Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism
Abstract With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy con...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | IET Generation, Transmission & Distribution |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/gtd2.13037 |
| _version_ | 1797356744790245376 |
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| author | Zhonghao Sun Tianguang Lu |
| author_facet | Zhonghao Sun Tianguang Lu |
| author_sort | Zhonghao Sun |
| collection | DOAJ |
| description | Abstract With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system. |
| first_indexed | 2024-03-08T14:32:03Z |
| format | Article |
| id | doaj.art-693ea6f8e96d485bb8c989cda28cc091 |
| institution | Directory Open Access Journal |
| issn | 1751-8687 1751-8695 |
| language | English |
| last_indexed | 2024-03-08T14:32:03Z |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Generation, Transmission & Distribution |
| spelling | doaj.art-693ea6f8e96d485bb8c989cda28cc0912024-01-12T08:04:57ZengWileyIET Generation, Transmission & Distribution1751-86871751-86952024-01-01181394910.1049/gtd2.13037Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanismZhonghao Sun0Tianguang Lu1School of Electrical Engineering Shandong University Jinan ChinaSchool of Electrical Engineering Shandong University Jinan ChinaAbstract With the increasing integration of distributed energy resources (DERs) into distribution systems, the optimization of system operation has become complex, facing challenges such as inadequate consideration of market participants’ benefits, poor computational efficiency, and data privacy concerns. This paper introduces the concept of a virtual power plant (VPP) as a solution for energy integration and management. To strike a balance between operational safety and the interests of market participants, a dual‐layer model is proposed. This model considers the benefits of both Distribution System Operators (DSO) and VPP, while also enhancing the consideration of distribution network constraints. The DSO considers AC optimal power flow and utilizes penalty functions to ensure network security in case of violations. To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This approach allows multiple intelligent agents to share a neural network model, effectively reducing the computational load. During the training process, only essential data is exchanged among the agents, ensuring the privacy of sensitive information. The effectiveness of the proposed model and the algorithm is validated through a case study on an IEEE 33‐node system.https://doi.org/10.1049/gtd2.13037cost optimal controldistributed power generationdistribution networksenergy management systemslearning (artificial intelligence) |
| spellingShingle | Zhonghao Sun Tianguang Lu Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism IET Generation, Transmission & Distribution cost optimal control distributed power generation distribution networks energy management systems learning (artificial intelligence) |
| title | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
| title_full | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
| title_fullStr | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
| title_full_unstemmed | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
| title_short | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
| title_sort | collaborative operation optimization of distribution system and virtual power plants using multi agent deep reinforcement learning with parameter sharing mechanism |
| topic | cost optimal control distributed power generation distribution networks energy management systems learning (artificial intelligence) |
| url | https://doi.org/10.1049/gtd2.13037 |
| work_keys_str_mv | AT zhonghaosun collaborativeoperationoptimizationofdistributionsystemandvirtualpowerplantsusingmultiagentdeepreinforcementlearningwithparametersharingmechanism AT tianguanglu collaborativeoperationoptimizationofdistributionsystemandvirtualpowerplantsusingmultiagentdeepreinforcementlearningwithparametersharingmechanism |