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Register a new userCounter-intuitive behaviour of energy system models under CO2 caps and prices
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The mitigation of climate change requires a fundamental transition of the energy system. Affordability, reliability and the reduction of greenhouse gas emissions constitute central but often conflicting targets for this energy transition. Against this context, we reveal limitations and counter-intuitive results in the model-based optimization of energy systems, which are often applied for policy advice. When system costs are minimized in the presence of a CO2 cap, efficiency gains free a part of the CO2 cap, allowing cheap technologies to replace expensive low-emission technologies. Even more striking results are observed in a setup where emissions are minimized in the presence of a budget constraint. Increasing CO2 prices can oust clean, but expensive technologies out of the system, and eventually lead to higher emissions. These effects robustly occur in models of different scope and complexity. Hence, extreme care is necessary in the application of energy system optimization models to avoid misleading policy advice.
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Ambitious targets for renewable energy and CO2 taxation both represent political instruments for decarbonisation of the energy system. We model a high number of coupled electricity and heating systems, where the primary sources of CO2 neutral energy are from variable renewable energy sources (VRES), i.e., wind and solar generators. The model includes hourly dispatch of all technologies for a full year for every country in Europe. In each model run, the amount of renewable energy and the level of CO2 tax are fixed exogenously, while the cost-optimal composition of energy generation, conversion, transmission and storage technologies and the corresponding CO2 emissions are calculated. We show that even for high penetrations of VRES, a significant CO2 tax of more than 100 euro/tCO2 is required to limit the combined CO2 emissions from the sectors to less than 5% of 1990 levels, because curtailment of VRES, combustion of fossil fuels and inefficient conversion technologies are economically favoured despite the presence of abundant VRES. A sufficiently high CO2 tax results in the more efficient use of VRES by means of heat pumps and hot water storage, in particular. We conclude that a renewable energy target on its own is not sufficient; in addition, a CO2 tax is required to decarbonise the electricity and heating sectors and incentivise the least cost combination of flexible and efficient energy conversion and storage.
Following the paradigm set by attraction-repulsion-alignment schemes, a myriad of individual based models have been proposed to calculate the evolution of abstract agents. While the emergent features of many agent systems have been described astonishingly well with force-based models, this is not the case for pedestrians. Many of the classical schemes have failed to capture the fine detail of crowd dynamics, and it is unlikely that a purely mechanical model will succeed. As a response to the mechanistic literature, we will consider a model for pedestrian dynamics that attempts to reproduce the rational behaviour of individual agents through the means of anticipation. Each pedestrian undergoes a two-step time evolution based on a perception stage and a decision stage. We will discuss the validity of this game theoretical based model in regimes with varying degrees of congestion, ultimately presenting a correction to the mechanistic model in order to achieve realistic high-density dynamics.
In the present paper, a decomposition formula for the call price due to Al`{o}s is transformed into a Taylor type formula containing an infinite series with stochastic terms. The new decomposition may be considered as an alternative to the decomposition of the call price found in a recent paper of Al`{o}s, Gatheral and Radoiv{c}i{c}. We use the new decomposition to obtain various approximations to the call price in the Heston model with sharper estimates of the error term than in the previously known approximations. One of the formulas obtained in the present paper has five significant terms and an error estimate of the form $O( u^{3}(left|rhoright|+ u))$, where $ u$ is the vol-vol parameter, and $rho$ is the correlation coefficient between the price and the volatility in the Heston model. Another approximation formula contains seven more terms and the error estimate is of the form $O( u^4(1+|rho|)$. For the uncorrelated Hestom model ($rho=0$), we obtain a formula with four significant terms and an error estimate $O( u^6)$. Numerical experiments show that the new approximations to the call price perform especially well in the high volatility mode.
We present a detailed study of the geometry, structure and energetics of carbon nanotube caps. We show that the structure of a cap uniquely determines the chirality of the nanotube that can be attached to it. The structure of the cap is specified in a geometrical way by defining the position of six pentagons on a hexagonal lattice. Moving one (or more) pentagons systematically creates caps for other nanotube chiralities. For the example of the (10,0) tube we study the formation energy of different nanotube caps using ab-initio calculations. The caps with isolated pentagons have an average formation energy 0.29+/-0.01eV/atom. A pair of adjacent pentagons requires a much larger formation energy of 1.5eV. We show that the formation energy of adjacent pentagon pairs explains the diameter distribution in small-diameter nanotube samples grown by chemical vapor deposition.
Power system expansion models are a widely used tool for planning powersystems, especially considering the integration of large shares of renewableresources. The backbone of these models is an optimization problem, whichdepends on a number of economic and technical parameters. Although theseparameters contain significant uncertainties, the sensitivity of power systemmodels to these uncertainties is barely investigated. In this work, we introduce a novel method to quantify the sensitivity ofpower system models to different model parameters based on measuring theadditional cost arising from misallocating generation capacities. The value ofthis method is proven by three prominent test cases: the definition of capitalcost, different weather periods and different spatial and temporal resolutions.We find that the model is most sensitive to the temporal resolution. Fur-thermore, we explain why the spatial resolution is of minor importance andwhy the underlying weather data should be chosen carefully.
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