{"query_echo":{"q":"","filters":{"geography":["Electric Reliability Council of Texas"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'Electric Reliability Council of Texas' 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=Electric+Reliability+Council+of+Texas","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":"ucs-001234","url":"https://climateinsightsdirectory.org/document/ucs-001234","title":"Used, But How Useful?","publisher":{"name":"Union of Concerned Scientists","organization":"union-of-concerned-scientists"},"document_type":"report","language":"en","date_published":"2020-05","source_url":"https://www.ucs.org/sites/default/files/2020-05/Used%20but%20How%20Useful%20May%202020.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The report argues that rate-regulated utilities use 'self-commitment' to bypass market merit order, running coal plants that are objectively uneconomic and passing the resulting losses to captive customers through state fuel-cost-recovery processes. Using PLEXOS modeling for the MISO region in 2018, the authors find that this practice cost customers $350 million and suppressed regional market clearing prices by 2.4%. The report identifies Cleco and DTE Energy as significant contributors to these distortions and recommends that state regulators disallow the recovery of imprudent, above-market costs to incentivize efficient dispatch.","ai_generated":true,"unit":"ucs-001234#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In 2018, the uneconomic self-commitment of coal plants in the MISO region resulted in costs of $350 million for customers, which equates to an average annual increase of approximately $60 on residential electric bills.","ai_generated":true,"unit":"ucs-001234#insight-1","date_modified":"2026-10-23"},{"text":"The practice of self-committing coal plants artificially lowers market clearing prices across all MISO transmission zones, reducing the regional average by $0.63 per MWh, or 2.4%.","ai_generated":true,"unit":"ucs-001234#insight-2","date_modified":"2026-10-23"},{"text":"While most coal resources in MISO are self-committed, a small group of companies and plants drive the majority of market distortion; Cleco (co-owner of Dolet Hills) and DTE Energy are identified as the worst actors.","ai_generated":true,"unit":"ucs-001234#insight-3","date_modified":"2026-10-23"},{"text":"Eliminating uneconomic self-commitment in MISO would have increased the system's market surplus by 66%, rising from $1.5 billion to $2.5 billion, thereby improving overall market efficiency and profitability for power plants.","ai_generated":true,"unit":"ucs-001234#insight-4","date_modified":"2026-10-23"},{"text":"Rate-regulated utilities can exploit the self-commitment loophole because they can recover market losses through state fuel-cost-recovery processes, a financial cushion not available to independent power providers (IPPs).","ai_generated":true,"unit":"ucs-001234#insight-5","date_modified":"2026-10-23"},{"text":"The traditional view of coal as a 'baseload resource' is outdated; current MISO data shows coal plants often cycle to follow load patterns and frequently operate when the cost to burn coal exceeds the market price.","ai_generated":true,"unit":"ucs-001234#insight-6","date_modified":"2026-10-23"},{"text":"Some utilities, such as Xcel Energy and Cleco, have begun reducing uneconomic dispatch by converting coal plants to seasonal operations, which the report suggests can save customers tens of millions of dollars.","ai_generated":true,"unit":"ucs-001234#insight-7","date_modified":"2026-10-23"},{"text":"The report concludes that market rule reforms alone are unlikely to stop the behavior; instead, state regulators must disallow the recovery of imprudent, above-market costs to create a real incentive for utilities to operate economically.","ai_generated":true,"unit":"ucs-001234#insight-8","date_modified":"2026-10-23"}],"climate_tags":["coal-fired power station","electricity market","energy transition","economic dispatch"],"geography_discussed":["PJM Interconnection","Southwest Power Pool","Electric Reliability Council of Texas","Louisiana","Texas","North Dakota","Michigan","Minnesota","Illinois","Iowa","Arkansas","Missouri","Mississippi","Kentucky","Indiana","Wisconsin"],"frame_perspective":[],"analysis_type":["economic modeling","policy analysis"]},{"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"]}]}