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A novel stochastic optimization model to design concentrated photovoltaic/thermal systems: A case to meet hotel energy demands compared to conventional photovoltaic system
Energy Conversion and Management ( IF 9.9 ) Pub Date : 2020-11-01 , DOI: 10.1016/j.enconman.2020.113383
Bruno S.M.C. Borba , Letícia F. Henrique , Diego C. Malagueta

Abstract This study offered a novel optimization model to design hybrid solar photovoltaic systems. In addition, the model was able to compare results from conventional photovoltaic panel systems to combined cooling, heating and power systems coupled with concentrated photovoltaic/thermal technology. In the trigeneration system, the concentrated photovoltaic/thermal collector, operating with an absorption chiller and hot water tank stratified, was modeled to supply the energy loads of hotels located in the city of Rio de Janeiro. Monte Carlo simulation considering uncertainty scenarios was applied to model the electric, cooling, and hot water demands, minute by minute, over a year. The optimal size of each system maximized the equivalent uniform annual cost through nonlinear programming, using the Basic Open-source Nonlinear Mixed Integer programming method. The decision variables for the combined cooling, heating and power system were the number of concentrated photovoltaic/thermal collectors and the hot water storage capacity. For the conventional photovoltaic system, the decision variable was the number of photovoltaic panels to be installed. In total, 16 hotels were optimized, showing that the total electrical demands met by the photovoltaic systems vary between 30% and 65%, decreasing as the hotel demands grow. Conversely, the trigeneration system would supply around 26–48% of electrical demands, 40–50% of cooling requirements, in addition to providing 20–45% of hot water for heating.

中文翻译:

设计集中光伏/热力系统的新型随机优化模型:与传统光伏系统相比满足酒店能源需求的案例

摘要 本研究为设计混合太阳能光伏系统提供了一种新的优化模型。此外,该模型还能够将传统光伏面板系统与结合了集中光伏/热力技术的冷、热和电力组合系统的结果进行比较。在三联产系统中,集中式光伏/集热器与吸收式冷却器和分层热水箱一起运行,模拟为里约热内卢市的酒店提供能源负荷。考虑到不确定性场景的蒙特卡罗模拟被应用于对电力、冷却和热水需求进行建模,每分钟,超过一年。每个系统的最优规模通过非线性规划最大化等效的统一年成本,使用基本开源非线性混合整数规划方法。冷热电联合系统的决策变量是集中式光伏/集热器的数量和热水存储容量。对于传统的光伏系统,决策变量是要安装的光伏面板数量。总共优化了 16 家酒店,显示光伏系统满足的总电力需求在 30% 到 65% 之间变化,随着酒店需求的增长而下降。相反,除了提供 20-45% 的热水供暖之外,三联产系统将提供大约 26-48% 的电力需求、40-50% 的冷却需求。热电系统分别是集中式光伏/集热器的数量和热水储存能力。对于传统的光伏系统,决策变量是要安装的光伏面板数量。总共优化了 16 家酒店,显示光伏系统满足的总电力需求在 30% 到 65% 之间变化,随着酒店需求的增长而下降。相反,除了提供 20-45% 的热水供暖之外,三联产系统将提供大约 26-48% 的电力需求、40-50% 的冷却需求。热电系统分别是集中式光伏/集热器的数量和热水储存能力。对于传统的光伏系统,决策变量是要安装的光伏面板数量。总共优化了 16 家酒店,显示光伏系统满足的总电力需求在 30% 到 65% 之间变化,随着酒店需求的增长而下降。相反,除了提供 20-45% 的热水供暖之外,三联产系统将提供大约 26-48% 的电力需求、40-50% 的冷却需求。显示光伏系统满足的总电力需求在 30% 到 65% 之间变化,随着酒店需求的增长而下降。相反,除了提供 20-45% 的热水供暖之外,三联产系统将提供大约 26-48% 的电力需求、40-50% 的冷却需求。显示光伏系统满足的总电力需求在 30% 到 65% 之间变化,随着酒店需求的增长而下降。相反,除了提供 20-45% 的热水供暖之外,三联产系统将提供大约 26-48% 的电力需求、40-50% 的冷却需求。
更新日期:2020-11-01
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