidelines (IPCC, 2006).
Within the European project “EvEmBi - Evaluation and reduction of different biogas plant concepts” (2018-2021, funded within the 11th ERA-NET bioenergy call) 15 partners from 5 European countries evaluate the existing technologies at biogas plants regarding their methane EFs and develop emission reduction strategies, respectively. The focus of the Austrian research group within this project is the evaluation of Austrian bio-waste plants.
In a first step, emissions from single sources as well as overall plant emissions are quantified. For the latter, the Austrian team uses an open-path technology (Open-Path Tunable-Diode-Laser-Spectroscopy) together with meteorological data (ultra-sonic anemometer) and inverse dispersion modelling (Backward Lagrangian Model). In order to determine comparable EFs, the applied methodologies are based on a measurement guideline developed in the previous project “MetHarmo – European harmonization of method to quantify methane emissions from biogas plants” (funded within the 9th ERA-NET bioenergy call). In addition, the determined EFs of the individual plant concepts are transferred to EFs of the entire plant inventory of the particular countries. For that, a model for EF quantification is used which is based on statistical information on the emissions from different plant components as well as on the distribution of certain technologies present in the participating countries. Furthermore, for the particular biogas plants emission reduction strategies are developed, implemented and verified.
In this presentation, the harmonised approach, first emission results from the Austrian measurement campaigns as well as emission reduction strategies are presented.

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

Chemical looping combustion is a highly efficient CO2 separation technology without direct contact between combustion air and fuel. A metal oxide is used as an oxygen carrier in dual fluidized beds to generate clean CO2. The use of biomass is the focus of current research because of the possibility of negative CO2 emissions and the utilization of biogenic carbon. The most commonly proposed OC are natural ores and residues, but complete combustion has not yet been achieved. In this work, the direct utilization of CLC exhaust gas for methane synthesis as an alternative route was investigated, where the gas components CO, CH4 and H2 are not disadvantageous but benefit the reactions in a methanation step. The whole process chain, the coupling of an 80 kWth pilot plant with gas cleaning and a 10 kW fluidized bed methanation unit were for this purpose established. As OC, ilmenite enhanced with limestone was used, combusting bark pellets in autothermal operation at over 1000 °C reaching high combustion efficiencies of up to 91.7%. The fuel reactor exhaust gas was mixed with hydrogen in the methanation reactor at 360 °C and converted with a methane yield of up to 97.3%. The study showed especially high carbon utilization efficiencies of 97% compared to competitor technologies. Based on the experimental results, a scale-up concept study showed the high potential of the combination of the technologies concerning the total efficiency and the adaptability to grid injection.

Zum Erreichen der Ziele der österreichischen Klimastrategie leisten Biomassefeuerungen einen entscheidenden Beitrag. Um dabei die Luftgüte nicht außer Acht zu lassen, wird in diesem Factsheet der aktuelle und zukünftige Status (bis 2050) von Staubemissionen in Österreich basierend auf Literaturdaten und eigenen Messungen dargelegt, und der aktuelle Kenntnisstand zu Emissionen aus Biomasse-Kleinfeuerungen zusammengefasst.

Global climate change will make it necessary to transform transportation and mobility away from what we know now towards a sustainable, flexible, and dynamic sector. A severe reduction of fossil-based CO₂ emissions in all energy-consuming sectors will be necessary to keep global warming below 2 °C above preindustrial levels. Thus, long-distance transportation will have to increase the share of renewable fuel consumed until alternative powertrains are ready to step in. Additionally, it is predicted that the share of renewables in the power generation sector grows worldwide. Thus, the need to store the excess electricity produced by fluctuating renewable sources is going to grow alike. The “Winddiesel” technology enables the integrative use of excess electricity combined with biomass-based fuel production. Surplus electricity can be converted to H₂ via electrolysis in a first step. The fluctuating H₂ source is combined with biomass-derived CO-rich syngas from gasification of lignocellulosic feedstock. Fischer-Tropsch synthesis converts the syngas to renewable hydrocarbons. This research article summarizes the experiments performed and presents new insights regarding the effects of load changes on the Fischer-Tropsch synthesis. Long-term campaigns were carried out, and performance-indicating parameters such as per-pass CO conversion, product distribution, and productivity were evaluated. The experiments showed that integrating renewable H₂ into a biomass-to-liquid Fischer-Tropsch concept could increase the productivity while product distribution remains almost the same. Furthermore, the economic assessment performed indicates good preconditions towards commercialization of the proposed system.

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.

Wastewater contains high amounts of unused energy in the form of dissolved ammonia, which can easily be converted into gaseous humidified ammonia via membrane distillation, thus providing a potential fuel for solid oxide fuel cells. This study presents comprehensive investigations of the use of humidified ammonia as the primary fuel component in high-fuel utilization conditions. For these investigations, large planar anode- and electrolyte-supported solid oxide single cells were operated at the respective appropriate temperatures, 800°C and 850°C. Fueled with ammonia, both cells exhibited excellent ammonia conversion ( > 99.5%) in addition to excellent performance output and fuel utilization. In 100 h stability tests performed at 80% fuel utilization, the cells exhibited stable performance, despite scanning electron microscopy analyzes revealing partial impairments to the nickel parts of both cells due to the formation and subsequent decomposition of nickel nitride. This study also demonstrates that methane is a perfect additional fuel component for humidified ammonia streams, as steam supports the internal reforming of methane. Alternating and direct current as well as electrochemical impedance measurements with a variety of ammonia/steam/methane/nitrogen fuel mixtures were used to evaluate the performance potential of the cells, and proved their stability over 48 h in highly polarized conditions.