chore(release): Release v0.2.0

Signed-off-by: Bobby Noelte <b0661n0e17e@gmail.com>

CHANGELOG.md

@@ -5,79 +5,142 @@ All notable changes to the akkudoktoreos project will be documented in this file
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
-## 0.1.0+dev (2025-10-26)
+## 0.2.0 (2025-11-09)
 
-### feat
-- setup default device configuration for automatic optimization
-- allow configuration of genetic algorithm parameters
-- allow configuration of home appliance time windows
-- mitigate old config
-- standardize measurement keys for battery/EV SoC measurements
-- feed-in tariff prediction support (incl. tests and docs)
-- energy management plan generation based on S2 standard instructions
-- make measurement keys configurable through EOS configuration
-- use pendulum types with pydantic via pydantic_extra_types.pendulum_dt
-- add Time, TimeWindow, TimeWindowSequence and to_time to datetimeutil
-- extend DataRecord with configurable field-like data
-- enrich health endpoints with version and optimization timestamps
-- add pydantic merge model tests
-- add plan tab to EOSdash
-- add predictions tab to EOSdash
-- add cache management to EOSdash admin tab
-- add about tab to EOSdash
-- adapt changelog & documentation for commitizen release flow
-- improve install and development documentation
+The most important new feature is **automatic optimization**.
+EOS can now independently perform optimization at regular intervals.
+This is based on the configured system parameters and forecasts, and also uses supplied
+measurement data, such as the current battery SoC.
+The result is an energy-management plan as well as the optimization output.
+The existing optimization interface using `POST /optimize` remains available and can still
+be used as before.
 
-### fix
-- automatic optimization (interval execution, locking, new endpoints)
-- recognize environment variables on EOS server startup
-- remove 0.0.0.0 → localhost translation on Windows
-- allow hostnames as well as IPs
-- access pydantic model fields via class instead of instance
-- downsampling in key_to_array
-- /v1/admin/cache/clear now clears all cache files; new /clear-expired endpoint
-- replace timezonefinder with tzfpy for accurate European timezones
-- explicit provider settings in config versus union
+In addition, bugs were fixed and new features were added:
+
+- Automatic optimization creates a **default configuration** if none is provided.
+  This is intended to make it easier to create a custom configuration by adapting the default.
+- The parameters of the genetic optimization algorithm (number of generations, etc.) are now
+  configurable.
+- For home appliances, start windows can now be specified (experimental).
+- Configuration files from previous versions are converted to the current format on first launch.
+- There are now measurement keys that are permanently assigned to a specific device simulation.
+  This simplifies providing measurement values for device simulations (e.g. battery SoC).
+- The infrastructure and first applications for **feed-in tariff forecasting**
+  (currently only fixed tariffs) are now integrated.
+- EOSdash has been expanded with new tabs for displaying the **energy-management plan**
+  and **predictions**.
+- The documentation has been updated and expanded in many places.
+
+### Feat
+
+- Energy-management plan generation based on S2 standard instructions
+- Feed-in-tariff prediction support (incl. tests & docs)
+- `LoadAkkudoktorAdjusted` load prediction variant
+- Standardized measurement keys for battery/EV SoC
+- Measurement keys configurable via EOS configuration
+- Setup default device configuration for automatic optimization
+- Health endpoints show version + last optimization timestamps
+- Configuration of genetic algorithm parameters
+- Configuration options for home-appliance time windows
+- Mitigation of legacy configuration
+- Config backup enhancements:
+
+  - Timestamp-based backup IDs
+  - API to list backups
+  - API to revert to a specific backup
+  - EOSdash Admin tab integration
+
+- Pendulum date types via `pydantic_extra_types.pendulum_dt`
+- `Time`, `TimeWindow`, `TimeWindowSequence`, and `to_time` helpers in `datetimeutil`
+- Extended `DataRecord` with configurable field-like semantics
+- EOSdash: Solution view now displays genetic optimization results and aggregated totals
+- EOSdash UI:
+
+  - Plan tab
+  - Predictions tab
+  - Cache management in Admin tab
+  - About tab
+
+- Pydantic merge model tests
+- Developer profiling entry in Makefile
+- Changelog & docs updated for commitizen release flow
+- Developer documentation updated
+- Improved install & development documentation
+
+### Changed
+
+- Battery simulation
+
+  - Performance improvements
+  - Charge + start times now reflect realistic simulation
+
+- Appliance simulation:
+
+  - Time windows may roll over to next day
+
+- Revised load prediction by splitting original `LoadAkkudoktor` into:
+
+  - `LoadAkkudoktor`
+  - `LoadAkkudoktorAdjusted`
+
+### Fixed
+
+- Correct URL/path for Akkudoktor forum in README
+- Automatic optimization:
+
+  - Reuses previous start solution
+  - Interval execution + locking + new endpoints
+  - Properly loads required data
+  - EV charge-rate migration for proper availability
+
+- Genetic common settings consistently available
+- Config markdown generation
+- Recognize environment variables on EOS server startup
+- Remove `0.0.0.0 → localhost` translation on Windows
+- Allow hostnames as well as IPs
+- Access Pydantic model fields via class instead of instance
+- Down-sampling in `key_to_array`
+- `/v1/admin/cache/clear` clears all cache files; added `/clear-expired`
+- Use `tzfpy` instead of timezonefinder for more accurate EU timezones
+- Explicit provider settings in config instead of union
 - ClearOutside weather prediction irradiance calculation
-- test config file priority without config_eos fixture
-- complete optimization sample request documentation
-- replace gitlint with commitizen
-- synchronize pre-commit config with real dependencies
-- add missing babel to requirements
-- fix documentation, tests, and implementation around optimization and predictions
+- Test config file priority without `config_eos` fixture
+- Complete optimization sample-request documentation
+- Replace gitlint with commitizen
+- Synchronize pre-commit config with real dependencies
+- Add missing `babel` to requirements
+- Fix documentation, tests, and implementation around optimization + predictions
 
-### chore
-- use memory cache for inverter interpolation
-- refactor genetic algorithm modules (split config, remove device singleton)
-- rename memory cache to CacheEnergyManagementStore
-- use class properties for config/ems/prediction mixins
-- skip matplotlib debug logs
-- auto-sync Bokeh JS CDN version
-- rename hello.py to about.py in EOSdash
-- remove demo page from EOSdash
-- split server test for system test
-- move doc utils to generate_config_md.py
-- improve documentation for pydantic merge models
-- remove pendulum warning from README
-- drop GitHub Discussions from contributing docs
-- bump version to 0.1.0+dev
-- rename or reorganize files/classes during refactoring
+### Chore
 
-### build
-- bump fastapi[standard] 0.115.14 → 0.117.1 and fix pytest-cov version
-- bump uvicorn 0.36.0 → 0.37.0
+- Use memory cache for inverter interpolation
+- Refactor genetic modules (split config, remove device singleton)
+- Rename memory cache to `CacheEnergyManagementStore`
+- Use class properties for config/EMS/prediction mixins
+- Skip matplotlib debug logs
+- Auto-sync Bokeh JS CDN version
+- Rename `hello.py` → `about.py` in EOSdash
+- Remove EOSdash demo page
+- Split server test from system test
+- Move doc utils to `generate_config_md.py`
+- Improve documentation for pydantic merge models
+- Remove pendulum warning from README
+- Drop GitHub Discussions from contributing docs
+- Rename or reorganize files / classes during refactors
 
-### BREAKING CHANGE
-EOS configuration and v1 API were changed:
+### BREAKING CHANGES
 
-- available_charge_rates_percent removed; replaced by new charge_rate config
-- optimization param hours renamed to horizon_hours
-- device config must now list devices and their properties explicitly
-- specific prediction provider configuration versus union
-- measurement keys provided as lists
-- new feed-in tariff providers must be configured
-- /v1/measurement/loadxxx endpoints removed (use generic measurement endpoints)
-- /v1/admin/cache/clear clears all cache files; use /v1/admin/cache/clear-expired for expired-only clearing
+EOS configuration + v1 API have changed:
+
+- `available_charge_rates_percent` removed → replaced by `charge_rate`
+- Optimization parameter `hours` → renamed to `horizon_hours`
+- Device config must explicitly list devices + properties
+- Prediction providers now explicit (instead of union)
+- Measurement keys provided as lists
+- Feed-in-tariff providers must be explicitly configured
+- `/v1/measurement/loadxxx` endpoints removed → use generic measurement endpoints
+- `/v1/admin/cache/clear` now clears **all*- cache files;
+  `/v1/admin/cache/clear-expired` only clears expired entries
 
 ## v0.1.0 (2025-09-30)
 

Makefile

@@ -162,7 +162,15 @@ docker-build:
 	@docker compose build --pull
 
 # Bump Akkudoktoreos version
+VERSION ?= 0.2.0
+NEW_VERSION ?= $(VERSION)+dev
+
 bump: pip-dev
-	@echo "Bumping akkudoktoreos version to release version"
+	@echo "Bumping akkudoktoreos version from $(VERSION) to $(NEW_VERSION) (dry-run: $(EXTRA_ARGS))"
 	.venv/bin/python scripts/convert_lightweight_tags.py
-	.venv/bin/cz bump
+	.venv/bin/python scripts/bump_version.py $(VERSION) $(NEW_VERSION) $(EXTRA_ARGS)
+
+bump-dry: pip-dev
+	@echo "Bumping akkudoktoreos version from $(VERSION) to $(NEW_VERSION) (dry-run: --dry-run)"
+	.venv/bin/python scripts/convert_lightweight_tags.py
+	.venv/bin/python scripts/bump_version.py $(VERSION) $(NEW_VERSION) --dry-run

README.md

@@ -1,4 +1,5 @@
-![AkkudoktorEOS](docs/_static/logo.png)
+![AkkudoktorEOS](./docs/_static/logo.png#gh-light-mode-only)
+![AkkudoktorEOS](./docs/_static/logo_dark.png#gh-dark-mode-only)
 
 **Build optimized energy management plans for your home automation**
 

docs/_generated/config.md

@@ -21,7 +21,7 @@ Properties:
 
 | Name | Environment Variable | Type | Read-Only | Default | Description |
 | ---- | -------------------- | ---- | --------- | ------- | ----------- |
-| version | `EOS_GENERAL__VERSION` | `str` | `rw` | `0.1.0+dev` | Configuration file version. Used to check compatibility. |
+| version | `EOS_GENERAL__VERSION` | `str` | `rw` | `0.2.0` | Configuration file version. Used to check compatibility. |
 | data_folder_path | `EOS_GENERAL__DATA_FOLDER_PATH` | `Optional[pathlib.Path]` | `rw` | `None` | Path to EOS data directory. |
 | data_output_subpath | `EOS_GENERAL__DATA_OUTPUT_SUBPATH` | `Optional[pathlib.Path]` | `rw` | `output` | Sub-path for the EOS output data directory. |
 | latitude | `EOS_GENERAL__LATITUDE` | `Optional[float]` | `rw` | `52.52` | Latitude in decimal degrees, between -90 and 90, north is positive (ISO 19115) (°) |
@@ -39,7 +39,7 @@ Properties:
 
    {
        "general": {
-           "version": "0.1.0+dev",
+           "version": "0.2.0",
            "data_folder_path": null,
            "data_output_subpath": "output",
            "latitude": 52.52,
@@ -55,7 +55,7 @@ Properties:
 
    {
        "general": {
-           "version": "0.1.0+dev",
+           "version": "0.2.0",
            "data_folder_path": null,
            "data_output_subpath": "output",
            "latitude": 52.52,
@@ -195,7 +195,7 @@ Properties:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -222,7 +222,7 @@ Properties:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -264,7 +264,7 @@ Properties:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -291,7 +291,7 @@ Properties:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -601,7 +601,7 @@ Properties:
 | levelized_cost_of_storage_kwh | `float` | `rw` | `0.0` | Levelized cost of storage (LCOS), the average lifetime cost of delivering one kWh [€/kWh]. |
 | max_charge_power_w | `Optional[float]` | `rw` | `5000` | Maximum charging power [W]. |
 | min_charge_power_w | `Optional[float]` | `rw` | `50` | Minimum charging power [W]. |
-| charge_rates | `Optional[list[float]]` | `rw` | `None` | Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available. |
+| charge_rates | `Optional[numpydantic.vendor.npbase_meta_classes.NDArray]` | `rw` | `[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]` | Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates. |
 | min_soc_percentage | `int` | `rw` | `0` | Minimum state of charge (SOC) as percentage of capacity [%]. This is the target SoC for charging |
 | max_soc_percentage | `int` | `rw` | `100` | Maximum state of charge (SOC) as percentage of capacity [%]. |
 | measurement_key_soc_factor | `str` | `ro` | `N/A` | Measurement key for the battery state of charge (SoC) as factor of total capacity [0.0 ... 1.0]. |
@@ -630,13 +630,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": [
-                       0.0,
-                       0.25,
-                       0.5,
-                       0.75,
-                       1.0
-                   ],
+                   "charge_rates": "[0.   0.25 0.5  0.75 1.  ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100
                },
@@ -648,7 +642,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100
                },
@@ -660,7 +654,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100
                },
@@ -672,7 +666,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100
                }
@@ -697,13 +691,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": [
-                       0.0,
-                       0.25,
-                       0.5,
-                       0.75,
-                       1.0
-                   ],
+                   "charge_rates": "[0.   0.25 0.5  0.75 1.  ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -727,7 +715,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "ev1-soc-factor",
@@ -751,7 +739,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "inverter1-soc-factor",
@@ -775,7 +763,7 @@ Battery SoC, power. |
                    "levelized_cost_of_storage_kwh": 0.12,
                    "max_charge_power_w": 5000.0,
                    "min_charge_power_w": 50.0,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 10,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "dishwasher-soc-factor",
@@ -1792,7 +1780,7 @@ Validators:
 
    {
        "general": {
-           "version": "0.1.0+dev",
+           "version": "0.2.0",
            "data_folder_path": null,
            "data_output_subpath": "output",
            "latitude": 52.52,
@@ -1821,7 +1809,7 @@ Validators:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",
@@ -1848,7 +1836,7 @@ Validators:
                    "levelized_cost_of_storage_kwh": 0.0,
                    "max_charge_power_w": 5000,
                    "min_charge_power_w": 50,
-                   "charge_rates": null,
+                   "charge_rates": "[0.  0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1. ]",
                    "min_soc_percentage": 0,
                    "max_soc_percentage": 100,
                    "measurement_key_soc_factor": "battery1-soc-factor",

docs/_generated/openapi.md

@@ -1,6 +1,6 @@
 # Akkudoktor-EOS
 
-**Version**: `v0.1.0+dev`
+**Version**: `v0.2.0`
 
 **Description**: This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.
 

docs/develop/install.md

@@ -138,16 +138,16 @@ This method is recommended for users who want a stable, tested version.
 ### 1) Download the Latest Release (M2)
 
 Visit the [Releases page](https://github.com/Akkudoktor-EOS/EOS/tags) and download the latest
-release package (e.g., `akkudoktoreos-v0.1.0.tar.gz` or `akkudoktoreos-v0.1.0.zip`).
+release package (e.g., `akkudoktoreos-v0.2.0.tar.gz` or `akkudoktoreos-v0.2.0.zip`).
 
 ### 2) Extract the Package (M2)
 
 ```bash
-tar -xzf akkudoktoreos-v0.1.0.tar.gz  # For .tar.gz
+tar -xzf akkudoktoreos-v0.2.0.tar.gz  # For .tar.gz
 # or
-unzip akkudoktoreos-v0.1.0.zip  # For .zip
+unzip akkudoktoreos-v0.2.0.zip  # For .zip
 
-cd akkudoktoreos-v0.1.0
+cd akkudoktoreos-v0.2.0
 ```
 
 ### 3) Create a virtual environment and run and configure EOS (M2)

docs/develop/release.md

@@ -4,7 +4,7 @@
 # Release Process
 
 This document describes how to prepare and publish a new release **via a Pull Request from a fork**,
-using **Commitizen** to manage versioning and changelogs — and how to set a **development version** after the release.
+and how to set a **development version** after the release.
 
 ## ✅ Overview of the Process
 
@@ -20,7 +20,7 @@ using **Commitizen** to manage versioning and changelogs — and how to set a **
 
 ### 1️⃣ Contributor: Prepare the Release in Your Fork
 
-**Clone and sync your fork:**
+#### Clone and sync your fork
 
 ```bash
 git clone https://github.com/<your-username>/EOS
@@ -32,25 +32,50 @@ git checkout main
 git pull eos main
 ````
 
-**Create the release branch:**
+#### Create the release branch
 
 ```bash
 git checkout -b release/vX.Y.Z
 ```
 
-**Run Commitizen to bump version and update changelog:**
+#### Bump the version information
+
+At least update
+
+- pyproject.toml
+- src/akkudoktoreos/core/version.py
+- src/akkudoktoreos/data/default.config.json
+- Makefile
+
+and the generated documentation:
 
 ```bash
-make bump
+make bump VERSION=0.1.0+dev NEW_VERSION=X.Y.Z
+make gen-docs
 ```
 
-> ✅ This updates version files and `CHANGELOG.md` in a single commit.
-> 🚫 **Do not push tags** — tags are created by the maintainer via GitHub Releases.
-
-**Push the branch to your fork:**
+You may check the changes by:
 
 ```bash
-git push origin release/vX.Y.Z
+git diff
+```
+
+#### Create a new CHANGELOG.md entry
+
+Edit CHANGELOG.md
+
+#### Create the new release commit
+
+```bash
+git add pyproject.toml src/akkudoktoreos/core/version.py \
+    src/akkudoktoreos/data/default.config.json Makefile CHANGELOG.md
+git commit -s -m "chore(release): Release vX.Y.Z"
+```
+
+#### Push the branch to your fork
+
+```bash
+git push --set-upstream origin release/vX.Y.Z
 ```
 
 ### 2️⃣ Contributor: Open the Release Pull Request
@@ -62,7 +87,7 @@ git push origin release/vX.Y.Z
 **PR Title:**
 
 ```text
-Release vX.Y.Z
+chore(release): release vX.Y.Z
 ```
 
 **PR Description Template:**
@@ -73,7 +98,7 @@ Release vX.Y.Z
 This pull request prepares release **vX.Y.Z**.
 
 ### Changes
-- Version bump via Commitizen
+- Version bump
 - Changelog update
 
 ### Changelog Summary
@@ -86,15 +111,15 @@ See `CHANGELOG.md` for full details.
 
 **Review Checklist:**
 
-* ✅ Only version files and `CHANGELOG.md` are modified
-* ✅ Version numbers are consistent
-* ✅ Changelog is complete and properly formatted
-* ✅ No unrelated changes are included
+- ✅ Only version files and `CHANGELOG.md` are modified
+- ✅ Version numbers are consistent
+- ✅ Changelog is complete and properly formatted
+- ✅ No unrelated changes are included
 
 **Merge Strategy:**
 
-* Prefer **Merge Commit** (or **Squash Merge**, per project preference)
-* Use commit message: `Release vX.Y.Z`
+- Prefer **Merge Commit** (or **Squash Merge**, per project preference)
+- Use commit message: `chore(release): Release vX.Y.Z`
 
 ### 4️⃣ Maintainer: Publish the GitHub Release
 
@@ -121,21 +146,21 @@ git pull eos main
 git checkout -b release/vX.Y.Z_dev
 ```
 
-**Set development marker manually:**
-
-The following files have to be updated:
-
-* pyproject.toml
-* src/akkudoktoreos/core/version.py
-* src/data/default.config.json
-
-Example for pyproject.toml
+**Set development version marker manually:**
 
 ```bash
-sed -i 's/version = "\(.*\)"/version = "\1+dev"/' pyproject.toml
-git add pyproject.toml
-git commit -m "chore: set development version marker"
-git push origin release/vX.Y.Z_dev
+make bump VERSION=X.Y.Z NEW_VERSION=X.Y.Z+dev
+make gen-docs
+```
+
+```bash
+git add pyproject.toml src/akkudoktoreos/core/version.py \
+    src/akkudoktoreos/data/default.config.json Makefile
+git commit -s -m "chore: set development version marker X.Y.Z+dev"
+```
+
+```bash
+git push --set-upstream origin release/vX.Y.Z_dev
 ```
 
 ### 6️⃣ Maintainer (or Contributor): Open the Development Version PR
@@ -147,13 +172,13 @@ git push origin release/vX.Y.Z_dev
 **PR Title:**
 
 ```text
-Release vX.Y.Z+dev
+chore: development version vX.Y.Z+dev
 ```
 
 **PR Description Template:**
 
 ```markdown
-## Release vX.Y.Z_dev
+## Development version vX.Y.Z+dev
 
 This pull request marks the repository as back in active development.
 
@@ -167,12 +192,12 @@ No changelog entry is needed.
 
 **Checklist:**
 
-* ✅ Only version files updated to `+dev`
-* ✅ No unintended changes
+- ✅ Only version files updated to `+dev`
+- ✅ No unintended changes
 
 **Merge Strategy:**
 
-* Merge with commit message: `Release vX.Y.Z_dev`
+- Merge with commit message: `chore: development version vX.Y.Z+dev`
 
 ## ✅ Quick Reference
 

openapi.json

@@ -3,7 +3,7 @@
   "info": {
     "title": "Akkudoktor-EOS",
     "description": "This project provides a comprehensive solution for simulating and optimizing an energy system based on renewable energy sources. With a focus on photovoltaic (PV) systems, battery storage (batteries), load management (consumer requirements), heat pumps, electric vehicles, and consideration of electricity price data, this system enables forecasting and optimization of energy flow and costs over a specified period.",
-    "version": "v0.1.0+dev"
+    "version": "v0.2.0"
   },
   "paths": {
     "/v1/admin/cache/clear": {
@@ -2122,7 +2122,7 @@
               }
             ],
             "title": "Charge Rates",
-            "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
+            "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates.",
             "examples": [
               [
                 0.0,
@@ -2263,7 +2263,7 @@
               }
             ],
             "title": "Charge Rates",
-            "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
+            "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None triggers fallback to default charge-rates.",
             "examples": [
               [
                 0.0,
@@ -2406,7 +2406,7 @@
           "general": {
             "$ref": "#/components/schemas/GeneralSettings-Output",
             "default": {
-              "version": "0.1.0+dev",
+              "version": "0.2.0",
               "data_output_subpath": "output",
               "latitude": 52.52,
               "longitude": 13.405,
@@ -4062,7 +4062,7 @@
             "type": "string",
             "title": "Version",
             "description": "Configuration file version. Used to check compatibility.",
-            "default": "0.1.0+dev"
+            "default": "0.2.0"
           },
           "data_folder_path": {
             "anyOf": [
@@ -4136,7 +4136,7 @@
             "type": "string",
             "title": "Version",
             "description": "Configuration file version. Used to check compatibility.",
-            "default": "0.1.0+dev"
+            "default": "0.2.0"
           },
           "data_folder_path": {
             "anyOf": [
@@ -5921,6 +5921,15 @@
             "title": "Total Costs Amt",
             "description": "The total costs [money amount]."
           },
+          "fitness_score": {
+            "items": {
+              "type": "number"
+            },
+            "type": "array",
+            "uniqueItems": true,
+            "title": "Fitness Score",
+            "description": "The fitness score as a set of fitness values."
+          },
           "prediction": {
             "$ref": "#/components/schemas/PydanticDateTimeDataFrame",
             "description": "Datetime data frame with time series prediction data per optimization interval:- pv_energy_wh: PV energy prediction (positive) in wh- elec_price_amt_kwh: Electricity price prediction in money per kwh- feed_in_tariff_amt_kwh: Feed in tariff prediction in money per kwh- weather_temp_air_celcius: Temperature in \u00b0C- loadforecast_energy_wh: Load mean energy prediction in wh- loadakkudoktor_std_energy_wh: Load energy standard deviation prediction in wh- loadakkudoktor_mean_energy_wh: Load mean energy prediction in wh"
@@ -5937,6 +5946,7 @@
           "total_losses_energy_wh",
           "total_revenues_amt",
           "total_costs_amt",
+          "fitness_score",
           "prediction",
           "solution"
         ],
@@ -7585,6 +7595,31 @@
             "title": "Max Soc Percentage",
             "description": "An integer representing the maximum state of charge (SOC) of the battery in percentage.",
             "default": 100
+          },
+          "charge_rates": {
+            "anyOf": [
+              {
+                "items": {
+                  "type": "number"
+                },
+                "type": "array"
+              },
+              {
+                "type": "null"
+              }
+            ],
+            "title": "Charge Rates",
+            "description": "Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
+            "examples": [
+              [
+                0.0,
+                0.25,
+                0.5,
+                0.75,
+                1.0
+              ],
+              null
+            ]
           }
         },
         "additionalProperties": false,

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "akkudoktor-eos"
-version = "0.0.1"
+version = "0.2.0"
 authors = [
   { name="Andreas Schmitz", email="author@example.com" },
 ]
@@ -111,7 +111,7 @@ ignore_missing_imports = true
 [tool.commitizen]
 name = "cz_conventional_commits"
 version_scheme = "semver"
-version = "0.1.0+dev"            # <-- Set your current version heretag_format = "v$version"
+version = "0.2.0"            # <-- Set your current version heretag_format = "v$version"
 
 # Files to automatically update when bumping version
 update_changelog_on_bump = true
@@ -133,5 +133,5 @@ template = "keepachangelog"
 version = [
   "pyproject.toml",          # Auto-update project version
   "src/akkudoktoreos/core/version.py",
-  "src/data/default.config.json"
+  "src/akkudoktoreos/data/default.config.json"
 ]

requirements-dev.txt

@@ -25,5 +25,5 @@ myst-parser==4.0.1
 # Pytest
 pytest==8.4.2
 pytest-cov==7.0.0
-coverage==7.11.0
+coverage==7.11.1
 pytest-xprocess==1.0.2

requirements.txt

@@ -10,9 +10,9 @@ fastapi_cli==0.0.14
 rich-toolkit==0.15.1
 python-fasthtml==0.12.33
 MonsterUI==1.0.32
-markdown-it-py==4.0.0
+markdown-it-py==3.0.0
 mdit-py-plugins==0.5.0
-bokeh==3.8.0
+bokeh==3.8.1
 uvicorn==0.38.0
 scikit-learn==1.7.2
 tzfpy==1.1.0
@@ -23,7 +23,7 @@ pendulum==3.1.0
 platformdirs==4.5.0
 psutil==7.1.3
 pvlib==0.13.1
-pydantic==2.12.3
+pydantic==2.12.4
 pydantic_extra_types==2.10.6
 statsmodels==0.14.5
 pydantic-settings==2.11.0

scripts/bump_version.py

@@ -0,0 +1,170 @@
+"""Update version strings in multiple project files only if the old version matches.
+
+This script updates version information in:
+- pyproject.toml
+- src/akkudoktoreos/core/version.py
+- src/akkudoktoreos/data/default.config.json
+- Makefile
+
+Supported version formats:
+- __version__ = "<version>"
+- version = "<version>"
+- "version": "<version>"
+- VERSION ?: <version>
+
+It will:
+- Replace VERSION → NEW_VERSION if the old version is found.
+- Report which files were updated.
+- Report which files contained mismatched versions.
+- Report which files had no version.
+
+Usage:
+    python bump_version.py VERSION NEW_VERSION
+
+Args:
+    VERSION (str): Version expected before replacement.
+    NEW_VERSION (str): Version to write.
+
+"""
+#!/usr/bin/env python3
+import argparse
+import glob
+import os
+import re
+import shutil
+from pathlib import Path
+from typing import List, Tuple
+
+# Patterns to match version strings
+VERSION_PATTERNS = [
+    re.compile(r'(__version__\s*=\s*")(?P<ver>[^"]+)(")'),
+    re.compile(r'(version\s*=\s*")(?P<ver>[^"]+)(")'),
+    re.compile(r'("version"\s*:\s*")(?P<ver>[^"]+)(")'),
+    re.compile(r'(VERSION\s*\?=\s*)(?P<ver>[^\s]+)'),  # For Makefile: VERSION ?= 0.2.0
+]
+
+# Default files to process
+DEFAULT_FILES = [
+    "pyproject.toml",
+    "src/akkudoktoreos/core/version.py",
+    "src/akkudoktoreos/data/default.config.json",
+    "Makefile",
+]
+
+
+def backup_file(file_path: str) -> str:
+    """Create a backup of the given file with a .bak suffix.
+
+    Args:
+        file_path: Path to the file to backup.
+
+    Returns:
+        Path to the backup file.
+    """
+    backup_path = f"{file_path}.bak"
+    shutil.copy2(file_path, backup_path)
+    return backup_path
+
+
+def replace_version_in_file(
+    file_path: Path, old_version: str, new_version: str, dry_run: bool = False
+) -> Tuple[bool, bool]:
+    """
+    Replace old_version with new_version in the given file if it matches.
+
+    Args:
+        file_path: Path to the file to modify.
+        old_version: The old version to replace.
+        new_version: The new version to set.
+        dry_run: If True, don't actually modify files.
+
+    Returns:
+        Tuple[bool, bool]: (file_would_be_updated, old_version_found)
+    """
+    content = file_path.read_text()
+    new_content = content
+    old_version_found = False
+    file_would_be_updated = False
+
+    for pattern in VERSION_PATTERNS:
+        def repl(match):
+            nonlocal old_version_found, file_would_be_updated
+            ver = match.group("ver")
+            if ver == old_version:
+                old_version_found = True
+                file_would_be_updated = True
+                # Some patterns have 3 groups (like quotes)
+                if len(match.groups()) == 3:
+                    return f"{match.group(1)}{new_version}{match.group(3)}"
+                else:
+                    return f"{match.group(1)}{new_version}"
+            return match.group(0)
+
+        new_content = pattern.sub(repl, new_content)
+
+    if file_would_be_updated:
+        if dry_run:
+            print(f"[DRY-RUN] Would update {file_path}")
+        else:
+            backup_path = file_path.with_suffix(file_path.suffix + ".bak")
+            shutil.copy(file_path, backup_path)
+            file_path.write_text(new_content)
+            print(f"Updated {file_path} (backup saved to {backup_path})")
+    elif not old_version_found:
+        print(f"[SKIP] {file_path}: old version '{old_version}' not found")
+
+    return file_would_be_updated, old_version_found
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Bump version across project files.")
+    parser.add_argument("old_version", help="Old version to replace")
+    parser.add_argument("new_version", help="New version to set")
+    parser.add_argument(
+        "--dry-run", action="store_true", help="Show what would be changed without modifying files"
+    )
+    parser.add_argument(
+        "--glob", nargs="*", help="Optional glob patterns to include additional files"
+    )
+    args = parser.parse_args()
+
+    updated_files = []
+    not_found_files = []
+
+    # Determine files to update
+    files_to_update: List[Path] = [Path(f) for f in DEFAULT_FILES]
+    if args.glob:
+        for pattern in args.glob:
+            files_to_update.extend(Path(".").glob(pattern))
+
+    files_to_update = list(dict.fromkeys(files_to_update))  # remove duplicates
+
+    any_updated = False
+    for file_path in files_to_update:
+        if file_path.exists() and file_path.is_file():
+            updated, _ = replace_version_in_file(
+                file_path, args.old_version, args.new_version, args.dry_run
+            )
+            any_updated |= updated
+            if updated:
+                updated_files.append(file_path)
+        else:
+            print(f"[SKIP] {file_path}: file does not exist")
+            not_found_files.append(file_path)
+
+    print("\nSummary:")
+    if updated_files:
+        print(f"Updated files ({len(updated_files)}):")
+        for f in updated_files:
+            print(f"  {f}")
+    else:
+        print("No files were updated.")
+
+    if not_found_files:
+        print(f"Files where old version was not found ({len(not_found_files)}):")
+        for f in not_found_files:
+            print(f"  {f}")
+
+
+if __name__ == "__main__":
+    main()

src/akkudoktoreos/config/configmigrate.py

@@ -21,7 +21,7 @@ if TYPE_CHECKING:
 #   - tuple[str, Callable[[Any], Any]] (new path + transform)
 #   - None (drop)
 MIGRATION_MAP: Dict[str, Union[str, Tuple[str, Callable[[Any], Any]], None]] = {
-    # 0.1.0 -> now
+    # 0.1.0 -> 0.2.0
     "devices/batteries/0/initial_soc_percentage": None,
     "devices/electric_vehicles/0/initial_soc_percentage": None,
     "elecprice/provider_settings/import_file_path": "elecprice/provider_settings/ElecPriceImport/import_file_path",

src/akkudoktoreos/core/version.py

@@ -2,4 +2,4 @@
 
 # For development add `+dev` to previous release
 # For release omit `+dev`.
-__version__ = "0.1.0+dev"
+__version__ = "0.2.0"

src/akkudoktoreos/data/default.config.json

@@ -1,5 +1,5 @@
 {
   "general": {
-    "version": "0.1.0+dev"
+    "version": "0.2.0"
   }
 }

src/akkudoktoreos/devices/devices.py

@@ -1,10 +1,13 @@
 """General configuration settings for simulated devices for optimization."""
 
 import json
+import re
 from typing import Any, Optional, TextIO, cast
 
+import numpy as np
 from loguru import logger
-from pydantic import Field, computed_field, model_validator
+from numpydantic import NDArray, Shape
+from pydantic import Field, computed_field, field_validator, model_validator
 
 from akkudoktoreos.config.configabc import SettingsBaseModel
 from akkudoktoreos.core.cache import CacheFileStore
@@ -14,6 +17,9 @@ from akkudoktoreos.core.pydantic import ConfigDict, PydanticBaseModel
 from akkudoktoreos.devices.devicesabc import DevicesBaseSettings
 from akkudoktoreos.utils.datetimeutil import DateTime, TimeWindowSequence, to_datetime
 
+# Default charge rates for battery
+BATTERY_DEFAULT_CHARGE_RATES = np.linspace(0.0, 1.0, 11)  # 0.0, 0.1, ..., 1.0
+
 
 class BatteriesCommonSettings(DevicesBaseSettings):
     """Battery devices base settings."""
@@ -61,9 +67,12 @@ class BatteriesCommonSettings(DevicesBaseSettings):
         examples=[50],
     )
 
-    charge_rates: Optional[list[float]] = Field(
-        default=None,
-        description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
+    charge_rates: Optional[NDArray[Shape["*"], float]] = Field(
+        default=BATTERY_DEFAULT_CHARGE_RATES,
+        description=(
+            "Charge rates as factor of maximum charging power [0.00 ... 1.00]. "
+            "None triggers fallback to default charge-rates."
+        ),
         examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None],
     )
 
@@ -71,7 +80,10 @@ class BatteriesCommonSettings(DevicesBaseSettings):
         default=0,
         ge=0,
         le=100,
-        description="Minimum state of charge (SOC) as percentage of capacity [%]. This is the target SoC for charging",
+        description=(
+            "Minimum state of charge (SOC) as percentage of capacity [%]. "
+            "This is the target SoC for charging"
+        ),
         examples=[10],
     )
 
@@ -83,6 +95,36 @@ class BatteriesCommonSettings(DevicesBaseSettings):
         examples=[100],
     )
 
+    @field_validator("charge_rates", mode="before")
+    def validate_and_sort_charge_rates(cls, v: Any) -> NDArray[Shape["*"], float]:
+        # None means fallback to default values
+        if v is None:
+            return BATTERY_DEFAULT_CHARGE_RATES.copy()
+
+        # Convert to numpy array
+        if isinstance(v, str):
+            # Remove brackets and split by comma or whitespace
+            numbers = re.split(r"[,\s]+", v.strip("[]"))
+
+            # Filter out any empty strings and convert to floats
+            arr = np.array([float(x) for x in numbers if x])
+        else:
+            arr = np.array(v, dtype=float)
+
+        # Must not be empty
+        if arr.size == 0:
+            raise ValueError("charge_rates must contain at least one value.")
+
+        # Enforce bounds: 0.0 ≤ x ≤ 1.0
+        if (arr < 0.0).any() or (arr > 1.0).any():
+            raise ValueError("charge_rates must be within [0.0, 1.0].")
+
+        # Remove duplicates + sort
+        arr = np.unique(arr)
+        arr.sort()
+
+        return arr
+
     @computed_field  # type: ignore[prop-decorator]
     @property
     def measurement_key_soc_factor(self) -> str:

src/akkudoktoreos/devices/genetic/battery.py

@@ -1,7 +1,8 @@
-from typing import Any, Optional
+from typing import Any, Iterator, Optional
 
 import numpy as np
 
+from akkudoktoreos.devices.devices import BATTERY_DEFAULT_CHARGE_RATES
 from akkudoktoreos.optimization.genetic.geneticdevices import (
     BaseBatteryParameters,
     SolarPanelBatteryParameters,
@@ -17,12 +18,20 @@ class Battery:
         self._setup()
 
     def _setup(self) -> None:
-        """Sets up the battery parameters based on configuration or provided parameters."""
+        """Sets up the battery parameters based on provided parameters."""
         self.capacity_wh = self.parameters.capacity_wh
         self.initial_soc_percentage = self.parameters.initial_soc_percentage
         self.charging_efficiency = self.parameters.charging_efficiency
         self.discharging_efficiency = self.parameters.discharging_efficiency
 
+        # Charge rates, in case of None use default
+        self.charge_rates = BATTERY_DEFAULT_CHARGE_RATES
+        if self.parameters.charge_rates:
+            charge_rates = np.array(self.parameters.charge_rates, dtype=float)
+            charge_rates = np.unique(charge_rates)
+            charge_rates.sort()
+            self.charge_rates = charge_rates
+
         # Only assign for storage battery
         self.min_soc_percentage = (
             self.parameters.min_soc_percentage
@@ -36,12 +45,30 @@ class Battery:
             self.max_charge_power_w = self.parameters.max_charge_power_w
         else:
             self.max_charge_power_w = self.capacity_wh  # TODO this should not be equal capacity_wh
-        self.discharge_array = np.full(self.prediction_hours, 1)
-        self.charge_array = np.full(self.prediction_hours, 1)
+        self.discharge_array = np.full(self.prediction_hours, 0)
+        self.charge_array = np.full(self.prediction_hours, 0)
         self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
         self.min_soc_wh = (self.min_soc_percentage / 100) * self.capacity_wh
         self.max_soc_wh = (self.max_soc_percentage / 100) * self.capacity_wh
 
+    def _lower_charge_rates_desc(self, start_rate: float) -> Iterator[float]:
+        """Yield all charge rates lower than a given rate in descending order.
+
+        Args:
+            charge_rates (np.ndarray): Sorted 1D array of available charge rates.
+            start_rate (float): The reference charge rate.
+
+        Yields:
+            float: Charge rates lower than `start_rate`, in descending order.
+        """
+        charge_rates_fast = self.charge_rates
+
+        # Find the insertion index for start_rate (left-most position)
+        idx = np.searchsorted(charge_rates_fast, start_rate, side="left")
+
+        # Yield values before idx in reverse (descending)
+        return (charge_rates_fast[j] for j in range(idx - 1, -1, -1))
+
     def to_dict(self) -> dict[str, Any]:
         """Converts the object to a dictionary representation."""
         return {
@@ -61,8 +88,8 @@ class Battery:
         """Resets the battery state to its initial values."""
         self.soc_wh = (self.initial_soc_percentage / 100) * self.capacity_wh
         self.soc_wh = min(max(self.soc_wh, self.min_soc_wh), self.max_soc_wh)
-        self.discharge_array = np.full(self.prediction_hours, 1)
-        self.charge_array = np.full(self.prediction_hours, 1)
+        self.discharge_array = np.full(self.prediction_hours, 0)
+        self.charge_array = np.full(self.prediction_hours, 0)
 
     def set_discharge_per_hour(self, discharge_array: np.ndarray) -> None:
         """Sets the discharge values for each hour."""
@@ -80,70 +107,172 @@ class Battery:
             )
         self.charge_array = np.array(charge_array)
 
-    def set_charge_allowed_for_hour(self, charge: float, hour: int) -> None:
-        """Sets the charge for a specific hour."""
-        if hour >= self.prediction_hours:
-            raise ValueError(
-                f"Hour {hour} is out of range. Must be less than {self.prediction_hours}."
-            )
-        self.charge_array[hour] = charge
-
     def current_soc_percentage(self) -> float:
         """Calculates the current state of charge in percentage."""
         return (self.soc_wh / self.capacity_wh) * 100
 
     def discharge_energy(self, wh: float, hour: int) -> tuple[float, float]:
-        """Discharges energy from the battery."""
+        """Discharge energy from the battery.
+
+        Discharge is limited by:
+        * Requested delivered energy
+        * Remaining energy above minimum SoC
+        * Maximum discharge power
+        * Discharge efficiency
+
+        Args:
+            wh (float): Requested delivered energy in watt-hours.
+            hour (int): Time index. If `self.discharge_array[hour] == 0`,
+                no discharge occurs.
+
+        Returns:
+            tuple[float, float]:
+                delivered_wh (float): Actual delivered energy [Wh].
+                losses_wh (float): Conversion losses [Wh].
+
+        """
         if self.discharge_array[hour] == 0:
             return 0.0, 0.0
 
-        max_possible_discharge_wh = (self.soc_wh - self.min_soc_wh) * self.discharging_efficiency
-        max_possible_discharge_wh = max(max_possible_discharge_wh, 0.0)
+        # Raw extractable energy above minimum SoC
+        raw_available_wh = max(self.soc_wh - self.min_soc_wh, 0.0)
 
-        max_possible_discharge_wh = min(
-            max_possible_discharge_wh, self.max_charge_power_w
-        )  # TODO make a new cfg variable max_discharge_power_w
+        # Maximum raw discharge due to power limit
+        max_raw_wh = self.max_charge_power_w  # TODO rename to max_discharge_power_w
 
-        actual_discharge_wh = min(wh, max_possible_discharge_wh)
-        actual_withdrawal_wh = (
-            actual_discharge_wh / self.discharging_efficiency
-            if self.discharging_efficiency > 0
-            else 0.0
-        )
+        # Actual raw withdrawal (internal)
+        raw_withdrawal_wh = min(raw_available_wh, max_raw_wh)
 
-        self.soc_wh -= actual_withdrawal_wh
+        # Convert raw to delivered
+        max_deliverable_wh = raw_withdrawal_wh * self.discharging_efficiency
+
+        # Cap by requested delivered energy
+        delivered_wh = min(wh, max_deliverable_wh)
+
+        # Effective raw withdrawal based on what is delivered
+        raw_used_wh = delivered_wh / self.discharging_efficiency
+
+        # Update SoC
+        self.soc_wh -= raw_used_wh
         self.soc_wh = max(self.soc_wh, self.min_soc_wh)
 
-        losses_wh = actual_withdrawal_wh - actual_discharge_wh
-        return actual_discharge_wh, losses_wh
+        # Losses
+        losses_wh = raw_used_wh - delivered_wh
+
+        return delivered_wh, losses_wh
 
     def charge_energy(
-        self, wh: Optional[float], hour: int, relative_power: float = 0.0
+        self,
+        wh: Optional[float],
+        hour: int,
+        charge_factor: float = 0.0,
     ) -> tuple[float, float]:
-        """Charges energy into the battery."""
+        """Charge energy into the battery.
+
+        Two **exclusive** modes:
+
+        Mode 1:
+            - `wh is not None` and `charge_factor == 0`
+            → The raw requested charge energy is `wh` (pre-efficiency).
+            → If remaining capacity is insufficient, charging is automatically limited.
+            → No exception is raised due to capacity limits.
+
+        Mode 2:
+            - `wh is None` and `charge_factor > 0`
+            → The raw requested energy is `max_charge_power_w * charge_factor`.
+            → If the request exceeds remaining capacity, the algorithm tries to
+              find a lower charge_factor that is compatible. If such a charge factor
+              exists, this hour’s charge_factor is replaced.
+            → If no charge factor can accommodate charging, the request is ignored
+              (`(0.0, 0.0)` is returned) and a penalty is applied elsewhere.
+
+        Charging is constrained by:
+            • Available SoC headroom (max_soc_wh − soc_wh)
+            • max_charge_power_w
+            • charging_efficiency
+
+        Args:
+            wh (float | None):
+                Requested raw energy [Wh] before efficiency.
+                Must be provided only for Mode 1 (charge_factor must be 0).
+
+            hour (int):
+                Time index. If charging is disabled at this hour (charge_array[hour] == 0),
+                returns `(0.0, 0.0)`.
+
+            charge_factor (float):
+                Fraction (0–1) of max charge power.
+                Must be >0 only in Mode 2 (`wh is None`).
+
+        Returns:
+            tuple[float, float]:
+                stored_wh : float
+                    Energy stored after efficiency [Wh].
+                losses_wh : float
+                    Conversion losses [Wh].
+
+        Raises:
+            ValueError:
+                - If the mode is ambiguous (neither Mode 1 nor Mode 2).
+                - If the final new SoC would exceed capacity_wh.
+
+        Notes:
+            stored_wh = raw_input_wh * charging_efficiency
+            losses_wh = raw_input_wh − stored_wh
+        """
+        # Charging allowed in this hour?
         if hour is not None and self.charge_array[hour] == 0:
-            return 0.0, 0.0  # Charging not allowed in this hour
+            return 0.0, 0.0
 
-        if relative_power > 0.0:
-            wh = self.max_charge_power_w * relative_power
+        # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access
+        soc_wh_fast = self.soc_wh
+        max_charge_power_w_fast = self.max_charge_power_w
+        charging_efficiency_fast = self.charging_efficiency
 
-        wh = wh if wh is not None else self.max_charge_power_w
-
-        max_possible_charge_wh = (
-            (self.max_soc_wh - self.soc_wh) / self.charging_efficiency
-            if self.charging_efficiency > 0
-            else 0.0
+        # Decide mode & determine raw_request_wh and raw_charge_wh
+        if wh is not None and charge_factor == 0.0:  # mode 1
+            raw_request_wh = wh
+            raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
+        elif wh is None and charge_factor > 0.0:  # mode 2
+            raw_request_wh = max_charge_power_w_fast * charge_factor
+            raw_charge_wh = max(self.max_soc_wh - soc_wh_fast, 0.0) / charging_efficiency_fast
+            if raw_request_wh > raw_charge_wh:
+                # Use a lower charge factor
+                lower_charge_factors = self._lower_charge_rates_desc(charge_factor)
+                for charge_factor in lower_charge_factors:
+                    raw_request_wh = max_charge_power_w_fast * charge_factor
+                    if raw_request_wh <= raw_charge_wh:
+                        self.charge_array[hour] = charge_factor
+                        break
+                if raw_request_wh > raw_charge_wh:
+                    # ignore request - penalty for missing SoC will be applied
+                    self.charge_array[hour] = 0
+                    return 0.0, 0.0
+        else:
+            raise ValueError(
+                f"{self.parameters.device_id}: charge_energy must be called either "
+                "with wh != None and charge_factor == 0, or with wh == None and charge_factor > 0."
             )
-        max_possible_charge_wh = max(max_possible_charge_wh, 0.0)
 
-        effective_charge_wh = min(wh, max_possible_charge_wh)
-        charged_wh = effective_charge_wh * self.charging_efficiency
+        # Remaining capacity
+        max_raw_wh = min(raw_charge_wh, max_charge_power_w_fast)
 
-        self.soc_wh += charged_wh
-        self.soc_wh = min(self.soc_wh, self.max_soc_wh)
+        # Actual raw intake
+        raw_input_wh = raw_request_wh if raw_request_wh < max_raw_wh else max_raw_wh
 
-        losses_wh = effective_charge_wh - charged_wh
-        return charged_wh, losses_wh
+        # Apply efficiency
+        stored_wh = raw_input_wh * charging_efficiency_fast
+        new_soc = soc_wh_fast + stored_wh
+
+        if new_soc > self.capacity_wh:
+            raise ValueError(
+                f"{self.parameters.device_id}: SoC {new_soc} Wh exceeds capacity {self.capacity_wh} Wh"
+            )
+
+        self.soc_wh = new_soc
+        losses_wh = raw_input_wh - stored_wh
+
+        return stored_wh, losses_wh
 
     def current_energy_content(self) -> float:
         """Returns the current usable energy in the battery."""

src/akkudoktoreos/devices/genetic/homeappliance.py

@@ -1,5 +1,3 @@
-from typing import Optional
-
 import numpy as np
 
 from akkudoktoreos.optimization.genetic.geneticdevices import HomeApplianceParameters
@@ -28,7 +26,6 @@ class HomeAppliance:
         self.load_curve = np.zeros(self.prediction_hours)  # Initialize the load curve with zeros
         self.duration_h = self.parameters.duration_h
         self.consumption_wh = self.parameters.consumption_wh
-        self.appliance_start: Optional[int] = None
         # setup possible start times
         if self.parameters.time_windows is None:
             self.parameters.time_windows = TimeWindowSequence(
@@ -59,33 +56,32 @@ class HomeAppliance:
         else:
             self.start_latest = 23
 
-    def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> None:
+    def set_starting_time(self, start_hour: int, global_start_hour: int = 0) -> int:
         """Sets the start time of the device and generates the corresponding load curve.
 
         :param start_hour: The hour at which the device should start.
         """
-        self.reset_load_curve()
-
-        # Check if the duration of use is within the available time windows
         if not self.start_allowed[start_hour]:
-            # No available time window to start home appliance
-            # Use the earliest one
-            start_hour = self.start_earliest
-
-        # Check if it is possibility to start the appliance
-        if start_hour < global_start_hour:
-            # Start is before current time
-            # Use the latest one
+            # It is not allowed (by the time windows) to start the application at this time
+            if global_start_hour <= self.start_latest:
+                # There is a time window left to start the appliance. Use it
                 start_hour = self.start_latest
+            else:
+                # There is no time window left to run the application
+                # Set the start into tomorrow
+                start_hour = self.start_earliest + 24
+
+        self.reset_load_curve()
 
         # Calculate power per hour based on total consumption and duration
         power_per_hour = self.consumption_wh / self.duration_h  # Convert to watt-hours
 
         # Set the power for the duration of use in the load curve array
-        self.load_curve[start_hour : start_hour + self.duration_h] = power_per_hour
+        if start_hour < len(self.load_curve):
+            end_hour = min(start_hour + self.duration_h, self.prediction_hours)
+            self.load_curve[start_hour:end_hour] = power_per_hour
 
-        # Set the selected start hour
-        self.appliance_start = start_hour
+        return start_hour
 
     def reset_load_curve(self) -> None:
         """Resets the load curve."""
@@ -107,6 +103,3 @@ class HomeAppliance:
             )
 
         return self.load_curve[hour]
-
-    def get_appliance_start(self) -> Optional[int]:
-        return self.appliance_start

src/akkudoktoreos/optimization/genetic/genetic.py

@@ -69,7 +69,16 @@ class GeneticSimulation(PydanticBaseModel):
 
     ac_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
     dc_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
+    bat_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None, description="TBD"
+    )
     ev_charge_hours: Optional[NDArray[Shape["*"], float]] = Field(default=None, description="TBD")
+    ev_discharge_hours: Optional[NDArray[Shape["*"], float]] = Field(
+        default=None, description="TBD"
+    )
+    home_appliance_start_hour: Optional[int] = Field(
+        default=None, description="Home appliance start hour - None denotes no start."
+    )
 
     def prepare(
         self,
@@ -100,8 +109,11 @@ class GeneticSimulation(PydanticBaseModel):
         self.home_appliance = home_appliance
         self.inverter = inverter
         self.ac_charge_hours = np.full(self.prediction_hours, 0.0)
-        self.dc_charge_hours = np.full(self.prediction_hours, 1.0)
+        self.dc_charge_hours = np.full(self.prediction_hours, 0.0)
+        self.bat_discharge_hours = np.full(self.prediction_hours, 0.0)
         self.ev_charge_hours = np.full(self.prediction_hours, 0.0)
+        self.ev_discharge_hours = np.full(self.prediction_hours, 0.0)
+        self.home_appliance_start_hour = None
         """Prepare simulation runs."""
         self.load_energy_array = np.array(parameters.gesamtlast, float)
         self.pv_prediction_wh = np.array(parameters.pv_prognose_wh, float)
@@ -114,28 +126,12 @@ class GeneticSimulation(PydanticBaseModel):
             )
         )
 
-    def set_akku_discharge_hours(self, ds: np.ndarray) -> None:
-        if self.battery:
-            self.battery.set_discharge_per_hour(ds)
-
-    def set_akku_ac_charge_hours(self, ds: np.ndarray) -> None:
-        self.ac_charge_hours = ds
-
-    def set_akku_dc_charge_hours(self, ds: np.ndarray) -> None:
-        self.dc_charge_hours = ds
-
-    def set_ev_charge_hours(self, ds: np.ndarray) -> None:
-        self.ev_charge_hours = ds
-
-    def set_home_appliance_start(self, ds: int, global_start_hour: int = 0) -> None:
-        if self.home_appliance:
-            self.home_appliance.set_starting_time(ds, global_start_hour=global_start_hour)
-
     def reset(self) -> None:
         if self.ev:
             self.ev.reset()
         if self.battery:
             self.battery.reset()
+        self.home_appliance_start_hour = None
 
     def simulate(self, start_hour: int) -> dict[str, Any]:
         """Simulate energy usage and costs for the given start hour.
@@ -146,45 +142,66 @@ class GeneticSimulation(PydanticBaseModel):
         # Remember start hour
         self.start_hour = start_hour
 
-        # Check for simulation integrity
-        required_attrs = [
-            "load_energy_array",
-            "pv_prediction_wh",
-            "elect_price_hourly",
-            "ev_charge_hours",
-            "ac_charge_hours",
-            "dc_charge_hours",
-            "elect_revenue_per_hour_arr",
-        ]
-        missing_data = [
-            attr.replace("_", " ").title() for attr in required_attrs if getattr(self, attr) is None
-        ]
+        # Provide fast (3x..5x) local read access (vs. self.xxx) for repetitive read access
+        load_energy_array_fast = self.load_energy_array
+        ev_charge_hours_fast = self.ev_charge_hours
+        ev_discharge_hours_fast = self.ev_discharge_hours
+        ac_charge_hours_fast = self.ac_charge_hours
+        dc_charge_hours_fast = self.dc_charge_hours
+        bat_discharge_hours_fast = self.bat_discharge_hours
+        elect_price_hourly_fast = self.elect_price_hourly
+        elect_revenue_per_hour_arr_fast = self.elect_revenue_per_hour_arr
+        pv_prediction_wh_fast = self.pv_prediction_wh
+        battery_fast = self.battery
+        ev_fast = self.ev
+        home_appliance_fast = self.home_appliance
+        inverter_fast = self.inverter
 
-        if missing_data:
-            logger.error("Mandatory data missing - %s", ", ".join(missing_data))
-            raise ValueError(f"Mandatory data missing: {', '.join(missing_data)}")
+        # Check for simulation integrity (in a way that mypy understands)
+        if (
+            load_energy_array_fast is None
+            or pv_prediction_wh_fast is None
+            or elect_price_hourly_fast is None
+            or ev_charge_hours_fast is None
+            or ac_charge_hours_fast is None
+            or dc_charge_hours_fast is None
+            or elect_revenue_per_hour_arr_fast is None
+            or bat_discharge_hours_fast is None
+            or ev_discharge_hours_fast is None
+        ):
+            missing = []
+            if load_energy_array_fast is None:
+                missing.append("Load Energy Array")
+            if pv_prediction_wh_fast is None:
+                missing.append("PV Prediction Wh")
+            if elect_price_hourly_fast is None:
+                missing.append("Electricity Price Hourly")
+            if ev_charge_hours_fast is None:
+                missing.append("EV Charge Hours")
+            if ac_charge_hours_fast is None:
+                missing.append("AC Charge Hours")
+            if dc_charge_hours_fast is None:
+                missing.append("DC Charge Hours")
+            if elect_revenue_per_hour_arr_fast is None:
+                missing.append("Electricity Revenue Per Hour")
+            if bat_discharge_hours_fast is None:
+                missing.append("Battery Discharge Hours")
+            if ev_discharge_hours_fast is None:
+                missing.append("EV Discharge Hours")
+            msg = ", ".join(missing)
+            logger.error("Mandatory data missing - %s", msg)
+            raise ValueError(f"Mandatory data missing: {msg}")
 
-        # Pre-fetch data
-        load_energy_array = np.array(self.load_energy_array)
-        pv_prediction_wh = np.array(self.pv_prediction_wh)
-        elect_price_hourly = np.array(self.elect_price_hourly)
-        ev_charge_hours = np.array(self.ev_charge_hours)
-        ac_charge_hours = np.array(self.ac_charge_hours)
-        dc_charge_hours = np.array(self.dc_charge_hours)
-        elect_revenue_per_hour_arr = np.array(self.elect_revenue_per_hour_arr)
-
-        # Fetch objects
-        battery = self.battery
-        ev = self.ev
-        home_appliance = self.home_appliance
-        inverter = self.inverter
-
-        if not (len(load_energy_array) == len(pv_prediction_wh) == len(elect_price_hourly)):
-            error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array)}, PV Forecast = {len(pv_prediction_wh)}, Electricity Price = {len(elect_price_hourly)}"
+        if not (
+            len(load_energy_array_fast)
+            == len(pv_prediction_wh_fast)
+            == len(elect_price_hourly_fast)
+        ):
+            error_msg = f"Array sizes do not match: Load Curve = {len(load_energy_array_fast)}, PV Forecast = {len(pv_prediction_wh_fast)}, Electricity Price = {len(elect_price_hourly_fast)}"
             logger.error(error_msg)
             raise ValueError(error_msg)
 
-        end_hour = len(load_energy_array)
+        end_hour = len(load_energy_array_fast)
         total_hours = end_hour - start_hour
 
         # Pre-allocate arrays for the results, optimized for speed
@@ -200,34 +217,59 @@ class GeneticSimulation(PydanticBaseModel):
         electricity_price_per_hour = np.full((total_hours), np.nan)
 
         # Set initial state
-        if battery:
-            soc_per_hour[0] = battery.current_soc_percentage()
-        if ev:
-            soc_ev_per_hour[0] = ev.current_soc_percentage()
+        if battery_fast:
+            soc_per_hour[0] = battery_fast.current_soc_percentage()
+            # Fill the charge array of the battery
+            dc_charge_hours_fast[0:start_hour] = 0
+            dc_charge_hours_fast[end_hour:] = 0
+            ac_charge_hours_fast[0:start_hour] = 0
+            dc_charge_hours_fast[end_hour:] = 0
+            battery_fast.charge_array = np.where(
+                ac_charge_hours_fast != 0, ac_charge_hours_fast, dc_charge_hours_fast
+            )
+            # Fill the discharge array of the battery
+            bat_discharge_hours_fast[0:start_hour] = 0
+            bat_discharge_hours_fast[end_hour:] = 0
+            battery_fast.discharge_array = bat_discharge_hours_fast
+
+        if ev_fast:
+            soc_ev_per_hour[0] = ev_fast.current_soc_percentage()
+            # Fill the charge array of the ev
+            ev_charge_hours_fast[0:start_hour] = 0
+            ev_charge_hours_fast[end_hour:] = 0
+            ev_fast.charge_array = ev_charge_hours_fast
+            # Fill the discharge array of the ev
+            ev_discharge_hours_fast[0:start_hour] = 0
+            ev_discharge_hours_fast[end_hour:] = 0
+            ev_fast.discharge_array = ev_discharge_hours_fast
+
+        if home_appliance_fast and self.home_appliance_start_hour:
+            home_appliance_enabled = True
+            self.home_appliance_start_hour = home_appliance_fast.set_starting_time(
+                self.home_appliance_start_hour, start_hour
+            )
+        else:
+            home_appliance_enabled = False
 
         for hour in range(start_hour, end_hour):
             hour_idx = hour - start_hour
 
-            # save begin states
-            if battery:
-                soc_per_hour[hour_idx] = battery.current_soc_percentage()
-            if ev:
-                soc_ev_per_hour[hour_idx] = ev.current_soc_percentage()
-
             # Accumulate loads and PV generation
-            consumption = load_energy_array[hour]
+            consumption = load_energy_array_fast[hour]
             losses_wh_per_hour[hour_idx] = 0.0
 
             # Home appliances
-            if home_appliance:
-                ha_load = home_appliance.get_load_for_hour(hour)
+            if home_appliance_enabled:
+                ha_load = home_appliance_fast.get_load_for_hour(hour)  # type: ignore[union-attr]
                 consumption += ha_load
                 home_appliance_wh_per_hour[hour_idx] = ha_load
 
             # E-Auto handling
-            if ev and ev_charge_hours[hour] > 0:
-                loaded_energy_ev, verluste_eauto = ev.charge_energy(
-                    None, hour, relative_power=ev_charge_hours[hour]
+            if ev_fast:
+                soc_ev_per_hour[hour_idx] = ev_fast.current_soc_percentage()  # save begin state
+                if ev_charge_hours_fast[hour] > 0:
+                    loaded_energy_ev, verluste_eauto = ev_fast.charge_energy(
+                        wh=None, hour=hour, charge_factor=ev_charge_hours_fast[hour]
                     )
                     consumption += loaded_energy_ev
                     losses_wh_per_hour[hour_idx] += verluste_eauto
@@ -237,28 +279,22 @@ class GeneticSimulation(PydanticBaseModel):
                 0.0
             )
 
-            hour_ac_charge = ac_charge_hours[hour]
-            hour_dc_charge = dc_charge_hours[hour]
-            hourly_electricity_price = elect_price_hourly[hour]
-            hourly_energy_revenue = elect_revenue_per_hour_arr[hour]
-
-            if battery:
-                battery.set_charge_allowed_for_hour(hour_dc_charge, hour)
-
-            if inverter:
-                energy_produced = pv_prediction_wh[hour]
+            if inverter_fast:
+                energy_produced = pv_prediction_wh_fast[hour]
                 (
                     energy_feedin_grid_actual,
                     energy_consumption_grid_actual,
                     losses,
                     eigenverbrauch,
-                ) = inverter.process_energy(energy_produced, consumption, hour)
+                ) = inverter_fast.process_energy(energy_produced, consumption, hour)
 
             # AC PV Battery Charge
-            if battery and hour_ac_charge > 0.0:
-                battery.set_charge_allowed_for_hour(1, hour)
-                battery_charged_energy_actual, battery_losses_actual = battery.charge_energy(
-                    None, hour, relative_power=hour_ac_charge
+            if battery_fast:
+                soc_per_hour[hour_idx] = battery_fast.current_soc_percentage()  # save begin state
+                hour_ac_charge = ac_charge_hours_fast[hour]
+                if hour_ac_charge > 0.0:
+                    battery_charged_energy_actual, battery_losses_actual = (
+                        battery_fast.charge_energy(None, hour, charge_factor=hour_ac_charge)
                     )
 
                     total_battery_energy = battery_charged_energy_actual + battery_losses_actual
@@ -271,11 +307,14 @@ class GeneticSimulation(PydanticBaseModel):
             consumption_energy_per_hour[hour_idx] = energy_consumption_grid_actual
             losses_wh_per_hour[hour_idx] += losses
             loads_energy_per_hour[hour_idx] = consumption
+            hourly_electricity_price = elect_price_hourly_fast[hour]
             electricity_price_per_hour[hour_idx] = hourly_electricity_price
 
             # Financial calculations
             costs_per_hour[hour_idx] = energy_consumption_grid_actual * hourly_electricity_price
-            revenue_per_hour[hour_idx] = energy_feedin_grid_actual * hourly_energy_revenue
+            revenue_per_hour[hour_idx] = (
+                energy_feedin_grid_actual * elect_revenue_per_hour_arr_fast[hour]
+            )
 
         total_cost = np.nansum(costs_per_hour)
         total_losses = np.nansum(losses_wh_per_hour)
@@ -289,7 +328,7 @@ class GeneticSimulation(PydanticBaseModel):
             "Kosten_Euro_pro_Stunde": costs_per_hour,
             "akku_soc_pro_stunde": soc_per_hour,
             "Einnahmen_Euro_pro_Stunde": revenue_per_hour,
-            "Gesamtbilanz_Euro": total_cost - total_revenue,
+            "Gesamtbilanz_Euro": total_cost - total_revenue,  # Fitness score ("FitnessMin")
             "EAuto_SoC_pro_Stunde": soc_ev_per_hour,
             "Gesamteinnahmen_Euro": total_revenue,
             "Gesamtkosten_Euro": total_cost,
@@ -574,27 +613,33 @@ class GeneticOptimization(OptimizationBase):
         discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
             individual
         )
+
         if self.opti_param.get("home_appliance", 0) > 0 and washingstart_int:
-            self.simulation.set_home_appliance_start(
-                washingstart_int, global_start_hour=self.ems.start_datetime.hour
+            # Set start hour for appliance
+            self.simulation.home_appliance_start_hour = washingstart_int
+
+        ac_charge_hours, dc_charge_hours, discharge = self.decode_charge_discharge(
+            discharge_hours_bin
         )
 
-        ac, dc, discharge = self.decode_charge_discharge(discharge_hours_bin)
-
-        self.simulation.set_akku_discharge_hours(discharge)
+        self.simulation.bat_discharge_hours = discharge
         # Set DC charge hours only if DC optimization is enabled
         if self.optimize_dc_charge:
-            self.simulation.set_akku_dc_charge_hours(dc)
-        self.simulation.set_akku_ac_charge_hours(ac)
+            self.simulation.dc_charge_hours = dc_charge_hours
+        else:
+            self.simulation.dc_charge_hours = np.full(self.config.prediction.hours, 1)
+        self.simulation.ac_charge_hours = ac_charge_hours
 
         if eautocharge_hours_index is not None:
             eautocharge_hours_float = np.array(
                 [self.ev_possible_charge_values[i] for i in eautocharge_hours_index],
                 float,
             )
-            self.simulation.set_ev_charge_hours(eautocharge_hours_float)
+            # discharge is set to 0 by default
+            self.simulation.ev_charge_hours = eautocharge_hours_float
         else:
-            self.simulation.set_ev_charge_hours(np.full(self.config.prediction.hours, 0))
+            # discharge is set to 0 by default
+            self.simulation.ev_charge_hours = np.full(self.config.prediction.hours, 0)
 
         # Do the simulation and return result.
         return self.simulation.simulate(self.ems.start_datetime.hour)
@@ -606,21 +651,57 @@ class GeneticOptimization(OptimizationBase):
         start_hour: int,
         worst_case: bool,
     ) -> tuple[float]:
-        """Evaluate the fitness of an individual solution based on the simulation results."""
+        """Evaluate the fitness score of a single individual in the DEAP genetic algorithm.
+
+        This method runs a simulation based on the provided individual genome and
+        optimization parameters. The resulting performance is converted into a
+        fitness score compatible with DEAP (i.e., returned as a 1-tuple).
+
+        Args:
+            individual (list[int]):
+                The genome representing one candidate solution.
+            parameters (GeneticOptimizationParameters):
+                Optimization parameters that influence simulation behavior,
+                constraints, and scoring logic.
+            start_hour (int):
+                The simulation start hour (0–23 or domain-specific).
+                Used to initialize time-based scheduling or constraints.
+            worst_case (bool):
+                If True, evaluates the solution under worst-case assumptions
+                (e.g., pessimistic forecasts or boundary conditions).
+                If False, uses nominal assumptions.
+
+        Returns:
+            tuple[float]:
+                A single-element tuple containing the computed fitness score.
+                Lower score is better: "FitnessMin".
+
+        Raises:
+            ValueError: If input arguments are invalid or the individual structure
+                is not compatible with the simulation.
+            RuntimeError: If the simulation fails or cannot produce results.
+
+        Notes:
+            The resulting score should match DEAP's expected format: a tuple, even
+            if only a single scalar fitness value is returned.
+        """
         try:
-            o = self.evaluate_inner(individual)
+            simulation_result = self.evaluate_inner(individual)
         except Exception as e:
-            return (100000.0,)  # Return a high penalty in case of an exception
+            # Return bad fitness score ("FitnessMin") in case of an exception
+            return (100000.0,)
 
-        gesamtbilanz = o["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
+        gesamtbilanz = simulation_result["Gesamtbilanz_Euro"] * (-1.0 if worst_case else 1.0)
 
+        # EV 100% & charge not allowed
+        if self.optimize_ev:
             discharge_hours_bin, eautocharge_hours_index, washingstart_int = self.split_individual(
                 individual
             )
 
-        # EV 100% & charge not allowed
-        if self.optimize_ev:
-            eauto_soc_per_hour = np.array(o.get("EAuto_SoC_pro_Stunde", []))  # Beispielkey
+            eauto_soc_per_hour = np.array(
+                simulation_result.get("EAuto_SoC_pro_Stunde", [])
+            )  # Beispielkey
 
             if eauto_soc_per_hour is None or eautocharge_hours_index is None:
                 raise ValueError("eauto_soc_per_hour or eautocharge_hours_index is None")
@@ -686,8 +767,8 @@ class GeneticOptimization(OptimizationBase):
 
         # More metrics
         individual.extra_data = (  # type: ignore[attr-defined]
-            o["Gesamtbilanz_Euro"],
-            o["Gesamt_Verluste"],
+            simulation_result["Gesamtbilanz_Euro"],
+            simulation_result["Gesamt_Verluste"],
             parameters.eauto.min_soc_percentage - self.simulation.ev.current_soc_percentage()
             if parameters.eauto and self.simulation.ev
             else 0,
@@ -701,7 +782,7 @@ class GeneticOptimization(OptimizationBase):
             )
             gesamtbilanz += -restwert_akku
 
-        if self.optimize_ev:
+        if self.optimize_ev and parameters.eauto and self.simulation.ev:
             try:
                 penalty = self.config.optimization.genetic.penalties["ev_soc_miss"]
             except:
@@ -710,15 +791,13 @@ class GeneticOptimization(OptimizationBase):
                 logger.error(
                     "Penalty function parameter `ev_soc_miss` not configured, using {}.", penalty
                 )
-            gesamtbilanz += max(
-                0,
-                (
-                    parameters.eauto.min_soc_percentage
-                    - self.simulation.ev.current_soc_percentage()
-                    if parameters.eauto and self.simulation.ev
-                    else 0
-                )
-                * penalty,
+            ev_soc_percentage = self.simulation.ev.current_soc_percentage()
+            if (
+                ev_soc_percentage < parameters.eauto.min_soc_percentage
+                or ev_soc_percentage > parameters.eauto.max_soc_percentage
+            ):
+                gesamtbilanz += (
+                    abs(parameters.eauto.min_soc_percentage - ev_soc_percentage) * penalty
                 )
 
         return (gesamtbilanz,)
@@ -825,7 +904,7 @@ class GeneticOptimization(OptimizationBase):
                 parameters.pv_akku,
                 prediction_hours=self.config.prediction.hours,
             )
-            akku.set_charge_per_hour(np.full(self.config.prediction.hours, 1))
+            akku.set_charge_per_hour(np.full(self.config.prediction.hours, 0))
 
         eauto: Optional[Battery] = None
         if parameters.eauto:
@@ -917,7 +996,7 @@ class GeneticOptimization(OptimizationBase):
         )
         # home appliance may have choosen a different appliance start hour
         if self.simulation.home_appliance:
-            washingstart_int = self.simulation.home_appliance.get_appliance_start()
+            washingstart_int = self.simulation.home_appliance_start_hour
 
         eautocharge_hours_float = (
             [self.ev_possible_charge_values[i] for i in eautocharge_hours_index]
@@ -925,12 +1004,28 @@ class GeneticOptimization(OptimizationBase):
             else None
         )
 
-        ac_charge, dc_charge, discharge = self.decode_charge_discharge(discharge_hours_bin)
+        # Simulation may have changed something, use simulation values
+        ac_charge_hours = self.simulation.ac_charge_hours
+        if ac_charge_hours is None:
+            ac_charge_hours = []
+        else:
+            ac_charge_hours = ac_charge_hours.tolist()
+        dc_charge_hours = self.simulation.dc_charge_hours
+        if dc_charge_hours is None:
+            dc_charge_hours = []
+        else:
+            dc_charge_hours = dc_charge_hours.tolist()
+        discharge = self.simulation.bat_discharge_hours
+        if discharge is None:
+            discharge = []
+        else:
+            discharge = discharge.tolist()
+
         # Visualize the results
         visualize = {
-            "ac_charge": ac_charge.tolist(),
-            "dc_charge": dc_charge.tolist(),
-            "discharge_allowed": discharge.tolist(),
+            "ac_charge": ac_charge_hours,
+            "dc_charge": dc_charge_hours,
+            "discharge_allowed": discharge,
             "eautocharge_hours_float": eautocharge_hours_float,
             "result": simulation_result,
             "eauto_obj": self.simulation.ev.to_dict() if self.simulation.ev else None,
@@ -946,8 +1041,8 @@ class GeneticOptimization(OptimizationBase):
 
         return GeneticSolution(
             **{
-                "ac_charge": ac_charge,
-                "dc_charge": dc_charge,
+                "ac_charge": ac_charge_hours,
+                "dc_charge": dc_charge_hours,
                 "discharge_allowed": discharge,
                 "eautocharge_hours_float": eautocharge_hours_float,
                 "result": GeneticSimulationResult(**simulation_result),

src/akkudoktoreos/optimization/genetic/geneticdevices.py

@@ -74,6 +74,11 @@ class BaseBatteryParameters(DeviceParameters):
         le=100,
         description="An integer representing the maximum state of charge (SOC) of the battery in percentage.",
     )
+    charge_rates: Optional[list[float]] = Field(
+        default=None,
+        description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
+        examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None],
+    )
 
 
 class SolarPanelBatteryParameters(BaseBatteryParameters):
@@ -90,11 +95,6 @@ class ElectricVehicleParameters(BaseBatteryParameters):
     initial_soc_percentage: int = initial_soc_percentage_field(
         "An integer representing the current state of charge (SOC) of the battery in percentage."
     )
-    charge_rates: Optional[list[float]] = Field(
-        default=None,
-        description="Charge rates as factor of maximum charging power [0.00 ... 1.00]. None denotes all charge rates are available.",
-        examples=[[0.0, 0.25, 0.5, 0.75, 1.0], None],
-    )
 
 
 class HomeApplianceParameters(DeviceParameters):

src/akkudoktoreos/optimization/genetic/geneticparams.py

@@ -457,7 +457,7 @@ class GeneticOptimizationParameters(
                         {
                             "device_id": "ev11",
                             "capacity_wh": 50000,
-                            "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                            "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
                             "min_soc_percentage": 70,
                         }
                     ]
@@ -483,7 +483,7 @@ class GeneticOptimizationParameters(
                         {
                             "device_id": "ev12",
                             "capacity_wh": 50000,
-                            "charge_rates": [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                            "charge_rates": [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0],
                             "min_soc_percentage": 70,
                         }
                     ]

src/akkudoktoreos/optimization/genetic/geneticsolution.py

@@ -6,7 +6,9 @@ import pandas as pd
 from loguru import logger
 from pydantic import Field, field_validator
 
-from akkudoktoreos.config.config import get_config
+from akkudoktoreos.core.coreabc import (
+    ConfigMixin,
+)
 from akkudoktoreos.core.emplan import (
     DDBCInstruction,
     EnergyManagementPlan,
@@ -109,7 +111,7 @@ class GeneticSimulationResult(GeneticParametersBaseModel):
         return NumpyEncoder.convert_numpy(field)[0]
 
 
-class GeneticSolution(GeneticParametersBaseModel):
+class GeneticSolution(ConfigMixin, GeneticParametersBaseModel):
     """**Note**: The first value of "Last_Wh_per_hour", "Netzeinspeisung_Wh_per_hour", and "Netzbezug_Wh_per_hour", will be set to null in the JSON output and represented as NaN or None in the corresponding classes' data returns. This approach is adopted to ensure that the current hour's processing remains unchanged."""
 
     ac_charge: list[float] = Field(
@@ -228,18 +230,20 @@ class GeneticSolution(GeneticParametersBaseModel):
         """
         from akkudoktoreos.core.ems import get_ems
 
-        config = get_config()
         start_datetime = get_ems().start_datetime
+        start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
         interval_hours = 1
         power_to_energy_per_interval_factor = 1.0
 
         # --- Create index based on list length and interval ---
-        n_points = len(self.result.Kosten_Euro_pro_Stunde)
+        # Ensure we only use the minimum of results and commands if differing
+        periods = min(len(self.result.Kosten_Euro_pro_Stunde), len(self.ac_charge) - start_day_hour)
         time_index = pd.date_range(
             start=start_datetime,
-            periods=n_points,
+            periods=periods,
             freq=f"{interval_hours}h",
         )
+        n_points = len(time_index)
         end_datetime = start_datetime.add(hours=n_points)
 
         # Fill solution into dataframe with correct column names
@@ -256,26 +260,42 @@ class GeneticSolution(GeneticParametersBaseModel):
         solution = pd.DataFrame(
             {
                 "date_time": time_index,
-                "load_energy_wh": self.result.Last_Wh_pro_Stunde,
-                "grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde,
-                "grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde,
-                "elec_price_prediction_amt_kwh": [v * 1000 for v in self.result.Electricity_price],
-                "costs_amt": self.result.Kosten_Euro_pro_Stunde,
-                "revenue_amt": self.result.Einnahmen_Euro_pro_Stunde,
-                "losses_energy_wh": self.result.Verluste_Pro_Stunde,
+                # result starts at start_day_hour
+                "load_energy_wh": self.result.Last_Wh_pro_Stunde[:n_points],
+                "grid_feedin_energy_wh": self.result.Netzeinspeisung_Wh_pro_Stunde[:n_points],
+                "grid_consumption_energy_wh": self.result.Netzbezug_Wh_pro_Stunde[:n_points],
+                "costs_amt": self.result.Kosten_Euro_pro_Stunde[:n_points],
+                "revenue_amt": self.result.Einnahmen_Euro_pro_Stunde[:n_points],
+                "losses_energy_wh": self.result.Verluste_Pro_Stunde[:n_points],
             },
             index=time_index,
         )
 
         # Add battery data
-        solution["battery1_soc_factor"] = [v / 100 for v in self.result.akku_soc_pro_stunde]
-        operation: dict[str, list[float]] = {}
-        for hour, rate in enumerate(self.ac_charge):
-            if hour >= n_points:
+        solution["battery1_soc_factor"] = [
+            v / 100
+            for v in self.result.akku_soc_pro_stunde[:n_points]  # result starts at start_day_hour
+        ]
+        operation: dict[str, list[float]] = {
+            "genetic_ac_charge_factor": [],
+            "genetic_dc_charge_factor": [],
+            "genetic_discharge_allowed_factor": [],
+        }
+        # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
+        for hour_idx, rate in enumerate(self.ac_charge):
+            if hour_idx < start_day_hour:
+                continue
+            if hour_idx >= start_day_hour + n_points:
                 break
+            ac_charge_hour = self.ac_charge[hour_idx]
+            dc_charge_hour = self.dc_charge[hour_idx]
+            discharge_allowed_hour = bool(self.discharge_allowed[hour_idx])
             operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour])
+                ac_charge_hour, dc_charge_hour, discharge_allowed_hour
             )
+            operation["genetic_ac_charge_factor"].append(ac_charge_hour)
+            operation["genetic_dc_charge_factor"].append(dc_charge_hour)
+            operation["genetic_discharge_allowed_factor"].append(discharge_allowed_hour)
             for mode in BatteryOperationMode:
                 mode_key = f"battery1_{mode.lower()}_op_mode"
                 factor_key = f"battery1_{mode.lower()}_op_factor"
@@ -289,15 +309,22 @@ class GeneticSolution(GeneticParametersBaseModel):
                     operation[mode_key].append(0.0)
                     operation[factor_key].append(0.0)
         for key in operation.keys():
+            if len(operation[key]) != n_points:
+                error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
+                logger.error(error_msg)
+                raise ValueError(error_msg)
             solution[key] = operation[key]
 
         # Add EV battery solution
+        # eautocharge_hours_float start at hour 0 of start day
+        # result.EAuto_SoC_pro_Stunde start at start_datetime.hour
         if self.eauto_obj:
             if self.eautocharge_hours_float is None:
                 # Electric vehicle is full enough. No load times.
                 solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
                     self.eauto_obj.initial_soc_percentage / 100.0
                 ] * n_points
+                solution["genetic_ev_charge_factor"] = [0.0] * n_points
                 # operation modes
                 operation_mode = BatteryOperationMode.IDLE
                 for mode in BatteryOperationMode:
@@ -311,12 +338,17 @@ class GeneticSolution(GeneticParametersBaseModel):
                         solution[factor_key] = [0.0] * n_points
             else:
                 solution[f"{self.eauto_obj.device_id}_soc_factor"] = [
-                    v / 100 for v in self.result.EAuto_SoC_pro_Stunde
+                    v / 100 for v in self.result.EAuto_SoC_pro_Stunde[:n_points]
                 ]
-                operation = {}
-                for hour, rate in enumerate(self.eautocharge_hours_float):
-                    if hour >= n_points:
+                operation = {
+                    "genetic_ev_charge_factor": [],
+                }
+                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
+                    if hour_idx < start_day_hour:
+                        continue
+                    if hour_idx >= start_day_hour + n_points:
                         break
+                    operation["genetic_ev_charge_factor"].append(rate)
                     operation_mode, operation_mode_factor = self._battery_operation_from_solution(
                         rate, 0.0, False
                     )
@@ -333,11 +365,16 @@ class GeneticSolution(GeneticParametersBaseModel):
                             operation[mode_key].append(0.0)
                             operation[factor_key].append(0.0)
                 for key in operation.keys():
+                    if len(operation[key]) != n_points:
+                        error_msg = f"instruction {key} has invalid length {len(operation[key])} - expected {n_points}"
+                        logger.error(error_msg)
+                        raise ValueError(error_msg)
                     solution[key] = operation[key]
 
         # Add home appliance data
         if self.washingstart:
-            solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour
+            # result starts at start_day_hour
+            solution["homeappliance1_energy_wh"] = self.result.Home_appliance_wh_per_hour[:n_points]
 
         # Fill prediction into dataframe with correct column names
         # - pvforecast_ac_energy_wh_energy_wh: PV energy prediction (positive) in wh
@@ -445,10 +482,13 @@ class GeneticSolution(GeneticParametersBaseModel):
             generated_at=to_datetime(),
             comment="Optimization solution derived from GeneticSolution.",
             valid_from=start_datetime,
-            valid_until=start_datetime.add(hours=config.optimization.horizon_hours),
+            valid_until=start_datetime.add(hours=self.config.optimization.horizon_hours),
             total_losses_energy_wh=self.result.Gesamt_Verluste,
             total_revenues_amt=self.result.Gesamteinnahmen_Euro,
             total_costs_amt=self.result.Gesamtkosten_Euro,
+            fitness_score={
+                self.result.Gesamtkosten_Euro,
+            },
             prediction=PydanticDateTimeDataFrame.from_dataframe(prediction),
             solution=PydanticDateTimeDataFrame.from_dataframe(solution),
         )
@@ -460,6 +500,7 @@ class GeneticSolution(GeneticParametersBaseModel):
         from akkudoktoreos.core.ems import get_ems
 
         start_datetime = get_ems().start_datetime
+        start_day_hour = start_datetime.in_timezone(self.config.general.timezone).hour
         plan = EnergyManagementPlan(
             id=f"plan-genetic@{to_datetime(as_string=True)}",
             generated_at=to_datetime(),
@@ -471,10 +512,15 @@ class GeneticSolution(GeneticParametersBaseModel):
         last_operation_mode: Optional[str] = None
         last_operation_mode_factor: Optional[float] = None
         resource_id = "battery1"
-        logger.debug("BAT: {} - {}", resource_id, self.ac_charge)
-        for hour, rate in enumerate(self.ac_charge):
+        # ac_charge, dc_charge, discharge_allowed start at hour 0 of start day
+        logger.debug("BAT: {} - {}", resource_id, self.ac_charge[start_day_hour:])
+        for hour_idx, rate in enumerate(self.ac_charge):
+            if hour_idx < start_day_hour:
+                continue
             operation_mode, operation_mode_factor = self._battery_operation_from_solution(
-                self.ac_charge[hour], self.dc_charge[hour], bool(self.discharge_allowed[hour])
+                self.ac_charge[hour_idx],
+                self.dc_charge[hour_idx],
+                bool(self.discharge_allowed[hour_idx]),
             )
             if (
                 operation_mode == last_operation_mode
@@ -484,7 +530,7 @@ class GeneticSolution(GeneticParametersBaseModel):
                 continue
             last_operation_mode = operation_mode
             last_operation_mode_factor = operation_mode_factor
-            execution_time = start_datetime.add(hours=hour)
+            execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
             plan.add_instruction(
                 FRBCInstruction(
                     resource_id=resource_id,
@@ -496,6 +542,7 @@ class GeneticSolution(GeneticParametersBaseModel):
             )
 
         # Add EV battery instructions (fill rate based control)
+        # eautocharge_hours_float start at hour 0 of start day
         if self.eauto_obj:
             resource_id = self.eauto_obj.device_id
             if self.eautocharge_hours_float is None:
@@ -513,8 +560,12 @@ class GeneticSolution(GeneticParametersBaseModel):
             else:
                 last_operation_mode = None
                 last_operation_mode_factor = None
-                logger.debug("EV: {} - {}", resource_id, self.eauto_obj.charge_array)
-                for hour, rate in enumerate(self.eautocharge_hours_float):
+                logger.debug(
+                    "EV: {} - {}", resource_id, self.eautocharge_hours_float[start_day_hour:]
+                )
+                for hour_idx, rate in enumerate(self.eautocharge_hours_float):
+                    if hour_idx < start_day_hour:
+                        continue
                     operation_mode, operation_mode_factor = self._battery_operation_from_solution(
                         rate, 0.0, False
                     )
@@ -526,7 +577,7 @@ class GeneticSolution(GeneticParametersBaseModel):
                         continue
                     last_operation_mode = operation_mode
                     last_operation_mode_factor = operation_mode_factor
-                    execution_time = start_datetime.add(hours=hour)
+                    execution_time = start_datetime.add(hours=hour_idx - start_day_hour)
                     plan.add_instruction(
                         FRBCInstruction(
                             resource_id=resource_id,
@@ -542,7 +593,7 @@ class GeneticSolution(GeneticParametersBaseModel):
             resource_id = "homeappliance1"
             operation_mode = ApplianceOperationMode.RUN  # type: ignore[assignment]
             operation_mode_factor = 1.0
-            execution_time = start_datetime.add(hours=self.washingstart)
+            execution_time = start_datetime.add(hours=self.washingstart - start_day_hour)
             plan.add_instruction(
                 DDBCInstruction(
                     resource_id=resource_id,

src/akkudoktoreos/optimization/optimization.py

@@ -110,6 +110,8 @@ class OptimizationSolution(PydanticBaseModel):
 
     total_costs_amt: float = Field(description="The total costs [money amount].")
 
+    fitness_score: set[float] = Field(description="The fitness score as a set of fitness values.")
+
     prediction: PydanticDateTimeDataFrame = Field(
         description=(
             "Datetime data frame with time series prediction data per optimization interval:"

src/akkudoktoreos/server/dash/plan.py

@@ -54,8 +54,21 @@ color_palette = {
     "pink-500": "#EC4899",  # pink-500
     "rose-500": "#F43F5E",  # rose-500
 }
+# Color names
 colors = list(color_palette.keys())
 
+# Colums that are exclude from the the solution card display
+# They are currently not used or are covered by others
+solution_excludes = [
+    "date_time",
+    "_op_mode",
+    "_fault_",
+    "_outage_supply_",
+    "_reserve_backup_",
+    "_ramp_rate_control_",
+    "_frequency_regulation_",
+]
+
 # Current state of solution displayed
 solution_visible: dict[str, bool] = {
     "pv_energy_wh": True,
@@ -122,7 +135,9 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
     instruction_columns = [
         instruction
         for instruction in solution_columns
-        if instruction.endswith("op_mode") or instruction.endswith("op_factor")
+        if instruction.endswith("op_mode")
+        or instruction.endswith("op_factor")
+        or instruction.startswith("genetic_")
     ]
     solution_columns = [x for x in solution_columns if x not in instruction_columns]
 
@@ -140,13 +155,26 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
     prediction_columns_to_join = prediction_df.columns.difference(df.columns)
     df = df.join(prediction_df[prediction_columns_to_join], how="inner")
 
-    # Remove time offset from UTC to get naive local time and make bokey plot in local time
+    # Exclude columns that currently do not have a value
+    excludes = solution_excludes
+    for instruction in instruction_columns:
+        if instruction.endswith("op_mode") and df[instruction].eq(0).all():
+            # Exclude op_mode and op_factor if all op_mode is 0
+            excludes.append(instruction)
+            excludes.append(f"{instruction[:-4]}factor")
+
+    # Make bokey plot in local time at location
+    # Determine daylight saving time change
     dst_offsets = df.index.map(lambda x: x.dst().total_seconds() / 3600)
+    # Determine desired timezone
     if config.general is None or config.general.timezone is None:
         date_time_tz = "Europe/Berlin"
     else:
         date_time_tz = config.general.timezone
-    df["date_time"] = pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz)
+    # Ensure original date_time is parsed as UTC and convert to local time
+    df["date_time_local"] = (
+        pd.to_datetime(df["date_time"], utc=True).dt.tz_convert(date_time_tz).dt.tz_localize(None)
+    )
 
     # There is a special case if we have daylight saving time change in the time series
     if dst_offsets.nunique() > 1:
@@ -241,21 +269,12 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
     # Create line renderers for each column
     renderers = {}
 
+    # Have an index for the colors of predictions, solutions and instructions.
+    prediction_color_idx = 0
+    solution_color_idx = int(len(colors) * 0.33) + 1
+    instruction_color_idx = int(len(colors) * 0.66) + 1
     for i, col in enumerate(sorted(df.columns)):
         # Exclude some columns that are currently not used or are covered by others
-        excludes = [
-            "date_time",
-            "_op_mode",
-            "_fault_",
-            "_forced_discharge_",
-            "_outage_supply_",
-            "_reserve_backup_",
-            "_ramp_rate_control_",
-            "_frequency_regulation_",
-            "_grid_support_export_",
-            "_peak_shaving_",
-        ]
-        # excludes = ["date_time"]
         if any(exclude in col for exclude in excludes):
             continue
         if col in solution_visible:
@@ -265,73 +284,85 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
             solution_visible[col] = visible
         if col in solution_color:
             color = solution_color[col]
-        elif col == "pv_energy_wh":
-            color = "yellow-500"
-            solution_color[col] = color
-        elif col == "elec_price_amt_kwh":
-            color = "red-500"
-            solution_color[col] = color
         else:
-            color = colors[i % len(colors)]
+            if col in prediction_columns:
+                color = colors[prediction_color_idx % len(colors)]
+                prediction_color_idx += 3
+            elif col in solution_columns:
+                color = colors[solution_color_idx % len(colors)]
+                solution_color_idx += 3
+            else:
+                color = colors[instruction_color_idx % len(colors)]
+                instruction_color_idx += 3
+            # Remember the color of this column
             solution_color[col] = color
+        if col in prediction_columns:
+            line_dash = "dotted"
+        else:
+            line_dash = "solid"
         if visible:
-            if col == "pv_energy_wh":
-                r = plot.vbar(
-                    x="date_time",
-                    top=col,
-                    source=source,
-                    width=BAR_WIDTH_1HOUR * 0.8,
-                    legend_label=col,
-                    color=color_palette[color],
-                    level="underlay",
-                )
-            elif col.endswith("energy_wh"):
+            if col.endswith("energy_wh"):
                 r = plot.step(
-                    x="date_time",
+                    x="date_time_local",
                     y=col,
-                    mode="before",
+                    mode="after",
                     source=source,
                     legend_label=col,
                     color=color_palette[color],
+                    line_dash=line_dash,
+                )
+            elif col.endswith("soc_factor"):
+                r = plot.line(
+                    x="date_time_local",
+                    y=col,
+                    source=source,
+                    legend_label=col,
+                    color=color_palette[color],
+                    line_dash=line_dash,
+                    y_range_name="factor",
                 )
             elif col.endswith("factor"):
                 r = plot.step(
-                    x="date_time",
+                    x="date_time_local",
                     y=col,
-                    mode="before",
+                    mode="after",
                     source=source,
                     legend_label=col,
                     color=color_palette[color],
+                    line_dash=line_dash,
                     y_range_name="factor",
                 )
             elif col.endswith("mode"):
                 r = plot.step(
-                    x="date_time",
+                    x="date_time_local",
                     y=col,
-                    mode="before",
+                    mode="after",
                     source=source,
                     legend_label=col,
                     color=color_palette[color],
+                    line_dash=line_dash,
                     y_range_name="factor",
                 )
             elif col.endswith("amt_kwh"):
                 r = plot.step(
-                    x="date_time",
+                    x="date_time_local",
                     y=col,
-                    mode="before",
+                    mode="after",
                     source=source,
                     legend_label=col,
                     color=color_palette[color],
+                    line_dash=line_dash,
                     y_range_name="amt_kwh",
                 )
             elif col.endswith("amt"):
                 r = plot.step(
-                    x="date_time",
+                    x="date_time_local",
                     y=col,
-                    mode="before",
+                    mode="after",
                     source=source,
                     legend_label=col,
                     color=color_palette[color],
+                    line_dash=line_dash,
                     y_range_name="amt",
                 )
             else:
@@ -430,7 +461,16 @@ def SolutionCard(solution: OptimizationSolution, config: SettingsEOS, data: Opti
     )
 
     return Grid(
+        Grid(
             Bokeh(plot),
+            Card(
+                P(f"Total revenues: {solution.total_revenues_amt}"),
+                P(f"Total costs: {solution.total_costs_amt}"),
+                P(f"Total losses: {solution.total_losses_energy_wh / 1000} kWh"),
+                P(f"Fitness score: {solution.fitness_score}"),
+            ),
+            cols=1,
+        ),
         Checkbox,
         cls="w-full space-y-3 space-x-3",
     )

tests/test_battery.py

@@ -6,20 +6,53 @@ from akkudoktoreos.devices.genetic.battery import Battery, SolarPanelBatteryPara
 
 @pytest.fixture
 def setup_pv_battery():
+    device_id="battery1"
+    capacity_wh=10000
+    initial_soc_percentage=50
+    charging_efficiency=0.88
+    discharging_efficiency=0.88
+    min_soc_percentage=20
+    max_soc_percentage=80
+    max_charge_power_w=8000
+    hours=24
+
     params = SolarPanelBatteryParameters(
-        device_id="battery1",
-        capacity_wh=10000,
-        initial_soc_percentage=50,
-        min_soc_percentage=20,
-        max_soc_percentage=80,
-        max_charge_power_w=8000,
-        hours=24,
+        device_id=device_id,
+        capacity_wh=capacity_wh,
+        initial_soc_percentage=initial_soc_percentage,
+        charging_efficiency=charging_efficiency,
+        discharging_efficiency=discharging_efficiency,
+        min_soc_percentage=min_soc_percentage,
+        max_soc_percentage=max_soc_percentage,
+        max_charge_power_w=max_charge_power_w,
+        hours=hours,
     )
     battery = Battery(
         params,
         prediction_hours=48,
     )
     battery.reset()
+
+    assert battery.parameters.device_id==device_id
+    assert battery.capacity_wh==capacity_wh
+    assert battery.initial_soc_percentage==initial_soc_percentage
+    assert battery.charging_efficiency==charging_efficiency
+    assert battery.initial_soc_percentage==initial_soc_percentage
+    assert battery.discharging_efficiency==discharging_efficiency
+    assert battery.max_soc_percentage==max_soc_percentage
+    assert battery.max_charge_power_w==max_charge_power_w
+    assert battery.soc_wh==float((initial_soc_percentage / 100) * capacity_wh)
+    assert battery.min_soc_wh==float((min_soc_percentage / 100) * capacity_wh)
+    assert battery.max_soc_wh==float((max_soc_percentage / 100) * capacity_wh)
+    assert np.all(battery.charge_array == 0)
+    assert np.all(battery.discharge_array == 0)
+
+    # Init for test
+    battery.charge_array = np.full(battery.prediction_hours, 1)
+    battery.discharge_array = np.full(battery.prediction_hours, 1)
+    assert np.all(battery.charge_array == 1)
+    assert np.all(battery.discharge_array == 1)
+
     return battery
 
 
@@ -164,17 +197,47 @@ def test_charge_energy_not_allowed_hour(setup_pv_battery):
     ), "SOC should remain unchanged"
 
 
-def test_charge_energy_relative_power(setup_pv_battery):
-    battery = setup_pv_battery
-
-    relative_power = 0.5  # 50% of max charge power
-    charged_wh, losses_wh = battery.charge_energy(wh=None, hour=4, relative_power=relative_power)
-
-    assert charged_wh > 0, "Charging should occur with relative power"
-    assert losses_wh >= 0, "Losses should not be negative"
-    assert charged_wh <= battery.max_charge_power_w * relative_power, (
-        "Charging should respect relative power limit"
+@pytest.mark.parametrize(
+    "wh, charge_factor, expected_raises",
+    [
+        (None, 0.5, False),  # Expected to work normally (if capacity allows)
+        (None, 1.0, False),  # Often still OK, depending on fixture capacity
+        (None, 2.0, False),   # Exceeds max charge → always ValueError
+        (1000, 0, False),
+        (1000, 1.0, True),
+    ],
 )
+def test_charge_energy_with_charge_factor(setup_pv_battery, wh, charge_factor, expected_raises):
+    battery = setup_pv_battery
+    hour = 4
+
+    if wh is not None and charge_factor == 0.0:  # mode 1
+        raw_request_wh = wh
+    else:
+        raw_request_wh = battery.max_charge_power_w * charge_factor
+    raw_capacity_wh = max(battery.max_soc_wh - battery.soc_wh, 0.0)
+
+    if expected_raises:
+        # Should raise
+        with pytest.raises(ValueError):
+            battery.charge_energy(
+                wh=wh,
+                hour=hour,
+                charge_factor=charge_factor,
+            )
+        return
+
+    # Should NOT raise
+    charged_wh, losses_wh = battery.charge_energy(
+        wh=wh,
+        hour=hour,
+        charge_factor=charge_factor,
+    )
+
+    # Expectations
+    assert charged_wh > 0, "Charging should occur with charge factor"
+    assert losses_wh >= 0, "Losses must not be negative"
+    assert charged_wh <= raw_request_wh, "Charging must not exceed request"
     assert battery.soc_wh > 0, "SOC should increase after charging"
 
 
@@ -198,6 +261,13 @@ def setup_car_battery():
         prediction_hours=48,
     )
     battery.reset()
+
+    # Init for test
+    battery.charge_array = np.full(battery.prediction_hours, 1)
+    battery.discharge_array = np.full(battery.prediction_hours, 1)
+    assert np.all(battery.charge_array == 1)
+    assert np.all(battery.discharge_array == 1)
+
     return battery
 
 

tests/test_config.py

@@ -3,11 +3,24 @@ from pathlib import Path
 from typing import Any, Optional, Union
 from unittest.mock import patch
 
+import numpy as np
 import pytest
 from loguru import logger
 from pydantic import IPvAnyAddress, ValidationError
 
 from akkudoktoreos.config.config import ConfigEOS, GeneralSettings
+from akkudoktoreos.devices.devices import BATTERY_DEFAULT_CHARGE_RATES
+
+
+def assert_values_equal(actual, expected):
+    """Compare values, handling lists and numpy arrays."""
+    if isinstance(actual, (list, np.ndarray)) or isinstance(expected, (list, np.ndarray)):
+        # Convert both to numpy arrays for comparison
+        actual_arr = np.array(actual)
+        expected_arr = np.array(expected)
+        assert np.array_equal(actual_arr, expected_arr), f"Expected {expected_arr}, but got {actual_arr}"
+    else:
+        assert actual == expected, f"Expected {expected}, but got {actual}"
 
 
 # overwrite config_mixin fixture from conftest
@@ -352,7 +365,7 @@ def test_config_common_settings_timezone_none_when_coordinates_missing():
             [
                 (
                     "devices.electric_vehicles[0].charge_rates",
-                    [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                    BATTERY_DEFAULT_CHARGE_RATES,
                 )
             ],
             ValueError,
@@ -364,10 +377,10 @@ def test_config_common_settings_timezone_none_when_coordinates_missing():
             [
                 (
                     "devices.electric_vehicles[0].charge_rates",
-                    [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                    BATTERY_DEFAULT_CHARGE_RATES,
                 )
             ],
-            TypeError,
+            KeyError,
         ),
         # Invalid index (no number)
         (
@@ -376,10 +389,10 @@ def test_config_common_settings_timezone_none_when_coordinates_missing():
             [
                 (
                     "devices.electric_vehicles[0].charge_rates",
-                    [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0],
+                    BATTERY_DEFAULT_CHARGE_RATES,
                 )
             ],
-            IndexError,
+            KeyError,
         ),
         # Unset value (set None)
         (
@@ -388,7 +401,7 @@ def test_config_common_settings_timezone_none_when_coordinates_missing():
             [
                 (
                     "devices.electric_vehicles[0].charge_rates",
-                    None,
+                    BATTERY_DEFAULT_CHARGE_RATES,
                 )
             ],
             None,
@@ -400,17 +413,13 @@ def test_set_nested_key(path, value, expected, exception, config_eos):
         config_eos.set_nested_value(path, value)
         for expected_path, expected_value in expected:
             actual_value = eval(f"config_eos.{expected_path}")
-            assert actual_value == expected_value, (
-                f"Expected {expected_value} at {expected_path}, but got {actual_value}"
-            )
+            assert_values_equal(actual_value, expected_value)
     else:
         try:
             config_eos.set_nested_value(path, value)
             for expected_path, expected_value in expected:
                 actual_value = eval(f"config_eos.{expected_path}")
-                assert actual_value == expected_value, (
-                    f"Expected {expected_value} at {expected_path}, but got {actual_value}"
-                )
+                assert_values_equal(actual_value, expected_value)
             pytest.fail(
                 f"Expected exception {exception} but none was raised. Set '{expected_path}' to '{actual_value}'"
             )
@@ -508,19 +517,19 @@ def test_merge_settings_partial(config_eos):
     config_eos.merge_settings_from_dict(partial_settings)
     assert config_eos.devices.max_electric_vehicles == 1
     assert len(config_eos.devices.electric_vehicles) == 1
-    assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]
+    assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])
 
     # Assure re-apply generates the same config
     config_eos.merge_settings_from_dict(partial_settings)
     assert config_eos.devices.max_electric_vehicles == 1
     assert len(config_eos.devices.electric_vehicles) == 1
-    assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]
+    assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])
 
     # Assure update keeps same values
     config_eos.update()
     assert config_eos.devices.max_electric_vehicles == 1
     assert len(config_eos.devices.electric_vehicles) == 1
-    assert config_eos.devices.electric_vehicles[0].charge_rates == [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0]
+    assert_values_equal(config_eos.devices.electric_vehicles[0].charge_rates, [0.0, 0.375, 0.5, 0.625, 0.75, 0.875, 1.0])
 
 
 def test_merge_settings_empty(config_eos):

tests/test_geneticsimulation.py

@@ -65,7 +65,6 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
         optimization_hours = config_eos.optimization.horizon_hours,
         prediction_hours = config_eos.prediction.hours,
     )
-    home_appliance.set_starting_time(2)
 
     # Example initialization of electric car battery
     eauto = Battery(
@@ -250,6 +249,17 @@ def genetic_simulation(config_eos) -> GeneticSimulation:
         home_appliance=home_appliance,
     )
 
+    # Init for test
+    assert simulation.ac_charge_hours is not None
+    assert simulation.dc_charge_hours is not None
+    assert simulation.bat_discharge_hours is not None
+    assert simulation.ev_charge_hours is not None
+    simulation.ac_charge_hours[start_hour] = 1.0
+    simulation.dc_charge_hours[start_hour] = 1.0
+    simulation.bat_discharge_hours[start_hour] = 1.0
+    simulation.ev_charge_hours[start_hour] = 1.0
+    simulation.home_appliance_start_hour = 2
+
     return simulation
 
 
@@ -321,34 +331,34 @@ def test_simulation(genetic_simulation):
 
     # Verify that the value at index 0 is 'None'
     # Check that 'Netzeinspeisung_Wh_pro_Stunde' and 'Netzbezug_Wh_pro_Stunde' are consistent
-    assert result["Netzbezug_Wh_pro_Stunde"][1] == 0, (
-        "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 0."
+    assert result["Netzbezug_Wh_pro_Stunde"][1] == 1527.13, (
+        "The value at index 1 of 'Netzbezug_Wh_pro_Stunde' should be 1527.13."
     )
 
     # Verify the total balance
-    assert abs(result["Gesamtbilanz_Euro"] - 1.958185274567674) < 1e-5, (
-        "Total balance should be 1.958185274567674."
+    assert abs(result["Gesamtbilanz_Euro"] - 6.612835813556755) < 1e-5, (
+        "Total balance should be 6.612835813556755."
     )
 
     # Check total revenue and total costs
-    assert abs(result["Gesamteinnahmen_Euro"] - 1.168863124510214) < 1e-5, (
-        "Total revenue should be 1.168863124510214."
+    assert abs(result["Gesamteinnahmen_Euro"] - 1.964301131937134) < 1e-5, (
+        "Total revenue should be 1.964301131937134."
     )
-    assert abs(result["Gesamtkosten_Euro"] - 3.127048399077888) < 1e-5, (
-        "Total costs should be 3.127048399077888 ."
+    assert abs(result["Gesamtkosten_Euro"] - 8.577136945493889) < 1e-5, (
+        "Total costs should be 8.577136945493889 ."
     )
 
     # Check the losses
-    assert abs(result["Gesamt_Verluste"] - 2871.5330639359036) < 1e-5, (
-        "Total losses should be 2871.5330639359036 ."
+    assert abs(result["Gesamt_Verluste"] - 1620.0) < 1e-5, (
+        "Total losses should be 1620.0 ."
     )
 
     # Check the values in 'akku_soc_pro_stunde'
-    assert result["akku_soc_pro_stunde"][-1] == 42.151590909090906, (
-        "The value at index -1 of 'akku_soc_pro_stunde' should be 42.151590909090906."
+    assert result["akku_soc_pro_stunde"][-1] == 98.0, (
+        "The value at index -1 of 'akku_soc_pro_stunde' should be 98.0."
     )
-    assert result["akku_soc_pro_stunde"][1] == 60.08659090909091, (
-        "The value at index 1 of 'akku_soc_pro_stunde' should be 60.08659090909091."
+    assert result["akku_soc_pro_stunde"][1] == 98.0, (
+        "The value at index 1 of 'akku_soc_pro_stunde' should be 98.0."
     )
 
     # Check home appliances

tests/test_geneticsimulation2.py

@@ -65,7 +65,6 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
         optimization_hours = config_eos.optimization.horizon_hours,
         prediction_hours = config_eos.prediction.hours,
     )
-    home_appliance.set_starting_time(2)
 
     # Example initialization of electric car battery
     eauto = Battery(
@@ -158,10 +157,11 @@ def genetic_simulation_2(config_eos) -> GeneticSimulation:
 
     ac = np.full(config_eos.prediction.hours, 0.0)
     ac[20] = 1
-    simulation.set_akku_ac_charge_hours(ac)
+    simulation.ac_charge_hours = ac
     dc = np.full(config_eos.prediction.hours, 0.0)
     dc[11] = 1
-    simulation.set_akku_dc_charge_hours(dc)
+    simulation.dc_charge_hours = dc
+    simulation.home_appliance_start_hour = 2
 
     return simulation
 
@@ -242,26 +242,26 @@ def test_simulation(genetic_simulation_2):
     )
 
     # Verfify DC and AC Charge Bins
-    assert abs(result["akku_soc_pro_stunde"][2] - 44.70681818181818) < 1e-5, (
-        "'akku_soc_pro_stunde[2]' should be 44.70681818181818."
+    assert abs(result["akku_soc_pro_stunde"][2] - 80.0) < 1e-5, (
+        "'akku_soc_pro_stunde[2]' should be 80.0."
     )
-    assert abs(result["akku_soc_pro_stunde"][10] - 10.0) < 1e-5, (
-        "'akku_soc_pro_stunde[10]' should be 10."
+    assert abs(result["akku_soc_pro_stunde"][10] - 80.0) < 1e-5, (
+        "'akku_soc_pro_stunde[10]' should be 80."
     )
 
     assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][10] - 3946.93) < 1e-3, (
         "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 3946.93."
     )
 
-    assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 0.0) < 1e-3, (
-        "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 0.0."
+    assert abs(result["Netzeinspeisung_Wh_pro_Stunde"][11] - 2799.7263636361786) < 1e-3, (
+        "'Netzeinspeisung_Wh_pro_Stunde[11]' should be 2799.7263636361786."
     )
 
-    assert abs(result["akku_soc_pro_stunde"][20] - 10) < 1e-5, (
-        "'akku_soc_pro_stunde[20]' should be 10."
+    assert abs(result["akku_soc_pro_stunde"][20] - 100) < 1e-5, (
+        "'akku_soc_pro_stunde[20]' should be 100."
     )
-    assert abs(result["Last_Wh_pro_Stunde"][20] - 6050.98) < 1e-3, (
-        "'Last_Wh_pro_Stunde[20]' should be 6050.98."
+    assert abs(result["Last_Wh_pro_Stunde"][20] - 1050.98) < 1e-3, (
+        "'Last_Wh_pro_Stunde[20]' should be 1050.98."
     )
 
     print("All tests passed successfully.")
@@ -280,46 +280,6 @@ def test_set_parameters(genetic_simulation_2):
     )
 
 
-def test_set_akku_discharge_hours(genetic_simulation_2):
-    """Test the set_akku_discharge_hours method of EnergyManagement."""
-    simulation = genetic_simulation_2
-    discharge_hours = np.full(simulation.prediction_hours, 1.0)
-    simulation.set_akku_discharge_hours(discharge_hours)
-    assert np.array_equal(simulation.battery.discharge_array, discharge_hours), (
-        "Discharge hours should be set correctly"
-    )
-
-
-def test_set_akku_ac_charge_hours(genetic_simulation_2):
-    """Test the set_akku_ac_charge_hours method of EnergyManagement."""
-    simulation = genetic_simulation_2
-    ac_charge_hours = np.full(simulation.prediction_hours, 1.0)
-    simulation.set_akku_ac_charge_hours(ac_charge_hours)
-    assert np.array_equal(simulation.ac_charge_hours, ac_charge_hours), (
-        "AC charge hours should be set correctly"
-    )
-
-
-def test_set_akku_dc_charge_hours(genetic_simulation_2):
-    """Test the set_akku_dc_charge_hours method of EnergyManagement."""
-    simulation = genetic_simulation_2
-    dc_charge_hours = np.full(simulation.prediction_hours, 1.0)
-    simulation.set_akku_dc_charge_hours(dc_charge_hours)
-    assert np.array_equal(simulation.dc_charge_hours, dc_charge_hours), (
-        "DC charge hours should be set correctly"
-    )
-
-
-def test_set_ev_charge_hours(genetic_simulation_2):
-    """Test the set_ev_charge_hours method of EnergyManagement."""
-    simulation = genetic_simulation_2
-    ev_charge_hours = np.full(simulation.prediction_hours, 1.0)
-    simulation.set_ev_charge_hours(ev_charge_hours)
-    assert np.array_equal(simulation.ev_charge_hours, ev_charge_hours), (
-        "EV charge hours should be set correctly"
-    )
-
-
 def test_reset(genetic_simulation_2):
     """Test the reset method of EnergyManagement."""
     simulation = genetic_simulation_2

tests/testdata/optimize_result_1.json

@@ -2,14 +2,14 @@
     "ac_charge": [
         0.0,
         0.0,
-        0.875,
         0.0,
-        0.875,
         0.0,
-        0.625,
-        0.75,
         0.0,
-        0.375,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
+        0.0,
         0.0,
         0.0,
         0.0,
@@ -50,16 +50,16 @@
         0.0
     ],
     "dc_charge": [
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
-        1.0,
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