A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm
Abstract A multi-resource balanced allocation method using a genetic-heuristic fusion algorithm is proposed to address the imbalance in distributed power generation resource allocation and the over-generation problem in virtual power plants. By establishing models of wind, solar, storage, and contro...
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Springer
2025-08-01
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| Series: | International Journal of Computational Intelligence Systems |
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| Online Access: | https://doi.org/10.1007/s44196-025-00941-1 |
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| author | Haifeng Li Tao Jin Xian Xu Lin Shi |
| author_facet | Haifeng Li Tao Jin Xian Xu Lin Shi |
| author_sort | Haifeng Li |
| collection | DOAJ |
| description | Abstract A multi-resource balanced allocation method using a genetic-heuristic fusion algorithm is proposed to address the imbalance in distributed power generation resource allocation and the over-generation problem in virtual power plants. By establishing models of wind, solar, storage, and controllable load characteristics, an optimization model is constructed with objectives of resource allocation balance and minimization of call costs, subject to constraints such as power balance. Combining the global search capability of a genetic algorithm and the local optimization capability of an ant colony algorithm, the genetic algorithm stage adopts real-number encoding and a dynamic crossover-mutation strategy, while the ant colony algorithm stage optimizes the pheromone update mechanism to avoid premature convergence. The experimental results show that this method achieves 100% accurate allocation of resources without any over-generation occurrences and reduces the resource allocation deviation rate by 32–67% compared to alternative methods. The algorithm demonstrates fast convergence, yielding solutions in less than 0.6 s across 14 repeated experiments, with an average convergence time reduction of 42% compared to traditional algorithms. Under a comprehensive fluctuation scenario with 30% renewable energy fluctuation rate and 15% load forecasting error, the system stability index remains at 0.865, demonstrating the algorithm’s efficiency and robustness under complex conditions and providing an effective approach for optimizing virtual power plant resource allocation. |
| format | Article |
| id | doaj-art-4985eeccebc44cad8f4a41b7568f7c03 |
| institution | Kabale University |
| issn | 1875-6883 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | International Journal of Computational Intelligence Systems |
| spelling | doaj-art-4985eeccebc44cad8f4a41b7568f7c032025-08-20T03:41:56ZengSpringerInternational Journal of Computational Intelligence Systems1875-68832025-08-0118112210.1007/s44196-025-00941-1A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic AlgorithmHaifeng Li0Tao Jin1Xian Xu2Lin Shi3State Grid Jiangsu Electric Power Company, LtdState Grid Jiangsu Electric Power Company, LtdState Grid Jiangsu Electric Power Company, LtdState Grid Jiangsu Electric Power Company, LtdAbstract A multi-resource balanced allocation method using a genetic-heuristic fusion algorithm is proposed to address the imbalance in distributed power generation resource allocation and the over-generation problem in virtual power plants. By establishing models of wind, solar, storage, and controllable load characteristics, an optimization model is constructed with objectives of resource allocation balance and minimization of call costs, subject to constraints such as power balance. Combining the global search capability of a genetic algorithm and the local optimization capability of an ant colony algorithm, the genetic algorithm stage adopts real-number encoding and a dynamic crossover-mutation strategy, while the ant colony algorithm stage optimizes the pheromone update mechanism to avoid premature convergence. The experimental results show that this method achieves 100% accurate allocation of resources without any over-generation occurrences and reduces the resource allocation deviation rate by 32–67% compared to alternative methods. The algorithm demonstrates fast convergence, yielding solutions in less than 0.6 s across 14 repeated experiments, with an average convergence time reduction of 42% compared to traditional algorithms. Under a comprehensive fluctuation scenario with 30% renewable energy fluctuation rate and 15% load forecasting error, the system stability index remains at 0.865, demonstrating the algorithm’s efficiency and robustness under complex conditions and providing an effective approach for optimizing virtual power plant resource allocation.https://doi.org/10.1007/s44196-025-00941-1Genetic-heuristic algorithmVirtual power plantDistributed generation resource allocationAnt colony |
| spellingShingle | Haifeng Li Tao Jin Xian Xu Lin Shi A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm International Journal of Computational Intelligence Systems Genetic-heuristic algorithm Virtual power plant Distributed generation resource allocation Ant colony |
| title | A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm |
| title_full | A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm |
| title_fullStr | A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm |
| title_full_unstemmed | A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm |
| title_short | A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm |
| title_sort | study of multi distributed resource equalization allocation for virtual power plants based on genetic heuristic algorithm |
| topic | Genetic-heuristic algorithm Virtual power plant Distributed generation resource allocation Ant colony |
| url | https://doi.org/10.1007/s44196-025-00941-1 |
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