{"title":"热电联产经济调度的改进遗传算法","authors":"Deliang Li, Chunyu Yang","doi":"10.1007/s42235-024-00569-5","DOIUrl":null,"url":null,"abstract":"<div><p>Combined Heat and Power Economic Dispatch (CHPED) is an important problem in the energy field, and it is beneficial for improving the utilization efficiency of power and heat energies. This paper proposes a Modified Genetic Algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. In comparison with the other algorithms, MGA has reduced generation costs by at least 562.73$, 1068.7$, 522.68$ and 1016.24$, respectively, for instances 3, 4, 7 and 8, and it has reduced generation costs by at most 848.22$, 3642.85$, 897.63$ and 3812.65$, respectively, for instances 3, 4, 7 and 8. Therefore, MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.</p></div>","PeriodicalId":614,"journal":{"name":"Journal of Bionic Engineering","volume":"21 5","pages":"2569 - 2586"},"PeriodicalIF":4.9000,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Modified Genetic Algorithm for Combined Heat and Power Economic Dispatch\",\"authors\":\"Deliang Li, Chunyu Yang\",\"doi\":\"10.1007/s42235-024-00569-5\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Combined Heat and Power Economic Dispatch (CHPED) is an important problem in the energy field, and it is beneficial for improving the utilization efficiency of power and heat energies. This paper proposes a Modified Genetic Algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. In comparison with the other algorithms, MGA has reduced generation costs by at least 562.73$, 1068.7$, 522.68$ and 1016.24$, respectively, for instances 3, 4, 7 and 8, and it has reduced generation costs by at most 848.22$, 3642.85$, 897.63$ and 3812.65$, respectively, for instances 3, 4, 7 and 8. Therefore, MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.</p></div>\",\"PeriodicalId\":614,\"journal\":{\"name\":\"Journal of Bionic Engineering\",\"volume\":\"21 5\",\"pages\":\"2569 - 2586\"},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-07-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Bionic Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s42235-024-00569-5\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Bionic Engineering","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s42235-024-00569-5","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
A Modified Genetic Algorithm for Combined Heat and Power Economic Dispatch
Combined Heat and Power Economic Dispatch (CHPED) is an important problem in the energy field, and it is beneficial for improving the utilization efficiency of power and heat energies. This paper proposes a Modified Genetic Algorithm (MGA) to determine the power and heat outputs of three kinds of units for CHPED. First, MGA replaces the simulated binary crossover by a new one based on the uniform and guassian distributions, and its convergence can be enhanced. Second, MGA modifies the mutation operator by introducing a disturbance coefficient based on guassian distribution, which can decrease the risk of being trapped into local optima. Eight instances with or without prohibited operating zones are used to investigate the efficiencies of MGA and other four genetic algorithms for CHPED. In comparison with the other algorithms, MGA has reduced generation costs by at least 562.73$, 1068.7$, 522.68$ and 1016.24$, respectively, for instances 3, 4, 7 and 8, and it has reduced generation costs by at most 848.22$, 3642.85$, 897.63$ and 3812.65$, respectively, for instances 3, 4, 7 and 8. Therefore, MGA has desirable convergence and stability for CHPED in comparison with the other four genetic algorithms.
期刊介绍:
The Journal of Bionic Engineering (JBE) is a peer-reviewed journal that publishes original research papers and reviews that apply the knowledge learned from nature and biological systems to solve concrete engineering problems. The topics that JBE covers include but are not limited to:
Mechanisms, kinematical mechanics and control of animal locomotion, development of mobile robots with walking (running and crawling), swimming or flying abilities inspired by animal locomotion.
Structures, morphologies, composition and physical properties of natural and biomaterials; fabrication of new materials mimicking the properties and functions of natural and biomaterials.
Biomedical materials, artificial organs and tissue engineering for medical applications; rehabilitation equipment and devices.
Development of bioinspired computation methods and artificial intelligence for engineering applications.