{"query_echo":{"q":"","filters":{"analysis_type":["variability analysis"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'variability analysis' for facet 'analysis_type' is not in the controlled vocabulary."}]},"total":{"value":1,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?analysis_type=variability+analysis","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"rmi-001063","url":"https://climateinsightsdirectory.org/document/rmi-001063","title":"SPATIAL AND TEMPORAL INTERACTIONS OF SOLAR AND WIND RESOURCES IN THE NEXT GENERATION UTILITY","publisher":{"name":"RMI","organization":"rmi"},"document_type":"research paper","language":"en","date_published":"2008","source_url":"https://rmi.org/app/uploads/2017/05/RMI_Document_Repository_Public-Reprts_Palmintier-Hansen-Levine-Solar-2008-WindSolar-in-NGU-correct-names_090924.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The research demonstrates that creating optimized portfolios of geographically diverse wind and solar sites significantly reduces power output variability compared to individual sites. In the modeled U.S. Midwest region for 2004, combined wind and solar portfolios achieved an average variability reduction of 55% below the average of individual sites. The study finds that a small subset of available sites is often sufficient to achieve most of these variability reductions. To manage remaining variability, the authors propose integrating responsive loads, energy storage via electric vehicles, and intelligent grid communications.","ai_generated":true,"unit":"rmi-001063#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Optimized portfolios of wind and solar resources together provide greater reductions in power variability than portfolios consisting of either resource alone, with an average decrease in variability of 55% compared to individual sites.","ai_generated":true,"unit":"rmi-001063#insight-1","date_modified":"2026-10-23"},{"text":"Wind-only optimized portfolios in the Midwest Reliability Organization (MRO) for 2004 reduced output variability by an average of 45% relative to individual sites and increased the capacity factor from 0.19 to 0.25.","ai_generated":true,"unit":"rmi-001063#insight-2","date_modified":"2026-10-23"},{"text":"Solar-only optimized portfolios in the MRO for 2004 reduced output variability by an average of 15% compared to individual sites and increased the capacity factor from 0.23 to 0.25.","ai_generated":true,"unit":"rmi-001063#insight-3","date_modified":"2026-10-23"},{"text":"The reduction in variability for solar-only portfolios is less significant than for wind-only portfolios because solar radiation is more highly correlated between sites; the minimum covariance matrix for solar-only is 50 times higher than for wind-only.","ai_generated":true,"unit":"rmi-001063#insight-4","date_modified":"2026-10-23"},{"text":"A small number of optimally selected sites can achieve the majority of variability reductions; portfolios of only six sites were found to be nearly indistinguishable from unconstrained optimizations.","ai_generated":true,"unit":"rmi-001063#insight-5","date_modified":"2026-10-23"},{"text":"The 'next generation utility' model suggests managing remaining renewable variability through the use of responsive loads, energy storage (specifically plug-in hybrid and full electric vehicles), and advanced intelligent grid communications.","ai_generated":true,"unit":"rmi-001063#insight-6","date_modified":"2026-10-23"}],"climate_tags":["wind power","photovoltaics","grid flexibility","demand response","energy storage","electric vehicle","smart grid","decarbonization"],"geography_discussed":["United States"],"frame_perspective":[],"analysis_type":["portfolio optimization","variability analysis"]}]}