{"id":"W4407392183","doi":"10.1016/j.ijhydene.2025.01.413","title":"Multi-agent reinforcement learning for energy management in microgrids with shared hydrogen storage","year":2025,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reinforcement learning; Hydrogen storage; Computer science; Energy storage; Energy management; Energy (signal processing); Distributed computing; Chemical engineering; Hydrogen; Chemistry; Power (physics); Artificial intelligence; Physics; Engineering; Thermodynamics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009778953,0.0005641187,0.0009149248,0.0002148679,0.0003867316,0.0005483075,0.0008177832,0.0006214894,0.001278254],"category_scores_gemma":[0.002099507,0.0002690399,0.0003121721,0.0001835849,0.0005258326,0.0005636202,0.0008578369,0.0006945798,0.0001204673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005675406,"about_ca_system_score_gemma":0.0006852428,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006683502,"about_ca_topic_score_gemma":0.0051059,"domain_scores_codex":[0.9997417,0.0001085661,0.00001248024,0.00004322208,0.00004459359,0.00004939978],"domain_scores_gemma":[0.9991648,0.0004865515,0.0001133157,0.00004079858,0.0001329588,0.0000616073],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005419319,0.00002722919,0.0002417138,0.00001797338,0.00001992145,0.00003154718,0.00001691855,0.9919264,0.0003847385,0.001084193,0.0001473038,0.006047853],"study_design_scores_gemma":[0.000004971182,0.00001400988,0.00003056481,8.850574e-7,0.000001993612,0.000002075745,0.000002327124,0.9995454,0.00005584692,0.0003113151,0.00002965722,9.098653e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2325238,0.0005273855,0.760641,0.0004786948,0.0001131712,0.0001037159,0.00006180169,0.0003637147,0.005186801],"genre_scores_gemma":[0.9930527,0.0000349425,0.006198726,0.00001680258,0.000009160907,0.00002403542,0.00001105359,0.000005616922,0.0006469589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006683502,"threshold_uncertainty_score":0.01328921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005595673368767631,"score_gpt":0.213730935297173,"score_spread":0.2081352619284054,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}