可再生能源制氢系统多目标优化调度. (Chinese)

Multi-objective Optimal Scheduling of Renewable Energy Hydrogen Production System. (English)

With the implementation of carbon peaking and carbon neutralization, hydrogen energy plays an important role in the process of energy decarbonization. Hydrogen production from renewable energy can further realize low-carbon energy. In response to the weakness of the stability of renewable energy systems, the conflicting objectives of system benefits and environmental costs were considered as a simultaneously consideration, the multi-objective golden eagle algorithm (MOGEO) was used to run and optimize the Pareto optimal solution set for the renewable energy hydrogen production system. In order to verify the feasibility of the proposed method, four typical days at the location of Winter Olympic were taken as an example, and compared with the commercial solver CPLEX and the traditional multi-objective particle swarm optimization (MOPSO) algorithm respectively. The results show that the proposed method can achieve better optimization results. [ABSTRACT FROM AUTHOR]

随着碳达峰碳中和的推行,氢能在能源去碳化进程中扮演着重要的地位.利用可再生能源制氢可以进一步实现能源低碳化.针对可再生能源系统稳定性较弱的缺点,将系统收益和环境成本这一对相互矛盾的目标进行折中考虑,提出使用多目标金鹰算法 (multi-objective golden eagle algorithm,MOGEO) 对可再生能源制氢系统运行优化求解帕累托最优解集.为验证该方法的可行性,以冬奥场馆所在地的典型日为例,分别与商业求解器 CPLEX 及传统的多目标粒子群算法(multi-objective particle swarm optimization, MOPSO) 进行对比,结果表明所提方法可以取得更好的优化结果. [ABSTRACT FROM AUTHOR]

Copyright of Science Technology & Engineering is the property of Chinese Society of Technology Economics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)