Importance subsampling: improving power system planning under climate-based uncertainty

Recent studies indicate that the effects of inter-annual climate-based variability in power system planning are significant and that long samples of demand & weather data (spanning multiple decades) should be considered. At the same time, modelling renewable generation such as solar and wind requires high temporal resolution to capture fluctuations in output levels. In many realistic power system models, using long samples at high temporal resolution is computationally unfeasible. This paper introduces a novel subsampling approach, referred to as importance subsampling, allowing the use of multiple decades of demand & weather data in power system planning models at reduced computational cost. The methodology can be applied in a wide class of optimisation based power system simulations. A test case is performed on a model of the United Kingdom created using the open-source modelling framework Calliope and 36 years of hourly demand and wind data. Standard data reduction approaches such as using individual years or clustering into representative days lead to significant errors in estimates of optimal system design. Furthermore, the resultant power systems lead to supply capacity shortages, raising questions of generation capacity adequacy. In contrast, importance subsampling leads to accurate estimates of optimal system design at greatly reduced computational cost, with resultant power systems able to meet demand across all 36 years of demand & weather scenarios

Coarse Grained Molecular Dynamics Simulations of the Fusion of Vesicles Incorporating Water Channels

As the dynamics of the cell membrane and the working mechanisms of proteins cannot be readily asserted at a molecular level, many different hypotheses exist that try to predict and explain these processes, for instance vesicle fusion. Therefore, we use coarse grained molecular dynamics simulations to elucidate the fusion mechanism of vesicles. The implementation of this method with hydrophilic and hydrophobic particles is known for its valid representation of bilayers. With a minimalistic approach, using only 3 atom types, 12 atoms per two-tailed phospholipids and incorporating only a bond potential and Lennard-Jones potential, phospholipid bilayers and vesicles can be simulated exhibiting authentic dynamics. We have simulated the spontaneous full fusion of both tiny (6 nm diameter) and larger (13 nm diameter) vesicles. We showed that, without applying constraints to the vesicles, the initial contact between two fusing vesicles, the stalk, is initiated by a bridging lipid tail that extends from the membrane spontaneously. Subsequently it is observed that the evolution of the stalk can proceed via two pathways, anisotropic and radial expansion, which is in accordance with literature. Contrary to the spherical vesicles of in vitro experiments, the fused vesicles remain tubular since the internal volume of these vesicles is too small compared to their membrane area. While the lipid bilayer has some permeability for water, it is not high enough to allow for the large flux required to equilibrate the vesicle content in the time accessible to our simulations. To increase the membrane permeability, we incorporate proteinaceous water channels, by applying the coarse grained technique to aquaporin. Even though incorporating water channels in the vesicles does significantly increase water permeability, the vesicles do not become spherical. Presumably the lipids have to be redistributed as well

Combined effects of land use and hunting on distributions of tropical mammals

Land use and hunting are 2 major pressures on biodiversity in the tropics. Yet, their combined impacts have not been systematically quantified at a large scale. We estimated the effects of both pressures on the distributions of 1884 tropical mammal species by integrating species’ range maps, detailed land-use maps (1992 and 2015), species-specific habitat preference data, and a hunting pressure model. We further identified areas where the combined impacts were greatest (hotspots) and least (coolspots) to determine priority areas for mitigation or prevention of the pressures. Land use was the main driver of reduced distribution of all mammal species considered. Yet, hunting pressure caused additional reductions in large-bodied species’ distributions. Together, land use and hunting reduced distributions of species by 41% (SD 30) on average (year 2015). Overlap between impacts was only 2% on average. Land use contributed more to the loss of distribution (39% on average) than hunting (4% on average). However, hunting reduced the distribution of large mammals by 29% on average; hence, large mammals lost a disproportional amount of area due to the combination of both pressures. Gran Chaco, the Atlantic Forest, and Thailand had high levels of impact across the species (hotspots of area loss). In contrast, the Amazon and Congo Basins, the Guianas, and Borneo had relatively low levels of impact (coolspots of area loss). Overall, hunting pressure and human land use increased from 1992 to 2015 and corresponding losses in distribution increased from 38% to 41% on average across the species. To effectively protect tropical mammals, conservation policies should address both pressures simultaneously because their effects are highly complementary. Our spatially detailed and species-specific results may support future national and global conservation agendas, including the design of post-2020 protected area targets and strategies

Reducing climate risk in energy system planning: a posteriori time series aggregation for models with storage

The growth in variable renewables such as solar and wind is increasing the impact of climate uncertainty in energy system planning. Addressing this ideally requires high-resolution time series spanning at least a few decades. However, solving capacity expansion planning models across such datasets often requires too much computing time or memory. To reduce computational cost, users often employ time series aggregation to compress demand and weather time series into a smaller number of time steps. Methods are usually a priori, employing information about the input time series only. Recent studies highlight the limitations of this approach, since reducing statistical error metrics on input time series does not in general lead to more accurate model outputs. Furthermore, many aggregation schemes are unsuitable for models with storage since they distort chronology. In this paper, we introduce a posteriori time series aggregation schemes that preserve chronology and hence allow modelling of storage technologies. Our methods adapt to the underlying energy system model; aggregation may differ in systems with different technologies or topologies even with the same time series inputs. They do this by using operational variables (generation, transmission and storage patterns) in addition to time series inputs when aggregating. We investigate a number of approaches. We find that a posteriori methods can perform better than a priori ones, primarily through a systematic identification and preservation of relevant extreme events. We hope that these tools render long demand and weather time series more manageable in capacity expansion planning studies. We make our models, data, and code publicly available