4.7 Article

Performance evaluation of solar hybrid combined cooling, heating and power systems: A multi-objective arithmetic optimization algorithm

期刊

ENERGY CONVERSION AND MANAGEMENT
卷 258, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.enconman.2022.115541

关键词

Solar thermal collectors; Multi-objective arithmetic optimization & nbsp;algorithm ; Hybrid combined cooling,& nbsp;heating and power & nbsp;system ; Following the state of battery strategy

资金

  1. key project of Tianjin Natural Sci-ence Foundation [19JCZDJC32100]
  2. Natural Science Foundation of Hebei Province of China [E2018202282]

向作者/读者索取更多资源

This study developed a mathematical model to optimize the configuration of a hybrid system combining solar thermal and photovoltaic technologies with combined cooling, heating, and power functions. The results showed that compared to other strategies, adopting the proposed strategy achieved better energy savings and carbon emission reductions.
The coupling of solar thermal and photovoltaic technologies with combined cooling, heating and power systems has significant impacts on the reduction of fossil fuel consumption and pollutant emissions. In this study, a mathematical model of a hybrid combined cooling, heating, and power system consisting of thermal storage units, batteries, microturbines, photovoltaic units, and solar thermal collectors, is developed. Meanwhile, based on the following thermal load strategy and following electric load strategy, the following the state of battery strategy is proposed. A multi-objective arithmetic optimization algorithm is proposed by using non-dominated sorting, mutation operations, and external archive mechanism to optimize the configuration of the hybrid system under different strategies. Besides, an optimal compromise is obtained by technique for order preference by similarity to an ideal solution method. A large hotel case is used to evaluate the performance of the hybrid system under different strategies. The optimization results show that the Pareto solutions obtained by the developed optimization algorithm are uniformly distributed. Moreover, compared with the hybrid system under the following electric load and following thermal load strategies, the hybrid system under the proposed strategy achieves better primary energy saving ratio, carbon dioxide emission reduction ratio, and energy efficiency, and these indicators reach 46.56%, 54.64%, and 78.51%, respectively.

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