where $c_0$ is cost at start, $c_1$ is material cost and $x_t$ is cumulative
capacity in the investment interval $t$. Thus, $x_0$ is the initial cumulative CSP capacity.
Additionally, there are **nuclear** and **coal** generators for which there is no potential for reducing their LCOE.
We build an optimisation to minimise the cost of supplying a flat demand $d=100$ with the given technologies between 2020 and 2050, where a CO$_2$ budget cap is applied.
> **Hint:** Problem formulation is to be found further along this notebook.
**Task:** Explore different discount rates, learning rates, CO$_2$ budgets. For instance
* No learning for CSP and no CO$_2$ budget would result in a coal-reliant system.
* A CO$_2$ budget and no learning prefers a system built on nuclear.
* A CO$_2$ budget and learning results in a system with CSP.
> **Note:** We use [`pyomo`](https://pyomo.readthedocs.io/en/stable/) for building optimisation problems in python. This is also what `pypsa` uses under the hood.
# + (parameters.at["LCOE",tech] - parameters.at["base LCOE",tech])*exp(-sum(model.generators[tech,yeart] for yeart in years if yeart < year)/parameters.at["volume",tech])
