Hi @ramaroesilva,
Thank you for your in-depth replies. Thanks to them as well as previous conversations above I have managed to solve the problem.
In case anyone is interested, here is a quick explanation:
To model these time dependant batteries, I used unique energy carriers with their respective conversion, supply and demand technologies for each electric vehicle parking spot (time-dependant battery).
The time series could be linked to the supply and demand to represent a car arriving and leaving, and the parking occupancy schedules could be used to deactivate the batteries when no car is present.
The ev charging capacity is 'offloaded' to the conversion techs so that the battery can be instantly charged or discharged when a car arrives or leaves.
There is a scrappy example in this thread.
3 replies
Using ruamel.yaml.dump as shown in https://yaml.readthedocs.io/en/latest/example.html should be possible, but seems a bit clunky.
Is there maybe a way native to Calliope?
4 replies
more or less, if the charging power is high, also the system efficency of whole system is high, is there a way to model that ? efficiency or costs change with charging power ?
Hello all
I have a problem with my model that I can not get fixed even after extensive troubleshooting. I do not even know how this can happen. In my model, electricity is generated in the transmission lines. It drives me crazy. My model is a relatively complex model with many regions, carriers and techniques and running in plan mode. But for debugging, I lowered the complexibilty and even then the problem occurs.
My links.yaml file looks something like this:
links:
...
AMPR4,AMPR5:
techs:
Interconnector:
constraints:
energy_cap_equals: 6500000.0In my techs.yaml the interconnectors are defined like this
techs:
...
Interconnector:
constraints:
energy_cap_max: inf
energy_eff: 1.0
costs:
monetary:
om_prod: 0.0
essentials:
carrier: ELC
color: '#190707'
name: 'Interconnectors'
parent: transmissionI don't see any difference here from the model in the tutorial. However, I get a solution that is calculated quickly but is unfortunately wrong.
When I look at carrier_prod and carrier_con for the interconnectors, I see the following.
carrier_con
AMPR4::Interconnector:AMPR5::ELC AMPR5::Interconnector:AMPR4::ELC
timesteps
2050-01-01 00:00:00 -6.500000e+06 0.0
2050-01-01 01:00:00 -4.191075e+06 0.0
2050-01-01 02:00:00 -4.315738e+06 0.0
2050-01-01 03:00:00 0.000000e+00 0.0
2050-01-01 04:00:00 0.000000e+00 0.0carrier_prod
AMPR4::Interconnector:AMPR5::ELC AMPR5::Interconnector:AMPR4::ELC
timesteps
2050-01-01 00:00:00 6500000.0 6500000.0
2050-01-01 01:00:00 0.0 6500000.0
2050-01-01 02:00:00 0.0 6500000.0
2050-01-01 03:00:00 0.0 6500000.0
2050-01-01 04:00:00 6500000.0 6500000.0According to this, I see two problems. On the one hand the line is used in both directions at the same time and on the other hand the use of the line generates electricity.
carrier_con.sum()
AMPR4::Interconnector:AMPR5::ELC -2.217436e+10
AMPR5::Interconnector:AMPR4::ELC -6.269706e+08carrier_prod.sum()
AMPR4::Interconnector:AMPR5::ELC 3.499195e+10
AMPR5::Interconnector:AMPR4::ELC 5.625508e+10Does anyone have any idea what this could be? Thanks for your feedback
4 replies
Hi @All,
the online documentation tells the following:
"By default, only the monetary cost class is used in the objective function, i.e., the default objective is to minimize total costs."
I read the discription about objective_function and that stuff, i did not really understud all of them or how to use... i would like to optimze (miximize) the earnings, how to do that ?
i have a simple model:
more ore less only demand, exept two supplys (grid and PV generator) grid costs are variable, from my point of view it makes sense, to use battery stored energy (costs 14..16ct/kWh in happy hour, not the whole day) in time of pv-production and export (feed in tariff 27ct/kWh) all of the pv generation. how to achive an optimzation on earnings ? minimzie costs work, but i don't have a starting point how to maximize earnings optimum on earnings (revneue (export ??) - costs) maximize export, does not help
8 replies
2 replies
I have another question. Is there any way to model a time-dependent storage? For example, I have a battery that looks like this
techs:
example_storage:
essentials:
name: 'example storage
color: '#000000'
parent: 'storage'
carrier: ELC
constraint:
energy_cap_equals: 1000
energy_eff: 1.0
lifetime: 10
storage_cap_max: inf
costs:
monetary:
interest_rate: 0.05
om_prod: 1
energy_cap: 100
storage_cap: 10However, energy_cap_equals is time-dependent. Is there a constraint I can use or any other way to model this? Similar to resource but for energy_cap?
2 replies
I want to model a demand response technique like load shift. However, the available load that can be shifted is time-dependent.
As an example:
Company 1 cannot shift load between 0 and 8 am,
between 8 am and 12 pm, it can shift 4 MW
between 12 and 4 pm, it can shift 6 MW
between 4 and 8 pm, it can shift 3 MW
and between 8 pm and 0 am it can shift 1 MW.
But in my model, there are many more different possible states. So I don't want to model all possible states as a separate technology.
10 replies
Hi @All! I have used calliope in the past to analyze planning questions but now I am considering it for its operational mode for scenario analysis in existing districts.
In particular, I'd like to test how systems of given sizes/design behave under different load curves and environmental conditions. For these reasons, I had some doubts regarding how the receding horizon in the operational mode works, with its two parameters horizon and window.
To understand if I got it correctly: the energy systems "see" parameters for #horizon timesteps in advance (e.g reading them from the external files provided) and the dispatch variables for #window timesteps are computed, then the horizon recedes for #window timesteps? Basically an optimization over the window horizon's timesteps
Thanks in advance!
Hi @All!
I have started using Calliope very recently and I am using it to support a DSO supporting a local DSO in planning the development of their electric and thermal grids. Just as described in the documentation for "demand_share_per_timestep_decision" (https://calliope.readthedocs.io/en/stable/user/advanced_constraints.html) I would am trying to use this constraint to make sure the relative share of heating technologies supplying the low temperature (ltheat) demand in different locations is consistent.
When I use the "group_share" constraint the model runs smoothly but does not provide the per time-step capabilities, however the "group_constraint" fails.
For example, I have defined the constraints of the Air-to-Water Heat Pump as in the attached snapshots:
I would be very grateful if you could please give me a hint as to why I am having this error (and why it is seems to be considering diesel when I specify the technology AWHP and the carrier (ltheat) ?
3 replies
Is there an easy way for a supply to have multiple carriers as outputs?
I want to model photovoltaic thermal hybrid collectors (PVT) which output both electricity and heat.
I can do this by combining a supply with a conversion tech with a unique carrier, but I was hoping to avoid this.
I suppose it can also be done using the Pyomo backend, but I have no experience there yet.
What would you recommend?
Thanks!
1 reply
Hello again.
I have a question about the group_constraints:
My group_constraint looks something like this
group_constraints:
elc_prod_region1:
locs: ['region1']
carrier_prod_min:
ELC: 10000000I thought so I can force that region1 must be generated at least 10000000 units of electricity. However, I get the following error message
[2021-04-16 16:00:00] WARNING Warning: Possible issues found during model processing:
* Unrecognised group constraint `carrier_prod_min`in group `elc_prod_region1` will be ignored - possibly a misspelling?
Error in print_warning_and_raise_errors:
Errors during model processing:
* Invalid carrier tier found at group_constraints.elc_prod_region1.carrier_prod_min_ELC. Only `carrier_`+ [`in`, `out`, `in_2`, `out_2`, `in_3`, `out_3`] is valid.So I changed my code to this:
group_constraints:
elc_prod_region1:
locs: ['region1']
carrier_prod_min:
carrier_out:
ELC: 10000000The model is now running, but I am not sure if it will really do what I want. I'm afraid that the constraint only considers technologies that have an input and only the output ELC. So supply, supply_plus, and conversion_plus are ignored. Has anyone already had experience with this and can tell me if this constraint as I have it now takes into account all power generating techniques. I have the constraint from here:
https://calliope.readthedocs.io/en/stable/user/advanced_constraints.html#group-constraints
Thanks, everybody
3 replies
18 replies
Hi everyone,
I am currently looking at different open-source energy modelling tools with the goal of designing minigrids (small stand-alone energy grids without a connection to the main grid). A lot of tools should theoretically work for this purpose, but do not support it explicitly. Therefore, the selection is difficult.
The long-term goal is to implement e-mobility and vehicle-to-grid options as well. This will be used to analyse possible synergies between new minigrids and e-mobility in rural African villages.
Since you are all experienced with Calliope already i would really appreciate a short opinion whether you think it is the right tool for this job. If you have any other suggestions I would appreciate those as well.
Thank you very much and best regards.
3 replies
Hi everyone,
I am trying to prevent a conversion tech from operating on certain hours of the year using both the 'energy_prod' and 'energy_con' constraints and a time series csv file. Running it doesn't show any errors, but it does not seem to be constrained properly as it is operating at the times it should be disabled. I tried using 0 and 1 as well as TRUE and FALSE in the csv, but both don't seem to constrain it.
If anyone has any pointers of what may be going on, I'd really appreciate it.
The conversion tech looks something like this:
e_x_conversion:
essentials:
name: e-x conversion
color: '#65AFC5'
parent: conversion
carrier_in: electricity
carrier_out: x
constraints:
energy_cap_max: 22
energy_prod: file=Y:(path).csv:column
energy_con: file=Y:(path).csv:column
3 replies
Hi everyone,
I am currently looking at different open-source energy modelling tools with the goal of designing minigrids (small stand-alone energy grids without a connection to the main grid). A lot of tools should theoretically work for this purpose, but do not support it explicitly. Therefore, the selection is difficult.
The long-term goal is to implement e-mobility and vehicle-to-grid options as well. This will be used to analyse possible synergies between new minigrids and e-mobility in rural African villages.
Since you are all experienced with Calliope already i would really appreciate a short opinion whether you think it is the right tool for this job. If you have any other suggestions I would appreciate those as well.
Thank you very much and best regards.
Hi @DEickholt , yes I think Calliope is particularly suited for this kind of job, more so than other options, because it was conceived originally precisely to be easily adaptable to multiple scales, including the local scale. On the Calliope model gallery you can see some examples of district-scale systems, and some more are available in the list of publications. I have myself used it in the past to model a district-scale energy system, for which Calliope allowed me to have a lot more versatility than other commercial software I used in the past
4 replies
Hi everyone,
I am trying to prevent a conversion tech from operating on certain hours of the year using both the 'energy_prod' and 'energy_con' constraints and a time series csv file. Running it doesn't show any errors, but it does not seem to be constrained properly as it is operating at the times it should be disabled. I tried using 0 and 1 as well as TRUE and FALSE in the csv, but both don't seem to constrain it.
If anyone has any pointers of what may be going on, I'd really appreciate it.The conversion tech looks something like this:
e_x_conversion:
essentials:
name: e-x conversion
color: '#65AFC5'
parent: conversion
carrier_in: electricity
carrier_out: x
constraints:
energy_cap_max: 22
energy_prod: file=Y:(path).csv:column
energy_con: file=Y:(path).csv:column
hi, never tried myself to constrain simultaneously consumption and production, so not sure, I should try; but what are you exactly trying to model? that could help figuring out if there are other/better options anyway
http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=134543
2 replies
transmission technology, which is then free for you to customise at your need. For instance, you can impose transmission capacity limits, transmission costs and/or a transmission efficiency which are tailored to your specific type of transmission (i.e. a pipeline for specific types of gases). Does this answer your question?
Hi everyone,
I am trying to model the European energy-system using the euro-calliope model. In my project I will run a model in planning mode, followed by a run in "operate mode" with the capacity obtained by the planning run.
Unfortunately I am running into a pretty annoying issue when trying to run the model in operation mode. The model does not run, as it is unfeasible. Saving the model as a .lp file, I discovered that the issue comes from this biofuel constraint in Italy:
c_e_balance_supply_plus_constraint(ITAbiofuel_2016_01_01_00_0000):
+1 carrier_prod(ITAbiofuelelectricity_2016_01_01_00_00_00)
-1 resource_con(ITAbiofuel_2016_01_01_00_00_00)
+1 storage(ITA__biofuel_2016_01_01_00_00_00)
= 2058
I don't know why 2058 is the constraint here, it is zero for all other timesteps of this technology and all other loc_techs.
If I remove biofuel from Italy, the constraint of biofuel in Slovenia (which was 0 when biofuel was allowed in Italy) changes and makes the model unfeasible again. This time the constraint making the model unfeasible is:
c_e_balance_supply_plus_constraint(SVNbiofuel_2016_01_02_12_0000):
+1 carrier_prod(SVNbiofuelelectricity_2016_01_02_12_00_00)
-1 resource_con(SVNbiofuel_2016_01_02_12_00_00)
+1 storage(SVN__biofuel_2016_01_02_12_00_00)
= 2932.8656850801376
After removing biofuel from Slovenia, however, the model runs and produces an output.
My first thought was that there seems to be an issue with the biofuel technology class or the way I link the constraints to the planning part of the model. But there is still biofuel capacity in other countries which is used in the operation mode and all links between the two models are automized, so the way constraints are implemented must be exactly the same for Italy and Slovenia compared to other locations.
I hope I described my issue in an comprehensible way, if not please let me know.
Is this issue familiar to anyone? Does anyone know were I could change anything to not run into this problem?
I am very confused, because there are 34 locations and around 14 technologies that work and the issue only arises at these two instances.
I would be incredibly grateful if anyone could give me a hint what is going wrong!
Many thanks!!
Hi, I have another question if somebody could help. Is it possible to force a technology to supply or convert the carrier in specific time (for example during the night) ?
@FAYDI yes, that's possible, particularly for supply techs. You could, for instance, set the resource of the technology as a time series (rather than 'inf', if you have a dispatchable supply plant). The time series should then have: resource = a very high value, or inf, during daylight hours; resource = 0 otherwise