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.

Toxicological characterisation of combustion emissions in vitro are often conducted with macrophage cell lines, and the majority of these experiments are based on responses measured at 24 h after the exposure. The aim of this study was to investigate how significant role time course plays on toxicological endpoints that are commonly measured in vitro. The RAW264.7 macrophage cell line was exposed to PM₁ samples (150 μg/ml) from biomass combustion devices representing old and modern combustion technologies for 2, 4, 8, 12, 24 and 32 h. After the exposure, cellular metabolic activity, cell membrane integrity, cellular DNA content, DNA damage and production of inflammatory markers were assessed. The present study revealed major differences in the time courses of the responses, statistical differences between the studied samples mostly limiting to differences between modern and old technology samples. Early stage responses consisted of disturbances in metabolic activity and cell membrane integrity. Middle time points revealed increases in chemokine production, whereas late-phase responses exhibited mostly increased DNA-damage, decreased membrane integrity and apoptotic activity. Altogether, these results implicate that the time point of measurement has to be considered carefully, when the toxicity of emission particles is characterised in in vitro study set-ups.

Achieving the autonomous deployment of aerial robots in unknown outdoor environments using only onboard computation is a challenging task. In this study, we have developed a solution to demonstrate the feasibility of autonomously deploying drones in unknown outdoor environments, with the main capability of providing an obstacle map of the area of interest in a short period of time. We focus on use cases where no obstacle maps are available beforehand, for instance, in search and rescue scenarios, and on increasing the autonomy of drones in such situations. Our vision‐based mapping approach consists of two separate steps. First, the drone performs an overview flight at a safe altitude acquiring overlapping nadir images, while creating a high‐quality sparse map of the environment by using a state‐of‐the‐art photogrammetry method. Second, this map is georeferenced, densified by fitting a mesh model and converted into an Octomap obstacle map, which can be continuously updated while performing a task of interest near the ground or in the vicinity of objects. The generation of the overview obstacle map is performed in almost real time on the onboard computer of the drone, a map of size is created in , therefore, with enough time remaining for the drone to execute other tasks inside the area of interest during the same flight. We evaluate quantitatively the accuracy of the acquired map and the characteristics of the planned trajectories. We further demonstrate experimentally the safe navigation of the drone in an area mapped with our proposed approach.

We briefly review the general concept and expected market potential of microgrids, then discuss the optimization challengesassociated with planning local cross-sectorial energy systems. A fair technology-neutral approach to this optimization task leads to a hard problem, which has to be tackled with advanced methods of mathematical optimization.The power of this approach is illustrated ina case study, concerning the replacement of heating systems in an alpine valley. In this case study we see both the potential for cost reduction and for the reduction of CO2emissions by an integrated planning approach.

An efficient utilization of biomass fuels in power plants is often limited by the melting behavior of the biomass ash, which causes unplanned shutdowns of the plants. If the melting temperature of the ash is locally exceeded, deposits can form on the walls of the combustion chamber. In this paper, a bubbling fluidized bed combustion chamber with 50 MW biomass input is investigated that severely suffers deposit build-up in the freeboard during operation. The deposit layers affect the operation negatively in two ways: they act as an additional heat resistance in regions of heat extraction, and they can come off the wall and fall into the bed and negatively influence the fluidization behavior. To detect zones where ash melting can occur, the temperature distribution in the combustion chamber is calculated numerically using the commercial CPFD (computational particle fluid dynamics) code, Barracuda Version 15. Regions where the ash melting temperature is exceeded are compared with the fouling observed on the walls in the freeboard. The numerically predicted regions agree well with the observed location of the deposits on the walls. Next, the model is used to find an optimized operating point with fewer regions in which the ash melting temperature is exceeded. Therefore, three cases with different distributions of the inlet gas streams are simulated. The simulations show if the air inlet streams are moved from the freeboard to the necking area above the bed a more even temperature distribution is obtained over the combustion chamber. Hence, the areas where the ash melting temperatures are exceeded are reduced significantly and the formation of deposits in the optimized operational mode is much less likely.

The design and optimisation of a biomass grate furnace requires accurate and efficient models for the
combustion process on the grate as well as the turbulent reactive flow in the combustion chamber. Computational Fluid Dynamics (CFD) have been successfully applied for gas phase combustion. However, no numerical models for the biomass packed bed combustion, which can be used as engineering design tools, are commercially available at present. This paper presents an innovative 3D CFD model for biomass packed bed combustion consisting of an Euler-Granular model for hydrodynamics of gas-particle multiphase flow and a thermally thin particle model for combustion of biomass particles. Modelling the particle trajectories and the thermal conversion of each particle in the bed constitutes the simulation of the entire bed combustion. The simulation of a small-scale underfeed stoker furnace of KWB has been successfully performed by the application of the new packed bed combustion model. The positions of the drying, pyrolysis and char burnout zones in the fuel bed as well as the temperature distribution among the particles seem to be plausible and could be confirmed by observations. Furthermore, a good qualitative agreement concerning the flue gas temperatures measured by thermocouples at different positions in the combustion chamber, and CO emissions measured at boiler outlet could be achieved. The new packed bed model provides the advantages of considering the release profiles of species and energy from the fuel bed close to reality and enables to consider the chemical compositions, size and physical properties of the fuel particles as well as the influence of primary air
distribution and grate motion on the particle trajectories.

The availability of energy is important to our every day lives. Biomass-fuelled heating systems are comfortable and reach an efficiency form over 90 %. With a thermoelectric generator (TEG) it’s possible to convert a part of the heat directly into electrical power and so become self sufficient from electicity. The purpose of this thesis was to optimise an existing prototype of a combined heat and power (CHP) plant based on a pellet heating system and a thermoelectric generator. Balancing the energy flows, especially the losses, was also part of the thesis.
Tests with the prototype were done. Some with the originial prototype, some with additional insulation and some with preheating the combustion air. To examine the part load behaviour, tests were done at 10, 7 and 4 kW fuelheat input.
By insulating the TEG the performance rose from 153 W to 174 W. The insulation and the preheating of the combustion air from room temperature to 350 degree lead to an power output from 194 watt. All at 10 kW fuellheat input. Finally the following conclusions can be drawn: For the series product it is recommended to optimize the insulation of the TEG. As the preheating of the combustion air didn’t lead to the expected effects it should be left out.

The use of biomass in manually charged room heating appliances to cover the domestic heating demand has traditionally been of a high percentage in the European Union. As a result of continued or even increased use of firewood stoves, the enhancement of available stoves is a declared objective of the European Union.
Therefore, an example-optimization of the firewood stove Stûv 16/78-in (available on the European market) was performed in preparation of this paper. There was a primary- and a secondary optimization carried out to quantify the potential of optimization. When optimizing, the gas and particulate emissions was considered. The operating behavior were measured and evaluated by performing combustion experiments on a specially designed test rig.
The primary optimization is divided into five sub-steps. Each Step was quantified by gas analysis. The achieved reduction of particulate emissions was measured before and after the entire primary optimization.
In comparison to the delivery condition it was possible to reduce the CO emissions to one quarter and the particulate emissions from 100 mg / mn³ to 39 mg / mn³ over the course of the primary optimization.
As a secondary optimization, a Catalyst was implemented. The used catalyst is a solid-state catalyst in the modification of a heterogeneous supported catalyst in the shape of honeycomb, which is marketed by Clariant International Ltd. under the name "EnviCat ® Longlife Plus". The catalytically active materials platinum and palladium are used.
After a strictly implemented primary optimizations, a further reduction to half of emissions was achieved by the integration of the catalyst even though a bypass of 20 % had to be integrated to ensure the operating safety.

The optimal design of microgrids with thermal energy system requires optimization techniques that can provide investment and scheduling of the technology portfolio involved. In the modeling of such systems with seasonal storage capability, the two main challenges include the low temporal resolution of available data and the non-linear cost versus capacity relationship of solar thermal and heat storage technologies. This work overcomes these challenges by developing two different optimization models based on mixed-integer linear programming with objectives to minimize the total energy costs and carbon dioxide emissions. Piecewise affine functions are used to approximate the non-linear cost versus capacity behavior. The developed methods are applied to the optimal planning of a case study in Austria. The results of the models are compared based on the accuracy and real-time performance together with the impact of piecewise affine cost functions versus non-piecewise affine fixed cost functions. The results show that the investment decisions of both models are in good agreement with each other while the computational time for the 8760-h based model is significantly greater than the model having three representative periods. The models with piecewise affine cost functions show larger capacities of technologies than non-piecewise affine fixed cost function based models.

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.