Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm
INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption...
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| Format: | Article |
| Language: | English |
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European Alliance for Innovation (EAI)
2024-04-01
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| Series: | EAI Endorsed Transactions on Energy Web |
| Subjects: | |
| Online Access: | https://publications.eai.eu/index.php/ew/article/view/5696 |
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| author | Fengyi Liu Pan Duan |
| author_facet | Fengyi Liu Pan Duan |
| author_sort | Fengyi Liu |
| collection | DOAJ |
| description |
INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.
OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs
METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.
RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.
CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.
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| first_indexed | 2024-04-24T11:43:14Z |
| format | Article |
| id | doaj.art-eade80acd3b1494e8f78dc0a669079dd |
| institution | Directory Open Access Journal |
| issn | 2032-944X |
| language | English |
| last_indexed | 2024-04-24T11:43:14Z |
| publishDate | 2024-04-01 |
| publisher | European Alliance for Innovation (EAI) |
| record_format | Article |
| series | EAI Endorsed Transactions on Energy Web |
| spelling | doaj.art-eade80acd3b1494e8f78dc0a669079dd2024-04-09T19:01:34ZengEuropean Alliance for Innovation (EAI)EAI Endorsed Transactions on Energy Web2032-944X2024-04-011110.4108/ew.5696Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithmFengyi Liu0Pan Duan1Chongqing University of Posts and Telecommunications Chongqing University of Posts and Telecommunications INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution. OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm. RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks. CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids. https://publications.eai.eu/index.php/ew/article/view/5696QPSOOptimal schedulingLevy flight strategymicro-power systems |
| spellingShingle | Fengyi Liu Pan Duan Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm EAI Endorsed Transactions on Energy Web QPSO Optimal scheduling Levy flight strategy micro-power systems |
| title | Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| title_full | Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| title_fullStr | Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| title_full_unstemmed | Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| title_short | Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| title_sort | research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm |
| topic | QPSO Optimal scheduling Levy flight strategy micro-power systems |
| url | https://publications.eai.eu/index.php/ew/article/view/5696 |
| work_keys_str_mv | AT fengyiliu researchonoptimalschedulingofmicrogridbasedonimprovedquantumparticleswarmoptimizationalgorithm AT panduan researchonoptimalschedulingofmicrogridbasedonimprovedquantumparticleswarmoptimizationalgorithm |