Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling
Scheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9076700/ |
| _version_ | 1827325699412721664 |
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| author | Md. Juel Rana Forhad Zaman Tapabrata Ray Ruhul Sarker |
| author_facet | Md. Juel Rana Forhad Zaman Tapabrata Ray Ruhul Sarker |
| author_sort | Md. Juel Rana |
| collection | DOAJ |
| description | Scheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these algorithms is far from ideal due to the presence of different complex equality and inequality constraints in CMGs. Furthermore, most of the current works ignore energy storage (ES) degradation costs in the optimization model, which has a significant impact on the life of ES. This paper develops both single and bi-objective optimization models by considering the life of ES along with the operating cost for scheduling a CMG. An efficient heuristic-enhanced Differential Evolution (DE) approach is proposed to solve these models; by exploiting the structure of equality constraints, the proposed heuristic is able to generate feasible solutions quickly. The significance of the proposed heuristic is that it can generate a high-quality solution with a considerably lower computational effort. Numerical simulations were performed to evaluate the performance of the proposed method, and obtained results were compared with the state-of-the-art algorithm. The simulation results corroborate the efficacy of the proposed method. |
| first_indexed | 2024-03-07T14:33:05Z |
| format | Article |
| id | doaj.art-8b110bb7908e4525aeb70df823527782 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-07T14:33:05Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-8b110bb7908e4525aeb70df8235277822024-03-06T00:00:33ZengIEEEIEEE Access2169-35362020-01-018765007651510.1109/ACCESS.2020.29897959076700Heuristic Enhanced Evolutionary Algorithm for Community Microgrid SchedulingMd. Juel Rana0https://orcid.org/0000-0002-3739-7131Forhad Zaman1https://orcid.org/0000-0001-5078-8252Tapabrata Ray2Ruhul Sarker3School of Engineering and Information Technology, University of New South Wales Canberra, Canberra, ACT, AustraliaSchool of Engineering and Information Technology, University of New South Wales Canberra, Canberra, ACT, AustraliaSchool of Engineering and Information Technology, University of New South Wales Canberra, Canberra, ACT, AustraliaSchool of Engineering and Information Technology, University of New South Wales Canberra, Canberra, ACT, AustraliaScheduling of community microgrids (CMGs) is an important and challenging optimization problem. Generally, the optimization is performed to schedule resources of CMGs at minimum cost. In recent years, a number of algorithms have been proposed to solve such problems. However, the performance of these algorithms is far from ideal due to the presence of different complex equality and inequality constraints in CMGs. Furthermore, most of the current works ignore energy storage (ES) degradation costs in the optimization model, which has a significant impact on the life of ES. This paper develops both single and bi-objective optimization models by considering the life of ES along with the operating cost for scheduling a CMG. An efficient heuristic-enhanced Differential Evolution (DE) approach is proposed to solve these models; by exploiting the structure of equality constraints, the proposed heuristic is able to generate feasible solutions quickly. The significance of the proposed heuristic is that it can generate a high-quality solution with a considerably lower computational effort. Numerical simulations were performed to evaluate the performance of the proposed method, and obtained results were compared with the state-of-the-art algorithm. The simulation results corroborate the efficacy of the proposed method.https://ieeexplore.ieee.org/document/9076700/Community microgridenergy schedulingenergy storage systemdifferential evolutiondistributed generatorsheuristic |
| spellingShingle | Md. Juel Rana Forhad Zaman Tapabrata Ray Ruhul Sarker Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling IEEE Access Community microgrid energy scheduling energy storage system differential evolution distributed generators heuristic |
| title | Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling |
| title_full | Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling |
| title_fullStr | Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling |
| title_full_unstemmed | Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling |
| title_short | Heuristic Enhanced Evolutionary Algorithm for Community Microgrid Scheduling |
| title_sort | heuristic enhanced evolutionary algorithm for community microgrid scheduling |
| topic | Community microgrid energy scheduling energy storage system differential evolution distributed generators heuristic |
| url | https://ieeexplore.ieee.org/document/9076700/ |
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