Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties
A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on us...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/22/7669 |
| _version_ | 1797459409755963392 |
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| author | Mingshan Mo Xinrui Xiong Yunlong Wu Zuyao Yu |
| author_facet | Mingshan Mo Xinrui Xiong Yunlong Wu Zuyao Yu |
| author_sort | Mingshan Mo |
| collection | DOAJ |
| description | A community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios. |
| first_indexed | 2024-03-09T16:51:49Z |
| format | Article |
| id | doaj.art-7f8fb439845b47c7bac0c2c99d8ce177 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T16:51:49Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-7f8fb439845b47c7bac0c2c99d8ce1772023-11-24T14:40:46ZengMDPI AGEnergies1996-10732023-11-011622766910.3390/en16227669Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple UncertaintiesMingshan Mo0Xinrui Xiong1Yunlong Wu2Zuyao Yu3School of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Electrical and Electronic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaA community-integrated energy system under a multiple-uncertainty low-carbon economic dispatch model based on the deep reinforcement learning method is developed to promote electricity low carbonization and complementary utilization of community-integrated energy. A demand response model based on users’ willingness is proposed for the uncertainty of users’ demand response behavior; a training scenario set of a reinforcement learning agent is generated with a Latin hypercube sampling method for the uncertainties of power, load, temperature, and electric vehicle trips. Based on the proposed demand response model, low-carbon economic dispatch of the community-integrated energy system under multiple uncertainties is achieved by training the agent to interact with the environment in the training scenario set and reach convergence after 250 training rounds. The simulation results show that the reinforcement learning agent achieves low-carbon economic dispatch under 5%, 10%, and 15% renewable energy/load fluctuation scenarios, temperature fluctuation scenarios, and uncertain scenarios of the number of trips, time periods, and mileage of electric vehicles, with good generalization performance under uncertain scenarios.https://www.mdpi.com/1996-1073/16/22/7669demand response uncertaintydeep reinforcement learningcommunity-integrated energy systemlow-carbon economic dispatch |
| spellingShingle | Mingshan Mo Xinrui Xiong Yunlong Wu Zuyao Yu Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties Energies demand response uncertainty deep reinforcement learning community-integrated energy system low-carbon economic dispatch |
| title | Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties |
| title_full | Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties |
| title_fullStr | Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties |
| title_full_unstemmed | Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties |
| title_short | Deep-Reinforcement-Learning-Based Low-Carbon Economic Dispatch for Community-Integrated Energy System under Multiple Uncertainties |
| title_sort | deep reinforcement learning based low carbon economic dispatch for community integrated energy system under multiple uncertainties |
| topic | demand response uncertainty deep reinforcement learning community-integrated energy system low-carbon economic dispatch |
| url | https://www.mdpi.com/1996-1073/16/22/7669 |
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