> ## Documentation Index
> Fetch the complete documentation index at: https://docs.transitionzero.org/llms.txt
> Use this file to discover all available pages before exploring further.

# Understanding Infeasibilities

> What are infeasible models? How can you prevent them? How can you resolve them?

## **What is an Infeasible Model?**

An **infeasible model scenario** occurs when a model cannot be solved because the constraints and inputs defined are mathematically or logically contradictory.

In the context of **Scenario Builder**, this means the optimisation engine fails to find a valid solution that meets all the technical, economic, and policy constraints over the specified time horizon.

## **Common Causes of Infeasibilities**

Infeasibilities result from unrealistic or overly constrained model assumptions. Common causes include:

1. **Demand-supply mismatch**
   * **High demand growth** that cannot be met by investment in generation
   * **High peak demand** which cannot be met by available dispatchable generation
2. **Over-constrained inputs**
   * **Emissions targets that are too strict**: For example, setting net-zero emissions too early without allowing enough low-carbon technologies to be built or without enabling carbon removal.
   * **Generation or capacity targets that exceed growth rates**: If you require a high share of renewables without increasing their build rates or potentials, the model may not be able to meet demand.
   * **Simultaneous targets that clash**: For example, requiring high fossil generation and low emissions.
   * **Growth rates that are too restrictive**: If growth rates prevent the model from deploying needed capacity in time, it may be impossible to meet demand or targets.
   * **Potentials are too low**: If you limit how much of a technology (like solar or wind) can be installed, and then rely on that technology to meet a generation target, the model won’t have enough options.
   * **Cost edits that discourage viable solutions**: If clean technologies are made too expensive, or fossil fuels too cheap, the model may not be able to find a clean pathway within emissions limits.
   * **No storage or dispatchable capacity available**: If variable renewables dominate and no storage, hydro, or gas is allowed, the model may fail to balance the grid.
   * **Fuel costs set to extreme values**: Unrealistically low or high fuel prices may skew the model away from feasible combinations of supply.

## **How to Resolve Infeasibilities**

When you get an infeasible result, it helps to **relax**, **adjust**, and **review** your scenario inputs. Here’s a structured way to troubleshoot and resolve infeasibilities:

1. **Check for Conflicts**
   * **Relax tight emissions or capacity targets**, especially in early years. Try intermediate targets and see if the model can find a solution.
   * **Review overlapping targets**. If you have multiple targets (e.g., capacity *and* emissions), try removing one and rerun.
2. **Loosen Constraints**
   * **Increase build rates** for key technologies like wind, solar, batteries, or gas.
   * **Increase renewable potentials** if you’re limiting generation capacities.
3. **Review Assumptions**
   * **Double-check edited costs**: Are renewables too expensive? Are fossil fuels unrealistically cheap? Are fuel prices too steep?
   * **Balance between ambition and realism**: Consider whether your inputs reflect plausible system development pathways.