{"query_echo":{"q":"","filters":{"geography":["Midwest Reliability Organization"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'Midwest Reliability Organization' for facet 'geography' is not in the controlled vocabulary."}]},"total":{"value":2,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?geography=Midwest+Reliability+Organization","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-001071","url":"https://climateinsightsdirectory.org/document/rmi-001071","title":"SPATIAL AND TEMPORAL INTERACTIONS OF WIND AND SOLAR IN THE NEXT GENERATION UTILITY","publisher":{"name":"RMI","organization":"rmi"},"document_type":"research paper","language":"en","date_published":"2008-06-03","source_url":"https://rmi.org/app/uploads/2017/05/RMI_Document_Repository_Public-Reprts_RMI_Windpower-NGU-paper_fnl.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The paper argues that the variability of renewable energy can be managed through optimized geographic dispersion and the cross-firming of wind and solar resources. By analyzing data from the MRO, SPP, and ERCOT regions, the study demonstrates that expanding the geographic scope of a renewable portfolio significantly reduces system variability and the frequency of zero-power production. It also finds that combining solar and wind resources further lowers variability compared to wind-only portfolios. The authors conclude that these strategies, combined with demand response, energy storage (such as xEVs), and smart grid communications, can reduce the reliance on conventional baseload power.","ai_generated":true,"unit":"rmi-001071#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Expanding the geographic area of a renewable portfolio across three reliability regions (MRO, SPP, and ERCOT) reduced system variability by 28% compared to the average individual region.","ai_generated":true,"unit":"rmi-001071#insight-1","date_modified":"2026-10-23"},{"text":"Geographic diversification significantly reduces the amount of time a system produces no power; a portfolio spanning the three analyzed regions produced no power for only 6 hours in a year, compared to 16 hours for the best individual region (ERCOT) and 90 hours for the average individual region.","ai_generated":true,"unit":"rmi-001071#insight-2","date_modified":"2026-10-23"},{"text":"Combining solar and wind resources further reduces variability; an optimized wind/solar portfolio showed 18% lower variability than an optimized wind-only portfolio.","ai_generated":true,"unit":"rmi-001071#insight-3","date_modified":"2026-10-23"},{"text":"In an optimized portfolio for the expanded region's 'bin of greatest return', capacity was distributed with approximately 80% allocated to wind and 20% to solar.","ai_generated":true,"unit":"rmi-001071#insight-4","date_modified":"2026-10-23"},{"text":"The length of the analysis period (one year versus three years) did not significantly impact the results for portfolio power output or variability, provided the single year used was typical.","ai_generated":true,"unit":"rmi-001071#insight-5","date_modified":"2026-10-23"},{"text":"The 'next generation utility' concept proposes reducing reliance on baseload coal and nuclear power by integrating variable renewables with demand response, energy storage via plug-in hybrid and electric vehicles (xEVs), and intelligent grid communications.","ai_generated":true,"unit":"rmi-001071#insight-6","date_modified":"2026-10-23"}],"climate_tags":["wind power","solar energy","grid flexibility","demand response","energy storage","electric vehicle","smart grid","decarbonization","energy efficiency"],"geography_discussed":["United States","California","Nevada"],"frame_perspective":[],"analysis_type":["quantitative","modeling"]},{"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"]}]}