混合增量粒子群优化和深度学习方法求解微电网中具有不确定性影响的经济调度

N. Rohiem, A. Soeprijanto, D. F. U. Putra, M. Syai’in, I. Sulistiawati, M. Zahoor, Luqman Ali Shah
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引用次数: 0

摘要

微电网是低压分布式发电模式的一个例子,可以覆盖各种能源,如传统发电机和可再生能源。经济调度(ED)是微电网中电力系统运行的重要功能和关键。有几个程序可以找到最佳生成。第一步是找到热发电机ED的每个可行状态(FS)。第二步是使用增量粒子群优化(IPSO)在FS的基础上找到最优发电,FS假设所有机组都被激活。第三步是将IPSO的输入和输出训练为深度学习(DL)。最后一步是将DL输出与IPSO进行比较。本文中的微电网系统考虑了10个火电机组和一个基于概率数据的风力发电厂。IPSO能够在24小时内每小时生成一个总发电量作为负载需求,显示出良好的结果。然而,IPSO在执行时间方面存在弱点,从10个实验来看,IPSO过程平均需要30分钟。基于IPSO的DL可以通过11输入10输出架构使其ED功能的执行时间更快。从IPSO的相同实验来看,DL可以产生与IPSO相同的输出,但执行时间更快。从总成本方面来看,风能正在影响降低总成本,IPSO和DL分别降低了2286万美元和2289万美元。
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Resolving Economic Dispatch with Uncertainty Effect in Microgrids Using Hybrid Incremental Particle Swarm Optimization and Deep Learning Method
Microgrids are one example of a low voltage distributed generation pattern that can cover a variety of energy, such as conventional generators and renewable energy. Economic dispatch (ED) is an important function and a key of a power system operation in microgrids. There are several procedures to find the optimum generation. The first step is to find every feasible state (FS) for thermal generator ED. The second step is to find optimum generation based on FS using incremental particle swarm optimization (IPSO), FS is assumed that all units are activated. The third step is to train the input and output of the IPSO into deep learning (DL). And the last step is to compare DL output with IPSO. The microgrids system in this paper considered 10 thermal units and a wind plant with power generation based on probabilistic data. IPSO shows good results by being capable to generate a total generation as the load requirement every hour for 24 h. However, IPSO has a weakness in execution times, from 10 experiments the average IPSO process takes 30 min. DL based on IPSO can make the execution time of its ED function faster with an 11 input and 10 output architecture. From the same experiments with IPSO, DL can produce the same output as IPSO but with a faster execution time. From the total cost side, wind energy is affecting to reduce total cost until USD 22.86 million from IPSO and USD 22.89 million from DL.
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Proceedings of the Pakistan Academy of Sciences: Part A
Proceedings of the Pakistan Academy of Sciences: Part A Computer Science-Computer Science (all)
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15
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