Replies: 9 comments 6 replies
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Nice overview! I have a few initial comments on the technologies/formats mentioned. Complementary information files: splitting these between extra energy system information and simulation configuration is a good idea. As far as formats go, CSV or another table-based format can make sense for the energy system info, but the configuration I would keep in a configuration format such as TOML. In my experience with EYE, extra information about the energy system outside of ESDL is always much more of a hassle to use and maintain - so I would recommend really keeping as much in ESDL as possible (though I realise this is not always possible). Profiles database: at ACE, we use InfluxDB or PostgreSQL for most of our databases. SQLite is nice and simple, but it's not great when you want things like multiple users, concurrent accesses and connections to other tools such as Grafana or Apache Superset. In my experience, it's not much more complicated to maintain either when using the official Docker images. I realise these subjects are very much something to be discussed still, but I figured I'd throw in my two cents here - do with them what you will! :) |
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@datejada, we should think about typical execution environments. What you have described is a typical data processing + computation pipeline. Depending on resource requirements, access restrictions, usability considerations, the preferred solution will differ. |
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Following our discussion at TNO 30/Aug/2023, we came up with things:
This involves accessing ESDL information in Julia, and we came up we 4 different approaches, ordered by estimated time to implement:
Option 1 might be harder to maintain, given the usual issues handling Python executables and environments. |
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Did anyone manage to run the notebook (https://github.com/EnergyTransition/ESDL-PyEcore-Tutorial/blob/master/edrreader.py)? Reproduction: import edrreader
import requests
url = edrreader.EDR_config['EDR_host'] + edrreader.EDR_config['EDR_path'] + '55ef73b3-fa4f-49e8-8ad6-2b1801c25dfe' + '?format=xml'
headers = {
'Content-Type': "application/json",
'Accept': "application/xml",
'User-Agent': "ESDL Tutorials"
}
r = requests.get(url, headers=headers)
r.text |
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Here's the ESDL file for the Norse case study to use. (Should be .esdl but GitHub gets mad.) |
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@Lokkij, I've included below the screenshot of the whole representation and the simplified versions of the Norse Case Study. For the whole representation, we have the ESDL file you developed before. We would like to have an ESDL file for the simplified version, keeping in mind that all the assets in the whole representation can have a profile associated with them. We also would like to have the JSON files for each ESDL representation. Thanks! @suvayu @sjvrijn @lsoucasse Any additional comment? |
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Actually I already created the ESDL file for this simplified network a couple of weeks ago but without data in it. @Lokkij, I will send you the file by mail, I cannot do it in the chat. |
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@Lokkij @datejada @lsoucasse |
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I think this discussion can be closed. I'll link it on the User Workflow discussion for future reference. |
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I can foresee three data files in the model that need to be considered as inputs and outputs for the model; see Figure below.
Maybe we need more types of files; I'm not sure. In addition, the pre-processing and post-processing can be either in Julia or Python; these steps should be able to link the information in all the data sources with the optimization model.
I hope these comments and general overview help in the discussion for next week's meeting to talk about the design of the model.
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