Academic Journal
Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism
| العنوان: | Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing mechanism |
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| المؤلفون: | Zhonghao Sun, Tianguang Lu |
| المصدر: | IET Generation, Transmission & Distribution, Vol 18, Iss 1, Pp 39-49 (2024) |
| بيانات النشر: | Wiley, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Production of electric energy or power. Powerplants. Central stations |
| مصطلحات موضوعية: | cost optimal control, distributed power generation, distribution networks, energy management systems, learning (artificial intelligence), Distribution or transmission of electric power, TK3001-3521, Production of electric energy or power. Powerplants. Central stations, TK1001-1841 |
| الوصف: | 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1751-8695 1751-8687 |
| Relation: | https://doaj.org/toc/1751-8687; https://doaj.org/toc/1751-8695 |
| DOI: | 10.1049/gtd2.13037 |
| URL الوصول: | https://doaj.org/article/693ea6f8e96d485bb8c989cda28cc091 |
| رقم الانضمام: | edsdoj.693ea6f8e96d485bb8c989cda28cc091 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 17518695 17518687 |
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| DOI: | 10.1049/gtd2.13037 |