太阳场大小对抛物槽式聚光太阳能发电站卡诺电池应用多目标技术经济优化的影响

IF 7.1 2区 工程技术 Q1 ENERGY & FUELS Sustainable Energy Technologies and Assessments Pub Date : 2024-10-10 DOI:10.1016/j.seta.2024.103984
L.G. Redelinghuys, C. McGregor
{"title":"太阳场大小对抛物槽式聚光太阳能发电站卡诺电池应用多目标技术经济优化的影响","authors":"L.G. Redelinghuys,&nbsp;C. McGregor","doi":"10.1016/j.seta.2024.103984","DOIUrl":null,"url":null,"abstract":"<div><div>This research extends our previous work by investigating the critical influence of the solar field size, quantified through the solar multiple (SM), on the multi-objective optimisation (MOO) of concentrating solar power (CSP) Carnot battery applications. The levelised costs of electricity and storage (LCOE and LCOS) and the capacity factor (CF) are our objective functions. Design variables are the thermal energy storage (TES) and heater capacities and the solar multiple (SM) for solar field size. Our main findings show that: (1) higher SMs decrease the trade-off between LCOE and LCOS; (2) For smaller SMs, Pareto-optimal TES and heater capacities have a one-to-one pairing and correlate positively. For higher SMs, one TES capacity can be paired with multiple heater capacities for Pareto optimality; (3) Regardless of the SM, higher TES capacities are paired with a single, higher heater capacity for Pareto optimality; (4) All Pareto-optimal solutions lie on the boundary of the LCOE-based graphical solution method with high accuracy, providing MOO estimates especially for lower SMs; (6) Pareto-optimal design ranges are: <span><math><mrow><mn>0</mn><mo>≤</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>500</mn></mrow></math></span> MW (all SMs), <span><math><mrow><mn>1</mn><mo>.</mo><mn>6</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>17</mn><mo>.</mo><mn>5</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>2</mn></mrow></math></span>), <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>2</mn><mo>.</mo><mn>6</mn></mrow></math></span>), <span><math><mrow><mn>5</mn><mo>.</mo><mn>9</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>3</mn></mrow></math></span>), <span><math><mrow><mn>10</mn><mo>.</mo><mn>5</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>4</mn></mrow></math></span>); (7) Utopian results are: <span><math><mrow><msub><mrow><mi>LCOE</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>3</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>5</mn><mo>.</mo><mn>9</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>0</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>10</mn><mo>.</mo><mn>71</mn></mrow></math></span> ¢/kWh, <span><math><mrow><msub><mrow><mi>LCOS</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>4</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>8</mn><mo>.</mo><mn>8</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>119</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>20</mn><mo>.</mo><mn>57</mn></mrow></math></span> ¢/kWh, <span><math><mrow><msub><mrow><mi>CF</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>4</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>20</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>500</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>92</mn><mo>.</mo><mn>8</mn></mrow></math></span> %.</div></div>","PeriodicalId":56019,"journal":{"name":"Sustainable Energy Technologies and Assessments","volume":"71 ","pages":"Article 103984"},"PeriodicalIF":7.1000,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Influence of solar field size on the multi-objective techno-economic optimisation of a Carnot battery application in a parabolic trough concentrating solar power plant\",\"authors\":\"L.G. Redelinghuys,&nbsp;C. McGregor\",\"doi\":\"10.1016/j.seta.2024.103984\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>This research extends our previous work by investigating the critical influence of the solar field size, quantified through the solar multiple (SM), on the multi-objective optimisation (MOO) of concentrating solar power (CSP) Carnot battery applications. The levelised costs of electricity and storage (LCOE and LCOS) and the capacity factor (CF) are our objective functions. Design variables are the thermal energy storage (TES) and heater capacities and the solar multiple (SM) for solar field size. Our main findings show that: (1) higher SMs decrease the trade-off between LCOE and LCOS; (2) For smaller SMs, Pareto-optimal TES and heater capacities have a one-to-one pairing and correlate positively. For higher SMs, one TES capacity can be paired with multiple heater capacities for Pareto optimality; (3) Regardless of the SM, higher TES capacities are paired with a single, higher heater capacity for Pareto optimality; (4) All Pareto-optimal solutions lie on the boundary of the LCOE-based graphical solution method with high accuracy, providing MOO estimates especially for lower SMs; (6) Pareto-optimal design ranges are: <span><math><mrow><mn>0</mn><mo>≤</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>500</mn></mrow></math></span> MW (all SMs), <span><math><mrow><mn>1</mn><mo>.</mo><mn>6</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>17</mn><mo>.</mo><mn>5</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>2</mn></mrow></math></span>), <span><math><mrow><mn>3</mn><mo>.</mo><mn>4</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>2</mn><mo>.</mo><mn>6</mn></mrow></math></span>), <span><math><mrow><mn>5</mn><mo>.</mo><mn>9</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>3</mn></mrow></math></span>), <span><math><mrow><mn>10</mn><mo>.</mo><mn>5</mn><mo>≤</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>≤</mo><mn>20</mn></mrow></math></span> h (<span><math><mrow><mi>SM</mi><mo>=</mo><mn>4</mn></mrow></math></span>); (7) Utopian results are: <span><math><mrow><msub><mrow><mi>LCOE</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>3</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>5</mn><mo>.</mo><mn>9</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>0</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>10</mn><mo>.</mo><mn>71</mn></mrow></math></span> ¢/kWh, <span><math><mrow><msub><mrow><mi>LCOS</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>4</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>8</mn><mo>.</mo><mn>8</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>119</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>20</mn><mo>.</mo><mn>57</mn></mrow></math></span> ¢/kWh, <span><math><mrow><msub><mrow><mi>CF</mi></mrow><mrow><mi>U</mi></mrow></msub><mrow><mo>(</mo><msup><mrow><mi>SM</mi></mrow><mrow><mo>∗</mo></mrow></msup><mo>=</mo><mn>4</mn><mo>,</mo><msubsup><mrow><mi>t</mi></mrow><mrow><mtext>TES</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>20</mn><mspace></mspace><mtext>h</mtext><mo>,</mo><msubsup><mrow><mi>H</mi></mrow><mrow><mtext>cap</mtext></mrow><mrow><mo>∗</mo></mrow></msubsup><mo>=</mo><mn>500</mn><mspace></mspace><mtext>MW</mtext><mo>)</mo></mrow><mo>=</mo><mn>92</mn><mo>.</mo><mn>8</mn></mrow></math></span> %.</div></div>\",\"PeriodicalId\":56019,\"journal\":{\"name\":\"Sustainable Energy Technologies and Assessments\",\"volume\":\"71 \",\"pages\":\"Article 103984\"},\"PeriodicalIF\":7.1000,\"publicationDate\":\"2024-10-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Sustainable Energy Technologies and Assessments\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213138824003801\",\"RegionNum\":2,\"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":"Sustainable Energy Technologies and Assessments","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213138824003801","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

摘要

这项研究扩展了我们之前的工作,研究了通过太阳倍率(SM)量化的太阳场大小对聚光太阳能(CSP)卡诺电池应用的多目标优化(MOO)的关键影响。电力和储能的平准化成本(LCOE 和 LCOS)以及容量因子(CF)是我们的目标函数。设计变量包括热能储存(TES)和加热器容量,以及太阳能发电场规模的太阳能倍数(SM)。我们的主要研究结果表明(1) 较高的 SM 会降低 LCOE 和 LCOS 之间的权衡;(2) 对于较小的 SM,帕累托最优 TES 和加热器容量具有一对一的配对关系,并呈正相关。对于较高的 SM,一个 TES 容量可与多个加热器容量配对,以实现帕累托最优;(3) 无论 SM 如何,较高的 TES 容量与一个较高的加热器容量配对,以实现帕累托最优;(4) 所有帕累托最优解都位于基于 LCOE 的图形求解方法的边界上,且精确度高,特别是对于较低的 SM,可提供 MOO 估计值;(6) 帕累托最优设计范围为:0≤Hcap∗≤500µm;(7) 帕累托最优设计范围为:0≤Hcap∗≤500µm:0≤Hcap∗≤500 MW(所有 SM),1.6≤tTES∗≤17.5 h(SM=2),3.4≤tTES∗≤20 h(SM=2.6),5.9≤tTES∗≤20 h(SM=3),10.5≤tTES∗≤20 h(SM=4);(7)乌托邦结果为:LCOEU(SM∗=2.6)LCOEU(SM∗=3,tTES∗=5.9h,Hcap∗=0MW)=10.71 ¢/kWh,LCOSU(SM∗=4,tTES∗=8.8h,Hcap∗=119MW)=20.57 ¢/kWh,CFU(SM∗=4,tTES∗=20h,Hcap∗=500MW)=92.8 %。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Influence of solar field size on the multi-objective techno-economic optimisation of a Carnot battery application in a parabolic trough concentrating solar power plant
This research extends our previous work by investigating the critical influence of the solar field size, quantified through the solar multiple (SM), on the multi-objective optimisation (MOO) of concentrating solar power (CSP) Carnot battery applications. The levelised costs of electricity and storage (LCOE and LCOS) and the capacity factor (CF) are our objective functions. Design variables are the thermal energy storage (TES) and heater capacities and the solar multiple (SM) for solar field size. Our main findings show that: (1) higher SMs decrease the trade-off between LCOE and LCOS; (2) For smaller SMs, Pareto-optimal TES and heater capacities have a one-to-one pairing and correlate positively. For higher SMs, one TES capacity can be paired with multiple heater capacities for Pareto optimality; (3) Regardless of the SM, higher TES capacities are paired with a single, higher heater capacity for Pareto optimality; (4) All Pareto-optimal solutions lie on the boundary of the LCOE-based graphical solution method with high accuracy, providing MOO estimates especially for lower SMs; (6) Pareto-optimal design ranges are: 0Hcap500 MW (all SMs), 1.6tTES17.5 h (SM=2), 3.4tTES20 h (SM=2.6), 5.9tTES20 h (SM=3), 10.5tTES20 h (SM=4); (7) Utopian results are: LCOEU(SM=3,tTES=5.9h,Hcap=0MW)=10.71 ¢/kWh, LCOSU(SM=4,tTES=8.8h,Hcap=119MW)=20.57 ¢/kWh, CFU(SM=4,tTES=20h,Hcap=500MW)=92.8 %.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Sustainable Energy Technologies and Assessments
Sustainable Energy Technologies and Assessments Energy-Renewable Energy, Sustainability and the Environment
CiteScore
12.70
自引率
12.50%
发文量
1091
期刊介绍: Encouraging a transition to a sustainable energy future is imperative for our world. Technologies that enable this shift in various sectors like transportation, heating, and power systems are of utmost importance. Sustainable Energy Technologies and Assessments welcomes papers focusing on a range of aspects and levels of technological advancements in energy generation and utilization. The aim is to reduce the negative environmental impact associated with energy production and consumption, spanning from laboratory experiments to real-world applications in the commercial sector.
期刊最新文献
Designing an integrative strategy to introduce electric vehicles in the tourism sector in an outermost region of the European Union Advances in smart cities with system integration and energy digitalization technologies: A state-of-the-art review Building-integrated passive and renewable solar technologies: A review from 3E perspectives Ceramic Air-to-Air Recuperator for energy recovery in HVAC systems: CFD analysis and comparison with experimental tests Day-ahead energy management in green microgrids: Impact of long-term scheduling of hydrogen storage systems
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1