Comments (1)
For the moment, you can workaround the issue by giving costs per time point, e.g. storage_costs[0] + 2*[-1]
.
Some considerations:
- For optimisation using an assumed cyclic time, it does not make sense to consider both, the 0th and N+1st time point. This can be seen in particular as the storage is typically balanced, so it is really one value that gains twice the weight.
- If time is assumed linear (no balanced storage), there are N+1 points in time for N time steps. Thus, the implementation makes sense to me. However, typically the initial content is predefined, so it just gives an offset to the objective value.
Thus, as a first approach to solve this here, I'd suggest to drop the costs for the 0th time step.
from oemof-solph.
Related Issues (20)
- Allow multiple outputs at OffsetConverter HOT 4
- Results processing should check if optimisation succeeded
- Using `custom_attributes` of `Source` and `Sink` classes fails HOT 1
- Add periodic values to invest relation of generic storage
- Implement alternative back-end to Pyomo (e.g. linopy) HOT 9
- How to define a switching Converter ? HOT 7
- The `OffsetConverter` is broken in the new implementation HOT 4
- The basic_example.py is broken HOT 1
- Aggregate all sorts of tools configurations in `pyproject.toml` HOT 1
- The online documentation could have a more appealing/modern look
- Investment limit AttributeError if storage investment only HOT 1
- Remove cellular model stub HOT 2
- Replace constraint tests HOT 5
- Refine timeindex
- running oemof_installation_test on venv HOT 6
- Formulation of "gradient limit" needs rework
- Compare `master` with `dev` in the bagde "commits since ..." HOT 2
- Update Indexing of Pandas Objects to the New Version HOT 1
- NonConvex initial_status=1 does not work as assumed HOT 1
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from oemof-solph.