Mengjie Zhang, Lei Yan, Yash Amonkar, Adam Nayak, Upmanu Lall
{"title":"鉴于厄尔尼诺/南方涛动隐含状态，得克萨斯州可再生能源供需的潜在气候可预测性","authors":"Mengjie Zhang, Lei Yan, Yash Amonkar, Adam Nayak, Upmanu Lall","doi":"10.1126/sciadv.ado3517","DOIUrl":null,"url":null,"abstract":"<div >Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño–Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.</div>","PeriodicalId":21609,"journal":{"name":"Science Advances","volume":null,"pages":null},"PeriodicalIF":11.7000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.science.org/doi/reader/10.1126/sciadv.ado3517","citationCount":"0","resultStr":"{\"title\":\"Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state\",\"authors\":\"Mengjie Zhang, Lei Yan, Yash Amonkar, Adam Nayak, Upmanu Lall\",\"doi\":\"10.1126/sciadv.ado3517\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div >Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño–Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.</div>\",\"PeriodicalId\":21609,\"journal\":{\"name\":\"Science Advances\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":11.7000,\"publicationDate\":\"2024-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.science.org/doi/reader/10.1126/sciadv.ado3517\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Science Advances\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://www.science.org/doi/10.1126/sciadv.ado3517\",\"RegionNum\":1,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Advances","FirstCategoryId":"103","ListUrlMain":"https://www.science.org/doi/10.1126/sciadv.ado3517","RegionNum":1,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
Potential climate predictability of renewable energy supply and demand for Texas given the ENSO hidden state
Climate variability influences renewable electricity supply and demand and hence system reliability. Using the hidden states of the sea surface temperature of tropical Pacific Ocean that reflect El Niño–Southern Oscillation (ENSO) dynamics that is objectively identified by a nonhomogeneous hidden Markov model, we provide a first example of the potential predictability of monthly wind and solar energy and heating and cooling energy demand for 1 to 6 months ahead for Texas, United States, a region that has a high penetration of renewable electricity and is susceptible to disruption by climate-driven supply-demand imbalances. We find a statistically significant potential for oversupply or undersupply of energy and anomalous heating/cooling demand depending on the ENSO state and the calendar month. Implications for financial securitization and the potential application of forecasts are discussed.
期刊介绍：
Science Advances, an open-access journal by AAAS, publishes impactful research in diverse scientific areas. It aims for fair, fast, and expert peer review, providing freely accessible research to readers. Led by distinguished scientists, the journal supports AAAS's mission by extending Science magazine's capacity to identify and promote significant advances. Evolving digital publishing technologies play a crucial role in advancing AAAS's global mission for science communication and benefitting humankind.
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