Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm
Algorithms used for day ahead generation scheduling are crucial for a power system operator to balance conflicting objectives and the network constraints. Practically feasible algorithms using parallel computing, low-cost hardware and open-source software in power system parlance are rarely attempte...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2020-01-01
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| Series: | Cogent Engineering |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/23311916.2020.1767019 |
| _version_ | 1797764929708621824 |
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| author | Kiran Babu Vakkapatla Srinivasa Varma Pinni |
| author_facet | Kiran Babu Vakkapatla Srinivasa Varma Pinni |
| author_sort | Kiran Babu Vakkapatla |
| collection | DOAJ |
| description | Algorithms used for day ahead generation scheduling are crucial for a power system operator to balance conflicting objectives and the network constraints. Practically feasible algorithms using parallel computing, low-cost hardware and open-source software in power system parlance are rarely attempted in the literature. In this paper, a multicore processing-based genetic algorithm is proposed for finding the optimum solution of economic emission dispatch considering the reliability indices. Continuous genetic algorithm is used to improve the speed of the algorithm. Cost minimization and emission minimization are considered as the objectives to find the set of pareto-optimal solutions. The final solution is selected from the pareto-optimal set based on the reliability of generating stations. The insight used to improve the search space is the usage of two cores of a dual core processor in parallel, with different parameters of genetic algorithm. The constraints are handled using repair function and penalty factors, based on the feasibility of implementation. The algorithm is tested on IEEE 30 Bus, 6 generator system and IEEE 57 Bus system. The results show that the multicore processing using different parameters of genetic algorithm has improved the performance. |
| first_indexed | 2024-03-12T20:03:47Z |
| format | Article |
| id | doaj.art-6b96cfbdcee14fe28a259cb52f54eedf |
| institution | Directory Open Access Journal |
| issn | 2331-1916 |
| language | English |
| last_indexed | 2024-03-12T20:03:47Z |
| publishDate | 2020-01-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Cogent Engineering |
| spelling | doaj.art-6b96cfbdcee14fe28a259cb52f54eedf2023-08-02T02:16:34ZengTaylor & Francis GroupCogent Engineering2331-19162020-01-017110.1080/23311916.2020.17670191767019Multiobjective generation scheduling using multicore processing-based continuous genetic algorithmKiran Babu Vakkapatla0Srinivasa Varma Pinni1Koneru Lakshmaiah Education FoundationKoneru Lakshmaiah Education FoundationAlgorithms used for day ahead generation scheduling are crucial for a power system operator to balance conflicting objectives and the network constraints. Practically feasible algorithms using parallel computing, low-cost hardware and open-source software in power system parlance are rarely attempted in the literature. In this paper, a multicore processing-based genetic algorithm is proposed for finding the optimum solution of economic emission dispatch considering the reliability indices. Continuous genetic algorithm is used to improve the speed of the algorithm. Cost minimization and emission minimization are considered as the objectives to find the set of pareto-optimal solutions. The final solution is selected from the pareto-optimal set based on the reliability of generating stations. The insight used to improve the search space is the usage of two cores of a dual core processor in parallel, with different parameters of genetic algorithm. The constraints are handled using repair function and penalty factors, based on the feasibility of implementation. The algorithm is tested on IEEE 30 Bus, 6 generator system and IEEE 57 Bus system. The results show that the multicore processing using different parameters of genetic algorithm has improved the performance.http://dx.doi.org/10.1080/23311916.2020.1767019generation schedulingmultiobjective optimizationgenetic algorithmpower system reliabilityparallel computing |
| spellingShingle | Kiran Babu Vakkapatla Srinivasa Varma Pinni Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm Cogent Engineering generation scheduling multiobjective optimization genetic algorithm power system reliability parallel computing |
| title | Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm |
| title_full | Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm |
| title_fullStr | Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm |
| title_full_unstemmed | Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm |
| title_short | Multiobjective generation scheduling using multicore processing-based continuous genetic algorithm |
| title_sort | multiobjective generation scheduling using multicore processing based continuous genetic algorithm |
| topic | generation scheduling multiobjective optimization genetic algorithm power system reliability parallel computing |
| url | http://dx.doi.org/10.1080/23311916.2020.1767019 |
| work_keys_str_mv | AT kiranbabuvakkapatla multiobjectivegenerationschedulingusingmulticoreprocessingbasedcontinuousgeneticalgorithm AT srinivasavarmapinni multiobjectivegenerationschedulingusingmulticoreprocessingbasedcontinuousgeneticalgorithm |