{"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, 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, 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}
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: MW (all SMs), h (), h (), h (), h (); (7) Utopian results are: ¢/kWh, ¢/kWh, %.
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.