Meeting global temperature targets-the role of bioenergy with carbon capture and storage

In order to meet stringent temperature targets, active removal of CO2 from the atmosphere may be required in the long run. Such negative emissions can be materialized when well-performing bioenergy systems are combined with carbon capture and storage (BECCS). Here, we develop an integrated global energy system and climate model to evaluate the role of BECCS in reaching ambitious temperature targets. We present emission, concentration and temperature pathways towards 1.5 and 2 degrees C targets. Our model results demonstrate that BECCS makes it feasible to reach temperature targets that are otherwise out of reach, provided that a temporary overshoot of the target is accepted. Additionally, stringent temperature targets can be met at considerably lower cost if BECCS is available. However, the economic benefit of BECCS nearly vanishes if an overshoot of the temperature target is not allowed. Finally, the least-cost emission pathway over the next 50 years towards a 1.5 degrees C overshoot target with BECCS is almost identical to a pathway leading to a 2 degrees C ceiling target

An Autopilot for Energy Models – Automatic Generation of Renewable Supply Curves, Hourly Capacity Factors and Hourly Synthetic Electricity Demand for Arbitrary World Regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand

An autopilot for energy models – Automatic generation of renewable supply curves, hourly capacity factors and hourly synthetic electricity demand for arbitrary world regions

Energy system models are increasingly being used to explore scenarios with large shares of variable renewables. This requires input data of high spatial and temporal resolution and places a considerable preprocessing burden on the modeling team. Here we present a new code set with an open source license for automatic generation of input data for large-scale energy system models for arbitrary regions of the world, including sub-national regions, along with an associated generic capacity expansion model of the electricity system. We use ECMWF ERA5 global reanalysis data along with other public geospatial datasets to generate detailed supply curves and hourly capacity factors for solar photovoltaic power, concentrated solar power, onshore and offshore wind power, and existing and future hydropower. Further, we use a machine learning approach to generate synthetic hourly electricity demand series that describe current demand, which we extend to future years using regional SSP scenarios. Finally, our code set automatically generates costs and losses for HVDC interconnections between neighboring regions. The usefulness of our approach is demonstrated by several different case studies based on input data generated by our code. We show that our model runs of a future European electricity system with high share of renewables are in line with results from more detailed models, despite our use of global datasets and synthetic demand

Historical wind deployment and implications for energy system models

A critical parameter in modeling studies of future decarbonized energy systems is the potential future capacity for onshore wind power. Wind power potential in energy system models is subject to assumptions regarding: (i) constraints on land availability for wind deployment; (ii) how densely wind turbines may be placed over larger areas, and (iii) allocation of capacity with respect to wind speed. By analyzing comprehensive databases of wind turbine locations and other GIS data in eleven countries and seventeen states in Australia, Canada, and the US; all with high penetration levels of wind power, we find that: i) large wind turbines are installed on most land types, even protected areas and land areas with high population density; ii) it is not uncommon with a deployment density up to 0.5 MW/km2 on municipality or county level, with rare outlier municipalities reaching up to 1.5 MW/km2 installed capacity; and iii) wind power has historically been allocated to relatively windy sites with average wind speed above 6 m/s. In many cases, allocation methods used in energy system models do not consistently reflect actual installations. For instance, we find no evidence of concentration of installations at the windiest sites, as is frequently assumed in energy system models. We conclude that assumptions made in models regarding wind power potentials are poorly reflective of historical installation patterns, and we provide new data to enable assumptions that have a more robust empirical foundation

Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets

Circulating proteins have important functions in inflammation and a broad range of diseases. To identify genetic influences on inflammation-related proteins, we conducted a genome-wide protein quantitative trait locus (pQTL) study of 91 plasma proteins measured using the Olink Target platform in 14,824 participants. We identified 180 pQTLs (59 cis, 121 trans). Integration of pQTL data with eQTL and disease genome-wide association studies provided insight into pathogenesis, implicating lymphotoxin-alpha in multiple sclerosis. Using Mendelian randomization (MR) to assess causality in disease etiology, we identified both shared and distinct effects of specific proteins across immune-mediated diseases, including directionally discordant effects of CD40 on risk of rheumatoid arthritis versus multiple sclerosis and inflammatory bowel disease. MR implicated CXCL5 in the etiology of ulcerative colitis (UC) and we show elevated gut CXCL5 transcript expression in patients with UC. These results identify targets of existing drugs and provide a powerful resource to facilitate future drug target prioritization. Here the authors identify genetic effectors of the level of inflammation-related plasma proteins and use Mendelian randomization to identify proteins that contribute to immune-mediated disease risk

Learning by modeling energy systems

Meeting the 2\ub0C climate target would likely require reducing carbon dioxide emissions from the global energy system to virtually zero within 50-100 years, and within 30-50 years for the 1.5\ub0C target. Both cases would involve a complete transition of the global energy system to zero-emission technologies like renewables or nuclear power at unprecedented rates. This complex challenge can only be analyzed with energy system models, i.e. large computer models that can generate future energy scenarios. This thesis presents five papers that develop methodology for modeling the global energy transition.In papers 1-2, we develop new methods for representing technological development of emerging technologies like solar or wind power in energy models. We use “experience curves”, empirical relationships that describe how costs tend to fall for new technologies as a function of their market growth. We find that by investing in solar and wind at a global scale we can drive down costs to a point where they compete with conventional fossil energy sources.Paper 3 is a study of meeting climate targets with bioenergy with carbon capture and storage (BECCS) using an integrated energy-climate model. BECCS is a technology that can produce negative emissions; i.e., it can deliver energy while actively removing CO2 from the atmosphere. We find that if BECCS is used on a global scale, it can significantly reduce costs of meeting the 1.5\ub0C target and potentially reverse global warming in the long run.Paper 4 addresses another modeling problem. Many global energy models are too large to use an hourly time resolution which may be necessary to represent very high penetration levels of variable renewables like solar and wind power. We present a method called “resource-based slicing” that can capture sufficient variability in just 16 annual time periods.Finally, in paper 5, we develop an open-source code base that uses global meteorological datasets to generate all input data an energy model needs to study solar-, wind- and hydropower in arbitrary world regions. Our GIS-based approach produces both hourly capacity factors and regional potentials for installed capacity, and our simple generic model performs on par with more detailed dedicated models of European electricity generation

Introducing Uncertain Learning in an Energy System Model: A Pilot Study Using GENIE

Energy system models based on experience curves are superior to conventional models in their treatment of the dynamics of technological development. However, assuming perfect foresight means that future learning rates are known with certainty, which is not realistic. An optimizing model for the global electricity system, GENIE, has been extended to include imperfect foresight of learning rates. Technology cost trajectories are still determined by experience curves, but progress along the curves may follow alternative branches. Information about exactly which branch is currently being followed is not initially available, but may be subsequently revealed and acted upon. Unlike most applications of stochastic programming with recourse, the learning rate uncertainties are not resolved at a predetermined point in time. Instead, this information is only revealed once a certain threshold level of experience has been obtained for the particular technology. To minimize computational difficulty, only two technologies feature experience curve uncertainty: photovoltaic solar cells and fuel cells. The learning rates for these technologies can independently assume high or low values.Model results emphasize the importance of early learning investments in emerging energy technologies. The optimal hedging strategy calculated by GENIE involves significant early investments in both solar PV and fuel cells. An early commitment to emerging technologies is not only a good investment plan when high learning rates are expected, but also an efficient hedging strategy when future learning rates are uncertain. A sensitivity analysis also shows that this investment strategy is surprisingly robust even if high future learning rates are regarded to be improbable