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EOS/docs/_generated/configoptimization.md
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1abdd345c4 fix: unify mypy environments for local checks and CI (#1291)
The isolated pre-commit mypy hook previously omitted runtime type information that
make mypy used, hiding errors involving dependencies such as Pydantic and Pendulum.
Makefile, pre-commit and CI now run the same full-project typing policy in the
development environment defined by uv.lock.

- Use uv run --locked --exact --extra dev and the same mypy arguments for Makefile
  and the local hook. Check all of src and tests, including on configuration-only
  changes.
- Pin Python 3.13 for local development and the pre-commit CI job, and install the
  locked pre-commit version in CI.
- Disable incremental analysis because existing Pendulum cache state changes mypy 2.3.1
  diagnostics. Document the policy, the performance tradeoff and the existing typing debt.
- Add a regression test that exercises Makefile, the hook and the CI command in a
  temporary project, accepting valid dependency types and detecting deliberate
  Pydantic/Pendulum assignment errors.

Resolve the newly detected mypy diagnostics.

- Enable the numpydantic and Pydantic mypy plugins, retaining strict Pydantic
  constructor typing with init_typed = true. Validate raw/coercible payloads through model_validate.
- Propagate concrete record, provider and time-window types through generic collections,
  factories and lookup methods. Preserve runtime field inspection and generated time-window
  documentation.
- Align Pendulum annotations with actual factory/arithmetic results while retaining Pydantic
  validation adapters at runtime. Correct optional values, array boundaries, REST handlers
  and plotting interfaces.
- Add pinned scipy-stubs and types-psutil, update uv.lock, and supply the plugins' dependencies.
- Add runtime regression coverage for validated path defaults, normalized time-series metadata,
  generic field inspection, invalid timestamps and unsupported provider imports.

Runtime and compatibility details:

- Validate path defaults as Path objects while retaining raw string defaults needed by
  migration serialization with exclude_defaults.
- Normalize feed-in tariff lists and default charge rates to NumPy arrays; reject missing
  timestamps/uninitialized values explicitly. Importing into a provider without import support
  returns HTTP 400.
- Public JSON schemas and OpenAPI structure match main (excluding the generated version).

Signed-off-by: dr-dimitry

Signed-off-by: dr-dimitry
Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>
Co-authored-by: dr-dimitri <87113560+dr-dimitri@users.noreply.github.com>
Co-authored-by: Normann <github@koldrack.com>
2026-09-10 23:20:35 +02:00

6.9 KiB

General Optimization Configuration

:::{table} optimization :widths: 10 20 10 5 5 30 :align: left

Name Environment Variable Type Read-Only Default Description
algorithm EOS_OPTIMIZATION__ALGORITHM <enum 'OptimizationAlgorithm'> rw required Optimization algorithm [GENETIC
algorithms list[str] ro N/A Available optimization algorithms.
genetic EOS_OPTIMIZATION__GENETIC GeneticCommonSettings rw required GENETIC optimization algorithm configuration.
genetic0 EOS_OPTIMIZATION__GENETIC0 Genetic0CommonSettings rw required GENETIC0 optimization algorithm configuration.
keys list[str] ro N/A The keys of the solution.
:::

Example Input

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "algorithm": "GENETIC",
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           },
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 400,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           },
           "algorithms": [
               "GENETIC",
               "GENETIC0"
           ],
           "keys": []
       }
   }

GENETIC0 Optimization Algorithm Configuration

:::{table} optimization::genetic0 :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
generations Optional[int] rw 400 Number of generations to evolve [>= 10]. Defaults to 400.
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals Optional[int] rw 300 Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec int ro N/A The optimization interval [sec]. Fixed to 1 hour (3600 seconds).
penalties dict[str, Union[float, int, str]] rw required Penalty parameters used in fitness evaluation.
seed Optional[int] rw None Random seed for reproducibility. None = random.
:::

Example Input

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic0": {
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "interval_sec": 3600,
               "horizon": 24
           }
       }
   }

GENETIC Optimization Algorithm Configuration

:::{table} optimization::genetic :widths: 10 10 5 5 30 :align: left

Name Type Read-Only Default Description
generations Optional[int] rw 400 Number of generations to evolve [>= 10]. Defaults to 400.
horizon int ro N/A Number of optimization steps.
horizon_hours int rw 24 The general time window within which the energy optimization goal shall be achieved [h]. Defaults to 24 hours.
individuals Optional[int] rw 300 Number of individuals (solutions) in the population [>= 10]. Defaults to 300.
interval_sec int rw 3600 The optimization interval [sec]. Defaults to 3600 seconds (1 hour)
penalties dict[str, Union[float, int, str]] rw required Penalty parameters used in fitness evaluation.
seed Optional[int] rw None Random seed for reproducibility. None = random.
:::

Example Input

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               }
           }
       }
   }

Example Output

   {
       "optimization": {
           "genetic": {
               "interval_sec": 3600,
               "horizon_hours": 24,
               "individuals": 300,
               "generations": 400,
               "seed": null,
               "penalties": {
                   "ev_soc_miss": 10
               },
               "horizon": 24
           }
       }
   }