RL_VPP_Thesis
Thesis based on the development of a RL agent that manages a VPP through EVs charging stations. Main optimization objectives of the VPP are: Valley filling and peak shaving. Main action performed to reach objectives are: storage of Renewable energy resources and power push in the grid at high demand times. Assumptions of high number of vehicles connected for minimum time of 3-4 hours in the grid.
파일 탐색기
최종 버전 다운로드 (.zip)- A2C_VPP_agent_trainer.ipynb
- MaskablePPO_VPP_agent_trainer.ipynb
- RecurrentPPO_VPP_agent_trainer.ipynb
- TRPO_VPP_agent_trainer.ipynb
- 1-Random_VPP_simulator.ipynb
- A2C_VPP_simulator.ipynb
- MaskablePPO_VPP_simulator.ipynb
- RecurrentPPO_VPP_simulator.ipynb
- TRPO_VPP_simulator.ipynb
- VPP_simulator_20EVs.ipynb
- A2C_parameter_lines.png
- Maskable_parameter_lines.png
- Recurrent_parameter_lines.png
- TRPO_parameter_lines.png
- EV_experiments_testing.csv
- EV_experiments_validating.csv
- wandb_export_A2C.csv
- wandb_export_Best_runs.csv
- wandb_export_Maskable.csv
- wandb_export_Recurrent.csv
- wandb_export_TRPO.csv
- Algorithms_results_plots.ipynb
- Experiments_testing_plots.ipynb
- Experiments_validator_plots.ipynb
- wohnblock_household_simulation_adaptive.yaml
- wohnblock_household_simulation_adaptive_10.yaml
- wohnblock_household_simulation_adaptive_15.yaml
- wohnblock_household_simulation_adaptive_25.yaml
- wohnblock_household_simulation_adaptive_30.yaml
- wohnblock_household_simulation_adaptive_35.yaml
- Environment_data_2020.csv
- consumer-00000007_glimpse_TEST_year.csv
- consumer-00000008_glimpse_TEST_year.csv
- consumer-00000009_glimpse_TEST_year.csv
- consumer-00000010_glimpse_TEST_year.csv
- consumer-00000002_glimpse_TEST.csv
- Day-ahead Prices_202001010000-202101010000.csv
- ninja_pv_52.5170_13.3889_corrected_2020_MERRA_Berlin.csv
- ninja_wind_52.5170_13.3889_corrected_2020_MERRA_Berlin.csv
- households_load_profile.csv
- market_prices_2020_profile.csv
- PV_load_2020_profile.csv
- WT_load_2020_profile.csv
- create_eletricity_prices.ipynb
- create_household_load.ipynb
- create_MultipleHousehold_load.ipynb
- create_pv_load_profile.ipynb
- create_wt_load_profile.ipynb
- Environment_data_2019.csv
- Environment_data_year_15.csv
- consumer-00000003_glimpse_TEST_year.csv
- consumer-00000004_glimpse_TEST_year.csv
- consumer-00000005_glimpse_TEST_year.csv
- consumer-00000006_glimpse_TEST_year.csv
- consumer-00000001_glimpse_TEST_year.csv
- Day-ahead Prices_201901010000-202001010000.csv
- ninja_pv_52.5170_13.3889_corrected_2019_MERRA_Berlin.csv
- ninja_wind_52.5170_13.3889_corrected_2019_MERRA_Berlin.csv
- households_load_profile.csv
- market_prices_2019_profile.csv
- PV_load_2019_profile.csv
- WT_load_2019_profile.csv
- create_eletricity_prices.ipynb
- create_household_load.ipynb
- create_MultipleHousehold_load.ipynb
- create_pv_load_profile.ipynb
- create_wt_load_profile.ipynb
- Environment_data_2018.csv
- consumer-00000011_glimpse_TEST_year.csv
- consumer-00000012_glimpse_TEST_year.csv
- consumer-00000013_glimpse_TEST_year.csv
- consumer-00000014_glimpse_TEST_year.csv
- consumer-00000015_glimpse_TEST_year.csv
- Day-ahead Prices_201801010000-201901010000.csv
- ninja_pv_52.5170_13.3889_corrected_2018_MERRA_Berlin.csv
- ninja_wind_52.5170_13.3889_corrected_2018_MERRA_Berlin.csv
- households_load_profile.csv
- market_prices_2018_profile.csv
- PV_load_2018_profile.csv
- WT_load_2018_profile.csv
- create_eletricity_prices.ipynb
- create_household_load.ipynb
- create_MultipleHousehold_load.ipynb
- create_pv_load_profile.ipynb
- create_wt_load_profile.ipynb
- VPP_table.csv
- 3D_bubble_names_colors.png
- DAI_logo.png
- Elvis_config.png
- ELVIS_data_25.png
- Elvis_logo.png
- RL_VPP_Thesis_scenario.png
- testing_dataset_merger.ipynb
- training_dataset_merger.ipynb
- validating_dataset_merger.ipynb
- 10EVs_RecurrentPPO_VPP_simulator.ipynb
- 15EVs_RecurrentPPO_VPP_simulator.ipynb
- 20EVs_RecurrentPPO_VPP_simulator.ipynb
- 25EVs_RecurrentPPO_VPP_simulator.ipynb
- 30EVs_RecurrentPPO_VPP_simulator.ipynb
- 35EVs_RecurrentPPO_VPP_simulator.ipynb
- EVs_RecurrentPPO_VPP_tester.ipynb
- EVs_RecurrentPPO_VPP_validator.ipynb
- A2C_VPP_Hyperp_Sweep.ipynb
- MaskablePPO_VPP_Hyperp_Sweep.ipynb
- RecurrentPPO_VPP_Hyperp_Sweep.ipynb
- TRPO_VPP_Hyperp_Sweep.ipynb
- model_A2C_1u6pwi97.zip
- model_MaskablePPO_8mq440dz.zip
- model_RecurrentPPO_333ckz0i.zip
- model_RecurrentPPO_s37o8q0n.zip
- model_TRPO_2ydih28d.zip
- debug-cli.root.log
- Bibliography_RL-VPP_Maldonato_bbl.bbl
- MALDONATO-RL_control-strategies_for_EVs_fleet_VPP.pdf
- README.md
- VPP_environment.py
- VPP_simulator.ipynb
// repository documentation
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