{"title":"HERON作为放松管制市场中轻水反应堆市场互动的工具","authors":"P. Talbot, A. Gairola, Konor L Frick, C. Rabiti","doi":"10.1115/power2020-16916","DOIUrl":null,"url":null,"abstract":"\n This paper reports the development of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to technoeconomic analysis in a deregulated market. A short description of the HERON plugin is provided, and the release process is described. HERON as a plugin enables RAVEN to perform stochastic technoeconomic analysis of grid-energy systems in a generic approach. The primary function of HERON is to generate the complex RAVEN workflows necessary to optimize component capacities under stochastic systems. HERON is capable of analyzing systems with complex components transferring a variety of commodities, including production components and varied markets. HERON is capable of optimizing high-resolution dispatch for such systems and guiding stochastic optimization algorithms in RAVEN for finding optimal component capacities. In particular, this document demonstrates the application of HERON to systems with deregulated markets. A system including a hyrdogen market, an electricity market, hydrogen storage, a hydrogen producer, and a nuclear power plant is considered. Stochastic histories for electricity prices at the electricity market are employed to perform stochastic analysis for ideal sizing of the hydrogen production facility and hydrogen storage unit. The impact of hydrogen market price and volatility of electricity price are also shown.","PeriodicalId":282703,"journal":{"name":"ASME 2020 Power Conference","volume":"17 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"HERON As a Tool for Light Water Reactor Market Interaction in a Deregulated Market\",\"authors\":\"P. Talbot, A. Gairola, Konor L Frick, C. Rabiti\",\"doi\":\"10.1115/power2020-16916\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n This paper reports the development of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to technoeconomic analysis in a deregulated market. A short description of the HERON plugin is provided, and the release process is described. HERON as a plugin enables RAVEN to perform stochastic technoeconomic analysis of grid-energy systems in a generic approach. The primary function of HERON is to generate the complex RAVEN workflows necessary to optimize component capacities under stochastic systems. HERON is capable of analyzing systems with complex components transferring a variety of commodities, including production components and varied markets. HERON is capable of optimizing high-resolution dispatch for such systems and guiding stochastic optimization algorithms in RAVEN for finding optimal component capacities. In particular, this document demonstrates the application of HERON to systems with deregulated markets. A system including a hyrdogen market, an electricity market, hydrogen storage, a hydrogen producer, and a nuclear power plant is considered. Stochastic histories for electricity prices at the electricity market are employed to perform stochastic analysis for ideal sizing of the hydrogen production facility and hydrogen storage unit. The impact of hydrogen market price and volatility of electricity price are also shown.\",\"PeriodicalId\":282703,\"journal\":{\"name\":\"ASME 2020 Power Conference\",\"volume\":\"17 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-08-04\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"ASME 2020 Power Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/power2020-16916\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"ASME 2020 Power Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/power2020-16916","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
HERON As a Tool for Light Water Reactor Market Interaction in a Deregulated Market
This paper reports the development of HERON (Holistic Energy Resource Optimization Network), a newly-developed RAVEN (Risk Analysis Virtual ENvironment) plugin for grid and capacity optimization, to technoeconomic analysis in a deregulated market. A short description of the HERON plugin is provided, and the release process is described. HERON as a plugin enables RAVEN to perform stochastic technoeconomic analysis of grid-energy systems in a generic approach. The primary function of HERON is to generate the complex RAVEN workflows necessary to optimize component capacities under stochastic systems. HERON is capable of analyzing systems with complex components transferring a variety of commodities, including production components and varied markets. HERON is capable of optimizing high-resolution dispatch for such systems and guiding stochastic optimization algorithms in RAVEN for finding optimal component capacities. In particular, this document demonstrates the application of HERON to systems with deregulated markets. A system including a hyrdogen market, an electricity market, hydrogen storage, a hydrogen producer, and a nuclear power plant is considered. Stochastic histories for electricity prices at the electricity market are employed to perform stochastic analysis for ideal sizing of the hydrogen production facility and hydrogen storage unit. The impact of hydrogen market price and volatility of electricity price are also shown.