{"title":"模拟低成本大规模能源储存在英国电力网络中的影响","authors":"E. Barbour, A. Pimm, D. Parra","doi":"10.1109/OSES.2019.8867348","DOIUrl":null,"url":null,"abstract":"In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.","PeriodicalId":416860,"journal":{"name":"2019 Offshore Energy and Storage Summit (OSES)","volume":"145 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Modelling the effects of low-cost large-scale energy storage in the UK electricity network\",\"authors\":\"E. Barbour, A. Pimm, D. Parra\",\"doi\":\"10.1109/OSES.2019.8867348\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.\",\"PeriodicalId\":416860,\"journal\":{\"name\":\"2019 Offshore Energy and Storage Summit (OSES)\",\"volume\":\"145 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-07-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 Offshore Energy and Storage Summit (OSES)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OSES.2019.8867348\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 Offshore Energy and Storage Summit (OSES)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OSES.2019.8867348","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Modelling the effects of low-cost large-scale energy storage in the UK electricity network
In this paper we present a framework for modelling the impacts of large-scale electricity storage in the Great Britain (GB) electricity network. Our framework consists of two principle components; firstly, a data-driven model of the GB powerplant dispatch, and secondly, an energy storage module. The storage module takes the powerplant dispatch and modifies it considering the specified energy storage characteristics (capacity, charging/discharging power and efficiency) in order to minimize an objective function. In particular, we consider two objective functions, minimizing the system running cost and minimizing the system emissions. We demonstrate our approach using data from the GB electricity system in 2015. Our model is primarily built in python and is entirely open-source in nature.
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