A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm
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| Publicado en: | International Journal of Computational Intelligence Systems vol. 18, no. 1 (Dec 2025), p. 200 |
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| Otros Autores: | , , |
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Springer Nature B.V.
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| Acceso en línea: | Citation/Abstract Full Text Full Text - PDF |
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| 024 | 7 | |a 10.1007/s44196-025-00941-1 |2 doi | |
| 035 | |a 3267459296 | ||
| 045 | 2 | |b d20251201 |b d20251231 | |
| 100 | 1 | |a Li, Haifeng |u State Grid Jiangsu Electric Power Company, Ltd, Taizhou Jiangsu, China (GRID:grid.433158.8) (ISNI:0000 0000 8891 7315) | |
| 245 | 1 | |a A Study of Multi-distributed Resource Equalization Allocation for Virtual Power Plants Based on Genetic-heuristic Algorithm | |
| 260 | |b Springer Nature B.V. |c Dec 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Collaboration | ||
| 653 | |a Distributed generation | ||
| 653 | |a Power plants | ||
| 653 | |a Optimization techniques | ||
| 653 | |a Resource allocation | ||
| 653 | |a Virtual power plants | ||
| 653 | |a Supply & demand | ||
| 653 | |a Systems stability | ||
| 653 | |a Energy storage | ||
| 653 | |a Energy resources | ||
| 653 | |a Ant colony optimization | ||
| 653 | |a Heuristic | ||
| 653 | |a Fuzzy logic | ||
| 653 | |a Heuristic methods | ||
| 653 | |a Efficiency | ||
| 653 | |a Optimization models | ||
| 653 | |a Scheduling | ||
| 653 | |a Convergence | ||
| 653 | |a Genetic algorithms | ||
| 653 | |a Electricity | ||
| 653 | |a Alternative energy | ||
| 653 | |a Costs | ||
| 653 | |a Renewable resources | ||
| 653 | |a Energy management | ||
| 653 | |a Flexibility | ||
| 653 | |a Consumption | ||
| 653 | |a Power supply | ||
| 653 | |a Controllability | ||
| 653 | |a Local optimization | ||
| 653 | |a Optimization algorithms | ||
| 700 | 1 | |a Jin, Tao |u State Grid Jiangsu Electric Power Company, Ltd, Taizhou Jiangsu, China (GRID:grid.433158.8) (ISNI:0000 0000 8891 7315) | |
| 700 | 1 | |a Xu, Xian |u State Grid Jiangsu Electric Power Company, Ltd, Taizhou Jiangsu, China (GRID:grid.433158.8) (ISNI:0000 0000 8891 7315) | |
| 700 | 1 | |a Shi, Lin |u State Grid Jiangsu Electric Power Company, Ltd, Taizhou Jiangsu, China (GRID:grid.433158.8) (ISNI:0000 0000 8891 7315) | |
| 773 | 0 | |t International Journal of Computational Intelligence Systems |g vol. 18, no. 1 (Dec 2025), p. 200 | |
| 786 | 0 | |d ProQuest |t Computer Science Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3267459296/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3267459296/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3267459296/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |