Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system -- a system where electricity supply covers demand -- is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. In this analysis, we focused on two drivers of power system adequacy: the impact of temperature variations on electricity demand, and of water inflows changes on hydro generation. Using a post-processing approach, based on results found in the literature, the inputs of a large-scale electricity market model covering the European region were modified. The results show that climate change may decrease total LOLE (Loss of Load Expectation) hours in Europe by more than 50%, as demand will largely decrease because of a higher temperatures during winter. We found that the climate change impact on demand tends to decrease LOLE values, while the climate change effects on hydrological conditions tend to increase LOLE values. The study is built on a limited amount of open-source data and can flexibly incorporate various sets of assumptions. Outcomes also show the current difficulties to reliably model the effects of climate change on power system adequacy. Overall, our presented method displays the relevance of climate change effects in electricity network studies

Thermal Direct Oil Management of Power Electronics in Electric Mobility

Efficient thermal management technologies in electric mobility promote sustainable transportation. The energy-efficient and lightweight direct cooling and heating of batteries, inverters and motors are enabling principals to achieve high power densities in the powertrain. Silicone oil is a key resource in terms of thermal, viscous and dialectical characteristics. The target is to integrate the necessary cooling through direct contact with the cooling liquid and the electrical conducting parts. This direct contact eliminates the need for a thermal conductor and electrical insulator between the metal and liquid and uses the sealed battery housing as a reservoir. The outlined concept is based on silicone oil direct cooling and heating in the housing of lithium-ion 18650 battery cells with an integrated thermoelectric element. The proposed setup is validated in two electrical motorcycles ethec alpha and ethec city from ETH Zurich

Characterization of Aerodynamic Effects and Unbalance in Rotor Dynamics Whirl Orbit

Mobility and artificial intelligence are the key drivers for the semiconductor industry. The requirements for high-performance chips are constantly increasing and technologies, such as ultra-precision machining, are necessary for this value chain of manufacturing. The exact bearing of rotating axes is one of the main tasks to produce precision workpieces. The influences that prevent these axes from perfect rotation originate from unbalance, gravity, dynamic elasticities of air in the bearing, and process forces. This paper aims to characterize these disturbances according to their origin and to describe the motion error. In this way, Moore's law can go on in chip manufacturing

Aerostatic Stiffness and Damping Analysis for High-Speed Air Bearings in Ultra-Precision Machine Tools

The microsystems technology drives manufacturing industries with the need for smaller structures for ultra-precision requirements. Precision combined with the allowed dynamics of the moving components are basic principles. Aerostatic bearings provide smooth and contactless movements, which are key resources for ultra-precision machine tools. Providing sufficient high stiffness properties for manufacturing applications, the air gap height of gas bearings has a size of just a few micrometres and shows a clear sensitivity to temperature and, in the case of journal bearings, also to speed. An algorithm developed computes the stiffness and damping properties and performs a full sensitivity analysis investigating all physical parameters of a plain journal bearing. The advanced approach combines a 2D-Thin-Film Finite Difference Method (FDM) with the Infinitesimal Perturbation Method (IFP) to achieve an eccentricity and attitude angle-dependent pressure distribution. This study describes a fully parametrized approach to serve as a base for the sensitivity analysis during the design phase and for the model of the control system. The outlined concept is evaluated on a high-speed journal bearing with the dimensionless DN factor of 3 ∙ 106. The orifice design in combination with industry-standard inlet pressure ranges is the main design restriction. The described correlation matrices are evaluated with the Latin Hypercube Sampling Method and ensures together with the 2D Reynolds equation-based FDM algorithm a short calculation period. The proposed system is validated and further discussed through a rotor dynamic study on a high-speed aerostatic machine tool spindle

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system – a system where electricity supply covers demand – is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. In this analysis, we focused on two drivers of power system adequacy: the impact of temperature variations on electricity demand, and of water inflows changes on hydro generation. Using a post-processing approach, based on results found in the literature, the inputs of a large-scale electricity market model covering the European region were modified. The results show that climate change may decrease total LOLE (Loss of Load Expectation) hours in Europe by more than 50%, as demand will largely decrease because of a higher temperatures during winter. We found that the climate change impact on demand tends to decrease LOLE values, while the climate change effects on hydrological conditions tend to increase LOLE values. The study is built on a limited amount of open-source data and can flexibly incorporate various sets of assumptions. Outcomes also show the current difficulties to reliably model the effects of climate change on power system adequacy. Overall, our presented method displays the relevance of climate change effects in electricity network studies

Fleet learning of thermal error compensation in machine tools

Thermal error compensation of machine tools promotes sustainable production. The thermal adaptive learning control (TALC) and machine learning approaches are the required enabling principals. Fleet learnings are key resources to develop sustainable machine tool fleets in terms of thermally induced machine tool error. The target is to integrate each machine tool of the fleet in a learning network. Federated learning with a central cloud server and dedicated edge computing on the one hand keeps the independence of each individual machine tool high and on the other hand leverages the learning of the entire fleet. The outlined concept is based on the TALC, combined with a machine agnostic and machine specific characterization and communication. The proposed system is validated with environmental measurements for two machine tools of the same type, one situated at ETH Zurich and the other one at TU Wien

Condition monitoring system for machine tool auxiliaries

Failures on machine tools not only occur on main components, but also on auxiliaries like cooling units or oil mist separators, which causes productivity losses similar to failures on main machine components. Due to their separation from the machine’s control network, their health status is in most cases not monitored. In this study, a new approach for online condition monitoring of auxiliary units by the example of an oil mist separator connected to a 5-axis machine tool is presented. The data is analyzed via machine learning principles in order to deduct an adequate condition assessment, encompassing environmental influences.ISSN:2212-827

Multi-variable rotor dynamics optimization of an aerostatic spindle

Single-objective multi-variable rotor dynamics optimization promotes the design of spindles in sustainable production. The goal is efficient partial automation of the design process and the optimization of the spindle shaft. The Timoshenko-Ehrenfest beam theory together with the Latin hypercube sampling and direct optimization methods are selected principles. The parametric model and frequency analysis, as well as the transient analysis, are key resources for rapid system development. Challenges are the computational effort to find the steady state of the milling simulation for the optimization problem. In addition, this entire process must meet overall performance requirements during the system design phase. The outlined concept is based on rotor dynamics optimization and validated for an aerostatic spindle in a milling process. The validation measurements are performed according to ISO 230-7 on the aerostatic spindle.ISSN:1755-5817ISSN:1878-001

Incorporating climate change effects into the European power system adequacy assessment using a post-processing method

The demand-supply balance of electricity systems is fundamentally linked to climate conditions. In light of this, the present study aims to model the effect of climate change on the European electricity system, specifically on its long-term reliability. A resource adequate power system – a system where electricity supply covers demand – is sensitive to generation capacity, demand patterns, and the network structure and capacity. Climate change is foreseen to affect each of these components. In this analysis, we focused on two drivers of power system adequacy: the impact of temperature variations on electricity demand, and of water inflows changes on hydro generation. Using a post-processing approach, based on results found in the literature, the inputs of a large-scale electricity market model covering the European region were modified. The results show that climate change may decrease total LOLE (Loss of Load Expectation) hours in Europe by more than 50%, as demand will largely decrease because of a higher temperatures during winter. We found that the climate change impact on demand tends to decrease LOLE values, while the climate change effects on hydrological conditions tend to increase LOLE values. The study is built on a limited amount of open-source data and can flexibly incorporate various sets of assumptions. Outcomes also show the current difficulties to reliably model the effects of climate change on power system adequacy. Overall, our presented method displays the relevance of climate change effects in electricity network studies