arXiv subjects

Peter Regner

Publications and source records attributed to Peter Regner.

6 recordsLinked to original sources

arXiv2021

Explaining the decline of US wind output power density

US wind power generation has grown significantly over the last decades, in line with the number and average size of operating turbines. However, wind power density has declined, both measured in terms of wind power output per rotor swept area as well as per spacing area. To study this effect, we present a decomposition of US wind power generation data for the period 2001--2021 and examine how changes in input power density and system efficiency affected output power density. Here, input power density refers to the amount of wind available to turbines, system efficiency refers to the share of power in the wind flowing through rotor swept areas which is converted to electricity and output power density refers to the amount of wind power generated per rotor swept area. We show that, while power input available to turbines has increased in the period 2001--2021, system efficiency has decreased. In total, this has caused a decline in output power density in the last 10 years, explaining higher land-use requirements. The decrease in system efficiency is linked to the decrease in specific power, i.e. the ratio between the nameplate capacity of a turbine and its rotor swept area. Furthermore, we show that the wind available to turbines has increased substantially due to increases in the average hub height of turbines since 2001. However, site quality has slightly decreased in this period.

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

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↗

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↗

arXiv2019

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↗

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↗