Hi to everyone again. I am modelling the Spanish electric grid. There is a HVDC transmission line between the peninsula and the Balearic Islands through all renewable excess go directly to the islands. I know hourly the electricity flux that goes from the peninsula to the islands . The problem I have is how to model this "supply" tech regarding Balearic Islands and at the same time "demand" for the Peninsula. Any suggestions?
You could create a supply and demand tech with the same file as resource, e.g.
and
Does this help?
However if you want to consider transmission losses your demand is probably something like
techs.your_supply_tech.constraints.resource: file=your_data.csv:positive_valuesand
techs.your_demand_tech.constraints.resource: file=your_data.csv:negative_valuesDoes this help?
However if you want to consider transmission losses your demand is probably something like
demand = supply*eff ...
hi everyone again. When i run calliope in the command window i have the following problem:
"Error in process_timeseries_data:
Time series indices do not match between generation.csv and demand.csv"
generation.csv I have in columns hourly values of each tech, so the columns are named as "hydro", "wind", "coal", etc
demand.csv has hourly demand for the Peninsula and the indice is Z1 (Zone 1).
how can i solve the problem? I mean why does Calliope require to have same indices for different things?
"Error in process_timeseries_data:
Time series indices do not match between generation.csv and demand.csv"
generation.csv I have in columns hourly values of each tech, so the columns are named as "hydro", "wind", "coal", etc
demand.csv has hourly demand for the Peninsula and the indice is Z1 (Zone 1).
how can i solve the problem? I mean why does Calliope require to have same indices for different things?
Hi everyone again. I have problems when plotting solutions. The model comes to a solution and generates the folder results.csv although it gives me error as follows:
Saving CSV results to directory: results.csv
Saving HTML file with plots to: plots.html
Error in _get_var_data:
too many indices for array
Could you post this (alongside a full error log) as a GitHub issue on our page? It will need some investigation to work out why that pops up!
Hi everyone again. I have problems now with the interest_rate variable. The error says this File"C:\Program_Files\Anaconda3\envs\calliope\lib\site-packages\calliope\core\preprocess\locations.py", line 404, in compute_depreciation_rates
interest = tech_config.costs[cost].get_key('interest_rate', None)
AttributeError: 'NoneType' object has no attribute 'get_key'
interest = tech_config.costs[cost].get_key('interest_rate', None)
AttributeError: 'NoneType' object has no attribute 'get_key'
I have removed all interest_rate definitions and it continues giving me error, has anyone had problems with this? It is the first time it happens to me running the code :/
which version of Calliope are you using? I would advise adding all interest_rate values in. The easiest way to do this for different cost classes is to override the parent technology groups with an interest rate for each cost class, e.g.:
tech_groups:
supply.costs.monetary.interest_rate: 0 # this might need to be non-zero
supply.costs.carbon.interest_rate: 0
supply_plus.costs.monetary.interest_rate: 0
supply_plus.costs.carbon.interest_rate: 0It's important to know that there is a depreciation cost computed for your technologies, as it impacts the balance of investment and operational costs in the objective function. Hence why it tends to run into errors when they are removed...
supply_electricity_renewable:
essentials:
parent: supply
costs:
monetary:
interest_rate: 0.0432
supply_electricity_nuclear:
essentials:
parent: supply
costs:
monetary:
interest_rate: 0.0432
supply_electricity_fossil:
essentials:
parent: supply
costs:
monetary:
interest_rate: 0.0432
storage:
costs:
monetary:
interest_rate: 0.0432
I have checked that the main difference lies in LCOE calculation (operation mode does not consider investment and O&M_fix costs)
You're right, if you know all your capacities, the primary difference is that
operate mode doesn't consider any fixed/investment costs or decision variables. Additionally, it runs on a rolling horizon. This can mean that storage results will be different, because the optimisation doesn't know anything about conditions beyond your time horizon. The idea is that operate mode is more realistic in only knowing near-term operating conditions, whereas plan mode considers perfect foresight.
-operate mode: i have loaded for every technology hourly (as timeseries) the capacity available as resource: file=capacities.csv:cap_coal, for example. The problem is that in operate mode, calliope shows me that " * Energy capacity constraint removed from Z1::coal as force_resource is applied", but i have explicitly written force_resource: false even to avoid the problem but it still persists.
do you know how could i solve this? i.e., providing hourly availability for resources in operate mode?
Hello everyone. I'm having an issue with the energy_cap_min constraint on a technology's size.
I'm trying to give a technology some kind of economy of scale, making it more efficient and less expensive while its size increases. I then made three ranges of rated power for such technology (small, medium and large) limiting its size within the range with energy_cap_min and enegy_cap_max and putting a fake purchase cost with purchase in order to trigger a binary variable making it possible to purchase 0 kW of such technology. In code:
chiller_electric_large:
constraints:
energy_cap_min: 5001
energy_cap_max: 12000
costs:
monetary:
purchase: 1
energy_cap: 750
interest_rate: 0.10For some reason the algorithm places a size of the technology which is outside the boundaries (like 90 something kW) in some of my locations. Am I not getting right how the parameters work (maybe purchase overwrites cost*size) or it can be something else? Many thanks!
@alexsescu if you set the capacity by using
force_resource and resource=... then you shouldn't have to worry about what value you've set for energy_capacity_max etc., because the technology will just use all the available resource (hence why the energy capacity constraints are ignored in the optimisation).
@abart89 you're approach is exactly what I would do in order to make
energy_cap_min work in the way you'd like. I've jsut checked the constraints, and everything is in place as I'd expect. Can you check (looking at model.inputs) that there are no location specific overrides being applied in some places, without you realising? You can check something like model.get_formatted_array('energy_cap_min').loc[{'techs': 'chiller_electric_large'}]
@FLomb based on the fact that there is a setup file, it should be pip-installable. You can install directly from GitHub (or any other git remote repo) using pip install git+<clone link to the repo>, you should be able to get the clone link to a specific branch.
Otherwise, if you have it locally, you can just cd into the project folder and run pip install -e ./ (-e means it will be updated each time you update the source code, it's convenient if you are actively developing your version)
A good idea might be to create a separate conda environment for that, if you are relying on Anaconda
@FLomb if you use
pip install in the local folder, it should install whatever branch you have checked out, so just make sure you checkout the branch you want to use. Also make sure that you don't have calliope already installed through conda or something like that, and remember to add *.egg-info/ to your .gitignore
@GiorgioBalestrieri of course I have calliope already installed through conda, but I created a new environment for the clean "branch" installation. The thing is that it didn't install the branch, even though of course I checked out having the correct branch displayed in the local folder before installing.
which apparently are also the cause of a subsequent Python KeyError
is there something very basic that I'm missing to make locations "exist"?
apparently the problem only occurs for some random locations. But all of them are defined in the same way
It could be that one of CNOR or CSUD hasn't actually been defined in the model, so Calliope thinks that the user has deleted the location (whereas it just hasn't been defined). If those locations are definitely in the model, you could try explicitly setting them to exist (which is the default anyway), e.g.
locations.CNOR.exists: true
Thanks Bryn, I managed to fix it, there was a typo in a location definition. Now it works, but still there is something weird happening: everything works fine if running in "planning" mode, but if trying to run in "operational" mode (as I normally always do) it returns an error saying that I need to specify I finite capacity on each of the defined transmission lines. Which of course I did define, using finite numbers (which just work as expected in planning mode). Any hints? It's possible that there is still something wrong in how I defined such techs (considering that I used to work on a single-node model before), but it is quite weird that I have no problem if running in planning mode.
a finite capacity*
Operation mode is quite rigid in its requirements, since it is so easy to define an optimisation problem which is unphysical. All capacity maximum values are set as the decision variable value, instead of acting as a maximum. So any infinite capacity won't work. The easiest thing here is to check
model.inputs.energy_cap_max and/or model.inputs.energy_cap_equals to see if there is a rogue infinite or NaN value. NaN values will default to inf (as that is the default for energy_cap_max)
Also, making the same test in plan mode, transmission lines are no more set to "nan". They are given the specified capacity limit
there's just something that makes he remove cap_equals constraints on tramission lines in operation mode
it*
well, for the "big" transmission lines between bidding zones I'm setting the capacities at link level. Conversely, for the "small" lines that are conceived as free transmission I am setting a capacity constraint at tech level, the same for all of them, and it's like a big number because I originally wanted it to be "inf", but then I put like 10GW to try overcome this error, without success
I think the latter lines are those giving the problem
There's no reason for the inputs dataset to have different values for different solving modes, since there is nothing in the code that changes until after the model is sent to the solver. Are you sure that
model.inputs gives you two different arrays between loading the model with plan vs operate(i.e. without ever telling the model to run the optimisation)?
yes, absolutely. Anyway, I just tried to follow your hint and specify capacities at link level; also, I realised I was using
energy_cap_max instead of energy_cap_equals for the "big" lineas (even if Calliope should automatically set that to equals if running in operation mode, shouldn't it?). Well, after these two changes, it is happy to solve the model. The only thing that is still problematic is that it does the following: Resource capacity constraint removed from SICI::hydro_dam as force_resource is applied for every single supply_plus tech I have, in every region, even if for most of them I specifically stated "forse_resource: False".
lines*