mirror of
https://github.com/Akkudoktor-EOS/EOS.git
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f61665669f
* Migrate from Flask to FastAPI
* FastAPI migration:
- Use pydantic model classes as input parameters to the
data/calculation classes.
- Interface field names changed to constructor parameter names (for
simplicity only during transition, should be updated in a followup
PR).
- Add basic interface requirements (e.g. some values > 0, etc.).
* Update tests for new data format.
* Python requirement down to 3.9 (TypeGuard no longer needed)
* Makefile: Add helpful targets (e.g. development server with reload)
* Move API doc from README to pydantic model classes (swagger)
* Link to swagger.io with own openapi.yml.
* Commit openapi.json and check with pytest for changes so the
documentation is always up-to-date.
* Streamline docker
* FastAPI: Run startup action on dev server
* Fix config for /strompreis, endpoint still broken however.
* test_openapi: Compare against docs/.../openapi.json
* Move fastapi to server/ submodule
* See #187 for new repository structure.
329 lines
6.1 KiB
Python
329 lines
6.1 KiB
Python
#!/usr/bin/env python3
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import time
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import numpy as np
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from akkudoktoreos.class_numpy_encoder import NumpyEncoder
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from akkudoktoreos.class_optimize import (
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OptimizationParameters,
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OptimizeResponse,
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optimization_problem,
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)
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from akkudoktoreos.config import get_working_dir, load_config
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from akkudoktoreos.visualize import visualisiere_ergebnisse
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start_hour = 0
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# PV Forecast (in W)
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pv_forecast = np.zeros(48)
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pv_forecast[12] = 5000
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# [
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 8.05,
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# 352.91,
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# 728.51,
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# 930.28,
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# 1043.25,
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# 1106.74,
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# 1161.69,
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# 1018.82,
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# 1519.07,
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# 1969.88,
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# 1017.96,
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# 1043.07,
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# 1007.17,
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# 319.67,
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# 7.88,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 0,
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# 5.04,
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# 335.59,
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# 705.32,
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# 1121.12,
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# 1604.79,
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# 2157.38,
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# 1433.25,
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# 5718.49,
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# 4553.96,
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# 3027.55,
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# 2574.46,
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# 1720.4,
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# 963.4,
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# 383.3,
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# 0,
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# 0,
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# 0,
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# ]
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# Temperature Forecast (in degree C)
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temperature_forecast = [
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18.3,
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17.8,
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16.9,
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16.2,
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15.6,
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15.1,
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14.6,
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14.2,
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14.3,
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14.8,
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15.7,
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16.7,
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17.4,
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18.0,
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18.6,
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19.2,
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19.1,
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18.7,
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18.5,
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17.7,
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16.2,
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14.6,
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13.6,
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13.0,
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12.6,
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12.2,
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11.7,
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11.6,
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11.3,
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11.0,
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10.7,
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10.2,
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11.4,
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14.4,
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16.4,
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18.3,
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19.5,
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20.7,
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21.9,
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22.7,
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23.1,
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23.1,
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22.8,
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21.8,
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20.2,
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19.1,
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18.0,
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17.4,
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]
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# Electricity Price (in Euro per Wh)
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strompreis_euro_pro_wh = np.full(48, 0.001)
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strompreis_euro_pro_wh[0:10] = 0.00001
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strompreis_euro_pro_wh[11:15] = 0.00005
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strompreis_euro_pro_wh[20] = 0.00001
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# [
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# 0.0000384,
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# 0.0000318,
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# 0.0000284,
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# 0.0008283,
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# 0.0008289,
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# 0.0008334,
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# 0.0008290,
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# 0.0003302,
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# 0.0003042,
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# 0.0002430,
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# 0.0002280,
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# 0.0002212,
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# 0.0002093,
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# 0.0001879,
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# 0.0001838,
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# 0.0002004,
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# 0.0002198,
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# 0.0002270,
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# 0.0002997,
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# 0.0003195,
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# 0.0003081,
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# 0.0002969,
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# 0.0002921,
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# 0.0002780,
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# 0.0003384,
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# 0.0003318,
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# 0.0003284,
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# 0.0003283,
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# 0.0003289,
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# 0.0003334,
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# 0.0003290,
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# 0.0003302,
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# 0.0003042,
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# 0.0002430,
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# 0.0002280,
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# 0.0002212,
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# 0.0002093,
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# 0.0001879,
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# 0.0001838,
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# 0.0002004,
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# 0.0002198,
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# 0.0002270,
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# 0.0002997,
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# 0.0003195,
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# 0.0003081,
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# 0.0002969,
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# 0.0002921,
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# 0.0002780,
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# ]
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# Overall System Load (in W)
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gesamtlast = [
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676.71,
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876.19,
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527.13,
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468.88,
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531.38,
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517.95,
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483.15,
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472.28,
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1011.68,
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995.00,
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1053.07,
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1063.91,
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1320.56,
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1132.03,
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1163.67,
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1176.82,
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1216.22,
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1103.78,
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1129.12,
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1178.71,
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1050.98,
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988.56,
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912.38,
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704.61,
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516.37,
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868.05,
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694.34,
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608.79,
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556.31,
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488.89,
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506.91,
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804.89,
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1141.98,
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1056.97,
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992.46,
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1155.99,
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827.01,
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1257.98,
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1232.67,
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871.26,
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860.88,
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1158.03,
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1222.72,
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1221.04,
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949.99,
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987.01,
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733.99,
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592.97,
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]
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# Start Solution (binary)
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start_solution = None
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# Define parameters for the optimization problem
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parameters = OptimizationParameters(
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**{
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"ems": {
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# Value of energy in battery (per Wh)
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"preis_euro_pro_wh_akku": 0e-05,
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# Feed-in tariff for exporting electricity (per Wh)
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"einspeiseverguetung_euro_pro_wh": 7e-05,
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# Overall load on the system
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"gesamtlast": gesamtlast,
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# PV generation forecast (48 hours)
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"pv_prognose_wh": pv_forecast,
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# Electricity price forecast (48 hours)
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"strompreis_euro_pro_wh": strompreis_euro_pro_wh,
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},
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"pv_akku": {
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# Battery capacity (in Wh)
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"kapazitaet_wh": 26400,
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# Initial state of charge (SOC) of PV battery (%)
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"start_soc_prozent": 15,
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# Minimum Soc PV Battery
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"min_soc_prozent": 15,
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},
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"eauto": {
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# Minimum SOC for electric car
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"min_soc_prozent": 50,
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# Electric car battery capacity (Wh)
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"kapazitaet_wh": 60000,
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# Charging efficiency of the electric car
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"lade_effizienz": 0.95,
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# Charging power of the electric car (W)
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"max_ladeleistung_w": 11040,
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# Current SOC of the electric car (%)
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"start_soc_prozent": 5,
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},
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# "spuelmaschine": {
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# # Household appliance consumption (Wh)
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# "verbrauch_wh": 5000,
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# # Duration of appliance usage (hours)
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# "dauer_h": 0,
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# },
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# Temperature forecast (48 hours)
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"temperature_forecast": temperature_forecast,
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# Initial solution for the optimization
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"start_solution": start_solution,
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}
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)
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# Startzeit nehmen
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start_time = time.time()
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# Initialize the optimization problem using the default configuration
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working_dir = get_working_dir()
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config = load_config(working_dir)
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opt_class = optimization_problem(config, verbose=True, fixed_seed=42)
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# Perform the optimisation based on the provided parameters and start hour
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ergebnis = opt_class.optimierung_ems(parameters=parameters, start_hour=start_hour)
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# Endzeit nehmen
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end_time = time.time()
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# Berechnete Zeit ausgeben
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elapsed_time = end_time - start_time
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print(f"Elapsed time: {elapsed_time:.4f} seconds")
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ac_charge, dc_charge, discharge = (
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ergebnis["ac_charge"],
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ergebnis["dc_charge"],
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ergebnis["discharge_allowed"],
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)
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visualisiere_ergebnisse(
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parameters.ems.gesamtlast,
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parameters.ems.pv_prognose_wh,
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parameters.ems.strompreis_euro_pro_wh,
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ergebnis["result"],
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ac_charge,
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dc_charge,
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discharge,
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parameters.temperature_forecast,
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start_hour,
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einspeiseverguetung_euro_pro_wh=np.full(
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config.eos.feed_in_tariff_eur_per_wh, parameters.ems.einspeiseverguetung_euro_pro_wh
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),
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config=config,
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)
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json_data = NumpyEncoder.dumps(ergebnis)
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print(json_data)
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OptimizeResponse(**ergebnis)
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