my names Mark, I am new here. Currently, I am about to finalize my research proposal for my PhD, for which I will at some point create an energy system model. Which is why I stumbled upon calliope.
So far I still did not understand what calliope is really doing. I get that I can build my model (regions, demands for power / electricity, supply / energy production and transmission lines). But what is the part of the solver? What does it solve?
Hi everyone, I'm trying to update my old models to the latest functionality and I'm a little confused over how 'export' now works. Let's say I have some excess free power available in a model (e.g. some wind power that is beyond what is required to meet the demand in the model), and I want to be able to convert this spare energy into another carrier which has an external value. For the sake of an example, we have a machine which converts power into magic beans which are worth $1000/kWh of beans. I could state this as follows:
magic_bean_maker: essentials: name: 'Magic bean maker' color: '#3B61E3' parent: conversion carrier_in: power carrier_out: magic_beans export_carrier: magic_beans constraints: energy_eff: 1.0 lifetime: 25 costs: monetary: interest_rate: 0.10 energy_cap: 0 # USD per kW om_con: 0 export: -1000
However, when I put this into a model with
energy_cap_equals: 1000 at a location with free power, despite there being no cost to producing beans and a clear negative cost to exporting them, no beans are produced. Is this because there is no demand for beans defined anywhere in the model, and the framework can only optimise for carriers with a demand?
I am hoping to model electric vehicles (EV) as storage . I was trying to do so by using a storage technology with storage_cap_max being input as a time-series representing whether the electric parking spot is occupied or not:
battery: essentials: name: 'Battery storage' color: '#3B61E3' parent: storage carrier: electricity constraints: energy_cap_max: 22 # kW storage_cap_max: df=EV_max_charge:EV_0
However, it does not seem to work, ignoring the max capacity.
In other words, is it possible to input storage_cap_max as a time-series?
Or is there a better way to do this?
when I do it, the model crashes sending this message
Malformed term in expression
[2021-03-10 11:52:18] DEBUG Neighboring tokens: " inf x1224 +1 x835 <= 0 c_u_x3558_: "
[2021-03-10 11:52:18] DEBUG
[2021-03-10 11:52:18] DEBUG Unable to read file
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.
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: 6500000.0
In 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: transmission
I 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.
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 0.0
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 6500000.0
According 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.
AMPR4::Interconnector:AMPR5::ELC -2.217436e+10 AMPR5::Interconnector:AMPR4::ELC -6.269706e+08
AMPR4::Interconnector:AMPR5::ELC 3.499195e+10 AMPR5::Interconnector:AMPR4::ELC 5.625508e+10
Does anyone have any idea what this could be? Thanks for your feedback
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
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: 10
However, 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?
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.
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!
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) ?