{"id":"W2502931572","doi":"10.2172/1251315","title":"Integrating Renewable Generation into Grid Operations: Four International Experiences","year":2016,"lang":"en","type":"report","venue":"","topic":"Electric Power System Optimization","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Pacific Northwest National Laboratory; U.S. Department of Energy; European Commission; Department for Enterprise, Trade and Investment, UK Government; Argonne National Laboratory; Ministry of Economy, Trade and Industry; National Renewable Energy Laboratory; National Nuclear Security Administration; Queen's University; Commonwealth Scientific and Industrial Research Organisation; Heinrich Böll Stiftung; Department of Energy and Climate Change","keywords":"Restructuring; Resource (disambiguation); Renewable energy; Liberalization; Energy mix; Business; Electricity generation; Grid; Energy planning; Environmental economics; Electricity; Economics; Power (physics); Engineering; Geography; Market economy; Computer science; Finance","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003713703,0.000428483,0.000303167,0.0005674766,0.003801467,0.005472217,0.0008215635,0.001470612,0.001980497],"category_scores_gemma":[0.003080207,0.0002980224,0.0003570232,0.002648475,0.002810225,0.004733648,0.004604276,0.002601732,0.0003089413],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002487803,"about_ca_system_score_gemma":0.001968367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00732919,"about_ca_topic_score_gemma":0.01611943,"domain_scores_codex":[0.997855,0.001284103,0.00007767404,0.00008536634,0.000233326,0.0004645798],"domain_scores_gemma":[0.998702,0.0005638771,0.0001004697,0.0001388229,0.0001940876,0.0003007194],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004261589,0.001130514,0.01862615,0.00097734,0.00009367498,0.01231396,0.5301048,0.007386073,0.003457307,0.1129249,0.03283444,0.2797247],"study_design_scores_gemma":[0.0000490103,0.000460906,0.008982394,0.0005542719,0.00004283863,0.001842009,0.4535133,0.001201211,0.0018213,0.008593548,0.5228551,0.00008413537],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7760443,0.005708409,0.003558761,0.01097175,0.0003643775,0.00006978036,0.0001043724,0.00006413428,0.2031141],"genre_scores_gemma":[0.971292,0.009459559,0.001635891,0.001347582,0.00008429166,0.00003497901,0.00009255174,0.00005424624,0.01599893],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00732919,"threshold_uncertainty_score":0.01964015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02842573587764455,"score_gpt":0.2664515605926838,"score_spread":0.2380258247150392,"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."}}