{"id":"W2504724541","doi":"10.3390/en9080616","title":"DG Mix and Energy Storage Units for Optimal Planning of Self-Sufficient Micro Energy Grids","year":2016,"lang":"en","type":"article","venue":"Energies","topic":"Microgrid Control and Optimization","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Renewable energy; Sizing; Energy storage; Photovoltaic system; Thermal energy storage; Distributed generation; Grid; Smart grid; Computer science; Intermittent energy source; Wind power; Automotive engineering; Process engineering; Engineering; Reliability engineering; Power (physics); Electrical engineering","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003034151,0.0004871361,0.0004861703,0.0005447208,0.0002484174,0.0006869078,0.0003724374,0.0004143908,0.001557609],"category_scores_gemma":[0.0005859475,0.000471092,0.0002890641,0.0005116821,0.0003392253,0.0006367092,0.0002642147,0.000351785,0.0001471056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007617595,"about_ca_system_score_gemma":0.0009675559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005621766,"about_ca_topic_score_gemma":0.01001063,"domain_scores_codex":[0.9998697,0.00004361513,0.000006188655,0.00002583317,0.00003477128,0.00001979076],"domain_scores_gemma":[0.9998367,0.00008515399,0.00002775342,0.000007482215,0.00003093688,0.00001191688],"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":[0.00001748939,0.000007315009,0.0001925177,0.00001275584,0.000005676656,0.00001716896,0.00001062973,0.9933534,0.0004961269,0.001362974,0.0001112299,0.004412756],"study_design_scores_gemma":[0.000004636136,0.00001173105,0.00009484331,0.000002970513,0.000003576711,0.000003362125,0.00001048497,0.9983138,0.000269136,0.00109222,0.0001911657,0.000002076947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1759263,0.0006360306,0.8130742,0.0002503912,0.00003187437,0.0001975257,0.0002400683,0.000437323,0.009206261],"genre_scores_gemma":[0.9390526,0.0001488617,0.05856993,0.00001904002,0.000005892273,0.0001476342,0.00006620646,0.00003088915,0.001958792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005621766,"threshold_uncertainty_score":0.01117808,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005756417736058999,"score_gpt":0.1780858473071237,"score_spread":0.1723294295710646,"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."}}