arXiv subjects

Johannes Schmidt

Publications and source records attributed to Johannes Schmidt.

At least 37 records · Page 2Linked to original sources

arXiv2021

The perils of automated fitting of datasets: the case of a wind turbine cost model

Rinne et al. conduct an interesting analysis of the impact of wind turbine technology and land-use on wind power potentials, which allows profound insights into each factors contribution to overall potentials. The paper presents a detailed model of site-specific wind turbine investment cost (i.e. road- and grid access costs) complemented by a model used to estimate site-independent costs. We believe that propose a cutting edge model of site-specific investment costs. However, the site-independent cost model is flawed in our opinion. This flaw most likely does not impact the results presented in the paper, although we expect a considerable generalization error. Thus the application of the wind turbine cost model in other contexts may lead to unreasonable results. More generally, the derivation of the wind turbine cost model serves as an example of how applications of automated regression analysis can go wrong.

stat.AP↗

arXiv2021

Improvements to Modern Portfolio Theory based models applied to electricity systems

With the increase of variable renewable energy sources (VRES) share in electricity systems, manystudies were developed in order to determine their optimal technological and spatial mix. Modern PortfolioTheory (MPT) has been frequently applied in this context. However, some crucial aspects, important inenergy planning, are not addressed by these analyses. We, therefore, propose several improvements andevaluate how each change in formulation impacts results. More specifically, we address generation costs, system demand, and firm energy output, present a formal model and apply it to the case of Brazil. Wefound that, after including our proposed modifications, the resulting efficient frontier differs strongly fromthe one obtained in the original formulation. Portfolios with high output standard deviation are not ableto provide a firm output level at competitive costs. Furthermore, we show that diversification plays animportant role in smoothing output from VRES portfolios

econ.TH↗

arXiv2021

Computational Performance of Deep Reinforcement Learning to find Nash Equilibria

We test the performance of deep deterministic policy gradient (DDPG), a deep reinforcement learning algorithm, able to handle continuous state and action spaces, to learn Nash equilibria in a setting where firms compete in prices. These algorithms are typically considered model-free because they do not require transition probability functions (as in e.g., Markov games) or predefined functional forms. Despite being model-free, a large set of parameters are utilized in various steps of the algorithm. These are e.g., learning rates, memory buffers, state-space dimensioning, normalizations, or noise decay rates and the purpose of this work is to systematically test the effect of these parameter configurations on convergence to the analytically derived Bertrand equilibrium. We find parameter choices that can reach convergence rates of up to 99%. The reliable convergence may make the method a useful tool to study strategic behavior of firms even in more complex settings. Keywords: Bertrand Equilibrium, Competition in Uniform Price Auctions, Deep Deterministic Policy Gradient Algorithm, Parameter Sensitivity Analysis

cs.GT↗

arXiv2021

Winterization of Texan power system infrastructure is profitable but risky

We deliver the first analysis of the 2021 cold spell in Texas which combines temperature dependent load estimates with temperature dependent estimates of power plant outages to understand the frequency of loss of load events, using a 71 year long time series of climate data. The expected revenue from full winterization is 11.74bn$ over a 30 years investment period. We find that large-scale winterization, in particular of gas infrastructure and gas power plants, would be profitable, as related costs for winterization are substantially lower. At the same moment, the associated investment risks are high due to the low-frequency of events - the 2021 event was the largest and we observe only 8 other similar ones for the simulated 71 years. As risks to investors are considerable, regulatory measures may be necessary to enforce winterization.

q-fin.GN↗

arXiv2021

Reviewing methods and assumptions for high-resolution large-scale onshore wind energy potential assessments

The rapid uptake of renewable energy technologies in recent decades has increased the demand of energy researchers, policymakers and energy planners for reliable data on the spatial distribution of their costs and potentials. For onshore wind energy this has resulted in an active research field devoted to analysing these resources for regions, countries or globally. A particular thread of this research attempts to go beyond purely technical or spatial restrictions and determine the realistic, feasible or actual potential for wind energy. Motivated by these developments, this paper reviews methods and assumptions for analysing geographical, technical, economic and, finally, feasible onshore wind potentials. We address each of these potentials in turn, including aspects related to land eligibility criteria, energy meteorology, and technical developments relating to wind turbine characteristics such as power density, specific rotor power and spacing aspects. Economic aspects of potential assessments are central to future deployment and are discussed on a turbine and system level covering levelized costs depending on locations, and the system integration costs which are often overlooked in such analyses. Non-technical approaches include scenicness assessments of the landscape, expert and stakeholder workshops, willingness to pay / accept elicitations and socioeconomic cost-benefit studies. For each of these different potential estimations, the state of the art is critically discussed, with an attempt to derive best practice recommendations and highlight avenues for future research.

econ.GN↗

arXiv2021

Parameterized Complexity of Logic-Based Argumentation in Schaefer's Framework

Logic-based argumentation is a well-established formalism modelling nonmonotonic reasoning. It has been playing a major role in AI for decades, now. Informally, a set of formulas is the support for a given claim if it is consistent, subset-minimal, and implies the claim. In such a case, the pair of the support and the claim together is called an argument. In this paper, we study the propositional variants of the following three computational tasks studied in argumentation: ARG (exists a support for a given claim with respect to a given set of formulas), ARG-Check (is a given set a support for a given claim), and ARG-Rel (similarly as ARG plus requiring an additionally given formula to be contained in the support). ARG-Check is complete for the complexity class DP, and the other two problems are known to be complete for the second level of the polynomial hierarchy (Parson et al., J. Log. Comput., 2003) and, accordingly, are highly intractable. Analyzing the reason for this intractability, we perform a two-dimensional classification: first, we consider all possible propositional fragments of the problem within Schaefer's framework (STOC 1978), and then study different parameterizations for each of the fragment. We identify a list of reasonable structural parameters (size of the claim, support, knowledge-base) that are connected to the aforementioned decision problems. Eventually, we thoroughly draw a fine border of parameterized intractability for each of the problems showing where the problems are fixed-parameter tractable and when this exactly stops. Surprisingly, several cases are of very high intractability (paraNP and beyond).

cs.AI↗

arXiv2020

Towards a global dynamic wind atlas: A multi-country validation of wind power simulation from MERRA-2 and ERA-5 reanalyses bias-corrected with the Global Wind Atlas

Reanalysis data are widely used for simulating renewable energy and in particular wind power generation. While MERRA-2 has been a de-facto standard in many studies, the newer ERA5- reanalysis recently gained importance. Here, we use these two datasets to simulate wind power generation and evaluate the respective quality in terms of correlations and errors when validated against historical wind power generation. However, due to their coarse spatial resolution, reanalyses fail to adequately represent local climatic conditions. We therefore additionally apply mean bias correction with two versions of the Global Wind Atlas (GWA) and assess the respective quality of resulting simulations. Potential users of the dataset can also benefit from our analysis of the impact of spatial and temporal aggregation on simulation quality indicators. While similar studies have been conducted, they mainly cover limited areas in Europe. In contrast, we look into regions, which globally differ significantly in terms of the prevailing climate: the US, Brazil, South-Africa, and New Zealand. Our principal findings are that (i) ERA5 outperforms MERRA-2, (ii) no major improvements can be expected by using bias-correction with GWA2, while GWA3 even reduces simulation quality, and (iii) temporal aggregation increases correlations and reduces errors, while spatial aggregation does so only consistently when comparing very low and very high aggregation levels.

stat.AP↗

arXiv2020

The Cost of Undisturbed Landscapes

By 2030 Austria aims to meet 100% of its electricity demand from domestic renewable sources, with most of the additional generation coming from wind and solar energy. Apart from the benefit of reducing CO2 emissions and, potentially, system cost, wind power is associated with negative impacts at the local level, such as interference with landscape aesthetics. Some of these impacts might be avoided by using alternative renewable energy technologies. Thus, we quantify the opportunity cost of wind power versus its best feasible alternative solar photovoltaics, using the power system model medea. Our findings suggest that the cost of undisturbed landscapes is considerable, particularly when solar PV is mainly realized on roof-tops. Under a wide range of assumptions, the opportunity cost of wind power is high enough to allow for significant compensation of the ones affected by local, negative wind turbine externalities.

econ.GN↗

arXiv2020

A lattice gas model for generic one-dimensional Hamiltonian Systems

We present a three-lane exclusion process that exhibits the same universal fluctuation pattern as generic one-dimensional Hamiltonian dynamics with short-range interactions, viz., with two sound modes in the Kardar-Parisi-Zhang (KPZ) universality class (with dynamical exponent $z=3/2$ and symmetric Prähofer-Spohn scaling function) and a superdiffusive heat mode with dynamical exponent $z=5/3$ and symmetric Lévy scaling function. The lattice gas model is amenable to efficient numerical simulation. Our main findings, obtained from dynamical Monte-Carlo simulation, are: (i) The frequently observed numerical asymmetry of the sound modes is a finite time effect. (ii) The mode-coupling calculation of the scale factor for the $5/3$-Lévy-mode gives at least the right order of magnitude. (iii) There are significant diffusive corrections which are non-universal.

cond-mat.stat-mech↗

arXiv2020

Simulation of long-term time series of solar photovoltaic power: is the ERA5-land reanalysis the next big step?

Modelling long time series of photovoltaic electricity generation in high temporal resolution using reanalysis data has become a commonly used alternative to assess the viability of systems with high shares of renewables, their risks of failure and probability of extreme events. While there is a considerable amount of literature evaluating the accuracy of the original solar radiation and temperature variables in these data sets, the validation of the calculated output of photovoltaic installations is scarce and usually limited to locations in Europe. This work combines the new ERA5-land reanalysis data set and PV_LIB to generate hourly time series of photovoltaic electricity generation for several years and validates the results using individual data of 57 large photovoltaic plants located in Chile. Results are also compared with PV output for these locations calculated using renewables.ninja, a platform relying on MERRA-2, a global reanalysis with five times lower spatial resolution. Accuracy and bias indicators are satisfactory for plants that do not present severe anomalies in their generation profiles and where basic plant characteristics such as size and orientation match our model assumptions. However, the improvements in indicators over results obtained with renewables.ninja from MERRA-2 are minor. The validation process serves not only to confirm the suitability of the proposed workflow to model the output of individual photovoltaic plants, but also to list and discuss data quality and availability issues. Efforts towards availability and standardization of data of individual installations are necessary to improve the basis for further developments.

physics.soc-ph↗

arXiv2019

Machine learning models show similar performance to Renewables.ninja for generation of long-term wind power time series even without location information

Driven by climatic processes, wind power generation is inherently variable. Long-term simulated wind power time series are therefore an essential component for understanding the temporal availability of wind power and its integration into future renewable energy systems. In the recent past, mainly power curve based models such as Renewables.ninja (RN) have been used for deriving synthetic time series for wind power generation despite their need for accurate location information as well as for bias correction, and their insufficient replication of extreme events and short-term power ramps. We assess how time series generated by machine learning models (MLM) compare to RN in terms of their ability to replicate the characteristics of observed nationally aggregated wind power generation for Germany. Hence, we apply neural networks to one MERRA2 reanalysis wind speed input dataset with no location information and one with basic location information. The resulting time series and the RN time series are compared with actual generation. Both MLM time series feature equal or even better time series quality than RN depending on the characteristics considered. We conclude that MLM models can, even when reducing information on turbine locations and turbine types, produce time series of at least equal quality to RN.

eess.SP↗

arXiv2019

A parametric model for wind turbine power curves incorporating environmental conditions

A wind turbine's power curve relates its power production to the wind speed it experiences. The typical shape of a power curve is well known and has been studied extensively; however, the power curves of individual turbine models can vary widely from one another. This is due to both the technical features of the turbine (power density, cut-in and cut-out speeds, limits on rotational speed and aerodynamic efficiency), and environmental factors (turbulence intensity, air density, wind shear and wind veer). Data on individual power curves are often proprietary and only available through commercial databases. We therefore develop an open-source model which can generate the power curve of any turbine, adapted to the specific conditions of any site. This can employ one of six parametric models advanced in the literature, and accounts for the eleven variables mentioned above. The model is described, the impact of each technical and environmental feature is examined, and it is then validated against the manufacturer power curves of 91 turbine models. Versions of the model are made available in MATLAB, R and Python code for the community.

eess.SP↗

arXiv2019

Assessing the Global Wind Atlas and local measurements for bias correction of wind power generation simulated from MERRA-2 in Brazil

NASAs MERRA-2 reanalysis is a widely used dataset in renewable energy resource modelling. The Global Wind Atlas (GWA) has been used to bias-correct MERRA-2 data before. There is, however, a lack of an analysis of the performance of MERRA-2 with bias correction from GWA on different spatial levels - and for regions outside of Europe, China or the United States. This study therefore evaluates different methods for wind power simulation on four spatial resolution levels from wind park to national level in Brazil. In particular, spatial interpolation methods and spatial as well as spatiotemporal wind speed bias correction using local wind speed measurements and mean wind speeds from the GWA are assessed. By validating the resulting timeseries against observed generation it is assessed at which spatial levels the different methods improve results - and whether global information derived from the GWA can compete with locally measured wind speed data as a source of bias correction. Results show that (i) bias correction with the GWA improves results on state, sub-system, and national-level, but not on wind park level, that (ii) the GWA improves results comparably to local measurements, and that (iii) complex spatial interpolation methods do not contribute in improving quality of the simulation.

stat.AP↗

arXiv2019

Parameterised Complexity of Abduction in Schaefer's Framework

Abductive reasoning is a non-monotonic formalism stemming from the work of Peirce. It describes the process of deriving the most plausible explanations of known facts. Considering the positive version asking for sets of variables as explanations, we study, besides asking for existence of the set of explanations, two explanation size limited variants of this reasoning problem (less than or equal to, and equal to). In this paper, we present a thorough two-dimensional classification of these problems. The first dimension is regarding the parameterised complexity under a wealth of different parameterisations. The second dimension spans through all possible Boolean fragments of these problems in Schaefer's constraint satisfaction framework with co-clones (STOC 1978). Thereby, we almost complete the parameterised picture started by Fellows et al. (AAAI 2012), partially building on results of Nordh and Zanuttini (Artif. Intell. 2008). In this process, we outline a fine-grained analysis of the inherent parameterised intractability of these problems and pinpoint their FPT parts. As the standard algebraic approach is not applicable to our problems, we develop an alternative method that makes the algebraic tools partially available again.

cs.CC↗

arXiv2019

Kardar-Parisi-Zhang Universality of the Nagel-Schreckenberg Model

Dynamical universality classes are distinguished by their dynamical exponent $z$ and unique scaling functions encoding space-time asymmetry for, e.g. slow-relaxation modes or the distribution of time-integrated currents. So far the universality class of the Nagel-Schreckenberg (NaSch) model, which is a paradigmatic model for traffic flow on highways, was not known except for the special case $v_{\text{max}}=1$. Here the model corresponds to the TASEP (totally asymmetric simple exclusion process) that is known to belong to the superdiffusive Kardar-Parisi-Zhang (KPZ) class with $z=3/2$. In this paper, we show that the NaSch model also belongs to the KPZ class \cite{KPZ} for general maximum velocities $v_{\text{max}}>1$. Using nonlinear fluctuating hydrodynamics theory we calculate the nonuniversal coefficients, fixing the exact asymptotic solutions for the dynamical structure function and the distribution of time-integrated currents. Performing large-scale Monte-Carlo simulations we show that the simulation results match the exact asymptotic KPZ solutions without any fitting parameter left. Additionally, we find that nonuniversal early-time effects or the choice of initial conditions might have a strong impact on the numerical determination of the dynamical exponent and therefore lead to inconclusive results. We also show that the universality class is not changed by extending the model to a two-lane NaSch model with dynamical lane changing rules.

cond-mat.stat-mech↗

arXiv2019

A Bloch-Ogus Theorem for henselian local rings in mixed characteristic

We show a conditional exactness statement for the Nisnevich Gersten complex associated to an $\mathbb{A}^1$-invariant cohomology theory with Nisnevich descent for smooth schemes over a Dedekind ring with only infinite residue fields. As an application we derive a Nisnevich analogue of the Bloch-Ogus theorem for étale cohomology over a henselian discrete valuation ring with infinite residue field.

math.AG↗

arXiv2018

District heating systems under high CO2 emission prices: the role of the pass-through from emission cost to electricity prices

Low CO2 prices have prompted discussion about political measures aimed at increasing the cost of carbon dioxide emissions. These costs affect, inter alia, integrated district heating system operators (DHSO), often owned by municipalities with some political influence, that use a variety of (CO2 emis- sion intense) heat generation technologies. We examine whether DHSOs have an incentive to support measures that increase CO2 emission prices in the short term. Therefore, we (i) develop a simplified analytical framework to analyse optimal decisions of a district heating operator, and (ii) investigate the market-wide effects of increasing emission prices, in particular the pass- through from emission costs to electricity prices. Using a numerical model of the common Austrian and German power system, we estimate a pass-through from CO2 emission prices to power prices between 0.69 and 0.53 as of 2017, depending on the absolute emission price level. We find the CO2 emission cost pass-through to be sufficiently high so that low-emission district heating systems operating at least moderately efficient generation units benefit from rising CO2 emission prices in the short term.

econ.GN↗

arXiv2018

Proof of concept for an optogalvanic gas sensor for NO based on Rydberg excitations

We demonstrate the applicability of 2-photon Rydberg excitations of nitric oxide (NO) at room temperature in a gas mixture with helium (He) as an optogalvanic gas sensor. The charges created initially from succeeding collisions of excited NO Rydberg molecules with free electrons are measured as a current on metallic electrodes inside a glass cell and amplified using a custom-designed highbandwidth transimpedance amplifier attached to the cell. We fnd that this gas sensing method is capable of detecting NO concentrations lower than 10 ppm even at atmospheric pressures, currently only limited by the way we prepare the gas dilutions.

physics.atom-ph↗