Game theory-based electricity pricing and microgrids management using online deep reinforcement learning
This study addresses a bi-level problem involving a retailer and multiple residential microgrids. The retailer, at the upper level, disseminates selling and buying electricity price signals to maximize profit, while microgrid agents, at the lower level, manage their resources based on these signals...
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| Vydané v: | Applied soft computing Ročník 182; s. 113621 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier B.V
01.10.2025
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| ISSN: | 1568-4946 |
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| Abstract | This study addresses a bi-level problem involving a retailer and multiple residential microgrids. The retailer, at the upper level, disseminates selling and buying electricity price signals to maximize profit, while microgrid agents, at the lower level, manage their resources based on these signals to minimize costs. Additionally, a distribution system operator oversees network constraints. The interaction between microgrids and the retailer is modeled as a Stackelberg game, allowing for double-sided trading. To deal with uncertainties related to sustainable resources, loads, and wholesale market prices, a hybrid fuzzy/stochastic optimization (HFSO) approach is employed. This method combines fuzzy chance-constrained programming at the upper level with risk-neutral programming at the lower level. Due to privacy-preserving concerns, the deep reinforcement learning approach is used to solve this problem. This approach is evolved to online learning to prevent data drift, especially when the load profile changes, and attain an acceptable answer quickly. To prove this claim, the ability to predict profit over a relatively long period is investigated for both the offline learning method and the proposed online learning method. The results show that the offline learning method has a prediction error of 15.54 %, while the online learning method has only a 1.8 % error. Specifically, the online learning method can predict the profit that the retailer will obtain with 96.75 % accuracy, while the offline learning method's prediction fails with −150.64 % accuracy. Also, the online learning method can predict each microgrid’s power transactions with more than 89.6 % accuracy.
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•Proposed an online-iterative DRL that adapts to load growth, enables fast decisions, and ensures data privacy.•Used GMDH network for self-adaptive structure, making DRL retraining faster and more efficient.•Tackled energy price/load/renewable uncertainty via hybrid fuzzy-stochastic optimization in the neural model.•Designed a pricing scheme enabling DSO dual-sided signals while cutting wholesale market dependency.•Designed a pricing scheme enabling DSO dual-sided signals while cutting wholesale market dependency. |
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| AbstractList | This study addresses a bi-level problem involving a retailer and multiple residential microgrids. The retailer, at the upper level, disseminates selling and buying electricity price signals to maximize profit, while microgrid agents, at the lower level, manage their resources based on these signals to minimize costs. Additionally, a distribution system operator oversees network constraints. The interaction between microgrids and the retailer is modeled as a Stackelberg game, allowing for double-sided trading. To deal with uncertainties related to sustainable resources, loads, and wholesale market prices, a hybrid fuzzy/stochastic optimization (HFSO) approach is employed. This method combines fuzzy chance-constrained programming at the upper level with risk-neutral programming at the lower level. Due to privacy-preserving concerns, the deep reinforcement learning approach is used to solve this problem. This approach is evolved to online learning to prevent data drift, especially when the load profile changes, and attain an acceptable answer quickly. To prove this claim, the ability to predict profit over a relatively long period is investigated for both the offline learning method and the proposed online learning method. The results show that the offline learning method has a prediction error of 15.54 %, while the online learning method has only a 1.8 % error. Specifically, the online learning method can predict the profit that the retailer will obtain with 96.75 % accuracy, while the offline learning method's prediction fails with −150.64 % accuracy. Also, the online learning method can predict each microgrid’s power transactions with more than 89.6 % accuracy.
[Display omitted]
•Proposed an online-iterative DRL that adapts to load growth, enables fast decisions, and ensures data privacy.•Used GMDH network for self-adaptive structure, making DRL retraining faster and more efficient.•Tackled energy price/load/renewable uncertainty via hybrid fuzzy-stochastic optimization in the neural model.•Designed a pricing scheme enabling DSO dual-sided signals while cutting wholesale market dependency.•Designed a pricing scheme enabling DSO dual-sided signals while cutting wholesale market dependency. |
| ArticleNumber | 113621 |
| Author | Azizi, Ali Shademan, Mahdi Jadid, Shahram |
| Author_xml | – sequence: 1 givenname: Mahdi orcidid: 0000-0001-8425-9551 surname: Shademan fullname: Shademan, Mahdi organization: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran – sequence: 2 givenname: Ali orcidid: 0000-0002-8315-1992 surname: Azizi fullname: Azizi, Ali organization: Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran – sequence: 3 givenname: Shahram orcidid: 0000-0002-7761-2430 surname: Jadid fullname: Jadid, Shahram email: jadid@iust.ac.ir organization: Centre of Excellence for Power System Automation and Operation, Iran University of Science and Technology, Tehran, Iran |
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| Cites_doi | 10.1109/ACCESS.2023.3344209 10.1109/TSG.2019.2930299 10.1109/TSG.2022.3164080 10.1016/j.eswa.2023.119552 10.1016/j.scs.2022.104019 10.1016/j.rser.2023.113161 10.1016/j.scs.2023.104787 10.1049/rpg2.12292 10.1016/j.rser.2023.113170 10.1109/TSG.2021.3113573 10.1109/TSG.2022.3225814 10.1002/9781394186518.ch9 10.1016/j.apenergy.2022.119556 10.1016/j.ijepes.2022.108839 10.1016/j.rser.2021.111890 10.1109/TSG.2022.3168856 10.1016/j.ijepes.2022.108289 10.1016/j.engappai.2023.106865 10.1016/j.energy.2021.121915 10.1016/j.scs.2023.104946 10.1109/TSG.2020.3003244 10.1109/TSG.2021.3109103 10.1016/j.ijepes.2021.107642 10.1109/TSG.2020.3032889 |
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| Keywords | Model-drift Fuzzy chance constrained programming Deep reinforcement learning Demand response Resource management Microgrids |
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| SubjectTerms | Deep reinforcement learning Demand response Fuzzy chance constrained programming Microgrids Model-drift Resource management |
| Title | Game theory-based electricity pricing and microgrids management using online deep reinforcement learning |
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