planned future investments

[sld]

When I use NCAP_PASTI for planned future investments already known, which parameter do I have to use for its known investment cost? because, if I'm not wrong, NCAP_COST, doesn't apply to NCAP_PASTI.

Thank you

Sara

[Antti-L]

Hi Sara,

Despite the name of the investment cost parameter (NCAP_COST), it has been designed to apply equally to both new investments and past investments.

Should you see that the cost specified is nonetheless not being applied to past investments (as specified by NCAP_PASTI), please provide some more details of the problem.

Sorry for not a very prompt reply.

Best wishes,

Antti

[sld]

Hi, Antti
Thank you very much for your reply. It was a misunderstanding on my side. As the Times manual says "NCAP_COST=  Applied to the investment variable(VAR_NCAP)", I assumed that it was only for new investments, but I see now that I was wrong.
So, let's say my model starts in year 2000 (datayear), and I want to include old investments from 1990 (pastyear). How do I input the cost of a past investment in the model? I have to inflate that investment cost so that it reflects its value in the year 2000 (so, my input would be NCAP_COST in year 2000, not 1990, because I can't actually input NCAP_COST for a pastyear).... Because, NCAP_FOM doesn't include depreciation costs, so TIMES calculates them from NCAP_COST, is that correct? I think I'm getting confused, sorry. 
Thanks for your help as usual,
Best wishes
Sara
 

[Antti-L]

Ok, I see you must be using ANSWER-TIMES.

In ANSWER-TIMES the specification of parameters for PAST years is, indeed, a bit limited.
However, you still have reasonable ways of specifying e.g. the investment costs for past investments.

1. All the costs parameters are by default extrapolated backwards. Therefore, if you specify a cost for the year 2000 (and this is your first datayear), it will be applied to the past investments of that process as well (unless you use an interpolation option that prevents backwards extrapolation).
2. ANSWER supports datayear and modelyear independence. Therefore, you could add one or two past datayears into your TS spread, while still keeping 2000 as the first Milestone year (called Result Year in ANSWER) in your model. So, you could add 1990 as an additional datayear into the model database, and then you could specify the costs directly for the year 1990.

I am not sure what you mean by "depreciation costs" here. But TIMES of course takes into account the discount factor for the 1990 investments when adding the investment costs into the objective function. And in a certain sense it also takes into account depreciation, by taking into account the value of the remaining lifetime falling within the model horizon.

As an example, assume that your model horizon starts in 2000, and also the base year for discounting is 2000, and the discount rate is 5%. Now assume that you have specified NCAP_PASTI(1990)=1 with a lifetime of 20 years, and you specify NCAP_COST(2000)=100. The costs will be extrapolated backwards, and so we will also have NCAP_COST(1990)=100. When discounted forward, the 1990 investment has a value of 100*1.05^10 = 162.9 in terms of 2000 money value. However, because the plant has only 10 years of remaining lifetime within the model horizon, the depreciated value is 162.9*0.3804 = 62.0, which is the cost entered into the objective function. (The coefficient 0.3804 is the discounted value of the remaining lifetime as a fraction of the total discounted lifetime).

However, note that the costs of the past investments represent sunk costs that actually have no impact on the model solution, because they are a constant term in the objective. Threfore, it actually does not matter how the costs of the past investments are accounted in the objective function.

[sld]

Hi, Antti,
Thank you very much for your reply. For some reason, I didn't think Times would extrapolate backwards the investment cost for past investment, that's why I got confused.
Yet, in the end, as you said, they have no impact in the solution so I shouldn't have worried about it
Thank you very much!!
Sara