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.