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Performance assessment of combined cooling, heating and power system operation strategy based on multi-objective seagull optimization algorithm

机译：基于多目标海鸥优化算法的组合冷却，加热和电力系统运行策略性能评估

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摘要

This study proposes a novel operation strategy named following the state of thermal storage tank (FST) to enhance the economic, energy and environmental performance of combined cooling, heating and power system. A system incorporating thermal storage tanks, batteries, micro turbines and photovoltaic generation units is built to assess the performance of different strategies. A multi-objective seagull optimization algorithm (MOSOA) is proposed to optimize the economic, energy and environmental objectives simultaneously, by using constrained non-dominant sorting and external archive mechanism into seagull optimization algorithm. In addition, technique for order preference by similarity to an ideal solution method is used to obtain the optimal compromise solution under the three objective functions to choose MOSOA Pareto solution. A large hotel case is used to evaluate the performance of FST, following the electric load (FEL), and following the thermal load (FTL) strategies in practical application. The result indicates that the output MOSOA Pareto solutions are uniformly distributed and the system performance under FST is superior to FEL and FTL strategies. The system performance is improved with primary energy saving ratio, carbon dioxide emission reduction ratio, energy efficiency and boiler energy saving ratio reaching 31.18%, 41.74%, 55.64% and 1.
机译：本研究提出了一种新颖的操作策略，以后的热储罐（FST），以提高冷却，加热和电力系统的经济，能量和环境性能。建立了一种包含热储罐，电池，微型涡轮机和光伏发电单元的系统，以评估不同策略的性能。提出了一种多目标海鸥优化算法（MOSOA），以通过使用受限的非主导分类和外部归档机制进入Seagull优化算法来优化经济，能量和环境目标。另外，通过与理想解决方法的相似性的顺序偏好技术用于在三个目标函数下获得最佳折衷解决方案以选择MosoA帕匹官溶液。大型酒店套件用于评估FST，在电负载（FEL）之后的FST，以及在实际应用中的热负荷（FTL）策略之后。结果表明，输出MOSOA Pareto解决方案均匀分布，FST下的系统性能优于FEL和FTL策略。系统性能随着初级节能比，二氧化碳排放减少率，能源效率和锅炉节能率达到31.18％，41.74％，55.64％和1。

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