Principal spatiotemporal mismatch and electricity price patterns in a highly decarbonized networked European power system.

As the European power system decarbonizes, the variability of the mismatch between renewable generation and demand, as well as that of electricity prices, are expected to increase substantially. Because mismatch and prices show complex temporal and spatial interaction, we propose the use of principal component analysis (PCA) to investigate them. We unveil their main spatiotemporal patterns, examine their cross-correlation, and their dependence on the transmission capacity expansion and CO 2 emissions reduction in a highly renewable cost-optimal electricity model. We find that the majority of variance in both the mismatch and price time series is explained by just three principal components (PCs). Hence, a convenient switch of basis vectors allows expressing the time series as combinations of few components which are shown to have intuitively interpretable structures. Moreover, we find that the temporal coherence between the first three PCs of mismatch and prices are substantially reinforced as the system decarbonizes.

Early decarbonisation of the European energy system pays off.

For a given carbon budget over several decades, different transformation rates for the energy system yield starkly different results. Here we consider a budget of 33 GtCO2 for the cumulative carbon dioxide emissions from the European electricity, heating, and transport sectors between 2020 and 2050, which represents Europe's contribution to the Paris Agreement. We have found that following an early and steady path in which emissions are strongly reduced in the first decade is more cost-effective than following a late and rapid path in which low initial reduction targets quickly deplete the carbon budget and require a sharp reduction later. We show that solar photovoltaic, onshore and offshore wind can become the cornerstone of a fully decarbonised energy system and that installation rates similar to historical maxima are required to achieve timely decarbonisation. Key to those results is a proper representation of existing balancing strategies through an open, hourly-resolved, networked model of the sector-coupled European energy system.

A high-resolution hydro power time-series model for energy systems analysis: Validated with Chinese hydro reservoirs.

We expand the renewable technology model palette and present a validated high resolution hydro power time series model for energy systems analysis. Among the weather-based renewables, hydroelectricity shows unique storage-like flexibility, which is particularly important given the high variability of wind and solar power. Often limited by data availability or computational performance, a high resolution, globally applicable and validated hydro power time series model has not been available. For a demonstration, we focus on 41 Chinese reservoir-based hydro stations as a demo, determine their upstream basin areas, estimate their inflow based on gridded surface runoff data and validate their daily inflow time series in terms of both flow volume and potential power generation. Furthermore, we showcase an application of these time series with hydro cascades in energy system long term investment planning. Our method's novelty lies in: •it is based on highly resolved spatial-temporal datasets;•both data and algorithms used here are globally applicable;•it includes a hydro cascade model that can be integrated into energy system simulations.

Robustness of networks against fluctuation-induced cascading failures.

Fluctuating fluxes on a complex network lead to load fluctuations at the vertices, which may cause them to become overloaded and to induce a cascading failure. A characterization of the one-point load fluctuations is presented, revealing their dependence on the nature of the flux fluctuations and on the underlying network structure. Based on these findings, an alternate robustness layout of the network is proposed. Taking load correlations between the vertices into account, an analytical prediction of the probability for the network to remain fully efficient is confirmed by simulations. Compared to previously proposed mean-flux layouts, the alternate layout comes with significantly less investment costs in the high-confidence limit.

Finite-size scaling of two-point statistics and the turbulent energy cascade generators.

Within the framework of random multiplicative energy cascade models of fully developed turbulence, finite-size-scaling expressions for two-point correlators and cumulants are derived, taking into account the observationally unavoidable conversion from an ultrametric to an Euclidean two-point distance. The comparison with two-point statistics of the surrogate energy dissipation, extracted from various wind tunnel and atmospheric boundary layer records, allows an accurate deduction of multiscaling exponents and cumulants, even at moderate Reynolds numbers for which simple power-law fits are not feasible. The extracted exponents serve as input for parametric estimates of the probabilistic cascade generator. Various cascade generators are evaluated.

Intermittency exponent of the turbulent energy cascade.

We consider the turbulent energy dissipation from one-dimensional records in experiments using air and gaseous helium at cryogenic temperatures, and obtain the intermittency exponent via the two-point correlation function of the energy dissipation. The air data are obtained in a number of flows in a wind tunnel and the atmospheric boundary layer at a height of about 35 m above the ground. The helium data correspond to the centerline of a jet exhausting into a container. The air data on the intermittency exponent are consistent with each other and with a trend that increases with the Taylor microscale Reynolds number, R(lambda), of up to about 1000 and saturates thereafter. On the other hand, the helium data cluster around a constant value at nearly all R(lambda), this being about half of the asymptotic value for the air data. Some possible explanation is offered for this anomaly.

Proactive robustness control of heterogeneously loaded networks.

A proactive measure to increase the robustness of heterogeneously loaded networks against cascades of overload failures is proposed. It is based on load-dependent weights. Compared to simple hop weights, respective shortest flow paths turn a previously heterogeneous load distribution into a more homogeneous one for the nodes and links of the network. The use of these flow paths increases the networks robustness and at the same time reduces the investment costs into the networks capacity layout. These findings are of relevance for critical infrastructures like communication and transportation networks.