{"title":"面向近实时规划运行的时变天气条件下太阳能光伏发电态势感知指标","authors":"Michael Walters , Ganesh K. Venayagamoorthy","doi":"10.1016/j.apenergy.2025.126855","DOIUrl":null,"url":null,"abstract":"<div><div>Solar photovoltaic (PV) plant development and utilization is increasing worldwide but remains intrinsically challenged by its large dependence on highly variable weather conditions and operating states. This paper presents a framework to leverage three new situational awareness indices (SAIs), namely: weather condition index (WCI) to gauge operational performance based on environmental states, operational complexity index (OCI) to indicate the severity of power generation reductions, and photovoltaic generation index (PVGI) to provide a final determination of the impact on power generation and to bolster situational awareness in planning and operational contexts for solar PV plants. This is accomplished by exploiting the effects of weather conditions, operating states, and solar PV power generation performance in high spatial-temporal resolution contexts residing in solar PV power generation data with independent fuzzy inference systems (FISs) for each index. SAIs provide additional operational insights to evaluate solar PV plant performance over both short-term (minute(s)) and long-term (24 h) time intervals in a variety of areas, including weather condition classification studies, energy dispatch controllers, and power system voltage and frequency stability assurance. The proposed SAI framework is developed, demonstrated, and evaluated for a 1MWp solar plant located in Clemson, South Carolina, USA.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"402 ","pages":"Article 126855"},"PeriodicalIF":11.0000,"publicationDate":"2025-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Situational awareness indices of solar PV power generation under temporal weather conditions for near real-time planning and operation\",\"authors\":\"Michael Walters , Ganesh K. Venayagamoorthy\",\"doi\":\"10.1016/j.apenergy.2025.126855\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Solar photovoltaic (PV) plant development and utilization is increasing worldwide but remains intrinsically challenged by its large dependence on highly variable weather conditions and operating states. This paper presents a framework to leverage three new situational awareness indices (SAIs), namely: weather condition index (WCI) to gauge operational performance based on environmental states, operational complexity index (OCI) to indicate the severity of power generation reductions, and photovoltaic generation index (PVGI) to provide a final determination of the impact on power generation and to bolster situational awareness in planning and operational contexts for solar PV plants. This is accomplished by exploiting the effects of weather conditions, operating states, and solar PV power generation performance in high spatial-temporal resolution contexts residing in solar PV power generation data with independent fuzzy inference systems (FISs) for each index. SAIs provide additional operational insights to evaluate solar PV plant performance over both short-term (minute(s)) and long-term (24 h) time intervals in a variety of areas, including weather condition classification studies, energy dispatch controllers, and power system voltage and frequency stability assurance. The proposed SAI framework is developed, demonstrated, and evaluated for a 1MWp solar plant located in Clemson, South Carolina, USA.</div></div>\",\"PeriodicalId\":246,\"journal\":{\"name\":\"Applied Energy\",\"volume\":\"402 \",\"pages\":\"Article 126855\"},\"PeriodicalIF\":11.0000,\"publicationDate\":\"2025-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Energy\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0306261925015855\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Energy","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0306261925015855","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Situational awareness indices of solar PV power generation under temporal weather conditions for near real-time planning and operation
Solar photovoltaic (PV) plant development and utilization is increasing worldwide but remains intrinsically challenged by its large dependence on highly variable weather conditions and operating states. This paper presents a framework to leverage three new situational awareness indices (SAIs), namely: weather condition index (WCI) to gauge operational performance based on environmental states, operational complexity index (OCI) to indicate the severity of power generation reductions, and photovoltaic generation index (PVGI) to provide a final determination of the impact on power generation and to bolster situational awareness in planning and operational contexts for solar PV plants. This is accomplished by exploiting the effects of weather conditions, operating states, and solar PV power generation performance in high spatial-temporal resolution contexts residing in solar PV power generation data with independent fuzzy inference systems (FISs) for each index. SAIs provide additional operational insights to evaluate solar PV plant performance over both short-term (minute(s)) and long-term (24 h) time intervals in a variety of areas, including weather condition classification studies, energy dispatch controllers, and power system voltage and frequency stability assurance. The proposed SAI framework is developed, demonstrated, and evaluated for a 1MWp solar plant located in Clemson, South Carolina, USA.
期刊介绍:
Applied Energy serves as a platform for sharing innovations, research, development, and demonstrations in energy conversion, conservation, and sustainable energy systems. The journal covers topics such as optimal energy resource use, environmental pollutant mitigation, and energy process analysis. It welcomes original papers, review articles, technical notes, and letters to the editor. Authors are encouraged to submit manuscripts that bridge the gap between research, development, and implementation. The journal addresses a wide spectrum of topics, including fossil and renewable energy technologies, energy economics, and environmental impacts. Applied Energy also explores modeling and forecasting, conservation strategies, and the social and economic implications of energy policies, including climate change mitigation. It is complemented by the open-access journal Advances in Applied Energy.