[NEMO-devel] Sensitivity style testing and uncertainties analysis in NEMO

Yusak Tanoto yusak.tanoto at gmail.com
Sun Sep 24 20:39:48 AEST 2017

Thank you so much Dr. Ben for your generosity and time in responding this.
I will try to think about it further and slowly trying your suggestions.

Cheers, Yusak

On Sep 22, 2017 9:17 PM, "Ben Elliston" <bje at air.net.au> wrote:

> Hi Yusak
> On Fri, Sep 22, 2017 at 03:55:36PM +1000, Yusak Tanoto wrote:
> > When I want to do sensitivity analysis, what I understand is to
> > change the value of an input variable or some of them, for example
> > technology costs, fuel costs, and CO2 emission in order to observe
> > its impacts towards reliability, and then simulate NEMO.
> Many of the parameters you might want to modify in a sensitivity
> analysis can be specified on the command line. See the table under
> "Running an optimisation" in the notebook documentation.  For these,
> it's easy to run multiple evolutions from a shell script (or a batch
> file on Windows).  For example, you can run evolve.py in a 'for' loop,
> varying the gas price using --gas-price.
> For technology costs, the best way is to make a new cost class in
> costs.py, add it to the cost_scenarios table (at the bottom of
> costs.py) and then you can vary that similarly using --costs in a
> 'for' loop.
> > A bit different, I am thinking about how to incorporate
> > uncertainties in NEMO, for example in terms of future demand so that
> > NEMO will respond by giving a solution which satisfy the variation
> > of future demand. Is this, in principle, possible to be conducted in
> > NEMO, I mean to introduce uncertainties or variability in the input
> > and having a solution which may satisfy that particular range.
> That's possible. You could generate a range of inputs in any way you
> like (for example, in good ol' Excel) and then do repeated evolutions
> on each value in the range.  I'd previously considered adding some
> uncertanty to the simulations (eg. simulating random generator
> failures), but this would make the evolution algorithm confused. If it
> runs two simulations with the same set of input parameters, it should
> get the same output.  With stochastic behaviour, that's out the
> window.
> Happy to hear others' ideas, too!
> Cheers, Ben
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