粒子群优化
分类
多目标优化
遗传算法
进化算法
数学优化
帕累托原理
混合算法(约束满足)
最优化问题
功率(物理)
工程类
计算机科学
算法
数学
约束逻辑程序设计
约束规划
随机规划
物理
量子力学
作者
Xinyu Ren,Lin Li,Bing-Xiang Ji,Zhi-Feng Liu
出处
期刊:Energy
[Elsevier]
日期:2024-01-21
卷期号:292: 130362-130362
被引量:3
标识
DOI:10.1016/j.energy.2024.130362
摘要
The planning and operation optimization of hybrid combined cooling, heating and power (CCHP) systems is the prerequisite and foundation for its advantages such as economy, energy saving, and high efficiency. This study constructed a bi-level optimization model of a hybrid CCHP system. Firstly, an upper-level capacity planning model of the hybrid CCHP system is constructed considering system economy, energy, and environmental performance. Secondly, a lower-level operation optimization model is proposed, considering the operation and maintenance cost of the system. Thirdly, a multi-objective honey badger algorithm (MOHBA) is developed based on the characteristics of the studied problem and the features of the bi-level optimization model. Finally, the optimization results indicate that the hybrid CCHP can save 32.79 % of the total cost, 49.74 % of primary energy, and 60.18 % of carbon emissions compared to the separation production (SP) system. In addition, compared with improving the strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEAD), and multi-objective particle swarm optimization (MOPSO), the proposed MOHBA can provide superior optimization results. The proposed bi-level planning model achieves improved economic, energy, and environmental performance of the hybrid CCHP system compared to the single-level planning model for the given operation modes.
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