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The increasing share of intermittent and non-dispatchable power sources such as wind and solar photovoltaic (PV) power in the electrical energy generation mix pose operational challenges in the electric power system and corresponding markets. Co-locating wind and PV power parks, forming utility-scale hybrid power parks (HPPs), means that the power sources can share grid connection, land, permitting procedures as well as operation and maintenance work. According to the results of the thesis, the power output of co-located wind and PV power parks are generally negatively correlated, which results in a smoothed aggregated power output. The seasonal and diurnal time scales contribute the most to the negative correlation, where wind power parks are likely to be more negatively correlated than any randomly chosen site. The smoothing effect as a result of aggregation is also studied in terms of probabilistic forecasting, which corresponds to estimating the uncertainty of power production predictions by means of a probabilistic distribution. By forecasting co-located wind and PV power production, the probabilistic forecasts can be improved, which is explained by the aggregated time series being smoother and therefore more straightforward to predict. The value of improved forecasts is also realized in the day-ahead market, where sharper and more reliable probabilistic forecasts improve decision making by lowering imbalance costs. Furthermore, when trading energy from HPPs with storage, probabilistic forecasts reduce the energy throughput of the battery and is preferable over a deterministic model when the regulating prices are more difficult to forecast than the spot-prices, and when the battery energy capacity is low. Finally, a techno-economic simulation model to assess and forecast the potential to retrofit existing wind power parks with PV power parks was developed. Retrofitting means that a PV power park is connected behind the same point of interconnection to the elect

The increasing share of intermittent and non-dispatchable power sources such as wind and solar photovoltaic (PV) power in the electrical energy generation mix pose operational challenges in the electric power system and corresponding markets. Co-locating wind and PV power parks, forming utility-scale hybrid power parks (HPPs), means that the power sources can share grid connection, land, permitting procedures as well as operation and maintenance work. According to the results of the thesis, the power output of co-located wind and PV power parks are generally negatively correlated, which results in a smoothed aggregated power output. The seasonal and diurnal time scales contribute the most to the negative correlation, where wind power parks are likely to be more negatively correlated than any randomly chosen site. The smoothing effect as a result of aggregation is also studied in terms of probabilistic forecasting, which corresponds to estimating the uncertainty of power production predictions by means of a probabilistic distribution. By forecasting co-located wind and PV power production, the probabilistic forecasts can be improved, which is explained by the aggregated time series being smoother and therefore more straightforward to predict. The value of improved forecasts is also realized in the day-ahead market, where sharper and more reliable probabilistic forecasts improve decision making by lowering imbalance costs. Furthermore, when trading energy from HPPs with storage, probabilistic forecasts reduce the energy throughput of the battery and is preferable over a deterministic model when the regulating prices are more difficult to forecast than the spot-prices, and when the battery energy capacity is low. Finally, a techno-economic simulation model to assess and forecast the potential to retrofit existing wind power parks with PV power parks was developed. Retrofitting means that a PV power park is connected behind the same point of interconnection to the elect

The increasing share of intermittent and non-dispatchable power sources such as wind and solar photovoltaic (PV) power in the electrical energy generation mix pose operational challenges in the electric power system and corresponding markets. Co-locating wind and PV power parks, forming utility-scale hybrid power parks (HPPs), means that the power sources can share grid connection, land, permitting procedures as well as operation and maintenance work. According to the results of the thesis, the power output of co-located wind and PV power parks are generally negatively correlated, which results in a smoothed aggregated power output. The seasonal and diurnal time scales contribute the most to the negative correlation, where wind power parks are likely to be more negatively correlated than any randomly chosen site. The smoothing effect as a result of aggregation is also studied in terms of probabilistic forecasting, which corresponds to estimating the uncertainty of power production predictions by means of a probabilistic distribution. By forecasting co-located wind and PV power production, the probabilistic forecasts can be improved, which is explained by the aggregated time series being smoother and therefore more straightforward to predict. The value of improved forecasts is also realized in the day-ahead market, where sharper and more reliable probabilistic forecasts improve decision making by lowering imbalance costs. Furthermore, when trading energy from HPPs with storage, probabilistic forecasts reduce the energy throughput of the battery and is preferable over a deterministic model when the regulating prices are more difficult to forecast than the spot-prices, and when the battery energy capacity is low. Finally, a techno-economic simulation model to assess and forecast the potential to retrofit existing wind power parks with PV power parks was developed. Retrofitting means that a PV power park is connected behind the same point of interconnection to the elect

Decision making in daily maintenance requires consideration of multiple factors. The importance of each of the factors fluctuates depending on the repair scenario and the needs of the maintainer. In order to include the prioritisation of multiple criteria, a weighted decision making model is developed. The model evaluates all repair options and rates them individually for three decision making factors: survivability, cost, and downtime. The factor ratings are aggregated using designated weights, resulting in a final score for each of the repair options. This type of decision making evaluation provides flexibility in considering repair options that may otherwise be deemed unfavourable because of one factor. Case study results show one of five considered options as the best, for three of the four weight sets. The resulting best option of the other weight set demonstrates that definition of best repair is dependent on the priority of decision factors., Aerospace Transport & Operations

Decision making in daily maintenance requires consideration of multiple factors. The importance of each of the factors fluctuates depending on the repair scenario and the needs of the maintainer. In order to include the prioritisation of multiple criteria, a weighted decision making model is developed. The model evaluates all repair options and rates them individually for three decision making factors: survivability, cost, and downtime. The factor ratings are aggregated using designated weights, resulting in a final score for each of the repair options. This type of decision making evaluation provides flexibility in considering repair options that may otherwise be deemed unfavourable because of one factor. Case study results show one of five considered options as the best, for three of the four weight sets. The resulting best option of the other weight set demonstrates that definition of best repair is dependent on the priority of decision factors., Air Transport & Operations

Microgrids comprising multiple distributed energy resources are being increasingly integrated into electricity networks. Considering the multiplicity of the distributed energy resources, it is impractical to represent them as detailed models in power system simulations. Hence, it will be attractive for electricity utilities to employ simplified models of microgrids in dynamic studies. This paper proposes a method for representing grid-Tied microgrids as simplified models in such studies. The proposed aggregated modelling approach investigates the applicability of modal analysis as a tool for model aggregating of grid-Tied microgrids. A new step to automate the selection of dominant modes is introduced in the model aggregating procedure. Validity of the reduced order dynamic equivalents is tested under different operating conditions. The paper demonstrates that the enhanced modal analysis reduces the model order and enables the representation of a hybrid microgrid in terms of the states of the microgrid PCC, while preserving the important dynamics.

A large body of literature suggests that amyotrophic lateral sclerosis (ALS) pathology is intimately linked with neuroinflammation, specifically activation and recruitment of microglia and astrocytes. The actual cause of gliosis is unclear. Extracellular Cu/Zn superoxide dismutase (SOD1) has recently been shown to activate microglia in a CD14 dependant mechanism providing one potential pathway by which glial cells become activated. As protein inclusions are thought to be an important part of ALS pathology and are associated with all forms of ALS, we sought to determine if aggregated SOD1 would activate microglia. Recombinant SOD1 was aggregated and this, or monomeric forms of SOD1 were then added to EOC.13 microglial cells or primary microglial cells in culture. Although monomeric mutant SOD1 has been shown to promote microglial activation in the past, we found that aggregated SOD1 was able to much more efficiently activate microglia in culture when compared with the unaggregated form of mutant SOD1. In addition to CD14 dependant pathways, aggregated SOD1 also bound to the surface of glial cells and was internalized in a lipid raft and scavenger receptor dependent manner. We have for the first time shown that aggregated mutant SOD1 potently activates microglia. These results suggest that there may be a potential link between protein aggregation and microglial activation in ALS.

A large body of literature suggests that amyotrophic lateral sclerosis (ALS) pathology is intimately linked with neuroinflammation, specifically activation and recruitment of microglia and astrocytes. The actual cause of gliosis is unclear. Extracellular Cu/Zn superoxide dismutase (SOD1) has recently been shown to activate microglia in a CD14 dependant mechanism providing one potential pathway by which glial cells become activated. As protein inclusions are thought to be an important part of ALS pathology and are associated with all forms of ALS, we sought to determine if aggregated SOD1 would activate microglia. Recombinant SOD1 was aggregated and this, or monomeric forms of SOD1 were then added to EOC.13 microglial cells or primary microglial cells in culture. Although monomeric mutant SOD1 has been shown to promote microglial activation in the past, we found that aggregated SOD1 was able to much more efficiently activate microglia in culture when compared with the unaggregated form of mutant SOD1. In addition to CD14 dependant pathways, aggregated SOD1 also bound to the surface of glial cells and was internalized in a lipid raft and scavenger receptor dependent manner. We have for the first time shown that aggregated mutant SOD1 potently activates microglia. These results suggest that there may be a potential link between protein aggregation and microglial activation in ALS.

A large body of literature suggests that amyotrophic lateral sclerosis (ALS) pathology is intimately linked with neuroinflammation, specifically activation and recruitment of microglia and astrocytes. The actual cause of gliosis is unclear. Extracellular Cu/Zn superoxide dismutase (SOD1) has recently been shown to activate microglia in a CD14 dependant mechanism providing one potential pathway by which glial cells become activated. As protein inclusions are thought to be an important part of ALS pathology and are associated with all forms of ALS, we sought to determine if aggregated SOD1 would activate microglia. Recombinant SOD1 was aggregated and this, or monomeric forms of SOD1 were then added to EOC.13 microglial cells or primary microglial cells in culture. Although monomeric mutant SOD1 has been shown to promote microglial activation in the past, we found that aggregated SOD1 was able to much more efficiently activate microglia in culture when compared with the unaggregated form of mutant SOD1. In addition to CD14 dependant pathways, aggregated SOD1 also bound to the surface of glial cells and was internalized in a lipid raft and scavenger receptor dependent manner. We have for the first time shown that aggregated mutant SOD1 potently activates microglia. These results suggest that there may be a potential link between protein aggregation and microglial activation in ALS.

My research has focused on the precipitation of iron minerals, mostly oxides and hydroxides, in aqueous systems across steep pH and Eh gradients. Unlike most work in this area, which involves loose precipitates filtered out of solutions, I have focused on precipitated aggregates and, more specifically, on those that are self-organized into dis-crete structures or patterns. This topic is actually quite narrow because such types of natu-ral material organization are rare within the geochemical realm compared with the mor-phological richness of crystals, not to mention the phantasmagoria of life.My investigation of iron-based examples has included three types of physical sys-tems: 1) growth of tubular structures around bubbles coming off a charged cathode in a free solution where convection dominates; 2) development of Liesegang patterns within gelled solutions due to reactions dominated by diffusion; and 3) formation of a cement-ing matrix within the aqueous interface between particles of silica. The third case in-volves physical characteristics of the first two in that it is primarily a tightly packed, dif-fusion-limited process, but at least initially the generation of gases can create mechani-cally driven flows through the interstitial spaces.All three systems and studies are inextricably related for both tubular ('vermi-form') structures and Liesegang patterns are commonly found in natural iron-cemented sediments such as massive laterite, ironstone deposits, and banded iron formations. They are also found on a much smaller scale within discrete 'concretions' and represent the two poles of the gradient between convection-based and diffusion-based systems. As Seilacher (2001) states concerning concretions, "the distribution and precipitation of dis-solved constituents, such iron and manganese, proceeds in two radically different mor-phospaces, which are typified by dendrites [and I would include tubes and other linear growth] on the one hand and Liesegang rings on t

This chapter contains sections titled: Introduction and Overview Aggregate and Survey Data Availability Bias and Variance of Variance Component Estimators Based on Aggregate and Survey Data Simulation Studies Using Auxiliary Variables to Reduce Aggregation Effects Conclusions Acknowledgements

This chapter contains sections titled: Introduction and Overview Aggregate and Survey Data Availability Bias and Variance of Variance Component Estimators Based on Aggregate and Survey Data Simulation Studies Using Auxiliary Variables to Reduce Aggregation Effects Conclusions Acknowledgements

This chapter contains sections titled: Introduction and Overview Aggregate and Survey Data Availability Bias and Variance of Variance Component Estimators Based on Aggregate and Survey Data Simulation Studies Using Auxiliary Variables to Reduce Aggregation Effects Conclusions Acknowledgements

This chapter contains sections titled: Introduction and Overview Aggregate and Survey Data Availability Bias and Variance of Variance Component Estimators Based on Aggregate and Survey Data Simulation Studies Using Auxiliary Variables to Reduce Aggregation Effects Conclusions Acknowledgements

The increasing amount of renewable energy sources challenges power grid operators due to the dependence on mostly unpredictable power generation by nature. Virtual power plants provide a way to solve this problem by aggregating multiple generator units, controllable loads and storage systems to serve as just one power plant. Even flexibilities of small domestic energy components like electric water heaters (EWH), heat pumps, e-mobility etc. can be pooled to a virtual power plant to allow prosumers active participation in energy markets. The ultimative goal of the research project Flex+ is to develop and establish a concept to allow a marketing of these domestic flexibilities in Austria. This thesis is part of the project and focuses on aggregation of remotely controllable electric water heaters. Different methods for forecasting hot water consumption of EWHs were investigated to enable generation of optimized power schedules. After analyzing the available data, two statistical models (SARIMA, exponential smoothing) were compared with a machine learning approach utilizing a recurrent neural network (LSTM). Evaluation of these forecasting models showed a superior prediction skill of the SARIMA method to exponential smoothing and more complex artificial neural networks. Furthermore, the energy flexibility potential of EWHs is explained and strategies for controlling a cluster of heater devices were examined. Two methods for calculating a baseline are presented to facilitate determination of the operating point in case of balancing energy requests. A test of these calculation methods in a simulated environment showed that it is advantageous to periodically exchange heaters during activations to be able to satisfy balancing requests for a prolonged period of time. Moreover, the simulation demonstrated that the developed control algorithm is working as desired and both baseline modes can be used to support calculation of aggregated negative balancing energy from a virtual, Masterarbeit Universität Innsbruck 2020

The increasing amount of renewable energy sources challenges power grid operators due to the dependence on mostly unpredictable power generation by nature. Virtual power plants provide a way to solve this problem by aggregating multiple generator units, controllable loads and storage systems to serve as just one power plant. Even flexibilities of small domestic energy components like electric water heaters (EWH), heat pumps, e-mobility etc. can be pooled to a virtual power plant to allow prosumers active participation in energy markets. The ultimative goal of the research project Flex+ is to develop and establish a concept to allow a marketing of these domestic flexibilities in Austria. This thesis is part of the project and focuses on aggregation of remotely controllable electric water heaters. Different methods for forecasting hot water consumption of EWHs were investigated to enable generation of optimized power schedules. After analyzing the available data, two statistical models (SARIMA, exponential smoothing) were compared with a machine learning approach utilizing a recurrent neural network (LSTM). Evaluation of these forecasting models showed a superior prediction skill of the SARIMA method to exponential smoothing and more complex artificial neural networks. Furthermore, the energy flexibility potential of EWHs is explained and strategies for controlling a cluster of heater devices were examined. Two methods for calculating a baseline are presented to facilitate determination of the operating point in case of balancing energy requests. A test of these calculation methods in a simulated environment showed that it is advantageous to periodically exchange heaters during activations to be able to satisfy balancing requests for a prolonged period of time. Moreover, the simulation demonstrated that the developed control algorithm is working as desired and both baseline modes can be used to support calculation of aggregated negative balancing energy from a virtual, Masterarbeit Universität Innsbruck 2020