We revisit the problem of predicting the spin magnitude and direction of the black hole resulting from the merger of two black holes with arbitrary masses and spins inspiralling in quasi-circular orbits. We do this by analyzing a catalog of 619 recent numerical-relativity simulations collected from the literature and spanning a large variety of initial conditions. By combining information from the post-Newtonian approximation, the extreme mass-ratio limit and perturbative calculations, we improve our previously proposed phenomenological formulae for the final remnant spin. In contrast with alternative suggestions in the literature, and in analogy with our previous expressions, the new formula is a simple algebraic function of the initial system parameters and is not restricted to binaries with spins aligned/anti-aligned with the orbital angular momentum, but can be employed for fully generic binaries. The accuracy of the new expression is significantly improved, especially for almost extremal progenitor spins and for small mass ratios, yielding a root-mean-square error σ ≈ 0.002 for aligned/anti-aligned binaries and σ ≈ 0.006 for generic binaries. Our new formula is suitable for cosmological applications and can be employed robustly in the analysis of the gravitational waveforms from advanced interferometric detectors.
PyPSA-Eur, the first open model dataset of the European power system at the transmission network level to cover the full ENTSO-E area, is presented. It contains 6001 lines (alternating current lines at and above 220 kV voltage level and all high voltage direct current lines), 3657 substations, a new open database of conventional power plants, time series for electrical demand and variable renewable generator availability, and geographic potentials for the expansion of wind and solar power. The model is suitable both for operational studies and generation and transmission expansion planning studies. The continental scope and highly resolved spatial scale enables a proper description of the long-range smoothing effects for renewable power generation and their varying resource availability. The restriction to freely available and open data encourages the open exchange of model data developments and eases the comparison of model results. A further novelty of the dataset is the publication of the full, automated software pipeline to assemble the load-flow-ready model from the original datasets, which enables easy replacement and improvement of the individual parts. This paper focuses on the description of the network topology, the compilation of a European power plant database and a top-down load timeseries regionalisation. It summarises the derivation of renewable wind and solar availability time-series from re-analysis weather datasets and the estimation of renewable capacity potentials restricted by land-use. Finally, validations of the dataset are presented, including a new methodology to compare geo-referenced network datasets to one another. challenges for the efficient design and regulation of electricity markets; the need to decarbonise heating and transport is driving electrification of these sectors; and finally energy markets are being integrated across the continent [1].To study this transformation, accurate modelling of the transmission grid is required. The need to take account of international electricity trading and the possibility of smoothing variable renewable feed-in over large distances (wind generation has a typical correlation length of around 600 km [2]) mean that models should have a continental scope. At the same time, high spatial detail is required, since national grid bottlenecks are already hindering the uptake of renewable energy today [3], and given persistent public acceptance problems facing new transmission projects [4], severe grid bottlenecks will remain a feature of the energy system for decades to come.Currently there is no openly-available model of the full European transmission network with which researchers can investigate and compare different approaches to the energy transformation. The transmission grid dataset provided by the European Network of Transmission System Operators for Electricity (ENTSO-E) for the 2016 Ten Year Network Development Plan (TYNDP) [5] is rendered unusable by restrictive licensing, the exclusion of Finland, Norway and Sweden, and a lack of geographic...
Renewable energy sources are likely to build the backbone of the future global energy system. One important key to a successful energy transition is to analyse the weather-dependent energy outputs of existing and eligible renewable resources. atlite is an open Python software package for retrieving global historical weather data and converting it to power generation potentials and time series for renewable energy technologies like wind turbines or solar photovoltaic panels based on detailed mathematical models. It further provides weather-dependent output on the demand side like building heating demand and heat pump performance. The software is optimized to aggregate data over multiple large regions with user-defined weightings based on land use or energy yield. Statement of needDeriving weather-based time series and maximum capacity potentials for renewables over large regions is a common problem in energy system modelling. Websites with exposed open APIs such as renewables.ninja ) exist for such purpose but are difficult to use for local execution, e.g. in cluster environments, and restricted to non-commercial use. Further, by design, they neither expose the underlying datasets nor methods for deriving time series, here referred to as conversion functions/methods. This makes them unsuited for utilizing different weather datasets or exploring alternative conversion functions. The pvlib (Holmgren et al., 2018) is suited for local execution and allows interchangeable input data but is specialized to PV systems only and intended for single location modelling. Other packages like the Danish REatlas (Andresen et al., 2015) face obstacles with accessibility, are based on proprietary code, miss documentation and are restricted in flexibility regarding their inputs.
In energy modelling, open data and open source code can help enhance traceability and reproducibility of model exercises which contribute to facilitate controversial debates and improve policy advice. While the availability of open power plant databases increased in recent years, they often differ considerably from each other and their data quality has not been systematically compared to proprietary sources yet. Here, we introduce the python-based 'powerplantmatching' (PPM), an open source toolset for cleaning, standardizing and combining multiple power plant databases. We apply it once only with open databases and once with an additional proprietary database in order to discuss and elaborate the issue of data quality, by analysing capacities, countries, fuel types, geographic coordinates and commissioning years for conventional power plants. We find that a derived dataset purely based on open data is not yet on a par with one in which a proprietary database has been added to the matching, even though the statistical values for capacity matched to a large degree with both datasets. When commissioning years are needed for modelling purposes in the final dataset, the proprietary database helps crucially to increase the quality of the derived dataset.
In power systems, flow allocation (FA) methods enable to allocate the usage and costs of the transmission grid to each single market participant. Based on predefined assumptions, the power flow is split into isolated generator-specific or producer-specific sub-flows. Two prominent FA methods, Marginal Participation (MP) and Equivalent Bilateral Exchanges (EBEs), build upon the linearized power flow and thus on the Power Transfer Distribution Factors (PTDFs). Despite their intuitive and computationally efficient concepts, they are restricted to networks with passive transmission elements only. As soon as a significant number of controllable transmission elements, such as high-voltage direct current (HVDC) lines, operate in the system, they lose their applicability. This work reformulates the two methods in terms of Virtual Injection Patterns (VIPs), which allows one to efficiently introduce a shift parameter q to tune contributions of net sources and net sinks in the network. In this work, major properties and differences in the methods are pointed out, and it is shown how the MP and EBE algorithms can be applied to generic meshed AC-DC electricity grids: by introducing a pseudo-impedance ω ¯ , which reflects the operational state of controllable elements and allows one to extend the PTDF matrix under the assumption of knowing the current flow in the system. Basic properties from graph theory are used to solve for the pseudo-impedance in dependence of the position within the network. This directly enables, e.g., HVDC lines to be considered in the MP and EBE algorithms. The extended methods are applied to a low-carbon European network model (PyPSA-EUR) with a spatial resolution of 181 nodes and an 18% transmission expansion compared to today’s total transmission capacity volume. The allocations of MP and EBE show that countries with high wind potentials profit most from the transmission grid expansion. Based on the average usage of transmission system expansion, a method of distributing operational and capital expenditures is proposed. In addition, it is shown how injections from renewable resources strongly drive country-to-country allocations and thus cross-border electricity flows.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.