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Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model.

Abstract Electricity markets in Europe become increasingly interconnected due to new grid connections and market coupling regulations. This paper examines the interdependencies between the Swiss electricity market and those of neighbouring countries. The Swiss market serves as a good example for a smaller electricity market which is increasingly affected by developments in the large neighbouring countries. To study these cross-border effects, especially those on Swiss electricity prices, we apply two different methodologies, an econometric and a Nash-Cournot equilibrium model. The analyses show that the Swiss electricity price correlates strongly with the German electricity price in the summer, but tends to follow the French electricity price in the winter. Another finding is that gas prices and the electricity load of neighbouring countries have a significant influence on prices. In particular, the load of France and Italy is driving up Swiss prices in the winter, while the German electricity demand and renewable energy generation have a larger influence on Swiss prices in the summer.

Accelerators for heavy-ion inertial fusion energy (HIF) have an economic incentive to fit beam tubes tightly to beams, putting them at risk from electron clouds produced by emission of electrons and gas from walls. Theory and PIC simulations suggest that the electrons will be radially trapped in the /spl ges/1 kV ion-beam potential. We are beginning studies on the High-Current Experiment (HCX) with unique capabilities to characterize electron production and trapping, the effects on ion beams, and mitigation techniques. We are measuring the flux of electrons and gas evolved from a target, whose angle to the beam can be varied between 78/spl deg/ and 88/spl deg/ from normal incidence. Quadrupole magnets are operating with a variety of internal charged particle diagnostics to measure the beam halo loss, net charge, electron ionization rate, and gas density.

We study the role of technology subsidies in climate policies, using a simple dynamic equilibrium model with learning-by-doing. The optimal subsidy rate of a carbon-free technology is high when the technology is first adopted, but falls significantly over the next decades. However, the efficiency costs of uniform instead of optimal subsidies, may be low if there are introduction or expansion constraints for a new technology. Finally, supporting existing energy technologies only, may lead to technology lock-in, and the impacts of lock-in increase with the learning potential of new technologies as well as the possibilities for early entry and thight carbon constraints.

The main problem involved in utilization of solar energy is low efficiency of photovoltaic conversion and high cost. The objectives of the paper are to increase the efficiency of photovoltaic cells and to reduce the cost of the photovoltaic module. A novel method is presented in this paper to increase the efficiency of photovoltaic modules and reduce the cost by the use of specially designed solar concentrators. The efficiency of photovoltaic module is increased by covering an angle of nearly 180° so that the sun's radiation converges through out the day from all angles on to a fixed flat plate module. The cost is reduced by use of cheaper glass for module and covering a wider area for the radiation. The above objectives are achieved by the use of a special arrangement of three lenses for the concentrators.

Since the invention of fluorescent light sources, there is strong interest in Eu3+ activated phosphors as they are able to provide a high color rendering index (CRI) and luminous efficacy, which will also hold for phosphor converted light emitting diodes. Due to an efficient U6+ to Eu3+ energy transfer in Gd3Li3Te2O12:U6+,Eu3+, this inorganic composition shows red photoluminescence peaking at 611 nm. That means Eu3+ photoluminescence can be nicely sensitized via excitation into the U6+ excitation bands. Therefore, photoluminescence properties, such as temperature dependent emission and emission lifetime measurements, are presented. Charge transfer bands were investigated in detail. Additionally, density functional theory calculations reveal the band structure of the pure, i.e., non-doped host material. Obtained theoretical results were evaluated experimentally by the aid of diffuse UV reflectance spectroscopy.

Abstract The convergence characteristics of two Monte Carlo models are investigated in this paper. These two models or their variations have been used by the electric power industry. Model 1 is based on the random sampling of generator and transmission line status for each hour. Model 2 advances the generation and transmission using the next-event approach. Mathematical analysis and system studies indicate that model 2 converges slower than model 1. The simulation time for model 2 is generally more than twice the mean duration of loss of load. The CPU time per year of simulation is shorter for model 2. However, the total CPU time for model 2 is much longer than for model 1.

Global sensitivity analysis (GSA) of distribution system with respect to stochastic PV and load variations plays an important role in designing optimal voltage control schemes. This paper proposes a data-driven framework for GSA of distribution system. In particular, two representative surrogate modeling-based approaches are developed, including the traditional Gaussian process-based and the analysis of variance (ANOVA) kernel ones. The key idea is to develop a surrogate model that captures the hidden global relationship between voltage and real and reactive power injections from the historical data. With the surrogate model, the Sobol indices can be conveniently calculated through either the sampling-based method or the analytical method to assess the global sensitivity of voltage to variations of load and PV power injections. The sampling-based method estimates the Sobol indices using Monte Carlo simulations while the analytical method calculates them by resorting to the ANOVA expansion framework. Comparison results with other model-based GSA methods on the unbalanced three-phase IEEE 37-bus and 123-bus distribution systems show that the proposed framework can achieve much higher computational efficiency with negligible loss of accuracy. The results on a real 240-bus distribution system using actual smart meter data further validate the feasibility and scalability of the proposed framework.