{"id":"W2765830748","doi":"10.1109/tste.2017.2767064","title":"Optimal Day-Ahead Scheduling of Power-to-Gas Energy Storage and Gas Load Management in Wholesale Electricity and Gas Markets","year":2017,"lang":"en","type":"article","venue":"IEEE Transactions on Sustainable Energy","topic":"Integrated Energy Systems Optimization","field":"Engineering","cited_by":140,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Power to gas; Natural gas; Electricity; Energy storage; Gas consumption; Scheduling (production processes); Grid; Environmental science; Process engineering; Waste management; Power (physics); Engineering; Operations management; Electrical engineering; Chemistry","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003722479,0.0003411213,0.0003848127,0.0006654178,0.0003200021,0.0001528248,0.0002439109,0.0002034499,0.00002437282],"category_scores_gemma":[0.00001666393,0.000385203,0.00006039667,0.0003920654,0.00007556195,0.0004472984,0.0000104617,0.0002203609,0.000001234725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005211583,"about_ca_system_score_gemma":0.00006068948,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002730699,"about_ca_topic_score_gemma":0.0006403681,"domain_scores_codex":[0.998181,0.00007936405,0.0004184298,0.0004295155,0.0002821735,0.000609489],"domain_scores_gemma":[0.9989433,0.00005363056,0.00009839704,0.0005462265,0.0001946416,0.0001637907],"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":[0.0001207632,0.00008024558,0.000008078863,0.0001499791,0.0001110293,0.0001085823,0.0002332012,0.9769747,0.001282916,0.003153277,0.00006444793,0.01771273],"study_design_scores_gemma":[0.002546931,0.000352581,0.0003375556,0.0004093629,0.000113097,0.00004956359,0.002455494,0.8911535,0.0921018,0.0004667174,0.008969541,0.001043865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.260483,0.0003865839,0.7307721,0.0001044783,0.0002786667,0.0001732747,0.0000083088,0.0001360967,0.007657464],"genre_scores_gemma":[0.9926004,0.001222912,0.002433472,0.00002669108,0.00002579876,0.000111604,0.00000377853,0.00007791309,0.003497461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7321174,"threshold_uncertainty_score":0.99986,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004598923741815085,"score_gpt":0.2028749513594557,"score_spread":0.1982760276176406,"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."}}