Learning by modeling energy systems
Doctoral thesis, 2019
Meeting the 2°C 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°C 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°C 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.
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°C 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.
time slices
GIS
negative emissions
experience curves
BECCS
climate targets
reanalysis
variable renewables
open source
energy system models
KA (Kemi)
Opponent: Dr Ilkka Keppo, associate professor, Bartlett School Env, Energy & Resources, London
Author
Niclas Mattsson
Chalmers, Space, Earth and Environment, Physical Resource Theory
Assessing New Energy Technologies Using an Energy System Model with Endogenized Experience Curves
International Journal of Energy Research,; Vol. 21(1997)p. 385-393
Journal article
Introducing Uncertain Learning in an Energy System Model: A Pilot Study Using GENIE
International Journal of Global Energy Issues,; Vol. 18(2002)p. 253-265
Journal article
Meeting global temperature targets-the role of bioenergy with carbon capture and storage
Environmental Research Letters,; Vol. 8(2013)
Journal article
Using resource based slicing to capture the intermittency of variable renewables in energy system models
Energy Strategy Reviews,; Vol. 18(2017)p. 73-84
Journal article
Mattsson, N., Verendel, V., Hedenus, F., Reichenberg, L., 2019. An Autopilot for Energy Models – Automatic Generation of Renewable Supply Curves, Hourly Capacity Factors and Hourly Synthetic Electricity Demand for Arbitrary World Regions. Submitted to Energy Strategy Rev.
To stop global warming, we need to reduce emissions of carbon dioxide from the global energy system to zero. How can this be done?
Well, we’re not sure.
But we know how to find out. Using computer-based energy system models, we can construct scenarios of how the global energy system can develop over the next 50 or 100 years. By playing with different combinations of promising energy technologies, like solar cells, wind turbines or perhaps even nuclear power, we can study how different technologies interact and see how we can deal with situations when the sun isn’t shining and the wind isn’t blowing. And then we can run the scenario through a climate model to see if our proposed system meets climate targets.
This thesis is about energy system modeling. We develop new methods that can be used to analyze how new technologies like solar cells can gain market share and start competing with conventional technologies. Other methods can be used to study climate effects of having access to negative emission technologies and how to use them to potentially reverse global warming. We also discuss how to adapt energy models so they can study future power systems entirely based on renewables.
Well, we’re not sure.
But we know how to find out. Using computer-based energy system models, we can construct scenarios of how the global energy system can develop over the next 50 or 100 years. By playing with different combinations of promising energy technologies, like solar cells, wind turbines or perhaps even nuclear power, we can study how different technologies interact and see how we can deal with situations when the sun isn’t shining and the wind isn’t blowing. And then we can run the scenario through a climate model to see if our proposed system meets climate targets.
This thesis is about energy system modeling. We develop new methods that can be used to analyze how new technologies like solar cells can gain market share and start competing with conventional technologies. Other methods can be used to study climate effects of having access to negative emission technologies and how to use them to potentially reverse global warming. We also discuss how to adapt energy models so they can study future power systems entirely based on renewables.
Subject Categories
Energy Systems
Climate Research
ISBN
978-91-7905-229-4
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4696
Publisher
Chalmers
KA (Kemi)
Opponent: Dr Ilkka Keppo, associate professor, Bartlett School Env, Energy & Resources, London