Suitability of oemof-solph & other tools for modelling hybrid RE plant portfolio?

Dear open modelling community,

First, I’d like to express my appreciation of all the members of the open source energy modelling community. It’s a time consuming and often thankless task to maintain these models, but these modelling tools are so important for enabling the energy transition.

I’d like to solicit general experience of using different tools, specifically to model a mixed portfolio of renewable plants to meet a mixed load.

I work in a renewable energy developer which is looking to provide firm renewable electricity. My organisation would like to model how a portfolio of different RE plants across a country can meet a fixed load across hourly periods. This modelling would help optimise the best mix of locations, technologies and storage for cost and reliability. For example, I might narrow down 15 solar and wind sites across the country on the basis of network availability, resource, etc. And then I’d want to create and optimise a model to choose, say, the top 7 site, determining the spread of capacity across technologies. And then understand what % of 15 minute periods in a year it would meet a certain fixed load.

We are considering two different tools for this. One is PLEXOS; a commerical software used by energy system planners normally as a economic dispatch model. I understand that it has the required capabilities, but it would require an investment in money (for the licenses) and time, to learn how to use it. It’s also computationally quite demanding and can take a long time to run. I feel that the tool may be too complicated for our needs.

The other option would be to use pySAM (the python interface of NREL’s System Advisor Model) and oemof-solph. At a high level, I’d approach it like so:

  1. Choose the locations of the wind and solar plants I’d like to model
  2. Get a standard generation profile for a fixed capacity for each of the sites using SAM plus weather files
  3. Enter these generation profiles into oemof-solph as RE plants, add their costs, and add storage options
  4. Add an extra generator representing ‘shortfall’
  5. Optimise the model to find the optimal selection of plants across sites and capacities

The benefits of this approach would be that its free and accessible. It could be set up to be usable by people even without python skills. It should be fast to run. The use of SAM means that the generation could be modelled to high accuracy. The downsides would be that potentially the storage behaviour wouldn’t be as accurate, if just modelled simply by OEMOF-solph. And maybe the optimisation tools aren’t as extensive as PLEXOS. And that there may be an organisation preference to model the plants in a commercial tool like PLEXOS before making the final investment decision, so we’d need to use it eventually anyway.

Has anyone had this challenge before, and managed to solve it using oemof-solph? Or had relative experience of open source modelling tools vs PLEXOS? Is there another portfolio optimisation program which you’d recommend that I consider?

Thanks,
Henry

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