Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments
The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a mic...
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MDPI AG
2025-03-01
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| author | Zichong Wang Yingying Zheng |
| author_facet | Zichong Wang Yingying Zheng |
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| collection | DOAJ |
| description | The microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a microgrid integrated with a combined heat and power system is determining the optimal configuration and operation duration under different scenarios to meet users’ electricity and heat demands while minimizing both economic and environmental costs. Thus, this paper presents a bi-objective mathematical model to solve the optimal scheduling problem of the microgrid. The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. These strategies address the challenges posed by unpredictable energy load fluctuations and the complexity of solving such systems. Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, the PMOEVO developed in this paper performs excellently in solving the Scheduling Problem of Biomass-Hybrid microgrids with energy storage considering uncertainty. The work presented in this paper provides a new solution framework for the microgrid-scheduling problem considering uncertainty. In future research, this solution framework will be further advanced for application in real-world scenarios. |
| format | Article |
| id | doaj-art-2c878be1a2384fa1bf3130c65ad3c520 |
| institution | DOAJ |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-2c878be1a2384fa1bf3130c65ad3c5202025-08-20T02:52:35ZengMDPI AGApplied Sciences2076-34172025-03-01155270210.3390/app15052702Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain EnvironmentsZichong Wang0Yingying Zheng1College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaCollege of Information and Electrical Engineering, China Agricultural University, Beijing 100083, ChinaThe microgrid is a small-scale, independent power system that plays a crucial role in the transition to carbon-neutral energy systems. Combined heat and power (CHP) systems with energy storage reduce energy waste within microgrids, enhancing energy utilization efficiency. The key challenge for a microgrid integrated with a combined heat and power system is determining the optimal configuration and operation duration under different scenarios to meet users’ electricity and heat demands while minimizing both economic and environmental costs. Thus, this paper presents a bi-objective mathematical model to solve the optimal scheduling problem of the microgrid. The Long Short-Term Memory–Parallel Multi-Objective Energy Valley Optimizer (LSTM-PMOEVO) framework incorporates energy load prediction using LSTM and scheduling planning solved via PMOEVO. These strategies address the challenges posed by unpredictable energy load fluctuations and the complexity of solving such systems. Finally, a public dataset was utilized for the experiments to verify the performance of the proposed algorithm. Comparisons and discussions show that the proposed optimization strategies significantly improve the performance of PMOEVO, demonstrating marked advantages over six classical algorithms. In conclusion, the PMOEVO developed in this paper performs excellently in solving the Scheduling Problem of Biomass-Hybrid microgrids with energy storage considering uncertainty. The work presented in this paper provides a new solution framework for the microgrid-scheduling problem considering uncertainty. In future research, this solution framework will be further advanced for application in real-world scenarios.https://www.mdpi.com/2076-3417/15/5/2702microgridcombined heat and powerenergy storagebi-objectiveLSTM-PMOEVOunpredictable energy load fluctuations |
| spellingShingle | Zichong Wang Yingying Zheng Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments Applied Sciences microgrid combined heat and power energy storage bi-objective LSTM-PMOEVO unpredictable energy load fluctuations |
| title | Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments |
| title_full | Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments |
| title_fullStr | Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments |
| title_full_unstemmed | Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments |
| title_short | Optimal Scheduling of Biomass-Hybrid Microgrids with Energy Storage: An LSTM-PMOEVO Framework for Uncertain Environments |
| title_sort | optimal scheduling of biomass hybrid microgrids with energy storage an lstm pmoevo framework for uncertain environments |
| topic | microgrid combined heat and power energy storage bi-objective LSTM-PMOEVO unpredictable energy load fluctuations |
| url | https://www.mdpi.com/2076-3417/15/5/2702 |
| work_keys_str_mv | AT zichongwang optimalschedulingofbiomasshybridmicrogridswithenergystorageanlstmpmoevoframeworkforuncertainenvironments AT yingyingzheng optimalschedulingofbiomasshybridmicrogridswithenergystorageanlstmpmoevoframeworkforuncertainenvironments |