Multi-Microgrid Energy Trading Strategy Based on Multi-Agent Deep Deterministic Policy Gradient Algorithm

Compared to individual microgrid, multi-microgrid (MMG) system can enhance the overall utilization of renewable energy, effectively improve the operational stability of local microgrids, and reduce the dependence on main grid. However, energy management of MMG encounters significant challenges due t...

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Veröffentlicht in:Applied mathematics and nonlinear sciences Jg. 9; H. 1
Hauptverfasser: Qi, Genhong, Fu, Lingxiao, Wang, Meng, Zhu, Mingcheng, Yuan, Shaoqing, Xu, Jiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Beirut Sciendo 01.01.2024
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services
Schlagworte:
68T07
Algorithms
Alternative energy sources
deep reinforcement learning
energy scheduling
energy trading
multi-agent deep deterministic policy gradient
multi-microgrid
Renewable resources
ISSN:2444-8656, 2444-8656
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Abstract Compared to individual microgrid, multi-microgrid (MMG) system can enhance the overall utilization of renewable energy, effectively improve the operational stability of local microgrids, and reduce the dependence on main grid. However, energy management of MMG encounters significant challenges due to the complex interaction between different microgrids. To tackle this issue, this paper introduces a non-cooperative gamebased optimal scheduling market trading model for MMG composed of various renewable energy sources, completing trade decisions while ensuring information independence. Considering the real-time changes in environmental transition functions and complex scheduling scenarios, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed, which modifies the experience replay mechanism and Markov process of the basic deep deterministic policy gradient (DDPG) algorithm. Compared to traditional multi-microgrid system scheduling algorithms, the method presented in this paper does not require individual predictions of state variables, achieves end-to-end training from agent states to actions, and ensures the information security and autonomous decision-making of each microgrid.
AbstractList Compared to individual microgrid, multi-microgrid (MMG) system can enhance the overall utilization of renewable energy, effectively improve the operational stability of local microgrids, and reduce the dependence on main grid. However, energy management of MMG encounters significant challenges due to the complex interaction between different microgrids. To tackle this issue, this paper introduces a non-cooperative gamebased optimal scheduling market trading model for MMG composed of various renewable energy sources, completing trade decisions while ensuring information independence. Considering the real-time changes in environmental transition functions and complex scheduling scenarios, the multi-agent deep deterministic policy gradient (MADDPG) algorithm is employed, which modifies the experience replay mechanism and Markov process of the basic deep deterministic policy gradient (DDPG) algorithm. Compared to traditional multi-microgrid system scheduling algorithms, the method presented in this paper does not require individual predictions of state variables, achieves end-to-end training from agent states to actions, and ensures the information security and autonomous decision-making of each microgrid.
Author Xu, Jiang
Qi, Genhong
Wang, Meng
Fu, Lingxiao
Yuan, Shaoqing
Zhu, Mingcheng
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Cites_doi 10.1109/TASC.2024.3420296
10.1038/s41467-023-40670-7
10.1016/j.epsr.2022.109089
10.1109/TSG.2019.2896182
10.1016/j.apenergy.2022.119151
10.3390/su142214794
10.1109/TIA.2022.3218022
10.1016/j.energy.2020.117180
10.1007/s12083-024-01681-3
10.1109/TIM.2023.3301904
10.1109/JSYST.2023.3315190
10.1109/JIOT.2019.2921159
10.1109/TASE.2023.3236408
10.1109/TPWRS.2022.3162473
10.1016/j.jclepro.2023.140130
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References 2024121221430981065_j_amns-2024-3426_ref_007
2024121221430981065_j_amns-2024-3426_ref_018
2024121221430981065_j_amns-2024-3426_ref_006
2024121221430981065_j_amns-2024-3426_ref_017
2024121221430981065_j_amns-2024-3426_ref_009
2024121221430981065_j_amns-2024-3426_ref_008
2024121221430981065_j_amns-2024-3426_ref_019
2024121221430981065_j_amns-2024-3426_ref_003
2024121221430981065_j_amns-2024-3426_ref_014
2024121221430981065_j_amns-2024-3426_ref_002
2024121221430981065_j_amns-2024-3426_ref_013
2024121221430981065_j_amns-2024-3426_ref_024
2024121221430981065_j_amns-2024-3426_ref_005
2024121221430981065_j_amns-2024-3426_ref_016
2024121221430981065_j_amns-2024-3426_ref_004
2024121221430981065_j_amns-2024-3426_ref_015
2024121221430981065_j_amns-2024-3426_ref_010
2024121221430981065_j_amns-2024-3426_ref_021
2024121221430981065_j_amns-2024-3426_ref_020
2024121221430981065_j_amns-2024-3426_ref_001
2024121221430981065_j_amns-2024-3426_ref_012
2024121221430981065_j_amns-2024-3426_ref_023
2024121221430981065_j_amns-2024-3426_ref_011
2024121221430981065_j_amns-2024-3426_ref_022
References_xml – ident: 2024121221430981065_j_amns-2024-3426_ref_016
– ident: 2024121221430981065_j_amns-2024-3426_ref_004
  doi: 10.1109/TASC.2024.3420296
– ident: 2024121221430981065_j_amns-2024-3426_ref_005
  doi: 10.1038/s41467-023-40670-7
– ident: 2024121221430981065_j_amns-2024-3426_ref_009
  doi: 10.1016/j.epsr.2022.109089
– ident: 2024121221430981065_j_amns-2024-3426_ref_015
– ident: 2024121221430981065_j_amns-2024-3426_ref_017
– ident: 2024121221430981065_j_amns-2024-3426_ref_018
– ident: 2024121221430981065_j_amns-2024-3426_ref_014
  doi: 10.1109/TSG.2019.2896182
– ident: 2024121221430981065_j_amns-2024-3426_ref_019
  doi: 10.1016/j.apenergy.2022.119151
– ident: 2024121221430981065_j_amns-2024-3426_ref_013
– ident: 2024121221430981065_j_amns-2024-3426_ref_012
– ident: 2024121221430981065_j_amns-2024-3426_ref_001
  doi: 10.3390/su142214794
– ident: 2024121221430981065_j_amns-2024-3426_ref_002
  doi: 10.1109/TIA.2022.3218022
– ident: 2024121221430981065_j_amns-2024-3426_ref_008
  doi: 10.1016/j.energy.2020.117180
– ident: 2024121221430981065_j_amns-2024-3426_ref_021
  doi: 10.1007/s12083-024-01681-3
– ident: 2024121221430981065_j_amns-2024-3426_ref_003
  doi: 10.1109/TIM.2023.3301904
– ident: 2024121221430981065_j_amns-2024-3426_ref_010
  doi: 10.1109/JSYST.2023.3315190
– ident: 2024121221430981065_j_amns-2024-3426_ref_022
– ident: 2024121221430981065_j_amns-2024-3426_ref_020
  doi: 10.1109/JIOT.2019.2921159
– ident: 2024121221430981065_j_amns-2024-3426_ref_023
– ident: 2024121221430981065_j_amns-2024-3426_ref_024
– ident: 2024121221430981065_j_amns-2024-3426_ref_006
  doi: 10.1109/TASE.2023.3236408
– ident: 2024121221430981065_j_amns-2024-3426_ref_007
  doi: 10.1109/TPWRS.2022.3162473
– ident: 2024121221430981065_j_amns-2024-3426_ref_011
  doi: 10.1016/j.jclepro.2023.140130
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Snippet Compared to individual microgrid, multi-microgrid (MMG) system can enhance the overall utilization of renewable energy, effectively improve the operational...
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SubjectTerms 68T07
Algorithms
Alternative energy sources
deep reinforcement learning
energy scheduling
energy trading
multi-agent deep deterministic policy gradient
multi-microgrid
Renewable resources
Title Multi-Microgrid Energy Trading Strategy Based on Multi-Agent Deep Deterministic Policy Gradient Algorithm
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