{"title":"Screening Curve for Valuing Power Generation and Storage Technologies in the 21st Century Grid","authors":"Y. Pratama, Niall Mac Dowell","doi":"10.2139/ssrn.3821841","DOIUrl":null,"url":null,"abstract":"The 21st century grids are characterised by increasing complexity of the system: high intermittent renewable energy sources (iRES) penetration requires additional flexibility and ancillary services from the other technologies (for example inertia, frequency responses, etc.). Accordingly, evaluating power technologies, i.e., power generation and storage, with LCOE, that exclusively emphasises the value of energy, alone can be misleading. On the other hand, performing detailed modeling analysis requires an enormous amount of effort and data which may not be accessible. To fill this gap, this study proposes a new metric to evaluate the performance of power technologies in the system. In addition to energy, a range of ancillary services is also considered. To balance completeness and ease of use, only the most valuable services are included in the new concept. To quantify the value of those services, we employed the Electricity System Optimisation framework with ancillary services (ESO-ANCIL) using UK electricity system as the case. Our analysis shows that although all services are valuable, capacity and energy services are the most significant. Whilst the technology screening curve is a practical approach to value those services and to derive optimal technology mix based on the system’s load duration curve, such method cannot evaluate iRES and electricity storage in the system. Therefore, we introduced a new set of rules and formulations to allow the application of the analysis for those technologies. As such, the proposed concept is particularly valuable to estimate the optimal deployment level and role or power technologies in providing energy and capacity services to the grid. Our proposed concept proves to be an easy-to-use concept that is capable in valuing a more complete set of services offered by power generation and storage technologies and can be used as an alternative to the LCOE.","PeriodicalId":18255,"journal":{"name":"MatSciRN: Process & Device Modeling (Topic)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2021-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"MatSciRN: Process & Device Modeling (Topic)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3821841","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
The 21st century grids are characterised by increasing complexity of the system: high intermittent renewable energy sources (iRES) penetration requires additional flexibility and ancillary services from the other technologies (for example inertia, frequency responses, etc.). Accordingly, evaluating power technologies, i.e., power generation and storage, with LCOE, that exclusively emphasises the value of energy, alone can be misleading. On the other hand, performing detailed modeling analysis requires an enormous amount of effort and data which may not be accessible. To fill this gap, this study proposes a new metric to evaluate the performance of power technologies in the system. In addition to energy, a range of ancillary services is also considered. To balance completeness and ease of use, only the most valuable services are included in the new concept. To quantify the value of those services, we employed the Electricity System Optimisation framework with ancillary services (ESO-ANCIL) using UK electricity system as the case. Our analysis shows that although all services are valuable, capacity and energy services are the most significant. Whilst the technology screening curve is a practical approach to value those services and to derive optimal technology mix based on the system’s load duration curve, such method cannot evaluate iRES and electricity storage in the system. Therefore, we introduced a new set of rules and formulations to allow the application of the analysis for those technologies. As such, the proposed concept is particularly valuable to estimate the optimal deployment level and role or power technologies in providing energy and capacity services to the grid. Our proposed concept proves to be an easy-to-use concept that is capable in valuing a more complete set of services offered by power generation and storage technologies and can be used as an alternative to the LCOE.