Activities

Cradle-to-grave boundary conditions: Our LCA boundaries includes all activities involved in extracting natural resources, processing them, manufacturing energy equipment, operations of the power plant and the disposal or recycling considerations at the end of useful life. The following is the example of this cradle-to-grave life-cycle for a wind turbine. 

Boundary conditions graphic

Data collection and analysis: we use existing databases on environmental impacts input parameters but we also supplement with data from most recent research and, where possible, from companies in the field.

Data collections figure

Workflow: A linear approximation of our workflow is depicted in the following graphic. In reality, we circle through different steps of this workflow multiple times to ensure recency of data and consistency of assumptions with actual industry operations. 

workflow chart

Zachary Harner graduated with his master’s degree from UT Austin’s Energy and Earth Resources (EER) program. His thesis focused on the LCA across the lithium supply chain. In particular, he evaluated two scenarios on lithium supply chains as depicted in following graphics.

Lithium Supply 1
Lithium supply 2

 

 

 

 

 

 

 

 

Zachary’s poster at the SME Conference provides further details on his research.

Andrew Kleiman graduated with his master’s degree from UT Austin’s Energy and Earth Resources (EER) program. His thesis focused on the LCA across the cobalt supply chain. In particular, he evaluated three major cobalt supply chains as depicted in following graphic.

Cobalt supply graphic

 

Andrew’s poster at the SME Conference provides further details on his research.
Hazal Kirimli, another EER student, shared early results in her poster on nickel supply chains at the SME conference.

 

These figures demonstrate the importance of incorporating cradle-to-grave boundaries and all environmental impacts to identify areas in the supply change where impacts can be mitigated. For example, the following graphs compare where different impacts concentrate along the supply chains for two different generation technologies, capable of generating the same amount of electricity per year. The results are preliminary and provided for illustrative purposes only. 

graph
graph

 

Note that:

  1. Supply chains are global, yet the impacts can be locally concentrated, dispersed across wider regions or global. For example, impacts during operations are felt by communities near the power plant, whereas impacts during sourcing and processing of natural resources are felt by communities near extraction sites and processing facilities.
  2. Impacts have different units. Harmonizing all impacts into a one metric is difficult. We are working on this challenge. 

Temporal distribution of impacts is important in visualizing what to expect. For example, the following graph compares timelines of total GWP (i.e., including sourcing, processing, manufacturing, building and operating and EoL) for different generation technologies. Different assumptions of life of assets cause the spikes. The results are preliminary and provided for demonstration purposes only. We are working on developing similar representations for all 17 impacts considered in our LCA.

chart

 

All of our power plants generate the same amount of electricity in a year. With battery storage, we bring solar and wind facilities closer to dispatchability to match a CCGT. Hence, our LCAs approximate total amount of materials needed for “equivalent” facilities. Still, this approach does not capture the needs of an electric power system that is expected to balance demand and supply of electricity in real time, instantaneously. In phase 2 of the CEO study, we are aggregating LCA impacts we calculated for individual power plants for whole power systems. That is, we are including all generation and T&D assets that needs to be built and operated by 2050 under various scenarios. For example, following charts compare the GWP impacts across two scenarios of transitioning energy generation portfolios in an actual power grid. We will confirm reliability of these systems via high-frequency (e.g., hourly) dispatch modeling.

illustration
illustration

 


University of Texas at Austin

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