• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Solar Energy >Two-stages optimised design of the collector field of solar power tower plants
【24h】

Two-stages optimised design of the collector field of solar power tower plants

机译：太阳能塔式电站集热场的两阶段优化设计

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In solar power tower (SPT) systems, selecting the optimum location of thousands of heliostats and the most profitable tower height and receiver size remains a challenge. Given the complexity of the problem, breaking the optimisation process down into two consecutive steps is suggested here; first, a primary, or energy, optimisation, which is practically independent of the cost models, and then a main, or economic, optimisation. The primary optimisation seeks a heliostat layout supplying the maximum annual incident energy for all the explored combinations of receiver sizes and tower heights. The annual electric output is then calculated as the combination of the incident energy and the simplified (annual averaged) receiver thermal losses and power efficiencies. Finally, the figure of merit of the main optimisation is the levelised cost of electric energy (LCOE) where the capital cost models used for the LCOE calculation are reported by the System Advisor Model (SAM)-NREL and Sandia. This structured optimisation, splitting energy procedures from economic ones, enables the organisation of a rather complex process, and it is not limited to any particular power tower code. Moreover, as the heliostat field layout is already fully optimised before the economic optimisation, the profiles of the LCOE versus the receiver radius for the tower heights explored here are sharp enough to establish optima easily. As an example of the new procedure, we present a full thermo-economic optimisation for the design of the collector field of an actual SPT system (Gemasolar, 20 MWe, radially staggered surrounding field with 2650 heliostats, 15 h of storage). The optimum design found for Gemasolar is reasonably consistent with the scarce open data. Finally, optimum designs are strongly dependent on the receiver cost, the electricity tariff and the assumed maximum receiver surface temperature. (C) 2016 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license.
机译：在太阳能塔式（SPT）系统中，选择数千个定日镜的最佳位置以及最有利可图的塔高和接收器尺寸仍然是一个挑战。考虑到问题的复杂性，此处建议将优化过程分为两个连续的步骤。首先是基本的或能源的优化，它实际上与成本模型无关，然后是主要的或经济的优化。首要的优化工作是寻找一个定日镜布局，为接收器尺寸和塔高的所有探索组合提供最大的年度入射能量。然后，将年发电量计算为入射能量与简化的（年平均）接收器热损耗和功率效率的组合。最后，主要优化的优缺点是电能的平均成本（LCOE），其中用于系统LCOE计算的资本成本模型由系统顾问模型（SAM）-NREL和Sandia报告。这种结构化的优化，将能源程序与经济程序分开，可以组织一个相当复杂的过程，并且不限于任何特定的电力塔代码。此外，由于定日镜场布局在经济优化之前已经得到充分优化，因此对于此处探索的塔高，LCOE的轮廓与接收器半径之间的关系足够清晰，可以轻松地确定最佳位置。作为新程序的一个示例，我们为实际SPT系统的集热场（Gemasolar，20 MWe，带有2650个定日镜的放射状交错的周围场，存储15 h）提出了一个完整的热经济优化方案。为Gemasolar找到的最佳设计与稀缺的公开数据相当一致。最后，最佳设计在很大程度上取决于接收器成本，电价和假定的最大接收器表面温度。 （C）2016作者。由Elsevier Ltd.发行。这是CC BY-NC-ND许可下的开放获取文章。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号